diff --git a/.github/workflows/issues.yml b/.github/workflows/issues.yml index 6447f83ef..dca717752 100644 --- a/.github/workflows/issues.yml +++ b/.github/workflows/issues.yml @@ -21,6 +21,7 @@ jobs: days-before-pr-stale: 10 days-before-pr-close: 7 stale-pr-label: "stale" + exempt-issue-labels: "skip-stale" stale-pr-message: "This PR is stale because it has been open for 10 days with no activity." close-pr-message: "This PR was closed because it has been inactive for 7 days since being marked as stale." repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 4f93980a2..edd0d898b 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -5,8 +5,7 @@ on: branches: - main pull_request: - branches: - - main + # All PRs, including stacked PRs env: UV_FROZEN: "1" diff --git a/.gitignore b/.gitignore index 7dd22b88d..2e9b92379 100644 --- a/.gitignore +++ b/.gitignore @@ -141,4 +141,5 @@ cython_debug/ .ruff_cache/ # PyPI configuration file -.pypirc \ No newline at end of file +.pypirc +.aider* diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 000000000..a75c1414f --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,14 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python Debugger: Python File", + "type": "debugpy", + "request": "launch", + "program": "${file}" + } + ] +} \ No newline at end of file diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 000000000..291c31837 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,71 @@ +Welcome to the OpenAI Agents SDK repository. This file contains the main points for new contributors. + +## Repository overview + +- **Source code**: `src/agents/` contains the implementation. +- **Tests**: `tests/` with a short guide in `tests/README.md`. +- **Examples**: under `examples/`. +- **Documentation**: markdown pages live in `docs/` with `mkdocs.yml` controlling the site. +- **Utilities**: developer commands are defined in the `Makefile`. +- **PR template**: `.github/PULL_REQUEST_TEMPLATE/pull_request_template.md` describes the information every PR must include. + +## Local workflow + +1. Format, lint and type‑check your changes: + + ```bash + make format + make lint + make mypy + ``` + +2. Run the tests: + + ```bash + make tests + ``` + + To run a single test, use `uv run pytest -s -k `. + +3. Build the documentation (optional but recommended for docs changes): + + ```bash + make build-docs + ``` + + Coverage can be generated with `make coverage`. + +All python commands should be run via `uv run python ...` + +## Snapshot tests + +Some tests rely on inline snapshots. See `tests/README.md` for details on updating them: + +```bash +make snapshots-fix # update existing snapshots +make snapshots-create # create new snapshots +``` + +Run `make tests` again after updating snapshots to ensure they pass. + +## Style notes + +- Write comments as full sentences and end them with a period. + +## Pull request expectations + +PRs should use the template located at `.github/PULL_REQUEST_TEMPLATE/pull_request_template.md`. Provide a summary, test plan and issue number if applicable, then check that: + +- New tests are added when needed. +- Documentation is updated. +- `make lint` and `make format` have been run. +- The full test suite passes. + +Commit messages should be concise and written in the imperative mood. Small, focused commits are preferred. + +## What reviewers look for + +- Tests covering new behaviour. +- Consistent style: code formatted with `uv run ruff format`, imports sorted, and type hints passing `uv run mypy .`. +- Clear documentation for any public API changes. +- Clean history and a helpful PR description. diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 000000000..5e01a1c3d --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1 @@ +Read the AGENTS.md file for instructions. \ No newline at end of file diff --git a/Makefile b/Makefile index 5c6aba425..9a88f93a1 100644 --- a/Makefile +++ b/Makefile @@ -7,6 +7,10 @@ format: uv run ruff format uv run ruff check --fix +.PHONY: format-check +format-check: + uv run ruff format --check + .PHONY: lint lint: uv run ruff check @@ -55,5 +59,5 @@ serve-docs: deploy-docs: uv run mkdocs gh-deploy --force --verbose - - +.PHONY: check +check: format-check lint mypy tests diff --git a/README.md b/README.md index bbd4a5a05..638f94b21 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,149 @@ # OpenAI Agents SDK -The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. +The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs. Image of the Agents Tracing UI +> [!NOTE] +> Looking for the JavaScript/TypeScript version? Check out [Agents SDK JS/TS](https://github.com/openai/openai-agents-js). + ### Core concepts: 1. [**Agents**](https://openai.github.io/openai-agents-python/agents): LLMs configured with instructions, tools, guardrails, and handoffs 2. [**Handoffs**](https://openai.github.io/openai-agents-python/handoffs/): A specialized tool call used by the Agents SDK for transferring control between agents 3. [**Guardrails**](https://openai.github.io/openai-agents-python/guardrails/): Configurable safety checks for input and output validation -4. [**Tracing**](https://openai.github.io/openai-agents-python/tracing/): Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows +4. [**Sessions**](#sessions): Automatic conversation history management across agent runs +5. [**Tracing**](https://openai.github.io/openai-agents-python/tracing/): Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows Explore the [examples](examples) directory to see the SDK in action, and read our [documentation](https://openai.github.io/openai-agents-python/) for more details. -Notably, our SDK [is compatible](https://openai.github.io/openai-agents-python/models/) with any model providers that support the OpenAI Chat Completions API format. +## Sessions + +The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the need to manually handle `.to_input_list()` between turns. + +### Quick start + +```python +from agents import Agent, Runner, SQLiteSession + +# Create agent +agent = Agent( + name="Assistant", + instructions="Reply very concisely.", +) + +# Create a session instance +session = SQLiteSession("conversation_123") + +# First turn +result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session +) +print(result.final_output) # "San Francisco" + +# Second turn - agent automatically remembers previous context +result = await Runner.run( + agent, + "What state is it in?", + session=session +) +print(result.final_output) # "California" + +# Also works with synchronous runner +result = Runner.run_sync( + agent, + "What's the population?", + session=session +) +print(result.final_output) # "Approximately 39 million" +``` + +### Session options + +- **No memory** (default): No session memory when session parameter is omitted +- **`session: Session = DatabaseSession(...)`**: Use a Session instance to manage conversation history + +```python +from agents import Agent, Runner, SQLiteSession + +# Custom SQLite database file +session = SQLiteSession("user_123", "conversations.db") +agent = Agent(name="Assistant") + +# Different session IDs maintain separate conversation histories +result1 = await Runner.run( + agent, + "Hello", + session=session +) +result2 = await Runner.run( + agent, + "Hello", + session=SQLiteSession("user_456", "conversations.db") +) +``` + +### Custom session implementations + +You can implement your own session memory by creating a class that follows the `Session` protocol: + +```python +from agents.memory import Session +from typing import List + +class MyCustomSession: + """Custom session implementation following the Session protocol.""" + + def __init__(self, session_id: str): + self.session_id = session_id + # Your initialization here + + async def get_items(self, limit: int | None = None) -> List[dict]: + # Retrieve conversation history for the session + pass + + async def add_items(self, items: List[dict]) -> None: + # Store new items for the session + pass + + async def pop_item(self) -> dict | None: + # Remove and return the most recent item from the session + pass + + async def clear_session(self) -> None: + # Clear all items for the session + pass + +# Use your custom session +agent = Agent(name="Assistant") +result = await Runner.run( + agent, + "Hello", + session=MyCustomSession("my_session") +) +``` ## Get started 1. Set up your Python environment -``` +- Option A: Using venv (traditional method) +```bash python -m venv env -source env/bin/activate +source env/bin/activate # On Windows: env\Scripts\activate +``` + +- Option B: Using uv (recommended) +```bash +uv venv +source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 2. Install Agents SDK -``` +```bash pip install openai-agents ``` @@ -49,7 +166,7 @@ print(result.final_output) (_If running this, ensure you set the `OPENAI_API_KEY` environment variable_) -(_For Jupyter notebook users, see [hello_world_jupyter.py](examples/basic/hello_world_jupyter.py)_) +(_For Jupyter notebook users, see [hello_world_jupyter.ipynb](examples/basic/hello_world_jupyter.ipynb)_) ## Handoffs example @@ -162,10 +279,16 @@ make sync 2. (After making changes) lint/test +``` +make check # run tests linter and typechecker +``` + +Or to run them individually: ``` make tests # run tests make mypy # run typechecker make lint # run linter +make format-check # run style checker ``` ## Acknowledgements diff --git a/docs/agents.md b/docs/agents.md index 39d4afd57..b11a4dd68 100644 --- a/docs/agents.md +++ b/docs/agents.md @@ -6,6 +6,7 @@ Agents are the core building block in your apps. An agent is a large language mo The most common properties of an agent you'll configure are: +- `name`: A required string that identifies your agent. - `instructions`: also known as a developer message or system prompt. - `model`: which LLM to use, and optional `model_settings` to configure model tuning parameters like temperature, top_p, etc. - `tools`: Tools that the agent can use to achieve its tasks. diff --git a/docs/context.md b/docs/context.md index 4176ec51f..6e54565e0 100644 --- a/docs/context.md +++ b/docs/context.md @@ -38,7 +38,8 @@ class UserInfo: # (1)! @function_tool async def fetch_user_age(wrapper: RunContextWrapper[UserInfo]) -> str: # (2)! - return f"User {wrapper.context.name} is 47 years old" + """Fetch the age of the user. Call this function to get user's age information.""" + return f"The user {wrapper.context.name} is 47 years old" async def main(): user_info = UserInfo(name="John", uid=123) diff --git a/docs/guardrails.md b/docs/guardrails.md index 2f0be0f2a..8df904a4c 100644 --- a/docs/guardrails.md +++ b/docs/guardrails.md @@ -23,7 +23,7 @@ Input guardrails run in 3 steps: Output guardrails run in 3 steps: -1. First, the guardrail receives the same input passed to the agent. +1. First, the guardrail receives the output produced by the agent. 2. Next, the guardrail function runs to produce a [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput], which is then wrapped in an [`OutputGuardrailResult`][agents.guardrail.OutputGuardrailResult] 3. Finally, we check if [`.tripwire_triggered`][agents.guardrail.GuardrailFunctionOutput.tripwire_triggered] is true. If true, an [`OutputGuardrailTripwireTriggered`][agents.exceptions.OutputGuardrailTripwireTriggered] exception is raised, so you can appropriately respond to the user or handle the exception. diff --git a/docs/index.md b/docs/index.md index 8aef6574e..935c4be5b 100644 --- a/docs/index.md +++ b/docs/index.md @@ -5,6 +5,7 @@ The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) enables - **Agents**, which are LLMs equipped with instructions and tools - **Handoffs**, which allow agents to delegate to other agents for specific tasks - **Guardrails**, which enable the inputs to agents to be validated +- **Sessions**, which automatically maintains conversation history across agent runs In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real-world applications without a steep learning curve. In addition, the SDK comes with built-in **tracing** that lets you visualize and debug your agentic flows, as well as evaluate them and even fine-tune models for your application. @@ -21,6 +22,7 @@ Here are the main features of the SDK: - Python-first: Use built-in language features to orchestrate and chain agents, rather than needing to learn new abstractions. - Handoffs: A powerful feature to coordinate and delegate between multiple agents. - Guardrails: Run input validations and checks in parallel to your agents, breaking early if the checks fail. +- Sessions: Automatic conversation history management across agent runs, eliminating manual state handling. - Function tools: Turn any Python function into a tool, with automatic schema generation and Pydantic-powered validation. - Tracing: Built-in tracing that lets you visualize, debug and monitor your workflows, as well as use the OpenAI suite of evaluation, fine-tuning and distillation tools. diff --git a/docs/ja/agents.md b/docs/ja/agents.md index f4b108e8e..828b36355 100644 --- a/docs/ja/agents.md +++ b/docs/ja/agents.md @@ -1,14 +1,18 @@ +--- +search: + exclude: true +--- # エージェント -エージェントは、アプリケーションの中核となる基本コンポーネントです。エージェントとは、instructions とツールで構成された大規模言語モデル（LLM）のことです。 +エージェントはアプリの主要な構成ブロックです。エージェントは、大規模言語モデル ( LLM ) に instructions と tools を設定したものです。 ## 基本設定 -エージェントで最も一般的に設定するプロパティは以下の通りです。 +エージェントで最も一般的に設定するプロパティは次のとおりです。 -- `instructions`：developer message や システムプロンプト(system prompt)とも呼ばれます。 -- `model`：どの LLM を使用するか、また `model_settings` で temperature や top_p などのモデル調整パラメーターを設定できます。 -- `tools`：エージェントがタスクを達成するために使用できるツールです。 +- `instructions`: 開発者メッセージまたは system prompt とも呼ばれます。 +- `model`: 使用する LLM と、temperature や top_p などのモデル調整パラメーターを指定する任意の `model_settings`。 +- `tools`: エージェントがタスクを達成するために利用できるツール。 ```python from agents import Agent, ModelSettings, function_tool @@ -27,7 +31,7 @@ agent = Agent( ## コンテキスト -エージェントは `context` 型に対して汎用的です。コンテキストは依存性注入ツールであり、`Runner.run()` に渡すオブジェクトです。これはすべてのエージェント、ツール、ハンドオフなどに渡され、エージェント実行時の依存関係や状態をまとめて管理します。任意の Python オブジェクトを context として指定できます。 +エージェントはその `context` 型について汎用的です。コンテキストは依存性注入の手段で、`Runner.run()` に渡すオブジェクトです。これはすべてのエージェント、ツール、ハンドオフなどに渡され、エージェント実行時の依存関係や状態をまとめて保持します。任意の Python オブジェクトをコンテキストとして渡せます。 ```python @dataclass @@ -45,7 +49,7 @@ agent = Agent[UserContext]( ## 出力タイプ -デフォルトでは、エージェントはプレーンテキスト（つまり `str`）出力を生成します。特定の型の出力をエージェントに生成させたい場合は、`output_type` パラメーターを使用できます。一般的な選択肢として [Pydantic](https://docs.pydantic.dev/) オブジェクトがありますが、Pydantic の [TypeAdapter](https://docs.pydantic.dev/latest/api/type_adapter/) でラップできる型（dataclasses、リスト、TypedDict など）であればサポートしています。 +デフォルトでは、エージェントはプレーンテキスト ( つまり `str` ) を出力します。特定の型で出力させたい場合は `output_type` パラメーターを使用します。一般的には [Pydantic](https://docs.pydantic.dev/) オブジェクトを利用しますが、Pydantic の [TypeAdapter](https://docs.pydantic.dev/latest/api/type_adapter/) でラップ可能な型であれば何でも対応します。たとえば dataclass、list、TypedDict などです。 ```python from pydantic import BaseModel @@ -66,11 +70,11 @@ agent = Agent( !!! note - `output_type` を指定すると、モデルは通常のプレーンテキスト応答の代わりに [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) を使用するよう指示されます。 + `output_type` を渡すと、モデルは通常のプレーンテキスト応答の代わりに [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) を使用するよう指示されます。 ## ハンドオフ -ハンドオフは、エージェントが委任できるサブエージェントです。ハンドオフのリストを指定すると、エージェントは必要に応じてそれらに処理を委任できます。これは、単一タスクに特化したモジュール型のエージェントをオーケストレーションする強力なパターンです。詳細は [handoffs](handoffs.md) ドキュメントをご覧ください。 +ハンドオフは、エージェントが委譲できるサブエージェントです。ハンドオフのリストを渡しておくと、エージェントは必要に応じてそれらに処理を委譲できます。これにより、単一のタスクに特化したモジュール式エージェントを編成できる強力なパターンが実現します。詳細は [handoffs](handoffs.md) ドキュメントをご覧ください。 ```python from agents import Agent @@ -91,7 +95,7 @@ triage_agent = Agent( ## 動的 instructions -多くの場合、エージェント作成時に instructions を指定できますが、関数を使って動的に instructions を提供することも可能です。この関数はエージェントと context を受け取り、プロンプトを返す必要があります。通常の関数と `async` 関数の両方が利用可能です。 +通常はエージェント作成時に instructions を指定しますが、関数を介して動的に instructions を提供することもできます。その関数はエージェントとコンテキストを受け取り、プロンプトを返す必要があります。同期関数と `async` 関数の両方に対応しています。 ```python def dynamic_instructions( @@ -106,17 +110,17 @@ agent = Agent[UserContext]( ) ``` -## ライフサイクルイベント（フック） +## ライフサイクルイベント (hooks) -エージェントのライフサイクルを監視したい場合があります。たとえば、イベントを記録したり、特定のイベント発生時にデータを事前取得したりしたい場合です。`hooks` プロパティを使ってエージェントのライフサイクルにフックできます。[`AgentHooks`][agents.lifecycle.AgentHooks] クラスをサブクラス化し、関心のあるメソッドをオーバーライドしてください。 +場合によっては、エージェントのライフサイクルを観察したいことがあります。たとえば、イベントをログに記録したり、特定のイベント発生時にデータを事前取得したりする場合です。`hooks` プロパティを使ってエージェントのライフサイクルにフックできます。[`AgentHooks`][agents.lifecycle.AgentHooks] クラスをサブクラス化し、関心のあるメソッドをオーバーライドしてください。 ## ガードレール -ガードレールを使うと、エージェントの実行と並行して user 入力のチェックやバリデーションを行えます。たとえば、user の入力が関連性のある内容かどうかをスクリーニングできます。詳細は [guardrails](guardrails.md) ドキュメントをご覧ください。 +ガードレールを使うと、エージェントの実行と並行してユーザー入力に対するチェックやバリデーションを実行できます。たとえば、ユーザーの入力内容が関連しているかをスクリーニングできます。詳細は [guardrails](guardrails.md) ドキュメントをご覧ください。 -## エージェントのクローン／コピー +## エージェントの複製 -エージェントの `clone()` メソッドを使うことで、エージェントを複製し、任意のプロパティを変更できます。 +`clone()` メソッドを使用すると、エージェントを複製し、必要に応じて任意のプロパティを変更できます。 ```python pirate_agent = Agent( @@ -133,15 +137,15 @@ robot_agent = pirate_agent.clone( ## ツール使用の強制 -ツールのリストを指定しても、必ずしも LLM がツールを使用するとは限りません。[`ModelSettings.tool_choice`][agents.model_settings.ModelSettings.tool_choice] を設定することでツールの使用を強制できます。有効な値は以下の通りです。 +ツールの一覧を渡しても、LLM が必ずツールを使用するとは限りません。[`ModelSettings.tool_choice`][agents.model_settings.ModelSettings.tool_choice] を設定することでツール使用を強制できます。有効な値は次のとおりです。 -1. `auto`：LLM がツールを使うかどうかを自動で判断します。 -2. `required`：LLM にツールの使用を必須とします（どのツールを使うかは賢く選択されます）。 -3. `none`：LLM にツールを _使わない_ ことを要求します。 -4. 特定の文字列（例：`my_tool`）を指定すると、その特定のツールの使用を必須とします。 +1. `auto` — ツールを使用するかどうかを LLM が判断します。 +2. `required` — LLM にツール使用を必須化します ( ただし使用するツールは自動選択 )。 +3. `none` — LLM にツールを使用しないことを要求します。 +4. 特定の文字列 ( 例: `my_tool` ) — その特定のツールを LLM に使用させます。 !!! note - 無限ループを防ぐため、フレームワークはツール呼び出し後に自動的に `tool_choice` を "auto" にリセットします。この挙動は [`agent.reset_tool_choice`][agents.agent.Agent.reset_tool_choice] で設定可能です。無限ループは、ツールの execution results が LLM に送信され、`tool_choice` のために再度ツール呼び出しが発生し、これが繰り返されることで発生します。 + 無限ループを防ぐため、フレームワークはツール呼び出し後に `tool_choice` を自動的に "auto" にリセットします。この動作は [`agent.reset_tool_choice`][agents.agent.Agent.reset_tool_choice] で設定できます。無限ループが起こる理由は、ツールの結果が LLM に送られ、`tool_choice` により再びツール呼び出しが生成される、という流れが繰り返されるからです。 - ツール呼び出し後にエージェントを完全に停止させたい場合（auto モードで継続させたくない場合）は、[`Agent.tool_use_behavior="stop_on_first_tool"`] を設定できます。これにより、ツールの出力がそのまま最終応答として使用され、以降の LLM 処理は行われません。 \ No newline at end of file + ツール呼び出し後にエージェントを完全に停止させたい場合 ( auto モードで続行させたくない場合 ) は、[`Agent.tool_use_behavior="stop_on_first_tool"`] を設定してください。これにより、ツールの出力を LL M の追加処理なしにそのまま最終応答として返します。 \ No newline at end of file diff --git a/docs/ja/config.md b/docs/ja/config.md index c547a7936..bf76b9fb6 100644 --- a/docs/ja/config.md +++ b/docs/ja/config.md @@ -1,8 +1,12 @@ +--- +search: + exclude: true +--- # SDK の設定 ## API キーとクライアント -デフォルトでは、SDK はインポート時に LLM リクエストやトレーシングのために `OPENAI_API_KEY` 環境変数を探します。アプリの起動前にこの環境変数を設定できない場合は、[set_default_openai_key()][agents.set_default_openai_key] 関数を使ってキーを設定できます。 +デフォルトでは、 SDK はインポートされた時点で LLM リクエストとトレーシングに使用する `OPENAI_API_KEY` 環境変数を探します。アプリ起動前にこの環境変数を設定できない場合は、 [set_default_openai_key()][agents.set_default_openai_key] 関数を利用してキーを設定できます。 ```python from agents import set_default_openai_key @@ -10,7 +14,7 @@ from agents import set_default_openai_key set_default_openai_key("sk-...") ``` -また、使用する OpenAI クライアントを設定することも可能です。デフォルトでは、SDK は環境変数または上記で設定したデフォルトキーを使って `AsyncOpenAI` インスタンスを作成します。これを変更したい場合は、[set_default_openai_client()][agents.set_default_openai_client] 関数を利用してください。 +また、使用する OpenAI クライアントを構成することも可能です。デフォルトでは、 SDK は環境変数または上記で設定したデフォルトキーを用いて `AsyncOpenAI` インスタンスを作成します。これを変更するには、 [set_default_openai_client()][agents.set_default_openai_client] 関数を使用します。 ```python from openai import AsyncOpenAI @@ -20,7 +24,7 @@ custom_client = AsyncOpenAI(base_url="...", api_key="...") set_default_openai_client(custom_client) ``` -さらに、使用する OpenAI API をカスタマイズすることもできます。デフォルトでは OpenAI Responses API を使用していますが、[set_default_openai_api()][agents.set_default_openai_api] 関数を使って Chat Completions API を利用するように上書きできます。 +さらに、使用する OpenAI API をカスタマイズすることもできます。既定では OpenAI Responses API を利用します。これを Chat Completions API に変更するには、 [set_default_openai_api()][agents.set_default_openai_api] 関数を使用してください。 ```python from agents import set_default_openai_api @@ -30,7 +34,7 @@ set_default_openai_api("chat_completions") ## トレーシング -トレーシングはデフォルトで有効になっています。デフォルトでは、上記のセクションで説明した OpenAI API キー（環境変数または設定したデフォルトキー）を使用します。トレーシング専用の API キーを設定したい場合は、[`set_tracing_export_api_key`][agents.set_tracing_export_api_key] 関数を利用してください。 +トレーシングはデフォルトで有効になっています。前述の OpenAI API キー（環境変数または設定したデフォルトキー）が自動的に使用されます。トレーシングで使用する API キーを個別に設定したい場合は、 [`set_tracing_export_api_key`][agents.set_tracing_export_api_key] 関数を利用してください。 ```python from agents import set_tracing_export_api_key @@ -38,7 +42,7 @@ from agents import set_tracing_export_api_key set_tracing_export_api_key("sk-...") ``` -また、[`set_tracing_disabled()`][agents.set_tracing_disabled] 関数を使ってトレーシングを完全に無効化することもできます。 +トレーシングを完全に無効化するには、 [`set_tracing_disabled()`][agents.set_tracing_disabled] 関数を呼び出します。 ```python from agents import set_tracing_disabled @@ -48,9 +52,9 @@ set_tracing_disabled(True) ## デバッグログ -SDK には、ハンドラーが設定されていない 2 つの Python ロガーがあります。デフォルトでは、警告やエラーは `stdout` に送信されますが、それ以外のログは抑制されます。 + SDK にはハンドラーが設定されていない Python ロガーが 2 つあります。デフォルトでは、警告とエラーは `stdout` に出力されますが、それ以外のログは抑制されます。 -詳細なログ出力を有効にするには、[`enable_verbose_stdout_logging()`][agents.enable_verbose_stdout_logging] 関数を使用してください。 +詳細なログを有効にするには、 [`enable_verbose_stdout_logging()`][agents.enable_verbose_stdout_logging] 関数を使用します。 ```python from agents import enable_verbose_stdout_logging @@ -58,7 +62,7 @@ from agents import enable_verbose_stdout_logging enable_verbose_stdout_logging() ``` -また、ハンドラーやフィルター、フォーマッターなどを追加してログをカスタマイズすることも可能です。詳細は [Python ロギングガイド](https://docs.python.org/3/howto/logging.html) をご覧ください。 +必要に応じて、ハンドラー、フィルター、フォーマッターなどを追加してログをカスタマイズすることも可能です。詳しくは [Python ロギングガイド](https://docs.python.org/3/howto/logging.html) を参照してください。 ```python import logging @@ -77,17 +81,17 @@ logger.setLevel(logging.WARNING) logger.addHandler(logging.StreamHandler()) ``` -### ログ内の機微なデータ +### ログに含まれる機微情報 -一部のログには機微なデータ（たとえば ユーザー データ）が含まれる場合があります。これらのデータのログ出力を無効にしたい場合は、以下の環境変数を設定してください。 +特定のログには機微情報（たとえば ユーザー データ）が含まれる場合があります。この情報が記録されるのを防ぎたい場合は、次の環境変数を設定してください。 -LLM の入力および出力のログ出力を無効にするには: +LLM の入力および出力のログを無効にする: ```bash export OPENAI_AGENTS_DONT_LOG_MODEL_DATA=1 ``` -ツールの入力および出力のログ出力を無効にするには: +ツールの入力および出力のログを無効にする: ```bash export OPENAI_AGENTS_DONT_LOG_TOOL_DATA=1 diff --git a/docs/ja/context.md b/docs/ja/context.md index 4240ceeef..72c0938cf 100644 --- a/docs/ja/context.md +++ b/docs/ja/context.md @@ -1,29 +1,33 @@ +--- +search: + exclude: true +--- # コンテキスト管理 -コンテキストは多義的な用語です。主に関心を持つべきコンテキストには、次の 2 つの大きなクラスがあります。 +コンテキストという言葉には複数の意味があります。ここでは主に 2 つのコンテキストについて説明します。 -1. コード内でローカルに利用可能なコンテキスト：これは、ツール関数の実行時や `on_handoff` のようなコールバック、ライフサイクルフックなどで必要となるデータや依存関係です。 -2. LLM に利用可能なコンテキスト：これは、LLM がレスポンスを生成する際に参照できるデータです。 +1. コード内でローカルに利用できるコンテキスト: ツール関数の実行時や `on_handoff` などのコールバック、ライフサイクルフックで必要となるデータや依存関係です。 +2. LLM が参照できるコンテキスト: LLM がレスポンスを生成する際に見えるデータです。 ## ローカルコンテキスト -これは [`RunContextWrapper`][agents.run_context.RunContextWrapper] クラスおよびその中の [`context`][agents.run_context.RunContextWrapper.context] プロパティによって表現されます。仕組みは以下の通りです。 +ローカルコンテキストは [`RunContextWrapper`][agents.run_context.RunContextWrapper] クラスと、その中の [`context`][agents.run_context.RunContextWrapper.context] プロパティで表現されます。仕組みは次のとおりです。 -1. 任意の Python オブジェクトを作成します。一般的なパターンとしては、dataclass や Pydantic オブジェクトを使います。 -2. そのオブジェクトを各種 run メソッド（例：`Runner.run(..., **context=whatever**))`）に渡します。 -3. すべてのツール呼び出しやライフサイクルフックなどには、ラッパーオブジェクト `RunContextWrapper[T]` が渡されます。ここで `T` はコンテキストオブジェクトの型を表し、`wrapper.context` からアクセスできます。 +1. 任意の Python オブジェクトを作成します。一般的なパターンとして dataclass や Pydantic オブジェクトを使用します。 +2. そのオブジェクトを各種 run メソッド（例: `Runner.run(..., **context=whatever** )`）に渡します。 +3. すべてのツール呼び出しやライフサイクルフックには、ラッパーオブジェクト `RunContextWrapper[T]` が渡されます。ここで `T` はコンテキストオブジェクトの型で、`wrapper.context` からアクセスできます。 -**最も重要**な注意点：特定のエージェント実行において、すべてのエージェント、ツール関数、ライフサイクルなどは、同じ _型_ のコンテキストを使用する必要があります。 +**最重要ポイント**: あるエージェントの実行において、エージェント・ツール関数・ライフサイクルフックなどはすべて同じ _型_ のコンテキストを使用しなければなりません。 -コンテキストは以下のような用途で利用できます。 +コンテキストでは次のような用途が考えられます。 -- 実行時のコンテキストデータ（例：ユーザー名/uid やユーザーに関するその他の情報など） -- 依存関係（例：ロガーオブジェクト、データフェッチャーなど） +- 実行に関するデータ（例: ユーザー名 / uid やその他のユーザー情報） +- 依存オブジェクト（例: ロガー、データフェッチャーなど） - ヘルパー関数 -!!! danger "注意" +!!! danger "Note" - コンテキストオブジェクトは **LLM には送信されません**。これは純粋にローカルなオブジェクトであり、読み書きやメソッド呼び出しが可能です。 + コンテキストオブジェクトは LLM には送信されません。あくまでローカルのオブジェクトであり、読み書きやメソッド呼び出しが可能です。 ```python import asyncio @@ -61,17 +65,17 @@ if __name__ == "__main__": asyncio.run(main()) ``` -1. これはコンテキストオブジェクトです。ここでは dataclass を使用していますが、任意の型を利用できます。 -2. これはツールです。`RunContextWrapper[UserInfo]` を受け取っていることが分かります。ツールの実装はコンテキストから値を読み取ります。 -3. エージェントにはジェネリック型 `UserInfo` を指定しています。これにより、型チェッカーがエラーを検出できます（例えば、異なるコンテキスト型を受け取るツールを渡そうとした場合など）。 -4. コンテキストは `run` 関数に渡されます。 -5. エージェントは正しくツールを呼び出し、年齢を取得します。 +1. これがコンテキストオブジェクトです。ここでは dataclass を使っていますが、任意の型を使用できます。 +2. これはツールです。`RunContextWrapper[UserInfo]` を受け取り、実装内でコンテキストを参照しています。 +3. エージェントにジェネリック `UserInfo` を付与することで、型チェッカーが誤りを検出できます（たとえば別のコンテキスト型を受け取るツールを渡した場合など）。 +4. `run` 関数にコンテキストを渡します。 +5. エージェントはツールを正しく呼び出し、年齢を取得します。 -## エージェント／LLM コンテキスト +## エージェント / LLM コンテキスト -LLM が呼び出される際、**唯一** 参照できるデータは会話履歴からのものです。つまり、LLM に新しいデータを利用させたい場合は、そのデータを履歴に含める必要があります。これを実現する方法はいくつかあります。 +LLM が呼び出されるとき、LLM が参照できるデータは会話履歴に含まれるものだけです。したがって、新しいデータを LLM に渡したい場合は、そのデータを履歴に含める形で提供する必要があります。方法はいくつかあります。 -1. エージェントの `instructions` に追加する。この方法は「システムプロンプト」や「開発者メッセージ」とも呼ばれます。システムプロンプトは静的な文字列でも、コンテキストを受け取って文字列を出力する動的な関数でも構いません。たとえば、ユーザー名や現在の日付など、常に有用な情報に適しています。 -2. `Runner.run` 関数を呼び出す際に `input` に追加する。この方法は `instructions` と似ていますが、[chain of command](https://cdn.openai.com/spec/model-spec-2024-05-08.html#follow-the-chain-of-command) の下位メッセージとして追加できます。 -3. 関数ツールを通じて公開する。この方法は _オンデマンド_ のコンテキストに適しています。LLM が必要なタイミングでツールを呼び出し、データを取得できます。 -4. リトリーバルや Web 検索を利用する。これらはファイルやデータベース（リトリーバル）、または Web（Web 検索）から関連データを取得できる特別なツールです。関連するコンテキストデータに基づいたレスポンスを「グラウンディング」するのに役立ちます。 \ No newline at end of file +1. Agent の `instructions` に追加する。いわゆる「system prompt」や「developer message」と呼ばれるものです。システムプロンプトは静的な文字列でも、コンテキストを受け取って文字列を返す動的な関数でも構いません。ユーザー名や現在の日付など、常に有用な情報を渡す際によく使われます。 +2. `Runner.run` 呼び出し時の `input` に追加する。`instructions` と似ていますが、[chain of command](https://cdn.openai.com/spec/model-spec-2024-05-08.html#follow-the-chain-of-command) の下位レイヤーにメッセージを配置できます。 +3. 関数ツール経由で公開する。オンデマンドで取得するコンテキストに適しており、LLM が必要に応じてツールを呼び出してデータを取得します。 +4. retrieval や web search を使う。これらは特別なツールで、ファイルやデータベースから関連データを取得する（retrieval）、もしくは Web から取得する（web search）ことができます。レスポンスを関連コンテキストで「グラウンディング」するのに有効です。 \ No newline at end of file diff --git a/docs/ja/examples.md b/docs/ja/examples.md index c2393c8e6..00f634ec1 100644 --- a/docs/ja/examples.md +++ b/docs/ja/examples.md @@ -1,40 +1,45 @@ +--- +search: + exclude: true +--- # コード例 -SDK のさまざまなサンプル実装については、[リポジトリ](https://github.com/openai/openai-agents-python/tree/main/examples) のコード例セクションをご覧ください。これらのコード例は、異なるパターンや機能を示すいくつかのカテゴリーに整理されています。 +リポジトリの [examples セクション](https://github.com/openai/openai-agents-python/tree/main/examples) には、 SDK のさまざまなサンプル実装が用意されています。これらの例は、異なるパターンや機能を示す複数のカテゴリーに整理されています。 + ## カテゴリー - **[agent_patterns](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns):** - このカテゴリーのコード例では、よく使われるエージェント設計パターンを紹介しています。 + このカテゴリーの例では、一般的なエージェント設計パターンを紹介しています。 - - 決定論的なワークフロー - - ツールとしてのエージェント - - エージェントの並列実行 + - 決定論的ワークフロー + - ツールとしてのエージェント + - エージェントの並列実行 - **[basic](https://github.com/openai/openai-agents-python/tree/main/examples/basic):** - これらのコード例では、SDK の基本的な機能を紹介しています。 + SDK の基礎的な機能を示す例です。 - - 動的なシステムプロンプト - - ストリーミング出力 - - ライフサイクルイベント + - 動的なシステムプロンプト + - ストリーミング出力 + - ライフサイクルイベント - **[tool examples](https://github.com/openai/openai-agents-python/tree/main/examples/tools):** - OpenAI がホストするツール（Web 検索やファイル検索など）の実装方法や、それらをエージェントに統合する方法を学べます。 + Web 検索やファイル検索など、 OpenAI がホストするツールの実装方法と、それらをエージェントに統合する方法を学べます。 - **[model providers](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers):** - OpenAI 以外のモデルを SDK で利用する方法を紹介しています。 + OpenAI 以外のモデルを SDK で利用する方法を探ります。 - **[handoffs](https://github.com/openai/openai-agents-python/tree/main/examples/handoffs):** - エージェントのハンドオフの実践的なコード例をご覧いただけます。 + エージェントのハンドオフを実践的に示す例です。 - **[mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp):** - Model context protocol (MCP) を使ったエージェントの構築方法を学べます。 + MCP を使ったエージェントの構築方法を学べます。 -- **[customer_service](https://github.com/openai/openai-agents-python/tree/main/examples/customer_service)** および **[research_bot](https://github.com/openai/openai-agents-python/tree/main/examples/research_bot):** - 実際のユースケースを示す、より発展的な 2 つのコード例です。 +- **[customer_service](https://github.com/openai/openai-agents-python/tree/main/examples/customer_service)** と **[research_bot](https://github.com/openai/openai-agents-python/tree/main/examples/research_bot):** + より実践的なユースケースを示す、拡張された 2 つの例です。 - - **customer_service**: 航空会社向けカスタマーサービスシステムの例。 - - **research_bot**: シンプルなディープリサーチクローン。 + - **customer_service**: 航空会社向けカスタマーサービスシステムの例 + - **research_bot**: シンプルなディープリサーチクローン - **[voice](https://github.com/openai/openai-agents-python/tree/main/examples/voice):** - TTS および STT モデルを利用した音声エージェントのコード例をご覧いただけます。 \ No newline at end of file + TTS と STT モデルを用いた音声エージェントの例をご覧ください。 \ No newline at end of file diff --git a/docs/ja/guardrails.md b/docs/ja/guardrails.md index 2525f8c20..b67bb8bad 100644 --- a/docs/ja/guardrails.md +++ b/docs/ja/guardrails.md @@ -1,43 +1,47 @@ +--- +search: + exclude: true +--- # ガードレール -ガードレールは、エージェントと _並行して_ 実行され、ユーザー入力のチェックやバリデーションを行うことができます。例えば、非常に賢い（そのため遅くて高価な）モデルを使ってカスタマーリクエストに対応するエージェントがあるとします。悪意のあるユーザーがモデルに数学の宿題を手伝わせるようなリクエストを送ることは避けたいでしょう。そこで、ガードレールを高速かつ安価なモデルで実行できます。ガードレールが悪意のある利用を検知した場合、即座にエラーを発生させ、高価なモデルの実行を止めて時間やコストを節約できます。 +ガードレールは エージェント と _並行して_ 実行され、ユーザー入力のチェックとバリデーションを行えます。例えば、とても賢い（つまり遅く/高価な）モデルを使用してカスタマーリクエストを処理するエージェントがあるとします。悪意のある ユーザー がモデルに数学の宿題を手伝わせようとするのは避けたいでしょう。そこで、速く/安価なモデルで動くガードレールを実行できます。ガードレールが悪意のある利用を検知すると、直ちにエラーを送出して高価なモデルの実行を停止し、時間とコストを節約できます。 -ガードレールには 2 種類あります： +ガードレールには 2 種類あります: -1. 入力ガードレール：最初のユーザー入力に対して実行されます -2. 出力ガードレール：最終的なエージェント出力に対して実行されます +1. 入力ガードレール は初期 ユーザー 入力に対して実行されます +2. 出力ガードレール は最終的なエージェント出力に対して実行されます ## 入力ガードレール -入力ガードレールは 3 ステップで実行されます： +入力ガードレールは 3 ステップで実行されます: -1. まず、ガードレールはエージェントに渡されたものと同じ入力を受け取ります。 -2. 次に、ガードレール関数が実行され、[`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を生成し、それが [`InputGuardrailResult`][agents.guardrail.InputGuardrailResult] でラップされます。 -3. 最後に、[`.tripwire_triggered`][agents.guardrail.GuardrailFunctionOutput.tripwire_triggered] が true かどうかを確認します。true の場合、[`InputGuardrailTripwireTriggered`][agents.exceptions.InputGuardrailTripwireTriggered] 例外が発生し、ユーザーへの適切な対応や例外処理が可能です。 +1. まず、ガードレールはエージェントに渡されたものと同じ入力を受け取ります。 +2. 次に、ガードレール関数が実行され [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を生成し、それが [`InputGuardrailResult`][agents.guardrail.InputGuardrailResult] にラップされます。 +3. 最後に [.tripwire_triggered][agents.guardrail.GuardrailFunctionOutput.tripwire_triggered] が true かどうかを確認します。true の場合、[`InputGuardrailTripwireTriggered`][agents.exceptions.InputGuardrailTripwireTriggered] 例外が送出されるので、適切に ユーザー に応答したり例外を処理できます。 !!! Note - 入力ガードレールはユーザー入力に対して実行されることを想定しているため、エージェントのガードレールは *最初* のエージェントでのみ実行されます。「なぜ `guardrails` プロパティがエージェントにあり、`Runner.run` に渡さないのか？」と疑問に思うかもしれません。これは、ガードレールが実際のエージェントに関連することが多いためです。異なるエージェントごとに異なるガードレールを実行するため、コードを同じ場所にまとめておくと可読性が向上します。 + 入力ガードレールは ユーザー 入力に対して実行されることを意図しているため、ガードレールは *最初* のエージェントでのみ実行されます。「なぜ `guardrails` プロパティがエージェントにあり、`Runner.run` に渡さないのか」と疑問に思うかもしれません。これは、ガードレールが実際の エージェント と密接に関連していることが多いからです。異なるエージェントには異なるガードレールを実行するため、コードを同じ場所に置くことで可読性が向上します。 ## 出力ガードレール -出力ガードレールも 3 ステップで実行されます： +出力ガードレールは 3 ステップで実行されます: -1. まず、ガードレールはエージェントに渡されたものと同じ入力を受け取ります。 -2. 次に、ガードレール関数が実行され、[`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を生成し、それが [`OutputGuardrailResult`][agents.guardrail.OutputGuardrailResult] でラップされます。 -3. 最後に、[`.tripwire_triggered`][agents.guardrail.GuardrailFunctionOutput.tripwire_triggered] が true かどうかを確認します。true の場合、[`OutputGuardrailTripwireTriggered`][agents.exceptions.OutputGuardrailTripwireTriggered] 例外が発生し、ユーザーへの適切な対応や例外処理が可能です。 +1. まず、ガードレールはエージェントが生成した出力を受け取ります。 +2. 次に、ガードレール関数が実行され [`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を生成し、それが [`OutputGuardrailResult`][agents.guardrail.OutputGuardrailResult] にラップされます。 +3. 最後に [.tripwire_triggered][agents.guardrail.GuardrailFunctionOutput.tripwire_triggered] が true かどうかを確認します。true の場合、[`OutputGuardrailTripwireTriggered`][agents.exceptions.OutputGuardrailTripwireTriggered] 例外が送出されるので、適切に ユーザー に応答したり例外を処理できます。 !!! Note - 出力ガードレールは最終的なエージェント出力に対して実行されることを想定しているため、エージェントのガードレールは *最後* のエージェントでのみ実行されます。入力ガードレールと同様に、ガードレールが実際のエージェントに関連することが多いため、コードを同じ場所にまとめておくと可読性が向上します。 + 出力ガードレールは最終的なエージェント出力に対して実行されることを意図しているため、ガードレールは *最後* のエージェントでのみ実行されます。入力ガードレールの場合と同様、ガードレールが実際の エージェント と密接に関連していることが多いため、コードを同じ場所に置くことで可読性が向上します。 ## トリップワイヤー -入力または出力がガードレールに失敗した場合、ガードレールはトリップワイヤーでこれを通知できます。トリップワイヤーが発動したガードレールを検知した時点で、即座に `{Input,Output}GuardrailTripwireTriggered` 例外を発生させ、エージェントの実行を停止します。 +入力または出力がガードレールを通過できなかった場合、ガードレールはトリップワイヤーでそれを示すことができます。トリップワイヤーがトリガーされたガードレールを検知した時点で、直ちに `{Input,Output}GuardrailTripwireTriggered` 例外を送出し、エージェントの実行を停止します。 ## ガードレールの実装 -入力を受け取り、[`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を返す関数を用意する必要があります。この例では、内部でエージェントを実行することでこれを実現します。 +入力を受け取り、[`GuardrailFunctionOutput`][agents.guardrail.GuardrailFunctionOutput] を返す関数を提供する必要があります。この例では、内部で エージェント を実行してこれを行います。 ```python from pydantic import BaseModel @@ -90,10 +94,10 @@ async def main(): print("Math homework guardrail tripped") ``` -1. このエージェントをガードレール関数内で使用します。 -2. これはエージェントの入力やコンテキストを受け取り、結果を返すガードレール関数です。 -3. ガードレールの結果に追加情報を含めることができます。 -4. これはワークフローを定義する実際のエージェントです。 +1. このエージェントをガードレール関数内で使用します。 +2. これはエージェントの入力/コンテキストを受け取り、結果を返すガードレール関数です。 +3. ガードレール結果に追加情報を含めることができます。 +4. これはワークフローを定義する実際のエージェントです。 出力ガードレールも同様です。 @@ -148,7 +152,7 @@ async def main(): print("Math output guardrail tripped") ``` -1. これは実際のエージェントの出力型です。 -2. これはガードレールの出力型です。 -3. これはエージェントの出力を受け取り、結果を返すガードレール関数です。 -4. これはワークフローを定義する実際のエージェントです。 \ No newline at end of file +1. これは実際のエージェントの出力型です。 +2. これはガードレールの出力型です。 +3. これはエージェントの出力を受け取り、結果を返すガードレール関数です。 +4. これはワークフローを定義する実際のエージェントです。 \ No newline at end of file diff --git a/docs/ja/handoffs.md b/docs/ja/handoffs.md index ae3b8b12e..c0e99556e 100644 --- a/docs/ja/handoffs.md +++ b/docs/ja/handoffs.md @@ -1,18 +1,22 @@ +--- +search: + exclude: true +--- # ハンドオフ -ハンドオフは、エージェントが他のエージェントにタスクを委任できる仕組みです。これは、異なるエージェントがそれぞれ異なる分野に特化しているシナリオで特に有用です。たとえば、カスタマーサポートアプリでは、注文状況、返金、FAQ などのタスクをそれぞれ専門に扱うエージェントが存在する場合があります。 +ハンドオフを使用すると、エージェント がタスクを別の エージェント に委譲できます。これは、複数の エージェント がそれぞれ異なる分野を専門とするシナリオで特に便利です。たとえばカスタマーサポートアプリでは、注文状況、返金、 FAQ などのタスクを個別に担当する エージェント を用意できます。 -ハンドオフは LLM からはツールとして認識されます。たとえば、`Refund Agent` という名前のエージェントへのハンドオフがある場合、そのツール名は `transfer_to_refund_agent` となります。 +ハンドオフは LLM からはツールとして認識されます。そのため、`Refund Agent` という エージェント へのハンドオフであれば、ツール名は `transfer_to_refund_agent` になります。 ## ハンドオフの作成 -すべてのエージェントは [`handoffs`][agents.agent.Agent.handoffs] パラメーターを持っており、これは直接 `Agent` を指定することも、ハンドオフをカスタマイズする `Handoff` オブジェクトを指定することもできます。 +すべての エージェント には [`handoffs`][agents.agent.Agent.handoffs] パラメーターがあり、直接 `Agent` を渡すことも、ハンドオフをカスタマイズする `Handoff` オブジェクトを渡すこともできます。 -Agents SDK で提供されている [`handoff()`][agents.handoffs.handoff] 関数を使ってハンドオフを作成できます。この関数では、ハンドオフ先のエージェントや、オプションのオーバーライドや入力フィルターを指定できます。 +Agents SDK が提供する [`handoff()`][agents.handoffs.handoff] 関数を使ってハンドオフを作成できます。この関数では、引き継ぎ先の エージェント を指定し、オーバーライドや入力フィルターをオプションで設定できます。 ### 基本的な使い方 -シンプルなハンドオフの作成方法は以下の通りです。 +シンプルなハンドオフを作成する例を示します。 ```python from agents import Agent, handoff @@ -24,18 +28,18 @@ refund_agent = Agent(name="Refund agent") triage_agent = Agent(name="Triage agent", handoffs=[billing_agent, handoff(refund_agent)]) ``` -1. エージェントを直接指定する（例：`billing_agent`）ことも、`handoff()` 関数を使うこともできます。 +1. `billing_agent` のように エージェント を直接指定することも、`handoff()` 関数を使用することもできます。 ### `handoff()` 関数によるハンドオフのカスタマイズ -[`handoff()`][agents.handoffs.handoff] 関数では、さまざまなカスタマイズが可能です。 +[`handoff()`][agents.handoffs.handoff] 関数を使うと、ハンドオフを細かくカスタマイズできます。 -- `agent`: ハンドオフ先のエージェントです。 -- `tool_name_override`: デフォルトでは `Handoff.default_tool_name()` 関数が使われ、`transfer_to_` となります。これを上書きできます。 -- `tool_description_override`: デフォルトのツール説明（`Handoff.default_tool_description()`）を上書きできます。 -- `on_handoff`: ハンドオフが呼び出されたときに実行されるコールバック関数です。たとえば、ハンドオフが呼び出されたタイミングでデータ取得を開始するなどに便利です。この関数はエージェントコンテキストを受け取り、オプションで LLM が生成した入力も受け取れます。入力データは `input_type` パラメーターで制御します。 -- `input_type`: ハンドオフで期待される入力の型（オプション）です。 -- `input_filter`: 次のエージェントが受け取る入力をフィルタリングできます。詳細は下記をご覧ください。 +- `agent`: ここで指定した エージェント に処理が引き渡されます。 +- `tool_name_override`: デフォルトでは `Handoff.default_tool_name()` が使用され、`transfer_to_` という名前になります。これを上書きできます。 +- `tool_description_override`: `Handoff.default_tool_description()` が返すデフォルトのツール説明を上書きします。 +- `on_handoff`: ハンドオフ実行時に呼び出されるコールバック関数です。ハンドオフが呼ばれたタイミングでデータ取得を開始するなどに便利です。この関数は エージェント のコンテキストを受け取り、オプションで LLM が生成した入力も受け取れます。渡されるデータは `input_type` パラメーターで制御します。 +- `input_type`: ハンドオフが受け取る入力の型（任意）。 +- `input_filter`: 次の エージェント が受け取る入力をフィルタリングできます。詳細は後述します。 ```python from agents import Agent, handoff, RunContextWrapper @@ -55,7 +59,7 @@ handoff_obj = handoff( ## ハンドオフ入力 -状況によっては、LLM にハンドオフ時に何らかのデータを提供してほしい場合があります。たとえば、「エスカレーションエージェント」へのハンドオフを考えてみましょう。この場合、理由を提供して記録できるようにしたいことがあります。 +場合によっては、 LLM がハンドオフを呼び出す際に追加のデータを渡してほしいことがあります。たとえば「Escalation エージェント」へのハンドオフでは、ログ用に理由を渡してもらいたいかもしれません。 ```python from pydantic import BaseModel @@ -79,9 +83,9 @@ handoff_obj = handoff( ## 入力フィルター -ハンドオフが発生すると、新しいエージェントが会話を引き継ぎ、これまでの会話履歴全体を見ることができます。これを変更したい場合は、[`input_filter`][agents.handoffs.Handoff.input_filter] を設定できます。入力フィルターは、[`HandoffInputData`][agents.handoffs.HandoffInputData] を受け取り、新しい `HandoffInputData` を返す関数です。 +ハンドオフが発生すると、新しい エージェント が会話を引き継ぎ、これまでの会話履歴全体を閲覧できる状態になります。これを変更したい場合は [`input_filter`][agents.handoffs.Handoff.input_filter] を設定してください。入力フィルターは、[`HandoffInputData`][agents.handoffs.HandoffInputData] として渡される既存の入力を受け取り、新しい `HandoffInputData` を返す関数です。 -よくあるパターン（たとえば履歴からすべてのツール呼び出しを削除するなど）は、[`agents.extensions.handoff_filters`][] で実装されています。 +よくあるパターン（たとえば履歴からすべてのツール呼び出しを削除するなど）は [`agents.extensions.handoff_filters`][] に実装済みです。 ```python from agents import Agent, handoff @@ -95,11 +99,11 @@ handoff_obj = handoff( ) ``` -1. これにより、`FAQ agent` が呼び出されたときに履歴からすべてのツールが自動的に削除されます。 +1. これにより `FAQ agent` が呼ばれた際に、履歴からすべてのツール呼び出しが自動で削除されます。 ## 推奨プロンプト -LLM がハンドオフを正しく理解できるようにするため、エージェントにハンドオフに関する情報を含めることを推奨します。[`agents.extensions.handoff_prompt.RECOMMENDED_PROMPT_PREFIX`][] で推奨されるプレフィックスを利用するか、[`agents.extensions.handoff_prompt.prompt_with_handoff_instructions`][] を呼び出して、推奨データをプロンプトに自動追加できます。 +LLM がハンドオフを正しく理解できるよう、エージェント にハンドオフに関する情報を含めることを推奨します。事前に用意したプレフィックス [`agents.extensions.handoff_prompt.RECOMMENDED_PROMPT_PREFIX`][] を利用するか、[`agents.extensions.handoff_prompt.prompt_with_handoff_instructions`][] を呼び出してプロンプトに推奨情報を自動で追加できます。 ```python from agents import Agent diff --git a/docs/ja/index.md b/docs/ja/index.md index 3b3064644..39692a166 100644 --- a/docs/ja/index.md +++ b/docs/ja/index.md @@ -1,28 +1,32 @@ +--- +search: + exclude: true +--- # OpenAI Agents SDK -[OpenAI Agents SDK](https://github.com/openai/openai-agents-python) は、非常に少ない抽象化で、軽量かつ使いやすいパッケージで エージェント 型 AI アプリを構築できる SDK です。これは、以前のエージェント向け実験プロジェクト [Swarm](https://github.com/openai/swarm/tree/main) の本番運用向けアップグレード版です。Agents SDK には、非常に少数の基本コンポーネントが含まれています。 +[OpenAI Agents SDK](https://github.com/openai/openai-agents-python) は、抽象化をほとんど排した軽量で使いやすいパッケージにより、エージェントベースの AI アプリを構築できるようにします。これは、以前のエージェント向け実験プロジェクトである [Swarm](https://github.com/openai/swarm/tree/main) をプロダクションレベルへとアップグレードしたものです。Agents SDK にはごく少数の基本コンポーネントがあります。 -- **エージェント**： instructions と tools を備えた LLM -- **ハンドオフ**：特定のタスクを他のエージェントに委任できる仕組み -- **ガードレール**：エージェントへの入力を検証できる仕組み +- **エージェント**: instructions と tools を備えた LLM +- **ハンドオフ**: エージェントが特定タスクを他のエージェントへ委任するしくみ +- **ガードレール**: エージェントへの入力を検証する機能 -Python と組み合わせることで、これらの基本コンポーネントは tools と エージェント 間の複雑な関係を表現でき、急な学習コストなしに実用的なアプリケーションを構築できます。さらに、SDK には組み込みの **トレーシング** 機能があり、エージェント フローの可視化やデバッグ、評価、さらにはアプリケーション向けモデルのファインチューニングも可能です。 +Python と組み合わせることで、これらのコンポーネントはツールとエージェント間の複雑な関係を表現でき、学習コストを抑えつつ実際のアプリケーションを構築できます。さらに SDK には、エージェントフローを可視化・デバッグできる **トレーシング** が標準搭載されており、評価やファインチューニングにも活用可能です。 -## なぜ Agents SDK を使うのか +## Agents SDK を使用する理由 SDK には 2 つの設計原則があります。 -1. 使う価値のある十分な機能を持ちつつ、学習が容易なほど少ない基本コンポーネントで構成されていること。 -2. すぐに使い始められるが、動作を細かくカスタマイズできること。 +1. 使う価値のある十分な機能を備えつつ、学習が早いようコンポーネント数を絞る。 +2. すぐに使い始められる初期設定で動作しつつ、挙動を細かくカスタマイズできる。 -SDK の主な特徴は以下の通りです。 +主な機能は次のとおりです。 -- エージェントループ： tools の呼び出し、LLM への実行結果の送信、LLM が完了するまでのループ処理を自動で行います。 -- Python ファースト：新しい抽象化を学ぶ必要なく、Python の言語機能でエージェントのオーケストレーションや連携が可能です。 -- ハンドオフ：複数のエージェント間での調整や委任を実現する強力な機能です。 -- ガードレール：エージェントと並行して入力検証やチェックを実行し、チェックに失敗した場合は早期に処理を中断します。 -- 関数ツール：任意の Python 関数を自動でスキーマ生成・Pydantic ベースのバリデーション付きツールに変換できます。 -- トレーシング：組み込みのトレーシングでワークフローの可視化・デバッグ・モニタリングができ、OpenAI の評価・ファインチューニング・蒸留ツールも利用可能です。 +- エージェントループ: ツール呼び出し、結果を LLM に送信、LLM が完了するまでのループを自動で処理。 +- Python ファースト: 新しい抽象化を学ばずに、言語標準機能でエージェントをオーケストレーション。 +- ハンドオフ: 複数エージェント間の協調と委譲を実現する強力な機能。 +- ガードレール: エージェントと並列で入力バリデーションを実行し、失敗時に早期終了。 +- 関数ツール: 任意の Python 関数をツール化し、自動スキーマ生成と Pydantic での検証を提供。 +- トレーシング: フローの可視化・デバッグ・モニタリングに加え、OpenAI の評価・ファインチューニング・蒸留ツールを利用可能。 ## インストール @@ -30,7 +34,7 @@ SDK の主な特徴は以下の通りです。 pip install openai-agents ``` -## Hello World サンプル +## Hello World の例 ```python from agents import Agent, Runner @@ -45,7 +49,7 @@ print(result.final_output) # Infinite loop's dance. ``` -(_実行する場合は、`OPENAI_API_KEY` 環境変数を設定してください_) +(_これを実行する場合は、`OPENAI_API_KEY` 環境変数を設定してください_) ```bash export OPENAI_API_KEY=sk-... diff --git a/docs/ja/mcp.md b/docs/ja/mcp.md index d93252fba..1e394a5e6 100644 --- a/docs/ja/mcp.md +++ b/docs/ja/mcp.md @@ -1,35 +1,47 @@ +--- +search: + exclude: true +--- # Model context protocol (MCP) -[Model context protocol](https://modelcontextprotocol.io/introduction)（通称 MCP）は、 LLM にツールやコンテキストを提供するための方法です。MCP ドキュメントからの引用です： +[Model context protocol](https://modelcontextprotocol.io/introduction)（通称 MCP）は、 LLM にツールとコンテキストを提供するための仕組みです。MCP のドキュメントでは次のように説明されています。 -> MCP は、アプリケーションが LLM にコンテキストを提供する方法を標準化するオープンプロトコルです。MCP を AI アプリケーションのための USB-C ポートのようなものと考えてください。USB-C がさまざまな周辺機器やアクセサリにデバイスを接続する標準的な方法を提供するのと同様に、MCP は AI モデルをさまざまなデータソースやツールに接続する標準的な方法を提供します。 +> MCP は、アプリケーションが LLM にコンテキストを提供する方法を標準化するオープンプロトコルです。MCP は AI アプリケーションにとっての USB‑C ポートのようなものと考えてください。USB‑C が各種デバイスを周辺機器と接続するための標準化された方法を提供するのと同様に、MCP は AI モデルをさまざまなデータソースやツールと接続するための標準化された方法を提供します。 -Agents SDK は MCP をサポートしています。これにより、幅広い MCP サーバーを利用して、エージェントにツールを提供することができます。 +Agents SDK は MCP をサポートしており、これにより幅広い MCP サーバーをエージェントにツールとして追加できます。 ## MCP サーバー -現在、MCP 仕様では、使用するトランスポートメカニズムに基づいて 2 種類のサーバーが定義されています： +現在、MCP 仕様では使用するトランスポート方式に基づき 3 種類のサーバーが定義されています。 -1. **stdio** サーバーは、アプリケーションのサブプロセスとして実行されます。ローカルで実行されるものと考えることができます。 -2. **HTTP over SSE** サーバーはリモートで実行されます。URL を介して接続します。 +1. **stdio** サーバー: アプリケーションのサブプロセスとして実行されます。ローカルで動かすイメージです。 +2. **HTTP over SSE** サーバー: リモートで動作し、 URL 経由で接続します。 +3. **Streamable HTTP** サーバー: MCP 仕様に定義された Streamable HTTP トランスポートを使用してリモートで動作します。 -これらのサーバーに接続するには、[`MCPServerStdio`][agents.mcp.server.MCPServerStdio] および [`MCPServerSse`][agents.mcp.server.MCPServerSse] クラスを使用できます。 +これらのサーバーへは [`MCPServerStdio`][agents.mcp.server.MCPServerStdio]、[`MCPServerSse`][agents.mcp.server.MCPServerSse]、[`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp] クラスを使用して接続できます。 -例えば、[公式 MCP ファイルシステムサーバー](https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem) を使用する場合は、次のようになります。 +たとえば、[公式 MCP filesystem サーバー](https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem)を利用する場合は次のようになります。 ```python +from agents.run_context import RunContextWrapper + async with MCPServerStdio( params={ "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], } ) as server: - tools = await server.list_tools() + # 注意：実際には通常は MCP サーバーをエージェントに追加し、 + # フレームワークがツール一覧の取得を自動的に処理するようにします。 + # list_tools() への直接呼び出しには run_context と agent パラメータが必要です。 + run_context = RunContextWrapper(context=None) + agent = Agent(name="test", instructions="test") + tools = await server.list_tools(run_context, agent) ``` ## MCP サーバーの利用 -MCP サーバーはエージェントに追加できます。Agents SDK は、エージェントが実行されるたびに MCP サーバーの `list_tools()` を呼び出します。これにより、LLM は MCP サーバーのツールを認識できるようになります。LLM が MCP サーバーのツールを呼び出すと、SDK はそのサーバーの `call_tool()` を呼び出します。 +MCP サーバーはエージェントに追加できます。Agents SDK はエージェント実行時に毎回 MCP サーバーへ `list_tools()` を呼び出し、 LLM に MCP サーバーのツールを認識させます。LLM が MCP サーバーのツールを呼び出すと、SDK はそのサーバーへ `call_tool()` を実行します。 ```python @@ -42,19 +54,19 @@ agent=Agent( ## キャッシュ -エージェントが実行されるたびに、MCP サーバーの `list_tools()` が呼び出されます。特にサーバーがリモートの場合、これはレイテンシの原因となることがあります。ツールリストを自動的にキャッシュするには、[`MCPServerStdio`][agents.mcp.server.MCPServerStdio] および [`MCPServerSse`][agents.mcp.server.MCPServerSse] の両方に `cache_tools_list=True` を渡すことができます。ツールリストが変更されないことが確実な場合のみ、この設定を行ってください。 +エージェントが実行されるたびに、MCP サーバーへ `list_tools()` が呼び出されます。サーバーがリモートの場合は特にレイテンシが発生します。ツール一覧を自動でキャッシュしたい場合は、[`MCPServerStdio`][agents.mcp.server.MCPServerStdio]、[`MCPServerSse`][agents.mcp.server.MCPServerSse]、[`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp] の各クラスに `cache_tools_list=True` を渡してください。ツール一覧が変更されないと確信できる場合のみ使用してください。 -キャッシュを無効化したい場合は、サーバーで `invalidate_tools_cache()` を呼び出すことができます。 +キャッシュを無効化したい場合は、サーバーで `invalidate_tools_cache()` を呼び出します。 -## エンドツーエンドの code examples +## エンドツーエンドのコード例 -[examples/mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp) で、完全な動作 code examples をご覧いただけます。 +完全な動作例は [examples/mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp) をご覧ください。 ## トレーシング -[トレーシング](../tracing.md) は、以下を含む MCP の操作を自動的に記録します： +[トレーシング](./tracing.md) は MCP の操作を自動的にキャプチャします。具体的には次の内容が含まれます。 -1. MCP サーバーへのツールリスト取得の呼び出し -2. 関数呼び出しに関する MCP 関連情報 +1. ツール一覧取得のための MCP サーバー呼び出し +2. 関数呼び出しに関する MCP 情報 -![MCP トレーシングのスクリーンショット](../assets/images/mcp-tracing.jpg) \ No newline at end of file +![MCP Tracing Screenshot](../assets/images/mcp-tracing.jpg) \ No newline at end of file diff --git a/docs/ja/models.md b/docs/ja/models.md deleted file mode 100644 index 5a76d60ec..000000000 --- a/docs/ja/models.md +++ /dev/null @@ -1,106 +0,0 @@ -# モデル - -Agents SDK には、OpenAI モデルの 2 種類のサポートが標準で用意されています。 - -- **推奨**: [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] は、新しい [Responses API](https://platform.openai.com/docs/api-reference/responses) を使って OpenAI API を呼び出します。 -- [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] は、[Chat Completions API](https://platform.openai.com/docs/api-reference/chat) を使って OpenAI API を呼び出します。 - -## モデルの組み合わせ - -1 つのワークフロー内で、各エージェントごとに異なるモデルを使いたい場合があります。たとえば、トリアージには小型で高速なモデルを使い、複雑なタスクにはより大きく高性能なモデルを使うことができます。[`Agent`][agents.Agent] を設定する際、以下のいずれかの方法で特定のモデルを選択できます。 - -1. OpenAI モデル名を直接渡す。 -2. 任意のモデル名と、その名前を Model インスタンスにマッピングできる [`ModelProvider`][agents.models.interface.ModelProvider] を渡す。 -3. [`Model`][agents.models.interface.Model] 実装を直接指定する。 - -!!!note - - SDK は [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] と [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] の両方の形状をサポートしていますが、各ワークフローで 1 つのモデル形状のみを使うことを推奨します。なぜなら、2 つの形状はサポートする機能やツールが異なるためです。ワークフローでモデル形状を組み合わせて使う場合は、利用するすべての機能が両方で利用可能かご確認ください。 - -```python -from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel -import asyncio - -spanish_agent = Agent( - name="Spanish agent", - instructions="You only speak Spanish.", - model="o3-mini", # (1)! -) - -english_agent = Agent( - name="English agent", - instructions="You only speak English", - model=OpenAIChatCompletionsModel( # (2)! - model="gpt-4o", - openai_client=AsyncOpenAI() - ), -) - -triage_agent = Agent( - name="Triage agent", - instructions="Handoff to the appropriate agent based on the language of the request.", - handoffs=[spanish_agent, english_agent], - model="gpt-3.5-turbo", -) - -async def main(): - result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") - print(result.final_output) -``` - -1. OpenAI モデル名を直接設定します。 -2. [`Model`][agents.models.interface.Model] 実装を指定します。 - -エージェントで使用するモデルをさらに細かく設定したい場合は、[`ModelSettings`][agents.models.interface.ModelSettings] を渡すことができます。これにより、temperature などのオプションのモデル設定パラメーターを指定できます。 - -```python -from agents import Agent, ModelSettings - -english_agent = Agent( - name="English agent", - instructions="You only speak English", - model="gpt-4o", - model_settings=ModelSettings(temperature=0.1), -) -``` - -## 他の LLM プロバイダーの利用 - -他の LLM プロバイダーは、3 つの方法で利用できます（[こちら](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/) に code examples があります）。 - -1. [`set_default_openai_client`][agents.set_default_openai_client] は、`AsyncOpenAI` のインスタンスを LLM クライアントとしてグローバルに利用したい場合に便利です。これは、LLM プロバイダーが OpenAI 互換の API エンドポイントを持ち、`base_url` と `api_key` を設定できる場合に使います。[examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py) に設定例があります。 -2. [`ModelProvider`][agents.models.interface.ModelProvider] は `Runner.run` レベルで利用します。これにより、「この実行のすべてのエージェントでカスタムモデルプロバイダーを使う」と指定できます。[examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py) に設定例があります。 -3. [`Agent.model`][agents.agent.Agent.model] で、特定のエージェントインスタンスにモデルを指定できます。これにより、エージェントごとに異なるプロバイダーを組み合わせて使うことができます。[examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py) に設定例があります。 - -`platform.openai.com` の API キーがない場合は、`set_tracing_disabled()` でトレーシングを無効にするか、[別のトレーシングプロセッサー](tracing.md) を設定することを推奨します。 - -!!! note - - これらの code examples では Chat Completions API/モデルを使っています。なぜなら、ほとんどの LLM プロバイダーはまだ Responses API をサポートしていないためです。もし LLM プロバイダーが Responses API をサポートしている場合は、Responses の利用を推奨します。 - -## 他の LLM プロバイダー利用時のよくある問題 - -### Tracing クライアントの 401 エラー - -トレーシングに関連するエラーが発生した場合、これはトレースが OpenAI サーバーにアップロードされるため、OpenAI API キーがないことが原因です。解決方法は 3 つあります。 - -1. トレーシングを完全に無効化する: [`set_tracing_disabled(True)`][agents.set_tracing_disabled]。 -2. トレーシング用の OpenAI キーを設定する: [`set_tracing_export_api_key(...)`][agents.set_tracing_export_api_key]。この API キーはトレースのアップロードのみに使われ、[platform.openai.com](https://platform.openai.com/) のものが必要です。 -3. OpenAI 以外のトレースプロセッサーを使う。[トレーシングのドキュメント](tracing.md#custom-tracing-processors) をご覧ください。 - -### Responses API サポート - -SDK はデフォルトで Responses API を使いますが、ほとんどの他の LLM プロバイダーはまだ対応していません。そのため、404 エラーなどが発生する場合があります。解決方法は 2 つあります。 - -1. [`set_default_openai_api("chat_completions")`][agents.set_default_openai_api] を呼び出します。これは、環境変数で `OPENAI_API_KEY` と `OPENAI_BASE_URL` を設定している場合に有効です。 -2. [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] を使います。[こちら](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/) に code examples があります。 - -### structured outputs サポート - -一部のモデルプロバイダーは [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) をサポートしていません。その場合、次のようなエラーが発生することがあります。 - -``` -BadRequestError: Error code: 400 - {'error': {'message': "'response_format.type' : value is not one of the allowed values ['text','json_object']", 'type': 'invalid_request_error'}} -``` - -これは一部のモデルプロバイダーの制限で、JSON 出力には対応していても、出力に使う `json_schema` を指定できない場合があります。現在この問題の修正に取り組んでいますが、JSON schema 出力をサポートしているプロバイダーの利用を推奨します。そうでない場合、不正な JSON によりアプリが頻繁に動作しなくなる可能性があります。 \ No newline at end of file diff --git a/docs/ja/models/index.md b/docs/ja/models/index.md new file mode 100644 index 000000000..410c01676 --- /dev/null +++ b/docs/ja/models/index.md @@ -0,0 +1,155 @@ +--- +search: + exclude: true +--- +# モデル + +Agents SDK は OpenAI モデルを 2 つの形態で即利用できます。 + +- **推奨**: [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] は、新しい [Responses API](https://platform.openai.com/docs/api-reference/responses) を使用して OpenAI API を呼び出します。 +- [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] は、[Chat Completions API](https://platform.openai.com/docs/api-reference/chat) を使用して OpenAI API を呼び出します。 + +## 非 OpenAI モデル + +ほとんどの非 OpenAI モデルは [LiteLLM インテグレーション](./litellm.md) 経由で利用できます。まず、litellm 依存グループをインストールします: + +```bash +pip install "openai-agents[litellm]" +``` + +次に、`litellm/` 接頭辞を付けて任意の [サポート対象モデル](https://docs.litellm.ai/docs/providers) を使用します: + +```python +claude_agent = Agent(model="litellm/anthropic/claude-3-5-sonnet-20240620", ...) +gemini_agent = Agent(model="litellm/gemini/gemini-2.5-flash-preview-04-17", ...) +``` + +### 非 OpenAI モデルを利用するその他の方法 + +他の LLM プロバイダーを統合する方法は、あと 3 つあります（[こちら](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/) に例があります）。 + +1. [`set_default_openai_client`][agents.set_default_openai_client] + `AsyncOpenAI` インスタンスを LLM クライアントとしてグローバルに使用したい場合に便利です。LLM プロバイダーが OpenAI 互換の API エンドポイントを持ち、`base_url` と `api_key` を設定できる場合に使用します。設定例は [examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py) にあります。 +2. [`ModelProvider`][agents.models.interface.ModelProvider] + `Runner.run` レベルでカスタムモデルプロバイダーを指定できます。これにより「この run のすべてのエージェントでカスタムプロバイダーを使う」と宣言できます。設定例は [examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py) にあります。 +3. [`Agent.model`][agents.agent.Agent.model] + 特定のエージェントインスタンスにモデルを指定できます。エージェントごとに異なるプロバイダーを組み合わせることが可能です。設定例は [examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py) にあります。ほとんどのモデルを簡単に利用する方法として [LiteLLM インテグレーション](./litellm.md) を利用できます。 + +`platform.openai.com` の API キーを持っていない場合は、`set_tracing_disabled()` でトレーシングを無効化するか、[別のトレーシングプロセッサー](../tracing.md) を設定することをお勧めします。 + +!!! note + これらの例では、Responses API をまだサポートしていない LLM プロバイダーが多いため、Chat Completions API/モデルを使用しています。LLM プロバイダーが Responses API をサポートしている場合は、Responses を使用することを推奨します。 + +## モデルの組み合わせ + +1 つのワークフロー内でエージェントごとに異なるモデルを使用したい場合があります。たとえば、振り分けには小さく高速なモデルを、複雑なタスクには大きく高性能なモデルを使用するといったケースです。[`Agent`][agents.Agent] を設定する際、次のいずれかの方法でモデルを選択できます。 + +1. モデル名を直接指定する +2. 任意のモデル名と、その名前を Model インスタンスへマッピングできる [`ModelProvider`][agents.models.interface.ModelProvider] を指定する +3. [`Model`][agents.models.interface.Model] 実装を直接渡す + +!!!note + SDK は [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] と [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] の両形態をサポートしていますが、各ワークフローで 1 つのモデル形態に統一することを推奨します。2 つの形態はサポートする機能とツールが異なるためです。混在させる場合は、使用する機能が双方で利用可能かを必ず確認してください。 + +```python +from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel +import asyncio + +spanish_agent = Agent( + name="Spanish agent", + instructions="You only speak Spanish.", + model="o3-mini", # (1)! +) + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model=OpenAIChatCompletionsModel( # (2)! + model="gpt-4o", + openai_client=AsyncOpenAI() + ), +) + +triage_agent = Agent( + name="Triage agent", + instructions="Handoff to the appropriate agent based on the language of the request.", + handoffs=[spanish_agent, english_agent], + model="gpt-3.5-turbo", +) + +async def main(): + result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?") + print(result.final_output) +``` + +1. OpenAI のモデル名を直接設定 +2. [`Model`][agents.models.interface.Model] 実装を提供 + +エージェントで使用するモデルをさらに構成したい場合は、`temperature` などのオプションパラメーターを指定できる [`ModelSettings`][agents.models.interface.ModelSettings] を渡せます。 + +```python +from agents import Agent, ModelSettings + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model="gpt-4o", + model_settings=ModelSettings(temperature=0.1), +) +``` + +OpenAI の Responses API を使用する場合、`user` や `service_tier` など[その他のオプションパラメーター](https://platform.openai.com/docs/api-reference/responses/create) があります。トップレベルで指定できない場合は、`extra_args` で渡してください。 + +```python +from agents import Agent, ModelSettings + +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model="gpt-4o", + model_settings=ModelSettings( + temperature=0.1, + extra_args={"service_tier": "flex", "user": "user_12345"}, + ), +) +``` + +## 他の LLM プロバイダー使用時の一般的な問題 + +### Tracing クライアントの 401 エラー + +Tracing 関連のエラーが発生する場合、トレースは OpenAI サーバーへアップロードされるため、OpenAI API キーが必要です。対応方法は次の 3 つです。 + +1. トレーシングを完全に無効化する: [`set_tracing_disabled(True)`][agents.set_tracing_disabled] +2. トレース用に OpenAI キーを設定する: [`set_tracing_export_api_key(...)`][agents.set_tracing_export_api_key] + この API キーはトレースのアップロードのみに使用され、[platform.openai.com](https://platform.openai.com/) で取得したものが必要です。 +3. OpenAI 以外のトレースプロセッサーを使用する。詳細は [tracing のドキュメント](../tracing.md#custom-tracing-processors) を参照してください。 + +### Responses API のサポート + +SDK はデフォルトで Responses API を使用しますが、ほとんどの LLM プロバイダーはまだ非対応です。その結果、404 などのエラーが発生することがあります。対処方法は次の 2 つです。 + +1. [`set_default_openai_api("chat_completions")`][agents.set_default_openai_api] を呼び出す + `OPENAI_API_KEY` と `OPENAI_BASE_URL` を環境変数で設定している場合に有効です。 +2. [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] を使用する + 例は [こちら](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/) にあります。 + +### structured outputs のサポート + +一部のモデルプロバイダーは [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) をサポートしていません。その場合、次のようなエラーが発生することがあります。 + +``` + +BadRequestError: Error code: 400 - {'error': {'message': "'response_format.type' : value is not one of the allowed values ['text','json_object']", 'type': 'invalid_request_error'}} + +``` + +これは一部プロバイダーの制限で、JSON 出力自体はサポートしていても `json_schema` を指定できないことが原因です。修正に向けて取り組んでいますが、JSON スキーマ出力をサポートしているプロバイダーを使用することをお勧めします。そうでないと、不正な JSON が返されてアプリが頻繁に壊れる可能性があります。 + +## プロバイダーを跨いだモデルの組み合わせ + +モデルプロバイダーごとの機能差に注意しないと、エラーが発生します。たとえば OpenAI は structured outputs、マルチモーダル入力、ホスト型の file search や web search をサポートしていますが、多くの他プロバイダーは非対応です。以下の制限に留意してください。 + +- 対応していないプロバイダーには未サポートの `tools` を送らない +- テキストのみのモデルを呼び出す前にマルチモーダル入力を除外する +- structured JSON 出力をサポートしていないプロバイダーでは、不正な JSON が返ることがある点に注意する \ No newline at end of file diff --git a/docs/ja/models/litellm.md b/docs/ja/models/litellm.md new file mode 100644 index 000000000..651d7a51b --- /dev/null +++ b/docs/ja/models/litellm.md @@ -0,0 +1,77 @@ +--- +search: + exclude: true +--- +# LiteLLM 経由でのモデル利用 + +!!! note + + LiteLLM との統合は現在ベータ版です。特に小規模なモデルプロバイダーでは問題が発生する可能性があります。問題を見つけた場合は、[GitHub Issues](https://github.com/openai/openai-agents-python/issues) からご報告ください。迅速に対応いたします。 + +[LiteLLM](https://docs.litellm.ai/docs/) は、1 つのインターフェースで 100 以上のモデルを利用できるライブラリです。Agents SDK では LiteLLM との統合により、任意の AI モデルを使用できます。 + +## セットアップ + +`litellm` がインストールされていることを確認してください。オプションの `litellm` 依存関係グループをインストールすることで対応できます。 + +```bash +pip install "openai-agents[litellm]" +``` + +インストール後、任意のエージェントで [`LitellmModel`][agents.extensions.models.litellm_model.LitellmModel] を利用できます。 + +## 例 + +以下は動作する完全なサンプルです。実行するとモデル名と API キーの入力を求められます。例えば次のように入力できます。 + +- `openai/gpt-4.1` をモデル名に、OpenAI API キーを入力 +- `anthropic/claude-3-5-sonnet-20240620` をモデル名に、Anthropic API キーを入力 +- その他 + +LiteLLM でサポートされているモデルの全リストは、[litellm providers docs](https://docs.litellm.ai/docs/providers) を参照してください。 + +```python +from __future__ import annotations + +import asyncio + +from agents import Agent, Runner, function_tool, set_tracing_disabled +from agents.extensions.models.litellm_model import LitellmModel + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(model: str, api_key: str): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model=LitellmModel(model=model, api_key=api_key), + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + # First try to get model/api key from args + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=False) + parser.add_argument("--api-key", type=str, required=False) + args = parser.parse_args() + + model = args.model + if not model: + model = input("Enter a model name for Litellm: ") + + api_key = args.api_key + if not api_key: + api_key = input("Enter an API key for Litellm: ") + + asyncio.run(main(model, api_key)) +``` \ No newline at end of file diff --git a/docs/ja/multi_agent.md b/docs/ja/multi_agent.md index e5604f6fe..a179fed36 100644 --- a/docs/ja/multi_agent.md +++ b/docs/ja/multi_agent.md @@ -1,37 +1,41 @@ -# 複数のエージェントのオーケストレーション +--- +search: + exclude: true +--- +# 複数エージェントのオーケストレーション -オーケストレーションとは、アプリ内でのエージェントの流れを指します。どのエージェントが、どの順番で実行され、次に何が起こるかをどのように決定するかということです。エージェントをオーケストレーションする主な方法は 2 つあります。 +オーケストレーションとは、アプリ内でエージェントがどのように流れるかを指します。どのエージェントが、どの順序で実行され、その後どう決定するかを制御します。エージェントをオーケストレーションする主な方法は次の 2 つです。 -1. LLM に意思決定を任せる方法：これは LLM の知能を活用し、計画・推論・意思決定を行わせるものです。 -2. コードによるオーケストレーション：コードでエージェントの流れを制御する方法です。 +1. LLM に判断させる: LLM の知能を活用し、計画・推論を行い、その結果に基づいて次のステップを決定します。 +2. コードでオーケストレーションする: コード側でエージェントの流れを定義します。 -これらのパターンは組み合わせて使うこともできます。それぞれにメリット・デメリットがあり、以下で説明します。 +これらのパターンは組み合わせて使用できます。それぞれにトレードオフがあり、以下で説明します。 ## LLM によるオーケストレーション -エージェントとは、instructions、tools、handoffs を備えた LLM です。つまり、オープンエンドなタスクが与えられた場合、LLM は自律的にタスクへの取り組み方を計画し、tools を使ってアクションを実行・データを取得し、handoffs を使ってサブエージェントにタスクを委任できます。たとえば、リサーチエージェントには以下のような tools を持たせることができます。 +エージェントとは、 instructions、ツール、ハンドオフを備えた LLM です。オープンエンドなタスクが与えられた場合、 LLM はタスクをどのように進めるかを自律的に計画し、ツールを使ってアクションやデータ取得を行い、ハンドオフでサブエージェントへタスクを委譲できます。たとえば、リサーチエージェントには次のようなツールを装備できます。 -- Web 検索でオンライン情報を探す -- ファイル検索やリトリーバルで独自データや接続先を検索する -- コンピュータ操作でコンピュータ上のアクションを実行する -- コード実行でデータ分析を行う -- 計画やレポート作成などに特化したエージェントへの handoffs +- Web 検索でオンライン情報を取得する +- ファイル検索で独自データや接続を調べる +- コンピュータ操作でコンピュータ上のアクションを実行する +- コード実行でデータ分析を行う +- 計画立案やレポート作成などに長けた専門エージェントへのハンドオフ -このパターンは、タスクがオープンエンドで LLM の知能に頼りたい場合に最適です。ここで重要なポイントは次のとおりです。 +このパターンはタスクがオープンエンドで、 LLM の知能に頼りたい場合に最適です。重要な戦術は次のとおりです。 -1. 良いプロンプトに投資しましょう。利用可能な tools、使い方、守るべきパラメーターを明確に伝えます。 -2. アプリをモニタリングし、改善を繰り返しましょう。問題が発生した箇所を確認し、プロンプトを改善します。 -3. エージェントに内省と改善を促しましょう。たとえばループで実行し、自己批評させたり、エラーメッセージを与えて改善させたりします。 -4. 何でもできる汎用エージェントではなく、1 つのタスクに特化したエージェントを用意しましょう。 -5. [evals](https://platform.openai.com/docs/guides/evals) に投資しましょう。これによりエージェントを訓練し、タスクの精度を向上させることができます。 +1. 良いプロンプトに投資する。利用可能なツール、使い方、守るべきパラメーターを明確に示します。 +2. アプリを監視し、改善を繰り返す。問題が起きた箇所を特定し、プロンプトを改善します。 +3. エージェントに内省と改善を許可する。たとえばループで実行し自己批評させたり、エラーメッセージを渡して修正させたりします。 +4. 何でもこなす汎用エージェントより、特定タスクに特化したエージェントを用意します。 +5. [evals](https://platform.openai.com/docs/guides/evals) に投資する。これによりエージェントを訓練し、タスク性能を向上できます。 ## コードによるオーケストレーション -LLM によるオーケストレーションは強力ですが、コードによるオーケストレーションは、速度・コスト・パフォーマンスの面でより決定的かつ予測可能になります。よく使われるパターンは次のとおりです。 +LLM によるオーケストレーションは強力ですが、コードでオーケストレーションすると速度・コスト・性能の面でより決定的かつ予測可能になります。よく使われるパターンは次のとおりです。 -- [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) を使い、コードで検査できる適切な形式のデータを生成する。たとえば、エージェントにタスクをいくつかのカテゴリーに分類させ、そのカテゴリーに応じて次のエージェントを選択することができます。 -- 複数のエージェントを連鎖させ、一方の出力を次の入力に変換する。たとえば、ブログ記事の作成タスクを「リサーチ→アウトライン作成→記事執筆→批評→改善」といった一連のステップに分解できます。 -- タスクを実行するエージェントと、評価・フィードバックを行うエージェントを `while` ループで回し、評価者が基準を満たしたと判断するまで繰り返します。 -- 複数のエージェントを並列で実行する（例：Python の基本コンポーネントである `asyncio.gather` を利用）。これは、互いに依存しない複数のタスクを高速に処理したい場合に有効です。 +- [structured outputs](https://platform.openai.com/docs/guides/structured-outputs) を使って、コード側で検査できる 適切な形式のデータ を生成する。たとえばエージェントにタスクをいくつかのカテゴリーに分類させ、そのカテゴリーに応じて次のエージェントを選択します。 +- あるエージェントの出力を次のエージェントの入力に変換して複数エージェントをチェーンする。ブログ記事執筆を「リサーチ → アウトライン作成 → 記事執筆 → 批評 → 改善」という一連のステップに分解できます。 +- タスクを実行するエージェントを `while` ループで回し、評価とフィードバックを行うエージェントと組み合わせ、評価者が基準を満たしたと判断するまで繰り返します。 +- `asyncio.gather` など Python の基本コンポーネントを用いて複数エージェントを並列実行する。互いに依存しない複数タスクがある場合に高速化できます。 -[`examples/agent_patterns`](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns) には、さまざまな code examples をご用意しています。 \ No newline at end of file +[`examples/agent_patterns`](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns) には多数のコード例があります。 \ No newline at end of file diff --git a/docs/ja/quickstart.md b/docs/ja/quickstart.md index 1a4913c70..30c3219d7 100644 --- a/docs/ja/quickstart.md +++ b/docs/ja/quickstart.md @@ -1,8 +1,12 @@ +--- +search: + exclude: true +--- # クイックスタート ## プロジェクトと仮想環境の作成 -この作業は一度だけ行えば十分です。 +これは一度だけ行えば十分です。 ```bash mkdir my_project @@ -12,7 +16,7 @@ python -m venv .venv ### 仮想環境の有効化 -新しいターミナルセッションを開始するたびに、これを実行してください。 +新しいターミナルセッションを開始するたびに実行してください。 ```bash source .venv/bin/activate @@ -32,9 +36,9 @@ pip install openai-agents # or `uv add openai-agents`, etc export OPENAI_API_KEY=sk-... ``` -## 最初のエージェントを作成する +## 最初のエージェントの作成 -エージェントは instructions、名前、およびオプションの設定（例： `model_config` ）で定義します。 +エージェントは instructions 、名前、`model_config` などのオプション設定で定義します。 ```python from agents import Agent @@ -45,9 +49,9 @@ agent = Agent( ) ``` -## さらにエージェントを追加する +## さらにエージェントを追加 -追加のエージェントも同様の方法で定義できます。 `handoff_descriptions` は、ハンドオフのルーティングを判断するための追加コンテキストを提供します。 +追加のエージェントも同様の方法で定義できます。`handoff_descriptions` はハンドオフのルーティングを判断するための追加コンテキストを提供します。 ```python from agents import Agent @@ -65,9 +69,9 @@ math_tutor_agent = Agent( ) ``` -## ハンドオフを定義する +## ハンドオフの定義 -各エージェントで、タスクを進めるために選択できる送信先ハンドオフオプションのインベントリを定義できます。 +各エージェントに対して、タスクを進める際に選択できるハンドオフ先の一覧を定義できます。 ```python triage_agent = Agent( @@ -77,9 +81,9 @@ triage_agent = Agent( ) ``` -## エージェントのオーケストレーションを実行する +## エージェントオーケストレーションの実行 -ワークフローが正しく動作し、トリアージエージェントが 2 つの専門エージェント間で正しくルーティングするか確認しましょう。 +ワークフローが実行され、トリアージエージェントが 2 つの専門エージェント間で正しくルーティングすることを確認しましょう。 ```python from agents import Runner @@ -89,9 +93,9 @@ async def main(): print(result.final_output) ``` -## ガードレールを追加する +## ガードレールの追加 -入力または出力に対して実行するカスタムガードレールを定義できます。 +入力または出力に対して実行されるカスタムガードレールを定義できます。 ```python from agents import GuardrailFunctionOutput, Agent, Runner @@ -118,7 +122,7 @@ async def homework_guardrail(ctx, agent, input_data): ## すべてをまとめる -すべてを組み合わせて、ハンドオフと入力ガードレールを使ったワークフロー全体を実行してみましょう。 +ハンドオフと入力ガードレールを組み合わせて、ワークフロー全体を実行してみましょう。 ```python from agents import Agent, InputGuardrail, GuardrailFunctionOutput, Runner @@ -176,14 +180,14 @@ if __name__ == "__main__": asyncio.run(main()) ``` -## トレースを確認する +## トレースの表示 -エージェントの実行中に何が起こったかを確認するには、[OpenAI ダッシュボードの Trace viewer](https://platform.openai.com/traces) にアクセスして、エージェント実行のトレースを表示してください。 +エージェントの実行内容を確認するには、[OpenAI ダッシュボードの Trace viewer](https://platform.openai.com/traces) に移動してトレースを閲覧してください。 ## 次のステップ -より複雑なエージェントフローの構築方法を学びましょう： +より複雑なエージェントフローの構築方法を学びましょう。 -- [エージェント](agents.md) の設定方法について学ぶ -- [エージェントの実行](running_agents.md) について学ぶ -- [tools](tools.md)、[guardrails](guardrails.md)、[models](models.md) について学ぶ \ No newline at end of file +- [エージェント](agents.md) の設定方法を学ぶ。 +- [エージェントの実行](running_agents.md) について学ぶ。 +- [ツール](tools.md)、[ガードレール](guardrails.md)、[モデル](models/index.md) について学ぶ。 \ No newline at end of file diff --git a/docs/ja/repl.md b/docs/ja/repl.md new file mode 100644 index 000000000..108c12b90 --- /dev/null +++ b/docs/ja/repl.md @@ -0,0 +1,22 @@ +--- +search: + exclude: true +--- +# REPL ユーティリティ + +`run_demo_loop` を使うと、ターミナルから手軽にエージェントを試せます。 + +```python +import asyncio +from agents import Agent, run_demo_loop + +async def main() -> None: + agent = Agent(name="Assistant", instructions="あなたは親切なアシスタントです") + await run_demo_loop(agent) + +if __name__ == "__main__": + asyncio.run(main()) +``` + +`run_demo_loop` は入力を繰り返し受け取り、会話履歴を保持したままエージェントを実行します。既定ではストリーミング出力を表示します。 +`quit` または `exit` と入力するか `Ctrl-D` を押すと終了します。 diff --git a/docs/ja/results.md b/docs/ja/results.md index 6b2c0cccb..cedd92402 100644 --- a/docs/ja/results.md +++ b/docs/ja/results.md @@ -1,52 +1,56 @@ +--- +search: + exclude: true +--- # 結果 -`Runner.run` メソッドを呼び出すと、以下のいずれかが返されます: +`Runner.run` メソッドを呼び出すと、以下のいずれかが返されます。 -- `run` または `run_sync` を呼び出した場合は、[`RunResult`][agents.result.RunResult] -- `run_streamed` を呼び出した場合は、[`RunResultStreaming`][agents.result.RunResultStreaming] +- `run` または `run_sync` を呼び出した場合は [`RunResult`][agents.result.RunResult] +- `run_streamed` を呼び出した場合は [`RunResultStreaming`][agents.result.RunResultStreaming] -これらはどちらも [`RunResultBase`][agents.result.RunResultBase] を継承しており、ほとんどの有用な情報はここに含まれています。 +これらはどちらも [`RunResultBase`][agents.result.RunResultBase] を継承しており、ほとんどの有用な情報はここに格納されています。 ## 最終出力 -[`final_output`][agents.result.RunResultBase.final_output] プロパティには、最後に実行されたエージェントの最終出力が格納されます。これは以下のいずれかです: +[`final_output`][agents.result.RunResultBase.final_output] プロパティには、最後に実行されたエージェントの最終出力が格納されます。内容は以下のいずれかです。 -- 最後のエージェントに `output_type` が定義されていない場合は `str` -- エージェントに `output_type` が定義されている場合は、`last_agent.output_type` 型のオブジェクト +- `output_type` が定義されていない場合は `str` +- `output_type` が定義されている場合は `last_agent.output_type` 型のオブジェクト !!! note - `final_output` の型は `Any` です。ハンドオフのため、静的に型を決定することはできません。ハンドオフが発生した場合、どのエージェントが最後になるか分からないため、出力型の集合を静的に知ることができません。 + `final_output` の型は `Any` です。ハンドオフが発生する可能性があるため、静的に型付けできません。ハンドオフが発生すると、どのエージェントでも最後になり得るため、可能性のある出力型を静的に特定できないのです。 ## 次のターンへの入力 -[`result.to_input_list()`][agents.result.RunResultBase.to_input_list] を使うことで、実行結果を入力リストに変換できます。これは、あなたが提供した元の入力と、エージェント実行中に生成されたアイテムを連結したものです。これにより、あるエージェント実行の出力を別の実行に渡したり、ループで実行して毎回新しいユーザー入力を追加したりするのが簡単になります。 +[`result.to_input_list()`][agents.result.RunResultBase.to_input_list] を使用すると、エージェント実行中に生成されたアイテムを元の入力に連結した入力リストへ変換できます。これにより、あるエージェント実行の出力を別の実行へ渡したり、ループで実行して毎回新しいユーザー入力を追加したりすることが容易になります。 ## 最後のエージェント -[`last_agent`][agents.result.RunResultBase.last_agent] プロパティには、最後に実行されたエージェントが格納されます。アプリケーションによっては、次回ユーザーが何か入力した際にこれが役立つことがよくあります。たとえば、フロントラインのトリアージエージェントが言語特化エージェントにハンドオフする場合、最後のエージェントを保存しておき、次回ユーザーがエージェントにメッセージを送る際に再利用できます。 +[`last_agent`][agents.result.RunResultBase.last_agent] プロパティには、最後に実行されたエージェントが格納されています。アプリケーションによっては、次回ユーザーが入力する際にこれが役立つことがよくあります。例えば、フロントラインのトリアージ エージェントが言語専用のエージェントにハンドオフする場合、最後のエージェントを保存しておき、ユーザーが次にメッセージを送ったときに再利用できます。 ## 新しいアイテム -[`new_items`][agents.result.RunResultBase.new_items] プロパティには、実行中に生成された新しいアイテムが格納されます。これらのアイテムは [`RunItem`][agents.items.RunItem] です。RunItem は LLM によって生成された raw アイテムをラップします。 +[`new_items`][agents.result.RunResultBase.new_items] プロパティには、実行中に生成された新しいアイテムが含まれます。これらのアイテムは [`RunItem`][agents.items.RunItem] です。RunItem は、 LLM が生成した raw アイテムをラップします。 -- [`MessageOutputItem`][agents.items.MessageOutputItem] は LLM からのメッセージを示します。raw アイテムは生成されたメッセージです。 -- [`HandoffCallItem`][agents.items.HandoffCallItem] は LLM がハンドオフツールを呼び出したことを示します。raw アイテムは LLM からのツールコールアイテムです。 -- [`HandoffOutputItem`][agents.items.HandoffOutputItem] はハンドオフが発生したことを示します。raw アイテムはハンドオフツールコールへのツールレスポンスです。また、アイテムからソース/ターゲットエージェントにもアクセスできます。 -- [`ToolCallItem`][agents.items.ToolCallItem] は LLM がツールを呼び出したことを示します。 -- [`ToolCallOutputItem`][agents.items.ToolCallOutputItem] はツールが呼び出されたことを示します。raw アイテムはツールレスポンスです。また、アイテムからツール出力にもアクセスできます。 -- [`ReasoningItem`][agents.items.ReasoningItem] は LLM からの推論アイテムを示します。raw アイテムは生成された推論です。 +- [`MessageOutputItem`][agents.items.MessageOutputItem] — LLM からのメッセージを示します。 raw アイテムは生成されたメッセージです。 +- [`HandoffCallItem`][agents.items.HandoffCallItem] — LLM がハンドオフ ツールを呼び出したことを示します。 raw アイテムは LLM からのツール呼び出しアイテムです。 +- [`HandoffOutputItem`][agents.items.HandoffOutputItem] — ハンドオフが発生したことを示します。 raw アイテムはハンドオフ ツール呼び出しに対するツール応答です。また、アイテムから送信元 / 送信先エージェントにもアクセスできます。 +- [`ToolCallItem`][agents.items.ToolCallItem] — LLM がツールを呼び出したことを示します。 +- [`ToolCallOutputItem`][agents.items.ToolCallOutputItem] — ツールが呼び出されたことを示します。 raw アイテムはツール応答です。また、アイテムからツール出力にもアクセスできます。 +- [`ReasoningItem`][agents.items.ReasoningItem] — LLM からの推論アイテムを示します。 raw アイテムは生成された推論内容です。 ## その他の情報 ### ガードレール結果 -[`input_guardrail_results`][agents.result.RunResultBase.input_guardrail_results] および [`output_guardrail_results`][agents.result.RunResultBase.output_guardrail_results] プロパティには、ガードレールの結果（存在する場合）が格納されます。ガードレール結果には、ログや保存に役立つ有用な情報が含まれることがあるため、これらを利用できるようにしています。 +[`input_guardrail_results`][agents.result.RunResultBase.input_guardrail_results] と [`output_guardrail_results`][agents.result.RunResultBase.output_guardrail_results] プロパティには、ガードレールの結果が存在する場合に格納されます。ガードレール結果には、ログや保存を行いたい有用な情報が含まれることがあるため、これらを参照できるようにしています。 ### raw レスポンス -[`raw_responses`][agents.result.RunResultBase.raw_responses] プロパティには、LLM によって生成された [`ModelResponse`][agents.items.ModelResponse] が格納されます。 +[`raw_responses`][agents.result.RunResultBase.raw_responses] プロパティには、 LLM が生成した [`ModelResponse`][agents.items.ModelResponse] が格納されます。 ### 元の入力 -[`input`][agents.result.RunResultBase.input] プロパティには、`run` メソッドに提供した元の入力が格納されます。ほとんどの場合これは必要ありませんが、必要な場合のために利用可能です。 \ No newline at end of file +[`input`][agents.result.RunResultBase.input] プロパティには、`run` メソッドに渡した元の入力が格納されます。ほとんどの場合は必要ありませんが、必要に応じて参照できるように用意されています。 \ No newline at end of file diff --git a/docs/ja/running_agents.md b/docs/ja/running_agents.md index 1a56fcc7f..83d5bec64 100644 --- a/docs/ja/running_agents.md +++ b/docs/ja/running_agents.md @@ -1,10 +1,15 @@ +--- +search: + exclude: true +--- # エージェントの実行 -エージェントは [`Runner`][agents.run.Runner] クラスを使って実行できます。3 つのオプションがあります。 +`Runner` クラス [`Runner`][agents.run.Runner] を使用して エージェント を実行できます。方法は 3 つあります。 -1. [`Runner.run()`][agents.run.Runner.run]：非同期で実行され、[`RunResult`][agents.result.RunResult] を返します。 -2. [`Runner.run_sync()`][agents.run.Runner.run_sync]：同期メソッドで、内部的には `.run()` を実行します。 -3. [`Runner.run_streamed()`][agents.run.Runner.run_streamed]：非同期で実行され、[`RunResultStreaming`][agents.result.RunResultStreaming] を返します。LLM をストリーミングモードで呼び出し、受信したイベントをリアルタイムでストリームします。 +1. 非同期で実行し、[`RunResult`][agents.result.RunResult] を返す [`Runner.run()`][agents.run.Runner.run] +2. 同期メソッドで、内部的には `.run()` を呼び出す [`Runner.run_sync()`][agents.run.Runner.run_sync] +3. 非同期で実行し、[`RunResultStreaming`][agents.result.RunResultStreaming] を返す [`Runner.run_streamed()`][agents.run.Runner.run_streamed] + LLM をストリーミングモードで呼び出し、受信したイベントを逐次 ストリーミング します。 ```python from agents import Agent, Runner @@ -19,53 +24,52 @@ async def main(): # Infinite loop's dance. ``` -詳細は [results guide](results.md) をご覧ください。 +詳細は [結果ガイド](results.md) を参照してください。 ## エージェントループ -`Runner` の run メソッドを使用する際、開始するエージェントと入力を渡します。入力は文字列（ユーザーメッセージと見なされます）または入力アイテムのリスト（OpenAI Responses API のアイテム）を指定できます。 +`Runner` の run メソッドを使用する際は、開始 エージェント と入力を渡します。入力は文字列（ユーザー メッセージと見なされます）または入力項目のリスト（OpenAI Responses API の項目）です。 -runner は次のようなループを実行します。 +Runner は以下のループを実行します。 -1. 現在のエージェントと入力で LLM を呼び出します。 -2. LLM が出力を生成します。 - 1. LLM が `final_output` を返した場合、ループは終了し、結果を返します。 - 2. LLM がハンドオフを行った場合、現在のエージェントと入力を更新し、ループを再実行します。 - 3. LLM がツール呼び出しを生成した場合、それらのツール呼び出しを実行し、結果を追加してループを再実行します。 -3. 渡された `max_turns` を超えた場合、[`MaxTurnsExceeded`][agents.exceptions.MaxTurnsExceeded] 例外を発生させます。 +1. 現在の エージェント と現在の入力で LLM を呼び出します。 +2. LLM が出力を生成します。 + 1. `final_output` が返された場合、ループを終了して結果を返します。 + 2. ハンドオフ が発生した場合、現在の エージェント と入力を更新し、ループを再実行します。 + 3. ツール呼び出し がある場合、それらを実行し、結果を追加してループを再実行します。 +3. 指定した `max_turns` を超えた場合、[`MaxTurnsExceeded`][agents.exceptions.MaxTurnsExceeded] 例外を送出します。 !!! note - - LLM の出力が「final output」と見なされるルールは、希望する型のテキスト出力が生成され、ツール呼び出しがない場合です。 + LLM の出力が「最終出力」と見なされる条件は、望ましい型のテキスト出力であり、ツール呼び出しがないことです。 ## ストリーミング -ストリーミングを利用すると、LLM の実行中にストリーミングイベントを受け取ることができます。ストリームが完了すると、[`RunResultStreaming`][agents.result.RunResultStreaming] に実行に関するすべての情報（新たに生成されたすべての出力を含む）が格納されます。ストリーミングイベントは `.stream_events()` で取得できます。詳細は [streaming guide](streaming.md) をご覧ください。 +ストリーミング を使用すると、LLM の実行中に ストリーミング イベントを受け取れます。ストリーム完了後、[`RunResultStreaming`][agents.result.RunResultStreaming] には実行に関する完全な情報（新しく生成されたすべての出力を含む）が格納されます。`.stream_events()` を呼び出して ストリーミング イベントを取得できます。詳しくは [ストリーミングガイド](streaming.md) をご覧ください。 -## Run config +## Run 設定 -`run_config` パラメーターでは、エージェント実行のグローバル設定をいくつか構成できます。 +`run_config` パラメーターにより、エージェント実行のグローバル設定を行えます。 -- [`model`][agents.run.RunConfig.model]：各エージェントの `model` 設定に関わらず、グローバルで使用する LLM モデルを指定できます。 -- [`model_provider`][agents.run.RunConfig.model_provider]：モデル名を検索するためのモデルプロバイダーで、デフォルトは OpenAI です。 -- [`model_settings`][agents.run.RunConfig.model_settings]：エージェント固有の設定を上書きします。たとえば、グローバルな `temperature` や `top_p` を設定できます。 -- [`input_guardrails`][agents.run.RunConfig.input_guardrails], [`output_guardrails`][agents.run.RunConfig.output_guardrails]：すべての実行に含める入力または出力ガードレールのリストです。 -- [`handoff_input_filter`][agents.run.RunConfig.handoff_input_filter]：すべてのハンドオフに適用するグローバルな入力フィルターです（ハンドオフに既にフィルターがない場合）。入力フィルターを使うと、新しいエージェントに送信する入力を編集できます。詳細は [`Handoff.input_filter`][agents.handoffs.Handoff.input_filter] のドキュメントをご覧ください。 -- [`tracing_disabled`][agents.run.RunConfig.tracing_disabled]：実行全体の [トレーシング](tracing.md) を無効にできます。 -- [`trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data]：トレースに LLM やツール呼び出しの入出力など、機密性のあるデータを含めるかどうかを設定します。 -- [`workflow_name`][agents.run.RunConfig.workflow_name], [`trace_id`][agents.run.RunConfig.trace_id], [`group_id`][agents.run.RunConfig.group_id]：実行のトレーシングワークフロー名、トレース ID、トレースグループ ID を設定します。少なくとも `workflow_name` の設定を推奨します。グループ ID は複数の実行にまたがるトレースをリンクするためのオプション項目です。 -- [`trace_metadata`][agents.run.RunConfig.trace_metadata]：すべてのトレースに含めるメタデータです。 +- [`model`][agents.run.RunConfig.model]: 各 Agent の `model` 設定に関わらず使用するグローバル LLM モデルを指定します。 +- [`model_provider`][agents.run.RunConfig.model_provider]: モデル名を解決する モデルプロバイダー。デフォルトは OpenAI です。 +- [`model_settings`][agents.run.RunConfig.model_settings]: エージェント固有設定を上書きします。例としてグローバル `temperature` や `top_p` の設定など。 +- [`input_guardrails`][agents.run.RunConfig.input_guardrails], [`output_guardrails`][agents.run.RunConfig.output_guardrails]: すべての実行に適用する入力 / 出力 ガードレール のリスト。 +- [`handoff_input_filter`][agents.run.RunConfig.handoff_input_filter]: ハンドオフ に入力フィルターが設定されていない場合に適用されるグローバル入力フィルター。新しい エージェント へ送信される入力を編集できます。詳細は [`Handoff.input_filter`][agents.handoffs.Handoff.input_filter] を参照してください。 +- [`tracing_disabled`][agents.run.RunConfig.tracing_disabled]: 実行全体の [トレーシング](tracing.md) を無効化します。 +- [`trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data]: トレースに LLM やツール呼び出しの入出力など、機微なデータを含めるかどうかを設定します。 +- [`workflow_name`][agents.run.RunConfig.workflow_name], [`trace_id`][agents.run.RunConfig.trace_id], [`group_id`][agents.run.RunConfig.group_id]: 実行のトレーシング ワークフロー名、トレース ID、トレース グループ ID を設定します。少なくとも `workflow_name` の設定を推奨します。`group_id` を設定すると、複数の実行にまたがるトレースをリンクできます。 +- [`trace_metadata`][agents.run.RunConfig.trace_metadata]: すべてのトレースに付与するメタデータ。 -## 会話・チャットスレッド +## 会話 / チャットスレッド -いずれかの run メソッドを呼び出すと、1 つまたは複数のエージェント（および 1 つまたは複数の LLM 呼び出し）が実行されますが、チャット会話における 1 回の論理的なターンを表します。例： +いずれかの run メソッドを呼び出すと、1 つ以上の エージェント が実行され（つまり 1 つ以上の LLM 呼び出しが行われ）、チャット会話の 1 つの論理ターンを表します。例: -1. ユーザーのターン：ユーザーがテキストを入力 -2. Runner の実行：最初のエージェントが LLM を呼び出し、ツールを実行し、2 番目のエージェントにハンドオフ、2 番目のエージェントがさらにツールを実行し、出力を生成 +1. ユーザーターン: ユーザー がテキストを入力 +2. Runner 実行: 最初の エージェント が LLM を呼び出し、ツールを実行し、2 番目の エージェント へハンドオフ。2 番目の エージェント がさらにツールを実行し、最終出力を生成。 -エージェントの実行が終わったら、ユーザーに何を表示するか選択できます。たとえば、エージェントが生成したすべての新しいアイテムをユーザーに見せることも、最終出力だけを見せることもできます。いずれの場合も、ユーザーが追加の質問をした場合は、再度 run メソッドを呼び出せます。 +エージェント実行の終了時に、ユーザー に何を表示するかは自由です。たとえば、エージェント が生成したすべての新しい項目を表示する、または最終出力のみを表示する等です。いずれの場合でも、ユーザー がフォローアップ質問をしたら、再度 run メソッドを呼び出せます。 -次のターンの入力を取得するには、ベースの [`RunResultBase.to_input_list()`][agents.result.RunResultBase.to_input_list] メソッドを使用できます。 +次ターンの入力は、基底クラス [`RunResultBase.to_input_list()`][agents.result.RunResultBase.to_input_list] を使用して取得できます。 ```python async def main(): @@ -86,10 +90,10 @@ async def main(): ## 例外 -SDK は特定のケースで例外を発生させます。全リストは [`agents.exceptions`][] にあります。概要は以下の通りです。 +特定の状況で SDK は例外を送出します。完全な一覧は [`agents.exceptions`][] にあります。概要は以下のとおりです。 -- [`AgentsException`][agents.exceptions.AgentsException]：SDK で発生するすべての例外の基底クラスです。 -- [`MaxTurnsExceeded`][agents.exceptions.MaxTurnsExceeded]：run メソッドに渡した `max_turns` を超えた場合に発生します。 -- [`ModelBehaviorError`][agents.exceptions.ModelBehaviorError]：モデルが不正な出力（例：不正な JSON や存在しないツールの使用）を生成した場合に発生します。 -- [`UserError`][agents.exceptions.UserError]：SDK を使用する際に、あなた（SDK を使ってコードを書く人）がエラーを起こした場合に発生します。 -- [`InputGuardrailTripwireTriggered`][agents.exceptions.InputGuardrailTripwireTriggered], [`OutputGuardrailTripwireTriggered`][agents.exceptions.OutputGuardrailTripwireTriggered]：[ガードレール](guardrails.md) が作動した場合に発生します。 \ No newline at end of file +- [`AgentsException`][agents.exceptions.AgentsException]: SDK が送出するすべての例外の基底クラス +- [`MaxTurnsExceeded`][agents.exceptions.MaxTurnsExceeded]: 実行が `max_turns` を超えた場合に送出 +- [`ModelBehaviorError`][agents.exceptions.ModelBehaviorError]: モデルが不正な出力（例: JSON 形式違反、存在しないツールの呼び出しなど）を生成した場合に送出 +- [`UserError`][agents.exceptions.UserError]: SDK の使用方法に誤りがある場合に送出 +- [`InputGuardrailTripwireTriggered`][agents.exceptions.InputGuardrailTripwireTriggered], [`OutputGuardrailTripwireTriggered`][agents.exceptions.OutputGuardrailTripwireTriggered]: [ガードレール](guardrails.md) が発火した場合に送出 \ No newline at end of file diff --git a/docs/ja/streaming.md b/docs/ja/streaming.md index b820bae59..a8a46cca8 100644 --- a/docs/ja/streaming.md +++ b/docs/ja/streaming.md @@ -1,14 +1,18 @@ +--- +search: + exclude: true +--- # ストリーミング -ストリーミングを利用すると、エージェントの実行が進行するにつれて、その更新情報を購読できます。これは、エンドユーザーに進捗状況や部分的な応答を表示するのに役立ちます。 +ストリーミングを使用すると、 エージェント の実行が進行するにつれて発生する更新を購読できます。これにより、エンド ユーザーに進捗状況や部分的な応答を表示するのに役立ちます。 -ストリーミングを行うには、[`Runner.run_streamed()`][agents.run.Runner.run_streamed] を呼び出します。これにより、[`RunResultStreaming`][agents.result.RunResultStreaming] が返されます。`result.stream_events()` を呼び出すと、下記で説明する [`StreamEvent`][agents.stream_events.StreamEvent] オブジェクトの非同期ストリームが得られます。 +ストリーミングを行うには、 [`Runner.run_streamed()`][agents.run.Runner.run_streamed] を呼び出します。これにより [`RunResultStreaming`][agents.result.RunResultStreaming] が返されます。続いて `result.stream_events()` を呼び出すと、後述する [`StreamEvent`][agents.stream_events.StreamEvent] オブジェクトの非同期ストリームを取得できます。 -## raw response events +## raw response イベント -[`RawResponsesStreamEvent`][agents.stream_events.RawResponsesStreamEvent] は、LLM から直接渡される raw イベントです。これらは OpenAI Responses API フォーマットであり、各イベントには type（例：`response.created`、`response.output_text.delta` など）とデータが含まれます。これらのイベントは、応答メッセージが生成され次第、ユーザーにストリーミングしたい場合に便利です。 +[`RawResponsesStreamEvent`][agents.stream_events.RawResponsesStreamEvent] は、 LLM から直接渡される raw なイベントです。これらは OpenAI Responses API 形式であり、各イベントには `response.created` や `response.output_text.delta` などの type とデータが含まれます。生成されたメッセージを即座にユーザーへストリーミングしたい場合に便利です。 -例えば、以下の例では LLM が生成したテキストをトークンごとに出力します。 +たとえば、以下のコードは LLM が生成したテキストをトークンごとに出力します。 ```python import asyncio @@ -31,11 +35,11 @@ if __name__ == "__main__": asyncio.run(main()) ``` -## Run item イベントとエージェントイベント +## Run item イベントと エージェント イベント -[`RunItemStreamEvent`][agents.stream_events.RunItemStreamEvent] は、より高レベルなイベントです。アイテムが完全に生成されたタイミングを通知します。これにより、「メッセージが生成された」「ツールが実行された」など、各トークン単位ではなく、進捗状況をまとめてユーザーに伝えることができます。同様に、[`AgentUpdatedStreamEvent`][agents.stream_events.AgentUpdatedStreamEvent] は、現在のエージェントが変更されたとき（例：ハンドオフの結果として）に更新情報を提供します。 +[`RunItemStreamEvent`][agents.stream_events.RunItemStreamEvent] は、より高レベルなイベントです。アイテムが完全に生成されたタイミングを通知するため、トークン単位ではなく「メッセージが生成された」「ツールが実行された」といったレベルで進捗をプッシュできます。同様に、 [`AgentUpdatedStreamEvent`][agents.stream_events.AgentUpdatedStreamEvent] はハンドオフなどで現在の エージェント が変わった際に更新を提供します。 -例えば、以下の例では raw イベントを無視し、ユーザーに更新情報のみをストリーミングします。 +たとえば、以下のコードは raw イベントを無視し、ユーザーへ更新のみをストリーミングします。 ```python import asyncio diff --git a/docs/ja/tools.md b/docs/ja/tools.md index 2444891cb..cd80092d5 100644 --- a/docs/ja/tools.md +++ b/docs/ja/tools.md @@ -1,18 +1,26 @@ +--- +search: + exclude: true +--- # ツール -ツールは エージェント がアクションを実行するためのものです。たとえば、データの取得、コードの実行、外部 API の呼び出し、さらにはコンピュータ操作などが含まれます。Agents SDK には 3 種類のツールクラスがあります。 +ツールはエージェントがアクションを実行できるようにします。たとえばデータの取得、コードの実行、外部 API の呼び出し、さらにはコンピュータ操作などです。Agents SDK には次の 3 種類のツールがあります。 -- Hosted tools: これらは LLM サーバー上で AI モデルとともに実行されます。OpenAI は retrieval、Web 検索、コンピュータ操作を Hosted tools として提供しています。 -- Function calling: 任意の Python 関数をツールとして利用できます。 -- Agents as tools: エージェントをツールとして利用でき、エージェントが他のエージェントをハンドオフせずに呼び出すことができます。 +- ホストツール: これらは LLM サーバー上で AI モデルと一緒に実行されます。OpenAI は retrieval、Web 検索、コンピュータ操作をホストツールとして提供しています。 +- 関数呼び出し: 任意の Python 関数をツールとして利用できます。 +- ツールとしてのエージェント: ハンドオフせずに、エージェントから他のエージェントを呼び出すことができます。 -## Hosted tools +## ホストツール OpenAI は [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] を使用する際に、いくつかの組み込みツールを提供しています。 -- [`WebSearchTool`][agents.tool.WebSearchTool] は、エージェントが Web 検索を行うことを可能にします。 -- [`FileSearchTool`][agents.tool.FileSearchTool] は、OpenAI ベクトルストアから情報を取得できます。 -- [`ComputerTool`][agents.tool.ComputerTool] は、コンピュータ操作タスクの自動化を可能にします。 +- [`WebSearchTool`][agents.tool.WebSearchTool] はエージェントに Web 検索を行わせます。 +- [`FileSearchTool`][agents.tool.FileSearchTool] は OpenAI ベクトルストアから情報を取得します。 +- [`ComputerTool`][agents.tool.ComputerTool] はコンピュータ操作タスクを自動化します。 +- [`CodeInterpreterTool`][agents.tool.CodeInterpreterTool] はサンドボックス環境でコードを実行します。 +- [`HostedMCPTool`][agents.tool.HostedMCPTool] はリモート MCP サーバーのツールをモデルから直接利用できるようにします。 +- [`ImageGenerationTool`][agents.tool.ImageGenerationTool] はプロンプトから画像を生成します。 +- [`LocalShellTool`][agents.tool.LocalShellTool] はローカルマシンでシェルコマンドを実行します。 ```python from agents import Agent, FileSearchTool, Runner, WebSearchTool @@ -33,16 +41,16 @@ async def main(): print(result.final_output) ``` -## Function tools +## 関数ツール -任意の Python 関数をツールとして利用できます。Agents SDK が自動的にツールをセットアップします。 +任意の Python 関数をツールとして使用できます。Agents SDK が自動的に設定を行います。 -- ツール名は Python 関数名になります（または任意の名前を指定できます） -- ツールの説明は関数の docstring から取得されます（または任意の説明を指定できます） -- 関数の引数から自動的に入力スキーマが作成されます -- 各入力の説明は、関数の docstring から取得されます（無効化も可能です） +- ツールの名前は Python 関数の名前になります（任意で名前を指定することも可能です） +- ツールの説明は関数の docstring から取得されます（任意で説明を指定することも可能です） +- 関数の引数から自動的に入力スキーマを生成します +- 各入力の説明は、無効化しない限り docstring から取得されます -Python の `inspect` モジュールを使って関数シグネチャを抽出し、[`griffe`](https://mkdocstrings.github.io/griffe/) で docstring を解析し、`pydantic` でスキーマを作成します。 +Python の `inspect` モジュールを使用して関数シグネチャを抽出し、[`griffe`](https://mkdocstrings.github.io/griffe/) で docstring を解析し、`pydantic` でスキーマを作成します。 ```python import json @@ -94,12 +102,12 @@ for tool in agent.tools: ``` -1. 関数の引数には任意の Python 型を利用でき、同期・非同期どちらの関数も利用可能です。 -2. docstring があれば、説明や引数の説明として利用されます。 -3. 関数はオプションで `context` を最初の引数として受け取れます。また、ツール名や説明、docstring スタイルの指定などのオーバーライドも可能です。 -4. デコレートした関数をツールのリストに渡すことができます。 +1. 関数の引数には任意の Python 型を使用でき、同期・非同期どちらの関数も利用できます。 +2. docstring が存在する場合、ツールと引数の説明を取得します。 +3. 関数はオプションで `context` を受け取れます（最初の引数である必要があります）。ツール名、説明、docstring のスタイルなどを上書き設定することも可能です。 +4. デコレートされた関数をツールのリストに渡してください。 -??? note "出力を展開して表示" +??? note "展開して出力を確認" ``` fetch_weather @@ -169,14 +177,14 @@ for tool in agent.tools: } ``` -### カスタム function tools +### カスタム関数ツール -場合によっては、Python 関数をツールとして使いたくないこともあります。その場合は、[`FunctionTool`][agents.tool.FunctionTool] を直接作成できます。必要な情報は以下の通りです。 +Python 関数をそのままツールにしたくない場合は、[`FunctionTool`][agents.tool.FunctionTool] を直接作成できます。次を指定する必要があります。 - `name` - `description` - `params_json_schema`（引数の JSON スキーマ） -- `on_invoke_tool`（context と引数（JSON 文字列）を受け取り、ツールの出力を文字列で返す async 関数） +- `on_invoke_tool`（context と引数の JSON 文字列を受け取り、ツールの出力を文字列で返す async 関数） ```python from typing import Any @@ -211,16 +219,16 @@ tool = FunctionTool( ### 引数と docstring の自動解析 -前述の通り、関数シグネチャを自動解析してツールのスキーマを抽出し、docstring からツールや各引数の説明を抽出します。主なポイントは以下の通りです。 +前述のとおり、関数シグネチャを自動解析してツールのスキーマを生成し、docstring を解析してツールおよび個別引数の説明を抽出します。主な注意点は次のとおりです。 -1. シグネチャの解析は `inspect` モジュールで行います。型アノテーションを利用して引数の型を把握し、Pydantic モデルを動的に構築して全体のスキーマを表現します。Python の基本コンポーネント、Pydantic モデル、TypedDict など、ほとんどの型をサポートしています。 -2. docstring の解析には `griffe` を使用します。サポートされている docstring フォーマットは `google`、`sphinx`、`numpy` です。docstring フォーマットは自動検出を試みますが、`function_tool` 呼び出し時に明示的に指定することもできます。`use_docstring_info` を `False` に設定することで docstring 解析を無効化できます。 +1. シグネチャ解析は `inspect` モジュールで行います。型アノテーションを用いて引数の型を認識し、Pydantic モデルを動的に構築して全体のスキーマを表現します。Python の基本型、Pydantic モデル、TypedDict などほとんどの型をサポートします。 +2. `griffe` を使用して docstring を解析します。対応する docstring 形式は `google`、`sphinx`、`numpy` です。形式は自動検出を試みますが、`function_tool` 呼び出し時に明示的に指定することもできます。`use_docstring_info` を `False` に設定すると docstring 解析を無効化できます。 スキーマ抽出のコードは [`agents.function_schema`][] にあります。 -## Agents as tools +## ツールとしてのエージェント -一部のワークフローでは、ハンドオフせずに中央のエージェントが専門エージェントのネットワークをオーケストレーションしたい場合があります。その場合、エージェントをツールとしてモデル化することで実現できます。 +一部のワークフローでは、ハンドオフせずに中央のエージェントが複数の専門エージェントをオーケストレーションしたい場合があります。そのような場合、エージェントをツールとしてモデル化できます。 ```python from agents import Agent, Runner @@ -259,12 +267,33 @@ async def main(): print(result.final_output) ``` -## Function tools でのエラー処理 +### ツールエージェントのカスタマイズ -`@function_tool` で function tool を作成する際、`failure_error_function` を渡すことができます。これは、ツール呼び出しがクラッシュした場合に LLM へエラー応答を提供する関数です。 +`agent.as_tool` 関数はエージェントを簡単にツール化するためのヘルパーです。ただし、すべての設定に対応しているわけではありません（例: `max_turns` は設定不可）。高度なユースケースでは、ツール実装内で `Runner.run` を直接使用してください。 -- デフォルト（何も渡さない場合）では、`default_tool_error_function` が実行され、LLM にエラーが発生したことを伝えます。 -- 独自のエラー関数を渡した場合は、それが実行され、その応答が LLM に送信されます。 -- 明示的に `None` を渡した場合、ツール呼び出し時のエラーは再スローされ、ユーザー側で処理できます。たとえば、モデルが無効な JSON を生成した場合は `ModelBehaviorError`、コードがクラッシュした場合は `UserError` などです。 +```python +@function_tool +async def run_my_agent() -> str: + """A tool that runs the agent with custom configs". + + agent = Agent(name="My agent", instructions="...") + + result = await Runner.run( + agent, + input="...", + max_turns=5, + run_config=... + ) + + return str(result.final_output) +``` + +## 関数ツールでのエラー処理 + +`@function_tool` で関数ツールを作成する際、`failure_error_function` を渡せます。これはツール呼び出しが失敗した場合に LLM へ返すエラーレスポンスを生成する関数です。 + +- 何も指定しない場合、`default_tool_error_function` が実行され、LLM にエラー発生を伝えます。 +- 独自のエラー関数を渡した場合はそちらが実行され、そのレスポンスが LLM へ送信されます。 +- 明示的に `None` を渡すと、ツール呼び出し時のエラーは再送出されます。モデルが無効な JSON を生成した場合は `ModelBehaviorError`、コードがクラッシュした場合は `UserError` などになります。 -`FunctionTool` オブジェクトを手動で作成する場合は、`on_invoke_tool` 関数内でエラー処理を行う必要があります。 \ No newline at end of file +`FunctionTool` オブジェクトを手動で作成する場合は、`on_invoke_tool` 関数内でエラーを処理する必要があります。 \ No newline at end of file diff --git a/docs/ja/tracing.md b/docs/ja/tracing.md index b7cb18dc4..d67200ce4 100644 --- a/docs/ja/tracing.md +++ b/docs/ja/tracing.md @@ -1,51 +1,55 @@ +--- +search: + exclude: true +--- # トレーシング -Agents SDK にはトレーシング機能が組み込まれており、エージェントの実行中に発生するイベント（ LLM 生成、ツール呼び出し、ハンドオフ、ガードレール、カスタムイベントなど）の包括的な記録を収集します。[Traces ダッシュボード](https://platform.openai.com/traces) を使用することで、開発中や本番環境でワークフローのデバッグ、可視化、監視が可能です。 +Agents SDK にはビルトインのトレーシング機能があり、エージェントの実行中に発生するイベント―― LLM 生成、ツール呼び出し、ハンドオフ、ガードレール、さらにカスタムイベントまで――を網羅的に記録します。開発時と本番環境の両方で [Traces dashboard](https://platform.openai.com/traces) を使用すると、ワークフローをデバッグ・可視化・モニタリングできます。 !!!note - トレーシングはデフォルトで有効になっています。トレーシングを無効にする方法は 2 つあります: + トレーシングはデフォルトで有効です。無効化する方法は次の 2 つです: - 1. 環境変数 `OPENAI_AGENTS_DISABLE_TRACING=1` を設定することで、グローバルにトレーシングを無効化できます。 - 2. 単一の実行に対しては [`agents.run.RunConfig.tracing_disabled`][] を `True` に設定することで無効化できます。 + 1. 環境変数 `OPENAI_AGENTS_DISABLE_TRACING=1` を設定してグローバルに無効化する + 2. 単一の実行に対しては [`agents.run.RunConfig.tracing_disabled`][] を `True` に設定する -***OpenAI の API を使用し、Zero Data Retention (ZDR) ポリシーの下で運用している組織では、トレーシングは利用できません。*** +***OpenAI の API を Zero Data Retention (ZDR) ポリシーで利用している組織では、トレーシングを利用できません。*** ## トレースとスパン -- **トレース** は 1 つの「ワークフロー」のエンドツーエンドの操作を表します。トレースはスパンで構成されます。トレースには以下のプロパティがあります: - - `workflow_name`: 論理的なワークフローやアプリ名です。例: "Code generation" や "Customer service" など。 - - `trace_id`: トレースの一意な ID です。指定しない場合は自動生成されます。フォーマットは `trace_<32_alphanumeric>` である必要があります。 - - `group_id`: オプションのグループ ID で、同じ会話からの複数のトレースをリンクするために使用します。例: チャットスレッド ID など。 - - `disabled`: True の場合、このトレースは記録されません。 - - `metadata`: トレースに付加するオプションのメタデータです。 -- **スパン** は開始時刻と終了時刻を持つ操作を表します。スパンには以下があります: - - `started_at` および `ended_at` タイムスタンプ - - 所属するトレースを示す `trace_id` - - このスパンの親スパンを指す `parent_id`（存在する場合） - - スパンに関する情報を含む `span_data`。例: `AgentSpanData` はエージェントに関する情報、`GenerationSpanData` は LLM 生成に関する情報など。 +- **トレース** は 1 度のワークフロー全体を表します。複数のスパンで構成され、次のプロパティを持ちます: + - `workflow_name`: 論理的なワークフローまたはアプリ名。例: 「Code generation」や「Customer service」 + - `trace_id`: トレースを一意に識別する ID。指定しない場合は自動生成されます。形式は `trace_<32_alphanumeric>` である必要があります。 + - `group_id`: オプションのグループ ID。会話内の複数トレースを関連付けます。たとえばチャットスレッド ID など。 + - `disabled`: `True` の場合、このトレースは記録されません。 + - `metadata`: トレースに付随する任意のメタデータ。 +- **スパン** は開始時刻と終了時刻を持つ個々の処理を表します。スパンは以下を保持します: + - `started_at` と `ended_at` タイムスタンプ + - 所属トレースを示す `trace_id` + - 親スパンを指す `parent_id` (存在する場合) + - スパンに関する情報を格納する `span_data`。たとえば `AgentSpanData` にはエージェント情報が、`GenerationSpanData` には LLM 生成情報が含まれます。 ## デフォルトのトレーシング -デフォルトでは、 SDK は以下をトレースします: +デフォルトで SDK は以下をトレースします: -- `Runner.{run, run_sync, run_streamed}()` 全体が `trace()` でラップされます。 -- エージェントが実行されるたびに `agent_span()` でラップされます。 -- LLM 生成は `generation_span()` でラップされます。 -- 関数ツール呼び出しはそれぞれ `function_span()` でラップされます。 -- ガードレールは `guardrail_span()` でラップされます。 -- ハンドオフは `handoff_span()` でラップされます。 -- 音声入力（音声からテキスト）は `transcription_span()` でラップされます。 -- 音声出力（テキストから音声）は `speech_span()` でラップされます。 -- 関連する音声スパンは `speech_group_span()` の下にまとめられる場合があります。 +- `Runner.{run, run_sync, run_streamed}()` 全体を `trace()` でラップ +- エージェントが実行されるたびに `agent_span()` でラップ +- LLM 生成を `generation_span()` でラップ +- 関数ツール呼び出しを `function_span()` でラップ +- ガードレールを `guardrail_span()` でラップ +- ハンドオフを `handoff_span()` でラップ +- 音声入力 (speech‑to‑text) を `transcription_span()` でラップ +- 音声出力 (text‑to‑speech) を `speech_span()` でラップ +- 関連する音声スパンは `speech_group_span()` の下にネストされる場合があります -デフォルトでは、トレース名は "Agent trace" です。`trace` を使用する場合、この名前を設定できます。また、[`RunConfig`][agents.run.RunConfig] で名前やその他のプロパティを設定することも可能です。 +トレース名はデフォルトで「Agent trace」です。`trace` を使用して指定したり、[`RunConfig`][agents.run.RunConfig] で名前やその他のプロパティを設定できます。 -さらに、[カスタムトレースプロセッサー](#custom-tracing-processors) を設定して、トレースを他の宛先に送信することもできます（置き換えや追加の宛先として）。 +さらに [カスタムトレーシングプロセッサー](#custom-tracing-processors) を設定して、トレースを別の送信先に出力（置き換えまたは追加）することも可能です。 -## より高レベルのトレース +## 上位レベルのトレース -複数回の `run()` 呼び出しを 1 つのトレースにまとめたい場合があります。その場合、コード全体を `trace()` でラップしてください。 +複数回の `run()` 呼び出しを 1 つのトレースにまとめたい場合があります。その場合、コード全体を `trace()` でラップします。 ```python from agents import Agent, Runner, trace @@ -60,48 +64,50 @@ async def main(): print(f"Rating: {second_result.final_output}") ``` -1. 2 回の `Runner.run` 呼び出しが `with trace()` でラップされているため、個々の実行は 2 つのトレースを作成するのではなく、全体のトレースの一部となります。 +1. `with trace()` で 2 つの `Runner.run` 呼び出しをラップしているため、それぞれが個別のトレースを作成せず、全体で 1 つのトレースになります。 ## トレースの作成 -[`trace()`][agents.tracing.trace] 関数を使ってトレースを作成できます。トレースは開始と終了が必要です。方法は 2 つあります: +[`trace()`][agents.tracing.trace] 関数を使ってトレースを作成できます。開始と終了が必要で、方法は 2 つあります。 -1. **推奨**: トレースをコンテキストマネージャーとして使用します。例: `with trace(...) as my_trace`。これにより、トレースの開始と終了が自動的に行われます。 -2. [`trace.start()`][agents.tracing.Trace.start] および [`trace.finish()`][agents.tracing.Trace.finish] を手動で呼び出すこともできます。 +1. **推奨**: `with trace(...) as my_trace` のようにコンテキストマネージャーとして使用する。開始と終了が自動で行われます。 +2. [`trace.start()`][agents.tracing.Trace.start] と [`trace.finish()`][agents.tracing.Trace.finish] を手動で呼び出す。 -現在のトレースは Python の [`contextvar`](https://docs.python.org/3/library/contextvars.html) で管理されています。これにより、自動的に並行処理にも対応します。トレースを手動で開始・終了する場合は、`start()`/`finish()` に `mark_as_current` および `reset_current` を渡して現在のトレースを更新する必要があります。 +現在のトレースは Python の [`contextvar`](https://docs.python.org/3/library/contextvars.html) で管理されているため、並行処理でも自動で機能します。手動で開始／終了する場合は `start()`／`finish()` に `mark_as_current` と `reset_current` を渡して現在のトレースを更新してください。 ## スパンの作成 -さまざまな [`*_span()`][agents.tracing.create] メソッドを使ってスパンを作成できます。通常、スパンを手動で作成する必要はありません。カスタムスパン情報を追跡するための [`custom_span()`][agents.tracing.custom_span] 関数も用意されています。 +各種 [`*_span()`][agents.tracing.create] メソッドでスパンを作成できます。一般的には手動で作成する必要はありません。カスタム情報を追跡するための [`custom_span()`][agents.tracing.custom_span] も利用できます。 -スパンは自動的に現在のトレースの一部となり、最も近い現在のスパンの下にネストされます。これは Python の [`contextvar`](https://docs.python.org/3/library/contextvars.html) で管理されています。 +スパンは自動的に現在のトレースの一部となり、最も近い現在のスパンの下にネストされます。これも Python の [`contextvar`](https://docs.python.org/3/library/contextvars.html) で管理されています。 -## 機微なデータ +## 機密データ -一部のスパンは、機微なデータを記録する場合があります。 +一部のスパンでは機密データが収集される可能性があります。 -`generation_span()` は LLM 生成の入力・出力を保存し、`function_span()` は関数呼び出しの入力・出力を保存します。これらには機微なデータが含まれる場合があるため、[`RunConfig.trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data] でそのデータの記録を無効化できます。 +`generation_span()` には LLM の入力と出力、`function_span()` には関数呼び出しの入力と出力が保存されます。これらに機密データが含まれる場合、[`RunConfig.trace_include_sensitive_data`][agents.run.RunConfig.trace_include_sensitive_data] を使用して記録を無効化できます。 -同様に、音声スパンはデフォルトで入力・出力音声の base64 エンコード PCM データを含みます。[`VoicePipelineConfig.trace_include_sensitive_audio_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_audio_data] を設定することで、この音声データの記録を無効化できます。 +同様に、音声スパンにはデフォルトで base64 エンコードされた PCM 音声データが含まれます。[`VoicePipelineConfig.trace_include_sensitive_audio_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_audio_data] を設定して音声データの記録を無効化できます。 -## カスタムトレースプロセッサー +## カスタムトレーシングプロセッサー -トレーシングの高レベルなアーキテクチャは以下の通りです: +トレーシングの高レベル構成は次のとおりです。 -- 初期化時にグローバルな [`TraceProvider`][agents.tracing.setup.TraceProvider] を作成し、トレースの生成を担当します。 -- `TraceProvider` は [`BatchTraceProcessor`][agents.tracing.processors.BatchTraceProcessor] で構成されており、トレースやスパンをバッチで [`BackendSpanExporter`][agents.tracing.processors.BackendSpanExporter] に送信します。これにより、スパンやトレースが OpenAI バックエンドにバッチでエクスポートされます。 +- 初期化時にグローバルな [`TraceProvider`][agents.tracing.setup.TraceProvider] を作成し、トレースを生成。 +- `TraceProvider` は [`BatchTraceProcessor`][agents.tracing.processors.BatchTraceProcessor] を用いてスパン／トレースをバッチ送信し、[`BackendSpanExporter`][agents.tracing.processors.BackendSpanExporter] が OpenAI バックエンドへバッチでエクスポートします。 -このデフォルト設定をカスタマイズし、トレースを別のバックエンドや追加のバックエンドに送信したり、エクスポーターの動作を変更したりするには、2 つの方法があります: +デフォルト設定を変更して別のバックエンドへ送信したり、エクスポーターの挙動を修正するには次の 2 通りがあります。 -1. [`add_trace_processor()`][agents.tracing.add_trace_processor] を使うと、**追加の** トレースプロセッサーを追加できます。これにより、トレースやスパンが準備できた時点で独自の処理を行うことができ、OpenAI バックエンドへの送信に加えて独自の処理が可能です。 -2. [`set_trace_processors()`][agents.tracing.set_trace_processors] を使うと、デフォルトのプロセッサーを独自のトレースプロセッサーに**置き換える**ことができます。この場合、OpenAI バックエンドにトレースが送信されるのは、`TracingProcessor` を含めた場合のみです。 +1. [`add_trace_processor()`][agents.tracing.add_trace_processor] + 既定の送信に加え、**追加** のトレースプロセッサーを登録できます。これにより OpenAI バックエンドへの送信に加えて独自処理が可能です。 +2. [`set_trace_processors()`][agents.tracing.set_trace_processors] + 既定のプロセッサーを置き換え、**独自** のトレースプロセッサーだけを使用します。OpenAI バックエンドへ送信する場合は、その機能を持つ `TracingProcessor` を含める必要があります。 -## 外部トレースプロセッサー一覧 +## 外部トレーシングプロセッサー一覧 - [Weights & Biases](https://weave-docs.wandb.ai/guides/integrations/openai_agents) -- [Arize-Phoenix](https://docs.arize.com/phoenix/tracing/integrations-tracing/openai-agents-sdk) -- [MLflow (self-hosted/OSS](https://mlflow.org/docs/latest/tracing/integrations/openai-agent) +- [Arize‑Phoenix](https://docs.arize.com/phoenix/tracing/integrations-tracing/openai-agents-sdk) +- [MLflow (self‑hosted/OSS](https://mlflow.org/docs/latest/tracing/integrations/openai-agent) - [MLflow (Databricks hosted](https://docs.databricks.com/aws/en/mlflow/mlflow-tracing#-automatic-tracing) - [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk) - [Pydantic Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents) @@ -113,4 +119,5 @@ async def main(): - [Comet Opik](https://www.comet.com/docs/opik/tracing/integrations/openai_agents) - [Langfuse](https://langfuse.com/docs/integrations/openaiagentssdk/openai-agents) - [Langtrace](https://docs.langtrace.ai/supported-integrations/llm-frameworks/openai-agents-sdk) -- [Okahu-Monocle](https://github.com/monocle2ai/monocle) \ No newline at end of file +- [Okahu‑Monocle](https://github.com/monocle2ai/monocle) +- [Portkey AI](https://portkey.ai/docs/integrations/agents/openai-agents) diff --git a/docs/ja/visualization.md b/docs/ja/visualization.md index c93b3836d..9093bb659 100644 --- a/docs/ja/visualization.md +++ b/docs/ja/visualization.md @@ -1,10 +1,14 @@ +--- +search: + exclude: true +--- # エージェントの可視化 -エージェントの可視化では、 **Graphviz** を使用してエージェントとその関係性を構造的にグラフィカルに表現できます。これは、アプリケーション内でエージェント、ツール、ハンドオフがどのように相互作用しているかを理解するのに役立ちます。 +エージェントの可視化を使用すると、 ** Graphviz ** を用いてエージェントとその関係を構造化されたグラフィカル表現として生成できます。これは、アプリケーション内でエージェント、ツール、handoffs がどのように相互作用するかを理解するのに役立ちます。 ## インストール -オプションの `viz` 依存グループをインストールします: +オプションの `viz` 依存関係グループをインストールします: ```bash pip install "openai-agents[viz]" @@ -12,11 +16,11 @@ pip install "openai-agents[viz]" ## グラフの生成 -`draw_graph` 関数を使ってエージェントの可視化を生成できます。この関数は、以下のような有向グラフを作成します: +`draw_graph` 関数を使用してエージェントの可視化を生成できます。この関数は有向グラフを作成し、以下のように表現します。 - **エージェント** は黄色のボックスで表されます。 - **ツール** は緑色の楕円で表されます。 -- **ハンドオフ** は、あるエージェントから別のエージェントへの有向エッジで表されます。 +- **handoffs** はエージェント間の有向エッジで示されます。 ### 使用例 @@ -50,35 +54,34 @@ draw_graph(triage_agent) ![Agent Graph](../assets/images/graph.png) -これにより、 **triage agent** の構造とサブエージェントやツールとの接続が視覚的に表現されたグラフが生成されます。 - +これにより、 **triage agent** の構造と、それがサブエージェントやツールとどのようにつながっているかを視覚的に表すグラフが生成されます。 ## 可視化の理解 -生成されたグラフには以下が含まれます: +生成されたグラフには次の要素が含まれます。 - エントリーポイントを示す **start node** (`__start__`) -- 黄色で塗りつぶされた **長方形** で表されるエージェント -- 緑色で塗りつぶされた **楕円** で表されるツール -- 相互作用を示す有向エッジ: - - エージェント間のハンドオフには **実線の矢印** - - ツール呼び出しには **点線の矢印** -- 実行が終了する場所を示す **end node** (`__end__`) +- 黄色の塗りつぶしを持つ **矩形** のエージェント +- 緑色の塗りつぶしを持つ **楕円** のツール +- 相互作用を示す有向エッジ + - エージェント間の handoffs には **実線の矢印** + - ツール呼び出しには **破線の矢印** +- 実行が終了する位置を示す **end node** (`__end__`) ## グラフのカスタマイズ ### グラフの表示 -デフォルトでは、`draw_graph` はグラフをインラインで表示します。グラフを別ウィンドウで表示したい場合は、次のように記述します: +デフォルトでは、`draw_graph` はグラフをインラインで表示します。別ウィンドウでグラフを表示するには、次のように記述します。 ```python draw_graph(triage_agent).view() ``` ### グラフの保存 -デフォルトでは、`draw_graph` はグラフをインラインで表示します。ファイルとして保存したい場合は、ファイル名を指定します: +デフォルトでは、`draw_graph` はグラフをインラインで表示します。ファイルとして保存するには、ファイル名を指定します: ```python -draw_graph(triage_agent, filename="agent_graph.png") +draw_graph(triage_agent, filename="agent_graph") ``` -これにより、作業ディレクトリに `agent_graph.png` が生成されます。 \ No newline at end of file +これにより、作業ディレクトリに `agent_graph.png` が生成されます。 diff --git a/docs/ja/voice/pipeline.md b/docs/ja/voice/pipeline.md index 506610763..d52fb77f7 100644 --- a/docs/ja/voice/pipeline.md +++ b/docs/ja/voice/pipeline.md @@ -1,6 +1,10 @@ -# パイプラインとワークフロー +--- +search: + exclude: true +--- +# パイプラインと ワークフロー -[`VoicePipeline`][agents.voice.pipeline.VoicePipeline] は、エージェントワークフローを音声アプリに簡単に変換できるクラスです。実行したいワークフローを渡すと、パイプラインが入力音声の文字起こし、音声終了の検出、適切なタイミングでのワークフロー呼び出し、ワークフロー出力の音声化までを自動で処理します。 +[`VoicePipeline`][agents.voice.pipeline.VoicePipeline] は、エージェント的なワークフローを音声アプリに簡単に変換できるクラスです。ワークフローを渡すと、パイプラインが入力音声の文字起こし、音声終了の検知、適切なタイミングでのワークフロー呼び出し、そしてワークフロー出力を音声へ変換する処理を担当します。 ```mermaid graph LR @@ -30,29 +34,31 @@ graph LR ## パイプラインの設定 -パイプラインを作成する際、以下の項目を設定できます。 +パイプラインを作成する際に、以下を設定できます。 -1. [`workflow`][agents.voice.workflow.VoiceWorkflowBase]：新しい音声が文字起こしされるたびに実行されるコードです。 +1. [`workflow`][agents.voice.workflow.VoiceWorkflowBase] ‐ 新しい音声が文字起こしされるたびに実行されるコード 2. 使用する [`speech-to-text`][agents.voice.model.STTModel] および [`text-to-speech`][agents.voice.model.TTSModel] モデル -3. [`config`][agents.voice.pipeline_config.VoicePipelineConfig]：以下のような設定が可能です。 - - モデルプロバイダー：モデル名をモデルにマッピングできます - - トレーシング：トレーシングの有効/無効、音声ファイルのアップロード有無、ワークフロー名、トレース ID など - - TTS および STT モデルの設定：プロンプト、言語、使用するデータ型など +3. [`config`][agents.voice.pipeline_config.VoicePipelineConfig] ‐ 以下のような内容を設定可能 + - モデルプロバイダー。モデル名をモデルにマッピングします + - トレーシング。トレーシングの無効化、音声ファイルのアップロード可否、ワークフロー名、トレース ID など + - TTS と STT モデルの設定。プロンプト、言語、使用するデータ型など ## パイプラインの実行 -パイプラインは [`run()`][agents.voice.pipeline.VoicePipeline.run] メソッドで実行できます。音声入力は 2 つの形式で渡せます。 +パイプラインは [`run()`][agents.voice.pipeline.VoicePipeline.run] メソッドで実行できます。音声入力は次の 2 形式で渡せます。 -1. [`AudioInput`][agents.voice.input.AudioInput]：完全な音声トランスクリプトがある場合に使用し、その内容に対する結果のみを生成します。話者が話し終えたタイミングを検出する必要がない場合（例：事前録音音声や push-to-talk アプリなど、ユーザーが話し終えたことが明確な場合）に便利です。 -2. [`StreamedAudioInput`][agents.voice.input.StreamedAudioInput]：ユーザーが話し終えたタイミングを検出する必要がある場合に使用します。音声チャンクを検出ごとにプッシュでき、VoicePipeline が「アクティビティ検出」と呼ばれるプロセスを通じて、適切なタイミングでエージェントワークフローを自動実行します。 +1. [`AudioInput`][agents.voice.input.AudioInput] + 完全な音声トランスクリプトがある場合に使用し、その結果だけを生成したいときに便利です。話者の発話終了を検知する必要がないケース、たとえば録音済み音声やプッシュトゥートーク型アプリのようにユーザーが話し終えたタイミングが明確な場合に向いています。 +2. [`StreamedAudioInput`][agents.voice.input.StreamedAudioInput] + ユーザーの発話終了検知が必要な場合に使用します。検出された音声チャンクを順次プッシュでき、音声パイプラインが「アクティビティ検知」と呼ばれるプロセスを通じて適切なタイミングでエージェント ワークフローを自動的に実行します。 ## 結果 -VoicePipeline 実行の結果は [`StreamedAudioResult`][agents.voice.result.StreamedAudioResult] です。これは、イベントが発生するたびにストリーミングで受け取れるオブジェクトです。いくつかの種類の [`VoiceStreamEvent`][agents.voice.events.VoiceStreamEvent] があります。 +音声パイプライン実行の結果は [`StreamedAudioResult`][agents.voice.result.StreamedAudioResult] です。これは発生したイベントをストリーミングで受け取れるオブジェクトです。いくつかの [`VoiceStreamEvent`][agents.voice.events.VoiceStreamEvent] があり、主なものは次のとおりです。 -1. [`VoiceStreamEventAudio`][agents.voice.events.VoiceStreamEventAudio]：音声チャンクを含みます。 -2. [`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle]：ターンの開始や終了など、ライフサイクルイベントを通知します。 -3. [`VoiceStreamEventError`][agents.voice.events.VoiceStreamEventError]：エラーイベントです。 +1. [`VoiceStreamEventAudio`][agents.voice.events.VoiceStreamEventAudio] ‐ 音声チャンクを含みます +2. [`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle] ‐ ターンの開始や終了などのライフサイクルイベントを通知します +3. [`VoiceStreamEventError`][agents.voice.events.VoiceStreamEventError] ‐ エラーイベントです ```python @@ -72,4 +78,4 @@ async for event in result.stream(): ### 割り込み -Agents SDK は現在、[`StreamedAudioInput`][agents.voice.input.StreamedAudioInput] に対する組み込みの割り込みサポートを提供していません。そのため、検出された各ターンごとにワークフローの個別実行がトリガーされます。アプリケーション内で割り込みを処理したい場合は、[`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle] イベントをリッスンできます。`turn_started` は新しいターンが文字起こしされ、処理が開始されたことを示します。`turn_ended` は該当ターンのすべての音声が送信された後にトリガーされます。これらのイベントを利用して、モデルがターンを開始した際に話者のマイクをミュートし、ターンに関連するすべての音声を送信し終えた後にアンミュートする、といった制御が可能です。 \ No newline at end of file +Agents SDK は現在 [`StreamedAudioInput`][agents.voice.input.StreamedAudioInput] に対して、組み込みの割り込み処理をサポートしていません。そのため、検出された各ターンごとにワークフローが個別に実行されます。アプリケーション内で割り込みを処理したい場合は、[`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle] イベントを監視できます。`turn_started` は新しいターンが文字起こしされ、処理が開始されたことを示します。`turn_ended` は該当ターンのすべての音声が送信された後にトリガーされます。たとえば、モデルがターンを開始した際にスピーカーのマイクをミュートし、そのターンに関連する音声をすべて送信し終えた後にアンミュートするといった制御に、これらのイベントを利用できます。 \ No newline at end of file diff --git a/docs/ja/voice/quickstart.md b/docs/ja/voice/quickstart.md index 9c8740203..291b9882a 100644 --- a/docs/ja/voice/quickstart.md +++ b/docs/ja/voice/quickstart.md @@ -1,8 +1,12 @@ +--- +search: + exclude: true +--- # クイックスタート ## 前提条件 -Agents SDK の[クイックスタート手順](../quickstart.md)に従い、仮想環境をセットアップしてください。その後、SDK からオプションの音声依存関係をインストールします。 +まずは [クイックスタート手順](../quickstart.md) に従って Agents SDK をセットアップし、仮想環境を作成してください。その後、SDK の音声関連のオプション依存関係をインストールします: ```bash pip install 'openai-agents[voice]' @@ -10,11 +14,11 @@ pip install 'openai-agents[voice]' ## コンセプト -主なコンセプトは [`VoicePipeline`][agents.voice.pipeline.VoicePipeline] です。これは 3 ステップのプロセスです： +押さえておくべき主な概念は [`VoicePipeline`][agents.voice.pipeline.VoicePipeline] です。これは次の 3 ステップから成るプロセスです。 -1. 音声をテキストに変換する音声認識モデル（speech-to-text）を実行します。 -2. 通常はエージェント的なワークフローであるあなたのコードを実行し、結果を生成します。 -3. 結果のテキストを音声に戻す音声合成モデル（text-to-speech）を実行します。 +1. speech-to-text モデルを実行して音声をテキストに変換します。 +2. 通常はエージェント的ワークフローであるあなたのコードを実行し、結果を生成します。 +3. text-to-speech モデルを実行して結果のテキストを再び音声に変換します。 ```mermaid graph LR @@ -44,7 +48,7 @@ graph LR ## エージェント -まず、いくつかのエージェントをセットアップしましょう。この SDK でエージェントを作成したことがあれば、馴染みがあるはずです。ここでは、複数のエージェント、ハンドオフ、ツールを用意します。 +まず、いくつかの エージェント をセットアップしましょう。この SDK でエージェントを構築したことがあれば、見覚えがあるはずです。ここでは複数の エージェント、ハンドオフ、そしてツールを用意します。 ```python import asyncio @@ -88,7 +92,7 @@ agent = Agent( ## 音声パイプライン -[`SingleAgentVoiceWorkflow`][agents.voice.workflow.SingleAgentVoiceWorkflow] をワークフローとして使い、シンプルな音声パイプラインをセットアップします。 +[`SingleAgentVoiceWorkflow`][agents.voice.workflow.SingleAgentVoiceWorkflow] をワークフローとして、シンプルな音声パイプラインを構築します。 ```python from agents.voice import SingleAgentVoiceWorkflow, VoicePipeline @@ -120,7 +124,7 @@ async for event in result.stream(): ``` -## すべてをまとめる +## まとめて実行 ```python import asyncio @@ -191,4 +195,4 @@ if __name__ == "__main__": asyncio.run(main()) ``` -この例を実行すると、エージェントがあなたに話しかけます！[examples/voice/static](https://github.com/openai/openai-agents-python/tree/main/examples/voice/static) のコード例をチェックして、実際にエージェントと会話できるデモをご覧ください。 \ No newline at end of file +この例を実行すると、エージェントがあなたに話しかけます。実際にエージェントと会話できるデモは、[examples/voice/static](https://github.com/openai/openai-agents-python/tree/main/examples/voice/static) をご覧ください。 \ No newline at end of file diff --git a/docs/ja/voice/tracing.md b/docs/ja/voice/tracing.md index 639a95f29..21f4788f9 100644 --- a/docs/ja/voice/tracing.md +++ b/docs/ja/voice/tracing.md @@ -1,14 +1,18 @@ +--- +search: + exclude: true +--- # トレーシング -[エージェントのトレーシング](../tracing.md)と同様に、音声パイプラインも自動的にトレーシングされます。 +[エージェントのトレーシング](../tracing.md) と同様に、音声パイプラインも自動的にトレーシングされます。 -基本的なトレーシング情報については上記のトレーシングドキュメントをご参照いただけますが、さらに [`VoicePipelineConfig`][agents.voice.pipeline_config.VoicePipelineConfig] を使ってパイプラインのトレーシングを設定することも可能です。 +基本的なトレーシング情報については上記のドキュメントを参照してください。さらに、[`VoicePipelineConfig`][agents.voice.pipeline_config.VoicePipelineConfig] でパイプラインのトレーシング設定を行えます。 -主なトレーシング関連フィールドは以下の通りです： +主なトレーシング関連フィールドは次のとおりです。 -- [`tracing_disabled`][agents.voice.pipeline_config.VoicePipelineConfig.tracing_disabled]：トレーシングを無効にするかどうかを制御します。デフォルトではトレーシングは有効です。 -- [`trace_include_sensitive_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_data]：トレースに音声の書き起こしなど、機密性の高いデータを含めるかどうかを制御します。これは特に音声パイプライン用であり、Workflow 内部で発生する内容には適用されません。 -- [`trace_include_sensitive_audio_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_audio_data]：トレースに音声データを含めるかどうかを制御します。 -- [`workflow_name`][agents.voice.pipeline_config.VoicePipelineConfig.workflow_name]：トレースワークフローの名前です。 -- [`group_id`][agents.voice.pipeline_config.VoicePipelineConfig.group_id]：トレースの `group_id` で、複数のトレースをリンクすることができます。 +- [`tracing_disabled`][agents.voice.pipeline_config.VoicePipelineConfig.tracing_disabled]：トレーシングを無効にするかどうかを制御します。デフォルトではトレーシングは有効です。 +- [`trace_include_sensitive_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_data]：トレースに音声テキストなどの機微なデータを含めるかどうかを制御します。これは音声パイプライン専用であり、Workflow 内部で発生する処理には影響しません。 +- [`trace_include_sensitive_audio_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_audio_data]：トレースに音声データを含めるかどうかを制御します。 +- [`workflow_name`][agents.voice.pipeline_config.VoicePipelineConfig.workflow_name]：トレース Workflow の名前です。 +- [`group_id`][agents.voice.pipeline_config.VoicePipelineConfig.group_id]：複数のトレースを関連付けるための `group_id` です。 - [`trace_metadata`][agents.voice.pipeline_config.VoicePipelineConfig.tracing_disabled]：トレースに追加するメタデータです。 \ No newline at end of file diff --git a/docs/mcp.md b/docs/mcp.md index e279a25e0..eef61a047 100644 --- a/docs/mcp.md +++ b/docs/mcp.md @@ -4,27 +4,35 @@ The [Model context protocol](https://modelcontextprotocol.io/introduction) (aka > MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools. -The Agents SDK has support for MCP. This enables you to use a wide range of MCP servers to provide tools to your Agents. +The Agents SDK has support for MCP. This enables you to use a wide range of MCP servers to provide tools and prompts to your Agents. ## MCP servers -Currently, the MCP spec defines two kinds of servers, based on the transport mechanism they use: +Currently, the MCP spec defines three kinds of servers, based on the transport mechanism they use: 1. **stdio** servers run as a subprocess of your application. You can think of them as running "locally". 2. **HTTP over SSE** servers run remotely. You connect to them via a URL. +3. **Streamable HTTP** servers run remotely using the Streamable HTTP transport defined in the MCP spec. -You can use the [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][agents.mcp.server.MCPServerSse] classes to connect to these servers. +You can use the [`MCPServerStdio`][agents.mcp.server.MCPServerStdio], [`MCPServerSse`][agents.mcp.server.MCPServerSse], and [`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp] classes to connect to these servers. For example, this is how you'd use the [official MCP filesystem server](https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem). ```python +from agents.run_context import RunContextWrapper + async with MCPServerStdio( params={ "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], } ) as server: - tools = await server.list_tools() + # Note: In practice, you typically add the server to an Agent + # and let the framework handle tool listing automatically. + # Direct calls to list_tools() require run_context and agent parameters. + run_context = RunContextWrapper(context=None) + agent = Agent(name="test", instructions="test") + tools = await server.list_tools(run_context, agent) ``` ## Using MCP servers @@ -40,9 +48,128 @@ agent=Agent( ) ``` +## Tool filtering + +You can filter which tools are available to your Agent by configuring tool filters on MCP servers. The SDK supports both static and dynamic tool filtering. + +### Static tool filtering + +For simple allow/block lists, you can use static filtering: + +```python +from agents.mcp import create_static_tool_filter + +# Only expose specific tools from this server +server = MCPServerStdio( + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + }, + tool_filter=create_static_tool_filter( + allowed_tool_names=["read_file", "write_file"] + ) +) + +# Exclude specific tools from this server +server = MCPServerStdio( + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + }, + tool_filter=create_static_tool_filter( + blocked_tool_names=["delete_file"] + ) +) + +``` + +**When both `allowed_tool_names` and `blocked_tool_names` are configured, the processing order is:** +1. First apply `allowed_tool_names` (allowlist) - only keep the specified tools +2. Then apply `blocked_tool_names` (blocklist) - exclude specified tools from the remaining tools + +For example, if you configure `allowed_tool_names=["read_file", "write_file", "delete_file"]` and `blocked_tool_names=["delete_file"]`, only `read_file` and `write_file` tools will be available. + +### Dynamic tool filtering + +For more complex filtering logic, you can use dynamic filters with functions: + +```python +from agents.mcp import ToolFilterContext + +# Simple synchronous filter +def custom_filter(context: ToolFilterContext, tool) -> bool: + """Example of a custom tool filter.""" + # Filter logic based on tool name patterns + return tool.name.startswith("allowed_prefix") + +# Context-aware filter +def context_aware_filter(context: ToolFilterContext, tool) -> bool: + """Filter tools based on context information.""" + # Access agent information + agent_name = context.agent.name + + # Access server information + server_name = context.server_name + + # Implement your custom filtering logic here + return some_filtering_logic(agent_name, server_name, tool) + +# Asynchronous filter +async def async_filter(context: ToolFilterContext, tool) -> bool: + """Example of an asynchronous filter.""" + # Perform async operations if needed + result = await some_async_check(context, tool) + return result + +server = MCPServerStdio( + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + }, + tool_filter=custom_filter # or context_aware_filter or async_filter +) +``` + +The `ToolFilterContext` provides access to: +- `run_context`: The current run context +- `agent`: The agent requesting the tools +- `server_name`: The name of the MCP server + +## Prompts + +MCP servers can also provide prompts that can be used to dynamically generate agent instructions. This allows you to create reusable instruction templates that can be customized with parameters. + +### Using prompts + +MCP servers that support prompts provide two key methods: + +- `list_prompts()`: Lists all available prompts on the server +- `get_prompt(name, arguments)`: Gets a specific prompt with optional parameters + +```python +# List available prompts +prompts_result = await server.list_prompts() +for prompt in prompts_result.prompts: + print(f"Prompt: {prompt.name} - {prompt.description}") + +# Get a specific prompt with parameters +prompt_result = await server.get_prompt( + "generate_code_review_instructions", + {"focus": "security vulnerabilities", "language": "python"} +) +instructions = prompt_result.messages[0].content.text + +# Use the prompt-generated instructions with an Agent +agent = Agent( + name="Code Reviewer", + instructions=instructions, # Instructions from MCP prompt + mcp_servers=[server] +) +``` + ## Caching -Every time an Agent runs, it calls `list_tools()` on the MCP server. This can be a latency hit, especially if the server is a remote server. To automatically cache the list of tools, you can pass `cache_tools_list=True` to both [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][agents.mcp.server.MCPServerSse]. You should only do this if you're certain the tool list will not change. +Every time an Agent runs, it calls `list_tools()` on the MCP server. This can be a latency hit, especially if the server is a remote server. To automatically cache the list of tools, you can pass `cache_tools_list=True` to [`MCPServerStdio`][agents.mcp.server.MCPServerStdio], [`MCPServerSse`][agents.mcp.server.MCPServerSse], and [`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp]. You should only do this if you're certain the tool list will not change. If you want to invalidate the cache, you can call `invalidate_tools_cache()` on the servers. diff --git a/docs/models.md b/docs/models/index.md similarity index 74% rename from docs/models.md rename to docs/models/index.md index 1ce6e8f2f..b3b2b7f0b 100644 --- a/docs/models.md +++ b/docs/models/index.md @@ -5,11 +5,40 @@ The Agents SDK comes with out-of-the-box support for OpenAI models in two flavor - **Recommended**: the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel], which calls OpenAI APIs using the new [Responses API](https://platform.openai.com/docs/api-reference/responses). - The [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel], which calls OpenAI APIs using the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). +## Non-OpenAI models + +You can use most other non-OpenAI models via the [LiteLLM integration](./litellm.md). First, install the litellm dependency group: + +```bash +pip install "openai-agents[litellm]" +``` + +Then, use any of the [supported models](https://docs.litellm.ai/docs/providers) with the `litellm/` prefix: + +```python +claude_agent = Agent(model="litellm/anthropic/claude-3-5-sonnet-20240620", ...) +gemini_agent = Agent(model="litellm/gemini/gemini-2.5-flash-preview-04-17", ...) +``` + +### Other ways to use non-OpenAI models + +You can integrate other LLM providers in 3 more ways (examples [here](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/)): + +1. [`set_default_openai_client`][agents.set_default_openai_client] is useful in cases where you want to globally use an instance of `AsyncOpenAI` as the LLM client. This is for cases where the LLM provider has an OpenAI compatible API endpoint, and you can set the `base_url` and `api_key`. See a configurable example in [examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py). +2. [`ModelProvider`][agents.models.interface.ModelProvider] is at the `Runner.run` level. This lets you say "use a custom model provider for all agents in this run". See a configurable example in [examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py). +3. [`Agent.model`][agents.agent.Agent.model] lets you specify the model on a specific Agent instance. This enables you to mix and match different providers for different agents. See a configurable example in [examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py). An easy way to use most available models is via the [LiteLLM integration](./litellm.md). + +In cases where you do not have an API key from `platform.openai.com`, we recommend disabling tracing via `set_tracing_disabled()`, or setting up a [different tracing processor](../tracing.md). + +!!! note + + In these examples, we use the Chat Completions API/model, because most LLM providers don't yet support the Responses API. If your LLM provider does support it, we recommend using Responses. + ## Mixing and matching models Within a single workflow, you may want to use different models for each agent. For example, you could use a smaller, faster model for triage, while using a larger, more capable model for complex tasks. When configuring an [`Agent`][agents.Agent], you can select a specific model by either: -1. Passing the name of an OpenAI model. +1. Passing the name of a model. 2. Passing any model name + a [`ModelProvider`][agents.models.interface.ModelProvider] that can map that name to a Model instance. 3. Directly providing a [`Model`][agents.models.interface.Model] implementation. @@ -64,19 +93,21 @@ english_agent = Agent( ) ``` -## Using other LLM providers +Also, when you use OpenAI's Responses API, [there are a few other optional parameters](https://platform.openai.com/docs/api-reference/responses/create) (e.g., `user`, `service_tier`, and so on). If they are not available at the top level, you can use `extra_args` to pass them as well. -You can use other LLM providers in 3 ways (examples [here](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/)): - -1. [`set_default_openai_client`][agents.set_default_openai_client] is useful in cases where you want to globally use an instance of `AsyncOpenAI` as the LLM client. This is for cases where the LLM provider has an OpenAI compatible API endpoint, and you can set the `base_url` and `api_key`. See a configurable example in [examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py). -2. [`ModelProvider`][agents.models.interface.ModelProvider] is at the `Runner.run` level. This lets you say "use a custom model provider for all agents in this run". See a configurable example in [examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py). -3. [`Agent.model`][agents.agent.Agent.model] lets you specify the model on a specific Agent instance. This enables you to mix and match different providers for different agents. See a configurable example in [examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py). - -In cases where you do not have an API key from `platform.openai.com`, we recommend disabling tracing via `set_tracing_disabled()`, or setting up a [different tracing processor](tracing.md). - -!!! note +```python +from agents import Agent, ModelSettings - In these examples, we use the Chat Completions API/model, because most LLM providers don't yet support the Responses API. If your LLM provider does support it, we recommend using Responses. +english_agent = Agent( + name="English agent", + instructions="You only speak English", + model="gpt-4o", + model_settings=ModelSettings( + temperature=0.1, + extra_args={"service_tier": "flex", "user": "user_12345"}, + ), +) +``` ## Common issues with using other LLM providers @@ -86,7 +117,7 @@ If you get errors related to tracing, this is because traces are uploaded to Ope 1. Disable tracing entirely: [`set_tracing_disabled(True)`][agents.set_tracing_disabled]. 2. Set an OpenAI key for tracing: [`set_tracing_export_api_key(...)`][agents.set_tracing_export_api_key]. This API key will only be used for uploading traces, and must be from [platform.openai.com](https://platform.openai.com/). -3. Use a non-OpenAI trace processor. See the [tracing docs](tracing.md#custom-tracing-processors). +3. Use a non-OpenAI trace processor. See the [tracing docs](../tracing.md#custom-tracing-processors). ### Responses API support @@ -100,7 +131,17 @@ The SDK uses the Responses API by default, but most other LLM providers don't ye Some model providers don't have support for [structured outputs](https://platform.openai.com/docs/guides/structured-outputs). This sometimes results in an error that looks something like this: ``` + BadRequestError: Error code: 400 - {'error': {'message': "'response_format.type' : value is not one of the allowed values ['text','json_object']", 'type': 'invalid_request_error'}} + ``` This is a shortcoming of some model providers - they support JSON outputs, but don't allow you to specify the `json_schema` to use for the output. We are working on a fix for this, but we suggest relying on providers that do have support for JSON schema output, because otherwise your app will often break because of malformed JSON. + +## Mixing models across providers + +You need to be aware of feature differences between model providers, or you may run into errors. For example, OpenAI supports structured outputs, multimodal input, and hosted file search and web search, but many other providers don't support these features. Be aware of these limitations: + +- Don't send unsupported `tools` to providers that don't understand them +- Filter out multimodal inputs before calling models that are text-only +- Be aware that providers that don't support structured JSON outputs will occasionally produce invalid JSON. diff --git a/docs/models/litellm.md b/docs/models/litellm.md new file mode 100644 index 000000000..90572a28c --- /dev/null +++ b/docs/models/litellm.md @@ -0,0 +1,73 @@ +# Using any model via LiteLLM + +!!! note + + The LiteLLM integration is in beta. You may run into issues with some model providers, especially smaller ones. Please report any issues via [Github issues](https://github.com/openai/openai-agents-python/issues) and we'll fix quickly. + +[LiteLLM](https://docs.litellm.ai/docs/) is a library that allows you to use 100+ models via a single interface. We've added a LiteLLM integration to allow you to use any AI model in the Agents SDK. + +## Setup + +You'll need to ensure `litellm` is available. You can do this by installing the optional `litellm` dependency group: + +```bash +pip install "openai-agents[litellm]" +``` + +Once done, you can use [`LitellmModel`][agents.extensions.models.litellm_model.LitellmModel] in any agent. + +## Example + +This is a fully working example. When you run it, you'll be prompted for a model name and API key. For example, you could enter: + +- `openai/gpt-4.1` for the model, and your OpenAI API key +- `anthropic/claude-3-5-sonnet-20240620` for the model, and your Anthropic API key +- etc + +For a full list of models supported in LiteLLM, see the [litellm providers docs](https://docs.litellm.ai/docs/providers). + +```python +from __future__ import annotations + +import asyncio + +from agents import Agent, Runner, function_tool, set_tracing_disabled +from agents.extensions.models.litellm_model import LitellmModel + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(model: str, api_key: str): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model=LitellmModel(model=model, api_key=api_key), + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + # First try to get model/api key from args + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=False) + parser.add_argument("--api-key", type=str, required=False) + args = parser.parse_args() + + model = args.model + if not model: + model = input("Enter a model name for Litellm: ") + + api_key = args.api_key + if not api_key: + api_key = input("Enter an API key for Litellm: ") + + asyncio.run(main(model, api_key)) +``` diff --git a/docs/quickstart.md b/docs/quickstart.md index 9513fdb8f..213d16e5d 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -186,4 +186,4 @@ Learn how to build more complex agentic flows: - Learn about how to configure [Agents](agents.md). - Learn about [running agents](running_agents.md). -- Learn about [tools](tools.md), [guardrails](guardrails.md) and [models](models.md). +- Learn about [tools](tools.md), [guardrails](guardrails.md) and [models](models/index.md). diff --git a/docs/ref/extensions/litellm.md b/docs/ref/extensions/litellm.md new file mode 100644 index 000000000..7bd67fde4 --- /dev/null +++ b/docs/ref/extensions/litellm.md @@ -0,0 +1,3 @@ +# `LiteLLM Models` + +::: agents.extensions.models.litellm_model diff --git a/docs/ref/memory.md b/docs/ref/memory.md new file mode 100644 index 000000000..04a2258bf --- /dev/null +++ b/docs/ref/memory.md @@ -0,0 +1,8 @@ +# Memory + +::: agents.memory + + options: + members: + - Session + - SQLiteSession diff --git a/docs/ref/repl.md b/docs/ref/repl.md new file mode 100644 index 000000000..a064a9bff --- /dev/null +++ b/docs/ref/repl.md @@ -0,0 +1,6 @@ +# `repl` + +::: agents.repl + options: + members: + - run_demo_loop diff --git a/docs/release.md b/docs/release.md new file mode 100644 index 000000000..a86103f96 --- /dev/null +++ b/docs/release.md @@ -0,0 +1,24 @@ +# Release process/changelog + +The project follows a slightly modified version of semantic versioning using the form `0.Y.Z`. The leading `0` indicates the SDK is still evolving rapidly. Increment the components as follows: + +## Minor (`Y`) versions + +We will increase minor versions `Y` for **breaking changes** to any public interfaces that are not marked as beta. For example, going from `0.0.x` to `0.1.x` might include breaking changes. + +If you don't want breaking changes, we recommend pinning to `0.0.x` versions in your project. + +## Patch (`Z`) versions + +We will increment `Z` for non-breaking changes: + +- Bug fixes +- New features +- Changes to private interfaces +- Updates to beta features + +## Breaking change changelog + +### 0.1.0 + +In this version, [`MCPServer.list_tools()`][agents.mcp.server.MCPServer] has two new params: `run_context` and `agent`. You'll need to add these params to any classes that subclass `MCPServer`. diff --git a/docs/repl.md b/docs/repl.md new file mode 100644 index 000000000..073b87f51 --- /dev/null +++ b/docs/repl.md @@ -0,0 +1,19 @@ +# REPL utility + +The SDK provides `run_demo_loop` for quick interactive testing. + +```python +import asyncio +from agents import Agent, run_demo_loop + +async def main() -> None: + agent = Agent(name="Assistant", instructions="You are a helpful assistant.") + await run_demo_loop(agent) + +if __name__ == "__main__": + asyncio.run(main()) +``` + +`run_demo_loop` prompts for user input in a loop, keeping the conversation +history between turns. By default it streams model output as it is produced. +Type `quit` or `exit` (or press `Ctrl-D`) to leave the loop. diff --git a/docs/running_agents.md b/docs/running_agents.md index f631cf46f..6898f5101 100644 --- a/docs/running_agents.md +++ b/docs/running_agents.md @@ -65,7 +65,9 @@ Calling any of the run methods can result in one or more agents running (and hen At the end of the agent run, you can choose what to show to the user. For example, you might show the user every new item generated by the agents, or just the final output. Either way, the user might then ask a followup question, in which case you can call the run method again. -You can use the base [`RunResultBase.to_input_list()`][agents.result.RunResultBase.to_input_list] method to get the inputs for the next turn. +### Manual conversation management + +You can manually manage conversation history using the [`RunResultBase.to_input_list()`][agents.result.RunResultBase.to_input_list] method to get the inputs for the next turn: ```python async def main(): @@ -84,6 +86,39 @@ async def main(): # California ``` +### Automatic conversation management with Sessions + +For a simpler approach, you can use [Sessions](sessions.md) to automatically handle conversation history without manually calling `.to_input_list()`: + +```python +from agents import Agent, Runner, SQLiteSession + +async def main(): + agent = Agent(name="Assistant", instructions="Reply very concisely.") + + # Create session instance + session = SQLiteSession("conversation_123") + + with trace(workflow_name="Conversation", group_id=thread_id): + # First turn + result = await Runner.run(agent, "What city is the Golden Gate Bridge in?", session=session) + print(result.final_output) + # San Francisco + + # Second turn - agent automatically remembers previous context + result = await Runner.run(agent, "What state is it in?", session=session) + print(result.final_output) + # California +``` + +Sessions automatically: + +- Retrieves conversation history before each run +- Stores new messages after each run +- Maintains separate conversations for different session IDs + +See the [Sessions documentation](sessions.md) for more details. + ## Exceptions The SDK raises exceptions in certain cases. The full list is in [`agents.exceptions`][]. As an overview: diff --git a/docs/scripts/translate_docs.py b/docs/scripts/translate_docs.py index f261daa04..ac40b6fa8 100644 --- a/docs/scripts/translate_docs.py +++ b/docs/scripts/translate_docs.py @@ -1,5 +1,7 @@ # ruff: noqa import os +import sys +import argparse from openai import OpenAI from concurrent.futures import ThreadPoolExecutor @@ -7,7 +9,18 @@ # logging.basicConfig(level=logging.INFO) # logging.getLogger("openai").setLevel(logging.DEBUG) -OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "gpt-4.1") +OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "o3") + +ENABLE_CODE_SNIPPET_EXCLUSION = True +# gpt-4.5 needed this for better quality +ENABLE_SMALL_CHUNK_TRANSLATION = False + +SEARCH_EXCLUSION = """--- +search: + exclude: true +--- +""" + # Define the source and target directories source_dir = "docs" @@ -25,6 +38,7 @@ "Agents SDK", "Hello World", "Model context protocol", + "MCP", "structured outputs", "Chain-of-Thought", "Chat Completions", @@ -109,6 +123,8 @@ def built_instructions(target_language: str, lang_code: str) -> str: - Keep the tone **natural** and concise. - Do not omit any content. If a segment should stay in English, copy it verbatim. - Do not change the markdown data structure, including the indentations. +- Section titles starting with # or ## must be a noun form rather than a sentence. +- Section titles must be translated except for the Do-Not-Translate list. - Keep all placeholders such as `CODE_BLOCK_*` and `CODE_LINE_PREFIX` unchanged. - Convert asset paths: `./assets/…` → `../assets/…`. *Example:* `![img](./assets/pic.png)` → `![img](../assets/pic.png)` @@ -178,10 +194,15 @@ def translate_file(file_path: str, target_path: str, lang_code: str) -> None: code_blocks: list[str] = [] code_block_chunks: list[str] = [] for line in lines: - if len(current_chunk) >= 120 and not in_code_block and line.startswith("#"): + if ( + ENABLE_SMALL_CHUNK_TRANSLATION is True + and len(current_chunk) >= 120 # required for gpt-4.5 + and not in_code_block + and line.startswith("#") + ): chunks.append("\n".join(current_chunk)) current_chunk = [] - if line.strip().startswith("```"): + if ENABLE_CODE_SNIPPET_EXCLUSION is True and line.strip().startswith("```"): code_block_chunks.append(line) if in_code_block is True: code_blocks.append("\n".join(code_block_chunks)) @@ -220,6 +241,8 @@ def translate_file(file_path: str, target_path: str, lang_code: str) -> None: for idx, code_block in enumerate(code_blocks): translated_text = translated_text.replace(f"CODE_BLOCK_{idx:02}", code_block) + # FIXME: enable mkdocs search plugin to seamlessly work with i18n plugin + translated_text = SEARCH_EXCLUSION + translated_text # Save the combined translated content with open(target_path, "w", encoding="utf-8") as f: f.write(translated_text) @@ -242,24 +265,47 @@ def translate_single_source_file(file_path: str) -> None: def main(): - # Traverse the source directory - for root, _, file_names in os.walk(source_dir): - # Skip the target directories - if any(lang in root for lang in languages): - continue - # Increasing this will make the translation faster; you can decide considering the model's capacity - concurrency = 6 - with ThreadPoolExecutor(max_workers=concurrency) as executor: - futures = [] - for file_name in file_names: - filepath = os.path.join(root, file_name) - futures.append(executor.submit(translate_single_source_file, filepath)) - if len(futures) >= concurrency: - for future in futures: - future.result() - futures.clear() - - print("Translation completed.") + parser = argparse.ArgumentParser(description="Translate documentation files") + parser.add_argument( + "--file", type=str, help="Specific file to translate (relative to docs directory)" + ) + args = parser.parse_args() + + if args.file: + # Translate a single file + # Handle both "foo.md" and "docs/foo.md" formats + if args.file.startswith("docs/"): + # Remove "docs/" prefix if present + relative_file = args.file[5:] + else: + relative_file = args.file + + file_path = os.path.join(source_dir, relative_file) + if os.path.exists(file_path): + translate_single_source_file(file_path) + print(f"Translation completed for {relative_file}") + else: + print(f"Error: File {file_path} does not exist") + sys.exit(1) + else: + # Traverse the source directory (original behavior) + for root, _, file_names in os.walk(source_dir): + # Skip the target directories + if any(lang in root for lang in languages): + continue + # Increasing this will make the translation faster; you can decide considering the model's capacity + concurrency = 6 + with ThreadPoolExecutor(max_workers=concurrency) as executor: + futures = [] + for file_name in file_names: + filepath = os.path.join(root, file_name) + futures.append(executor.submit(translate_single_source_file, filepath)) + if len(futures) >= concurrency: + for future in futures: + future.result() + futures.clear() + + print("Translation completed.") if __name__ == "__main__": diff --git a/docs/sessions.md b/docs/sessions.md new file mode 100644 index 000000000..8906f0e6d --- /dev/null +++ b/docs/sessions.md @@ -0,0 +1,319 @@ +# Sessions + +The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the need to manually handle `.to_input_list()` between turns. + +Sessions stores conversation history for a specific session, allowing agents to maintain context without requiring explicit manual memory management. This is particularly useful for building chat applications or multi-turn conversations where you want the agent to remember previous interactions. + +## Quick start + +```python +from agents import Agent, Runner, SQLiteSession + +# Create agent +agent = Agent( + name="Assistant", + instructions="Reply very concisely.", +) + +# Create a session instance with a session ID +session = SQLiteSession("conversation_123") + +# First turn +result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session +) +print(result.final_output) # "San Francisco" + +# Second turn - agent automatically remembers previous context +result = await Runner.run( + agent, + "What state is it in?", + session=session +) +print(result.final_output) # "California" + +# Also works with synchronous runner +result = Runner.run_sync( + agent, + "What's the population?", + session=session +) +print(result.final_output) # "Approximately 39 million" +``` + +## How it works + +When session memory is enabled: + +1. **Before each run**: The runner automatically retrieves the conversation history for the session and prepends it to the input items. +2. **After each run**: All new items generated during the run (user input, assistant responses, tool calls, etc.) are automatically stored in the session. +3. **Context preservation**: Each subsequent run with the same session includes the full conversation history, allowing the agent to maintain context. + +This eliminates the need to manually call `.to_input_list()` and manage conversation state between runs. + +## Memory operations + +### Basic operations + +Sessions supports several operations for managing conversation history: + +```python +from agents import SQLiteSession + +session = SQLiteSession("user_123", "conversations.db") + +# Get all items in a session +items = await session.get_items() + +# Add new items to a session +new_items = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"} +] +await session.add_items(new_items) + +# Remove and return the most recent item +last_item = await session.pop_item() +print(last_item) # {"role": "assistant", "content": "Hi there!"} + +# Clear all items from a session +await session.clear_session() +``` + +### Using pop_item for corrections + +The `pop_item` method is particularly useful when you want to undo or modify the last item in a conversation: + +```python +from agents import Agent, Runner, SQLiteSession + +agent = Agent(name="Assistant") +session = SQLiteSession("correction_example") + +# Initial conversation +result = await Runner.run( + agent, + "What's 2 + 2?", + session=session +) +print(f"Agent: {result.final_output}") + +# User wants to correct their question +user_item = await session.pop_item() # Remove user's question +assistant_item = await session.pop_item() # Remove agent's response + +# Ask a corrected question +result = await Runner.run( + agent, + "What's 2 + 3?", + session=session +) +print(f"Agent: {result.final_output}") +``` + +## Memory options + +### No memory (default) + +```python +# Default behavior - no session memory +result = await Runner.run(agent, "Hello") +``` + +### SQLite memory + +```python +from agents import SQLiteSession + +# In-memory database (lost when process ends) +session = SQLiteSession("user_123") + +# Persistent file-based database +session = SQLiteSession("user_123", "conversations.db") + +# Use the session +result = await Runner.run( + agent, + "Hello", + session=session +) +``` + +### Multiple sessions + +```python +from agents import Agent, Runner, SQLiteSession + +agent = Agent(name="Assistant") + +# Different sessions maintain separate conversation histories +session_1 = SQLiteSession("user_123", "conversations.db") +session_2 = SQLiteSession("user_456", "conversations.db") + +result1 = await Runner.run( + agent, + "Hello", + session=session_1 +) +result2 = await Runner.run( + agent, + "Hello", + session=session_2 +) +``` + +## Custom memory implementations + +You can implement your own session memory by creating a class that follows the [`Session`][agents.memory.session.Session] protocol: + +````python +from agents.memory import Session +from typing import List + +class MyCustomSession: + """Custom session implementation following the Session protocol.""" + + def __init__(self, session_id: str): + self.session_id = session_id + # Your initialization here + + async def get_items(self, limit: int | None = None) -> List[dict]: + """Retrieve conversation history for this session.""" + # Your implementation here + pass + + async def add_items(self, items: List[dict]) -> None: + """Store new items for this session.""" + # Your implementation here + pass + + async def pop_item(self) -> dict | None: + """Remove and return the most recent item from this session.""" + # Your implementation here + pass + + async def clear_session(self) -> None: + """Clear all items for this session.""" + # Your implementation here + pass + +# Use your custom session +agent = Agent(name="Assistant") +result = await Runner.run( + agent, + "Hello", + session=MyCustomSession("my_session") +) + +## Session management + +### Session ID naming + +Use meaningful session IDs that help you organize conversations: + +- User-based: `"user_12345"` +- Thread-based: `"thread_abc123"` +- Context-based: `"support_ticket_456"` + +### Memory persistence + +- Use in-memory SQLite (`SQLiteSession("session_id")`) for temporary conversations +- Use file-based SQLite (`SQLiteSession("session_id", "path/to/db.sqlite")`) for persistent conversations +- Consider implementing custom session backends for production systems (Redis, PostgreSQL, etc.) + +### Session management + +```python +# Clear a session when conversation should start fresh +await session.clear_session() + +# Different agents can share the same session +support_agent = Agent(name="Support") +billing_agent = Agent(name="Billing") +session = SQLiteSession("user_123") + +# Both agents will see the same conversation history +result1 = await Runner.run( + support_agent, + "Help me with my account", + session=session +) +result2 = await Runner.run( + billing_agent, + "What are my charges?", + session=session +) +```` + +## Complete example + +Here's a complete example showing session memory in action: + +```python +import asyncio +from agents import Agent, Runner, SQLiteSession + + +async def main(): + # Create an agent + agent = Agent( + name="Assistant", + instructions="Reply very concisely.", + ) + + # Create a session instance that will persist across runs + session = SQLiteSession("conversation_123", "conversation_history.db") + + print("=== Sessions Example ===") + print("The agent will remember previous messages automatically.\n") + + # First turn + print("First turn:") + print("User: What city is the Golden Gate Bridge in?") + result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session + ) + print(f"Assistant: {result.final_output}") + print() + + # Second turn - the agent will remember the previous conversation + print("Second turn:") + print("User: What state is it in?") + result = await Runner.run( + agent, + "What state is it in?", + session=session + ) + print(f"Assistant: {result.final_output}") + print() + + # Third turn - continuing the conversation + print("Third turn:") + print("User: What's the population of that state?") + result = await Runner.run( + agent, + "What's the population of that state?", + session=session + ) + print(f"Assistant: {result.final_output}") + print() + + print("=== Conversation Complete ===") + print("Notice how the agent remembered the context from previous turns!") + print("Sessions automatically handles conversation history.") + + +if __name__ == "__main__": + asyncio.run(main()) +``` + +## API Reference + +For detailed API documentation, see: + +- [`Session`][agents.memory.Session] - Protocol interface +- [`SQLiteSession`][agents.memory.SQLiteSession] - SQLite implementation diff --git a/docs/tools.md b/docs/tools.md index 5fe2ecedb..6dba1a853 100644 --- a/docs/tools.md +++ b/docs/tools.md @@ -13,6 +13,10 @@ OpenAI offers a few built-in tools when using the [`OpenAIResponsesModel`][agent - The [`WebSearchTool`][agents.tool.WebSearchTool] lets an agent search the web. - The [`FileSearchTool`][agents.tool.FileSearchTool] allows retrieving information from your OpenAI Vector Stores. - The [`ComputerTool`][agents.tool.ComputerTool] allows automating computer use tasks. +- The [`CodeInterpreterTool`][agents.tool.CodeInterpreterTool] lets the LLM execute code in a sandboxed environment. +- The [`HostedMCPTool`][agents.tool.HostedMCPTool] exposes a remote MCP server's tools to the model. +- The [`ImageGenerationTool`][agents.tool.ImageGenerationTool] generates images from a prompt. +- The [`LocalShellTool`][agents.tool.LocalShellTool] runs shell commands on your machine. ```python from agents import Agent, FileSearchTool, Runner, WebSearchTool @@ -266,7 +270,7 @@ The `agent.as_tool` function is a convenience method to make it easy to turn an ```python @function_tool async def run_my_agent() -> str: - """A tool that runs the agent with custom configs". + """A tool that runs the agent with custom configs""" agent = Agent(name="My agent", instructions="...") @@ -280,6 +284,33 @@ async def run_my_agent() -> str: return str(result.final_output) ``` +### Custom output extraction + +In certain cases, you might want to modify the output of the tool-agents before returning it to the central agent. This may be useful if you want to: + +- Extract a specific piece of information (e.g., a JSON payload) from the sub-agent's chat history. +- Convert or reformat the agent’s final answer (e.g., transform Markdown into plain text or CSV). +- Validate the output or provide a fallback value when the agent’s response is missing or malformed. + +You can do this by supplying the `custom_output_extractor` argument to the `as_tool` method: + +```python +async def extract_json_payload(run_result: RunResult) -> str: + # Scan the agent’s outputs in reverse order until we find a JSON-like message from a tool call. + for item in reversed(run_result.new_items): + if isinstance(item, ToolCallOutputItem) and item.output.strip().startswith("{"): + return item.output.strip() + # Fallback to an empty JSON object if nothing was found + return "{}" + + +json_tool = data_agent.as_tool( + tool_name="get_data_json", + tool_description="Run the data agent and return only its JSON payload", + custom_output_extractor=extract_json_payload, +) +``` + ## Handling errors in function tools When you create a function tool via `@function_tool`, you can pass a `failure_error_function`. This is a function that provides an error response to the LLM in case the tool call crashes. diff --git a/docs/tracing.md b/docs/tracing.md index ea48a2e28..c7776ad7b 100644 --- a/docs/tracing.md +++ b/docs/tracing.md @@ -101,6 +101,7 @@ To customize this default setup, to send traces to alternative or additional bac - [Weights & Biases](https://weave-docs.wandb.ai/guides/integrations/openai_agents) - [Arize-Phoenix](https://docs.arize.com/phoenix/tracing/integrations-tracing/openai-agents-sdk) +- [Future AGI](https://docs.futureagi.com/future-agi/products/observability/auto-instrumentation/openai_agents) - [MLflow (self-hosted/OSS](https://mlflow.org/docs/latest/tracing/integrations/openai-agent) - [MLflow (Databricks hosted](https://docs.databricks.com/aws/en/mlflow/mlflow-tracing#-automatic-tracing) - [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk) @@ -114,3 +115,5 @@ To customize this default setup, to send traces to alternative or additional bac - [Langfuse](https://langfuse.com/docs/integrations/openaiagentssdk/openai-agents) - [Langtrace](https://docs.langtrace.ai/supported-integrations/llm-frameworks/openai-agents-sdk) - [Okahu-Monocle](https://github.com/monocle2ai/monocle) +- [Galileo](https://v2docs.galileo.ai/integrations/openai-agent-integration#openai-agent-integration) +- [Portkey AI](https://portkey.ai/docs/integrations/agents/openai-agents) diff --git a/docs/visualization.md b/docs/visualization.md index 00f3126db..409803f76 100644 --- a/docs/visualization.md +++ b/docs/visualization.md @@ -78,9 +78,7 @@ draw_graph(triage_agent).view() By default, `draw_graph` displays the graph inline. To save it as a file, specify a filename: ```python -draw_graph(triage_agent, filename="agent_graph.png") +draw_graph(triage_agent, filename="agent_graph") ``` This will generate `agent_graph.png` in the working directory. - - diff --git a/examples/agent_patterns/input_guardrails.py b/examples/agent_patterns/input_guardrails.py index 154535511..18ab9d2a7 100644 --- a/examples/agent_patterns/input_guardrails.py +++ b/examples/agent_patterns/input_guardrails.py @@ -20,7 +20,7 @@ Guardrails are checks that run in parallel to the agent's execution. They can be used to do things like: - Check if input messages are off-topic -- Check that output messages don't violate any policies +- Check that input messages don't violate any policies - Take over control of the agent's execution if an unexpected input is detected In this example, we'll setup an input guardrail that trips if the user is asking to do math homework. diff --git a/examples/agent_patterns/llm_as_a_judge.py b/examples/agent_patterns/llm_as_a_judge.py index 5a46cc3eb..81dec7501 100644 --- a/examples/agent_patterns/llm_as_a_judge.py +++ b/examples/agent_patterns/llm_as_a_judge.py @@ -32,7 +32,7 @@ class EvaluationFeedback: instructions=( "You evaluate a story outline and decide if it's good enough." "If it's not good enough, you provide feedback on what needs to be improved." - "Never give it a pass on the first try." + "Never give it a pass on the first try. After 5 attempts, you can give it a pass if story outline is good enough - do not go for perfection" ), output_type=EvaluationFeedback, ) diff --git a/examples/basic/agent_lifecycle_example.py b/examples/basic/agent_lifecycle_example.py index 29bb18c96..b4334a83b 100644 --- a/examples/basic/agent_lifecycle_example.py +++ b/examples/basic/agent_lifecycle_example.py @@ -101,12 +101,10 @@ async def main() -> None: ### (Start Agent) 1: Agent Start Agent started ### (Start Agent) 2: Agent Start Agent started tool random_number ### (Start Agent) 3: Agent Start Agent ended tool random_number with result 37 -### (Start Agent) 4: Agent Start Agent started -### (Start Agent) 5: Agent Start Agent handed off to Multiply Agent +### (Start Agent) 4: Agent Start Agent handed off to Multiply Agent ### (Multiply Agent) 1: Agent Multiply Agent started ### (Multiply Agent) 2: Agent Multiply Agent started tool multiply_by_two ### (Multiply Agent) 3: Agent Multiply Agent ended tool multiply_by_two with result 74 -### (Multiply Agent) 4: Agent Multiply Agent started -### (Multiply Agent) 5: Agent Multiply Agent ended with output number=74 +### (Multiply Agent) 4: Agent Multiply Agent ended with output number=74 Done! """ diff --git a/examples/basic/hello_world_jupyter.ipynb b/examples/basic/hello_world_jupyter.ipynb new file mode 100644 index 000000000..8dd3bb379 --- /dev/null +++ b/examples/basic/hello_world_jupyter.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8a77ee2e-22f2-409c-837d-b994978b0aa2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A function calls self, \n", + "Unraveling layers deep, \n", + "Base case ends the quest. \n", + "\n", + "Infinite loops lurk, \n", + "Mind the base condition well, \n", + "Or it will not work. \n", + "\n", + "Trees and lists unfold, \n", + "Elegant solutions bloom, \n", + "Recursion's art told.\n" + ] + } + ], + "source": [ + "from agents import Agent, Runner\n", + "\n", + "agent = Agent(name=\"Assistant\", instructions=\"You are a helpful assistant\")\n", + "\n", + "# Intended for Jupyter notebooks where there's an existing event loop\n", + "result = await Runner.run(agent, \"Write a haiku about recursion in programming.\") # type: ignore[top-level-await] # noqa: F704\n", + "print(result.final_output)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/basic/hello_world_jupyter.py b/examples/basic/hello_world_jupyter.py deleted file mode 100644 index c929a7c68..000000000 --- a/examples/basic/hello_world_jupyter.py +++ /dev/null @@ -1,11 +0,0 @@ -from agents import Agent, Runner - -agent = Agent(name="Assistant", instructions="You are a helpful assistant") - -# Intended for Jupyter notebooks where there's an existing event loop -result = await Runner.run(agent, "Write a haiku about recursion in programming.") # type: ignore[top-level-await] # noqa: F704 -print(result.final_output) - -# Code within code loops, -# Infinite mirrors reflect— -# Logic folds on self. diff --git a/examples/basic/lifecycle_example.py b/examples/basic/lifecycle_example.py index 285bfecd6..02ce449f4 100644 --- a/examples/basic/lifecycle_example.py +++ b/examples/basic/lifecycle_example.py @@ -105,14 +105,12 @@ async def main() -> None: Enter a max number: 250 ### 1: Agent Start Agent started. Usage: 0 requests, 0 input tokens, 0 output tokens, 0 total tokens ### 2: Tool random_number started. Usage: 1 requests, 148 input tokens, 15 output tokens, 163 total tokens -### 3: Tool random_number ended with result 101. Usage: 1 requests, 148 input tokens, 15 output tokens, 163 total tokens -### 4: Agent Start Agent started. Usage: 1 requests, 148 input tokens, 15 output tokens, 163 total tokens -### 5: Handoff from Start Agent to Multiply Agent. Usage: 2 requests, 323 input tokens, 30 output tokens, 353 total tokens -### 6: Agent Multiply Agent started. Usage: 2 requests, 323 input tokens, 30 output tokens, 353 total tokens -### 7: Tool multiply_by_two started. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens -### 8: Tool multiply_by_two ended with result 202. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens -### 9: Agent Multiply Agent started. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens -### 10: Agent Multiply Agent ended with output number=202. Usage: 4 requests, 714 input tokens, 63 output tokens, 777 total tokens +### 3: Tool random_number ended with result 101. Usage: 1 requests, 148 input tokens, 15 output tokens, 163 total token +### 4: Handoff from Start Agent to Multiply Agent. Usage: 2 requests, 323 input tokens, 30 output tokens, 353 total tokens +### 5: Agent Multiply Agent started. Usage: 2 requests, 323 input tokens, 30 output tokens, 353 total tokens +### 6: Tool multiply_by_two started. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens +### 7: Tool multiply_by_two ended with result 202. Usage: 3 requests, 504 input tokens, 46 output tokens, 550 total tokens +### 8: Agent Multiply Agent ended with output number=202. Usage: 4 requests, 714 input tokens, 63 output tokens, 777 total tokens Done! """ diff --git a/examples/basic/local_image.py b/examples/basic/local_image.py new file mode 100644 index 000000000..d4a784ba2 --- /dev/null +++ b/examples/basic/local_image.py @@ -0,0 +1,48 @@ +import asyncio +import base64 +import os + +from agents import Agent, Runner + +FILEPATH = os.path.join(os.path.dirname(__file__), "media/image_bison.jpg") + + +def image_to_base64(image_path): + with open(image_path, "rb") as image_file: + encoded_string = base64.b64encode(image_file.read()).decode("utf-8") + return encoded_string + + +async def main(): + # Print base64-encoded image + b64_image = image_to_base64(FILEPATH) + + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + ) + + result = await Runner.run( + agent, + [ + { + "role": "user", + "content": [ + { + "type": "input_image", + "detail": "auto", + "image_url": f"data:image/jpeg;base64,{b64_image}", + } + ], + }, + { + "role": "user", + "content": "What do you see in this image?", + }, + ], + ) + print(result.final_output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/basic/media/image_bison.jpg b/examples/basic/media/image_bison.jpg new file mode 100644 index 000000000..b113c91f6 Binary files /dev/null and b/examples/basic/media/image_bison.jpg differ diff --git a/examples/basic/non_strict_output_type.py b/examples/basic/non_strict_output_type.py new file mode 100644 index 000000000..49fcc4e2c --- /dev/null +++ b/examples/basic/non_strict_output_type.py @@ -0,0 +1,81 @@ +import asyncio +import json +from dataclasses import dataclass +from typing import Any + +from agents import Agent, AgentOutputSchema, AgentOutputSchemaBase, Runner + +"""This example demonstrates how to use an output type that is not in strict mode. Strict mode +allows us to guarantee valid JSON output, but some schemas are not strict-compatible. + +In this example, we define an output type that is not strict-compatible, and then we run the +agent with strict_json_schema=False. + +We also demonstrate a custom output type. + +To understand which schemas are strict-compatible, see: +https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses#supported-schemas +""" + + +@dataclass +class OutputType: + jokes: dict[int, str] + """A list of jokes, indexed by joke number.""" + + +class CustomOutputSchema(AgentOutputSchemaBase): + """A demonstration of a custom output schema.""" + + def is_plain_text(self) -> bool: + return False + + def name(self) -> str: + return "CustomOutputSchema" + + def json_schema(self) -> dict[str, Any]: + return { + "type": "object", + "properties": {"jokes": {"type": "object", "properties": {"joke": {"type": "string"}}}}, + } + + def is_strict_json_schema(self) -> bool: + return False + + def validate_json(self, json_str: str) -> Any: + json_obj = json.loads(json_str) + # Just for demonstration, we'll return a list. + return list(json_obj["jokes"].values()) + + +async def main(): + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + output_type=OutputType, + ) + + input = "Tell me 3 short jokes." + + # First, let's try with a strict output type. This should raise an exception. + try: + result = await Runner.run(agent, input) + raise AssertionError("Should have raised an exception") + except Exception as e: + print(f"Error (expected): {e}") + + # Now let's try again with a non-strict output type. This should work. + # In some cases, it will raise an error - the schema isn't strict, so the model may + # produce an invalid JSON object. + agent.output_type = AgentOutputSchema(OutputType, strict_json_schema=False) + result = await Runner.run(agent, input) + print(result.final_output) + + # Finally, let's try a custom output type. + agent.output_type = CustomOutputSchema() + result = await Runner.run(agent, input) + print(result.final_output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/basic/prompt_template.py b/examples/basic/prompt_template.py new file mode 100644 index 000000000..59251935e --- /dev/null +++ b/examples/basic/prompt_template.py @@ -0,0 +1,79 @@ +import argparse +import asyncio +import random + +from agents import Agent, GenerateDynamicPromptData, Runner + +""" +NOTE: This example will not work out of the box, because the default prompt ID will not be available +in your project. + +To use it, please: +1. Go to https://platform.openai.com/playground/prompts +2. Create a new prompt variable, `poem_style`. +3. Create a system prompt with the content: +``` +Write a poem in {{poem_style}} +``` +4. Run the example with the `--prompt-id` flag. +""" + +DEFAULT_PROMPT_ID = "pmpt_6850729e8ba481939fd439e058c69ee004afaa19c520b78b" + + +class DynamicContext: + def __init__(self, prompt_id: str): + self.prompt_id = prompt_id + self.poem_style = random.choice(["limerick", "haiku", "ballad"]) + print(f"[debug] DynamicContext initialized with poem_style: {self.poem_style}") + + +async def _get_dynamic_prompt(data: GenerateDynamicPromptData): + ctx: DynamicContext = data.context.context + return { + "id": ctx.prompt_id, + "version": "1", + "variables": { + "poem_style": ctx.poem_style, + }, + } + + +async def dynamic_prompt(prompt_id: str): + context = DynamicContext(prompt_id) + + agent = Agent( + name="Assistant", + prompt=_get_dynamic_prompt, + ) + + result = await Runner.run(agent, "Tell me about recursion in programming.", context=context) + print(result.final_output) + + +async def static_prompt(prompt_id: str): + agent = Agent( + name="Assistant", + prompt={ + "id": prompt_id, + "version": "1", + "variables": { + "poem_style": "limerick", + }, + }, + ) + + result = await Runner.run(agent, "Tell me about recursion in programming.") + print(result.final_output) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--dynamic", action="store_true") + parser.add_argument("--prompt-id", type=str, default=DEFAULT_PROMPT_ID) + args = parser.parse_args() + + if args.dynamic: + asyncio.run(dynamic_prompt(args.prompt_id)) + else: + asyncio.run(static_prompt(args.prompt_id)) diff --git a/examples/basic/remote_image.py b/examples/basic/remote_image.py new file mode 100644 index 000000000..948a22d9e --- /dev/null +++ b/examples/basic/remote_image.py @@ -0,0 +1,31 @@ +import asyncio + +from agents import Agent, Runner + +URL = "https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg" + + +async def main(): + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + ) + + result = await Runner.run( + agent, + [ + { + "role": "user", + "content": [{"type": "input_image", "detail": "auto", "image_url": URL}], + }, + { + "role": "user", + "content": "What do you see in this image?", + }, + ], + ) + print(result.final_output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/basic/session_example.py b/examples/basic/session_example.py new file mode 100644 index 000000000..63d1d1b7c --- /dev/null +++ b/examples/basic/session_example.py @@ -0,0 +1,77 @@ +""" +Example demonstrating session memory functionality. + +This example shows how to use session memory to maintain conversation history +across multiple agent runs without manually handling .to_input_list(). +""" + +import asyncio + +from agents import Agent, Runner, SQLiteSession + + +async def main(): + # Create an agent + agent = Agent( + name="Assistant", + instructions="Reply very concisely.", + ) + + # Create a session instance that will persist across runs + session_id = "conversation_123" + session = SQLiteSession(session_id) + + print("=== Session Example ===") + print("The agent will remember previous messages automatically.\n") + + # First turn + print("First turn:") + print("User: What city is the Golden Gate Bridge in?") + result = await Runner.run( + agent, + "What city is the Golden Gate Bridge in?", + session=session, + ) + print(f"Assistant: {result.final_output}") + print() + + # Second turn - the agent will remember the previous conversation + print("Second turn:") + print("User: What state is it in?") + result = await Runner.run(agent, "What state is it in?", session=session) + print(f"Assistant: {result.final_output}") + print() + + # Third turn - continuing the conversation + print("Third turn:") + print("User: What's the population of that state?") + result = await Runner.run( + agent, + "What's the population of that state?", + session=session, + ) + print(f"Assistant: {result.final_output}") + print() + + print("=== Conversation Complete ===") + print("Notice how the agent remembered the context from previous turns!") + print("Sessions automatically handles conversation history.") + + # Demonstrate the limit parameter - get only the latest 2 items + print("\n=== Latest Items Demo ===") + latest_items = await session.get_items(limit=2) + print("Latest 2 items:") + for i, msg in enumerate(latest_items, 1): + role = msg.get("role", "unknown") + content = msg.get("content", "") + print(f" {i}. {role}: {content}") + + print(f"\nFetched {len(latest_items)} out of total conversation history.") + + # Get all items to show the difference + all_items = await session.get_items() + print(f"Total items in session: {len(all_items)}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/financial_research_agent/main.py b/examples/financial_research_agent/main.py index 3fa8a7e08..b5b6cfdfd 100644 --- a/examples/financial_research_agent/main.py +++ b/examples/financial_research_agent/main.py @@ -4,7 +4,7 @@ # Entrypoint for the financial bot example. -# Run this as `python -m examples.financial_bot.main` and enter a +# Run this as `python -m examples.financial_research_agent.main` and enter a # financial research query, for example: # "Write up an analysis of Apple Inc.'s most recent quarter." async def main() -> None: diff --git a/examples/hosted_mcp/__init__.py b/examples/hosted_mcp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/hosted_mcp/approvals.py b/examples/hosted_mcp/approvals.py new file mode 100644 index 000000000..3080a1d63 --- /dev/null +++ b/examples/hosted_mcp/approvals.py @@ -0,0 +1,61 @@ +import argparse +import asyncio + +from agents import ( + Agent, + HostedMCPTool, + MCPToolApprovalFunctionResult, + MCPToolApprovalRequest, + Runner, +) + +"""This example demonstrates how to use the hosted MCP support in the OpenAI Responses API, with +approval callbacks.""" + + +def approval_callback(request: MCPToolApprovalRequest) -> MCPToolApprovalFunctionResult: + answer = input(f"Approve running the tool `{request.data.name}`? (y/n) ") + result: MCPToolApprovalFunctionResult = {"approve": answer == "y"} + if not result["approve"]: + result["reason"] = "User denied" + return result + + +async def main(verbose: bool, stream: bool): + agent = Agent( + name="Assistant", + tools=[ + HostedMCPTool( + tool_config={ + "type": "mcp", + "server_label": "gitmcp", + "server_url": "https://gitmcp.io/openai/codex", + "require_approval": "always", + }, + on_approval_request=approval_callback, + ) + ], + ) + + if stream: + result = Runner.run_streamed(agent, "Which language is this repo written in?") + async for event in result.stream_events(): + if event.type == "run_item_stream_event": + print(f"Got event of type {event.item.__class__.__name__}") + print(f"Done streaming; final result: {result.final_output}") + else: + res = await Runner.run(agent, "Which language is this repo written in?") + print(res.final_output) + + if verbose: + for item in res.new_items: + print(item) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--verbose", action="store_true", default=False) + parser.add_argument("--stream", action="store_true", default=False) + args = parser.parse_args() + + asyncio.run(main(args.verbose, args.stream)) diff --git a/examples/hosted_mcp/simple.py b/examples/hosted_mcp/simple.py new file mode 100644 index 000000000..895fdfbe0 --- /dev/null +++ b/examples/hosted_mcp/simple.py @@ -0,0 +1,47 @@ +import argparse +import asyncio + +from agents import Agent, HostedMCPTool, Runner + +"""This example demonstrates how to use the hosted MCP support in the OpenAI Responses API, with +approvals not required for any tools. You should only use this for trusted MCP servers.""" + + +async def main(verbose: bool, stream: bool): + agent = Agent( + name="Assistant", + tools=[ + HostedMCPTool( + tool_config={ + "type": "mcp", + "server_label": "gitmcp", + "server_url": "https://gitmcp.io/openai/codex", + "require_approval": "never", + } + ) + ], + ) + + if stream: + result = Runner.run_streamed(agent, "Which language is this repo written in?") + async for event in result.stream_events(): + if event.type == "run_item_stream_event": + print(f"Got event of type {event.item.__class__.__name__}") + print(f"Done streaming; final result: {result.final_output}") + else: + res = await Runner.run(agent, "Which language is this repo written in?") + print(res.final_output) + # The repository is primarily written in multiple languages, including Rust and TypeScript... + + if verbose: + for item in res.new_items: + print(item) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--verbose", action="store_true", default=False) + parser.add_argument("--stream", action="store_true", default=False) + args = parser.parse_args() + + asyncio.run(main(args.verbose, args.stream)) diff --git a/examples/mcp/prompt_server/README.md b/examples/mcp/prompt_server/README.md new file mode 100644 index 000000000..c1b1c3b37 --- /dev/null +++ b/examples/mcp/prompt_server/README.md @@ -0,0 +1,29 @@ +# MCP Prompt Server Example + +This example uses a local MCP prompt server in [server.py](server.py). + +Run the example via: + +``` +uv run python examples/mcp/prompt_server/main.py +``` + +## Details + +The example uses the `MCPServerStreamableHttp` class from `agents.mcp`. The server runs in a sub-process at `http://localhost:8000/mcp` and provides user-controlled prompts that generate agent instructions. + +The server exposes prompts like `generate_code_review_instructions` that take parameters such as focus area and programming language. The agent calls these prompts to dynamically generate its system instructions based on user-provided parameters. + +## Workflow + +The example demonstrates two key functions: + +1. **`show_available_prompts`** - Lists all available prompts on the MCP server, showing users what prompts they can select from. This demonstrates the discovery aspect of MCP prompts. + +2. **`demo_code_review`** - Shows the complete user-controlled prompt workflow: + - Calls `generate_code_review_instructions` with specific parameters (focus: "security vulnerabilities", language: "python") + - Uses the generated instructions to create an Agent with specialized code review capabilities + - Runs the agent against vulnerable sample code (command injection via `os.system`) + - The agent analyzes the code and provides security-focused feedback using available tools + +This pattern allows users to dynamically configure agent behavior through MCP prompts rather than hardcoded instructions. \ No newline at end of file diff --git a/examples/mcp/prompt_server/main.py b/examples/mcp/prompt_server/main.py new file mode 100644 index 000000000..8f2991fc0 --- /dev/null +++ b/examples/mcp/prompt_server/main.py @@ -0,0 +1,110 @@ +import asyncio +import os +import shutil +import subprocess +import time +from typing import Any + +from agents import Agent, Runner, gen_trace_id, trace +from agents.mcp import MCPServer, MCPServerStreamableHttp +from agents.model_settings import ModelSettings + + +async def get_instructions_from_prompt(mcp_server: MCPServer, prompt_name: str, **kwargs) -> str: + """Get agent instructions by calling MCP prompt endpoint (user-controlled)""" + print(f"Getting instructions from prompt: {prompt_name}") + + try: + prompt_result = await mcp_server.get_prompt(prompt_name, kwargs) + content = prompt_result.messages[0].content + if hasattr(content, 'text'): + instructions = content.text + else: + instructions = str(content) + print("Generated instructions") + return instructions + except Exception as e: + print(f"Failed to get instructions: {e}") + return f"You are a helpful assistant. Error: {e}" + + +async def demo_code_review(mcp_server: MCPServer): + """Demo: Code review with user-selected prompt""" + print("=== CODE REVIEW DEMO ===") + + # User explicitly selects prompt and parameters + instructions = await get_instructions_from_prompt( + mcp_server, + "generate_code_review_instructions", + focus="security vulnerabilities", + language="python", + ) + + agent = Agent( + name="Code Reviewer Agent", + instructions=instructions, # Instructions from MCP prompt + model_settings=ModelSettings(tool_choice="auto"), + ) + + message = """Please review this code: + +def process_user_input(user_input): + command = f"echo {user_input}" + os.system(command) + return "Command executed" + +""" + + print(f"Running: {message[:60]}...") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + print("\n" + "=" * 50 + "\n") + + +async def show_available_prompts(mcp_server: MCPServer): + """Show available prompts for user selection""" + print("=== AVAILABLE PROMPTS ===") + + prompts_result = await mcp_server.list_prompts() + print("User can select from these prompts:") + for i, prompt in enumerate(prompts_result.prompts, 1): + print(f" {i}. {prompt.name} - {prompt.description}") + print() + + +async def main(): + async with MCPServerStreamableHttp( + name="Simple Prompt Server", + params={"url": "http://localhost:8000/mcp"}, + ) as server: + trace_id = gen_trace_id() + with trace(workflow_name="Simple Prompt Demo", trace_id=trace_id): + print(f"Trace: https://platform.openai.com/traces/trace?trace_id={trace_id}\n") + + await show_available_prompts(server) + await demo_code_review(server) + + +if __name__ == "__main__": + if not shutil.which("uv"): + raise RuntimeError("uv is not installed") + + process: subprocess.Popen[Any] | None = None + try: + this_dir = os.path.dirname(os.path.abspath(__file__)) + server_file = os.path.join(this_dir, "server.py") + + print("Starting Simple Prompt Server...") + process = subprocess.Popen(["uv", "run", server_file]) + time.sleep(3) + print("Server started\n") + except Exception as e: + print(f"Error starting server: {e}") + exit(1) + + try: + asyncio.run(main()) + finally: + if process: + process.terminate() + print("Server terminated.") diff --git a/examples/mcp/prompt_server/server.py b/examples/mcp/prompt_server/server.py new file mode 100644 index 000000000..01dcbac34 --- /dev/null +++ b/examples/mcp/prompt_server/server.py @@ -0,0 +1,37 @@ +from mcp.server.fastmcp import FastMCP + +# Create server +mcp = FastMCP("Prompt Server") + + +# Instruction-generating prompts (user-controlled) +@mcp.prompt() +def generate_code_review_instructions( + focus: str = "general code quality", language: str = "python" +) -> str: + """Generate agent instructions for code review tasks""" + print(f"[debug-server] generate_code_review_instructions({focus}, {language})") + + return f"""You are a senior {language} code review specialist. Your role is to provide comprehensive code analysis with focus on {focus}. + +INSTRUCTIONS: +- Analyze code for quality, security, performance, and best practices +- Provide specific, actionable feedback with examples +- Identify potential bugs, vulnerabilities, and optimization opportunities +- Suggest improvements with code examples when applicable +- Be constructive and educational in your feedback +- Focus particularly on {focus} aspects + +RESPONSE FORMAT: +1. Overall Assessment +2. Specific Issues Found +3. Security Considerations +4. Performance Notes +5. Recommended Improvements +6. Best Practices Suggestions + +Use the available tools to check current time if you need timestamps for your analysis.""" + + +if __name__ == "__main__": + mcp.run(transport="streamable-http") diff --git a/examples/mcp/streamablehttp_example/README.md b/examples/mcp/streamablehttp_example/README.md new file mode 100644 index 000000000..a07fe19be --- /dev/null +++ b/examples/mcp/streamablehttp_example/README.md @@ -0,0 +1,13 @@ +# MCP Streamable HTTP Example + +This example uses a local Streamable HTTP server in [server.py](server.py). + +Run the example via: + +``` +uv run python examples/mcp/streamablehttp_example/main.py +``` + +## Details + +The example uses the `MCPServerStreamableHttp` class from `agents.mcp`. The server runs in a sub-process at `https://localhost:8000/mcp`. diff --git a/examples/mcp/streamablehttp_example/main.py b/examples/mcp/streamablehttp_example/main.py new file mode 100644 index 000000000..cc95e798b --- /dev/null +++ b/examples/mcp/streamablehttp_example/main.py @@ -0,0 +1,83 @@ +import asyncio +import os +import shutil +import subprocess +import time +from typing import Any + +from agents import Agent, Runner, gen_trace_id, trace +from agents.mcp import MCPServer, MCPServerStreamableHttp +from agents.model_settings import ModelSettings + + +async def run(mcp_server: MCPServer): + agent = Agent( + name="Assistant", + instructions="Use the tools to answer the questions.", + mcp_servers=[mcp_server], + model_settings=ModelSettings(tool_choice="required"), + ) + + # Use the `add` tool to add two numbers + message = "Add these numbers: 7 and 22." + print(f"Running: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + # Run the `get_weather` tool + message = "What's the weather in Tokyo?" + print(f"\n\nRunning: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + # Run the `get_secret_word` tool + message = "What's the secret word?" + print(f"\n\nRunning: {message}") + result = await Runner.run(starting_agent=agent, input=message) + print(result.final_output) + + +async def main(): + async with MCPServerStreamableHttp( + name="Streamable HTTP Python Server", + params={ + "url": "http://localhost:8000/mcp", + }, + ) as server: + trace_id = gen_trace_id() + with trace(workflow_name="Streamable HTTP Example", trace_id=trace_id): + print(f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}\n") + await run(server) + + +if __name__ == "__main__": + # Let's make sure the user has uv installed + if not shutil.which("uv"): + raise RuntimeError( + "uv is not installed. Please install it: https://docs.astral.sh/uv/getting-started/installation/" + ) + + # We'll run the Streamable HTTP server in a subprocess. Usually this would be a remote server, but for this + # demo, we'll run it locally at http://localhost:8000/mcp + process: subprocess.Popen[Any] | None = None + try: + this_dir = os.path.dirname(os.path.abspath(__file__)) + server_file = os.path.join(this_dir, "server.py") + + print("Starting Streamable HTTP server at http://localhost:8000/mcp ...") + + # Run `uv run server.py` to start the Streamable HTTP server + process = subprocess.Popen(["uv", "run", server_file]) + # Give it 3 seconds to start + time.sleep(3) + + print("Streamable HTTP server started. Running example...\n\n") + except Exception as e: + print(f"Error starting Streamable HTTP server: {e}") + exit(1) + + try: + asyncio.run(main()) + finally: + if process: + process.terminate() diff --git a/examples/mcp/streamablehttp_example/server.py b/examples/mcp/streamablehttp_example/server.py new file mode 100644 index 000000000..d8f839652 --- /dev/null +++ b/examples/mcp/streamablehttp_example/server.py @@ -0,0 +1,33 @@ +import random + +import requests +from mcp.server.fastmcp import FastMCP + +# Create server +mcp = FastMCP("Echo Server") + + +@mcp.tool() +def add(a: int, b: int) -> int: + """Add two numbers""" + print(f"[debug-server] add({a}, {b})") + return a + b + + +@mcp.tool() +def get_secret_word() -> str: + print("[debug-server] get_secret_word()") + return random.choice(["apple", "banana", "cherry"]) + + +@mcp.tool() +def get_current_weather(city: str) -> str: + print(f"[debug-server] get_current_weather({city})") + + endpoint = "https://wttr.in" + response = requests.get(f"{endpoint}/{city}") + return response.text + + +if __name__ == "__main__": + mcp.run(transport="streamable-http") diff --git a/examples/model_providers/litellm_auto.py b/examples/model_providers/litellm_auto.py new file mode 100644 index 000000000..12b1e8914 --- /dev/null +++ b/examples/model_providers/litellm_auto.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +import asyncio + +from agents import Agent, Runner, function_tool, set_tracing_disabled + +"""This example uses the built-in support for LiteLLM. To use this, ensure you have the +ANTHROPIC_API_KEY environment variable set. +""" + +set_tracing_disabled(disabled=True) + + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + # We prefix with litellm/ to tell the Runner to use the LitellmModel + model="litellm/anthropic/claude-3-5-sonnet-20240620", + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + import os + + if os.getenv("ANTHROPIC_API_KEY") is None: + raise ValueError( + "ANTHROPIC_API_KEY is not set. Please set it the environment variable and try again." + ) + + asyncio.run(main()) diff --git a/examples/model_providers/litellm_provider.py b/examples/model_providers/litellm_provider.py new file mode 100644 index 000000000..4a1a696fc --- /dev/null +++ b/examples/model_providers/litellm_provider.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +import asyncio + +from agents import Agent, Runner, function_tool, set_tracing_disabled +from agents.extensions.models.litellm_model import LitellmModel + +"""This example uses the LitellmModel directly, to hit any model provider. +You can run it like this: +uv run examples/model_providers/litellm_provider.py --model anthropic/claude-3-5-sonnet-20240620 +or +uv run examples/model_providers/litellm_provider.py --model gemini/gemini-2.0-flash + +Find more providers here: https://docs.litellm.ai/docs/providers +""" + +set_tracing_disabled(disabled=True) + + +@function_tool +def get_weather(city: str): + print(f"[debug] getting weather for {city}") + return f"The weather in {city} is sunny." + + +async def main(model: str, api_key: str): + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model=LitellmModel(model=model, api_key=api_key), + tools=[get_weather], + ) + + result = await Runner.run(agent, "What's the weather in Tokyo?") + print(result.final_output) + + +if __name__ == "__main__": + # First try to get model/api key from args + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=False) + parser.add_argument("--api-key", type=str, required=False) + args = parser.parse_args() + + model = args.model + if not model: + model = input("Enter a model name for Litellm: ") + + api_key = args.api_key + if not api_key: + api_key = input("Enter an API key for Litellm: ") + + asyncio.run(main(model, api_key)) diff --git a/examples/realtime/demo.py b/examples/realtime/demo.py new file mode 100644 index 000000000..61834a4e5 --- /dev/null +++ b/examples/realtime/demo.py @@ -0,0 +1,114 @@ +import asyncio +import os +import sys +from typing import TYPE_CHECKING + +import numpy as np + +from agents.realtime import RealtimeSession + +# Add the current directory to path so we can import ui +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +from agents import function_tool +from agents.realtime import RealtimeAgent, RealtimeRunner, RealtimeSessionEvent + +if TYPE_CHECKING: + from .ui import AppUI +else: + # Try both import styles + try: + # Try relative import first (when used as a package) + from .ui import AppUI + except ImportError: + # Fall back to direct import (when run as a script) + from ui import AppUI + + +@function_tool +def get_weather(city: str) -> str: + """Get the weather in a city.""" + return f"The weather in {city} is sunny." + + +agent = RealtimeAgent( + name="Assistant", + instructions="You always greet the user with 'Top of the morning to you'.", + tools=[get_weather], +) + + +class Example: + def __init__(self) -> None: + self.ui = AppUI() + self.ui.connected = asyncio.Event() + self.ui.last_audio_item_id = None + # Set the audio callback + self.ui.set_audio_callback(self.on_audio_recorded) + + self.session: RealtimeSession | None = None + + async def run(self) -> None: + # Start UI in a separate task instead of waiting for it to complete + ui_task = asyncio.create_task(self.ui.run_async()) + + # Set up session immediately without waiting for UI to finish + runner = RealtimeRunner(agent) + async with await runner.run() as session: + self.session = session + self.ui.set_is_connected(True) + async for event in session: + await self.on_event(event) + + # Wait for UI task to complete when session ends + await ui_task + + async def on_audio_recorded(self, audio_bytes: bytes) -> None: + """Called when audio is recorded by the UI.""" + try: + # Send the audio to the session + assert self.session is not None + await self.session.send_audio(audio_bytes) + except Exception as e: + self.ui.log_message(f"Error sending audio: {e}") + + async def on_event(self, event: RealtimeSessionEvent) -> None: + # Display event in the UI + try: + if event.type == "agent_start": + self.ui.add_transcript(f"Agent started: {event.agent.name}") + elif event.type == "agent_end": + self.ui.add_transcript(f"Agent ended: {event.agent.name}") + elif event.type == "handoff": + self.ui.add_transcript( + f"Handoff from {event.from_agent.name} to {event.to_agent.name}" + ) + elif event.type == "tool_start": + self.ui.add_transcript(f"Tool started: {event.tool.name}") + elif event.type == "tool_end": + self.ui.add_transcript(f"Tool ended: {event.tool.name}; output: {event.output}") + elif event.type == "audio_end": + self.ui.add_transcript("Audio ended") + elif event.type == "audio": + np_audio = np.frombuffer(event.audio.data, dtype=np.int16) + self.ui.play_audio(np_audio) + elif event.type == "audio_interrupted": + self.ui.add_transcript("Audio interrupted") + elif event.type == "error": + self.ui.add_transcript(f"Error: {event.error}") + elif event.type == "history_updated": + pass + elif event.type == "history_added": + pass + elif event.type == "raw_model_event": + self.ui.log_message(f"Raw model event: {event.data}") + else: + self.ui.log_message(f"Unknown event type: {event.type}") + except Exception as e: + # This can happen if the UI has already exited + self.ui.log_message(f"Event handling error: {str(e)}") + + +if __name__ == "__main__": + example = Example() + asyncio.run(example.run()) diff --git a/examples/realtime/ui.py b/examples/realtime/ui.py new file mode 100644 index 000000000..1ba055835 --- /dev/null +++ b/examples/realtime/ui.py @@ -0,0 +1,271 @@ +from __future__ import annotations + +import asyncio +from collections.abc import Coroutine +from typing import Any, Callable + +import numpy as np +import numpy.typing as npt +import sounddevice as sd +from textual import events +from textual.app import App, ComposeResult +from textual.containers import Container, Horizontal +from textual.reactive import reactive +from textual.widgets import RichLog, Static +from typing_extensions import override + +CHUNK_LENGTH_S = 0.05 # 50ms +SAMPLE_RATE = 24000 +FORMAT = np.int16 +CHANNELS = 1 + + +class Header(Static): + """A header widget.""" + + @override + def render(self) -> str: + return "Realtime Demo" + + +class AudioStatusIndicator(Static): + """A widget that shows the current audio recording status.""" + + is_recording = reactive(False) + + @override + def render(self) -> str: + status = ( + "🔴 Conversation started." + if self.is_recording + else "⚪ Press SPACE to start the conversation (q to quit)" + ) + return status + + +class AppUI(App[None]): + CSS = """ + Screen { + background: #1a1b26; /* Dark blue-grey background */ + } + + Container { + border: double rgb(91, 164, 91); + } + + #input-container { + height: 5; /* Explicit height for input container */ + margin: 1 1; + padding: 1 2; + } + + #bottom-pane { + width: 100%; + height: 82%; /* Reduced to make room for session display */ + border: round rgb(205, 133, 63); + } + + #status-indicator { + height: 3; + content-align: center middle; + background: #2a2b36; + border: solid rgb(91, 164, 91); + margin: 1 1; + } + + #session-display { + height: 3; + content-align: center middle; + background: #2a2b36; + border: solid rgb(91, 164, 91); + margin: 1 1; + } + + #transcripts { + width: 50%; + height: 100%; + border-right: solid rgb(91, 164, 91); + } + + #transcripts-header { + height: 2; + background: #2a2b36; + content-align: center middle; + border-bottom: solid rgb(91, 164, 91); + } + + #transcripts-content { + height: 100%; + } + + #event-log { + width: 50%; + height: 100%; + } + + #event-log-header { + height: 2; + background: #2a2b36; + content-align: center middle; + border-bottom: solid rgb(91, 164, 91); + } + + #event-log-content { + height: 100%; + } + + Static { + color: white; + } + """ + + should_send_audio: asyncio.Event + connected: asyncio.Event + last_audio_item_id: str | None + audio_callback: Callable[[bytes], Coroutine[Any, Any, None]] | None + + def __init__(self) -> None: + super().__init__() + self.audio_player = sd.OutputStream( + samplerate=SAMPLE_RATE, + channels=CHANNELS, + dtype=FORMAT, + ) + self.should_send_audio = asyncio.Event() + self.connected = asyncio.Event() + self.audio_callback = None + + @override + def compose(self) -> ComposeResult: + """Create child widgets for the app.""" + with Container(): + yield Header(id="session-display") + yield AudioStatusIndicator(id="status-indicator") + with Container(id="bottom-pane"): + with Horizontal(): + with Container(id="transcripts"): + yield Static("Conversation transcript", id="transcripts-header") + yield RichLog( + id="transcripts-content", wrap=True, highlight=True, markup=True + ) + with Container(id="event-log"): + yield Static("Raw event log", id="event-log-header") + yield RichLog( + id="event-log-content", wrap=True, highlight=True, markup=True + ) + + def set_is_connected(self, is_connected: bool) -> None: + self.connected.set() if is_connected else self.connected.clear() + + def set_audio_callback(self, callback: Callable[[bytes], Coroutine[Any, Any, None]]) -> None: + """Set a callback function to be called when audio is recorded.""" + self.audio_callback = callback + + # High-level methods for UI operations + def set_header_text(self, text: str) -> None: + """Update the header text.""" + header = self.query_one("#session-display", Header) + header.update(text) + + def set_recording_status(self, is_recording: bool) -> None: + """Set the recording status indicator.""" + status_indicator = self.query_one(AudioStatusIndicator) + status_indicator.is_recording = is_recording + + def log_message(self, message: str) -> None: + """Add a message to the event log.""" + try: + log_pane = self.query_one("#event-log-content", RichLog) + log_pane.write(message) + except Exception: + # Handle the case where the widget might not be available + pass + + def add_transcript(self, message: str) -> None: + """Add a transcript message to the transcripts panel.""" + try: + transcript_pane = self.query_one("#transcripts-content", RichLog) + transcript_pane.write(message) + except Exception: + # Handle the case where the widget might not be available + pass + + def play_audio(self, audio_data: npt.NDArray[np.int16]) -> None: + """Play audio data through the audio player.""" + try: + self.audio_player.write(audio_data) + except Exception as e: + self.log_message(f"Audio play error: {e}") + + async def on_mount(self) -> None: + """Set up audio player and start the audio capture worker.""" + self.audio_player.start() + self.run_worker(self.capture_audio()) + + async def capture_audio(self) -> None: + """Capture audio from the microphone and send to the session.""" + # Wait for connection to be established + await self.connected.wait() + + # Set up audio input stream + stream = sd.InputStream( + channels=CHANNELS, + samplerate=SAMPLE_RATE, + dtype=FORMAT, + ) + + try: + # Wait for user to press spacebar to start + await self.should_send_audio.wait() + + stream.start() + self.set_recording_status(True) + self.log_message("Recording started - speak to the agent") + + # Buffer size in samples + read_size = int(SAMPLE_RATE * CHUNK_LENGTH_S) + + while True: + # Check if there's enough data to read + if stream.read_available < read_size: + await asyncio.sleep(0.01) # Small sleep to avoid CPU hogging + continue + + # Read audio data + data, _ = stream.read(read_size) + + # Convert numpy array to bytes + audio_bytes = data.tobytes() + + # Call audio callback if set + if self.audio_callback: + try: + await self.audio_callback(audio_bytes) + except Exception as e: + self.log_message(f"Audio callback error: {e}") + + # Yield control back to event loop + await asyncio.sleep(0) + + except Exception as e: + self.log_message(f"Audio capture error: {e}") + finally: + if stream.active: + stream.stop() + stream.close() + + async def on_key(self, event: events.Key) -> None: + """Handle key press events.""" + # add the keypress to the log + self.log_message(f"Key pressed: {event.key}") + + if event.key == "q": + self.audio_player.stop() + self.audio_player.close() + self.exit() + return + + if event.key == "space": # Spacebar + if not self.should_send_audio.is_set(): + self.should_send_audio.set() + self.set_recording_status(True) diff --git a/examples/reasoning_content/__init__.py b/examples/reasoning_content/__init__.py new file mode 100644 index 000000000..f24b2606d --- /dev/null +++ b/examples/reasoning_content/__init__.py @@ -0,0 +1,3 @@ +""" +Examples demonstrating how to use models that provide reasoning content. +""" diff --git a/examples/reasoning_content/main.py b/examples/reasoning_content/main.py new file mode 100644 index 000000000..c23b04254 --- /dev/null +++ b/examples/reasoning_content/main.py @@ -0,0 +1,124 @@ +""" +Example demonstrating how to use the reasoning content feature with models that support it. + +Some models, like deepseek-reasoner, provide a reasoning_content field in addition to the regular content. +This example shows how to access and use this reasoning content from both streaming and non-streaming responses. + +To run this example, you need to: +1. Set your OPENAI_API_KEY environment variable +2. Use a model that supports reasoning content (e.g., deepseek-reasoner) +""" + +import asyncio +import os +from typing import Any, cast + +from agents import ModelSettings +from agents.models.interface import ModelTracing +from agents.models.openai_provider import OpenAIProvider +from agents.types import ResponseOutputRefusal, ResponseOutputText # type: ignore + +MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or "deepseek-reasoner" + + +async def stream_with_reasoning_content(): + """ + Example of streaming a response from a model that provides reasoning content. + The reasoning content will be emitted as separate events. + """ + provider = OpenAIProvider() + model = provider.get_model(MODEL_NAME) + + print("\n=== Streaming Example ===") + print("Prompt: Write a haiku about recursion in programming") + + reasoning_content = "" + regular_content = "" + + async for event in model.stream_response( + system_instructions="You are a helpful assistant that writes creative content.", + input="Write a haiku about recursion in programming", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ): + if event.type == "response.reasoning_summary_text.delta": + print( + f"\033[33m{event.delta}\033[0m", end="", flush=True + ) # Yellow for reasoning content + reasoning_content += event.delta + elif event.type == "response.output_text.delta": + print(f"\033[32m{event.delta}\033[0m", end="", flush=True) # Green for regular content + regular_content += event.delta + + print("\n\nReasoning Content:") + print(reasoning_content) + print("\nRegular Content:") + print(regular_content) + print("\n") + + +async def get_response_with_reasoning_content(): + """ + Example of getting a complete response from a model that provides reasoning content. + The reasoning content will be available as a separate item in the response. + """ + provider = OpenAIProvider() + model = provider.get_model(MODEL_NAME) + + print("\n=== Non-streaming Example ===") + print("Prompt: Explain the concept of recursion in programming") + + response = await model.get_response( + system_instructions="You are a helpful assistant that explains technical concepts clearly.", + input="Explain the concept of recursion in programming", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ) + + # Extract reasoning content and regular content from the response + reasoning_content = None + regular_content = None + + for item in response.output: + if hasattr(item, "type") and item.type == "reasoning": + reasoning_content = item.summary[0].text + elif hasattr(item, "type") and item.type == "message": + if item.content and len(item.content) > 0: + content_item = item.content[0] + if isinstance(content_item, ResponseOutputText): + regular_content = content_item.text + elif isinstance(content_item, ResponseOutputRefusal): + refusal_item = cast(Any, content_item) + regular_content = refusal_item.refusal + + print("\nReasoning Content:") + print(reasoning_content or "No reasoning content provided") + + print("\nRegular Content:") + print(regular_content or "No regular content provided") + + print("\n") + + +async def main(): + try: + await stream_with_reasoning_content() + await get_response_with_reasoning_content() + except Exception as e: + print(f"Error: {e}") + print("\nNote: This example requires a model that supports reasoning content.") + print("You may need to use a specific model like deepseek-reasoner or similar.") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/reasoning_content/runner_example.py b/examples/reasoning_content/runner_example.py new file mode 100644 index 000000000..e51f85799 --- /dev/null +++ b/examples/reasoning_content/runner_example.py @@ -0,0 +1,88 @@ +""" +Example demonstrating how to use the reasoning content feature with the Runner API. + +This example shows how to extract and use reasoning content from responses when using +the Runner API, which is the most common way users interact with the Agents library. + +To run this example, you need to: +1. Set your OPENAI_API_KEY environment variable +2. Use a model that supports reasoning content (e.g., deepseek-reasoner) +""" + +import asyncio +import os +from typing import Any + +from agents import Agent, Runner, trace +from agents.items import ReasoningItem + +MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or "deepseek-reasoner" + + +async def main(): + print(f"Using model: {MODEL_NAME}") + + # Create an agent with a model that supports reasoning content + agent = Agent( + name="Reasoning Agent", + instructions="You are a helpful assistant that explains your reasoning step by step.", + model=MODEL_NAME, + ) + + # Example 1: Non-streaming response + with trace("Reasoning Content - Non-streaming"): + print("\n=== Example 1: Non-streaming response ===") + result = await Runner.run( + agent, "What is the square root of 841? Please explain your reasoning." + ) + + # Extract reasoning content from the result items + reasoning_content = None + # RunResult has 'response' attribute which has 'output' attribute + for item in result.response.output: # type: ignore + if isinstance(item, ReasoningItem): + reasoning_content = item.summary[0].text # type: ignore + break + + print("\nReasoning Content:") + print(reasoning_content or "No reasoning content provided") + + print("\nFinal Output:") + print(result.final_output) + + # Example 2: Streaming response + with trace("Reasoning Content - Streaming"): + print("\n=== Example 2: Streaming response ===") + print("\nStreaming response:") + + # Buffers to collect reasoning and regular content + reasoning_buffer = "" + content_buffer = "" + + # RunResultStreaming is async iterable + stream = Runner.run_streamed(agent, "What is 15 x 27? Please explain your reasoning.") + + async for event in stream: # type: ignore + if isinstance(event, ReasoningItem): + # This is reasoning content + reasoning_item: Any = event + reasoning_buffer += reasoning_item.summary[0].text + print( + f"\033[33m{reasoning_item.summary[0].text}\033[0m", end="", flush=True + ) # Yellow for reasoning + elif hasattr(event, "text"): + # This is regular content + content_buffer += event.text + print( + f"\033[32m{event.text}\033[0m", end="", flush=True + ) # Green for regular content + + print("\n\nCollected Reasoning Content:") + print(reasoning_buffer) + + print("\nCollected Final Answer:") + print(content_buffer) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/research_bot/agents/search_agent.py b/examples/research_bot/agents/search_agent.py index 0212ce5b5..61f91701f 100644 --- a/examples/research_bot/agents/search_agent.py +++ b/examples/research_bot/agents/search_agent.py @@ -3,7 +3,7 @@ INSTRUCTIONS = ( "You are a research assistant. Given a search term, you search the web for that term and " - "produce a concise summary of the results. The summary must 2-3 paragraphs and less than 300 " + "produce a concise summary of the results. The summary must be 2-3 paragraphs and less than 300 " "words. Capture the main points. Write succinctly, no need to have complete sentences or good " "grammar. This will be consumed by someone synthesizing a report, so its vital you capture the " "essence and ignore any fluff. Do not include any additional commentary other than the summary " diff --git a/examples/tools/code_interpreter.py b/examples/tools/code_interpreter.py new file mode 100644 index 000000000..a5843ce3f --- /dev/null +++ b/examples/tools/code_interpreter.py @@ -0,0 +1,34 @@ +import asyncio + +from agents import Agent, CodeInterpreterTool, Runner, trace + + +async def main(): + agent = Agent( + name="Code interpreter", + instructions="You love doing math.", + tools=[ + CodeInterpreterTool( + tool_config={"type": "code_interpreter", "container": {"type": "auto"}}, + ) + ], + ) + + with trace("Code interpreter example"): + print("Solving math problem...") + result = Runner.run_streamed(agent, "What is the square root of273 * 312821 plus 1782?") + async for event in result.stream_events(): + if ( + event.type == "run_item_stream_event" + and event.item.type == "tool_call_item" + and event.item.raw_item.type == "code_interpreter_call" + ): + print(f"Code interpreter code:\n```\n{event.item.raw_item.code}\n```\n") + elif event.type == "run_item_stream_event": + print(f"Other event: {event.item.type}") + + print(f"Final output: {result.final_output}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/tools/image_generator.py b/examples/tools/image_generator.py new file mode 100644 index 000000000..fd6fcc6ba --- /dev/null +++ b/examples/tools/image_generator.py @@ -0,0 +1,54 @@ +import asyncio +import base64 +import os +import subprocess +import sys +import tempfile + +from agents import Agent, ImageGenerationTool, Runner, trace + + +def open_file(path: str) -> None: + if sys.platform.startswith("darwin"): + subprocess.run(["open", path], check=False) # macOS + elif os.name == "nt": # Windows + os.astartfile(path) # type: ignore + elif os.name == "posix": + subprocess.run(["xdg-open", path], check=False) # Linux/Unix + else: + print(f"Don't know how to open files on this platform: {sys.platform}") + + +async def main(): + agent = Agent( + name="Image generator", + instructions="You are a helpful agent.", + tools=[ + ImageGenerationTool( + tool_config={"type": "image_generation", "quality": "low"}, + ) + ], + ) + + with trace("Image generation example"): + print("Generating image, this may take a while...") + result = await Runner.run( + agent, "Create an image of a frog eating a pizza, comic book style." + ) + print(result.final_output) + for item in result.new_items: + if ( + item.type == "tool_call_item" + and item.raw_item.type == "image_generation_call" + and (img_result := item.raw_item.result) + ): + with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp: + tmp.write(base64.b64decode(img_result)) + temp_path = tmp.name + + # Open the image + open_file(temp_path) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/mkdocs.yml b/mkdocs.yml index 51d65e697..a465bfefa 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -19,6 +19,9 @@ theme: logo: assets/logo.svg favicon: images/favicon-platform.svg +repo_name: openai-agents-python +repo_url: https://github.com/openai/openai-agents-python + plugins: - search - mkdocstrings: @@ -54,8 +57,10 @@ plugins: - Documentation: - agents.md - running_agents.md + - sessions.md - results.md - streaming.md + - repl.md - tools.md - mcp.md - handoffs.md @@ -63,9 +68,12 @@ plugins: - context.md - guardrails.md - multi_agent.md - - models.md + - Models: + - models/index.md + - models/litellm.md - config.md - visualization.md + - release.md - Voice agents: - voice/quickstart.md - voice/pipeline.md @@ -75,6 +83,8 @@ plugins: - ref/index.md - ref/agent.md - ref/run.md + - ref/memory.md + - ref/repl.md - ref/tool.md - ref/result.md - ref/stream_events.md @@ -120,6 +130,7 @@ plugins: - Extensions: - ref/extensions/handoff_filters.md - ref/extensions/handoff_prompt.md + - ref/extensions/litellm.md - locale: ja name: 日本語 @@ -131,8 +142,10 @@ plugins: - ドキュメント: - agents.md - running_agents.md + - sessions.md - results.md - streaming.md + - repl.md - tools.md - mcp.md - handoffs.md @@ -140,7 +153,9 @@ plugins: - context.md - guardrails.md - multi_agent.md - - models.md + - モデル: + - models/index.md + - models/litellm.md - config.md - visualization.md - 音声エージェント: diff --git a/pyproject.toml b/pyproject.toml index 1eefadd93..dfceb88f9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,19 +1,19 @@ [project] name = "openai-agents" -version = "0.0.11" +version = "0.1.0" description = "OpenAI Agents SDK" readme = "README.md" requires-python = ">=3.9" license = "MIT" authors = [{ name = "OpenAI", email = "support@openai.com" }] dependencies = [ - "openai>=1.66.5", + "openai>=1.93.1, <2", "pydantic>=2.10, <3", "griffe>=1.5.6, <2", "typing-extensions>=4.12.2, <5", "requests>=2.0, <3", "types-requests>=2.0, <3", - "mcp>=1.6.0, <2; python_version >= '3.10'", + "mcp>=1.9.4, <2; python_version >= '3.10'", ] classifiers = [ "Typing :: Typed", @@ -23,19 +23,21 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", - "Intended Audience :: Developers", + "Programming Language :: Python :: 3.13", "Operating System :: OS Independent", "Topic :: Software Development :: Libraries :: Python Modules", "License :: OSI Approved :: MIT License", ] [project.urls] -Homepage = "https://github.com/openai/openai-agents-python" +Homepage = "https://openai.github.io/openai-agents-python/" Repository = "https://github.com/openai/openai-agents-python" [project.optional-dependencies] voice = ["numpy>=2.2.0, <3; python_version>='3.10'", "websockets>=15.0, <16"] viz = ["graphviz>=0.17"] +litellm = ["litellm>=1.67.4.post1, <2"] +realtime = ["websockets>=15.0, <16"] [dependency-groups] dev = [ @@ -44,7 +46,7 @@ dev = [ "pytest", "pytest-asyncio", "pytest-mock>=3.14.0", - "rich", + "rich>=13.1.0, <14", "mkdocs>=1.6.0", "mkdocs-material>=9.6.0", "mkdocstrings[python]>=0.28.0", @@ -60,6 +62,7 @@ dev = [ "graphviz", "mkdocs-static-i18n>=1.3.0", "eval-type-backport>=0.2.2", + "fastapi >= 0.110.0, <1", ] [tool.uv.workspace] diff --git a/src/agents/__init__.py b/src/agents/__init__.py index db7d312e4..7de17efdb 100644 --- a/src/agents/__init__.py +++ b/src/agents/__init__.py @@ -5,8 +5,8 @@ from openai import AsyncOpenAI from . import _config -from .agent import Agent, ToolsToFinalOutputFunction, ToolsToFinalOutputResult -from .agent_output import AgentOutputSchema +from .agent import Agent, AgentBase, ToolsToFinalOutputFunction, ToolsToFinalOutputResult +from .agent_output import AgentOutputSchema, AgentOutputSchemaBase from .computer import AsyncComputer, Button, Computer, Environment from .exceptions import ( AgentsException, @@ -14,6 +14,7 @@ MaxTurnsExceeded, ModelBehaviorError, OutputGuardrailTripwireTriggered, + RunErrorDetails, UserError, ) from .guardrail import ( @@ -39,11 +40,14 @@ TResponseInputItem, ) from .lifecycle import AgentHooks, RunHooks +from .memory import Session, SQLiteSession from .model_settings import ModelSettings from .models.interface import Model, ModelProvider, ModelTracing from .models.openai_chatcompletions import OpenAIChatCompletionsModel from .models.openai_provider import OpenAIProvider from .models.openai_responses import OpenAIResponsesModel +from .prompts import DynamicPromptFunction, GenerateDynamicPromptData, Prompt +from .repl import run_demo_loop from .result import RunResult, RunResultStreaming from .run import RunConfig, Runner from .run_context import RunContextWrapper, TContext @@ -54,10 +58,19 @@ StreamEvent, ) from .tool import ( + CodeInterpreterTool, ComputerTool, FileSearchTool, FunctionTool, FunctionToolResult, + HostedMCPTool, + ImageGenerationTool, + LocalShellCommandRequest, + LocalShellExecutor, + LocalShellTool, + MCPToolApprovalFunction, + MCPToolApprovalFunctionResult, + MCPToolApprovalRequest, Tool, WebSearchTool, default_tool_error_function, @@ -92,6 +105,7 @@ handoff_span, mcp_tools_span, set_trace_processors, + set_trace_provider, set_tracing_disabled, set_tracing_export_api_key, speech_group_span, @@ -147,9 +161,11 @@ def enable_verbose_stdout_logging(): __all__ = [ "Agent", + "AgentBase", "ToolsToFinalOutputFunction", "ToolsToFinalOutputResult", "Runner", + "run_demo_loop", "Model", "ModelProvider", "ModelTracing", @@ -158,6 +174,7 @@ def enable_verbose_stdout_logging(): "OpenAIProvider", "OpenAIResponsesModel", "AgentOutputSchema", + "AgentOutputSchemaBase", "Computer", "AsyncComputer", "Environment", @@ -165,6 +182,9 @@ def enable_verbose_stdout_logging(): "AgentsException", "InputGuardrailTripwireTriggered", "OutputGuardrailTripwireTriggered", + "DynamicPromptFunction", + "GenerateDynamicPromptData", + "Prompt", "MaxTurnsExceeded", "ModelBehaviorError", "UserError", @@ -188,12 +208,14 @@ def enable_verbose_stdout_logging(): "ToolCallItem", "ToolCallOutputItem", "ReasoningItem", - "ModelResponse", "ItemHelpers", "RunHooks", "AgentHooks", + "Session", + "SQLiteSession", "RunContextWrapper", "TContext", + "RunErrorDetails", "RunResult", "RunResultStreaming", "RunConfig", @@ -205,8 +227,17 @@ def enable_verbose_stdout_logging(): "FunctionToolResult", "ComputerTool", "FileSearchTool", + "CodeInterpreterTool", + "ImageGenerationTool", + "LocalShellCommandRequest", + "LocalShellExecutor", + "LocalShellTool", "Tool", "WebSearchTool", + "HostedMCPTool", + "MCPToolApprovalFunction", + "MCPToolApprovalRequest", + "MCPToolApprovalFunctionResult", "function_tool", "Usage", "add_trace_processor", @@ -219,6 +250,7 @@ def enable_verbose_stdout_logging(): "guardrail_span", "handoff_span", "set_trace_processors", + "set_trace_provider", "set_tracing_disabled", "speech_group_span", "transcription_span", diff --git a/src/agents/_run_impl.py b/src/agents/_run_impl.py index 94c181b7f..4ac8b316b 100644 --- a/src/agents/_run_impl.py +++ b/src/agents/_run_impl.py @@ -14,6 +14,9 @@ ResponseFunctionWebSearch, ResponseOutputMessage, ) +from openai.types.responses.response_code_interpreter_tool_call import ( + ResponseCodeInterpreterToolCall, +) from openai.types.responses.response_computer_tool_call import ( ActionClick, ActionDoubleClick, @@ -25,11 +28,21 @@ ActionType, ActionWait, ) -from openai.types.responses.response_input_param import ComputerCallOutput +from openai.types.responses.response_input_item_param import ( + ComputerCallOutputAcknowledgedSafetyCheck, +) +from openai.types.responses.response_input_param import ComputerCallOutput, McpApprovalResponse +from openai.types.responses.response_output_item import ( + ImageGenerationCall, + LocalShellCall, + McpApprovalRequest, + McpCall, + McpListTools, +) from openai.types.responses.response_reasoning_item import ResponseReasoningItem from .agent import Agent, ToolsToFinalOutputResult -from .agent_output import AgentOutputSchema +from .agent_output import AgentOutputSchemaBase from .computer import AsyncComputer, Computer from .exceptions import AgentsException, ModelBehaviorError, UserError from .guardrail import InputGuardrail, InputGuardrailResult, OutputGuardrail, OutputGuardrailResult @@ -38,6 +51,9 @@ HandoffCallItem, HandoffOutputItem, ItemHelpers, + MCPApprovalRequestItem, + MCPApprovalResponseItem, + MCPListToolsItem, MessageOutputItem, ModelResponse, ReasoningItem, @@ -52,7 +68,18 @@ from .models.interface import ModelTracing from .run_context import RunContextWrapper, TContext from .stream_events import RunItemStreamEvent, StreamEvent -from .tool import ComputerTool, FunctionTool, FunctionToolResult, Tool +from .tool import ( + ComputerTool, + ComputerToolSafetyCheckData, + FunctionTool, + FunctionToolResult, + HostedMCPTool, + LocalShellCommandRequest, + LocalShellTool, + MCPToolApprovalRequest, + Tool, +) +from .tool_context import ToolContext from .tracing import ( SpanError, Trace, @@ -112,15 +139,29 @@ class ToolRunComputerAction: computer_tool: ComputerTool +@dataclass +class ToolRunMCPApprovalRequest: + request_item: McpApprovalRequest + mcp_tool: HostedMCPTool + + +@dataclass +class ToolRunLocalShellCall: + tool_call: LocalShellCall + local_shell_tool: LocalShellTool + + @dataclass class ProcessedResponse: new_items: list[RunItem] handoffs: list[ToolRunHandoff] functions: list[ToolRunFunction] computer_actions: list[ToolRunComputerAction] + local_shell_calls: list[ToolRunLocalShellCall] tools_used: list[str] # Names of all tools used, including hosted tools + mcp_approval_requests: list[ToolRunMCPApprovalRequest] # Only requests with callbacks - def has_tools_to_run(self) -> bool: + def has_tools_or_approvals_to_run(self) -> bool: # Handoffs, functions and computer actions need local processing # Hosted tools have already run, so there's nothing to do. return any( @@ -128,6 +169,8 @@ def has_tools_to_run(self) -> bool: self.handoffs, self.functions, self.computer_actions, + self.local_shell_calls, + self.mcp_approval_requests, ] ) @@ -195,7 +238,7 @@ async def execute_tools_and_side_effects( pre_step_items: list[RunItem], new_response: ModelResponse, processed_response: ProcessedResponse, - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, @@ -226,7 +269,16 @@ async def execute_tools_and_side_effects( new_step_items.extend([result.run_item for result in function_results]) new_step_items.extend(computer_results) - # Second, check if there are any handoffs + # Next, run the MCP approval requests + if processed_response.mcp_approval_requests: + approval_results = await cls.execute_mcp_approval_requests( + agent=agent, + approval_requests=processed_response.mcp_approval_requests, + context_wrapper=context_wrapper, + ) + new_step_items.extend(approval_results) + + # Next, check if there are any handoffs if run_handoffs := processed_response.handoffs: return await cls.execute_handoffs( agent=agent, @@ -240,7 +292,7 @@ async def execute_tools_and_side_effects( run_config=run_config, ) - # Third, we'll check if the tool use should result in a final output + # Next, we'll check if the tool use should result in a final output check_tool_use = await cls._check_for_final_output_from_tools( agent=agent, tool_results=function_results, @@ -295,7 +347,7 @@ async def execute_tools_and_side_effects( ) elif ( not output_schema or output_schema.is_plain_text() - ) and not processed_response.has_tools_to_run(): + ) and not processed_response.has_tools_or_approvals_to_run(): return await cls.execute_final_output( agent=agent, original_input=original_input, @@ -335,7 +387,7 @@ def process_model_response( agent: Agent[Any], all_tools: list[Tool], response: ModelResponse, - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], ) -> ProcessedResponse: items: list[RunItem] = [] @@ -343,10 +395,20 @@ def process_model_response( run_handoffs = [] functions = [] computer_actions = [] + local_shell_calls = [] + mcp_approval_requests = [] tools_used: list[str] = [] handoff_map = {handoff.tool_name: handoff for handoff in handoffs} function_map = {tool.name: tool for tool in all_tools if isinstance(tool, FunctionTool)} computer_tool = next((tool for tool in all_tools if isinstance(tool, ComputerTool)), None) + local_shell_tool = next( + (tool for tool in all_tools if isinstance(tool, LocalShellTool)), None + ) + hosted_mcp_server_map = { + tool.tool_config["server_label"]: tool + for tool in all_tools + if isinstance(tool, HostedMCPTool) + } for output in response.output: if isinstance(output, ResponseOutputMessage): @@ -375,6 +437,57 @@ def process_model_response( computer_actions.append( ToolRunComputerAction(tool_call=output, computer_tool=computer_tool) ) + elif isinstance(output, McpApprovalRequest): + items.append(MCPApprovalRequestItem(raw_item=output, agent=agent)) + if output.server_label not in hosted_mcp_server_map: + _error_tracing.attach_error_to_current_span( + SpanError( + message="MCP server label not found", + data={"server_label": output.server_label}, + ) + ) + raise ModelBehaviorError(f"MCP server label {output.server_label} not found") + else: + server = hosted_mcp_server_map[output.server_label] + if server.on_approval_request: + mcp_approval_requests.append( + ToolRunMCPApprovalRequest( + request_item=output, + mcp_tool=server, + ) + ) + else: + logger.warning( + f"MCP server {output.server_label} has no on_approval_request hook" + ) + elif isinstance(output, McpListTools): + items.append(MCPListToolsItem(raw_item=output, agent=agent)) + elif isinstance(output, McpCall): + items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("mcp") + elif isinstance(output, ImageGenerationCall): + items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("image_generation") + elif isinstance(output, ResponseCodeInterpreterToolCall): + items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("code_interpreter") + elif isinstance(output, LocalShellCall): + items.append(ToolCallItem(raw_item=output, agent=agent)) + tools_used.append("local_shell") + if not local_shell_tool: + _error_tracing.attach_error_to_current_span( + SpanError( + message="Local shell tool not found", + data={}, + ) + ) + raise ModelBehaviorError( + "Model produced local shell call without a local shell tool." + ) + local_shell_calls.append( + ToolRunLocalShellCall(tool_call=output, local_shell_tool=local_shell_tool) + ) + elif not isinstance(output, ResponseFunctionToolCall): logger.warning(f"Unexpected output type, ignoring: {type(output)}") continue @@ -416,7 +529,9 @@ def process_model_response( handoffs=run_handoffs, functions=functions, computer_actions=computer_actions, + local_shell_calls=local_shell_calls, tools_used=tools_used, + mcp_approval_requests=mcp_approval_requests, ) @classmethod @@ -433,23 +548,24 @@ async def run_single_tool( func_tool: FunctionTool, tool_call: ResponseFunctionToolCall ) -> Any: with function_span(func_tool.name) as span_fn: + tool_context = ToolContext.from_agent_context(context_wrapper, tool_call.call_id) if config.trace_include_sensitive_data: span_fn.span_data.input = tool_call.arguments try: _, _, result = await asyncio.gather( - hooks.on_tool_start(context_wrapper, agent, func_tool), + hooks.on_tool_start(tool_context, agent, func_tool), ( - agent.hooks.on_tool_start(context_wrapper, agent, func_tool) + agent.hooks.on_tool_start(tool_context, agent, func_tool) if agent.hooks else _coro.noop_coroutine() ), - func_tool.on_invoke_tool(context_wrapper, tool_call.arguments), + func_tool.on_invoke_tool(tool_context, tool_call.arguments), ) await asyncio.gather( - hooks.on_tool_end(context_wrapper, agent, func_tool, result), + hooks.on_tool_end(tool_context, agent, func_tool, result), ( - agent.hooks.on_tool_end(context_wrapper, agent, func_tool, result) + agent.hooks.on_tool_end(tool_context, agent, func_tool, result) if agent.hooks else _coro.noop_coroutine() ), @@ -489,6 +605,30 @@ async def run_single_tool( for tool_run, result in zip(tool_runs, results) ] + @classmethod + async def execute_local_shell_calls( + cls, + *, + agent: Agent[TContext], + calls: list[ToolRunLocalShellCall], + context_wrapper: RunContextWrapper[TContext], + hooks: RunHooks[TContext], + config: RunConfig, + ) -> list[RunItem]: + results: list[RunItem] = [] + # Need to run these serially, because each call can affect the local shell state + for call in calls: + results.append( + await LocalShellAction.execute( + agent=agent, + call=call, + hooks=hooks, + context_wrapper=context_wrapper, + config=config, + ) + ) + return results + @classmethod async def execute_computer_actions( cls, @@ -502,6 +642,29 @@ async def execute_computer_actions( results: list[RunItem] = [] # Need to run these serially, because each action can affect the computer state for action in actions: + acknowledged: list[ComputerCallOutputAcknowledgedSafetyCheck] | None = None + if action.tool_call.pending_safety_checks and action.computer_tool.on_safety_check: + acknowledged = [] + for check in action.tool_call.pending_safety_checks: + data = ComputerToolSafetyCheckData( + ctx_wrapper=context_wrapper, + agent=agent, + tool_call=action.tool_call, + safety_check=check, + ) + maybe = action.computer_tool.on_safety_check(data) + ack = await maybe if inspect.isawaitable(maybe) else maybe + if ack: + acknowledged.append( + ComputerCallOutputAcknowledgedSafetyCheck( + id=check.id, + code=check.code, + message=check.message, + ) + ) + else: + raise UserError("Computer tool safety check was not acknowledged") + results.append( await ComputerAction.execute( agent=agent, @@ -509,6 +672,7 @@ async def execute_computer_actions( hooks=hooks, context_wrapper=context_wrapper, config=config, + acknowledged_safety_checks=acknowledged, ) ) @@ -643,6 +807,40 @@ async def execute_handoffs( next_step=NextStepHandoff(new_agent), ) + @classmethod + async def execute_mcp_approval_requests( + cls, + *, + agent: Agent[TContext], + approval_requests: list[ToolRunMCPApprovalRequest], + context_wrapper: RunContextWrapper[TContext], + ) -> list[RunItem]: + async def run_single_approval(approval_request: ToolRunMCPApprovalRequest) -> RunItem: + callback = approval_request.mcp_tool.on_approval_request + assert callback is not None, "Callback is required for MCP approval requests" + maybe_awaitable_result = callback( + MCPToolApprovalRequest(context_wrapper, approval_request.request_item) + ) + if inspect.isawaitable(maybe_awaitable_result): + result = await maybe_awaitable_result + else: + result = maybe_awaitable_result + reason = result.get("reason", None) + raw_item: McpApprovalResponse = { + "approval_request_id": approval_request.request_item.id, + "approve": result["approve"], + "type": "mcp_approval_response", + } + if not result["approve"] and reason: + raw_item["reason"] = reason + return MCPApprovalResponseItem( + raw_item=raw_item, + agent=agent, + ) + + tasks = [run_single_approval(approval_request) for approval_request in approval_requests] + return await asyncio.gather(*tasks) + @classmethod async def execute_final_output( cls, @@ -727,6 +925,11 @@ def stream_step_result_to_queue( event = RunItemStreamEvent(item=item, name="tool_output") elif isinstance(item, ReasoningItem): event = RunItemStreamEvent(item=item, name="reasoning_item_created") + elif isinstance(item, MCPApprovalRequestItem): + event = RunItemStreamEvent(item=item, name="mcp_approval_requested") + elif isinstance(item, MCPListToolsItem): + event = RunItemStreamEvent(item=item, name="mcp_list_tools") + else: logger.warning(f"Unexpected item type: {type(item)}") event = None @@ -823,6 +1026,7 @@ async def execute( hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], config: RunConfig, + acknowledged_safety_checks: list[ComputerCallOutputAcknowledgedSafetyCheck] | None = None, ) -> RunItem: output_func = ( cls._get_screenshot_async(action.computer_tool.computer, action.tool_call) @@ -861,6 +1065,7 @@ async def execute( "image_url": image_url, }, type="computer_call_output", + acknowledged_safety_checks=acknowledged_safety_checks, ), ) @@ -919,3 +1124,54 @@ async def _get_screenshot_async( await computer.wait() return await computer.screenshot() + + +class LocalShellAction: + @classmethod + async def execute( + cls, + *, + agent: Agent[TContext], + call: ToolRunLocalShellCall, + hooks: RunHooks[TContext], + context_wrapper: RunContextWrapper[TContext], + config: RunConfig, + ) -> RunItem: + await asyncio.gather( + hooks.on_tool_start(context_wrapper, agent, call.local_shell_tool), + ( + agent.hooks.on_tool_start(context_wrapper, agent, call.local_shell_tool) + if agent.hooks + else _coro.noop_coroutine() + ), + ) + + request = LocalShellCommandRequest( + ctx_wrapper=context_wrapper, + data=call.tool_call, + ) + output = call.local_shell_tool.executor(request) + if inspect.isawaitable(output): + result = await output + else: + result = output + + await asyncio.gather( + hooks.on_tool_end(context_wrapper, agent, call.local_shell_tool, result), + ( + agent.hooks.on_tool_end(context_wrapper, agent, call.local_shell_tool, result) + if agent.hooks + else _coro.noop_coroutine() + ), + ) + + return ToolCallOutputItem( + agent=agent, + output=output, + raw_item={ + "type": "local_shell_call_output", + "id": call.tool_call.call_id, + "output": result, + # "id": "out" + call.tool_call.id, # TODO remove this, it should be optional + }, + ) diff --git a/src/agents/agent.py b/src/agents/agent.py index a24456b06..e418b67da 100644 --- a/src/agents/agent.py +++ b/src/agents/agent.py @@ -1,13 +1,16 @@ from __future__ import annotations +import asyncio import dataclasses import inspect from collections.abc import Awaitable from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, cast +from openai.types.responses.response_prompt_param import ResponsePromptParam from typing_extensions import NotRequired, TypeAlias, TypedDict +from .agent_output import AgentOutputSchemaBase from .guardrail import InputGuardrail, OutputGuardrail from .handoffs import Handoff from .items import ItemHelpers @@ -15,8 +18,9 @@ from .mcp import MCPUtil from .model_settings import ModelSettings from .models.interface import Model +from .prompts import DynamicPromptFunction, Prompt, PromptUtil from .run_context import RunContextWrapper, TContext -from .tool import FunctionToolResult, Tool, function_tool +from .tool import FunctionTool, FunctionToolResult, Tool, function_tool from .util import _transforms from .util._types import MaybeAwaitable @@ -63,7 +67,63 @@ class MCPConfig(TypedDict): @dataclass -class Agent(Generic[TContext]): +class AgentBase(Generic[TContext]): + """Base class for `Agent` and `RealtimeAgent`.""" + + name: str + """The name of the agent.""" + + handoff_description: str | None = None + """A description of the agent. This is used when the agent is used as a handoff, so that an + LLM knows what it does and when to invoke it. + """ + + tools: list[Tool] = field(default_factory=list) + """A list of tools that the agent can use.""" + + mcp_servers: list[MCPServer] = field(default_factory=list) + """A list of [Model Context Protocol](https://modelcontextprotocol.io/) servers that + the agent can use. Every time the agent runs, it will include tools from these servers in the + list of available tools. + + NOTE: You are expected to manage the lifecycle of these servers. Specifically, you must call + `server.connect()` before passing it to the agent, and `server.cleanup()` when the server is no + longer needed. + """ + + mcp_config: MCPConfig = field(default_factory=lambda: MCPConfig()) + """Configuration for MCP servers.""" + + async def get_mcp_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]: + """Fetches the available tools from the MCP servers.""" + convert_schemas_to_strict = self.mcp_config.get("convert_schemas_to_strict", False) + return await MCPUtil.get_all_function_tools( + self.mcp_servers, convert_schemas_to_strict, run_context, self + ) + + async def get_all_tools(self, run_context: RunContextWrapper[Any]) -> list[Tool]: + """All agent tools, including MCP tools and function tools.""" + mcp_tools = await self.get_mcp_tools(run_context) + + async def _check_tool_enabled(tool: Tool) -> bool: + if not isinstance(tool, FunctionTool): + return True + + attr = tool.is_enabled + if isinstance(attr, bool): + return attr + res = attr(run_context, self) + if inspect.isawaitable(res): + return bool(await res) + return bool(res) + + results = await asyncio.gather(*(_check_tool_enabled(t) for t in self.tools)) + enabled: list[Tool] = [t for t, ok in zip(self.tools, results) if ok] + return [*mcp_tools, *enabled] + + +@dataclass +class Agent(AgentBase, Generic[TContext]): """An agent is an AI model configured with instructions, tools, guardrails, handoffs and more. We strongly recommend passing `instructions`, which is the "system prompt" for the agent. In @@ -72,10 +132,9 @@ class Agent(Generic[TContext]): Agents are generic on the context type. The context is a (mutable) object you create. It is passed to tool functions, handoffs, guardrails, etc. - """ - name: str - """The name of the agent.""" + See `AgentBase` for base parameters that are shared with `RealtimeAgent`s. + """ instructions: ( str @@ -93,9 +152,10 @@ class Agent(Generic[TContext]): return a string. """ - handoff_description: str | None = None - """A description of the agent. This is used when the agent is used as a handoff, so that an - LLM knows what it does and when to invoke it. + prompt: Prompt | DynamicPromptFunction | None = None + """A prompt object (or a function that returns a Prompt). Prompts allow you to dynamically + configure the instructions, tools and other config for an agent outside of your code. Only + usable with OpenAI models, using the Responses API. """ handoffs: list[Agent[Any] | Handoff[TContext]] = field(default_factory=list) @@ -115,22 +175,6 @@ class Agent(Generic[TContext]): """Configures model-specific tuning parameters (e.g. temperature, top_p). """ - tools: list[Tool] = field(default_factory=list) - """A list of tools that the agent can use.""" - - mcp_servers: list[MCPServer] = field(default_factory=list) - """A list of [Model Context Protocol](https://modelcontextprotocol.io/) servers that - the agent can use. Every time the agent runs, it will include tools from these servers in the - list of available tools. - - NOTE: You are expected to manage the lifecycle of these servers. Specifically, you must call - `server.connect()` before passing it to the agent, and `server.cleanup()` when the server is no - longer needed. - """ - - mcp_config: MCPConfig = field(default_factory=lambda: MCPConfig()) - """Configuration for MCP servers.""" - input_guardrails: list[InputGuardrail[TContext]] = field(default_factory=list) """A list of checks that run in parallel to the agent's execution, before generating a response. Runs only if the agent is the first agent in the chain. @@ -141,8 +185,14 @@ class Agent(Generic[TContext]): Runs only if the agent produces a final output. """ - output_type: type[Any] | None = None - """The type of the output object. If not provided, the output will be `str`.""" + output_type: type[Any] | AgentOutputSchemaBase | None = None + """The type of the output object. If not provided, the output will be `str`. In most cases, + you should pass a regular Python type (e.g. a dataclass, Pydantic model, TypedDict, etc). + You can customize this in two ways: + 1. If you want non-strict schemas, pass `AgentOutputSchema(MyClass, strict_json_schema=False)`. + 2. If you want to use a custom JSON schema (i.e. without using the SDK's automatic schema) + creation, subclass and pass an `AgentOutputSchemaBase` subclass. + """ hooks: AgentHooks[TContext] | None = None """A class that receives callbacks on various lifecycle events for this agent. @@ -234,12 +284,35 @@ async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> s return None - async def get_mcp_tools(self) -> list[Tool]: + async def get_prompt( + self, run_context: RunContextWrapper[TContext] + ) -> ResponsePromptParam | None: + """Get the prompt for the agent.""" + return await PromptUtil.to_model_input(self.prompt, run_context, self) + + async def get_mcp_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]: """Fetches the available tools from the MCP servers.""" convert_schemas_to_strict = self.mcp_config.get("convert_schemas_to_strict", False) - return await MCPUtil.get_all_function_tools(self.mcp_servers, convert_schemas_to_strict) + return await MCPUtil.get_all_function_tools( + self.mcp_servers, convert_schemas_to_strict, run_context, self + ) - async def get_all_tools(self) -> list[Tool]: + async def get_all_tools(self, run_context: RunContextWrapper[Any]) -> list[Tool]: """All agent tools, including MCP tools and function tools.""" - mcp_tools = await self.get_mcp_tools() - return mcp_tools + self.tools + mcp_tools = await self.get_mcp_tools(run_context) + + async def _check_tool_enabled(tool: Tool) -> bool: + if not isinstance(tool, FunctionTool): + return True + + attr = tool.is_enabled + if isinstance(attr, bool): + return attr + res = attr(run_context, self) + if inspect.isawaitable(res): + return bool(await res) + return bool(res) + + results = await asyncio.gather(*(_check_tool_enabled(t) for t in self.tools)) + enabled: list[Tool] = [t for t, ok in zip(self.tools, results) if ok] + return [*mcp_tools, *enabled] diff --git a/src/agents/agent_output.py b/src/agents/agent_output.py index 3262c57d6..ee14956e3 100644 --- a/src/agents/agent_output.py +++ b/src/agents/agent_output.py @@ -1,3 +1,4 @@ +import abc from dataclasses import dataclass from typing import Any @@ -12,8 +13,46 @@ _WRAPPER_DICT_KEY = "response" +class AgentOutputSchemaBase(abc.ABC): + """An object that captures the JSON schema of the output, as well as validating/parsing JSON + produced by the LLM into the output type. + """ + + @abc.abstractmethod + def is_plain_text(self) -> bool: + """Whether the output type is plain text (versus a JSON object).""" + pass + + @abc.abstractmethod + def name(self) -> str: + """The name of the output type.""" + pass + + @abc.abstractmethod + def json_schema(self) -> dict[str, Any]: + """Returns the JSON schema of the output. Will only be called if the output type is not + plain text. + """ + pass + + @abc.abstractmethod + def is_strict_json_schema(self) -> bool: + """Whether the JSON schema is in strict mode. Strict mode constrains the JSON schema + features, but guarantees valid JSON. See here for details: + https://platform.openai.com/docs/guides/structured-outputs#supported-schemas + """ + pass + + @abc.abstractmethod + def validate_json(self, json_str: str) -> Any: + """Validate a JSON string against the output type. You must return the validated object, + or raise a `ModelBehaviorError` if the JSON is invalid. + """ + pass + + @dataclass(init=False) -class AgentOutputSchema: +class AgentOutputSchema(AgentOutputSchemaBase): """An object that captures the JSON schema of the output, as well as validating/parsing JSON produced by the LLM into the output type. """ @@ -32,7 +71,7 @@ class AgentOutputSchema: _output_schema: dict[str, Any] """The JSON schema of the output.""" - strict_json_schema: bool + _strict_json_schema: bool """Whether the JSON schema is in strict mode. We **strongly** recommend setting this to True, as it increases the likelihood of correct JSON input. """ @@ -45,7 +84,7 @@ def __init__(self, output_type: type[Any], strict_json_schema: bool = True): setting this to True, as it increases the likelihood of correct JSON input. """ self.output_type = output_type - self.strict_json_schema = strict_json_schema + self._strict_json_schema = strict_json_schema if output_type is None or output_type is str: self._is_wrapped = False @@ -70,24 +109,35 @@ def __init__(self, output_type: type[Any], strict_json_schema: bool = True): self._type_adapter = TypeAdapter(output_type) self._output_schema = self._type_adapter.json_schema() - if self.strict_json_schema: - self._output_schema = ensure_strict_json_schema(self._output_schema) + if self._strict_json_schema: + try: + self._output_schema = ensure_strict_json_schema(self._output_schema) + except UserError as e: + raise UserError( + "Strict JSON schema is enabled, but the output type is not valid. " + "Either make the output type strict, or pass output_schema_strict=False to " + "your Agent()" + ) from e def is_plain_text(self) -> bool: """Whether the output type is plain text (versus a JSON object).""" return self.output_type is None or self.output_type is str + def is_strict_json_schema(self) -> bool: + """Whether the JSON schema is in strict mode.""" + return self._strict_json_schema + def json_schema(self) -> dict[str, Any]: """The JSON schema of the output type.""" if self.is_plain_text(): raise UserError("Output type is plain text, so no JSON schema is available") return self._output_schema - def validate_json(self, json_str: str, partial: bool = False) -> Any: + def validate_json(self, json_str: str) -> Any: """Validate a JSON string against the output type. Returns the validated object, or raises a `ModelBehaviorError` if the JSON is invalid. """ - validated = _json.validate_json(json_str, self._type_adapter, partial) + validated = _json.validate_json(json_str, self._type_adapter, partial=False) if self._is_wrapped: if not isinstance(validated, dict): _error_tracing.attach_error_to_current_span( @@ -113,7 +163,7 @@ def validate_json(self, json_str: str, partial: bool = False) -> Any: return validated[_WRAPPER_DICT_KEY] return validated - def output_type_name(self) -> str: + def name(self) -> str: """The name of the output type.""" return _type_to_str(self.output_type) diff --git a/src/agents/exceptions.py b/src/agents/exceptions.py index 78898f017..c00024c2e 100644 --- a/src/agents/exceptions.py +++ b/src/agents/exceptions.py @@ -1,12 +1,42 @@ -from typing import TYPE_CHECKING +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any if TYPE_CHECKING: + from .agent import Agent from .guardrail import InputGuardrailResult, OutputGuardrailResult + from .items import ModelResponse, RunItem, TResponseInputItem + from .run_context import RunContextWrapper + +from .util._pretty_print import pretty_print_run_error_details + + +@dataclass +class RunErrorDetails: + """Data collected from an agent run when an exception occurs.""" + + input: str | list[TResponseInputItem] + new_items: list[RunItem] + raw_responses: list[ModelResponse] + last_agent: Agent[Any] + context_wrapper: RunContextWrapper[Any] + input_guardrail_results: list[InputGuardrailResult] + output_guardrail_results: list[OutputGuardrailResult] + + def __str__(self) -> str: + return pretty_print_run_error_details(self) class AgentsException(Exception): """Base class for all exceptions in the Agents SDK.""" + run_data: RunErrorDetails | None + + def __init__(self, *args: object) -> None: + super().__init__(*args) + self.run_data = None + class MaxTurnsExceeded(AgentsException): """Exception raised when the maximum number of turns is exceeded.""" @@ -15,6 +45,7 @@ class MaxTurnsExceeded(AgentsException): def __init__(self, message: str): self.message = message + super().__init__(message) class ModelBehaviorError(AgentsException): @@ -26,6 +57,7 @@ class ModelBehaviorError(AgentsException): def __init__(self, message: str): self.message = message + super().__init__(message) class UserError(AgentsException): @@ -35,15 +67,16 @@ class UserError(AgentsException): def __init__(self, message: str): self.message = message + super().__init__(message) class InputGuardrailTripwireTriggered(AgentsException): """Exception raised when a guardrail tripwire is triggered.""" - guardrail_result: "InputGuardrailResult" + guardrail_result: InputGuardrailResult """The result data of the guardrail that was triggered.""" - def __init__(self, guardrail_result: "InputGuardrailResult"): + def __init__(self, guardrail_result: InputGuardrailResult): self.guardrail_result = guardrail_result super().__init__( f"Guardrail {guardrail_result.guardrail.__class__.__name__} triggered tripwire" @@ -53,10 +86,10 @@ def __init__(self, guardrail_result: "InputGuardrailResult"): class OutputGuardrailTripwireTriggered(AgentsException): """Exception raised when a guardrail tripwire is triggered.""" - guardrail_result: "OutputGuardrailResult" + guardrail_result: OutputGuardrailResult """The result data of the guardrail that was triggered.""" - def __init__(self, guardrail_result: "OutputGuardrailResult"): + def __init__(self, guardrail_result: OutputGuardrailResult): self.guardrail_result = guardrail_result super().__init__( f"Guardrail {guardrail_result.guardrail.__class__.__name__} triggered tripwire" diff --git a/src/agents/extensions/models/__init__.py b/src/agents/extensions/models/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/agents/extensions/models/litellm_model.py b/src/agents/extensions/models/litellm_model.py new file mode 100644 index 000000000..a06c61dc3 --- /dev/null +++ b/src/agents/extensions/models/litellm_model.py @@ -0,0 +1,409 @@ +from __future__ import annotations + +import json +import time +from collections.abc import AsyncIterator +from typing import Any, Literal, cast, overload + +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails + +from agents.exceptions import ModelBehaviorError + +try: + import litellm +except ImportError as _e: + raise ImportError( + "`litellm` is required to use the LitellmModel. You can install it via the optional " + "dependency group: `pip install 'openai-agents[litellm]'`." + ) from _e + +from openai import NOT_GIVEN, AsyncStream, NotGiven +from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageToolCall +from openai.types.chat.chat_completion_message import ( + Annotation, + AnnotationURLCitation, + ChatCompletionMessage, +) +from openai.types.chat.chat_completion_message_tool_call import Function +from openai.types.responses import Response + +from ... import _debug +from ...agent_output import AgentOutputSchemaBase +from ...handoffs import Handoff +from ...items import ModelResponse, TResponseInputItem, TResponseStreamEvent +from ...logger import logger +from ...model_settings import ModelSettings +from ...models.chatcmpl_converter import Converter +from ...models.chatcmpl_helpers import HEADERS +from ...models.chatcmpl_stream_handler import ChatCmplStreamHandler +from ...models.fake_id import FAKE_RESPONSES_ID +from ...models.interface import Model, ModelTracing +from ...tool import Tool +from ...tracing import generation_span +from ...tracing.span_data import GenerationSpanData +from ...tracing.spans import Span +from ...usage import Usage + + +class LitellmModel(Model): + """This class enables using any model via LiteLLM. LiteLLM allows you to acess OpenAPI, + Anthropic, Gemini, Mistral, and many other models. + See supported models here: [litellm models](https://docs.litellm.ai/docs/providers). + """ + + def __init__( + self, + model: str, + base_url: str | None = None, + api_key: str | None = None, + ): + self.model = model + self.base_url = base_url + self.api_key = api_key + + async def get_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + previous_response_id: str | None, + prompt: Any | None = None, + ) -> ModelResponse: + with generation_span( + model=str(self.model), + model_config=model_settings.to_json_dict() + | {"base_url": str(self.base_url or ""), "model_impl": "litellm"}, + disabled=tracing.is_disabled(), + ) as span_generation: + response = await self._fetch_response( + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + span_generation, + tracing, + stream=False, + prompt=prompt, + ) + + assert isinstance(response.choices[0], litellm.types.utils.Choices) + + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("Received model response") + else: + logger.debug( + f"""LLM resp:\n{ + json.dumps( + response.choices[0].message.model_dump(), indent=2, ensure_ascii=False + ) + }\n""" + ) + + if hasattr(response, "usage"): + response_usage = response.usage + usage = ( + Usage( + requests=1, + input_tokens=response_usage.prompt_tokens, + output_tokens=response_usage.completion_tokens, + total_tokens=response_usage.total_tokens, + input_tokens_details=InputTokensDetails( + cached_tokens=getattr( + response_usage.prompt_tokens_details, "cached_tokens", 0 + ) + or 0 + ), + output_tokens_details=OutputTokensDetails( + reasoning_tokens=getattr( + response_usage.completion_tokens_details, "reasoning_tokens", 0 + ) + or 0 + ), + ) + if response.usage + else Usage() + ) + else: + usage = Usage() + logger.warning("No usage information returned from Litellm") + + if tracing.include_data(): + span_generation.span_data.output = [response.choices[0].message.model_dump()] + span_generation.span_data.usage = { + "input_tokens": usage.input_tokens, + "output_tokens": usage.output_tokens, + } + + items = Converter.message_to_output_items( + LitellmConverter.convert_message_to_openai(response.choices[0].message) + ) + + return ModelResponse( + output=items, + usage=usage, + response_id=None, + ) + + async def stream_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + previous_response_id: str | None, + prompt: Any | None = None, + ) -> AsyncIterator[TResponseStreamEvent]: + with generation_span( + model=str(self.model), + model_config=model_settings.to_json_dict() + | {"base_url": str(self.base_url or ""), "model_impl": "litellm"}, + disabled=tracing.is_disabled(), + ) as span_generation: + response, stream = await self._fetch_response( + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + span_generation, + tracing, + stream=True, + prompt=prompt, + ) + + final_response: Response | None = None + async for chunk in ChatCmplStreamHandler.handle_stream(response, stream): + yield chunk + + if chunk.type == "response.completed": + final_response = chunk.response + + if tracing.include_data() and final_response: + span_generation.span_data.output = [final_response.model_dump()] + + if final_response and final_response.usage: + span_generation.span_data.usage = { + "input_tokens": final_response.usage.input_tokens, + "output_tokens": final_response.usage.output_tokens, + } + + @overload + async def _fetch_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + span: Span[GenerationSpanData], + tracing: ModelTracing, + stream: Literal[True], + prompt: Any | None = None, + ) -> tuple[Response, AsyncStream[ChatCompletionChunk]]: ... + + @overload + async def _fetch_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + span: Span[GenerationSpanData], + tracing: ModelTracing, + stream: Literal[False], + prompt: Any | None = None, + ) -> litellm.types.utils.ModelResponse: ... + + async def _fetch_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + span: Span[GenerationSpanData], + tracing: ModelTracing, + stream: bool = False, + prompt: Any | None = None, + ) -> litellm.types.utils.ModelResponse | tuple[Response, AsyncStream[ChatCompletionChunk]]: + converted_messages = Converter.items_to_messages(input) + + if system_instructions: + converted_messages.insert( + 0, + { + "content": system_instructions, + "role": "system", + }, + ) + if tracing.include_data(): + span.span_data.input = converted_messages + + parallel_tool_calls = ( + True + if model_settings.parallel_tool_calls and tools and len(tools) > 0 + else False + if model_settings.parallel_tool_calls is False + else None + ) + tool_choice = Converter.convert_tool_choice(model_settings.tool_choice) + response_format = Converter.convert_response_format(output_schema) + + converted_tools = [Converter.tool_to_openai(tool) for tool in tools] if tools else [] + + for handoff in handoffs: + converted_tools.append(Converter.convert_handoff_tool(handoff)) + + if _debug.DONT_LOG_MODEL_DATA: + logger.debug("Calling LLM") + else: + logger.debug( + f"Calling Litellm model: {self.model}\n" + f"{json.dumps(converted_messages, indent=2, ensure_ascii=False)}\n" + f"Tools:\n{json.dumps(converted_tools, indent=2, ensure_ascii=False)}\n" + f"Stream: {stream}\n" + f"Tool choice: {tool_choice}\n" + f"Response format: {response_format}\n" + ) + + reasoning_effort = model_settings.reasoning.effort if model_settings.reasoning else None + + stream_options = None + if stream and model_settings.include_usage is not None: + stream_options = {"include_usage": model_settings.include_usage} + + extra_kwargs = {} + if model_settings.extra_query: + extra_kwargs["extra_query"] = model_settings.extra_query + if model_settings.metadata: + extra_kwargs["metadata"] = model_settings.metadata + if model_settings.extra_body and isinstance(model_settings.extra_body, dict): + extra_kwargs.update(model_settings.extra_body) + + # Add kwargs from model_settings.extra_args, filtering out None values + if model_settings.extra_args: + extra_kwargs.update(model_settings.extra_args) + + ret = await litellm.acompletion( + model=self.model, + messages=converted_messages, + tools=converted_tools or None, + temperature=model_settings.temperature, + top_p=model_settings.top_p, + frequency_penalty=model_settings.frequency_penalty, + presence_penalty=model_settings.presence_penalty, + max_tokens=model_settings.max_tokens, + tool_choice=self._remove_not_given(tool_choice), + response_format=self._remove_not_given(response_format), + parallel_tool_calls=parallel_tool_calls, + stream=stream, + stream_options=stream_options, + reasoning_effort=reasoning_effort, + extra_headers={**HEADERS, **(model_settings.extra_headers or {})}, + api_key=self.api_key, + base_url=self.base_url, + **extra_kwargs, + ) + + if isinstance(ret, litellm.types.utils.ModelResponse): + return ret + + response = Response( + id=FAKE_RESPONSES_ID, + created_at=time.time(), + model=self.model, + object="response", + output=[], + tool_choice=cast(Literal["auto", "required", "none"], tool_choice) + if tool_choice != NOT_GIVEN + else "auto", + top_p=model_settings.top_p, + temperature=model_settings.temperature, + tools=[], + parallel_tool_calls=parallel_tool_calls or False, + reasoning=model_settings.reasoning, + ) + return response, ret + + def _remove_not_given(self, value: Any) -> Any: + if isinstance(value, NotGiven): + return None + return value + + +class LitellmConverter: + @classmethod + def convert_message_to_openai( + cls, message: litellm.types.utils.Message + ) -> ChatCompletionMessage: + if message.role != "assistant": + raise ModelBehaviorError(f"Unsupported role: {message.role}") + + tool_calls = ( + [LitellmConverter.convert_tool_call_to_openai(tool) for tool in message.tool_calls] + if message.tool_calls + else None + ) + + provider_specific_fields = message.get("provider_specific_fields", None) + refusal = ( + provider_specific_fields.get("refusal", None) if provider_specific_fields else None + ) + + return ChatCompletionMessage( + content=message.content, + refusal=refusal, + role="assistant", + annotations=cls.convert_annotations_to_openai(message), + audio=message.get("audio", None), # litellm deletes audio if not present + tool_calls=tool_calls, + ) + + @classmethod + def convert_annotations_to_openai( + cls, message: litellm.types.utils.Message + ) -> list[Annotation] | None: + annotations: list[litellm.types.llms.openai.ChatCompletionAnnotation] | None = message.get( + "annotations", None + ) + if not annotations: + return None + + return [ + Annotation( + type="url_citation", + url_citation=AnnotationURLCitation( + start_index=annotation["url_citation"]["start_index"], + end_index=annotation["url_citation"]["end_index"], + url=annotation["url_citation"]["url"], + title=annotation["url_citation"]["title"], + ), + ) + for annotation in annotations + ] + + @classmethod + def convert_tool_call_to_openai( + cls, tool_call: litellm.types.utils.ChatCompletionMessageToolCall + ) -> ChatCompletionMessageToolCall: + return ChatCompletionMessageToolCall( + id=tool_call.id, + type="function", + function=Function( + name=tool_call.function.name or "", arguments=tool_call.function.arguments + ), + ) diff --git a/src/agents/extensions/models/litellm_provider.py b/src/agents/extensions/models/litellm_provider.py new file mode 100644 index 000000000..5a2dc1666 --- /dev/null +++ b/src/agents/extensions/models/litellm_provider.py @@ -0,0 +1,21 @@ +from ...models.interface import Model, ModelProvider +from .litellm_model import LitellmModel + +DEFAULT_MODEL: str = "gpt-4.1" + + +class LitellmProvider(ModelProvider): + """A ModelProvider that uses LiteLLM to route to any model provider. You can use it via: + ```python + Runner.run(agent, input, run_config=RunConfig(model_provider=LitellmProvider())) + ``` + See supported models here: [litellm models](https://docs.litellm.ai/docs/providers). + + NOTE: API keys must be set via environment variables. If you're using models that require + additional configuration (e.g. Azure API base or version), those must also be set via the + environment variables that LiteLLM expects. If you have more advanced needs, we recommend + copy-pasting this class and making any modifications you need. + """ + + def get_model(self, model_name: str | None) -> Model: + return LitellmModel(model_name or DEFAULT_MODEL) diff --git a/src/agents/extensions/visualization.py b/src/agents/extensions/visualization.py index 5fb350626..be762a330 100644 --- a/src/agents/extensions/visualization.py +++ b/src/agents/extensions/visualization.py @@ -1,4 +1,4 @@ -from typing import Optional +from __future__ import annotations import graphviz # type: ignore @@ -31,7 +31,9 @@ def get_main_graph(agent: Agent) -> str: return "".join(parts) -def get_all_nodes(agent: Agent, parent: Optional[Agent] = None) -> str: +def get_all_nodes( + agent: Agent, parent: Agent | None = None, visited: set[str] | None = None +) -> str: """ Recursively generates the nodes for the given agent and its handoffs in DOT format. @@ -41,17 +43,23 @@ def get_all_nodes(agent: Agent, parent: Optional[Agent] = None) -> str: Returns: str: The DOT format string representing the nodes. """ + if visited is None: + visited = set() + if agent.name in visited: + return "" + visited.add(agent.name) + parts = [] # Start and end the graph - parts.append( - '"__start__" [label="__start__", shape=ellipse, style=filled, ' - "fillcolor=lightblue, width=0.5, height=0.3];" - '"__end__" [label="__end__", shape=ellipse, style=filled, ' - "fillcolor=lightblue, width=0.5, height=0.3];" - ) - # Ensure parent agent node is colored if not parent: + parts.append( + '"__start__" [label="__start__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" + '"__end__" [label="__end__", shape=ellipse, style=filled, ' + "fillcolor=lightblue, width=0.5, height=0.3];" + ) + # Ensure parent agent node is colored parts.append( f'"{agent.name}" [label="{agent.name}", shape=box, style=filled, ' "fillcolor=lightyellow, width=1.5, height=0.8];" @@ -71,17 +79,20 @@ def get_all_nodes(agent: Agent, parent: Optional[Agent] = None) -> str: f"fillcolor=lightyellow, width=1.5, height=0.8];" ) if isinstance(handoff, Agent): - parts.append( - f'"{handoff.name}" [label="{handoff.name}", ' - f"shape=box, style=filled, style=rounded, " - f"fillcolor=lightyellow, width=1.5, height=0.8];" - ) - parts.append(get_all_nodes(handoff)) + if handoff.name not in visited: + parts.append( + f'"{handoff.name}" [label="{handoff.name}", ' + f"shape=box, style=filled, style=rounded, " + f"fillcolor=lightyellow, width=1.5, height=0.8];" + ) + parts.append(get_all_nodes(handoff, agent, visited)) return "".join(parts) -def get_all_edges(agent: Agent, parent: Optional[Agent] = None) -> str: +def get_all_edges( + agent: Agent, parent: Agent | None = None, visited: set[str] | None = None +) -> str: """ Recursively generates the edges for the given agent and its handoffs in DOT format. @@ -92,6 +103,12 @@ def get_all_edges(agent: Agent, parent: Optional[Agent] = None) -> str: Returns: str: The DOT format string representing the edges. """ + if visited is None: + visited = set() + if agent.name in visited: + return "" + visited.add(agent.name) + parts = [] if not parent: @@ -109,7 +126,7 @@ def get_all_edges(agent: Agent, parent: Optional[Agent] = None) -> str: if isinstance(handoff, Agent): parts.append(f""" "{agent.name}" -> "{handoff.name}";""") - parts.append(get_all_edges(handoff, agent)) + parts.append(get_all_edges(handoff, agent, visited)) if not agent.handoffs and not isinstance(agent, Tool): # type: ignore parts.append(f'"{agent.name}" -> "__end__";') @@ -117,7 +134,7 @@ def get_all_edges(agent: Agent, parent: Optional[Agent] = None) -> str: return "".join(parts) -def draw_graph(agent: Agent, filename: Optional[str] = None) -> graphviz.Source: +def draw_graph(agent: Agent, filename: str | None = None) -> graphviz.Source: """ Draws the graph for the given agent and optionally saves it as a PNG file. @@ -132,6 +149,6 @@ def draw_graph(agent: Agent, filename: Optional[str] = None) -> graphviz.Source: graph = graphviz.Source(dot_code) if filename: - graph.render(filename, format="png") + graph.render(filename, format="png", cleanup=True) return graph diff --git a/src/agents/function_schema.py b/src/agents/function_schema.py index 0e5868965..e1a91e189 100644 --- a/src/agents/function_schema.py +++ b/src/agents/function_schema.py @@ -13,6 +13,7 @@ from .exceptions import UserError from .run_context import RunContextWrapper from .strict_schema import ensure_strict_json_schema +from .tool_context import ToolContext @dataclass @@ -222,7 +223,8 @@ def function_schema( doc_info = None param_descs = {} - func_name = name_override or doc_info.name if doc_info else func.__name__ + # Ensure name_override takes precedence even if docstring info is disabled. + func_name = name_override or (doc_info.name if doc_info else func.__name__) # 2. Inspect function signature and get type hints sig = inspect.signature(func) @@ -237,21 +239,21 @@ def function_schema( ann = type_hints.get(first_name, first_param.annotation) if ann != inspect._empty: origin = get_origin(ann) or ann - if origin is RunContextWrapper: + if origin is RunContextWrapper or origin is ToolContext: takes_context = True # Mark that the function takes context else: filtered_params.append((first_name, first_param)) else: filtered_params.append((first_name, first_param)) - # For parameters other than the first, raise error if any use RunContextWrapper. + # For parameters other than the first, raise error if any use RunContextWrapper or ToolContext. for name, param in params[1:]: ann = type_hints.get(name, param.annotation) if ann != inspect._empty: origin = get_origin(ann) or ann - if origin is RunContextWrapper: + if origin is RunContextWrapper or origin is ToolContext: raise UserError( - f"RunContextWrapper param found at non-first position in function" + f"RunContextWrapper/ToolContext param found at non-first position in function" f" {func.__name__}" ) filtered_params.append((name, param)) @@ -335,7 +337,8 @@ def function_schema( # 5. Return as a FuncSchema dataclass return FuncSchema( name=func_name, - description=description_override or doc_info.description if doc_info else None, + # Ensure description_override takes precedence even if docstring info is disabled. + description=description_override or (doc_info.description if doc_info else None), params_pydantic_model=dynamic_model, params_json_schema=json_schema, signature=sig, diff --git a/src/agents/handoffs.py b/src/agents/handoffs.py index 686191f3d..cb2752e4f 100644 --- a/src/agents/handoffs.py +++ b/src/agents/handoffs.py @@ -1,6 +1,7 @@ from __future__ import annotations import inspect +import json from collections.abc import Awaitable from dataclasses import dataclass from typing import TYPE_CHECKING, Any, Callable, Generic, cast, overload @@ -14,6 +15,7 @@ from .strict_schema import ensure_strict_json_schema from .tracing.spans import SpanError from .util import _error_tracing, _json, _transforms +from .util._types import MaybeAwaitable if TYPE_CHECKING: from .agent import Agent @@ -98,9 +100,13 @@ class Handoff(Generic[TContext]): True, as it increases the likelihood of correct JSON input. """ + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True + """Whether the handoff is enabled. Either a bool or a Callable that takes the run context and + agent and returns whether the handoff is enabled. You can use this to dynamically enable/disable + a handoff based on your context/state.""" + def get_transfer_message(self, agent: Agent[Any]) -> str: - base = f"{{'assistant': '{agent.name}'}}" - return base + return json.dumps({"assistant": agent.name}) @classmethod def default_tool_name(cls, agent: Agent[Any]) -> str: @@ -121,6 +127,7 @@ def handoff( tool_name_override: str | None = None, tool_description_override: str | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True, ) -> Handoff[TContext]: ... @@ -133,6 +140,7 @@ def handoff( tool_description_override: str | None = None, tool_name_override: str | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True, ) -> Handoff[TContext]: ... @@ -144,6 +152,7 @@ def handoff( tool_description_override: str | None = None, tool_name_override: str | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True, ) -> Handoff[TContext]: ... @@ -154,6 +163,7 @@ def handoff( on_handoff: OnHandoffWithInput[THandoffInput] | OnHandoffWithoutInput | None = None, input_type: type[THandoffInput] | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + is_enabled: bool | Callable[[RunContextWrapper[Any], Agent[Any]], MaybeAwaitable[bool]] = True, ) -> Handoff[TContext]: """Create a handoff from an agent. @@ -166,9 +176,12 @@ def handoff( input_type: the type of the input to the handoff. If provided, the input will be validated against this type. Only relevant if you pass a function that takes an input. input_filter: a function that filters the inputs that are passed to the next agent. + is_enabled: Whether the handoff is enabled. Can be a bool or a callable that takes the run + context and agent and returns whether the handoff is enabled. Disabled handoffs are + hidden from the LLM at runtime. """ assert (on_handoff and input_type) or not (on_handoff and input_type), ( - "You must provide either both on_input and input_type, or neither" + "You must provide either both on_handoff and input_type, or neither" ) type_adapter: TypeAdapter[Any] | None if input_type is not None: @@ -233,4 +246,5 @@ async def _invoke_handoff( on_invoke_handoff=_invoke_handoff, input_filter=input_filter, agent_name=agent.name, + is_enabled=is_enabled, ) diff --git a/src/agents/items.py b/src/agents/items.py index 8fb2b52a3..64797ad22 100644 --- a/src/agents/items.py +++ b/src/agents/items.py @@ -18,7 +18,22 @@ ResponseOutputText, ResponseStreamEvent, ) -from openai.types.responses.response_input_item_param import ComputerCallOutput, FunctionCallOutput +from openai.types.responses.response_code_interpreter_tool_call import ( + ResponseCodeInterpreterToolCall, +) +from openai.types.responses.response_input_item_param import ( + ComputerCallOutput, + FunctionCallOutput, + LocalShellCallOutput, + McpApprovalResponse, +) +from openai.types.responses.response_output_item import ( + ImageGenerationCall, + LocalShellCall, + McpApprovalRequest, + McpCall, + McpListTools, +) from openai.types.responses.response_reasoning_item import ResponseReasoningItem from pydantic import BaseModel from typing_extensions import TypeAlias @@ -108,6 +123,10 @@ class HandoffOutputItem(RunItemBase[TResponseInputItem]): ResponseComputerToolCall, ResponseFileSearchToolCall, ResponseFunctionWebSearch, + McpCall, + ResponseCodeInterpreterToolCall, + ImageGenerationCall, + LocalShellCall, ] """A type that represents a tool call item.""" @@ -123,10 +142,12 @@ class ToolCallItem(RunItemBase[ToolCallItemTypes]): @dataclass -class ToolCallOutputItem(RunItemBase[Union[FunctionCallOutput, ComputerCallOutput]]): +class ToolCallOutputItem( + RunItemBase[Union[FunctionCallOutput, ComputerCallOutput, LocalShellCallOutput]] +): """Represents the output of a tool call.""" - raw_item: FunctionCallOutput | ComputerCallOutput + raw_item: FunctionCallOutput | ComputerCallOutput | LocalShellCallOutput """The raw item from the model.""" output: Any @@ -147,6 +168,36 @@ class ReasoningItem(RunItemBase[ResponseReasoningItem]): type: Literal["reasoning_item"] = "reasoning_item" +@dataclass +class MCPListToolsItem(RunItemBase[McpListTools]): + """Represents a call to an MCP server to list tools.""" + + raw_item: McpListTools + """The raw MCP list tools call.""" + + type: Literal["mcp_list_tools_item"] = "mcp_list_tools_item" + + +@dataclass +class MCPApprovalRequestItem(RunItemBase[McpApprovalRequest]): + """Represents a request for MCP approval.""" + + raw_item: McpApprovalRequest + """The raw MCP approval request.""" + + type: Literal["mcp_approval_request_item"] = "mcp_approval_request_item" + + +@dataclass +class MCPApprovalResponseItem(RunItemBase[McpApprovalResponse]): + """Represents a response to an MCP approval request.""" + + raw_item: McpApprovalResponse + """The raw MCP approval response.""" + + type: Literal["mcp_approval_response_item"] = "mcp_approval_response_item" + + RunItem: TypeAlias = Union[ MessageOutputItem, HandoffCallItem, @@ -154,6 +205,9 @@ class ReasoningItem(RunItemBase[ResponseReasoningItem]): ToolCallItem, ToolCallOutputItem, ReasoningItem, + MCPListToolsItem, + MCPApprovalRequestItem, + MCPApprovalResponseItem, ] """An item generated by an agent.""" diff --git a/src/agents/lifecycle.py b/src/agents/lifecycle.py index 8643248b1..2cce496c8 100644 --- a/src/agents/lifecycle.py +++ b/src/agents/lifecycle.py @@ -1,25 +1,27 @@ from typing import Any, Generic -from .agent import Agent +from typing_extensions import TypeVar + +from .agent import Agent, AgentBase from .run_context import RunContextWrapper, TContext from .tool import Tool +TAgent = TypeVar("TAgent", bound=AgentBase, default=AgentBase) + -class RunHooks(Generic[TContext]): +class RunHooksBase(Generic[TContext, TAgent]): """A class that receives callbacks on various lifecycle events in an agent run. Subclass and override the methods you need. """ - async def on_agent_start( - self, context: RunContextWrapper[TContext], agent: Agent[TContext] - ) -> None: + async def on_agent_start(self, context: RunContextWrapper[TContext], agent: TAgent) -> None: """Called before the agent is invoked. Called each time the current agent changes.""" pass async def on_agent_end( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, output: Any, ) -> None: """Called when the agent produces a final output.""" @@ -28,8 +30,8 @@ async def on_agent_end( async def on_handoff( self, context: RunContextWrapper[TContext], - from_agent: Agent[TContext], - to_agent: Agent[TContext], + from_agent: TAgent, + to_agent: TAgent, ) -> None: """Called when a handoff occurs.""" pass @@ -37,7 +39,7 @@ async def on_handoff( async def on_tool_start( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, tool: Tool, ) -> None: """Called before a tool is invoked.""" @@ -46,7 +48,7 @@ async def on_tool_start( async def on_tool_end( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, tool: Tool, result: str, ) -> None: @@ -54,14 +56,14 @@ async def on_tool_end( pass -class AgentHooks(Generic[TContext]): +class AgentHooksBase(Generic[TContext, TAgent]): """A class that receives callbacks on various lifecycle events for a specific agent. You can set this on `agent.hooks` to receive events for that specific agent. Subclass and override the methods you need. """ - async def on_start(self, context: RunContextWrapper[TContext], agent: Agent[TContext]) -> None: + async def on_start(self, context: RunContextWrapper[TContext], agent: TAgent) -> None: """Called before the agent is invoked. Called each time the running agent is changed to this agent.""" pass @@ -69,7 +71,7 @@ async def on_start(self, context: RunContextWrapper[TContext], agent: Agent[TCon async def on_end( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, output: Any, ) -> None: """Called when the agent produces a final output.""" @@ -78,8 +80,8 @@ async def on_end( async def on_handoff( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], - source: Agent[TContext], + agent: TAgent, + source: TAgent, ) -> None: """Called when the agent is being handed off to. The `source` is the agent that is handing off to this agent.""" @@ -88,7 +90,7 @@ async def on_handoff( async def on_tool_start( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, tool: Tool, ) -> None: """Called before a tool is invoked.""" @@ -97,9 +99,16 @@ async def on_tool_start( async def on_tool_end( self, context: RunContextWrapper[TContext], - agent: Agent[TContext], + agent: TAgent, tool: Tool, result: str, ) -> None: """Called after a tool is invoked.""" pass + + +RunHooks = RunHooksBase[TContext, Agent] +"""Run hooks when using `Agent`.""" + +AgentHooks = AgentHooksBase[TContext, Agent] +"""Agent hooks for `Agent`s.""" diff --git a/src/agents/mcp/__init__.py b/src/agents/mcp/__init__.py index 1a72a89f0..da5a68b16 100644 --- a/src/agents/mcp/__init__.py +++ b/src/agents/mcp/__init__.py @@ -5,11 +5,20 @@ MCPServerSseParams, MCPServerStdio, MCPServerStdioParams, + MCPServerStreamableHttp, + MCPServerStreamableHttpParams, ) except ImportError: pass -from .util import MCPUtil +from .util import ( + MCPUtil, + ToolFilter, + ToolFilterCallable, + ToolFilterContext, + ToolFilterStatic, + create_static_tool_filter, +) __all__ = [ "MCPServer", @@ -17,5 +26,12 @@ "MCPServerSseParams", "MCPServerStdio", "MCPServerStdioParams", + "MCPServerStreamableHttp", + "MCPServerStreamableHttpParams", "MCPUtil", + "ToolFilter", + "ToolFilterCallable", + "ToolFilterContext", + "ToolFilterStatic", + "create_static_tool_filter", ] diff --git a/src/agents/mcp/server.py b/src/agents/mcp/server.py index e70d7ce69..91a9274fc 100644 --- a/src/agents/mcp/server.py +++ b/src/agents/mcp/server.py @@ -2,18 +2,27 @@ import abc import asyncio +import inspect from contextlib import AbstractAsyncContextManager, AsyncExitStack +from datetime import timedelta from pathlib import Path -from typing import Any, Literal +from typing import TYPE_CHECKING, Any, Literal, cast from anyio.streams.memory import MemoryObjectReceiveStream, MemoryObjectSendStream from mcp import ClientSession, StdioServerParameters, Tool as MCPTool, stdio_client from mcp.client.sse import sse_client -from mcp.types import CallToolResult, JSONRPCMessage +from mcp.client.streamable_http import GetSessionIdCallback, streamablehttp_client +from mcp.shared.message import SessionMessage +from mcp.types import CallToolResult, GetPromptResult, InitializeResult, ListPromptsResult from typing_extensions import NotRequired, TypedDict from ..exceptions import UserError from ..logger import logger +from ..run_context import RunContextWrapper +from .util import ToolFilter, ToolFilterCallable, ToolFilterContext, ToolFilterStatic + +if TYPE_CHECKING: + from ..agent import AgentBase class MCPServer(abc.ABC): @@ -41,7 +50,11 @@ async def cleanup(self): pass @abc.abstractmethod - async def list_tools(self) -> list[MCPTool]: + async def list_tools( + self, + run_context: RunContextWrapper[Any] | None = None, + agent: AgentBase | None = None, + ) -> list[MCPTool]: """List the tools available on the server.""" pass @@ -50,11 +63,30 @@ async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> C """Invoke a tool on the server.""" pass + @abc.abstractmethod + async def list_prompts( + self, + ) -> ListPromptsResult: + """List the prompts available on the server.""" + pass + + @abc.abstractmethod + async def get_prompt( + self, name: str, arguments: dict[str, Any] | None = None + ) -> GetPromptResult: + """Get a specific prompt from the server.""" + pass + class _MCPServerWithClientSession(MCPServer, abc.ABC): """Base class for MCP servers that use a `ClientSession` to communicate with the server.""" - def __init__(self, cache_tools_list: bool): + def __init__( + self, + cache_tools_list: bool, + client_session_timeout_seconds: float | None, + tool_filter: ToolFilter = None, + ): """ Args: cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be @@ -63,23 +95,110 @@ def __init__(self, cache_tools_list: bool): by calling `invalidate_tools_cache()`. You should set this to `True` if you know the server will not change its tools list, because it can drastically improve latency (by avoiding a round-trip to the server every time). + + client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + tool_filter: The tool filter to use for filtering tools. """ self.session: ClientSession | None = None self.exit_stack: AsyncExitStack = AsyncExitStack() self._cleanup_lock: asyncio.Lock = asyncio.Lock() self.cache_tools_list = cache_tools_list + self.server_initialize_result: InitializeResult | None = None + + self.client_session_timeout_seconds = client_session_timeout_seconds # The cache is always dirty at startup, so that we fetch tools at least once self._cache_dirty = True self._tools_list: list[MCPTool] | None = None + self.tool_filter = tool_filter + + async def _apply_tool_filter( + self, + tools: list[MCPTool], + run_context: RunContextWrapper[Any], + agent: AgentBase, + ) -> list[MCPTool]: + """Apply the tool filter to the list of tools.""" + if self.tool_filter is None: + return tools + + # Handle static tool filter + if isinstance(self.tool_filter, dict): + return self._apply_static_tool_filter(tools, self.tool_filter) + + # Handle callable tool filter (dynamic filter) + else: + return await self._apply_dynamic_tool_filter(tools, run_context, agent) + + def _apply_static_tool_filter( + self, tools: list[MCPTool], static_filter: ToolFilterStatic + ) -> list[MCPTool]: + """Apply static tool filtering based on allowlist and blocklist.""" + filtered_tools = tools + + # Apply allowed_tool_names filter (whitelist) + if "allowed_tool_names" in static_filter: + allowed_names = static_filter["allowed_tool_names"] + filtered_tools = [t for t in filtered_tools if t.name in allowed_names] + + # Apply blocked_tool_names filter (blacklist) + if "blocked_tool_names" in static_filter: + blocked_names = static_filter["blocked_tool_names"] + filtered_tools = [t for t in filtered_tools if t.name not in blocked_names] + + return filtered_tools + + async def _apply_dynamic_tool_filter( + self, + tools: list[MCPTool], + run_context: RunContextWrapper[Any], + agent: AgentBase, + ) -> list[MCPTool]: + """Apply dynamic tool filtering using a callable filter function.""" + + # Ensure we have a callable filter and cast to help mypy + if not callable(self.tool_filter): + raise ValueError("Tool filter must be callable for dynamic filtering") + tool_filter_func = cast(ToolFilterCallable, self.tool_filter) + + # Create filter context + filter_context = ToolFilterContext( + run_context=run_context, + agent=agent, + server_name=self.name, + ) + + filtered_tools = [] + for tool in tools: + try: + # Call the filter function with context + result = tool_filter_func(filter_context, tool) + + if inspect.isawaitable(result): + should_include = await result + else: + should_include = result + + if should_include: + filtered_tools.append(tool) + except Exception as e: + logger.error( + f"Error applying tool filter to tool '{tool.name}' on server '{self.name}': {e}" + ) + # On error, exclude the tool for safety + continue + + return filtered_tools + @abc.abstractmethod def create_streams( self, ) -> AbstractAsyncContextManager[ tuple[ - MemoryObjectReceiveStream[JSONRPCMessage | Exception], - MemoryObjectSendStream[JSONRPCMessage], + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -100,30 +219,54 @@ async def connect(self): """Connect to the server.""" try: transport = await self.exit_stack.enter_async_context(self.create_streams()) - read, write = transport - session = await self.exit_stack.enter_async_context(ClientSession(read, write)) - await session.initialize() + # streamablehttp_client returns (read, write, get_session_id) + # sse_client returns (read, write) + + read, write, *_ = transport + + session = await self.exit_stack.enter_async_context( + ClientSession( + read, + write, + timedelta(seconds=self.client_session_timeout_seconds) + if self.client_session_timeout_seconds + else None, + ) + ) + server_result = await session.initialize() + self.server_initialize_result = server_result self.session = session except Exception as e: logger.error(f"Error initializing MCP server: {e}") await self.cleanup() raise - async def list_tools(self) -> list[MCPTool]: + async def list_tools( + self, + run_context: RunContextWrapper[Any] | None = None, + agent: AgentBase | None = None, + ) -> list[MCPTool]: """List the tools available on the server.""" if not self.session: raise UserError("Server not initialized. Make sure you call `connect()` first.") # Return from cache if caching is enabled, we have tools, and the cache is not dirty if self.cache_tools_list and not self._cache_dirty and self._tools_list: - return self._tools_list - - # Reset the cache dirty to False - self._cache_dirty = False - - # Fetch the tools from the server - self._tools_list = (await self.session.list_tools()).tools - return self._tools_list + tools = self._tools_list + else: + # Reset the cache dirty to False + self._cache_dirty = False + # Fetch the tools from the server + self._tools_list = (await self.session.list_tools()).tools + tools = self._tools_list + + # Filter tools based on tool_filter + filtered_tools = tools + if self.tool_filter is not None: + if run_context is None or agent is None: + raise UserError("run_context and agent are required for dynamic tool filtering") + filtered_tools = await self._apply_tool_filter(filtered_tools, run_context, agent) + return filtered_tools async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> CallToolResult: """Invoke a tool on the server.""" @@ -132,14 +275,33 @@ async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> C return await self.session.call_tool(tool_name, arguments) + async def list_prompts( + self, + ) -> ListPromptsResult: + """List the prompts available on the server.""" + if not self.session: + raise UserError("Server not initialized. Make sure you call `connect()` first.") + + return await self.session.list_prompts() + + async def get_prompt( + self, name: str, arguments: dict[str, Any] | None = None + ) -> GetPromptResult: + """Get a specific prompt from the server.""" + if not self.session: + raise UserError("Server not initialized. Make sure you call `connect()` first.") + + return await self.session.get_prompt(name, arguments) + async def cleanup(self): """Cleanup the server.""" async with self._cleanup_lock: try: await self.exit_stack.aclose() - self.session = None except Exception as e: logger.error(f"Error cleaning up server: {e}") + finally: + self.session = None class MCPServerStdioParams(TypedDict): @@ -182,6 +344,8 @@ def __init__( params: MCPServerStdioParams, cache_tools_list: bool = False, name: str | None = None, + client_session_timeout_seconds: float | None = 5, + tool_filter: ToolFilter = None, ): """Create a new MCP server based on the stdio transport. @@ -198,8 +362,14 @@ def __init__( improve latency (by avoiding a round-trip to the server every time). name: A readable name for the server. If not provided, we'll create one from the command. + client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + tool_filter: The tool filter to use for filtering tools. """ - super().__init__(cache_tools_list) + super().__init__( + cache_tools_list, + client_session_timeout_seconds, + tool_filter, + ) self.params = StdioServerParameters( command=params["command"], @@ -216,8 +386,9 @@ def create_streams( self, ) -> AbstractAsyncContextManager[ tuple[ - MemoryObjectReceiveStream[JSONRPCMessage | Exception], - MemoryObjectSendStream[JSONRPCMessage], + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -256,6 +427,8 @@ def __init__( params: MCPServerSseParams, cache_tools_list: bool = False, name: str | None = None, + client_session_timeout_seconds: float | None = 5, + tool_filter: ToolFilter = None, ): """Create a new MCP server based on the HTTP with SSE transport. @@ -273,8 +446,15 @@ def __init__( name: A readable name for the server. If not provided, we'll create one from the URL. + + client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + tool_filter: The tool filter to use for filtering tools. """ - super().__init__(cache_tools_list) + super().__init__( + cache_tools_list, + client_session_timeout_seconds, + tool_filter, + ) self.params = params self._name = name or f"sse: {self.params['url']}" @@ -283,8 +463,9 @@ def create_streams( self, ) -> AbstractAsyncContextManager[ tuple[ - MemoryObjectReceiveStream[JSONRPCMessage | Exception], - MemoryObjectSendStream[JSONRPCMessage], + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -299,3 +480,90 @@ def create_streams( def name(self) -> str: """A readable name for the server.""" return self._name + + +class MCPServerStreamableHttpParams(TypedDict): + """Mirrors the params in`mcp.client.streamable_http.streamablehttp_client`.""" + + url: str + """The URL of the server.""" + + headers: NotRequired[dict[str, str]] + """The headers to send to the server.""" + + timeout: NotRequired[timedelta | float] + """The timeout for the HTTP request. Defaults to 5 seconds.""" + + sse_read_timeout: NotRequired[timedelta | float] + """The timeout for the SSE connection, in seconds. Defaults to 5 minutes.""" + + terminate_on_close: NotRequired[bool] + """Terminate on close""" + + +class MCPServerStreamableHttp(_MCPServerWithClientSession): + """MCP server implementation that uses the Streamable HTTP transport. See the [spec] + (https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http) + for details. + """ + + def __init__( + self, + params: MCPServerStreamableHttpParams, + cache_tools_list: bool = False, + name: str | None = None, + client_session_timeout_seconds: float | None = 5, + tool_filter: ToolFilter = None, + ): + """Create a new MCP server based on the Streamable HTTP transport. + + Args: + params: The params that configure the server. This includes the URL of the server, + the headers to send to the server, the timeout for the HTTP request, and the + timeout for the Streamable HTTP connection and whether we need to + terminate on close. + + cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be + cached and only fetched from the server once. If `False`, the tools list will be + fetched from the server on each call to `list_tools()`. The cache can be + invalidated by calling `invalidate_tools_cache()`. You should set this to `True` + if you know the server will not change its tools list, because it can drastically + improve latency (by avoiding a round-trip to the server every time). + + name: A readable name for the server. If not provided, we'll create one from the + URL. + + client_session_timeout_seconds: the read timeout passed to the MCP ClientSession. + tool_filter: The tool filter to use for filtering tools. + """ + super().__init__( + cache_tools_list, + client_session_timeout_seconds, + tool_filter, + ) + + self.params = params + self._name = name or f"streamable_http: {self.params['url']}" + + def create_streams( + self, + ) -> AbstractAsyncContextManager[ + tuple[ + MemoryObjectReceiveStream[SessionMessage | Exception], + MemoryObjectSendStream[SessionMessage], + GetSessionIdCallback | None, + ] + ]: + """Create the streams for the server.""" + return streamablehttp_client( + url=self.params["url"], + headers=self.params.get("headers", None), + timeout=self.params.get("timeout", 5), + sse_read_timeout=self.params.get("sse_read_timeout", 60 * 5), + terminate_on_close=self.params.get("terminate_on_close", True), + ) + + @property + def name(self) -> str: + """A readable name for the server.""" + return self._name diff --git a/src/agents/mcp/util.py b/src/agents/mcp/util.py index bbfe1885c..18cf4440a 100644 --- a/src/agents/mcp/util.py +++ b/src/agents/mcp/util.py @@ -1,34 +1,113 @@ import functools import json -from typing import TYPE_CHECKING, Any +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Callable, Optional, Union -from agents.strict_schema import ensure_strict_json_schema +from typing_extensions import NotRequired, TypedDict from .. import _debug from ..exceptions import AgentsException, ModelBehaviorError, UserError from ..logger import logger from ..run_context import RunContextWrapper +from ..strict_schema import ensure_strict_json_schema from ..tool import FunctionTool, Tool from ..tracing import FunctionSpanData, get_current_span, mcp_tools_span +from ..util._types import MaybeAwaitable if TYPE_CHECKING: from mcp.types import Tool as MCPTool + from ..agent import AgentBase from .server import MCPServer +@dataclass +class ToolFilterContext: + """Context information available to tool filter functions.""" + + run_context: RunContextWrapper[Any] + """The current run context.""" + + agent: "AgentBase" + """The agent that is requesting the tool list.""" + + server_name: str + """The name of the MCP server.""" + + +ToolFilterCallable = Callable[["ToolFilterContext", "MCPTool"], MaybeAwaitable[bool]] +"""A function that determines whether a tool should be available. + +Args: + context: The context information including run context, agent, and server name. + tool: The MCP tool to filter. + +Returns: + Whether the tool should be available (True) or filtered out (False). +""" + + +class ToolFilterStatic(TypedDict): + """Static tool filter configuration using allowlists and blocklists.""" + + allowed_tool_names: NotRequired[list[str]] + """Optional list of tool names to allow (whitelist). + If set, only these tools will be available.""" + + blocked_tool_names: NotRequired[list[str]] + """Optional list of tool names to exclude (blacklist). + If set, these tools will be filtered out.""" + + +ToolFilter = Union[ToolFilterCallable, ToolFilterStatic, None] +"""A tool filter that can be either a function, static configuration, or None (no filtering).""" + + +def create_static_tool_filter( + allowed_tool_names: Optional[list[str]] = None, + blocked_tool_names: Optional[list[str]] = None, +) -> Optional[ToolFilterStatic]: + """Create a static tool filter from allowlist and blocklist parameters. + + This is a convenience function for creating a ToolFilterStatic. + + Args: + allowed_tool_names: Optional list of tool names to allow (whitelist). + blocked_tool_names: Optional list of tool names to exclude (blacklist). + + Returns: + A ToolFilterStatic if any filtering is specified, None otherwise. + """ + if allowed_tool_names is None and blocked_tool_names is None: + return None + + filter_dict: ToolFilterStatic = {} + if allowed_tool_names is not None: + filter_dict["allowed_tool_names"] = allowed_tool_names + if blocked_tool_names is not None: + filter_dict["blocked_tool_names"] = blocked_tool_names + + return filter_dict + + class MCPUtil: """Set of utilities for interop between MCP and Agents SDK tools.""" @classmethod async def get_all_function_tools( - cls, servers: list["MCPServer"], convert_schemas_to_strict: bool + cls, + servers: list["MCPServer"], + convert_schemas_to_strict: bool, + run_context: RunContextWrapper[Any], + agent: "AgentBase", ) -> list[Tool]: """Get all function tools from a list of MCP servers.""" tools = [] tool_names: set[str] = set() for server in servers: - server_tools = await cls.get_function_tools(server, convert_schemas_to_strict) + server_tools = await cls.get_function_tools( + server, convert_schemas_to_strict, run_context, agent + ) server_tool_names = {tool.name for tool in server_tools} if len(server_tool_names & tool_names) > 0: raise UserError( @@ -42,12 +121,16 @@ async def get_all_function_tools( @classmethod async def get_function_tools( - cls, server: "MCPServer", convert_schemas_to_strict: bool + cls, + server: "MCPServer", + convert_schemas_to_strict: bool, + run_context: RunContextWrapper[Any], + agent: "AgentBase", ) -> list[Tool]: """Get all function tools from a single MCP server.""" with mcp_tools_span(server=server.name) as span: - tools = await server.list_tools() + tools = await server.list_tools(run_context, agent) span.span_data.result = [tool.name for tool in tools] return [cls.to_function_tool(tool, server, convert_schemas_to_strict) for tool in tools] @@ -116,7 +199,7 @@ async def invoke_mcp_tool( if len(result.content) == 1: tool_output = result.content[0].model_dump_json() elif len(result.content) > 1: - tool_output = json.dumps([item.model_dump() for item in result.content]) + tool_output = json.dumps([item.model_dump(mode="json") for item in result.content]) else: logger.error(f"Errored MCP tool result: {result}") tool_output = "Error running tool." diff --git a/src/agents/memory/__init__.py b/src/agents/memory/__init__.py new file mode 100644 index 000000000..059ca57ab --- /dev/null +++ b/src/agents/memory/__init__.py @@ -0,0 +1,3 @@ +from .session import Session, SQLiteSession + +__all__ = ["Session", "SQLiteSession"] diff --git a/src/agents/memory/session.py b/src/agents/memory/session.py new file mode 100644 index 000000000..8db0971eb --- /dev/null +++ b/src/agents/memory/session.py @@ -0,0 +1,369 @@ +from __future__ import annotations + +import asyncio +import json +import sqlite3 +import threading +from abc import ABC, abstractmethod +from pathlib import Path +from typing import TYPE_CHECKING, Protocol, runtime_checkable + +if TYPE_CHECKING: + from ..items import TResponseInputItem + + +@runtime_checkable +class Session(Protocol): + """Protocol for session implementations. + + Session stores conversation history for a specific session, allowing + agents to maintain context without requiring explicit manual memory management. + """ + + session_id: str + + async def get_items(self, limit: int | None = None) -> list[TResponseInputItem]: + """Retrieve the conversation history for this session. + + Args: + limit: Maximum number of items to retrieve. If None, retrieves all items. + When specified, returns the latest N items in chronological order. + + Returns: + List of input items representing the conversation history + """ + ... + + async def add_items(self, items: list[TResponseInputItem]) -> None: + """Add new items to the conversation history. + + Args: + items: List of input items to add to the history + """ + ... + + async def pop_item(self) -> TResponseInputItem | None: + """Remove and return the most recent item from the session. + + Returns: + The most recent item if it exists, None if the session is empty + """ + ... + + async def clear_session(self) -> None: + """Clear all items for this session.""" + ... + + +class SessionABC(ABC): + """Abstract base class for session implementations. + + Session stores conversation history for a specific session, allowing + agents to maintain context without requiring explicit manual memory management. + + This ABC is intended for internal use and as a base class for concrete implementations. + Third-party libraries should implement the Session protocol instead. + """ + + session_id: str + + @abstractmethod + async def get_items(self, limit: int | None = None) -> list[TResponseInputItem]: + """Retrieve the conversation history for this session. + + Args: + limit: Maximum number of items to retrieve. If None, retrieves all items. + When specified, returns the latest N items in chronological order. + + Returns: + List of input items representing the conversation history + """ + ... + + @abstractmethod + async def add_items(self, items: list[TResponseInputItem]) -> None: + """Add new items to the conversation history. + + Args: + items: List of input items to add to the history + """ + ... + + @abstractmethod + async def pop_item(self) -> TResponseInputItem | None: + """Remove and return the most recent item from the session. + + Returns: + The most recent item if it exists, None if the session is empty + """ + ... + + @abstractmethod + async def clear_session(self) -> None: + """Clear all items for this session.""" + ... + + +class SQLiteSession(SessionABC): + """SQLite-based implementation of session storage. + + This implementation stores conversation history in a SQLite database. + By default, uses an in-memory database that is lost when the process ends. + For persistent storage, provide a file path. + """ + + def __init__( + self, + session_id: str, + db_path: str | Path = ":memory:", + sessions_table: str = "agent_sessions", + messages_table: str = "agent_messages", + ): + """Initialize the SQLite session. + + Args: + session_id: Unique identifier for the conversation session + db_path: Path to the SQLite database file. Defaults to ':memory:' (in-memory database) + sessions_table: Name of the table to store session metadata. Defaults to + 'agent_sessions' + messages_table: Name of the table to store message data. Defaults to 'agent_messages' + """ + self.session_id = session_id + self.db_path = db_path + self.sessions_table = sessions_table + self.messages_table = messages_table + self._local = threading.local() + self._lock = threading.Lock() + + # For in-memory databases, we need a shared connection to avoid thread isolation + # For file databases, we use thread-local connections for better concurrency + self._is_memory_db = str(db_path) == ":memory:" + if self._is_memory_db: + self._shared_connection = sqlite3.connect(":memory:", check_same_thread=False) + self._shared_connection.execute("PRAGMA journal_mode=WAL") + self._init_db_for_connection(self._shared_connection) + else: + # For file databases, initialize the schema once since it persists + init_conn = sqlite3.connect(str(self.db_path), check_same_thread=False) + init_conn.execute("PRAGMA journal_mode=WAL") + self._init_db_for_connection(init_conn) + init_conn.close() + + def _get_connection(self) -> sqlite3.Connection: + """Get a database connection.""" + if self._is_memory_db: + # Use shared connection for in-memory database to avoid thread isolation + return self._shared_connection + else: + # Use thread-local connections for file databases + if not hasattr(self._local, "connection"): + self._local.connection = sqlite3.connect( + str(self.db_path), + check_same_thread=False, + ) + self._local.connection.execute("PRAGMA journal_mode=WAL") + assert isinstance(self._local.connection, sqlite3.Connection), ( + f"Expected sqlite3.Connection, got {type(self._local.connection)}" + ) + return self._local.connection + + def _init_db_for_connection(self, conn: sqlite3.Connection) -> None: + """Initialize the database schema for a specific connection.""" + conn.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.sessions_table} ( + session_id TEXT PRIMARY KEY, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """ + ) + + conn.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.messages_table} ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + session_id TEXT NOT NULL, + message_data TEXT NOT NULL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (session_id) REFERENCES {self.sessions_table} (session_id) + ON DELETE CASCADE + ) + """ + ) + + conn.execute( + f""" + CREATE INDEX IF NOT EXISTS idx_{self.messages_table}_session_id + ON {self.messages_table} (session_id, created_at) + """ + ) + + conn.commit() + + async def get_items(self, limit: int | None = None) -> list[TResponseInputItem]: + """Retrieve the conversation history for this session. + + Args: + limit: Maximum number of items to retrieve. If None, retrieves all items. + When specified, returns the latest N items in chronological order. + + Returns: + List of input items representing the conversation history + """ + + def _get_items_sync(): + conn = self._get_connection() + with self._lock if self._is_memory_db else threading.Lock(): + if limit is None: + # Fetch all items in chronological order + cursor = conn.execute( + f""" + SELECT message_data FROM {self.messages_table} + WHERE session_id = ? + ORDER BY created_at ASC + """, + (self.session_id,), + ) + else: + # Fetch the latest N items in chronological order + cursor = conn.execute( + f""" + SELECT message_data FROM {self.messages_table} + WHERE session_id = ? + ORDER BY created_at DESC + LIMIT ? + """, + (self.session_id, limit), + ) + + rows = cursor.fetchall() + + # Reverse to get chronological order when using DESC + if limit is not None: + rows = list(reversed(rows)) + + items = [] + for (message_data,) in rows: + try: + item = json.loads(message_data) + items.append(item) + except json.JSONDecodeError: + # Skip invalid JSON entries + continue + + return items + + return await asyncio.to_thread(_get_items_sync) + + async def add_items(self, items: list[TResponseInputItem]) -> None: + """Add new items to the conversation history. + + Args: + items: List of input items to add to the history + """ + if not items: + return + + def _add_items_sync(): + conn = self._get_connection() + + with self._lock if self._is_memory_db else threading.Lock(): + # Ensure session exists + conn.execute( + f""" + INSERT OR IGNORE INTO {self.sessions_table} (session_id) VALUES (?) + """, + (self.session_id,), + ) + + # Add items + message_data = [(self.session_id, json.dumps(item)) for item in items] + conn.executemany( + f""" + INSERT INTO {self.messages_table} (session_id, message_data) VALUES (?, ?) + """, + message_data, + ) + + # Update session timestamp + conn.execute( + f""" + UPDATE {self.sessions_table} + SET updated_at = CURRENT_TIMESTAMP + WHERE session_id = ? + """, + (self.session_id,), + ) + + conn.commit() + + await asyncio.to_thread(_add_items_sync) + + async def pop_item(self) -> TResponseInputItem | None: + """Remove and return the most recent item from the session. + + Returns: + The most recent item if it exists, None if the session is empty + """ + + def _pop_item_sync(): + conn = self._get_connection() + with self._lock if self._is_memory_db else threading.Lock(): + # Use DELETE with RETURNING to atomically delete and return the most recent item + cursor = conn.execute( + f""" + DELETE FROM {self.messages_table} + WHERE id = ( + SELECT id FROM {self.messages_table} + WHERE session_id = ? + ORDER BY created_at DESC + LIMIT 1 + ) + RETURNING message_data + """, + (self.session_id,), + ) + + result = cursor.fetchone() + conn.commit() + + if result: + message_data = result[0] + try: + item = json.loads(message_data) + return item + except json.JSONDecodeError: + # Return None for corrupted JSON entries (already deleted) + return None + + return None + + return await asyncio.to_thread(_pop_item_sync) + + async def clear_session(self) -> None: + """Clear all items for this session.""" + + def _clear_session_sync(): + conn = self._get_connection() + with self._lock if self._is_memory_db else threading.Lock(): + conn.execute( + f"DELETE FROM {self.messages_table} WHERE session_id = ?", + (self.session_id,), + ) + conn.execute( + f"DELETE FROM {self.sessions_table} WHERE session_id = ?", + (self.session_id,), + ) + conn.commit() + + await asyncio.to_thread(_clear_session_sync) + + def close(self) -> None: + """Close the database connection.""" + if self._is_memory_db: + if hasattr(self, "_shared_connection"): + self._shared_connection.close() + else: + if hasattr(self._local, "connection"): + self._local.connection.close() diff --git a/src/agents/model_settings.py b/src/agents/model_settings.py index ed9a01318..1e9edcbc6 100644 --- a/src/agents/model_settings.py +++ b/src/agents/model_settings.py @@ -1,11 +1,55 @@ from __future__ import annotations +import dataclasses +from collections.abc import Mapping from dataclasses import dataclass, fields, replace -from typing import Literal +from typing import Annotated, Any, Literal, Union +from openai import Omit as _Omit from openai._types import Body, Query +from openai.types.responses import ResponseIncludable from openai.types.shared import Reasoning +from pydantic import BaseModel, GetCoreSchemaHandler +from pydantic_core import core_schema +from typing_extensions import TypeAlias + + +class _OmitTypeAnnotation: + @classmethod + def __get_pydantic_core_schema__( + cls, + _source_type: Any, + _handler: GetCoreSchemaHandler, + ) -> core_schema.CoreSchema: + def validate_from_none(value: None) -> _Omit: + return _Omit() + + from_none_schema = core_schema.chain_schema( + [ + core_schema.none_schema(), + core_schema.no_info_plain_validator_function(validate_from_none), + ] + ) + return core_schema.json_or_python_schema( + json_schema=from_none_schema, + python_schema=core_schema.union_schema( + [ + # check if it's an instance first before doing any further work + core_schema.is_instance_schema(_Omit), + from_none_schema, + ] + ), + serialization=core_schema.plain_serializer_function_ser_schema(lambda instance: None), + ) +@dataclass +class MCPToolChoice: + server_label: str + name: str + +Omit = Annotated[_Omit, _OmitTypeAnnotation] +Headers: TypeAlias = Mapping[str, Union[str, Omit]] +ToolChoice: TypeAlias = Union[Literal["auto", "required", "none"], str, MCPToolChoice, None] @dataclass class ModelSettings: @@ -30,12 +74,17 @@ class ModelSettings: presence_penalty: float | None = None """The presence penalty to use when calling the model.""" - tool_choice: Literal["auto", "required", "none"] | str | None = None + tool_choice: ToolChoice | None = None """The tool choice to use when calling the model.""" parallel_tool_calls: bool | None = None - """Whether to use parallel tool calls when calling the model. - Defaults to False if not provided.""" + """Controls whether the model can make multiple parallel tool calls in a single turn. + If not provided (i.e., set to None), this behavior defers to the underlying + model provider's default. For most current providers (e.g., OpenAI), this typically + means parallel tool calls are enabled (True). + Set to True to explicitly enable parallel tool calls, or False to restrict the + model to at most one tool call per turn. + """ truncation: Literal["auto", "disabled"] | None = None """The truncation strategy to use when calling the model.""" @@ -59,6 +108,10 @@ class ModelSettings: """Whether to include usage chunk. Defaults to True if not provided.""" + response_include: list[ResponseIncludable] | None = None + """Additional output data to include in the model response. + [include parameter](https://platform.openai.com/docs/api-reference/responses/create#responses-create-include)""" + extra_query: Query | None = None """Additional query fields to provide with the request. Defaults to None if not provided.""" @@ -67,6 +120,15 @@ class ModelSettings: """Additional body fields to provide with the request. Defaults to None if not provided.""" + extra_headers: Headers | None = None + """Additional headers to provide with the request. + Defaults to None if not provided.""" + + extra_args: dict[str, Any] | None = None + """Arbitrary keyword arguments to pass to the model API call. + These will be passed directly to the underlying model provider's API. + Use with caution as not all models support all parameters.""" + def resolve(self, override: ModelSettings | None) -> ModelSettings: """Produce a new ModelSettings by overlaying any non-None values from the override on top of this instance.""" @@ -78,4 +140,27 @@ def resolve(self, override: ModelSettings | None) -> ModelSettings: for field in fields(self) if getattr(override, field.name) is not None } + + # Handle extra_args merging specially - merge dictionaries instead of replacing + if self.extra_args is not None or override.extra_args is not None: + merged_args = {} + if self.extra_args: + merged_args.update(self.extra_args) + if override.extra_args: + merged_args.update(override.extra_args) + changes["extra_args"] = merged_args if merged_args else None + return replace(self, **changes) + + def to_json_dict(self) -> dict[str, Any]: + dataclass_dict = dataclasses.asdict(self) + + json_dict: dict[str, Any] = {} + + for field_name, value in dataclass_dict.items(): + if isinstance(value, BaseModel): + json_dict[field_name] = value.model_dump(mode="json") + else: + json_dict[field_name] = value + + return json_dict diff --git a/src/agents/models/chatcmpl_converter.py b/src/agents/models/chatcmpl_converter.py new file mode 100644 index 000000000..d3c71c24e --- /dev/null +++ b/src/agents/models/chatcmpl_converter.py @@ -0,0 +1,495 @@ +from __future__ import annotations + +import json +from collections.abc import Iterable +from typing import Any, Literal, cast + +from openai import NOT_GIVEN, NotGiven +from openai.types.chat import ( + ChatCompletionAssistantMessageParam, + ChatCompletionContentPartImageParam, + ChatCompletionContentPartParam, + ChatCompletionContentPartTextParam, + ChatCompletionDeveloperMessageParam, + ChatCompletionMessage, + ChatCompletionMessageParam, + ChatCompletionMessageToolCallParam, + ChatCompletionSystemMessageParam, + ChatCompletionToolChoiceOptionParam, + ChatCompletionToolMessageParam, + ChatCompletionUserMessageParam, +) +from openai.types.chat.chat_completion_content_part_param import File, FileFile +from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam +from openai.types.chat.completion_create_params import ResponseFormat +from openai.types.responses import ( + EasyInputMessageParam, + ResponseFileSearchToolCallParam, + ResponseFunctionToolCall, + ResponseFunctionToolCallParam, + ResponseInputContentParam, + ResponseInputFileParam, + ResponseInputImageParam, + ResponseInputTextParam, + ResponseOutputMessage, + ResponseOutputMessageParam, + ResponseOutputRefusal, + ResponseOutputText, + ResponseReasoningItem, +) +from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message +from openai.types.responses.response_reasoning_item import Summary + +from ..agent_output import AgentOutputSchemaBase +from ..exceptions import AgentsException, UserError +from ..handoffs import Handoff +from ..items import TResponseInputItem, TResponseOutputItem +from ..model_settings import MCPToolChoice +from ..tool import FunctionTool, Tool +from .fake_id import FAKE_RESPONSES_ID + + +class Converter: + @classmethod + def convert_tool_choice( + cls, tool_choice: Literal["auto", "required", "none"] | str | MCPToolChoice | None + ) -> ChatCompletionToolChoiceOptionParam | NotGiven: + if tool_choice is None: + return NOT_GIVEN + elif isinstance(tool_choice, MCPToolChoice): + raise UserError("MCPToolChoice is not supported for Chat Completions models") + elif tool_choice == "auto": + return "auto" + elif tool_choice == "required": + return "required" + elif tool_choice == "none": + return "none" + else: + return { + "type": "function", + "function": { + "name": tool_choice, + }, + } + + @classmethod + def convert_response_format( + cls, final_output_schema: AgentOutputSchemaBase | None + ) -> ResponseFormat | NotGiven: + if not final_output_schema or final_output_schema.is_plain_text(): + return NOT_GIVEN + + return { + "type": "json_schema", + "json_schema": { + "name": "final_output", + "strict": final_output_schema.is_strict_json_schema(), + "schema": final_output_schema.json_schema(), + }, + } + + @classmethod + def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]: + items: list[TResponseOutputItem] = [] + + # Handle reasoning content if available + if hasattr(message, "reasoning_content") and message.reasoning_content: + items.append( + ResponseReasoningItem( + id=FAKE_RESPONSES_ID, + summary=[Summary(text=message.reasoning_content, type="summary_text")], + type="reasoning", + ) + ) + + message_item = ResponseOutputMessage( + id=FAKE_RESPONSES_ID, + content=[], + role="assistant", + type="message", + status="completed", + ) + if message.content: + message_item.content.append( + ResponseOutputText(text=message.content, type="output_text", annotations=[]) + ) + if message.refusal: + message_item.content.append( + ResponseOutputRefusal(refusal=message.refusal, type="refusal") + ) + if message.audio: + raise AgentsException("Audio is not currently supported") + + if message_item.content: + items.append(message_item) + + if message.tool_calls: + for tool_call in message.tool_calls: + items.append( + ResponseFunctionToolCall( + id=FAKE_RESPONSES_ID, + call_id=tool_call.id, + arguments=tool_call.function.arguments, + name=tool_call.function.name, + type="function_call", + ) + ) + + return items + + @classmethod + def maybe_easy_input_message(cls, item: Any) -> EasyInputMessageParam | None: + if not isinstance(item, dict): + return None + + keys = item.keys() + # EasyInputMessageParam only has these two keys + if keys != {"content", "role"}: + return None + + role = item.get("role", None) + if role not in ("user", "assistant", "system", "developer"): + return None + + if "content" not in item: + return None + + return cast(EasyInputMessageParam, item) + + @classmethod + def maybe_input_message(cls, item: Any) -> Message | None: + if ( + isinstance(item, dict) + and item.get("type") == "message" + and item.get("role") + in ( + "user", + "system", + "developer", + ) + ): + return cast(Message, item) + + return None + + @classmethod + def maybe_file_search_call(cls, item: Any) -> ResponseFileSearchToolCallParam | None: + if isinstance(item, dict) and item.get("type") == "file_search_call": + return cast(ResponseFileSearchToolCallParam, item) + return None + + @classmethod + def maybe_function_tool_call(cls, item: Any) -> ResponseFunctionToolCallParam | None: + if isinstance(item, dict) and item.get("type") == "function_call": + return cast(ResponseFunctionToolCallParam, item) + return None + + @classmethod + def maybe_function_tool_call_output( + cls, + item: Any, + ) -> FunctionCallOutput | None: + if isinstance(item, dict) and item.get("type") == "function_call_output": + return cast(FunctionCallOutput, item) + return None + + @classmethod + def maybe_item_reference(cls, item: Any) -> ItemReference | None: + if isinstance(item, dict) and item.get("type") == "item_reference": + return cast(ItemReference, item) + return None + + @classmethod + def maybe_response_output_message(cls, item: Any) -> ResponseOutputMessageParam | None: + # ResponseOutputMessage is only used for messages with role assistant + if ( + isinstance(item, dict) + and item.get("type") == "message" + and item.get("role") == "assistant" + ): + return cast(ResponseOutputMessageParam, item) + return None + + @classmethod + def extract_text_content( + cls, content: str | Iterable[ResponseInputContentParam] + ) -> str | list[ChatCompletionContentPartTextParam]: + all_content = cls.extract_all_content(content) + if isinstance(all_content, str): + return all_content + out: list[ChatCompletionContentPartTextParam] = [] + for c in all_content: + if c.get("type") == "text": + out.append(cast(ChatCompletionContentPartTextParam, c)) + return out + + @classmethod + def extract_all_content( + cls, content: str | Iterable[ResponseInputContentParam] + ) -> str | list[ChatCompletionContentPartParam]: + if isinstance(content, str): + return content + out: list[ChatCompletionContentPartParam] = [] + + for c in content: + if isinstance(c, dict) and c.get("type") == "input_text": + casted_text_param = cast(ResponseInputTextParam, c) + out.append( + ChatCompletionContentPartTextParam( + type="text", + text=casted_text_param["text"], + ) + ) + elif isinstance(c, dict) and c.get("type") == "input_image": + casted_image_param = cast(ResponseInputImageParam, c) + if "image_url" not in casted_image_param or not casted_image_param["image_url"]: + raise UserError( + f"Only image URLs are supported for input_image {casted_image_param}" + ) + out.append( + ChatCompletionContentPartImageParam( + type="image_url", + image_url={ + "url": casted_image_param["image_url"], + "detail": casted_image_param.get("detail", "auto"), + }, + ) + ) + elif isinstance(c, dict) and c.get("type") == "input_file": + casted_file_param = cast(ResponseInputFileParam, c) + if "file_data" not in casted_file_param or not casted_file_param["file_data"]: + raise UserError( + f"Only file_data is supported for input_file {casted_file_param}" + ) + out.append( + File( + type="file", + file=FileFile( + file_data=casted_file_param["file_data"], + ), + ) + ) + else: + raise UserError(f"Unknown content: {c}") + return out + + @classmethod + def items_to_messages( + cls, + items: str | Iterable[TResponseInputItem], + ) -> list[ChatCompletionMessageParam]: + """ + Convert a sequence of 'Item' objects into a list of ChatCompletionMessageParam. + + Rules: + - EasyInputMessage or InputMessage (role=user) => ChatCompletionUserMessageParam + - EasyInputMessage or InputMessage (role=system) => ChatCompletionSystemMessageParam + - EasyInputMessage or InputMessage (role=developer) => ChatCompletionDeveloperMessageParam + - InputMessage (role=assistant) => Start or flush a ChatCompletionAssistantMessageParam + - response_output_message => Also produces/flushes a ChatCompletionAssistantMessageParam + - tool calls get attached to the *current* assistant message, or create one if none. + - tool outputs => ChatCompletionToolMessageParam + """ + + if isinstance(items, str): + return [ + ChatCompletionUserMessageParam( + role="user", + content=items, + ) + ] + + result: list[ChatCompletionMessageParam] = [] + current_assistant_msg: ChatCompletionAssistantMessageParam | None = None + + def flush_assistant_message() -> None: + nonlocal current_assistant_msg + if current_assistant_msg is not None: + # The API doesn't support empty arrays for tool_calls + if not current_assistant_msg.get("tool_calls"): + del current_assistant_msg["tool_calls"] + result.append(current_assistant_msg) + current_assistant_msg = None + + def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: + nonlocal current_assistant_msg + if current_assistant_msg is None: + current_assistant_msg = ChatCompletionAssistantMessageParam(role="assistant") + current_assistant_msg["tool_calls"] = [] + return current_assistant_msg + + for item in items: + # 1) Check easy input message + if easy_msg := cls.maybe_easy_input_message(item): + role = easy_msg["role"] + content = easy_msg["content"] + + if role == "user": + flush_assistant_message() + msg_user: ChatCompletionUserMessageParam = { + "role": "user", + "content": cls.extract_all_content(content), + } + result.append(msg_user) + elif role == "system": + flush_assistant_message() + msg_system: ChatCompletionSystemMessageParam = { + "role": "system", + "content": cls.extract_text_content(content), + } + result.append(msg_system) + elif role == "developer": + flush_assistant_message() + msg_developer: ChatCompletionDeveloperMessageParam = { + "role": "developer", + "content": cls.extract_text_content(content), + } + result.append(msg_developer) + elif role == "assistant": + flush_assistant_message() + msg_assistant: ChatCompletionAssistantMessageParam = { + "role": "assistant", + "content": cls.extract_text_content(content), + } + result.append(msg_assistant) + else: + raise UserError(f"Unexpected role in easy_input_message: {role}") + + # 2) Check input message + elif in_msg := cls.maybe_input_message(item): + role = in_msg["role"] + content = in_msg["content"] + flush_assistant_message() + + if role == "user": + msg_user = { + "role": "user", + "content": cls.extract_all_content(content), + } + result.append(msg_user) + elif role == "system": + msg_system = { + "role": "system", + "content": cls.extract_text_content(content), + } + result.append(msg_system) + elif role == "developer": + msg_developer = { + "role": "developer", + "content": cls.extract_text_content(content), + } + result.append(msg_developer) + else: + raise UserError(f"Unexpected role in input_message: {role}") + + # 3) response output message => assistant + elif resp_msg := cls.maybe_response_output_message(item): + flush_assistant_message() + new_asst = ChatCompletionAssistantMessageParam(role="assistant") + contents = resp_msg["content"] + + text_segments = [] + for c in contents: + if c["type"] == "output_text": + text_segments.append(c["text"]) + elif c["type"] == "refusal": + new_asst["refusal"] = c["refusal"] + elif c["type"] == "output_audio": + # Can't handle this, b/c chat completions expects an ID which we dont have + raise UserError( + f"Only audio IDs are supported for chat completions, but got: {c}" + ) + else: + raise UserError(f"Unknown content type in ResponseOutputMessage: {c}") + + if text_segments: + combined = "\n".join(text_segments) + new_asst["content"] = combined + + new_asst["tool_calls"] = [] + current_assistant_msg = new_asst + + # 4) function/file-search calls => attach to assistant + elif file_search := cls.maybe_file_search_call(item): + asst = ensure_assistant_message() + tool_calls = list(asst.get("tool_calls", [])) + new_tool_call = ChatCompletionMessageToolCallParam( + id=file_search["id"], + type="function", + function={ + "name": "file_search_call", + "arguments": json.dumps( + { + "queries": file_search.get("queries", []), + "status": file_search.get("status"), + } + ), + }, + ) + tool_calls.append(new_tool_call) + asst["tool_calls"] = tool_calls + + elif func_call := cls.maybe_function_tool_call(item): + asst = ensure_assistant_message() + tool_calls = list(asst.get("tool_calls", [])) + arguments = func_call["arguments"] if func_call["arguments"] else "{}" + new_tool_call = ChatCompletionMessageToolCallParam( + id=func_call["call_id"], + type="function", + function={ + "name": func_call["name"], + "arguments": arguments, + }, + ) + tool_calls.append(new_tool_call) + asst["tool_calls"] = tool_calls + # 5) function call output => tool message + elif func_output := cls.maybe_function_tool_call_output(item): + flush_assistant_message() + msg: ChatCompletionToolMessageParam = { + "role": "tool", + "tool_call_id": func_output["call_id"], + "content": func_output["output"], + } + result.append(msg) + + # 6) item reference => handle or raise + elif item_ref := cls.maybe_item_reference(item): + raise UserError( + f"Encountered an item_reference, which is not supported: {item_ref}" + ) + + # 7) If we haven't recognized it => fail or ignore + else: + raise UserError(f"Unhandled item type or structure: {item}") + + flush_assistant_message() + return result + + @classmethod + def tool_to_openai(cls, tool: Tool) -> ChatCompletionToolParam: + if isinstance(tool, FunctionTool): + return { + "type": "function", + "function": { + "name": tool.name, + "description": tool.description or "", + "parameters": tool.params_json_schema, + }, + } + + raise UserError( + f"Hosted tools are not supported with the ChatCompletions API. Got tool type: " + f"{type(tool)}, tool: {tool}" + ) + + @classmethod + def convert_handoff_tool(cls, handoff: Handoff[Any]) -> ChatCompletionToolParam: + return { + "type": "function", + "function": { + "name": handoff.tool_name, + "description": handoff.tool_description, + "parameters": handoff.input_json_schema, + }, + } diff --git a/src/agents/models/chatcmpl_helpers.py b/src/agents/models/chatcmpl_helpers.py new file mode 100644 index 000000000..0cee21ecc --- /dev/null +++ b/src/agents/models/chatcmpl_helpers.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from openai import AsyncOpenAI + +from ..model_settings import ModelSettings +from ..version import __version__ + +_USER_AGENT = f"Agents/Python {__version__}" +HEADERS = {"User-Agent": _USER_AGENT} + + +class ChatCmplHelpers: + @classmethod + def is_openai(cls, client: AsyncOpenAI): + return str(client.base_url).startswith("https://api.openai.com") + + @classmethod + def get_store_param(cls, client: AsyncOpenAI, model_settings: ModelSettings) -> bool | None: + # Match the behavior of Responses where store is True when not given + default_store = True if cls.is_openai(client) else None + return model_settings.store if model_settings.store is not None else default_store + + @classmethod + def get_stream_options_param( + cls, client: AsyncOpenAI, model_settings: ModelSettings, stream: bool + ) -> dict[str, bool] | None: + if not stream: + return None + + default_include_usage = True if cls.is_openai(client) else None + include_usage = ( + model_settings.include_usage + if model_settings.include_usage is not None + else default_include_usage + ) + stream_options = {"include_usage": include_usage} if include_usage is not None else None + return stream_options diff --git a/src/agents/models/chatcmpl_stream_handler.py b/src/agents/models/chatcmpl_stream_handler.py new file mode 100644 index 000000000..83fa32abc --- /dev/null +++ b/src/agents/models/chatcmpl_stream_handler.py @@ -0,0 +1,428 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator +from dataclasses import dataclass, field + +from openai import AsyncStream +from openai.types.chat import ChatCompletionChunk +from openai.types.completion_usage import CompletionUsage +from openai.types.responses import ( + Response, + ResponseCompletedEvent, + ResponseContentPartAddedEvent, + ResponseContentPartDoneEvent, + ResponseCreatedEvent, + ResponseFunctionCallArgumentsDeltaEvent, + ResponseFunctionToolCall, + ResponseOutputItem, + ResponseOutputItemAddedEvent, + ResponseOutputItemDoneEvent, + ResponseOutputMessage, + ResponseOutputRefusal, + ResponseOutputText, + ResponseReasoningItem, + ResponseReasoningSummaryPartAddedEvent, + ResponseReasoningSummaryPartDoneEvent, + ResponseReasoningSummaryTextDeltaEvent, + ResponseRefusalDeltaEvent, + ResponseTextDeltaEvent, + ResponseUsage, +) +from openai.types.responses.response_reasoning_item import Summary +from openai.types.responses.response_reasoning_summary_part_added_event import ( + Part as AddedEventPart, +) +from openai.types.responses.response_reasoning_summary_part_done_event import Part as DoneEventPart +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails + +from ..items import TResponseStreamEvent +from .fake_id import FAKE_RESPONSES_ID + + +# Define a Part class for internal use +class Part: + def __init__(self, text: str, type: str): + self.text = text + self.type = type + + +@dataclass +class StreamingState: + started: bool = False + text_content_index_and_output: tuple[int, ResponseOutputText] | None = None + refusal_content_index_and_output: tuple[int, ResponseOutputRefusal] | None = None + reasoning_content_index_and_output: tuple[int, ResponseReasoningItem] | None = None + function_calls: dict[int, ResponseFunctionToolCall] = field(default_factory=dict) + + +class SequenceNumber: + def __init__(self): + self._sequence_number = 0 + + def get_and_increment(self) -> int: + num = self._sequence_number + self._sequence_number += 1 + return num + + +class ChatCmplStreamHandler: + @classmethod + async def handle_stream( + cls, + response: Response, + stream: AsyncStream[ChatCompletionChunk], + ) -> AsyncIterator[TResponseStreamEvent]: + usage: CompletionUsage | None = None + state = StreamingState() + sequence_number = SequenceNumber() + async for chunk in stream: + if not state.started: + state.started = True + yield ResponseCreatedEvent( + response=response, + type="response.created", + sequence_number=sequence_number.get_and_increment(), + ) + + # This is always set by the OpenAI API, but not by others e.g. LiteLLM + usage = chunk.usage if hasattr(chunk, "usage") else None + + if not chunk.choices or not chunk.choices[0].delta: + continue + + delta = chunk.choices[0].delta + + # Handle reasoning content + if hasattr(delta, "reasoning_content"): + reasoning_content = delta.reasoning_content + if reasoning_content and not state.reasoning_content_index_and_output: + state.reasoning_content_index_and_output = ( + 0, + ResponseReasoningItem( + id=FAKE_RESPONSES_ID, + summary=[Summary(text="", type="summary_text")], + type="reasoning", + ), + ) + yield ResponseOutputItemAddedEvent( + item=ResponseReasoningItem( + id=FAKE_RESPONSES_ID, + summary=[Summary(text="", type="summary_text")], + type="reasoning", + ), + output_index=0, + type="response.output_item.added", + sequence_number=sequence_number.get_and_increment(), + ) + + yield ResponseReasoningSummaryPartAddedEvent( + item_id=FAKE_RESPONSES_ID, + output_index=0, + summary_index=0, + part=AddedEventPart(text="", type="summary_text"), + type="response.reasoning_summary_part.added", + sequence_number=sequence_number.get_and_increment(), + ) + + if reasoning_content and state.reasoning_content_index_and_output: + yield ResponseReasoningSummaryTextDeltaEvent( + delta=reasoning_content, + item_id=FAKE_RESPONSES_ID, + output_index=0, + summary_index=0, + type="response.reasoning_summary_text.delta", + sequence_number=sequence_number.get_and_increment(), + ) + + # Create a new summary with updated text + current_summary = state.reasoning_content_index_and_output[1].summary[0] + updated_text = current_summary.text + reasoning_content + new_summary = Summary(text=updated_text, type="summary_text") + state.reasoning_content_index_and_output[1].summary[0] = new_summary + + # Handle regular content + if delta.content is not None: + if not state.text_content_index_and_output: + content_index = 0 + if state.reasoning_content_index_and_output: + content_index += 1 + if state.refusal_content_index_and_output: + content_index += 1 + + state.text_content_index_and_output = ( + content_index, + ResponseOutputText( + text="", + type="output_text", + annotations=[], + ), + ) + # Start a new assistant message stream + assistant_item = ResponseOutputMessage( + id=FAKE_RESPONSES_ID, + content=[], + role="assistant", + type="message", + status="in_progress", + ) + # Notify consumers of the start of a new output message + first content part + yield ResponseOutputItemAddedEvent( + item=assistant_item, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 + type="response.output_item.added", + sequence_number=sequence_number.get_and_increment(), + ) + yield ResponseContentPartAddedEvent( + content_index=state.text_content_index_and_output[0], + item_id=FAKE_RESPONSES_ID, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 + part=ResponseOutputText( + text="", + type="output_text", + annotations=[], + ), + type="response.content_part.added", + sequence_number=sequence_number.get_and_increment(), + ) + # Emit the delta for this segment of content + yield ResponseTextDeltaEvent( + content_index=state.text_content_index_and_output[0], + delta=delta.content, + item_id=FAKE_RESPONSES_ID, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 + type="response.output_text.delta", + sequence_number=sequence_number.get_and_increment(), + ) + # Accumulate the text into the response part + state.text_content_index_and_output[1].text += delta.content + + # Handle refusals (model declines to answer) + # This is always set by the OpenAI API, but not by others e.g. LiteLLM + if hasattr(delta, "refusal") and delta.refusal: + if not state.refusal_content_index_and_output: + refusal_index = 0 + if state.reasoning_content_index_and_output: + refusal_index += 1 + if state.text_content_index_and_output: + refusal_index += 1 + + state.refusal_content_index_and_output = ( + refusal_index, + ResponseOutputRefusal(refusal="", type="refusal"), + ) + # Start a new assistant message if one doesn't exist yet (in-progress) + assistant_item = ResponseOutputMessage( + id=FAKE_RESPONSES_ID, + content=[], + role="assistant", + type="message", + status="in_progress", + ) + # Notify downstream that assistant message + first content part are starting + yield ResponseOutputItemAddedEvent( + item=assistant_item, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 + type="response.output_item.added", + sequence_number=sequence_number.get_and_increment(), + ) + yield ResponseContentPartAddedEvent( + content_index=state.refusal_content_index_and_output[0], + item_id=FAKE_RESPONSES_ID, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 + part=ResponseOutputText( + text="", + type="output_text", + annotations=[], + ), + type="response.content_part.added", + sequence_number=sequence_number.get_and_increment(), + ) + # Emit the delta for this segment of refusal + yield ResponseRefusalDeltaEvent( + content_index=state.refusal_content_index_and_output[0], + delta=delta.refusal, + item_id=FAKE_RESPONSES_ID, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 + type="response.refusal.delta", + sequence_number=sequence_number.get_and_increment(), + ) + # Accumulate the refusal string in the output part + state.refusal_content_index_and_output[1].refusal += delta.refusal + + # Handle tool calls + # Because we don't know the name of the function until the end of the stream, we'll + # save everything and yield events at the end + if delta.tool_calls: + for tc_delta in delta.tool_calls: + if tc_delta.index not in state.function_calls: + state.function_calls[tc_delta.index] = ResponseFunctionToolCall( + id=FAKE_RESPONSES_ID, + arguments="", + name="", + type="function_call", + call_id="", + ) + tc_function = tc_delta.function + + state.function_calls[tc_delta.index].arguments += ( + tc_function.arguments if tc_function else "" + ) or "" + state.function_calls[tc_delta.index].name += ( + tc_function.name if tc_function else "" + ) or "" + state.function_calls[tc_delta.index].call_id = tc_delta.id or "" + + if state.reasoning_content_index_and_output: + yield ResponseReasoningSummaryPartDoneEvent( + item_id=FAKE_RESPONSES_ID, + output_index=0, + summary_index=0, + part=DoneEventPart( + text=state.reasoning_content_index_and_output[1].summary[0].text, + type="summary_text", + ), + type="response.reasoning_summary_part.done", + sequence_number=sequence_number.get_and_increment(), + ) + yield ResponseOutputItemDoneEvent( + item=state.reasoning_content_index_and_output[1], + output_index=0, + type="response.output_item.done", + sequence_number=sequence_number.get_and_increment(), + ) + + function_call_starting_index = 0 + if state.reasoning_content_index_and_output: + function_call_starting_index += 1 + + if state.text_content_index_and_output: + function_call_starting_index += 1 + # Send end event for this content part + yield ResponseContentPartDoneEvent( + content_index=state.text_content_index_and_output[0], + item_id=FAKE_RESPONSES_ID, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 + part=state.text_content_index_and_output[1], + type="response.content_part.done", + sequence_number=sequence_number.get_and_increment(), + ) + + if state.refusal_content_index_and_output: + function_call_starting_index += 1 + # Send end event for this content part + yield ResponseContentPartDoneEvent( + content_index=state.refusal_content_index_and_output[0], + item_id=FAKE_RESPONSES_ID, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 + part=state.refusal_content_index_and_output[1], + type="response.content_part.done", + sequence_number=sequence_number.get_and_increment(), + ) + + # Actually send events for the function calls + for function_call in state.function_calls.values(): + # First, a ResponseOutputItemAdded for the function call + yield ResponseOutputItemAddedEvent( + item=ResponseFunctionToolCall( + id=FAKE_RESPONSES_ID, + call_id=function_call.call_id, + arguments=function_call.arguments, + name=function_call.name, + type="function_call", + ), + output_index=function_call_starting_index, + type="response.output_item.added", + sequence_number=sequence_number.get_and_increment(), + ) + # Then, yield the args + yield ResponseFunctionCallArgumentsDeltaEvent( + delta=function_call.arguments, + item_id=FAKE_RESPONSES_ID, + output_index=function_call_starting_index, + type="response.function_call_arguments.delta", + sequence_number=sequence_number.get_and_increment(), + ) + # Finally, the ResponseOutputItemDone + yield ResponseOutputItemDoneEvent( + item=ResponseFunctionToolCall( + id=FAKE_RESPONSES_ID, + call_id=function_call.call_id, + arguments=function_call.arguments, + name=function_call.name, + type="function_call", + ), + output_index=function_call_starting_index, + type="response.output_item.done", + sequence_number=sequence_number.get_and_increment(), + ) + + # Finally, send the Response completed event + outputs: list[ResponseOutputItem] = [] + + # include Reasoning item if it exists + if state.reasoning_content_index_and_output: + outputs.append(state.reasoning_content_index_and_output[1]) + + # include text or refusal content if they exist + if state.text_content_index_and_output or state.refusal_content_index_and_output: + assistant_msg = ResponseOutputMessage( + id=FAKE_RESPONSES_ID, + content=[], + role="assistant", + type="message", + status="completed", + ) + if state.text_content_index_and_output: + assistant_msg.content.append(state.text_content_index_and_output[1]) + if state.refusal_content_index_and_output: + assistant_msg.content.append(state.refusal_content_index_and_output[1]) + outputs.append(assistant_msg) + + # send a ResponseOutputItemDone for the assistant message + yield ResponseOutputItemDoneEvent( + item=assistant_msg, + output_index=state.reasoning_content_index_and_output + is not None, # fixed 0 -> 0 or 1 + type="response.output_item.done", + sequence_number=sequence_number.get_and_increment(), + ) + + for function_call in state.function_calls.values(): + outputs.append(function_call) + + final_response = response.model_copy() + final_response.output = outputs + final_response.usage = ( + ResponseUsage( + input_tokens=usage.prompt_tokens, + output_tokens=usage.completion_tokens, + total_tokens=usage.total_tokens, + output_tokens_details=OutputTokensDetails( + reasoning_tokens=usage.completion_tokens_details.reasoning_tokens + if usage.completion_tokens_details + and usage.completion_tokens_details.reasoning_tokens + else 0 + ), + input_tokens_details=InputTokensDetails( + cached_tokens=usage.prompt_tokens_details.cached_tokens + if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens + else 0 + ), + ) + if usage + else None + ) + + yield ResponseCompletedEvent( + response=final_response, + type="response.completed", + sequence_number=sequence_number.get_and_increment(), + ) diff --git a/src/agents/models/interface.py b/src/agents/models/interface.py index bcf2c1a65..5a185806c 100644 --- a/src/agents/models/interface.py +++ b/src/agents/models/interface.py @@ -5,7 +5,9 @@ from collections.abc import AsyncIterator from typing import TYPE_CHECKING -from ..agent_output import AgentOutputSchema +from openai.types.responses.response_prompt_param import ResponsePromptParam + +from ..agent_output import AgentOutputSchemaBase from ..handoffs import Handoff from ..items import ModelResponse, TResponseInputItem, TResponseStreamEvent from ..tool import Tool @@ -41,11 +43,12 @@ async def get_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, *, previous_response_id: str | None, + prompt: ResponsePromptParam | None, ) -> ModelResponse: """Get a response from the model. @@ -59,6 +62,7 @@ async def get_response( tracing: Tracing configuration. previous_response_id: the ID of the previous response. Generally not used by the model, except for the OpenAI Responses API. + prompt: The prompt config to use for the model. Returns: The full model response. @@ -72,11 +76,12 @@ def stream_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, *, previous_response_id: str | None, + prompt: ResponsePromptParam | None, ) -> AsyncIterator[TResponseStreamEvent]: """Stream a response from the model. @@ -90,6 +95,7 @@ def stream_response( tracing: Tracing configuration. previous_response_id: the ID of the previous response. Generally not used by the model, except for the OpenAI Responses API. + prompt: The prompt config to use for the model. Returns: An iterator of response stream events, in OpenAI Responses format. diff --git a/src/agents/models/multi_provider.py b/src/agents/models/multi_provider.py new file mode 100644 index 000000000..d075ac9b6 --- /dev/null +++ b/src/agents/models/multi_provider.py @@ -0,0 +1,144 @@ +from __future__ import annotations + +from openai import AsyncOpenAI + +from ..exceptions import UserError +from .interface import Model, ModelProvider +from .openai_provider import OpenAIProvider + + +class MultiProviderMap: + """A map of model name prefixes to ModelProviders.""" + + def __init__(self): + self._mapping: dict[str, ModelProvider] = {} + + def has_prefix(self, prefix: str) -> bool: + """Returns True if the given prefix is in the mapping.""" + return prefix in self._mapping + + def get_mapping(self) -> dict[str, ModelProvider]: + """Returns a copy of the current prefix -> ModelProvider mapping.""" + return self._mapping.copy() + + def set_mapping(self, mapping: dict[str, ModelProvider]): + """Overwrites the current mapping with a new one.""" + self._mapping = mapping + + def get_provider(self, prefix: str) -> ModelProvider | None: + """Returns the ModelProvider for the given prefix. + + Args: + prefix: The prefix of the model name e.g. "openai" or "my_prefix". + """ + return self._mapping.get(prefix) + + def add_provider(self, prefix: str, provider: ModelProvider): + """Adds a new prefix -> ModelProvider mapping. + + Args: + prefix: The prefix of the model name e.g. "openai" or "my_prefix". + provider: The ModelProvider to use for the given prefix. + """ + self._mapping[prefix] = provider + + def remove_provider(self, prefix: str): + """Removes the mapping for the given prefix. + + Args: + prefix: The prefix of the model name e.g. "openai" or "my_prefix". + """ + del self._mapping[prefix] + + +class MultiProvider(ModelProvider): + """This ModelProvider maps to a Model based on the prefix of the model name. By default, the + mapping is: + - "openai/" prefix or no prefix -> OpenAIProvider. e.g. "openai/gpt-4.1", "gpt-4.1" + - "litellm/" prefix -> LitellmProvider. e.g. "litellm/openai/gpt-4.1" + + You can override or customize this mapping. + """ + + def __init__( + self, + *, + provider_map: MultiProviderMap | None = None, + openai_api_key: str | None = None, + openai_base_url: str | None = None, + openai_client: AsyncOpenAI | None = None, + openai_organization: str | None = None, + openai_project: str | None = None, + openai_use_responses: bool | None = None, + ) -> None: + """Create a new OpenAI provider. + + Args: + provider_map: A MultiProviderMap that maps prefixes to ModelProviders. If not provided, + we will use a default mapping. See the documentation for this class to see the + default mapping. + openai_api_key: The API key to use for the OpenAI provider. If not provided, we will use + the default API key. + openai_base_url: The base URL to use for the OpenAI provider. If not provided, we will + use the default base URL. + openai_client: An optional OpenAI client to use. If not provided, we will create a new + OpenAI client using the api_key and base_url. + openai_organization: The organization to use for the OpenAI provider. + openai_project: The project to use for the OpenAI provider. + openai_use_responses: Whether to use the OpenAI responses API. + """ + self.provider_map = provider_map + self.openai_provider = OpenAIProvider( + api_key=openai_api_key, + base_url=openai_base_url, + openai_client=openai_client, + organization=openai_organization, + project=openai_project, + use_responses=openai_use_responses, + ) + + self._fallback_providers: dict[str, ModelProvider] = {} + + def _get_prefix_and_model_name(self, model_name: str | None) -> tuple[str | None, str | None]: + if model_name is None: + return None, None + elif "/" in model_name: + prefix, model_name = model_name.split("/", 1) + return prefix, model_name + else: + return None, model_name + + def _create_fallback_provider(self, prefix: str) -> ModelProvider: + if prefix == "litellm": + from ..extensions.models.litellm_provider import LitellmProvider + + return LitellmProvider() + else: + raise UserError(f"Unknown prefix: {prefix}") + + def _get_fallback_provider(self, prefix: str | None) -> ModelProvider: + if prefix is None or prefix == "openai": + return self.openai_provider + elif prefix in self._fallback_providers: + return self._fallback_providers[prefix] + else: + self._fallback_providers[prefix] = self._create_fallback_provider(prefix) + return self._fallback_providers[prefix] + + def get_model(self, model_name: str | None) -> Model: + """Returns a Model based on the model name. The model name can have a prefix, ending with + a "/", which will be used to look up the ModelProvider. If there is no prefix, we will use + the OpenAI provider. + + Args: + model_name: The name of the model to get. + + Returns: + A Model. + """ + prefix, model_name = self._get_prefix_and_model_name(model_name) + + if prefix and self.provider_map and (provider := self.provider_map.get_provider(prefix)): + return provider.get_model(model_name) + else: + return self._get_fallback_provider(prefix).get_model(model_name) diff --git a/src/agents/models/openai_chatcompletions.py b/src/agents/models/openai_chatcompletions.py index a4f93cb71..6de431b4d 100644 --- a/src/agents/models/openai_chatcompletions.py +++ b/src/agents/models/openai_chatcompletions.py @@ -1,73 +1,31 @@ from __future__ import annotations -import dataclasses import json import time -from collections.abc import AsyncIterator, Iterable -from dataclasses import dataclass, field +from collections.abc import AsyncIterator from typing import TYPE_CHECKING, Any, Literal, cast, overload -from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream, NotGiven +from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream from openai.types import ChatModel -from openai.types.chat import ( - ChatCompletion, - ChatCompletionAssistantMessageParam, - ChatCompletionChunk, - ChatCompletionContentPartImageParam, - ChatCompletionContentPartParam, - ChatCompletionContentPartTextParam, - ChatCompletionDeveloperMessageParam, - ChatCompletionMessage, - ChatCompletionMessageParam, - ChatCompletionMessageToolCallParam, - ChatCompletionSystemMessageParam, - ChatCompletionToolChoiceOptionParam, - ChatCompletionToolMessageParam, - ChatCompletionUserMessageParam, -) -from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam -from openai.types.chat.completion_create_params import ResponseFormat -from openai.types.completion_usage import CompletionUsage -from openai.types.responses import ( - EasyInputMessageParam, - Response, - ResponseCompletedEvent, - ResponseContentPartAddedEvent, - ResponseContentPartDoneEvent, - ResponseCreatedEvent, - ResponseFileSearchToolCallParam, - ResponseFunctionCallArgumentsDeltaEvent, - ResponseFunctionToolCall, - ResponseFunctionToolCallParam, - ResponseInputContentParam, - ResponseInputImageParam, - ResponseInputTextParam, - ResponseOutputItem, - ResponseOutputItemAddedEvent, - ResponseOutputItemDoneEvent, - ResponseOutputMessage, - ResponseOutputMessageParam, - ResponseOutputRefusal, - ResponseOutputText, - ResponseRefusalDeltaEvent, - ResponseTextDeltaEvent, - ResponseUsage, -) -from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message +from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessage +from openai.types.chat.chat_completion import Choice +from openai.types.responses import Response +from openai.types.responses.response_prompt_param import ResponsePromptParam from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails from .. import _debug -from ..agent_output import AgentOutputSchema -from ..exceptions import AgentsException, UserError +from ..agent_output import AgentOutputSchemaBase from ..handoffs import Handoff -from ..items import ModelResponse, TResponseInputItem, TResponseOutputItem, TResponseStreamEvent +from ..items import ModelResponse, TResponseInputItem, TResponseStreamEvent from ..logger import logger -from ..tool import FunctionTool, Tool +from ..tool import Tool from ..tracing import generation_span from ..tracing.span_data import GenerationSpanData from ..tracing.spans import Span from ..usage import Usage -from ..version import __version__ +from .chatcmpl_converter import Converter +from .chatcmpl_helpers import HEADERS, ChatCmplHelpers +from .chatcmpl_stream_handler import ChatCmplStreamHandler from .fake_id import FAKE_RESPONSES_ID from .interface import Model, ModelTracing @@ -75,18 +33,6 @@ from ..model_settings import ModelSettings -_USER_AGENT = f"Agents/Python {__version__}" -_HEADERS = {"User-Agent": _USER_AGENT} - - -@dataclass -class _StreamingState: - started: bool = False - text_content_index_and_output: tuple[int, ResponseOutputText] | None = None - refusal_content_index_and_output: tuple[int, ResponseOutputRefusal] | None = None - function_calls: dict[int, ResponseFunctionToolCall] = field(default_factory=dict) - - class OpenAIChatCompletionsModel(Model): def __init__( self, @@ -105,15 +51,15 @@ async def get_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, previous_response_id: str | None, + prompt: ResponsePromptParam | None = None, ) -> ModelResponse: with generation_span( model=str(self.model), - model_config=dataclasses.asdict(model_settings) - | {"base_url": str(self._client.base_url)}, + model_config=model_settings.to_json_dict() | {"base_url": str(self._client.base_url)}, disabled=tracing.is_disabled(), ) as span_generation: response = await self._fetch_response( @@ -126,14 +72,26 @@ async def get_response( span_generation, tracing, stream=False, + prompt=prompt, ) + message: ChatCompletionMessage | None = None + first_choice: Choice | None = None + if response.choices and len(response.choices) > 0: + first_choice = response.choices[0] + message = first_choice.message + if _debug.DONT_LOG_MODEL_DATA: logger.debug("Received model response") else: - logger.debug( - f"LLM resp:\n{json.dumps(response.choices[0].message.model_dump(), indent=2)}\n" - ) + if message is not None: + logger.debug( + "LLM resp:\n%s\n", + json.dumps(message.model_dump(), indent=2, ensure_ascii=False), + ) + else: + finish_reason = first_choice.finish_reason if first_choice else "-" + logger.debug(f"LLM resp had no message. finish_reason: {finish_reason}") usage = ( Usage( @@ -141,18 +99,32 @@ async def get_response( input_tokens=response.usage.prompt_tokens, output_tokens=response.usage.completion_tokens, total_tokens=response.usage.total_tokens, + input_tokens_details=InputTokensDetails( + cached_tokens=getattr( + response.usage.prompt_tokens_details, "cached_tokens", 0 + ) + or 0, + ), + output_tokens_details=OutputTokensDetails( + reasoning_tokens=getattr( + response.usage.completion_tokens_details, "reasoning_tokens", 0 + ) + or 0, + ), ) if response.usage else Usage() ) if tracing.include_data(): - span_generation.span_data.output = [response.choices[0].message.model_dump()] + span_generation.span_data.output = ( + [message.model_dump()] if message is not None else [] + ) span_generation.span_data.usage = { "input_tokens": usage.input_tokens, "output_tokens": usage.output_tokens, } - items = _Converter.message_to_output_items(response.choices[0].message) + items = Converter.message_to_output_items(message) if message is not None else [] return ModelResponse( output=items, @@ -166,19 +138,18 @@ async def stream_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, - *, previous_response_id: str | None, + prompt: ResponsePromptParam | None = None, ) -> AsyncIterator[TResponseStreamEvent]: """ Yields a partial message as it is generated, as well as the usage information. """ with generation_span( model=str(self.model), - model_config=dataclasses.asdict(model_settings) - | {"base_url": str(self._client.base_url)}, + model_config=model_settings.to_json_dict() | {"base_url": str(self._client.base_url)}, disabled=tracing.is_disabled(), ) as span_generation: response, stream = await self._fetch_response( @@ -191,259 +162,23 @@ async def stream_response( span_generation, tracing, stream=True, + prompt=prompt, ) - usage: CompletionUsage | None = None - state = _StreamingState() - - async for chunk in stream: - if not state.started: - state.started = True - yield ResponseCreatedEvent( - response=response, - type="response.created", - ) - - # The usage is only available in the last chunk - usage = chunk.usage - - if not chunk.choices or not chunk.choices[0].delta: - continue - - delta = chunk.choices[0].delta - - # Handle text - if delta.content: - if not state.text_content_index_and_output: - # Initialize a content tracker for streaming text - state.text_content_index_and_output = ( - 0 if not state.refusal_content_index_and_output else 1, - ResponseOutputText( - text="", - type="output_text", - annotations=[], - ), - ) - # Start a new assistant message stream - assistant_item = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="in_progress", - ) - # Notify consumers of the start of a new output message + first content part - yield ResponseOutputItemAddedEvent( - item=assistant_item, - output_index=0, - type="response.output_item.added", - ) - yield ResponseContentPartAddedEvent( - content_index=state.text_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=ResponseOutputText( - text="", - type="output_text", - annotations=[], - ), - type="response.content_part.added", - ) - # Emit the delta for this segment of content - yield ResponseTextDeltaEvent( - content_index=state.text_content_index_and_output[0], - delta=delta.content, - item_id=FAKE_RESPONSES_ID, - output_index=0, - type="response.output_text.delta", - ) - # Accumulate the text into the response part - state.text_content_index_and_output[1].text += delta.content - - # Handle refusals (model declines to answer) - if delta.refusal: - if not state.refusal_content_index_and_output: - # Initialize a content tracker for streaming refusal text - state.refusal_content_index_and_output = ( - 0 if not state.text_content_index_and_output else 1, - ResponseOutputRefusal(refusal="", type="refusal"), - ) - # Start a new assistant message if one doesn't exist yet (in-progress) - assistant_item = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="in_progress", - ) - # Notify downstream that assistant message + first content part are starting - yield ResponseOutputItemAddedEvent( - item=assistant_item, - output_index=0, - type="response.output_item.added", - ) - yield ResponseContentPartAddedEvent( - content_index=state.refusal_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=ResponseOutputText( - text="", - type="output_text", - annotations=[], - ), - type="response.content_part.added", - ) - # Emit the delta for this segment of refusal - yield ResponseRefusalDeltaEvent( - content_index=state.refusal_content_index_and_output[0], - delta=delta.refusal, - item_id=FAKE_RESPONSES_ID, - output_index=0, - type="response.refusal.delta", - ) - # Accumulate the refusal string in the output part - state.refusal_content_index_and_output[1].refusal += delta.refusal - - # Handle tool calls - # Because we don't know the name of the function until the end of the stream, we'll - # save everything and yield events at the end - if delta.tool_calls: - for tc_delta in delta.tool_calls: - if tc_delta.index not in state.function_calls: - state.function_calls[tc_delta.index] = ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - arguments="", - name="", - type="function_call", - call_id="", - ) - tc_function = tc_delta.function - - state.function_calls[tc_delta.index].arguments += ( - tc_function.arguments if tc_function else "" - ) or "" - state.function_calls[tc_delta.index].name += ( - tc_function.name if tc_function else "" - ) or "" - state.function_calls[tc_delta.index].call_id += tc_delta.id or "" - - function_call_starting_index = 0 - if state.text_content_index_and_output: - function_call_starting_index += 1 - # Send end event for this content part - yield ResponseContentPartDoneEvent( - content_index=state.text_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=state.text_content_index_and_output[1], - type="response.content_part.done", - ) - - if state.refusal_content_index_and_output: - function_call_starting_index += 1 - # Send end event for this content part - yield ResponseContentPartDoneEvent( - content_index=state.refusal_content_index_and_output[0], - item_id=FAKE_RESPONSES_ID, - output_index=0, - part=state.refusal_content_index_and_output[1], - type="response.content_part.done", - ) - - # Actually send events for the function calls - for function_call in state.function_calls.values(): - # First, a ResponseOutputItemAdded for the function call - yield ResponseOutputItemAddedEvent( - item=ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - call_id=function_call.call_id, - arguments=function_call.arguments, - name=function_call.name, - type="function_call", - ), - output_index=function_call_starting_index, - type="response.output_item.added", - ) - # Then, yield the args - yield ResponseFunctionCallArgumentsDeltaEvent( - delta=function_call.arguments, - item_id=FAKE_RESPONSES_ID, - output_index=function_call_starting_index, - type="response.function_call_arguments.delta", - ) - # Finally, the ResponseOutputItemDone - yield ResponseOutputItemDoneEvent( - item=ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - call_id=function_call.call_id, - arguments=function_call.arguments, - name=function_call.name, - type="function_call", - ), - output_index=function_call_starting_index, - type="response.output_item.done", - ) + final_response: Response | None = None + async for chunk in ChatCmplStreamHandler.handle_stream(response, stream): + yield chunk - # Finally, send the Response completed event - outputs: list[ResponseOutputItem] = [] - if state.text_content_index_and_output or state.refusal_content_index_and_output: - assistant_msg = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="completed", - ) - if state.text_content_index_and_output: - assistant_msg.content.append(state.text_content_index_and_output[1]) - if state.refusal_content_index_and_output: - assistant_msg.content.append(state.refusal_content_index_and_output[1]) - outputs.append(assistant_msg) - - # send a ResponseOutputItemDone for the assistant message - yield ResponseOutputItemDoneEvent( - item=assistant_msg, - output_index=0, - type="response.output_item.done", - ) + if chunk.type == "response.completed": + final_response = chunk.response - for function_call in state.function_calls.values(): - outputs.append(function_call) - - final_response = response.model_copy() - final_response.output = outputs - final_response.usage = ( - ResponseUsage( - input_tokens=usage.prompt_tokens, - output_tokens=usage.completion_tokens, - total_tokens=usage.total_tokens, - output_tokens_details=OutputTokensDetails( - reasoning_tokens=usage.completion_tokens_details.reasoning_tokens - if usage.completion_tokens_details - and usage.completion_tokens_details.reasoning_tokens - else 0 - ), - input_tokens_details=InputTokensDetails( - cached_tokens=usage.prompt_tokens_details.cached_tokens - if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens - else 0 - ), - ) - if usage - else None - ) - - yield ResponseCompletedEvent( - response=final_response, - type="response.completed", - ) - if tracing.include_data(): + if tracing.include_data() and final_response: span_generation.span_data.output = [final_response.model_dump()] - if usage: + if final_response and final_response.usage: span_generation.span_data.usage = { - "input_tokens": usage.prompt_tokens, - "output_tokens": usage.completion_tokens, + "input_tokens": final_response.usage.input_tokens, + "output_tokens": final_response.usage.output_tokens, } @overload @@ -453,11 +188,12 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], span: Span[GenerationSpanData], tracing: ModelTracing, stream: Literal[True], + prompt: ResponsePromptParam | None = None, ) -> tuple[Response, AsyncStream[ChatCompletionChunk]]: ... @overload @@ -467,11 +203,12 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], span: Span[GenerationSpanData], tracing: ModelTracing, stream: Literal[False], + prompt: ResponsePromptParam | None = None, ) -> ChatCompletion: ... async def _fetch_response( @@ -480,13 +217,14 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], span: Span[GenerationSpanData], tracing: ModelTracing, stream: bool = False, + prompt: ResponsePromptParam | None = None, ) -> ChatCompletion | tuple[Response, AsyncStream[ChatCompletionChunk]]: - converted_messages = _Converter.items_to_messages(input) + converted_messages = Converter.items_to_messages(input) if system_instructions: converted_messages.insert( @@ -506,29 +244,29 @@ async def _fetch_response( if model_settings.parallel_tool_calls is False else NOT_GIVEN ) - tool_choice = _Converter.convert_tool_choice(model_settings.tool_choice) - response_format = _Converter.convert_response_format(output_schema) + tool_choice = Converter.convert_tool_choice(model_settings.tool_choice) + response_format = Converter.convert_response_format(output_schema) - converted_tools = [ToolConverter.to_openai(tool) for tool in tools] if tools else [] + converted_tools = [Converter.tool_to_openai(tool) for tool in tools] if tools else [] for handoff in handoffs: - converted_tools.append(ToolConverter.convert_handoff_tool(handoff)) + converted_tools.append(Converter.convert_handoff_tool(handoff)) if _debug.DONT_LOG_MODEL_DATA: logger.debug("Calling LLM") else: logger.debug( - f"{json.dumps(converted_messages, indent=2)}\n" - f"Tools:\n{json.dumps(converted_tools, indent=2)}\n" + f"{json.dumps(converted_messages, indent=2, ensure_ascii=False)}\n" + f"Tools:\n{json.dumps(converted_tools, indent=2, ensure_ascii=False)}\n" f"Stream: {stream}\n" f"Tool choice: {tool_choice}\n" f"Response format: {response_format}\n" ) reasoning_effort = model_settings.reasoning.effort if model_settings.reasoning else None - store = _Converter.get_store_param(self._get_client(), model_settings) + store = ChatCmplHelpers.get_store_param(self._get_client(), model_settings) - stream_options = _Converter.get_stream_options_param( + stream_options = ChatCmplHelpers.get_stream_options_param( self._get_client(), model_settings, stream=stream ) @@ -548,10 +286,11 @@ async def _fetch_response( stream_options=self._non_null_or_not_given(stream_options), store=self._non_null_or_not_given(store), reasoning_effort=self._non_null_or_not_given(reasoning_effort), - extra_headers=_HEADERS, + extra_headers={**HEADERS, **(model_settings.extra_headers or {})}, extra_query=model_settings.extra_query, extra_body=model_settings.extra_body, metadata=self._non_null_or_not_given(model_settings.metadata), + **(model_settings.extra_args or {}), ) if isinstance(ret, ChatCompletion): @@ -578,453 +317,3 @@ def _get_client(self) -> AsyncOpenAI: if self._client is None: self._client = AsyncOpenAI() return self._client - - -class _Converter: - @classmethod - def is_openai(cls, client: AsyncOpenAI): - return str(client.base_url).startswith("https://api.openai.com") - - @classmethod - def get_store_param(cls, client: AsyncOpenAI, model_settings: ModelSettings) -> bool | None: - # Match the behavior of Responses where store is True when not given - default_store = True if cls.is_openai(client) else None - return model_settings.store if model_settings.store is not None else default_store - - @classmethod - def get_stream_options_param( - cls, client: AsyncOpenAI, model_settings: ModelSettings, stream: bool - ) -> dict[str, bool] | None: - if not stream: - return None - - default_include_usage = True if cls.is_openai(client) else None - include_usage = ( - model_settings.include_usage - if model_settings.include_usage is not None - else default_include_usage - ) - stream_options = {"include_usage": include_usage} if include_usage is not None else None - return stream_options - - @classmethod - def convert_tool_choice( - cls, tool_choice: Literal["auto", "required", "none"] | str | None - ) -> ChatCompletionToolChoiceOptionParam | NotGiven: - if tool_choice is None: - return NOT_GIVEN - elif tool_choice == "auto": - return "auto" - elif tool_choice == "required": - return "required" - elif tool_choice == "none": - return "none" - else: - return { - "type": "function", - "function": { - "name": tool_choice, - }, - } - - @classmethod - def convert_response_format( - cls, final_output_schema: AgentOutputSchema | None - ) -> ResponseFormat | NotGiven: - if not final_output_schema or final_output_schema.is_plain_text(): - return NOT_GIVEN - - return { - "type": "json_schema", - "json_schema": { - "name": "final_output", - "strict": final_output_schema.strict_json_schema, - "schema": final_output_schema.json_schema(), - }, - } - - @classmethod - def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]: - items: list[TResponseOutputItem] = [] - - message_item = ResponseOutputMessage( - id=FAKE_RESPONSES_ID, - content=[], - role="assistant", - type="message", - status="completed", - ) - if message.content: - message_item.content.append( - ResponseOutputText(text=message.content, type="output_text", annotations=[]) - ) - if message.refusal: - message_item.content.append( - ResponseOutputRefusal(refusal=message.refusal, type="refusal") - ) - if message.audio: - raise AgentsException("Audio is not currently supported") - - if message_item.content: - items.append(message_item) - - if message.tool_calls: - for tool_call in message.tool_calls: - items.append( - ResponseFunctionToolCall( - id=FAKE_RESPONSES_ID, - call_id=tool_call.id, - arguments=tool_call.function.arguments, - name=tool_call.function.name, - type="function_call", - ) - ) - - return items - - @classmethod - def maybe_easy_input_message(cls, item: Any) -> EasyInputMessageParam | None: - if not isinstance(item, dict): - return None - - keys = item.keys() - # EasyInputMessageParam only has these two keys - if keys != {"content", "role"}: - return None - - role = item.get("role", None) - if role not in ("user", "assistant", "system", "developer"): - return None - - if "content" not in item: - return None - - return cast(EasyInputMessageParam, item) - - @classmethod - def maybe_input_message(cls, item: Any) -> Message | None: - if ( - isinstance(item, dict) - and item.get("type") == "message" - and item.get("role") - in ( - "user", - "system", - "developer", - ) - ): - return cast(Message, item) - - return None - - @classmethod - def maybe_file_search_call(cls, item: Any) -> ResponseFileSearchToolCallParam | None: - if isinstance(item, dict) and item.get("type") == "file_search_call": - return cast(ResponseFileSearchToolCallParam, item) - return None - - @classmethod - def maybe_function_tool_call(cls, item: Any) -> ResponseFunctionToolCallParam | None: - if isinstance(item, dict) and item.get("type") == "function_call": - return cast(ResponseFunctionToolCallParam, item) - return None - - @classmethod - def maybe_function_tool_call_output( - cls, - item: Any, - ) -> FunctionCallOutput | None: - if isinstance(item, dict) and item.get("type") == "function_call_output": - return cast(FunctionCallOutput, item) - return None - - @classmethod - def maybe_item_reference(cls, item: Any) -> ItemReference | None: - if isinstance(item, dict) and item.get("type") == "item_reference": - return cast(ItemReference, item) - return None - - @classmethod - def maybe_response_output_message(cls, item: Any) -> ResponseOutputMessageParam | None: - # ResponseOutputMessage is only used for messages with role assistant - if ( - isinstance(item, dict) - and item.get("type") == "message" - and item.get("role") == "assistant" - ): - return cast(ResponseOutputMessageParam, item) - return None - - @classmethod - def extract_text_content( - cls, content: str | Iterable[ResponseInputContentParam] - ) -> str | list[ChatCompletionContentPartTextParam]: - all_content = cls.extract_all_content(content) - if isinstance(all_content, str): - return all_content - out: list[ChatCompletionContentPartTextParam] = [] - for c in all_content: - if c.get("type") == "text": - out.append(cast(ChatCompletionContentPartTextParam, c)) - return out - - @classmethod - def extract_all_content( - cls, content: str | Iterable[ResponseInputContentParam] - ) -> str | list[ChatCompletionContentPartParam]: - if isinstance(content, str): - return content - out: list[ChatCompletionContentPartParam] = [] - - for c in content: - if isinstance(c, dict) and c.get("type") == "input_text": - casted_text_param = cast(ResponseInputTextParam, c) - out.append( - ChatCompletionContentPartTextParam( - type="text", - text=casted_text_param["text"], - ) - ) - elif isinstance(c, dict) and c.get("type") == "input_image": - casted_image_param = cast(ResponseInputImageParam, c) - if "image_url" not in casted_image_param or not casted_image_param["image_url"]: - raise UserError( - f"Only image URLs are supported for input_image {casted_image_param}" - ) - out.append( - ChatCompletionContentPartImageParam( - type="image_url", - image_url={ - "url": casted_image_param["image_url"], - "detail": casted_image_param["detail"], - }, - ) - ) - elif isinstance(c, dict) and c.get("type") == "input_file": - raise UserError(f"File uploads are not supported for chat completions {c}") - else: - raise UserError(f"Unknown content: {c}") - return out - - @classmethod - def items_to_messages( - cls, - items: str | Iterable[TResponseInputItem], - ) -> list[ChatCompletionMessageParam]: - """ - Convert a sequence of 'Item' objects into a list of ChatCompletionMessageParam. - - Rules: - - EasyInputMessage or InputMessage (role=user) => ChatCompletionUserMessageParam - - EasyInputMessage or InputMessage (role=system) => ChatCompletionSystemMessageParam - - EasyInputMessage or InputMessage (role=developer) => ChatCompletionDeveloperMessageParam - - InputMessage (role=assistant) => Start or flush a ChatCompletionAssistantMessageParam - - response_output_message => Also produces/flushes a ChatCompletionAssistantMessageParam - - tool calls get attached to the *current* assistant message, or create one if none. - - tool outputs => ChatCompletionToolMessageParam - """ - - if isinstance(items, str): - return [ - ChatCompletionUserMessageParam( - role="user", - content=items, - ) - ] - - result: list[ChatCompletionMessageParam] = [] - current_assistant_msg: ChatCompletionAssistantMessageParam | None = None - - def flush_assistant_message() -> None: - nonlocal current_assistant_msg - if current_assistant_msg is not None: - # The API doesn't support empty arrays for tool_calls - if not current_assistant_msg.get("tool_calls"): - del current_assistant_msg["tool_calls"] - result.append(current_assistant_msg) - current_assistant_msg = None - - def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: - nonlocal current_assistant_msg - if current_assistant_msg is None: - current_assistant_msg = ChatCompletionAssistantMessageParam(role="assistant") - current_assistant_msg["tool_calls"] = [] - return current_assistant_msg - - for item in items: - # 1) Check easy input message - if easy_msg := cls.maybe_easy_input_message(item): - role = easy_msg["role"] - content = easy_msg["content"] - - if role == "user": - flush_assistant_message() - msg_user: ChatCompletionUserMessageParam = { - "role": "user", - "content": cls.extract_all_content(content), - } - result.append(msg_user) - elif role == "system": - flush_assistant_message() - msg_system: ChatCompletionSystemMessageParam = { - "role": "system", - "content": cls.extract_text_content(content), - } - result.append(msg_system) - elif role == "developer": - flush_assistant_message() - msg_developer: ChatCompletionDeveloperMessageParam = { - "role": "developer", - "content": cls.extract_text_content(content), - } - result.append(msg_developer) - elif role == "assistant": - flush_assistant_message() - msg_assistant: ChatCompletionAssistantMessageParam = { - "role": "assistant", - "content": cls.extract_text_content(content), - } - result.append(msg_assistant) - else: - raise UserError(f"Unexpected role in easy_input_message: {role}") - - # 2) Check input message - elif in_msg := cls.maybe_input_message(item): - role = in_msg["role"] - content = in_msg["content"] - flush_assistant_message() - - if role == "user": - msg_user = { - "role": "user", - "content": cls.extract_all_content(content), - } - result.append(msg_user) - elif role == "system": - msg_system = { - "role": "system", - "content": cls.extract_text_content(content), - } - result.append(msg_system) - elif role == "developer": - msg_developer = { - "role": "developer", - "content": cls.extract_text_content(content), - } - result.append(msg_developer) - else: - raise UserError(f"Unexpected role in input_message: {role}") - - # 3) response output message => assistant - elif resp_msg := cls.maybe_response_output_message(item): - flush_assistant_message() - new_asst = ChatCompletionAssistantMessageParam(role="assistant") - contents = resp_msg["content"] - - text_segments = [] - for c in contents: - if c["type"] == "output_text": - text_segments.append(c["text"]) - elif c["type"] == "refusal": - new_asst["refusal"] = c["refusal"] - elif c["type"] == "output_audio": - # Can't handle this, b/c chat completions expects an ID which we dont have - raise UserError( - f"Only audio IDs are supported for chat completions, but got: {c}" - ) - else: - raise UserError(f"Unknown content type in ResponseOutputMessage: {c}") - - if text_segments: - combined = "\n".join(text_segments) - new_asst["content"] = combined - - new_asst["tool_calls"] = [] - current_assistant_msg = new_asst - - # 4) function/file-search calls => attach to assistant - elif file_search := cls.maybe_file_search_call(item): - asst = ensure_assistant_message() - tool_calls = list(asst.get("tool_calls", [])) - new_tool_call = ChatCompletionMessageToolCallParam( - id=file_search["id"], - type="function", - function={ - "name": "file_search_call", - "arguments": json.dumps( - { - "queries": file_search.get("queries", []), - "status": file_search.get("status"), - } - ), - }, - ) - tool_calls.append(new_tool_call) - asst["tool_calls"] = tool_calls - - elif func_call := cls.maybe_function_tool_call(item): - asst = ensure_assistant_message() - tool_calls = list(asst.get("tool_calls", [])) - arguments = func_call["arguments"] if func_call["arguments"] else "{}" - new_tool_call = ChatCompletionMessageToolCallParam( - id=func_call["call_id"], - type="function", - function={ - "name": func_call["name"], - "arguments": arguments, - }, - ) - tool_calls.append(new_tool_call) - asst["tool_calls"] = tool_calls - # 5) function call output => tool message - elif func_output := cls.maybe_function_tool_call_output(item): - flush_assistant_message() - msg: ChatCompletionToolMessageParam = { - "role": "tool", - "tool_call_id": func_output["call_id"], - "content": func_output["output"], - } - result.append(msg) - - # 6) item reference => handle or raise - elif item_ref := cls.maybe_item_reference(item): - raise UserError( - f"Encountered an item_reference, which is not supported: {item_ref}" - ) - - # 7) If we haven't recognized it => fail or ignore - else: - raise UserError(f"Unhandled item type or structure: {item}") - - flush_assistant_message() - return result - - -class ToolConverter: - @classmethod - def to_openai(cls, tool: Tool) -> ChatCompletionToolParam: - if isinstance(tool, FunctionTool): - return { - "type": "function", - "function": { - "name": tool.name, - "description": tool.description or "", - "parameters": tool.params_json_schema, - }, - } - - raise UserError( - f"Hosted tools are not supported with the ChatCompletions API. Got tool type: " - f"{type(tool)}, tool: {tool}" - ) - - @classmethod - def convert_handoff_tool(cls, handoff: Handoff[Any]) -> ChatCompletionToolParam: - return { - "type": "function", - "function": { - "name": handoff.tool_name, - "description": handoff.tool_description, - "parameters": handoff.input_json_schema, - }, - } diff --git a/src/agents/models/openai_responses.py b/src/agents/models/openai_responses.py index ab4617d46..d25613aee 100644 --- a/src/agents/models/openai_responses.py +++ b/src/agents/models/openai_responses.py @@ -10,20 +10,33 @@ from openai.types.responses import ( Response, ResponseCompletedEvent, + ResponseIncludable, ResponseStreamEvent, ResponseTextConfigParam, ToolParam, WebSearchToolParam, response_create_params, ) +from openai.types.responses.response_prompt_param import ResponsePromptParam from .. import _debug -from ..agent_output import AgentOutputSchema +from ..agent_output import AgentOutputSchemaBase from ..exceptions import UserError from ..handoffs import Handoff from ..items import ItemHelpers, ModelResponse, TResponseInputItem from ..logger import logger -from ..tool import ComputerTool, FileSearchTool, FunctionTool, Tool, WebSearchTool +from ..model_settings import MCPToolChoice +from ..tool import ( + CodeInterpreterTool, + ComputerTool, + FileSearchTool, + FunctionTool, + HostedMCPTool, + ImageGenerationTool, + LocalShellTool, + Tool, + WebSearchTool, +) from ..tracing import SpanError, response_span from ..usage import Usage from ..version import __version__ @@ -36,13 +49,6 @@ _USER_AGENT = f"Agents/Python {__version__}" _HEADERS = {"User-Agent": _USER_AGENT} -# From the Responses API -IncludeLiteral = Literal[ - "file_search_call.results", - "message.input_image.image_url", - "computer_call_output.output.image_url", -] - class OpenAIResponsesModel(Model): """ @@ -66,10 +72,11 @@ async def get_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, previous_response_id: str | None, + prompt: ResponsePromptParam | None = None, ) -> ModelResponse: with response_span(disabled=tracing.is_disabled()) as span_response: try: @@ -82,6 +89,7 @@ async def get_response( handoffs, previous_response_id, stream=False, + prompt=prompt, ) if _debug.DONT_LOG_MODEL_DATA: @@ -89,7 +97,13 @@ async def get_response( else: logger.debug( "LLM resp:\n" - f"{json.dumps([x.model_dump() for x in response.output], indent=2)}\n" + f"""{ + json.dumps( + [x.model_dump() for x in response.output], + indent=2, + ensure_ascii=False, + ) + }\n""" ) usage = ( @@ -98,6 +112,8 @@ async def get_response( input_tokens=response.usage.input_tokens, output_tokens=response.usage.output_tokens, total_tokens=response.usage.total_tokens, + input_tokens_details=response.usage.input_tokens_details, + output_tokens_details=response.usage.output_tokens_details, ) if response.usage else Usage() @@ -131,10 +147,11 @@ async def stream_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, previous_response_id: str | None, + prompt: ResponsePromptParam | None = None, ) -> AsyncIterator[ResponseStreamEvent]: """ Yields a partial message as it is generated, as well as the usage information. @@ -150,6 +167,7 @@ async def stream_response( handoffs, previous_response_id, stream=True, + prompt=prompt, ) final_response: Response | None = None @@ -182,10 +200,11 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], previous_response_id: str | None, stream: Literal[True], + prompt: ResponsePromptParam | None = None, ) -> AsyncStream[ResponseStreamEvent]: ... @overload @@ -195,10 +214,11 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], previous_response_id: str | None, stream: Literal[False], + prompt: ResponsePromptParam | None = None, ) -> Response: ... async def _fetch_response( @@ -207,10 +227,11 @@ async def _fetch_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], previous_response_id: str | None, stream: Literal[True] | Literal[False] = False, + prompt: ResponsePromptParam | None = None, ) -> Response | AsyncStream[ResponseStreamEvent]: list_input = ItemHelpers.input_to_new_input_list(input) @@ -226,13 +247,17 @@ async def _fetch_response( converted_tools = Converter.convert_tools(tools, handoffs) response_format = Converter.get_response_format(output_schema) + include: list[ResponseIncludable] = converted_tools.includes + if model_settings.response_include is not None: + include = list({*include, *model_settings.response_include}) + if _debug.DONT_LOG_MODEL_DATA: logger.debug("Calling LLM") else: logger.debug( f"Calling LLM {self.model} with input:\n" - f"{json.dumps(list_input, indent=2)}\n" - f"Tools:\n{json.dumps(converted_tools.tools, indent=2)}\n" + f"{json.dumps(list_input, indent=2, ensure_ascii=False)}\n" + f"Tools:\n{json.dumps(converted_tools.tools, indent=2, ensure_ascii=False)}\n" f"Stream: {stream}\n" f"Tool choice: {tool_choice}\n" f"Response format: {response_format}\n" @@ -244,8 +269,9 @@ async def _fetch_response( instructions=self._non_null_or_not_given(system_instructions), model=self.model, input=list_input, - include=converted_tools.includes, + include=include, tools=converted_tools.tools, + prompt=self._non_null_or_not_given(prompt), temperature=self._non_null_or_not_given(model_settings.temperature), top_p=self._non_null_or_not_given(model_settings.top_p), truncation=self._non_null_or_not_given(model_settings.truncation), @@ -253,13 +279,14 @@ async def _fetch_response( tool_choice=tool_choice, parallel_tool_calls=parallel_tool_calls, stream=stream, - extra_headers=_HEADERS, + extra_headers={**_HEADERS, **(model_settings.extra_headers or {})}, extra_query=model_settings.extra_query, extra_body=model_settings.extra_body, text=response_format, store=self._non_null_or_not_given(model_settings.store), reasoning=self._non_null_or_not_given(model_settings.reasoning), metadata=self._non_null_or_not_given(model_settings.metadata), + **(model_settings.extra_args or {}), ) def _get_client(self) -> AsyncOpenAI: @@ -271,16 +298,22 @@ def _get_client(self) -> AsyncOpenAI: @dataclass class ConvertedTools: tools: list[ToolParam] - includes: list[IncludeLiteral] + includes: list[ResponseIncludable] class Converter: @classmethod def convert_tool_choice( - cls, tool_choice: Literal["auto", "required", "none"] | str | None + cls, tool_choice: Literal["auto", "required", "none"] | str | MCPToolChoice | None ) -> response_create_params.ToolChoice | NotGiven: if tool_choice is None: return NOT_GIVEN + elif isinstance(tool_choice, MCPToolChoice): + return { + "server_label": tool_choice.server_label, + "type": "mcp", + "name": tool_choice.name, + } elif tool_choice == "required": return "required" elif tool_choice == "auto": @@ -299,6 +332,18 @@ def convert_tool_choice( return { "type": "computer_use_preview", } + elif tool_choice == "image_generation": + return { + "type": "image_generation", + } + elif tool_choice == "code_interpreter": + return { + "type": "code_interpreter", + } + elif tool_choice == "mcp": + # Note that this is still here for backwards compatibility, + # but migrating to MCPToolChoice is recommended. + return { "type": "mcp" } # type: ignore [typeddict-item] else: return { "type": "function", @@ -307,7 +352,7 @@ def convert_tool_choice( @classmethod def get_response_format( - cls, output_schema: AgentOutputSchema | None + cls, output_schema: AgentOutputSchemaBase | None ) -> ResponseTextConfigParam | NotGiven: if output_schema is None or output_schema.is_plain_text(): return NOT_GIVEN @@ -317,7 +362,7 @@ def get_response_format( "type": "json_schema", "name": "final_output", "schema": output_schema.json_schema(), - "strict": output_schema.strict_json_schema, + "strict": output_schema.is_strict_json_schema(), } } @@ -328,7 +373,7 @@ def convert_tools( handoffs: list[Handoff[Any]], ) -> ConvertedTools: converted_tools: list[ToolParam] = [] - includes: list[IncludeLiteral] = [] + includes: list[ResponseIncludable] = [] computer_tools = [tool for tool in tools if isinstance(tool, ComputerTool)] if len(computer_tools) > 1: @@ -346,7 +391,7 @@ def convert_tools( return ConvertedTools(tools=converted_tools, includes=includes) @classmethod - def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, IncludeLiteral | None]: + def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, ResponseIncludable | None]: """Returns converted tool and includes""" if isinstance(tool, FunctionTool): @@ -357,7 +402,7 @@ def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, IncludeLiteral | None]: "type": "function", "description": tool.description, } - includes: IncludeLiteral | None = None + includes: ResponseIncludable | None = None elif isinstance(tool, WebSearchTool): ws: WebSearchToolParam = { "type": "web_search_preview", @@ -387,7 +432,20 @@ def _convert_tool(cls, tool: Tool) -> tuple[ToolParam, IncludeLiteral | None]: "display_height": tool.computer.dimensions[1], } includes = None - + elif isinstance(tool, HostedMCPTool): + converted_tool = tool.tool_config + includes = None + elif isinstance(tool, ImageGenerationTool): + converted_tool = tool.tool_config + includes = None + elif isinstance(tool, CodeInterpreterTool): + converted_tool = tool.tool_config + includes = None + elif isinstance(tool, LocalShellTool): + converted_tool = { + "type": "local_shell", + } + includes = None else: raise UserError(f"Unknown tool type: {type(tool)}, tool") diff --git a/src/agents/prompts.py b/src/agents/prompts.py new file mode 100644 index 000000000..aa627d033 --- /dev/null +++ b/src/agents/prompts.py @@ -0,0 +1,76 @@ +from __future__ import annotations + +import inspect +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Callable + +from openai.types.responses.response_prompt_param import ( + ResponsePromptParam, + Variables as ResponsesPromptVariables, +) +from typing_extensions import NotRequired, TypedDict + +from agents.util._types import MaybeAwaitable + +from .exceptions import UserError +from .run_context import RunContextWrapper + +if TYPE_CHECKING: + from .agent import Agent + + +class Prompt(TypedDict): + """Prompt configuration to use for interacting with an OpenAI model.""" + + id: str + """The unique ID of the prompt.""" + + version: NotRequired[str] + """Optional version of the prompt.""" + + variables: NotRequired[dict[str, ResponsesPromptVariables]] + """Optional variables to substitute into the prompt.""" + + +@dataclass +class GenerateDynamicPromptData: + """Inputs to a function that allows you to dynamically generate a prompt.""" + + context: RunContextWrapper[Any] + """The run context.""" + + agent: Agent[Any] + """The agent for which the prompt is being generated.""" + + +DynamicPromptFunction = Callable[[GenerateDynamicPromptData], MaybeAwaitable[Prompt]] +"""A function that dynamically generates a prompt.""" + + +class PromptUtil: + @staticmethod + async def to_model_input( + prompt: Prompt | DynamicPromptFunction | None, + context: RunContextWrapper[Any], + agent: Agent[Any], + ) -> ResponsePromptParam | None: + if prompt is None: + return None + + resolved_prompt: Prompt + if isinstance(prompt, dict): + resolved_prompt = prompt + else: + func_result = prompt(GenerateDynamicPromptData(context=context, agent=agent)) + if inspect.isawaitable(func_result): + resolved_prompt = await func_result + else: + resolved_prompt = func_result + if not isinstance(resolved_prompt, dict): + raise UserError("Dynamic prompt function must return a Prompt") + + return { + "id": resolved_prompt["id"], + "version": resolved_prompt.get("version"), + "variables": resolved_prompt.get("variables"), + } diff --git a/src/agents/realtime/README.md b/src/agents/realtime/README.md new file mode 100644 index 000000000..9acc23160 --- /dev/null +++ b/src/agents/realtime/README.md @@ -0,0 +1,3 @@ +# Realtime + +Realtime agents are in beta: expect some breaking changes over the next few weeks as we find issues and fix them. diff --git a/src/agents/realtime/__init__.py b/src/agents/realtime/__init__.py new file mode 100644 index 000000000..52b59edc3 --- /dev/null +++ b/src/agents/realtime/__init__.py @@ -0,0 +1,69 @@ +from .agent import RealtimeAgent, RealtimeAgentHooks, RealtimeRunHooks +from .config import ( + RealtimeAudioFormat, + RealtimeClientMessage, + RealtimeInputAudioTranscriptionConfig, + RealtimeModelName, + RealtimeRunConfig, + RealtimeSessionModelSettings, + RealtimeTurnDetectionConfig, + RealtimeUserInput, + RealtimeUserInputMessage, + RealtimeUserInputText, +) +from .events import ( + RealtimeAgentEndEvent, + RealtimeAgentStartEvent, + RealtimeAudio, + RealtimeAudioEnd, + RealtimeAudioInterrupted, + RealtimeError, + RealtimeHandoffEvent, + RealtimeHistoryAdded, + RealtimeHistoryUpdated, + RealtimeRawModelEvent, + RealtimeSessionEvent, + RealtimeToolEnd, + RealtimeToolStart, +) +from .model import ( + RealtimeModel, + RealtimeModelConfig, + RealtimeModelListener, +) +from .runner import RealtimeRunner +from .session import RealtimeSession + +__all__ = [ + "RealtimeAgent", + "RealtimeAgentHooks", + "RealtimeRunHooks", + "RealtimeRunner", + "RealtimeRunConfig", + "RealtimeSessionModelSettings", + "RealtimeInputAudioTranscriptionConfig", + "RealtimeTurnDetectionConfig", + "RealtimeAudioFormat", + "RealtimeClientMessage", + "RealtimeUserInput", + "RealtimeUserInputMessage", + "RealtimeUserInputText", + "RealtimeModelName", + "RealtimeModel", + "RealtimeModelListener", + "RealtimeModelConfig", + "RealtimeSessionEvent", + "RealtimeAgentStartEvent", + "RealtimeAgentEndEvent", + "RealtimeHandoffEvent", + "RealtimeToolStart", + "RealtimeToolEnd", + "RealtimeRawModelEvent", + "RealtimeAudioEnd", + "RealtimeAudio", + "RealtimeAudioInterrupted", + "RealtimeError", + "RealtimeHistoryUpdated", + "RealtimeHistoryAdded", + "RealtimeSession", +] diff --git a/src/agents/realtime/agent.py b/src/agents/realtime/agent.py new file mode 100644 index 000000000..9bbed8cb4 --- /dev/null +++ b/src/agents/realtime/agent.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +import dataclasses +import inspect +from collections.abc import Awaitable +from dataclasses import dataclass +from typing import Any, Callable, Generic, cast + +from ..agent import AgentBase +from ..lifecycle import AgentHooksBase, RunHooksBase +from ..logger import logger +from ..run_context import RunContextWrapper, TContext +from ..util._types import MaybeAwaitable + +RealtimeAgentHooks = AgentHooksBase[TContext, "RealtimeAgent[TContext]"] +"""Agent hooks for `RealtimeAgent`s.""" + +RealtimeRunHooks = RunHooksBase[TContext, "RealtimeAgent[TContext]"] +"""Run hooks for `RealtimeAgent`s.""" + + +@dataclass +class RealtimeAgent(AgentBase, Generic[TContext]): + """A specialized agent instance that is meant to be used within a `RealtimeSession` to build + voice agents. Due to the nature of this agent, some configuration options are not supported + that are supported by regular `Agent` instances. For example: + - `model` choice is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `modelSettings` is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `outputType` is not supported, as RealtimeAgents do not support structured outputs. + - `toolUseBehavior` is not supported, as all RealtimeAgents will be handled by the same model + within a `RealtimeSession`. + - `voice` can be configured on an `Agent` level; however, it cannot be changed after the first + agent within a `RealtimeSession` has spoken. + + See `AgentBase` for base parameters that are shared with `Agent`s. + """ + + instructions: ( + str + | Callable[ + [RunContextWrapper[TContext], RealtimeAgent[TContext]], + MaybeAwaitable[str], + ] + | None + ) = None + """The instructions for the agent. Will be used as the "system prompt" when this agent is + invoked. Describes what the agent should do, and how it responds. + + Can either be a string, or a function that dynamically generates instructions for the agent. If + you provide a function, it will be called with the context and the agent instance. It must + return a string. + """ + + hooks: RealtimeAgentHooks | None = None + """A class that receives callbacks on various lifecycle events for this agent. + """ + + def clone(self, **kwargs: Any) -> RealtimeAgent[TContext]: + """Make a copy of the agent, with the given arguments changed. For example, you could do: + ``` + new_agent = agent.clone(instructions="New instructions") + ``` + """ + return dataclasses.replace(self, **kwargs) + + async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> str | None: + """Get the system prompt for the agent.""" + if isinstance(self.instructions, str): + return self.instructions + elif callable(self.instructions): + if inspect.iscoroutinefunction(self.instructions): + return await cast(Awaitable[str], self.instructions(run_context, self)) + else: + return cast(str, self.instructions(run_context, self)) + elif self.instructions is not None: + logger.error(f"Instructions must be a string or a function, got {self.instructions}") + + return None diff --git a/src/agents/realtime/config.py b/src/agents/realtime/config.py new file mode 100644 index 000000000..ece72e755 --- /dev/null +++ b/src/agents/realtime/config.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +from typing import ( + Any, + Literal, + Union, +) + +from typing_extensions import NotRequired, TypeAlias, TypedDict + +from ..guardrail import OutputGuardrail +from ..model_settings import ToolChoice +from ..tool import Tool + +RealtimeModelName: TypeAlias = Union[ + Literal[ + "gpt-4o-realtime-preview", + "gpt-4o-mini-realtime-preview", + "gpt-4o-realtime-preview-2025-06-03", + "gpt-4o-realtime-preview-2024-12-17", + "gpt-4o-realtime-preview-2024-10-01", + "gpt-4o-mini-realtime-preview-2024-12-17", + ], + str, +] +"""The name of a realtime model.""" + + +class RealtimeClientMessage(TypedDict): + type: str # explicitly required + other_data: NotRequired[dict[str, Any]] + + +class RealtimeUserInputText(TypedDict): + type: Literal["input_text"] + text: str + + +class RealtimeUserInputMessage(TypedDict): + type: Literal["message"] + role: Literal["user"] + content: list[RealtimeUserInputText] + + +RealtimeUserInput: TypeAlias = Union[str, RealtimeUserInputMessage] + + +RealtimeAudioFormat: TypeAlias = Union[Literal["pcm16", "g711_ulaw", "g711_alaw"], str] + + +class RealtimeInputAudioTranscriptionConfig(TypedDict): + language: NotRequired[str] + model: NotRequired[Literal["gpt-4o-transcribe", "gpt-4o-mini-transcribe", "whisper-1"] | str] + prompt: NotRequired[str] + + +class RealtimeTurnDetectionConfig(TypedDict): + """Turn detection config. Allows extra vendor keys if needed.""" + + type: NotRequired[Literal["semantic_vad", "server_vad"]] + create_response: NotRequired[bool] + eagerness: NotRequired[Literal["auto", "low", "medium", "high"]] + interrupt_response: NotRequired[bool] + prefix_padding_ms: NotRequired[int] + silence_duration_ms: NotRequired[int] + threshold: NotRequired[float] + + +class RealtimeSessionModelSettings(TypedDict): + """Model settings for a realtime model session.""" + + model_name: NotRequired[RealtimeModelName] + + instructions: NotRequired[str] + modalities: NotRequired[list[Literal["text", "audio"]]] + voice: NotRequired[str] + + input_audio_format: NotRequired[RealtimeAudioFormat] + output_audio_format: NotRequired[RealtimeAudioFormat] + input_audio_transcription: NotRequired[RealtimeInputAudioTranscriptionConfig] + turn_detection: NotRequired[RealtimeTurnDetectionConfig] + + tool_choice: NotRequired[ToolChoice] + tools: NotRequired[list[Tool]] + + tracing: NotRequired[RealtimeModelTracingConfig | None] + + +class RealtimeGuardrailsSettings(TypedDict): + """Settings for output guardrails in realtime sessions.""" + + debounce_text_length: NotRequired[int] + """ + The minimum number of characters to accumulate before running guardrails on transcript + deltas. Defaults to 100. Guardrails run every time the accumulated text reaches + 1x, 2x, 3x, etc. times this threshold. + """ + + +class RealtimeModelTracingConfig(TypedDict): + """Configuration for tracing in realtime model sessions.""" + + workflow_name: NotRequired[str] + """The workflow name to use for tracing.""" + + group_id: NotRequired[str] + """A group identifier to use for tracing, to link multiple traces together.""" + + metadata: NotRequired[dict[str, Any]] + """Additional metadata to include with the trace.""" + + +class RealtimeRunConfig(TypedDict): + model_settings: NotRequired[RealtimeSessionModelSettings] + + output_guardrails: NotRequired[list[OutputGuardrail[Any]]] + """List of output guardrails to run on the agent's responses.""" + + guardrails_settings: NotRequired[RealtimeGuardrailsSettings] + """Settings for guardrail execution.""" + + tracing_disabled: NotRequired[bool] + """Whether tracing is disabled for this run.""" + + # TODO (rm) Add history audio storage config diff --git a/src/agents/realtime/events.py b/src/agents/realtime/events.py new file mode 100644 index 000000000..e1b3cfea3 --- /dev/null +++ b/src/agents/realtime/events.py @@ -0,0 +1,215 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Literal, Union + +from typing_extensions import TypeAlias + +from ..guardrail import OutputGuardrailResult +from ..run_context import RunContextWrapper +from ..tool import Tool +from .agent import RealtimeAgent +from .items import RealtimeItem +from .model_events import RealtimeModelAudioEvent, RealtimeModelEvent + + +@dataclass +class RealtimeEventInfo: + context: RunContextWrapper + """The context for the event.""" + + +@dataclass +class RealtimeAgentStartEvent: + """A new agent has started.""" + + agent: RealtimeAgent + """The new agent.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["agent_start"] = "agent_start" + + +@dataclass +class RealtimeAgentEndEvent: + """An agent has ended.""" + + agent: RealtimeAgent + """The agent that ended.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["agent_end"] = "agent_end" + + +@dataclass +class RealtimeHandoffEvent: + """An agent has handed off to another agent.""" + + from_agent: RealtimeAgent + """The agent that handed off.""" + + to_agent: RealtimeAgent + """The agent that was handed off to.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["handoff"] = "handoff" + + +@dataclass +class RealtimeToolStart: + """An agent is starting a tool call.""" + + agent: RealtimeAgent + """The agent that updated.""" + + tool: Tool + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["tool_start"] = "tool_start" + + +@dataclass +class RealtimeToolEnd: + """An agent has ended a tool call.""" + + agent: RealtimeAgent + """The agent that ended the tool call.""" + + tool: Tool + """The tool that was called.""" + + output: Any + """The output of the tool call.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["tool_end"] = "tool_end" + + +@dataclass +class RealtimeRawModelEvent: + """Forwards raw events from the model layer.""" + + data: RealtimeModelEvent + """The raw data from the model layer.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["raw_model_event"] = "raw_model_event" + + +@dataclass +class RealtimeAudioEnd: + """Triggered when the agent stops generating audio.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["audio_end"] = "audio_end" + + +@dataclass +class RealtimeAudio: + """Triggered when the agent generates new audio to be played.""" + + audio: RealtimeModelAudioEvent + """The audio event from the model layer.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["audio"] = "audio" + + +@dataclass +class RealtimeAudioInterrupted: + """Triggered when the agent is interrupted. Can be listened to by the user to stop audio + playback or give visual indicators to the user. + """ + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["audio_interrupted"] = "audio_interrupted" + + +@dataclass +class RealtimeError: + """An error has occurred.""" + + error: Any + """The error that occurred.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["error"] = "error" + + +@dataclass +class RealtimeHistoryUpdated: + """The history has been updated. Contains the full history of the session.""" + + history: list[RealtimeItem] + """The full history of the session.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["history_updated"] = "history_updated" + + +@dataclass +class RealtimeHistoryAdded: + """A new item has been added to the history.""" + + item: RealtimeItem + """The new item that was added to the history.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["history_added"] = "history_added" + + +@dataclass +class RealtimeGuardrailTripped: + """A guardrail has been tripped and the agent has been interrupted.""" + + guardrail_results: list[OutputGuardrailResult] + """The results from all triggered guardrails.""" + + message: str + """The message that was being generated when the guardrail was triggered.""" + + info: RealtimeEventInfo + """Common info for all events, such as the context.""" + + type: Literal["guardrail_tripped"] = "guardrail_tripped" + +RealtimeSessionEvent: TypeAlias = Union[ + RealtimeAgentStartEvent, + RealtimeAgentEndEvent, + RealtimeHandoffEvent, + RealtimeToolStart, + RealtimeToolEnd, + RealtimeRawModelEvent, + RealtimeAudioEnd, + RealtimeAudio, + RealtimeAudioInterrupted, + RealtimeError, + RealtimeHistoryUpdated, + RealtimeHistoryAdded, + RealtimeGuardrailTripped, +] +"""An event emitted by the realtime session.""" diff --git a/src/agents/realtime/items.py b/src/agents/realtime/items.py new file mode 100644 index 000000000..a027b2a39 --- /dev/null +++ b/src/agents/realtime/items.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +from typing import Annotated, Literal, Union + +from pydantic import BaseModel, ConfigDict, Field + + +class InputText(BaseModel): + type: Literal["input_text"] = "input_text" + text: str + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class InputAudio(BaseModel): + type: Literal["input_audio"] = "input_audio" + audio: str | None = None + transcript: str | None = None + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class AssistantText(BaseModel): + type: Literal["text"] = "text" + text: str + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class AssistantAudio(BaseModel): + type: Literal["audio"] = "audio" + audio: str | None = None + transcript: str | None = None + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class SystemMessageItem(BaseModel): + item_id: str + previous_item_id: str | None = None + type: Literal["message"] = "message" + role: Literal["system"] = "system" + content: list[InputText] + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class UserMessageItem(BaseModel): + item_id: str + previous_item_id: str | None = None + type: Literal["message"] = "message" + role: Literal["user"] = "user" + content: list[InputText | InputAudio] + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +class AssistantMessageItem(BaseModel): + item_id: str + previous_item_id: str | None = None + type: Literal["message"] = "message" + role: Literal["assistant"] = "assistant" + status: Literal["in_progress", "completed", "incomplete"] | None = None + content: list[AssistantText | AssistantAudio] + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +RealtimeMessageItem = Annotated[ + Union[SystemMessageItem, UserMessageItem, AssistantMessageItem], + Field(discriminator="role"), +] + + +class RealtimeToolCallItem(BaseModel): + item_id: str + previous_item_id: str | None = None + type: Literal["function_call"] = "function_call" + status: Literal["in_progress", "completed"] + arguments: str + name: str + output: str | None = None + + # Allow extra data + model_config = ConfigDict(extra="allow") + + +RealtimeItem = Union[RealtimeMessageItem, RealtimeToolCallItem] + + +class RealtimeResponse(BaseModel): + id: str + output: list[RealtimeMessageItem] diff --git a/src/agents/realtime/model.py b/src/agents/realtime/model.py new file mode 100644 index 000000000..abb3a1eac --- /dev/null +++ b/src/agents/realtime/model.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import abc +from typing import Any, Callable + +from typing_extensions import NotRequired, TypedDict + +from ..util._types import MaybeAwaitable +from .config import ( + RealtimeClientMessage, + RealtimeSessionModelSettings, + RealtimeUserInput, +) +from .model_events import RealtimeModelEvent, RealtimeModelToolCallEvent + + +class RealtimeModelListener(abc.ABC): + """A listener for realtime transport events.""" + + @abc.abstractmethod + async def on_event(self, event: RealtimeModelEvent) -> None: + """Called when an event is emitted by the realtime transport.""" + pass + + +class RealtimeModelConfig(TypedDict): + """Options for connecting to a realtime model.""" + + api_key: NotRequired[str | Callable[[], MaybeAwaitable[str]]] + """The API key (or function that returns a key) to use when connecting. If unset, the model will + try to use a sane default. For example, the OpenAI Realtime model will try to use the + `OPENAI_API_KEY` environment variable. + """ + + url: NotRequired[str] + """The URL to use when connecting. If unset, the model will use a sane default. For example, + the OpenAI Realtime model will use the default OpenAI WebSocket URL. + """ + + initial_model_settings: NotRequired[RealtimeSessionModelSettings] + """The initial model settings to use when connecting.""" + + +class RealtimeModel(abc.ABC): + """Interface for connecting to a realtime model and sending/receiving events.""" + + @abc.abstractmethod + async def connect(self, options: RealtimeModelConfig) -> None: + """Establish a connection to the model and keep it alive.""" + pass + + @abc.abstractmethod + def add_listener(self, listener: RealtimeModelListener) -> None: + """Add a listener to the model.""" + pass + + @abc.abstractmethod + def remove_listener(self, listener: RealtimeModelListener) -> None: + """Remove a listener from the model.""" + pass + + @abc.abstractmethod + async def send_event(self, event: RealtimeClientMessage) -> None: + """Send an event to the model.""" + pass + + @abc.abstractmethod + async def send_message( + self, message: RealtimeUserInput, other_event_data: dict[str, Any] | None = None + ) -> None: + """Send a message to the model.""" + pass + + @abc.abstractmethod + async def send_audio(self, audio: bytes, *, commit: bool = False) -> None: + """Send a raw audio chunk to the model. + + Args: + audio: The audio data to send. + commit: Whether to commit the audio buffer to the model. If the model does not do turn + detection, this can be used to indicate the turn is completed. + """ + pass + + @abc.abstractmethod + async def send_tool_output( + self, tool_call: RealtimeModelToolCallEvent, output: str, start_response: bool + ) -> None: + """Send tool output to the model.""" + pass + + @abc.abstractmethod + async def interrupt(self) -> None: + """Interrupt the model. For example, could be triggered by a guardrail.""" + pass + + @abc.abstractmethod + async def close(self) -> None: + """Close the session.""" + pass diff --git a/src/agents/realtime/model_events.py b/src/agents/realtime/model_events.py new file mode 100644 index 000000000..a9769cebd --- /dev/null +++ b/src/agents/realtime/model_events.py @@ -0,0 +1,150 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Literal, Union + +from typing_extensions import TypeAlias + +from .items import RealtimeItem + +RealtimeConnectionStatus: TypeAlias = Literal["connecting", "connected", "disconnected"] + + +@dataclass +class RealtimeModelErrorEvent: + """Represents a transport‑layer error.""" + + error: Any + + type: Literal["error"] = "error" + + +@dataclass +class RealtimeModelToolCallEvent: + """Model attempted a tool/function call.""" + + name: str + call_id: str + arguments: str + + id: str | None = None + previous_item_id: str | None = None + + type: Literal["function_call"] = "function_call" + + +@dataclass +class RealtimeModelAudioEvent: + """Raw audio bytes emitted by the model.""" + + data: bytes + response_id: str + + type: Literal["audio"] = "audio" + + +@dataclass +class RealtimeModelAudioInterruptedEvent: + """Audio interrupted.""" + + type: Literal["audio_interrupted"] = "audio_interrupted" + + +@dataclass +class RealtimeModelAudioDoneEvent: + """Audio done.""" + + type: Literal["audio_done"] = "audio_done" + + +@dataclass +class RealtimeModelInputAudioTranscriptionCompletedEvent: + """Input audio transcription completed.""" + + item_id: str + transcript: str + + type: Literal["conversation.item.input_audio_transcription.completed"] = ( + "conversation.item.input_audio_transcription.completed" + ) + + +@dataclass +class RealtimeModelTranscriptDeltaEvent: + """Partial transcript update.""" + + item_id: str + delta: str + response_id: str + + type: Literal["transcript_delta"] = "transcript_delta" + + +@dataclass +class RealtimeModelItemUpdatedEvent: + """Item added to the history or updated.""" + + item: RealtimeItem + + type: Literal["item_updated"] = "item_updated" + + +@dataclass +class RealtimeModelItemDeletedEvent: + """Item deleted from the history.""" + + item_id: str + + type: Literal["item_deleted"] = "item_deleted" + + +@dataclass +class RealtimeModelConnectionStatusEvent: + """Connection status changed.""" + + status: RealtimeConnectionStatus + + type: Literal["connection_status"] = "connection_status" + + +@dataclass +class RealtimeModelTurnStartedEvent: + """Triggered when the model starts generating a response for a turn.""" + + type: Literal["turn_started"] = "turn_started" + + +@dataclass +class RealtimeModelTurnEndedEvent: + """Triggered when the model finishes generating a response for a turn.""" + + type: Literal["turn_ended"] = "turn_ended" + + +@dataclass +class RealtimeModelOtherEvent: + """Used as a catchall for vendor-specific events.""" + + data: Any + + type: Literal["other"] = "other" + + +# TODO (rm) Add usage events + + +RealtimeModelEvent: TypeAlias = Union[ + RealtimeModelErrorEvent, + RealtimeModelToolCallEvent, + RealtimeModelAudioEvent, + RealtimeModelAudioInterruptedEvent, + RealtimeModelAudioDoneEvent, + RealtimeModelInputAudioTranscriptionCompletedEvent, + RealtimeModelTranscriptDeltaEvent, + RealtimeModelItemUpdatedEvent, + RealtimeModelItemDeletedEvent, + RealtimeModelConnectionStatusEvent, + RealtimeModelTurnStartedEvent, + RealtimeModelTurnEndedEvent, + RealtimeModelOtherEvent, +] diff --git a/src/agents/realtime/openai_realtime.py b/src/agents/realtime/openai_realtime.py new file mode 100644 index 000000000..797753242 --- /dev/null +++ b/src/agents/realtime/openai_realtime.py @@ -0,0 +1,410 @@ +from __future__ import annotations + +import asyncio +import base64 +import inspect +import json +import os +from datetime import datetime +from typing import Any, Callable, Literal + +import websockets +from openai.types.beta.realtime.conversation_item import ConversationItem +from openai.types.beta.realtime.realtime_server_event import ( + RealtimeServerEvent as OpenAIRealtimeServerEvent, +) +from openai.types.beta.realtime.response_audio_delta_event import ResponseAudioDeltaEvent +from pydantic import TypeAdapter +from websockets.asyncio.client import ClientConnection + +from agents.util._types import MaybeAwaitable + +from ..exceptions import UserError +from ..logger import logger +from .config import ( + RealtimeClientMessage, + RealtimeModelTracingConfig, + RealtimeSessionModelSettings, + RealtimeUserInput, +) +from .items import RealtimeMessageItem, RealtimeToolCallItem +from .model import ( + RealtimeModel, + RealtimeModelConfig, + RealtimeModelListener, +) +from .model_events import ( + RealtimeModelAudioDoneEvent, + RealtimeModelAudioEvent, + RealtimeModelAudioInterruptedEvent, + RealtimeModelErrorEvent, + RealtimeModelEvent, + RealtimeModelInputAudioTranscriptionCompletedEvent, + RealtimeModelItemDeletedEvent, + RealtimeModelItemUpdatedEvent, + RealtimeModelToolCallEvent, + RealtimeModelTranscriptDeltaEvent, + RealtimeModelTurnEndedEvent, + RealtimeModelTurnStartedEvent, +) + + +async def get_api_key(key: str | Callable[[], MaybeAwaitable[str]] | None) -> str | None: + if isinstance(key, str): + return key + elif callable(key): + result = key() + if inspect.isawaitable(result): + return await result + return result + + return os.getenv("OPENAI_API_KEY") + + +class OpenAIRealtimeWebSocketModel(RealtimeModel): + """A model that uses OpenAI's WebSocket API.""" + + def __init__(self) -> None: + self.model = "gpt-4o-realtime-preview" # Default model + self._websocket: ClientConnection | None = None + self._websocket_task: asyncio.Task[None] | None = None + self._listeners: list[RealtimeModelListener] = [] + self._current_item_id: str | None = None + self._audio_start_time: datetime | None = None + self._audio_length_ms: float = 0.0 + self._ongoing_response: bool = False + self._current_audio_content_index: int | None = None + self._tracing_config: RealtimeModelTracingConfig | Literal["auto"] | None = None + + async def connect(self, options: RealtimeModelConfig) -> None: + """Establish a connection to the model and keep it alive.""" + assert self._websocket is None, "Already connected" + assert self._websocket_task is None, "Already connected" + + model_settings: RealtimeSessionModelSettings = options.get("initial_model_settings", {}) + + self.model = model_settings.get("model_name", self.model) + api_key = await get_api_key(options.get("api_key")) + + if "tracing" in model_settings: + self._tracing_config = model_settings["tracing"] + else: + self._tracing_config = "auto" + + if not api_key: + raise UserError("API key is required but was not provided.") + + url = options.get("url", f"wss://api.openai.com/v1/realtime?model={self.model}") + + headers = { + "Authorization": f"Bearer {api_key}", + "OpenAI-Beta": "realtime=v1", + } + self._websocket = await websockets.connect(url, additional_headers=headers) + self._websocket_task = asyncio.create_task(self._listen_for_messages()) + + async def _send_tracing_config( + self, tracing_config: RealtimeModelTracingConfig | Literal["auto"] | None + ) -> None: + """Update tracing configuration via session.update event.""" + if tracing_config is not None: + await self.send_event( + {"type": "session.update", "other_data": {"session": {"tracing": tracing_config}}} + ) + + def add_listener(self, listener: RealtimeModelListener) -> None: + """Add a listener to the model.""" + self._listeners.append(listener) + + def remove_listener(self, listener: RealtimeModelListener) -> None: + """Remove a listener from the model.""" + self._listeners.remove(listener) + + async def _emit_event(self, event: RealtimeModelEvent) -> None: + """Emit an event to the listeners.""" + for listener in self._listeners: + await listener.on_event(event) + + async def _listen_for_messages(self): + assert self._websocket is not None, "Not connected" + + try: + async for message in self._websocket: + parsed = json.loads(message) + await self._handle_ws_event(parsed) + + except websockets.exceptions.ConnectionClosed: + # TODO connection closed handling (event, cleanup) + logger.warning("WebSocket connection closed") + except Exception as e: + logger.error(f"WebSocket error: {e}") + + async def send_event(self, event: RealtimeClientMessage) -> None: + """Send an event to the model.""" + assert self._websocket is not None, "Not connected" + converted_event = { + "type": event["type"], + } + + converted_event.update(event.get("other_data", {})) + + await self._websocket.send(json.dumps(converted_event)) + + async def send_message( + self, message: RealtimeUserInput, other_event_data: dict[str, Any] | None = None + ) -> None: + """Send a message to the model.""" + message = ( + message + if isinstance(message, dict) + else { + "type": "message", + "role": "user", + "content": [{"type": "input_text", "text": message}], + } + ) + other_data = { + "item": message, + } + if other_event_data: + other_data.update(other_event_data) + + await self.send_event({"type": "conversation.item.create", "other_data": other_data}) + + await self.send_event({"type": "response.create"}) + + async def send_audio(self, audio: bytes, *, commit: bool = False) -> None: + """Send a raw audio chunk to the model. + + Args: + audio: The audio data to send. + commit: Whether to commit the audio buffer to the model. If the model does not do turn + detection, this can be used to indicate the turn is completed. + """ + assert self._websocket is not None, "Not connected" + base64_audio = base64.b64encode(audio).decode("utf-8") + await self.send_event( + { + "type": "input_audio_buffer.append", + "other_data": { + "audio": base64_audio, + }, + } + ) + if commit: + await self.send_event({"type": "input_audio_buffer.commit"}) + + async def send_tool_output( + self, tool_call: RealtimeModelToolCallEvent, output: str, start_response: bool + ) -> None: + """Send tool output to the model.""" + await self.send_event( + { + "type": "conversation.item.create", + "other_data": { + "item": { + "type": "function_call_output", + "output": output, + "call_id": tool_call.id, + }, + }, + } + ) + + tool_item = RealtimeToolCallItem( + item_id=tool_call.id or "", + previous_item_id=tool_call.previous_item_id, + type="function_call", + status="completed", + arguments=tool_call.arguments, + name=tool_call.name, + output=output, + ) + await self._emit_event(RealtimeModelItemUpdatedEvent(item=tool_item)) + + if start_response: + await self.send_event({"type": "response.create"}) + + async def interrupt(self) -> None: + """Interrupt the model.""" + if not self._current_item_id or not self._audio_start_time: + return + + await self._cancel_response() + + elapsed_time_ms = (datetime.now() - self._audio_start_time).total_seconds() * 1000 + if elapsed_time_ms > 0 and elapsed_time_ms < self._audio_length_ms: + await self._emit_event(RealtimeModelAudioInterruptedEvent()) + await self.send_event( + { + "type": "conversation.item.truncate", + "other_data": { + "item_id": self._current_item_id, + "content_index": self._current_audio_content_index, + "audio_end_ms": elapsed_time_ms, + }, + } + ) + + self._current_item_id = None + self._audio_start_time = None + self._audio_length_ms = 0.0 + self._current_audio_content_index = None + + async def _handle_audio_delta(self, parsed: ResponseAudioDeltaEvent) -> None: + """Handle audio delta events and update audio tracking state.""" + self._current_audio_content_index = parsed.content_index + self._current_item_id = parsed.item_id + if self._audio_start_time is None: + self._audio_start_time = datetime.now() + self._audio_length_ms = 0.0 + + audio_bytes = base64.b64decode(parsed.delta) + # Calculate audio length in ms using 24KHz pcm16le + self._audio_length_ms += self._calculate_audio_length_ms(audio_bytes) + await self._emit_event( + RealtimeModelAudioEvent(data=audio_bytes, response_id=parsed.response_id) + ) + + def _calculate_audio_length_ms(self, audio_bytes: bytes) -> float: + """Calculate audio length in milliseconds for 24KHz PCM16LE format.""" + return len(audio_bytes) / 24 / 2 + + async def _handle_output_item(self, item: ConversationItem) -> None: + """Handle response output item events (function calls and messages).""" + if item.type == "function_call" and item.status == "completed": + tool_call = RealtimeToolCallItem( + item_id=item.id or "", + previous_item_id=None, + type="function_call", + # We use the same item for tool call and output, so it will be completed by the + # output being added + status="in_progress", + arguments=item.arguments or "", + name=item.name or "", + output=None, + ) + await self._emit_event(RealtimeModelItemUpdatedEvent(item=tool_call)) + await self._emit_event( + RealtimeModelToolCallEvent( + call_id=item.id or "", + name=item.name or "", + arguments=item.arguments or "", + id=item.id or "", + ) + ) + elif item.type == "message": + # Handle message items from output_item events (no previous_item_id) + message_item: RealtimeMessageItem = TypeAdapter(RealtimeMessageItem).validate_python( + { + "item_id": item.id or "", + "type": item.type, + "role": item.role, + "content": item.content, + "status": "in_progress", + } + ) + await self._emit_event(RealtimeModelItemUpdatedEvent(item=message_item)) + + async def _handle_conversation_item( + self, item: ConversationItem, previous_item_id: str | None + ) -> None: + """Handle conversation item creation/retrieval events.""" + message_item: RealtimeMessageItem = TypeAdapter(RealtimeMessageItem).validate_python( + { + "item_id": item.id or "", + "previous_item_id": previous_item_id, + "type": item.type, + "role": item.role, + "content": item.content, + "status": "in_progress", + } + ) + await self._emit_event(RealtimeModelItemUpdatedEvent(item=message_item)) + + async def close(self) -> None: + """Close the session.""" + if self._websocket: + await self._websocket.close() + self._websocket = None + if self._websocket_task: + self._websocket_task.cancel() + self._websocket_task = None + + async def _cancel_response(self) -> None: + if self._ongoing_response: + await self.send_event({"type": "response.cancel"}) + self._ongoing_response = False + + async def _handle_ws_event(self, event: dict[str, Any]): + try: + parsed: OpenAIRealtimeServerEvent = TypeAdapter( + OpenAIRealtimeServerEvent + ).validate_python(event) + except Exception as e: + logger.error(f"Invalid event: {event} - {e}") + # await self._emit_event(RealtimeModelErrorEvent(error=f"Invalid event: {event} - {e}")) + return + + if parsed.type == "response.audio.delta": + await self._handle_audio_delta(parsed) + elif parsed.type == "response.audio.done": + await self._emit_event(RealtimeModelAudioDoneEvent()) + elif parsed.type == "input_audio_buffer.speech_started": + await self.interrupt() + elif parsed.type == "response.created": + self._ongoing_response = True + await self._emit_event(RealtimeModelTurnStartedEvent()) + elif parsed.type == "response.done": + self._ongoing_response = False + await self._emit_event(RealtimeModelTurnEndedEvent()) + elif parsed.type == "session.created": + await self._send_tracing_config(self._tracing_config) + elif parsed.type == "error": + await self._emit_event(RealtimeModelErrorEvent(error=parsed.error)) + elif parsed.type == "conversation.item.deleted": + await self._emit_event(RealtimeModelItemDeletedEvent(item_id=parsed.item_id)) + elif ( + parsed.type == "conversation.item.created" + or parsed.type == "conversation.item.retrieved" + ): + previous_item_id = ( + parsed.previous_item_id if parsed.type == "conversation.item.created" else None + ) + await self._handle_conversation_item(parsed.item, previous_item_id) + elif ( + parsed.type == "conversation.item.input_audio_transcription.completed" + or parsed.type == "conversation.item.truncated" + ): + await self.send_event( + { + "type": "conversation.item.retrieve", + "other_data": { + "item_id": self._current_item_id, + }, + } + ) + if parsed.type == "conversation.item.input_audio_transcription.completed": + await self._emit_event( + RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id=parsed.item_id, transcript=parsed.transcript + ) + ) + elif parsed.type == "response.audio_transcript.delta": + await self._emit_event( + RealtimeModelTranscriptDeltaEvent( + item_id=parsed.item_id, delta=parsed.delta, response_id=parsed.response_id + ) + ) + elif ( + parsed.type == "conversation.item.input_audio_transcription.delta" + or parsed.type == "response.text.delta" + or parsed.type == "response.function_call_arguments.delta" + ): + # No support for partials yet + pass + elif ( + parsed.type == "response.output_item.added" + or parsed.type == "response.output_item.done" + ): + await self._handle_output_item(parsed.item) diff --git a/src/agents/realtime/runner.py b/src/agents/realtime/runner.py new file mode 100644 index 000000000..a7047a6f5 --- /dev/null +++ b/src/agents/realtime/runner.py @@ -0,0 +1,118 @@ +"""Minimal realtime session implementation for voice agents.""" + +from __future__ import annotations + +import asyncio + +from ..run_context import RunContextWrapper, TContext +from .agent import RealtimeAgent +from .config import ( + RealtimeRunConfig, + RealtimeSessionModelSettings, +) +from .model import ( + RealtimeModel, + RealtimeModelConfig, +) +from .openai_realtime import OpenAIRealtimeWebSocketModel +from .session import RealtimeSession + + +class RealtimeRunner: + """A `RealtimeRunner` is the equivalent of `Runner` for realtime agents. It automatically + handles multiple turns by maintaining a persistent connection with the underlying model + layer. + + The session manages the local history copy, executes tools, runs guardrails and facilitates + handoffs between agents. + + Since this code runs on your server, it uses WebSockets by default. You can optionally create + your own custom model layer by implementing the `RealtimeModel` interface. + """ + + def __init__( + self, + starting_agent: RealtimeAgent, + *, + model: RealtimeModel | None = None, + config: RealtimeRunConfig | None = None, + ) -> None: + """Initialize the realtime runner. + + Args: + starting_agent: The agent to start the session with. + context: The context to use for the session. + model: The model to use. If not provided, will use a default OpenAI realtime model. + config: Override parameters to use for the entire run. + """ + self._starting_agent = starting_agent + self._config = config + self._model = model or OpenAIRealtimeWebSocketModel() + + async def run( + self, *, context: TContext | None = None, model_config: RealtimeModelConfig | None = None + ) -> RealtimeSession: + """Start and returns a realtime session. + + Returns: + RealtimeSession: A session object that allows bidirectional communication with the + realtime model. + + Example: + ```python + runner = RealtimeRunner(agent) + async with await runner.run() as session: + await session.send_message("Hello") + async for event in session: + print(event) + ``` + """ + model_settings = await self._get_model_settings( + agent=self._starting_agent, + disable_tracing=self._config.get("tracing_disabled", False) if self._config else False, + initial_settings=model_config.get("initial_model_settings") if model_config else None, + overrides=self._config.get("model_settings") if self._config else None, + ) + + model_config = model_config.copy() if model_config else {} + model_config["initial_model_settings"] = model_settings + + # Create and return the connection + session = RealtimeSession( + model=self._model, + agent=self._starting_agent, + context=context, + model_config=model_config, + run_config=self._config, + ) + + return session + + async def _get_model_settings( + self, + agent: RealtimeAgent, + disable_tracing: bool, + context: TContext | None = None, + initial_settings: RealtimeSessionModelSettings | None = None, + overrides: RealtimeSessionModelSettings | None = None, + ) -> RealtimeSessionModelSettings: + context_wrapper = RunContextWrapper(context) + model_settings = initial_settings.copy() if initial_settings else {} + + instructions, tools = await asyncio.gather( + agent.get_system_prompt(context_wrapper), + agent.get_all_tools(context_wrapper), + ) + + if instructions is not None: + model_settings["instructions"] = instructions + if tools is not None: + model_settings["tools"] = tools + + if overrides: + model_settings.update(overrides) + + if disable_tracing: + model_settings["tracing"] = None + + return model_settings diff --git a/src/agents/realtime/session.py b/src/agents/realtime/session.py new file mode 100644 index 000000000..0ca3bf7af --- /dev/null +++ b/src/agents/realtime/session.py @@ -0,0 +1,368 @@ +from __future__ import annotations + +import asyncio +from collections.abc import AsyncIterator +from typing import Any, cast + +from typing_extensions import assert_never + +from ..agent import Agent +from ..handoffs import Handoff +from ..run_context import RunContextWrapper, TContext +from ..tool import FunctionTool +from ..tool_context import ToolContext +from .agent import RealtimeAgent +from .config import RealtimeRunConfig, RealtimeUserInput +from .events import ( + RealtimeAgentEndEvent, + RealtimeAgentStartEvent, + RealtimeAudio, + RealtimeAudioEnd, + RealtimeAudioInterrupted, + RealtimeError, + RealtimeEventInfo, + RealtimeGuardrailTripped, + RealtimeHistoryAdded, + RealtimeHistoryUpdated, + RealtimeRawModelEvent, + RealtimeSessionEvent, + RealtimeToolEnd, + RealtimeToolStart, +) +from .items import InputAudio, InputText, RealtimeItem +from .model import RealtimeModel, RealtimeModelConfig, RealtimeModelListener +from .model_events import ( + RealtimeModelEvent, + RealtimeModelInputAudioTranscriptionCompletedEvent, + RealtimeModelToolCallEvent, +) + + +class RealtimeSession(RealtimeModelListener): + """A connection to a realtime model. It streams events from the model to you, and allows you to + send messages and audio to the model. + + Example: + ```python + runner = RealtimeRunner(agent) + async with await runner.run() as session: + # Send messages + await session.send_message("Hello") + await session.send_audio(audio_bytes) + + # Stream events + async for event in session: + if event.type == "audio": + # Handle audio event + pass + ``` + """ + + def __init__( + self, + model: RealtimeModel, + agent: RealtimeAgent, + context: TContext | None, + model_config: RealtimeModelConfig | None = None, + run_config: RealtimeRunConfig | None = None, + ) -> None: + """Initialize the session. + + Args: + model: The model to use. + agent: The current agent. + context: The context object. + model_config: Model configuration. + run_config: Runtime configuration including guardrails. + """ + self._model = model + self._current_agent = agent + self._context_wrapper = RunContextWrapper(context) + self._event_info = RealtimeEventInfo(context=self._context_wrapper) + self._history: list[RealtimeItem] = [] + self._model_config = model_config or {} + self._run_config = run_config or {} + self._event_queue: asyncio.Queue[RealtimeSessionEvent] = asyncio.Queue() + self._closed = False + + # Guardrails state tracking + self._interrupted_by_guardrail = False + self._item_transcripts: dict[str, str] = {} # item_id -> accumulated transcript + self._item_guardrail_run_counts: dict[str, int] = {} # item_id -> run count + self._debounce_text_length = self._run_config.get("guardrails_settings", {}).get( + "debounce_text_length", 100 + ) + + async def __aenter__(self) -> RealtimeSession: + """Start the session by connecting to the model. After this, you will be able to stream + events from the model and send messages and audio to the model. + """ + # Add ourselves as a listener + self._model.add_listener(self) + + # Connect to the model + await self._model.connect(self._model_config) + + # Emit initial history update + await self._put_event( + RealtimeHistoryUpdated( + history=self._history, + info=self._event_info, + ) + ) + + return self + + async def enter(self) -> RealtimeSession: + """Enter the async context manager. We strongly recommend using the async context manager + pattern instead of this method. If you use this, you need to manually call `close()` when + you are done. + """ + return await self.__aenter__() + + async def __aexit__(self, _exc_type: Any, _exc_val: Any, _exc_tb: Any) -> None: + """End the session.""" + await self.close() + + async def __aiter__(self) -> AsyncIterator[RealtimeSessionEvent]: + """Iterate over events from the session.""" + while not self._closed: + try: + event = await self._event_queue.get() + yield event + except asyncio.CancelledError: + break + + async def close(self) -> None: + """Close the session.""" + self._closed = True + self._model.remove_listener(self) + await self._model.close() + + async def send_message(self, message: RealtimeUserInput) -> None: + """Send a message to the model.""" + await self._model.send_message(message) + + async def send_audio(self, audio: bytes, *, commit: bool = False) -> None: + """Send a raw audio chunk to the model.""" + await self._model.send_audio(audio, commit=commit) + + async def interrupt(self) -> None: + """Interrupt the model.""" + await self._model.interrupt() + + async def on_event(self, event: RealtimeModelEvent) -> None: + await self._put_event(RealtimeRawModelEvent(data=event, info=self._event_info)) + + if event.type == "error": + await self._put_event(RealtimeError(info=self._event_info, error=event.error)) + elif event.type == "function_call": + await self._handle_tool_call(event) + elif event.type == "audio": + await self._put_event(RealtimeAudio(info=self._event_info, audio=event)) + elif event.type == "audio_interrupted": + await self._put_event(RealtimeAudioInterrupted(info=self._event_info)) + elif event.type == "audio_done": + await self._put_event(RealtimeAudioEnd(info=self._event_info)) + elif event.type == "conversation.item.input_audio_transcription.completed": + self._history = RealtimeSession._get_new_history(self._history, event) + await self._put_event( + RealtimeHistoryUpdated(info=self._event_info, history=self._history) + ) + elif event.type == "transcript_delta": + # Accumulate transcript text for guardrail debouncing per item_id + item_id = event.item_id + if item_id not in self._item_transcripts: + self._item_transcripts[item_id] = "" + self._item_guardrail_run_counts[item_id] = 0 + + self._item_transcripts[item_id] += event.delta + + # Check if we should run guardrails based on debounce threshold + current_length = len(self._item_transcripts[item_id]) + threshold = self._debounce_text_length + next_run_threshold = (self._item_guardrail_run_counts[item_id] + 1) * threshold + + if current_length >= next_run_threshold: + self._item_guardrail_run_counts[item_id] += 1 + await self._run_output_guardrails(self._item_transcripts[item_id]) + elif event.type == "item_updated": + is_new = not any(item.item_id == event.item.item_id for item in self._history) + self._history = self._get_new_history(self._history, event.item) + if is_new: + new_item = next( + item for item in self._history if item.item_id == event.item.item_id + ) + await self._put_event(RealtimeHistoryAdded(info=self._event_info, item=new_item)) + else: + await self._put_event( + RealtimeHistoryUpdated(info=self._event_info, history=self._history) + ) + elif event.type == "item_deleted": + deleted_id = event.item_id + self._history = [item for item in self._history if item.item_id != deleted_id] + await self._put_event( + RealtimeHistoryUpdated(info=self._event_info, history=self._history) + ) + elif event.type == "connection_status": + pass + elif event.type == "turn_started": + await self._put_event( + RealtimeAgentStartEvent( + agent=self._current_agent, + info=self._event_info, + ) + ) + elif event.type == "turn_ended": + # Clear guardrail state for next turn + self._item_transcripts.clear() + self._item_guardrail_run_counts.clear() + self._interrupted_by_guardrail = False + + await self._put_event( + RealtimeAgentEndEvent( + agent=self._current_agent, + info=self._event_info, + ) + ) + elif event.type == "other": + pass + else: + assert_never(event) + + async def _put_event(self, event: RealtimeSessionEvent) -> None: + """Put an event into the queue.""" + await self._event_queue.put(event) + + async def _handle_tool_call(self, event: RealtimeModelToolCallEvent) -> None: + """Handle a tool call event.""" + all_tools = await self._current_agent.get_all_tools(self._context_wrapper) + function_map = {tool.name: tool for tool in all_tools if isinstance(tool, FunctionTool)} + handoff_map = {tool.name: tool for tool in all_tools if isinstance(tool, Handoff)} + + if event.name in function_map: + await self._put_event( + RealtimeToolStart( + info=self._event_info, + tool=function_map[event.name], + agent=self._current_agent, + ) + ) + + func_tool = function_map[event.name] + tool_context = ToolContext.from_agent_context(self._context_wrapper, event.call_id) + result = await func_tool.on_invoke_tool(tool_context, event.arguments) + + await self._model.send_tool_output(event, str(result), True) + + await self._put_event( + RealtimeToolEnd( + info=self._event_info, + tool=func_tool, + output=result, + agent=self._current_agent, + ) + ) + elif event.name in handoff_map: + # TODO (rm) Add support for handoffs + pass + else: + # TODO (rm) Add error handling + pass + + @classmethod + def _get_new_history( + cls, + old_history: list[RealtimeItem], + event: RealtimeModelInputAudioTranscriptionCompletedEvent | RealtimeItem, + ) -> list[RealtimeItem]: + # Merge transcript into placeholder input_audio message. + if isinstance(event, RealtimeModelInputAudioTranscriptionCompletedEvent): + new_history: list[RealtimeItem] = [] + for item in old_history: + if item.item_id == event.item_id and item.type == "message" and item.role == "user": + content: list[InputText | InputAudio] = [] + for entry in item.content: + if entry.type == "input_audio": + copied_entry = entry.model_copy(update={"transcript": event.transcript}) + content.append(copied_entry) + else: + content.append(entry) # type: ignore + new_history.append( + item.model_copy(update={"content": content, "status": "completed"}) + ) + else: + new_history.append(item) + return new_history + + # Otherwise it's just a new item + # TODO (rm) Add support for audio storage config + + # If the item already exists, update it + existing_index = next( + (i for i, item in enumerate(old_history) if item.item_id == event.item_id), None + ) + if existing_index is not None: + new_history = old_history.copy() + new_history[existing_index] = event + return new_history + # Otherwise, insert it after the previous_item_id if that is set + elif event.previous_item_id: + # Insert the new item after the previous item + previous_index = next( + (i for i, item in enumerate(old_history) if item.item_id == event.previous_item_id), + None, + ) + if previous_index is not None: + new_history = old_history.copy() + new_history.insert(previous_index + 1, event) + return new_history + + # Otherwise, add it to the end + return old_history + [event] + + async def _run_output_guardrails(self, text: str) -> bool: + """Run output guardrails on the given text. Returns True if any guardrail was triggered.""" + output_guardrails = self._run_config.get("output_guardrails", []) + if not output_guardrails or self._interrupted_by_guardrail: + return False + + triggered_results = [] + + for guardrail in output_guardrails: + try: + result = await guardrail.run( + # TODO (rm) Remove this cast, it's wrong + self._context_wrapper, + cast(Agent[Any], self._current_agent), + text, + ) + if result.output.tripwire_triggered: + triggered_results.append(result) + except Exception: + # Continue with other guardrails if one fails + continue + + if triggered_results: + # Mark as interrupted to prevent multiple interrupts + self._interrupted_by_guardrail = True + + # Emit guardrail tripped event + await self._put_event( + RealtimeGuardrailTripped( + guardrail_results=triggered_results, + message=text, + info=self._event_info, + ) + ) + + # Interrupt the model + await self._model.interrupt() + + # Send guardrail triggered message + guardrail_names = [result.guardrail.get_name() for result in triggered_results] + await self._model.send_message(f"guardrail triggered: {', '.join(guardrail_names)}") + + return True + + return False diff --git a/src/agents/repl.py b/src/agents/repl.py new file mode 100644 index 000000000..f7142555f --- /dev/null +++ b/src/agents/repl.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +from typing import Any + +from openai.types.responses.response_text_delta_event import ResponseTextDeltaEvent + +from .agent import Agent +from .items import TResponseInputItem +from .result import RunResultBase +from .run import Runner +from .stream_events import AgentUpdatedStreamEvent, RawResponsesStreamEvent, RunItemStreamEvent + + +async def run_demo_loop(agent: Agent[Any], *, stream: bool = True) -> None: + """Run a simple REPL loop with the given agent. + + This utility allows quick manual testing and debugging of an agent from the + command line. Conversation state is preserved across turns. Enter ``exit`` + or ``quit`` to stop the loop. + + Args: + agent: The starting agent to run. + stream: Whether to stream the agent output. + """ + + current_agent = agent + input_items: list[TResponseInputItem] = [] + while True: + try: + user_input = input(" > ") + except (EOFError, KeyboardInterrupt): + print() + break + if user_input.strip().lower() in {"exit", "quit"}: + break + if not user_input: + continue + + input_items.append({"role": "user", "content": user_input}) + + result: RunResultBase + if stream: + result = Runner.run_streamed(current_agent, input=input_items) + async for event in result.stream_events(): + if isinstance(event, RawResponsesStreamEvent): + if isinstance(event.data, ResponseTextDeltaEvent): + print(event.data.delta, end="", flush=True) + elif isinstance(event, RunItemStreamEvent): + if event.item.type == "tool_call_item": + print("\n[tool called]", flush=True) + elif event.item.type == "tool_call_output_item": + print(f"\n[tool output: {event.item.output}]", flush=True) + elif isinstance(event, AgentUpdatedStreamEvent): + print(f"\n[Agent updated: {event.new_agent.name}]", flush=True) + print() + else: + result = await Runner.run(current_agent, input_items) + if result.final_output is not None: + print(result.final_output) + + current_agent = result.last_agent + input_items = result.to_input_list() diff --git a/src/agents/result.py b/src/agents/result.py index a2a6cc4a5..5cf0e74c8 100644 --- a/src/agents/result.py +++ b/src/agents/result.py @@ -10,14 +10,23 @@ from ._run_impl import QueueCompleteSentinel from .agent import Agent -from .agent_output import AgentOutputSchema -from .exceptions import InputGuardrailTripwireTriggered, MaxTurnsExceeded +from .agent_output import AgentOutputSchemaBase +from .exceptions import ( + AgentsException, + InputGuardrailTripwireTriggered, + MaxTurnsExceeded, + RunErrorDetails, +) from .guardrail import InputGuardrailResult, OutputGuardrailResult from .items import ItemHelpers, ModelResponse, RunItem, TResponseInputItem from .logger import logger +from .run_context import RunContextWrapper from .stream_events import StreamEvent from .tracing import Trace -from .util._pretty_print import pretty_print_result, pretty_print_run_result_streaming +from .util._pretty_print import ( + pretty_print_result, + pretty_print_run_result_streaming, +) if TYPE_CHECKING: from ._run_impl import QueueCompleteSentinel @@ -50,6 +59,9 @@ class RunResultBase(abc.ABC): output_guardrail_results: list[OutputGuardrailResult] """Guardrail results for the final output of the agent.""" + context_wrapper: RunContextWrapper[Any] + """The context wrapper for the agent run.""" + @property @abc.abstractmethod def last_agent(self) -> Agent[Any]: @@ -124,9 +136,9 @@ class RunResultStreaming(RunResultBase): final_output: Any """The final output of the agent. This is None until the agent has finished running.""" - _current_agent_output_schema: AgentOutputSchema | None = field(repr=False) + _current_agent_output_schema: AgentOutputSchemaBase | None = field(repr=False) - _trace: Trace | None = field(repr=False) + trace: Trace | None = field(repr=False) is_complete: bool = False """Whether the agent has finished running.""" @@ -152,6 +164,18 @@ def last_agent(self) -> Agent[Any]: """ return self.current_agent + def cancel(self) -> None: + """Cancels the streaming run, stopping all background tasks and marking the run as + complete.""" + self._cleanup_tasks() # Cancel all running tasks + self.is_complete = True # Mark the run as complete to stop event streaming + + # Optionally, clear the event queue to prevent processing stale events + while not self._event_queue.empty(): + self._event_queue.get_nowait() + while not self._input_guardrail_queue.empty(): + self._input_guardrail_queue.get_nowait() + async def stream_events(self) -> AsyncIterator[StreamEvent]: """Stream deltas for new items as they are generated. We're using the types from the OpenAI Responses API, so these are semantic events: each event has a `type` field that @@ -185,39 +209,58 @@ async def stream_events(self) -> AsyncIterator[StreamEvent]: yield item self._event_queue.task_done() - if self._trace: - self._trace.finish(reset_current=True) - self._cleanup_tasks() if self._stored_exception: raise self._stored_exception + def _create_error_details(self) -> RunErrorDetails: + """Return a `RunErrorDetails` object considering the current attributes of the class.""" + return RunErrorDetails( + input=self.input, + new_items=self.new_items, + raw_responses=self.raw_responses, + last_agent=self.current_agent, + context_wrapper=self.context_wrapper, + input_guardrail_results=self.input_guardrail_results, + output_guardrail_results=self.output_guardrail_results, + ) + def _check_errors(self): if self.current_turn > self.max_turns: - self._stored_exception = MaxTurnsExceeded(f"Max turns ({self.max_turns}) exceeded") + max_turns_exc = MaxTurnsExceeded(f"Max turns ({self.max_turns}) exceeded") + max_turns_exc.run_data = self._create_error_details() + self._stored_exception = max_turns_exc # Fetch all the completed guardrail results from the queue and raise if needed while not self._input_guardrail_queue.empty(): guardrail_result = self._input_guardrail_queue.get_nowait() if guardrail_result.output.tripwire_triggered: - self._stored_exception = InputGuardrailTripwireTriggered(guardrail_result) + tripwire_exc = InputGuardrailTripwireTriggered(guardrail_result) + tripwire_exc.run_data = self._create_error_details() + self._stored_exception = tripwire_exc # Check the tasks for any exceptions if self._run_impl_task and self._run_impl_task.done(): - exc = self._run_impl_task.exception() - if exc and isinstance(exc, Exception): - self._stored_exception = exc + run_impl_exc = self._run_impl_task.exception() + if run_impl_exc and isinstance(run_impl_exc, Exception): + if isinstance(run_impl_exc, AgentsException) and run_impl_exc.run_data is None: + run_impl_exc.run_data = self._create_error_details() + self._stored_exception = run_impl_exc if self._input_guardrails_task and self._input_guardrails_task.done(): - exc = self._input_guardrails_task.exception() - if exc and isinstance(exc, Exception): - self._stored_exception = exc + in_guard_exc = self._input_guardrails_task.exception() + if in_guard_exc and isinstance(in_guard_exc, Exception): + if isinstance(in_guard_exc, AgentsException) and in_guard_exc.run_data is None: + in_guard_exc.run_data = self._create_error_details() + self._stored_exception = in_guard_exc if self._output_guardrails_task and self._output_guardrails_task.done(): - exc = self._output_guardrails_task.exception() - if exc and isinstance(exc, Exception): - self._stored_exception = exc + out_guard_exc = self._output_guardrails_task.exception() + if out_guard_exc and isinstance(out_guard_exc, Exception): + if isinstance(out_guard_exc, AgentsException) and out_guard_exc.run_data is None: + out_guard_exc.run_data = self._create_error_details() + self._stored_exception = out_guard_exc def _cleanup_tasks(self): if self._run_impl_task and not self._run_impl_task.done(): diff --git a/src/agents/run.py b/src/agents/run.py index e2b0dbceb..2dd9524bb 100644 --- a/src/agents/run.py +++ b/src/agents/run.py @@ -2,10 +2,15 @@ import asyncio import copy +import inspect from dataclasses import dataclass, field -from typing import Any, cast +from typing import Any, Generic, cast from openai.types.responses import ResponseCompletedEvent +from openai.types.responses.response_prompt_param import ( + ResponsePromptParam, +) +from typing_extensions import NotRequired, TypedDict, Unpack from ._run_impl import ( AgentToolUseTracker, @@ -19,22 +24,30 @@ get_model_tracing_impl, ) from .agent import Agent -from .agent_output import AgentOutputSchema +from .agent_output import AgentOutputSchema, AgentOutputSchemaBase from .exceptions import ( AgentsException, InputGuardrailTripwireTriggered, MaxTurnsExceeded, ModelBehaviorError, OutputGuardrailTripwireTriggered, + RunErrorDetails, + UserError, +) +from .guardrail import ( + InputGuardrail, + InputGuardrailResult, + OutputGuardrail, + OutputGuardrailResult, ) -from .guardrail import InputGuardrail, InputGuardrailResult, OutputGuardrail, OutputGuardrailResult from .handoffs import Handoff, HandoffInputFilter, handoff from .items import ItemHelpers, ModelResponse, RunItem, TResponseInputItem from .lifecycle import RunHooks from .logger import logger +from .memory import Session from .model_settings import ModelSettings from .models.interface import Model, ModelProvider -from .models.openai_provider import OpenAIProvider +from .models.multi_provider import MultiProvider from .result import RunResult, RunResultStreaming from .run_context import RunContextWrapper, TContext from .stream_events import AgentUpdatedStreamEvent, RawResponsesStreamEvent @@ -46,6 +59,27 @@ DEFAULT_MAX_TURNS = 10 +DEFAULT_AGENT_RUNNER: AgentRunner = None # type: ignore +# the value is set at the end of the module + + +def set_default_agent_runner(runner: AgentRunner | None) -> None: + """ + WARNING: this class is experimental and not part of the public API + It should not be used directly. + """ + global DEFAULT_AGENT_RUNNER + DEFAULT_AGENT_RUNNER = runner or AgentRunner() + + +def get_default_agent_runner() -> AgentRunner: + """ + WARNING: this class is experimental and not part of the public API + It should not be used directly. + """ + global DEFAULT_AGENT_RUNNER + return DEFAULT_AGENT_RUNNER + @dataclass class RunConfig: @@ -56,7 +90,7 @@ class RunConfig: agent. The model_provider passed in below must be able to resolve this model name. """ - model_provider: ModelProvider = field(default_factory=OpenAIProvider) + model_provider: ModelProvider = field(default_factory=MultiProvider) """The model provider to use when looking up string model names. Defaults to OpenAI.""" model_settings: ModelSettings | None = None @@ -106,6 +140,28 @@ class RunConfig: """ +class RunOptions(TypedDict, Generic[TContext]): + """Arguments for ``AgentRunner`` methods.""" + + context: NotRequired[TContext | None] + """The context for the run.""" + + max_turns: NotRequired[int] + """The maximum number of turns to run for.""" + + hooks: NotRequired[RunHooks[TContext] | None] + """Lifecycle hooks for the run.""" + + run_config: NotRequired[RunConfig | None] + """Run configuration.""" + + previous_response_id: NotRequired[str | None] + """The ID of the previous response, if any.""" + + session: NotRequired[Session | None] + """The session for the run.""" + + class Runner: @classmethod async def run( @@ -118,6 +174,7 @@ async def run( hooks: RunHooks[TContext] | None = None, run_config: RunConfig | None = None, previous_response_id: str | None = None, + session: Session | None = None, ) -> RunResult: """Run a workflow starting at the given agent. The agent will run in a loop until a final output is generated. The loop runs like so: @@ -126,13 +183,10 @@ async def run( `agent.output_type`, the loop terminates. 3. If there's a handoff, we run the loop again, with the new agent. 4. Else, we run tool calls (if any), and re-run the loop. - In two cases, the agent may raise an exception: 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised. - Note that only the first agent's input guardrails are run. - Args: starting_agent: The starting agent to run. input: The initial input to the agent. You can pass a single string for a user message, @@ -144,16 +198,153 @@ async def run( run_config: Global settings for the entire agent run. previous_response_id: The ID of the previous response, if using OpenAI models via the Responses API, this allows you to skip passing in input from the previous turn. + Returns: + A run result containing all the inputs, guardrail results and the output of the last + agent. Agents may perform handoffs, so we don't know the specific type of the output. + """ + runner = DEFAULT_AGENT_RUNNER + return await runner.run( + starting_agent, + input, + context=context, + max_turns=max_turns, + hooks=hooks, + run_config=run_config, + previous_response_id=previous_response_id, + session=session, + ) + @classmethod + def run_sync( + cls, + starting_agent: Agent[TContext], + input: str | list[TResponseInputItem], + *, + context: TContext | None = None, + max_turns: int = DEFAULT_MAX_TURNS, + hooks: RunHooks[TContext] | None = None, + run_config: RunConfig | None = None, + previous_response_id: str | None = None, + session: Session | None = None, + ) -> RunResult: + """Run a workflow synchronously, starting at the given agent. Note that this just wraps the + `run` method, so it will not work if there's already an event loop (e.g. inside an async + function, or in a Jupyter notebook or async context like FastAPI). For those cases, use + the `run` method instead. + The agent will run in a loop until a final output is generated. The loop runs like so: + 1. The agent is invoked with the given input. + 2. If there is a final output (i.e. the agent produces something of type + `agent.output_type`, the loop terminates. + 3. If there's a handoff, we run the loop again, with the new agent. + 4. Else, we run tool calls (if any), and re-run the loop. + In two cases, the agent may raise an exception: + 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. + 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised. + Note that only the first agent's input guardrails are run. + Args: + starting_agent: The starting agent to run. + input: The initial input to the agent. You can pass a single string for a user message, + or a list of input items. + context: The context to run the agent with. + max_turns: The maximum number of turns to run the agent for. A turn is defined as one + AI invocation (including any tool calls that might occur). + hooks: An object that receives callbacks on various lifecycle events. + run_config: Global settings for the entire agent run. + previous_response_id: The ID of the previous response, if using OpenAI models via the + Responses API, this allows you to skip passing in input from the previous turn. Returns: A run result containing all the inputs, guardrail results and the output of the last agent. Agents may perform handoffs, so we don't know the specific type of the output. """ + runner = DEFAULT_AGENT_RUNNER + return runner.run_sync( + starting_agent, + input, + context=context, + max_turns=max_turns, + hooks=hooks, + run_config=run_config, + previous_response_id=previous_response_id, + session=session, + ) + + @classmethod + def run_streamed( + cls, + starting_agent: Agent[TContext], + input: str | list[TResponseInputItem], + context: TContext | None = None, + max_turns: int = DEFAULT_MAX_TURNS, + hooks: RunHooks[TContext] | None = None, + run_config: RunConfig | None = None, + previous_response_id: str | None = None, + session: Session | None = None, + ) -> RunResultStreaming: + """Run a workflow starting at the given agent in streaming mode. The returned result object + contains a method you can use to stream semantic events as they are generated. + The agent will run in a loop until a final output is generated. The loop runs like so: + 1. The agent is invoked with the given input. + 2. If there is a final output (i.e. the agent produces something of type + `agent.output_type`, the loop terminates. + 3. If there's a handoff, we run the loop again, with the new agent. + 4. Else, we run tool calls (if any), and re-run the loop. + In two cases, the agent may raise an exception: + 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. + 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised. + Note that only the first agent's input guardrails are run. + Args: + starting_agent: The starting agent to run. + input: The initial input to the agent. You can pass a single string for a user message, + or a list of input items. + context: The context to run the agent with. + max_turns: The maximum number of turns to run the agent for. A turn is defined as one + AI invocation (including any tool calls that might occur). + hooks: An object that receives callbacks on various lifecycle events. + run_config: Global settings for the entire agent run. + previous_response_id: The ID of the previous response, if using OpenAI models via the + Responses API, this allows you to skip passing in input from the previous turn. + Returns: + A result object that contains data about the run, as well as a method to stream events. + """ + runner = DEFAULT_AGENT_RUNNER + return runner.run_streamed( + starting_agent, + input, + context=context, + max_turns=max_turns, + hooks=hooks, + run_config=run_config, + previous_response_id=previous_response_id, + session=session, + ) + + +class AgentRunner: + """ + WARNING: this class is experimental and not part of the public API + It should not be used directly or subclassed. + """ + + async def run( + self, + starting_agent: Agent[TContext], + input: str | list[TResponseInputItem], + **kwargs: Unpack[RunOptions[TContext]], + ) -> RunResult: + context = kwargs.get("context") + max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS) + hooks = kwargs.get("hooks") + run_config = kwargs.get("run_config") + previous_response_id = kwargs.get("previous_response_id") + session = kwargs.get("session") if hooks is None: hooks = RunHooks[Any]() if run_config is None: run_config = RunConfig() + # Prepare input with session if enabled + prepared_input = await self._prepare_input_with_session(input, session) + tool_use_tracker = AgentToolUseTracker() with TraceCtxManager( @@ -164,7 +355,7 @@ async def run( disabled=run_config.tracing_disabled, ): current_turn = 0 - original_input: str | list[TResponseInputItem] = copy.deepcopy(input) + original_input: str | list[TResponseInputItem] = copy.deepcopy(prepared_input) generated_items: list[RunItem] = [] model_responses: list[ModelResponse] = [] @@ -180,12 +371,17 @@ async def run( try: while True: + all_tools = await AgentRunner._get_all_tools(current_agent, context_wrapper) + # Start an agent span if we don't have one. This span is ended if the current # agent changes, or if the agent loop ends. if current_span is None: - handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)] - if output_schema := cls._get_output_schema(current_agent): - output_type_name = output_schema.output_type_name() + handoff_names = [ + h.agent_name + for h in await AgentRunner._get_handoffs(current_agent, context_wrapper) + ] + if output_schema := AgentRunner._get_output_schema(current_agent): + output_type_name = output_schema.name() else: output_type_name = "str" @@ -195,8 +391,6 @@ async def run( output_type=output_type_name, ) current_span.start(mark_as_current=True) - - all_tools = await cls._get_all_tools(current_agent) current_span.span_data.tools = [t.name for t in all_tools] current_turn += 1 @@ -216,14 +410,14 @@ async def run( if current_turn == 1: input_guardrail_results, turn_result = await asyncio.gather( - cls._run_input_guardrails( + self._run_input_guardrails( starting_agent, starting_agent.input_guardrails + (run_config.input_guardrails or []), - copy.deepcopy(input), + copy.deepcopy(prepared_input), context_wrapper, ), - cls._run_single_turn( + self._run_single_turn( agent=current_agent, all_tools=all_tools, original_input=original_input, @@ -237,7 +431,7 @@ async def run( ), ) else: - turn_result = await cls._run_single_turn( + turn_result = await self._run_single_turn( agent=current_agent, all_tools=all_tools, original_input=original_input, @@ -256,13 +450,13 @@ async def run( generated_items = turn_result.generated_items if isinstance(turn_result.next_step, NextStepFinalOutput): - output_guardrail_results = await cls._run_output_guardrails( + output_guardrail_results = await self._run_output_guardrails( current_agent.output_guardrails + (run_config.output_guardrails or []), current_agent, turn_result.next_step.output, context_wrapper, ) - return RunResult( + result = RunResult( input=original_input, new_items=generated_items, raw_responses=model_responses, @@ -270,7 +464,13 @@ async def run( _last_agent=current_agent, input_guardrail_results=input_guardrail_results, output_guardrail_results=output_guardrail_results, + context_wrapper=context_wrapper, ) + + # Save the conversation to session if enabled + await self._save_result_to_session(session, input, result) + + return result elif isinstance(turn_result.next_step, NextStepHandoff): current_agent = cast(Agent[TContext], turn_result.next_step.new_agent) current_span.finish(reset_current=True) @@ -282,60 +482,39 @@ async def run( raise AgentsException( f"Unknown next step type: {type(turn_result.next_step)}" ) + except AgentsException as exc: + exc.run_data = RunErrorDetails( + input=original_input, + new_items=generated_items, + raw_responses=model_responses, + last_agent=current_agent, + context_wrapper=context_wrapper, + input_guardrail_results=input_guardrail_results, + output_guardrail_results=[], + ) + raise finally: if current_span: current_span.finish(reset_current=True) - @classmethod def run_sync( - cls, + self, starting_agent: Agent[TContext], input: str | list[TResponseInputItem], - *, - context: TContext | None = None, - max_turns: int = DEFAULT_MAX_TURNS, - hooks: RunHooks[TContext] | None = None, - run_config: RunConfig | None = None, - previous_response_id: str | None = None, + **kwargs: Unpack[RunOptions[TContext]], ) -> RunResult: - """Run a workflow synchronously, starting at the given agent. Note that this just wraps the - `run` method, so it will not work if there's already an event loop (e.g. inside an async - function, or in a Jupyter notebook or async context like FastAPI). For those cases, use - the `run` method instead. - - The agent will run in a loop until a final output is generated. The loop runs like so: - 1. The agent is invoked with the given input. - 2. If there is a final output (i.e. the agent produces something of type - `agent.output_type`, the loop terminates. - 3. If there's a handoff, we run the loop again, with the new agent. - 4. Else, we run tool calls (if any), and re-run the loop. - - In two cases, the agent may raise an exception: - 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. - 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised. + context = kwargs.get("context") + max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS) + hooks = kwargs.get("hooks") + run_config = kwargs.get("run_config") + previous_response_id = kwargs.get("previous_response_id") + session = kwargs.get("session") - Note that only the first agent's input guardrails are run. - - Args: - starting_agent: The starting agent to run. - input: The initial input to the agent. You can pass a single string for a user message, - or a list of input items. - context: The context to run the agent with. - max_turns: The maximum number of turns to run the agent for. A turn is defined as one - AI invocation (including any tool calls that might occur). - hooks: An object that receives callbacks on various lifecycle events. - run_config: Global settings for the entire agent run. - previous_response_id: The ID of the previous response, if using OpenAI models via the - Responses API, this allows you to skip passing in input from the previous turn. - - Returns: - A run result containing all the inputs, guardrail results and the output of the last - agent. Agents may perform handoffs, so we don't know the specific type of the output. - """ return asyncio.get_event_loop().run_until_complete( - cls.run( + self.run( starting_agent, input, + session=session, context=context, max_turns=max_turns, hooks=hooks, @@ -344,47 +523,19 @@ def run_sync( ) ) - @classmethod def run_streamed( - cls, + self, starting_agent: Agent[TContext], input: str | list[TResponseInputItem], - context: TContext | None = None, - max_turns: int = DEFAULT_MAX_TURNS, - hooks: RunHooks[TContext] | None = None, - run_config: RunConfig | None = None, - previous_response_id: str | None = None, + **kwargs: Unpack[RunOptions[TContext]], ) -> RunResultStreaming: - """Run a workflow starting at the given agent in streaming mode. The returned result object - contains a method you can use to stream semantic events as they are generated. + context = kwargs.get("context") + max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS) + hooks = kwargs.get("hooks") + run_config = kwargs.get("run_config") + previous_response_id = kwargs.get("previous_response_id") + session = kwargs.get("session") - The agent will run in a loop until a final output is generated. The loop runs like so: - 1. The agent is invoked with the given input. - 2. If there is a final output (i.e. the agent produces something of type - `agent.output_type`, the loop terminates. - 3. If there's a handoff, we run the loop again, with the new agent. - 4. Else, we run tool calls (if any), and re-run the loop. - - In two cases, the agent may raise an exception: - 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. - 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised. - - Note that only the first agent's input guardrails are run. - - Args: - starting_agent: The starting agent to run. - input: The initial input to the agent. You can pass a single string for a user message, - or a list of input items. - context: The context to run the agent with. - max_turns: The maximum number of turns to run the agent for. A turn is defined as one - AI invocation (including any tool calls that might occur). - hooks: An object that receives callbacks on various lifecycle events. - run_config: Global settings for the entire agent run. - previous_response_id: The ID of the previous response, if using OpenAI models via the - Responses API, this allows you to skip passing in input from the previous turn. - Returns: - A result object that contains data about the run, as well as a method to stream events. - """ if hooks is None: hooks = RunHooks[Any]() if run_config is None: @@ -404,12 +555,8 @@ def run_streamed( disabled=run_config.tracing_disabled, ) ) - # Need to start the trace here, because the current trace contextvar is captured at - # asyncio.create_task time - if new_trace: - new_trace.start(mark_as_current=True) - output_schema = cls._get_output_schema(starting_agent) + output_schema = AgentRunner._get_output_schema(starting_agent) context_wrapper: RunContextWrapper[TContext] = RunContextWrapper( context=context # type: ignore ) @@ -426,12 +573,13 @@ def run_streamed( input_guardrail_results=[], output_guardrail_results=[], _current_agent_output_schema=output_schema, - _trace=new_trace, + trace=new_trace, + context_wrapper=context_wrapper, ) # Kick off the actual agent loop in the background and return the streamed result object. streamed_result._run_impl_task = asyncio.create_task( - cls._run_streamed_impl( + self._start_streaming( starting_input=input, streamed_result=streamed_result, starting_agent=starting_agent, @@ -440,6 +588,7 @@ def run_streamed( context_wrapper=context_wrapper, run_config=run_config, previous_response_id=previous_response_id, + session=session, ) ) return streamed_result @@ -488,7 +637,7 @@ async def _run_input_guardrails_with_queue( streamed_result.input_guardrail_results = guardrail_results @classmethod - async def _run_streamed_impl( + async def _start_streaming( cls, starting_input: str | list[TResponseInputItem], streamed_result: RunResultStreaming, @@ -498,7 +647,11 @@ async def _run_streamed_impl( context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, previous_response_id: str | None, + session: Session | None, ): + if streamed_result.trace: + streamed_result.trace.start(mark_as_current=True) + current_span: Span[AgentSpanData] | None = None current_agent = starting_agent current_turn = 0 @@ -508,16 +661,27 @@ async def _run_streamed_impl( streamed_result._event_queue.put_nowait(AgentUpdatedStreamEvent(new_agent=current_agent)) try: + # Prepare input with session if enabled + prepared_input = await AgentRunner._prepare_input_with_session(starting_input, session) + + # Update the streamed result with the prepared input + streamed_result.input = prepared_input + while True: if streamed_result.is_complete: break + all_tools = await cls._get_all_tools(current_agent, context_wrapper) + # Start an agent span if we don't have one. This span is ended if the current # agent changes, or if the agent loop ends. if current_span is None: - handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)] + handoff_names = [ + h.agent_name + for h in await cls._get_handoffs(current_agent, context_wrapper) + ] if output_schema := cls._get_output_schema(current_agent): - output_type_name = output_schema.output_type_name() + output_type_name = output_schema.name() else: output_type_name = "str" @@ -527,8 +691,6 @@ async def _run_streamed_impl( output_type=output_type_name, ) current_span.start(mark_as_current=True) - - all_tools = await cls._get_all_tools(current_agent) tool_names = [t.name for t in all_tools] current_span.span_data.tools = tool_names current_turn += 1 @@ -551,7 +713,7 @@ async def _run_streamed_impl( cls._run_input_guardrails_with_queue( starting_agent, starting_agent.input_guardrails + (run_config.input_guardrails or []), - copy.deepcopy(ItemHelpers.input_to_new_input_list(starting_input)), + copy.deepcopy(ItemHelpers.input_to_new_input_list(prepared_input)), context_wrapper, streamed_result, current_span, @@ -605,9 +767,39 @@ async def _run_streamed_impl( streamed_result.output_guardrail_results = output_guardrail_results streamed_result.final_output = turn_result.next_step.output streamed_result.is_complete = True + + # Save the conversation to session if enabled + # Create a temporary RunResult for session saving + temp_result = RunResult( + input=streamed_result.input, + new_items=streamed_result.new_items, + raw_responses=streamed_result.raw_responses, + final_output=streamed_result.final_output, + _last_agent=current_agent, + input_guardrail_results=streamed_result.input_guardrail_results, + output_guardrail_results=streamed_result.output_guardrail_results, + context_wrapper=context_wrapper, + ) + await AgentRunner._save_result_to_session( + session, starting_input, temp_result + ) + streamed_result._event_queue.put_nowait(QueueCompleteSentinel()) elif isinstance(turn_result.next_step, NextStepRunAgain): pass + except AgentsException as exc: + streamed_result.is_complete = True + streamed_result._event_queue.put_nowait(QueueCompleteSentinel()) + exc.run_data = RunErrorDetails( + input=streamed_result.input, + new_items=streamed_result.new_items, + raw_responses=streamed_result.raw_responses, + last_agent=current_agent, + context_wrapper=context_wrapper, + input_guardrail_results=streamed_result.input_guardrail_results, + output_guardrail_results=streamed_result.output_guardrail_results, + ) + raise except Exception as e: if current_span: _error_tracing.attach_error_to_span( @@ -625,6 +817,8 @@ async def _run_streamed_impl( finally: if current_span: current_span.finish(reset_current=True) + if streamed_result.trace: + streamed_result.trace.finish(reset_current=True) @classmethod async def _run_single_turn_streamed( @@ -654,9 +848,12 @@ async def _run_single_turn_streamed( streamed_result.current_agent = agent streamed_result._current_agent_output_schema = output_schema - system_prompt = await agent.get_system_prompt(context_wrapper) + system_prompt, prompt_config = await asyncio.gather( + agent.get_system_prompt(context_wrapper), + agent.get_prompt(context_wrapper), + ) - handoffs = cls._get_handoffs(agent) + handoffs = await cls._get_handoffs(agent, context_wrapper) model = cls._get_model(agent, run_config) model_settings = agent.model_settings.resolve(run_config.model_settings) model_settings = RunImpl.maybe_reset_tool_choice(agent, tool_use_tracker, model_settings) @@ -678,6 +875,7 @@ async def _run_single_turn_streamed( run_config.tracing_disabled, run_config.trace_include_sensitive_data ), previous_response_id=previous_response_id, + prompt=prompt_config, ): if isinstance(event, ResponseCompletedEvent): usage = ( @@ -686,6 +884,8 @@ async def _run_single_turn_streamed( input_tokens=event.response.usage.input_tokens, output_tokens=event.response.usage.output_tokens, total_tokens=event.response.usage.total_tokens, + input_tokens_details=event.response.usage.input_tokens_details, + output_tokens_details=event.response.usage.output_tokens_details, ) if event.response.usage else Usage() @@ -695,6 +895,7 @@ async def _run_single_turn_streamed( usage=usage, response_id=event.response.id, ) + context_wrapper.usage.add(usage) streamed_result._event_queue.put_nowait(RawResponsesStreamEvent(data=event)) @@ -746,10 +947,13 @@ async def _run_single_turn( ), ) - system_prompt = await agent.get_system_prompt(context_wrapper) + system_prompt, prompt_config = await asyncio.gather( + agent.get_system_prompt(context_wrapper), + agent.get_prompt(context_wrapper), + ) output_schema = cls._get_output_schema(agent) - handoffs = cls._get_handoffs(agent) + handoffs = await cls._get_handoffs(agent, context_wrapper) input = ItemHelpers.input_to_new_input_list(original_input) input.extend([generated_item.to_input_item() for generated_item in generated_items]) @@ -764,6 +968,7 @@ async def _run_single_turn( run_config, tool_use_tracker, previous_response_id, + prompt_config, ) return await cls._get_single_step_result_from_response( @@ -789,7 +994,7 @@ async def _get_single_step_result_from_response( original_input: str | list[TResponseInputItem], pre_step_items: list[RunItem], new_response: ModelResponse, - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], @@ -900,13 +1105,14 @@ async def _get_new_response( agent: Agent[TContext], system_prompt: str | None, input: list[TResponseInputItem], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, all_tools: list[Tool], handoffs: list[Handoff], context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, tool_use_tracker: AgentToolUseTracker, previous_response_id: str | None, + prompt_config: ResponsePromptParam | None, ) -> ModelResponse: model = cls._get_model(agent, run_config) model_settings = agent.model_settings.resolve(run_config.model_settings) @@ -923,6 +1129,7 @@ async def _get_new_response( run_config.tracing_disabled, run_config.trace_include_sensitive_data ), previous_response_id=previous_response_id, + prompt=prompt_config, ) context_wrapper.usage.add(new_response.usage) @@ -930,25 +1137,43 @@ async def _get_new_response( return new_response @classmethod - def _get_output_schema(cls, agent: Agent[Any]) -> AgentOutputSchema | None: + def _get_output_schema(cls, agent: Agent[Any]) -> AgentOutputSchemaBase | None: if agent.output_type is None or agent.output_type is str: return None + elif isinstance(agent.output_type, AgentOutputSchemaBase): + return agent.output_type return AgentOutputSchema(agent.output_type) @classmethod - def _get_handoffs(cls, agent: Agent[Any]) -> list[Handoff]: + async def _get_handoffs( + cls, agent: Agent[Any], context_wrapper: RunContextWrapper[Any] + ) -> list[Handoff]: handoffs = [] for handoff_item in agent.handoffs: if isinstance(handoff_item, Handoff): handoffs.append(handoff_item) elif isinstance(handoff_item, Agent): handoffs.append(handoff(handoff_item)) - return handoffs + + async def _check_handoff_enabled(handoff_obj: Handoff) -> bool: + attr = handoff_obj.is_enabled + if isinstance(attr, bool): + return attr + res = attr(context_wrapper, agent) + if inspect.isawaitable(res): + return bool(await res) + return bool(res) + + results = await asyncio.gather(*(_check_handoff_enabled(h) for h in handoffs)) + enabled: list[Handoff] = [h for h, ok in zip(handoffs, results) if ok] + return enabled @classmethod - async def _get_all_tools(cls, agent: Agent[Any]) -> list[Tool]: - return await agent.get_all_tools() + async def _get_all_tools( + cls, agent: Agent[Any], context_wrapper: RunContextWrapper[Any] + ) -> list[Tool]: + return await agent.get_all_tools(context_wrapper) @classmethod def _get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model: @@ -960,3 +1185,58 @@ def _get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model: return agent.model return run_config.model_provider.get_model(agent.model) + + @classmethod + async def _prepare_input_with_session( + cls, + input: str | list[TResponseInputItem], + session: Session | None, + ) -> str | list[TResponseInputItem]: + """Prepare input by combining it with session history if enabled.""" + if session is None: + return input + + # Validate that we don't have both a session and a list input, as this creates + # ambiguity about whether the list should append to or replace existing session history + if isinstance(input, list): + raise UserError( + "Cannot provide both a session and a list of input items. " + "When using session memory, provide only a string input to append to the " + "conversation, or use session=None and provide a list to manually manage " + "conversation history." + ) + + # Get previous conversation history + history = await session.get_items() + + # Convert input to list format + new_input_list = ItemHelpers.input_to_new_input_list(input) + + # Combine history with new input + combined_input = history + new_input_list + + return combined_input + + @classmethod + async def _save_result_to_session( + cls, + session: Session | None, + original_input: str | list[TResponseInputItem], + result: RunResult, + ) -> None: + """Save the conversation turn to session.""" + if session is None: + return + + # Convert original input to list format if needed + input_list = ItemHelpers.input_to_new_input_list(original_input) + + # Convert new items to input format + new_items_as_input = [item.to_input_item() for item in result.new_items] + + # Save all items from this turn + items_to_save = input_list + new_items_as_input + await session.add_items(items_to_save) + + +DEFAULT_AGENT_RUNNER = AgentRunner() diff --git a/src/agents/stream_events.py b/src/agents/stream_events.py index bd37d11f3..a271e8acd 100644 --- a/src/agents/stream_events.py +++ b/src/agents/stream_events.py @@ -31,10 +31,13 @@ class RunItemStreamEvent: name: Literal[ "message_output_created", "handoff_requested", + # This is misspelled, but we can't change it because that would be a breaking change "handoff_occured", "tool_called", "tool_output", "reasoning_item_created", + "mcp_approval_requested", + "mcp_list_tools", ] """The name of the event.""" diff --git a/src/agents/tool.py b/src/agents/tool.py index c1c162423..0faf09846 100644 --- a/src/agents/tool.py +++ b/src/agents/tool.py @@ -4,12 +4,18 @@ import json from collections.abc import Awaitable from dataclasses import dataclass -from typing import Any, Callable, Literal, Union, overload +from typing import TYPE_CHECKING, Any, Callable, Literal, Union, overload from openai.types.responses.file_search_tool_param import Filters, RankingOptions +from openai.types.responses.response_computer_tool_call import ( + PendingSafetyCheck, + ResponseComputerToolCall, +) +from openai.types.responses.response_output_item import LocalShellCall, McpApprovalRequest +from openai.types.responses.tool_param import CodeInterpreter, ImageGeneration, Mcp from openai.types.responses.web_search_tool_param import UserLocation from pydantic import ValidationError -from typing_extensions import Concatenate, ParamSpec +from typing_extensions import Concatenate, NotRequired, ParamSpec, TypedDict from . import _debug from .computer import AsyncComputer, Computer @@ -18,16 +24,25 @@ from .items import RunItem from .logger import logger from .run_context import RunContextWrapper +from .tool_context import ToolContext from .tracing import SpanError from .util import _error_tracing from .util._types import MaybeAwaitable +if TYPE_CHECKING: + from .agent import Agent, AgentBase + ToolParams = ParamSpec("ToolParams") ToolFunctionWithoutContext = Callable[ToolParams, Any] ToolFunctionWithContext = Callable[Concatenate[RunContextWrapper[Any], ToolParams], Any] +ToolFunctionWithToolContext = Callable[Concatenate[ToolContext, ToolParams], Any] -ToolFunction = Union[ToolFunctionWithoutContext[ToolParams], ToolFunctionWithContext[ToolParams]] +ToolFunction = Union[ + ToolFunctionWithoutContext[ToolParams], + ToolFunctionWithContext[ToolParams], + ToolFunctionWithToolContext[ToolParams], +] @dataclass @@ -57,7 +72,7 @@ class FunctionTool: params_json_schema: dict[str, Any] """The JSON schema for the tool's parameters.""" - on_invoke_tool: Callable[[RunContextWrapper[Any], str], Awaitable[Any]] + on_invoke_tool: Callable[[ToolContext[Any], str], Awaitable[Any]] """A function that invokes the tool with the given context and parameters. The params passed are: 1. The tool run context. @@ -72,6 +87,11 @@ class FunctionTool: """Whether the JSON schema is in strict mode. We **strongly** recommend setting this to True, as it increases the likelihood of correct JSON input.""" + is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase], MaybeAwaitable[bool]] = True + """Whether the tool is enabled. Either a bool or a Callable that takes the run context and agent + and returns whether the tool is enabled. You can use this to dynamically enable/disable a tool + based on your context/state.""" + @dataclass class FileSearchTool: @@ -125,12 +145,140 @@ class ComputerTool: as well as implements the computer actions like click, screenshot, etc. """ + on_safety_check: Callable[[ComputerToolSafetyCheckData], MaybeAwaitable[bool]] | None = None + """Optional callback to acknowledge computer tool safety checks.""" + @property def name(self): return "computer_use_preview" -Tool = Union[FunctionTool, FileSearchTool, WebSearchTool, ComputerTool] +@dataclass +class ComputerToolSafetyCheckData: + """Information about a computer tool safety check.""" + + ctx_wrapper: RunContextWrapper[Any] + """The run context.""" + + agent: Agent[Any] + """The agent performing the computer action.""" + + tool_call: ResponseComputerToolCall + """The computer tool call.""" + + safety_check: PendingSafetyCheck + """The pending safety check to acknowledge.""" + + +@dataclass +class MCPToolApprovalRequest: + """A request to approve a tool call.""" + + ctx_wrapper: RunContextWrapper[Any] + """The run context.""" + + data: McpApprovalRequest + """The data from the MCP tool approval request.""" + + +class MCPToolApprovalFunctionResult(TypedDict): + """The result of an MCP tool approval function.""" + + approve: bool + """Whether to approve the tool call.""" + + reason: NotRequired[str] + """An optional reason, if rejected.""" + + +MCPToolApprovalFunction = Callable[ + [MCPToolApprovalRequest], MaybeAwaitable[MCPToolApprovalFunctionResult] +] +"""A function that approves or rejects a tool call.""" + + +@dataclass +class HostedMCPTool: + """A tool that allows the LLM to use a remote MCP server. The LLM will automatically list and + call tools, without requiring a a round trip back to your code. + If you want to run MCP servers locally via stdio, in a VPC or other non-publicly-accessible + environment, or you just prefer to run tool calls locally, then you can instead use the servers + in `agents.mcp` and pass `Agent(mcp_servers=[...])` to the agent.""" + + tool_config: Mcp + """The MCP tool config, which includes the server URL and other settings.""" + + on_approval_request: MCPToolApprovalFunction | None = None + """An optional function that will be called if approval is requested for an MCP tool. If not + provided, you will need to manually add approvals/rejections to the input and call + `Runner.run(...)` again.""" + + @property + def name(self): + return "hosted_mcp" + + +@dataclass +class CodeInterpreterTool: + """A tool that allows the LLM to execute code in a sandboxed environment.""" + + tool_config: CodeInterpreter + """The tool config, which includes the container and other settings.""" + + @property + def name(self): + return "code_interpreter" + + +@dataclass +class ImageGenerationTool: + """A tool that allows the LLM to generate images.""" + + tool_config: ImageGeneration + """The tool config, which image generation settings.""" + + @property + def name(self): + return "image_generation" + + +@dataclass +class LocalShellCommandRequest: + """A request to execute a command on a shell.""" + + ctx_wrapper: RunContextWrapper[Any] + """The run context.""" + + data: LocalShellCall + """The data from the local shell tool call.""" + + +LocalShellExecutor = Callable[[LocalShellCommandRequest], MaybeAwaitable[str]] +"""A function that executes a command on a shell.""" + + +@dataclass +class LocalShellTool: + """A tool that allows the LLM to execute commands on a shell.""" + + executor: LocalShellExecutor + """A function that executes a command on a shell.""" + + @property + def name(self): + return "local_shell" + + +Tool = Union[ + FunctionTool, + FileSearchTool, + WebSearchTool, + ComputerTool, + HostedMCPTool, + LocalShellTool, + ImageGenerationTool, + CodeInterpreterTool, +] """A tool that can be used in an agent.""" @@ -152,6 +300,7 @@ def function_tool( use_docstring_info: bool = True, failure_error_function: ToolErrorFunction | None = None, strict_mode: bool = True, + is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase], MaybeAwaitable[bool]] = True, ) -> FunctionTool: """Overload for usage as @function_tool (no parentheses).""" ... @@ -166,6 +315,7 @@ def function_tool( use_docstring_info: bool = True, failure_error_function: ToolErrorFunction | None = None, strict_mode: bool = True, + is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase], MaybeAwaitable[bool]] = True, ) -> Callable[[ToolFunction[...]], FunctionTool]: """Overload for usage as @function_tool(...).""" ... @@ -180,6 +330,7 @@ def function_tool( use_docstring_info: bool = True, failure_error_function: ToolErrorFunction | None = default_tool_error_function, strict_mode: bool = True, + is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase], MaybeAwaitable[bool]] = True, ) -> FunctionTool | Callable[[ToolFunction[...]], FunctionTool]: """ Decorator to create a FunctionTool from a function. By default, we will: @@ -208,6 +359,9 @@ def function_tool( If False, it allows non-strict JSON schemas. For example, if a parameter has a default value, it will be optional, additional properties are allowed, etc. See here for more: https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses#supported-schemas + is_enabled: Whether the tool is enabled. Can be a bool or a callable that takes the run + context and agent and returns whether the tool is enabled. Disabled tools are hidden + from the LLM at runtime. """ def _create_function_tool(the_func: ToolFunction[...]) -> FunctionTool: @@ -220,7 +374,7 @@ def _create_function_tool(the_func: ToolFunction[...]) -> FunctionTool: strict_json_schema=strict_mode, ) - async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> Any: + async def _on_invoke_tool_impl(ctx: ToolContext[Any], input: str) -> Any: try: json_data: dict[str, Any] = json.loads(input) if input else {} except Exception as e: @@ -269,7 +423,7 @@ async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> Any: return result - async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> Any: + async def _on_invoke_tool(ctx: ToolContext[Any], input: str) -> Any: try: return await _on_invoke_tool_impl(ctx, input) except Exception as e: @@ -297,6 +451,7 @@ async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> Any: params_json_schema=schema.params_json_schema, on_invoke_tool=_on_invoke_tool, strict_json_schema=strict_mode, + is_enabled=is_enabled, ) # If func is actually a callable, we were used as @function_tool with no parentheses diff --git a/src/agents/tool_context.py b/src/agents/tool_context.py new file mode 100644 index 000000000..c4329b8af --- /dev/null +++ b/src/agents/tool_context.py @@ -0,0 +1,29 @@ +from dataclasses import dataclass, field, fields +from typing import Any + +from .run_context import RunContextWrapper, TContext + + +def _assert_must_pass_tool_call_id() -> str: + raise ValueError("tool_call_id must be passed to ToolContext") + + +@dataclass +class ToolContext(RunContextWrapper[TContext]): + """The context of a tool call.""" + + tool_call_id: str = field(default_factory=_assert_must_pass_tool_call_id) + """The ID of the tool call.""" + + @classmethod + def from_agent_context( + cls, context: RunContextWrapper[TContext], tool_call_id: str + ) -> "ToolContext": + """ + Create a ToolContext from a RunContextWrapper. + """ + # Grab the names of the RunContextWrapper's init=True fields + base_values: dict[str, Any] = { + f.name: getattr(context, f.name) for f in fields(RunContextWrapper) if f.init + } + return cls(tool_call_id=tool_call_id, **base_values) diff --git a/src/agents/tracing/__init__.py b/src/agents/tracing/__init__.py index 9df944263..b45c06d75 100644 --- a/src/agents/tracing/__init__.py +++ b/src/agents/tracing/__init__.py @@ -18,7 +18,8 @@ ) from .processor_interface import TracingProcessor from .processors import default_exporter, default_processor -from .setup import GLOBAL_TRACE_PROVIDER +from .provider import DefaultTraceProvider, TraceProvider +from .setup import get_trace_provider, set_trace_provider from .span_data import ( AgentSpanData, CustomSpanData, @@ -45,10 +46,12 @@ "generation_span", "get_current_span", "get_current_trace", + "get_trace_provider", "guardrail_span", "handoff_span", "response_span", "set_trace_processors", + "set_trace_provider", "set_tracing_disabled", "trace", "Trace", @@ -67,6 +70,7 @@ "SpeechSpanData", "TranscriptionSpanData", "TracingProcessor", + "TraceProvider", "gen_trace_id", "gen_span_id", "speech_group_span", @@ -80,21 +84,21 @@ def add_trace_processor(span_processor: TracingProcessor) -> None: """ Adds a new trace processor. This processor will receive all traces/spans. """ - GLOBAL_TRACE_PROVIDER.register_processor(span_processor) + get_trace_provider().register_processor(span_processor) def set_trace_processors(processors: list[TracingProcessor]) -> None: """ Set the list of trace processors. This will replace the current list of processors. """ - GLOBAL_TRACE_PROVIDER.set_processors(processors) + get_trace_provider().set_processors(processors) def set_tracing_disabled(disabled: bool) -> None: """ Set whether tracing is globally disabled. """ - GLOBAL_TRACE_PROVIDER.set_disabled(disabled) + get_trace_provider().set_disabled(disabled) def set_tracing_export_api_key(api_key: str) -> None: @@ -104,10 +108,11 @@ def set_tracing_export_api_key(api_key: str) -> None: default_exporter().set_api_key(api_key) +set_trace_provider(DefaultTraceProvider()) # Add the default processor, which exports traces and spans to the backend in batches. You can # change the default behavior by either: # 1. calling add_trace_processor(), which adds additional processors, or # 2. calling set_trace_processors(), which replaces the default processor. add_trace_processor(default_processor()) -atexit.register(GLOBAL_TRACE_PROVIDER.shutdown) +atexit.register(get_trace_provider().shutdown) diff --git a/src/agents/tracing/create.py b/src/agents/tracing/create.py index b6fe46104..ac451abf5 100644 --- a/src/agents/tracing/create.py +++ b/src/agents/tracing/create.py @@ -4,7 +4,7 @@ from typing import TYPE_CHECKING, Any from ..logger import logger -from .setup import GLOBAL_TRACE_PROVIDER +from .setup import get_trace_provider from .span_data import ( AgentSpanData, CustomSpanData, @@ -56,13 +56,13 @@ def trace( Returns: The newly created trace object. """ - current_trace = GLOBAL_TRACE_PROVIDER.get_current_trace() + current_trace = get_trace_provider().get_current_trace() if current_trace: logger.warning( "Trace already exists. Creating a new trace, but this is probably a mistake." ) - return GLOBAL_TRACE_PROVIDER.create_trace( + return get_trace_provider().create_trace( name=workflow_name, trace_id=trace_id, group_id=group_id, @@ -73,12 +73,12 @@ def trace( def get_current_trace() -> Trace | None: """Returns the currently active trace, if present.""" - return GLOBAL_TRACE_PROVIDER.get_current_trace() + return get_trace_provider().get_current_trace() def get_current_span() -> Span[Any] | None: """Returns the currently active span, if present.""" - return GLOBAL_TRACE_PROVIDER.get_current_span() + return get_trace_provider().get_current_span() def agent_span( @@ -108,7 +108,7 @@ def agent_span( Returns: The newly created agent span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=AgentSpanData(name=name, handoffs=handoffs, tools=tools, output_type=output_type), span_id=span_id, parent=parent, @@ -141,7 +141,7 @@ def function_span( Returns: The newly created function span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=FunctionSpanData(name=name, input=input, output=output), span_id=span_id, parent=parent, @@ -183,7 +183,7 @@ def generation_span( Returns: The newly created generation span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=GenerationSpanData( input=input, output=output, @@ -215,7 +215,7 @@ def response_span( trace/span as the parent. disabled: If True, we will return a Span but the Span will not be recorded. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=ResponseSpanData(response=response), span_id=span_id, parent=parent, @@ -246,7 +246,7 @@ def handoff_span( Returns: The newly created handoff span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=HandoffSpanData(from_agent=from_agent, to_agent=to_agent), span_id=span_id, parent=parent, @@ -278,7 +278,7 @@ def custom_span( Returns: The newly created custom span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=CustomSpanData(name=name, data=data or {}), span_id=span_id, parent=parent, @@ -306,7 +306,7 @@ def guardrail_span( trace/span as the parent. disabled: If True, we will return a Span but the Span will not be recorded. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=GuardrailSpanData(name=name, triggered=triggered), span_id=span_id, parent=parent, @@ -344,7 +344,7 @@ def transcription_span( Returns: The newly created speech-to-text span. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=TranscriptionSpanData( input=input, input_format=input_format, @@ -386,7 +386,7 @@ def speech_span( trace/span as the parent. disabled: If True, we will return a Span but the Span will not be recorded. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=SpeechSpanData( model=model, input=input, @@ -419,7 +419,7 @@ def speech_group_span( trace/span as the parent. disabled: If True, we will return a Span but the Span will not be recorded. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=SpeechGroupSpanData(input=input), span_id=span_id, parent=parent, @@ -447,7 +447,7 @@ def mcp_tools_span( trace/span as the parent. disabled: If True, we will return a Span but the Span will not be recorded. """ - return GLOBAL_TRACE_PROVIDER.create_span( + return get_trace_provider().create_span( span_data=MCPListToolsSpanData(server=server, result=result), span_id=span_id, parent=parent, diff --git a/src/agents/tracing/processor_interface.py b/src/agents/tracing/processor_interface.py index 4dcd897c7..0a05bcae2 100644 --- a/src/agents/tracing/processor_interface.py +++ b/src/agents/tracing/processor_interface.py @@ -23,7 +23,7 @@ def on_trace_end(self, trace: "Trace") -> None: """Called when a trace is finished. Args: - trace: The trace that started. + trace: The trace that finished. """ pass diff --git a/src/agents/tracing/processors.py b/src/agents/tracing/processors.py index f929d05da..73f733125 100644 --- a/src/agents/tracing/processors.py +++ b/src/agents/tracing/processors.py @@ -102,6 +102,12 @@ def export(self, items: list[Trace | Span[Any]]) -> None: "OpenAI-Beta": "traces=v1", } + if self.organization: + headers["OpenAI-Organization"] = self.organization + + if self.project: + headers["OpenAI-Project"] = self.project + # Exponential backoff loop attempt = 0 delay = self.base_delay @@ -182,10 +188,27 @@ def __init__( # Track when we next *must* perform a scheduled export self._next_export_time = time.time() + self._schedule_delay - self._worker_thread = threading.Thread(target=self._run, daemon=True) - self._worker_thread.start() + # We lazily start the background worker thread the first time a span/trace is queued. + self._worker_thread: threading.Thread | None = None + self._thread_start_lock = threading.Lock() + + def _ensure_thread_started(self) -> None: + # Fast path without holding the lock + if self._worker_thread and self._worker_thread.is_alive(): + return + + # Double-checked locking to avoid starting multiple threads + with self._thread_start_lock: + if self._worker_thread and self._worker_thread.is_alive(): + return + + self._worker_thread = threading.Thread(target=self._run, daemon=True) + self._worker_thread.start() def on_trace_start(self, trace: Trace) -> None: + # Ensure the background worker is running before we enqueue anything. + self._ensure_thread_started() + try: self._queue.put_nowait(trace) except queue.Full: @@ -200,6 +223,9 @@ def on_span_start(self, span: Span[Any]) -> None: pass def on_span_end(self, span: Span[Any]) -> None: + # Ensure the background worker is running before we enqueue anything. + self._ensure_thread_started() + try: self._queue.put_nowait(span) except queue.Full: @@ -210,7 +236,13 @@ def shutdown(self, timeout: float | None = None): Called when the application stops. We signal our thread to stop, then join it. """ self._shutdown_event.set() - self._worker_thread.join(timeout=timeout) + + # Only join if we ever started the background thread; otherwise flush synchronously. + if self._worker_thread and self._worker_thread.is_alive(): + self._worker_thread.join(timeout=timeout) + else: + # No background thread: process any remaining items synchronously. + self._export_batches(force=True) def force_flush(self): """ diff --git a/src/agents/tracing/provider.py b/src/agents/tracing/provider.py new file mode 100644 index 000000000..38d3b7ab3 --- /dev/null +++ b/src/agents/tracing/provider.py @@ -0,0 +1,294 @@ +from __future__ import annotations + +import os +import threading +import uuid +from abc import ABC, abstractmethod +from datetime import datetime, timezone +from typing import Any + +from ..logger import logger +from .processor_interface import TracingProcessor +from .scope import Scope +from .spans import NoOpSpan, Span, SpanImpl, TSpanData +from .traces import NoOpTrace, Trace, TraceImpl + + +class SynchronousMultiTracingProcessor(TracingProcessor): + """ + Forwards all calls to a list of TracingProcessors, in order of registration. + """ + + def __init__(self): + # Using a tuple to avoid race conditions when iterating over processors + self._processors: tuple[TracingProcessor, ...] = () + self._lock = threading.Lock() + + def add_tracing_processor(self, tracing_processor: TracingProcessor): + """ + Add a processor to the list of processors. Each processor will receive all traces/spans. + """ + with self._lock: + self._processors += (tracing_processor,) + + def set_processors(self, processors: list[TracingProcessor]): + """ + Set the list of processors. This will replace the current list of processors. + """ + with self._lock: + self._processors = tuple(processors) + + def on_trace_start(self, trace: Trace) -> None: + """ + Called when a trace is started. + """ + for processor in self._processors: + processor.on_trace_start(trace) + + def on_trace_end(self, trace: Trace) -> None: + """ + Called when a trace is finished. + """ + for processor in self._processors: + processor.on_trace_end(trace) + + def on_span_start(self, span: Span[Any]) -> None: + """ + Called when a span is started. + """ + for processor in self._processors: + processor.on_span_start(span) + + def on_span_end(self, span: Span[Any]) -> None: + """ + Called when a span is finished. + """ + for processor in self._processors: + processor.on_span_end(span) + + def shutdown(self) -> None: + """ + Called when the application stops. + """ + for processor in self._processors: + logger.debug(f"Shutting down trace processor {processor}") + processor.shutdown() + + def force_flush(self): + """ + Force the processors to flush their buffers. + """ + for processor in self._processors: + processor.force_flush() + + +class TraceProvider(ABC): + """Interface for creating traces and spans.""" + + @abstractmethod + def register_processor(self, processor: TracingProcessor) -> None: + """Add a processor that will receive all traces and spans.""" + + @abstractmethod + def set_processors(self, processors: list[TracingProcessor]) -> None: + """Replace the list of processors with ``processors``.""" + + @abstractmethod + def get_current_trace(self) -> Trace | None: + """Return the currently active trace, if any.""" + + @abstractmethod + def get_current_span(self) -> Span[Any] | None: + """Return the currently active span, if any.""" + + @abstractmethod + def set_disabled(self, disabled: bool) -> None: + """Enable or disable tracing globally.""" + + @abstractmethod + def time_iso(self) -> str: + """Return the current time in ISO 8601 format.""" + + @abstractmethod + def gen_trace_id(self) -> str: + """Generate a new trace identifier.""" + + @abstractmethod + def gen_span_id(self) -> str: + """Generate a new span identifier.""" + + @abstractmethod + def gen_group_id(self) -> str: + """Generate a new group identifier.""" + + @abstractmethod + def create_trace( + self, + name: str, + trace_id: str | None = None, + group_id: str | None = None, + metadata: dict[str, Any] | None = None, + disabled: bool = False, + ) -> Trace: + """Create a new trace.""" + + @abstractmethod + def create_span( + self, + span_data: TSpanData, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, + ) -> Span[TSpanData]: + """Create a new span.""" + + @abstractmethod + def shutdown(self) -> None: + """Clean up any resources used by the provider.""" + + +class DefaultTraceProvider(TraceProvider): + def __init__(self) -> None: + self._multi_processor = SynchronousMultiTracingProcessor() + self._disabled = os.environ.get("OPENAI_AGENTS_DISABLE_TRACING", "false").lower() in ( + "true", + "1", + ) + + def register_processor(self, processor: TracingProcessor): + """ + Add a processor to the list of processors. Each processor will receive all traces/spans. + """ + self._multi_processor.add_tracing_processor(processor) + + def set_processors(self, processors: list[TracingProcessor]): + """ + Set the list of processors. This will replace the current list of processors. + """ + self._multi_processor.set_processors(processors) + + def get_current_trace(self) -> Trace | None: + """ + Returns the currently active trace, if any. + """ + return Scope.get_current_trace() + + def get_current_span(self) -> Span[Any] | None: + """ + Returns the currently active span, if any. + """ + return Scope.get_current_span() + + def set_disabled(self, disabled: bool) -> None: + """ + Set whether tracing is disabled. + """ + self._disabled = disabled + + def time_iso(self) -> str: + """Return the current time in ISO 8601 format.""" + return datetime.now(timezone.utc).isoformat() + + def gen_trace_id(self) -> str: + """Generate a new trace ID.""" + return f"trace_{uuid.uuid4().hex}" + + def gen_span_id(self) -> str: + """Generate a new span ID.""" + return f"span_{uuid.uuid4().hex[:24]}" + + def gen_group_id(self) -> str: + """Generate a new group ID.""" + return f"group_{uuid.uuid4().hex[:24]}" + + def create_trace( + self, + name: str, + trace_id: str | None = None, + group_id: str | None = None, + metadata: dict[str, Any] | None = None, + disabled: bool = False, + ) -> Trace: + """ + Create a new trace. + """ + if self._disabled or disabled: + logger.debug(f"Tracing is disabled. Not creating trace {name}") + return NoOpTrace() + + trace_id = trace_id or self.gen_trace_id() + + logger.debug(f"Creating trace {name} with id {trace_id}") + + return TraceImpl( + name=name, + trace_id=trace_id, + group_id=group_id, + metadata=metadata, + processor=self._multi_processor, + ) + + def create_span( + self, + span_data: TSpanData, + span_id: str | None = None, + parent: Trace | Span[Any] | None = None, + disabled: bool = False, + ) -> Span[TSpanData]: + """ + Create a new span. + """ + if self._disabled or disabled: + logger.debug(f"Tracing is disabled. Not creating span {span_data}") + return NoOpSpan(span_data) + + if not parent: + current_span = Scope.get_current_span() + current_trace = Scope.get_current_trace() + if current_trace is None: + logger.error( + "No active trace. Make sure to start a trace with `trace()` first" + "Returning NoOpSpan." + ) + return NoOpSpan(span_data) + elif isinstance(current_trace, NoOpTrace) or isinstance(current_span, NoOpSpan): + logger.debug( + f"Parent {current_span} or {current_trace} is no-op, returning NoOpSpan" + ) + return NoOpSpan(span_data) + + parent_id = current_span.span_id if current_span else None + trace_id = current_trace.trace_id + + elif isinstance(parent, Trace): + if isinstance(parent, NoOpTrace): + logger.debug(f"Parent {parent} is no-op, returning NoOpSpan") + return NoOpSpan(span_data) + trace_id = parent.trace_id + parent_id = None + elif isinstance(parent, Span): + if isinstance(parent, NoOpSpan): + logger.debug(f"Parent {parent} is no-op, returning NoOpSpan") + return NoOpSpan(span_data) + parent_id = parent.span_id + trace_id = parent.trace_id + + logger.debug(f"Creating span {span_data} with id {span_id}") + + return SpanImpl( + trace_id=trace_id, + span_id=span_id or self.gen_span_id(), + parent_id=parent_id, + processor=self._multi_processor, + span_data=span_data, + ) + + def shutdown(self) -> None: + if self._disabled: + return + + try: + logger.debug("Shutting down trace provider") + self._multi_processor.shutdown() + except Exception as e: + logger.error(f"Error shutting down trace provider: {e}") diff --git a/src/agents/tracing/setup.py b/src/agents/tracing/setup.py index 9e27d210b..3a56b728f 100644 --- a/src/agents/tracing/setup.py +++ b/src/agents/tracing/setup.py @@ -1,214 +1,21 @@ from __future__ import annotations -import os -import threading -from typing import Any +from typing import TYPE_CHECKING -from ..logger import logger -from . import util -from .processor_interface import TracingProcessor -from .scope import Scope -from .spans import NoOpSpan, Span, SpanImpl, TSpanData -from .traces import NoOpTrace, Trace, TraceImpl +if TYPE_CHECKING: + from .provider import TraceProvider +GLOBAL_TRACE_PROVIDER: TraceProvider | None = None -class SynchronousMultiTracingProcessor(TracingProcessor): - """ - Forwards all calls to a list of TracingProcessors, in order of registration. - """ - def __init__(self): - # Using a tuple to avoid race conditions when iterating over processors - self._processors: tuple[TracingProcessor, ...] = () - self._lock = threading.Lock() +def set_trace_provider(provider: TraceProvider) -> None: + """Set the global trace provider used by tracing utilities.""" + global GLOBAL_TRACE_PROVIDER + GLOBAL_TRACE_PROVIDER = provider - def add_tracing_processor(self, tracing_processor: TracingProcessor): - """ - Add a processor to the list of processors. Each processor will receive all traces/spans. - """ - with self._lock: - self._processors += (tracing_processor,) - def set_processors(self, processors: list[TracingProcessor]): - """ - Set the list of processors. This will replace the current list of processors. - """ - with self._lock: - self._processors = tuple(processors) - - def on_trace_start(self, trace: Trace) -> None: - """ - Called when a trace is started. - """ - for processor in self._processors: - processor.on_trace_start(trace) - - def on_trace_end(self, trace: Trace) -> None: - """ - Called when a trace is finished. - """ - for processor in self._processors: - processor.on_trace_end(trace) - - def on_span_start(self, span: Span[Any]) -> None: - """ - Called when a span is started. - """ - for processor in self._processors: - processor.on_span_start(span) - - def on_span_end(self, span: Span[Any]) -> None: - """ - Called when a span is finished. - """ - for processor in self._processors: - processor.on_span_end(span) - - def shutdown(self) -> None: - """ - Called when the application stops. - """ - for processor in self._processors: - logger.debug(f"Shutting down trace processor {processor}") - processor.shutdown() - - def force_flush(self): - """ - Force the processors to flush their buffers. - """ - for processor in self._processors: - processor.force_flush() - - -class TraceProvider: - def __init__(self): - self._multi_processor = SynchronousMultiTracingProcessor() - self._disabled = os.environ.get("OPENAI_AGENTS_DISABLE_TRACING", "false").lower() in ( - "true", - "1", - ) - - def register_processor(self, processor: TracingProcessor): - """ - Add a processor to the list of processors. Each processor will receive all traces/spans. - """ - self._multi_processor.add_tracing_processor(processor) - - def set_processors(self, processors: list[TracingProcessor]): - """ - Set the list of processors. This will replace the current list of processors. - """ - self._multi_processor.set_processors(processors) - - def get_current_trace(self) -> Trace | None: - """ - Returns the currently active trace, if any. - """ - return Scope.get_current_trace() - - def get_current_span(self) -> Span[Any] | None: - """ - Returns the currently active span, if any. - """ - return Scope.get_current_span() - - def set_disabled(self, disabled: bool) -> None: - """ - Set whether tracing is disabled. - """ - self._disabled = disabled - - def create_trace( - self, - name: str, - trace_id: str | None = None, - group_id: str | None = None, - metadata: dict[str, Any] | None = None, - disabled: bool = False, - ) -> Trace: - """ - Create a new trace. - """ - if self._disabled or disabled: - logger.debug(f"Tracing is disabled. Not creating trace {name}") - return NoOpTrace() - - trace_id = trace_id or util.gen_trace_id() - - logger.debug(f"Creating trace {name} with id {trace_id}") - - return TraceImpl( - name=name, - trace_id=trace_id, - group_id=group_id, - metadata=metadata, - processor=self._multi_processor, - ) - - def create_span( - self, - span_data: TSpanData, - span_id: str | None = None, - parent: Trace | Span[Any] | None = None, - disabled: bool = False, - ) -> Span[TSpanData]: - """ - Create a new span. - """ - if self._disabled or disabled: - logger.debug(f"Tracing is disabled. Not creating span {span_data}") - return NoOpSpan(span_data) - - if not parent: - current_span = Scope.get_current_span() - current_trace = Scope.get_current_trace() - if current_trace is None: - logger.error( - "No active trace. Make sure to start a trace with `trace()` first" - "Returning NoOpSpan." - ) - return NoOpSpan(span_data) - elif isinstance(current_trace, NoOpTrace) or isinstance(current_span, NoOpSpan): - logger.debug( - f"Parent {current_span} or {current_trace} is no-op, returning NoOpSpan" - ) - return NoOpSpan(span_data) - - parent_id = current_span.span_id if current_span else None - trace_id = current_trace.trace_id - - elif isinstance(parent, Trace): - if isinstance(parent, NoOpTrace): - logger.debug(f"Parent {parent} is no-op, returning NoOpSpan") - return NoOpSpan(span_data) - trace_id = parent.trace_id - parent_id = None - elif isinstance(parent, Span): - if isinstance(parent, NoOpSpan): - logger.debug(f"Parent {parent} is no-op, returning NoOpSpan") - return NoOpSpan(span_data) - parent_id = parent.span_id - trace_id = parent.trace_id - - logger.debug(f"Creating span {span_data} with id {span_id}") - - return SpanImpl( - trace_id=trace_id, - span_id=span_id, - parent_id=parent_id, - processor=self._multi_processor, - span_data=span_data, - ) - - def shutdown(self) -> None: - if self._disabled: - return - - try: - logger.debug("Shutting down trace provider") - self._multi_processor.shutdown() - except Exception as e: - logger.error(f"Error shutting down trace provider: {e}") - - -GLOBAL_TRACE_PROVIDER = TraceProvider() +def get_trace_provider() -> TraceProvider: + """Get the global trace provider used by tracing utilities.""" + if GLOBAL_TRACE_PROVIDER is None: + raise RuntimeError("Trace provider not set") + return GLOBAL_TRACE_PROVIDER diff --git a/src/agents/tracing/span_data.py b/src/agents/tracing/span_data.py index 1a4502803..cb3e8491d 100644 --- a/src/agents/tracing/span_data.py +++ b/src/agents/tracing/span_data.py @@ -338,7 +338,7 @@ def __init__( @property def type(self) -> str: - return "speech-group" + return "speech_group" def export(self) -> dict[str, Any]: return { diff --git a/src/agents/tracing/util.py b/src/agents/tracing/util.py index f546b4e50..7f436d019 100644 --- a/src/agents/tracing/util.py +++ b/src/agents/tracing/util.py @@ -1,22 +1,21 @@ -import uuid -from datetime import datetime, timezone +from .setup import get_trace_provider def time_iso() -> str: - """Returns the current time in ISO 8601 format.""" - return datetime.now(timezone.utc).isoformat() + """Return the current time in ISO 8601 format.""" + return get_trace_provider().time_iso() def gen_trace_id() -> str: - """Generates a new trace ID.""" - return f"trace_{uuid.uuid4().hex}" + """Generate a new trace ID.""" + return get_trace_provider().gen_trace_id() def gen_span_id() -> str: - """Generates a new span ID.""" - return f"span_{uuid.uuid4().hex[:24]}" + """Generate a new span ID.""" + return get_trace_provider().gen_span_id() def gen_group_id() -> str: - """Generates a new group ID.""" - return f"group_{uuid.uuid4().hex[:24]}" + """Generate a new group ID.""" + return get_trace_provider().gen_group_id() diff --git a/src/agents/usage.py b/src/agents/usage.py index 23d989b4b..843f62937 100644 --- a/src/agents/usage.py +++ b/src/agents/usage.py @@ -1,4 +1,6 @@ -from dataclasses import dataclass +from dataclasses import dataclass, field + +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails @dataclass @@ -9,9 +11,18 @@ class Usage: input_tokens: int = 0 """Total input tokens sent, across all requests.""" + input_tokens_details: InputTokensDetails = field( + default_factory=lambda: InputTokensDetails(cached_tokens=0) + ) + """Details about the input tokens, matching responses API usage details.""" output_tokens: int = 0 """Total output tokens received, across all requests.""" + output_tokens_details: OutputTokensDetails = field( + default_factory=lambda: OutputTokensDetails(reasoning_tokens=0) + ) + """Details about the output tokens, matching responses API usage details.""" + total_tokens: int = 0 """Total tokens sent and received, across all requests.""" @@ -20,3 +31,12 @@ def add(self, other: "Usage") -> None: self.input_tokens += other.input_tokens if other.input_tokens else 0 self.output_tokens += other.output_tokens if other.output_tokens else 0 self.total_tokens += other.total_tokens if other.total_tokens else 0 + self.input_tokens_details = InputTokensDetails( + cached_tokens=self.input_tokens_details.cached_tokens + + other.input_tokens_details.cached_tokens + ) + + self.output_tokens_details = OutputTokensDetails( + reasoning_tokens=self.output_tokens_details.reasoning_tokens + + other.output_tokens_details.reasoning_tokens + ) diff --git a/src/agents/util/_pretty_print.py b/src/agents/util/_pretty_print.py index afd3e2b1b..29df3562e 100644 --- a/src/agents/util/_pretty_print.py +++ b/src/agents/util/_pretty_print.py @@ -3,6 +3,7 @@ from pydantic import BaseModel if TYPE_CHECKING: + from ..exceptions import RunErrorDetails from ..result import RunResult, RunResultBase, RunResultStreaming @@ -38,6 +39,17 @@ def pretty_print_result(result: "RunResult") -> str: return output +def pretty_print_run_error_details(result: "RunErrorDetails") -> str: + output = "RunErrorDetails:" + output += f'\n- Last agent: Agent(name="{result.last_agent.name}", ...)' + output += f"\n- {len(result.new_items)} new item(s)" + output += f"\n- {len(result.raw_responses)} raw response(s)" + output += f"\n- {len(result.input_guardrail_results)} input guardrail result(s)" + output += "\n(See `RunErrorDetails` for more details)" + + return output + + def pretty_print_run_result_streaming(result: "RunResultStreaming") -> str: output = "RunResultStreaming:" output += f'\n- Current agent: Agent(name="{result.current_agent.name}", ...)' diff --git a/src/agents/voice/__init__.py b/src/agents/voice/__init__.py index 499c064c5..e11ee4467 100644 --- a/src/agents/voice/__init__.py +++ b/src/agents/voice/__init__.py @@ -7,6 +7,7 @@ STTModelSettings, TTSModel, TTSModelSettings, + TTSVoice, VoiceModelProvider, ) from .models.openai_model_provider import OpenAIVoiceModelProvider @@ -30,6 +31,7 @@ "STTModelSettings", "TTSModel", "TTSModelSettings", + "TTSVoice", "VoiceModelProvider", "StreamedAudioResult", "SingleAgentVoiceWorkflow", diff --git a/src/agents/voice/model.py b/src/agents/voice/model.py index 220d4b480..b048a452d 100644 --- a/src/agents/voice/model.py +++ b/src/agents/voice/model.py @@ -14,14 +14,15 @@ ) DEFAULT_TTS_BUFFER_SIZE = 120 +TTSVoice = Literal["alloy", "ash", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer"] +"""Exportable type for the TTSModelSettings voice enum""" + @dataclass class TTSModelSettings: """Settings for a TTS model.""" - voice: ( - Literal["alloy", "ash", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer"] | None - ) = None + voice: TTSVoice | None = None """ The voice to use for the TTS model. If not provided, the default voice for the respective model will be used. diff --git a/src/agents/voice/pipeline.py b/src/agents/voice/pipeline.py index d1dac57cf..5addd995f 100644 --- a/src/agents/voice/pipeline.py +++ b/src/agents/voice/pipeline.py @@ -125,6 +125,12 @@ async def _run_multi_turn(self, audio_input: StreamedAudioInput) -> StreamedAudi self._get_tts_model(), self.config.tts_settings, self.config ) + try: + async for intro_text in self.workflow.on_start(): + await output._add_text(intro_text) + except Exception as e: + logger.warning(f"on_start() failed: {e}") + transcription_session = await self._get_stt_model().create_session( audio_input, self.config.stt_settings, diff --git a/src/agents/voice/workflow.py b/src/agents/voice/workflow.py index c706ec413..538676ad1 100644 --- a/src/agents/voice/workflow.py +++ b/src/agents/voice/workflow.py @@ -32,6 +32,14 @@ def run(self, transcription: str) -> AsyncIterator[str]: """ pass + async def on_start(self) -> AsyncIterator[str]: + """ + Optional method that runs before any user input is received. Can be used + to deliver a greeting or instruction via TTS. Defaults to doing nothing. + """ + return + yield + class VoiceWorkflowHelper: @classmethod diff --git a/tests/conftest.py b/tests/conftest.py index ba0d88221..b73d734d1 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,8 +5,9 @@ from agents.models import _openai_shared from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel from agents.models.openai_responses import OpenAIResponsesModel +from agents.run import set_default_agent_runner from agents.tracing import set_trace_processors -from agents.tracing.setup import GLOBAL_TRACE_PROVIDER +from agents.tracing.setup import get_trace_provider from .testing_processor import SPAN_PROCESSOR_TESTING @@ -33,11 +34,16 @@ def clear_openai_settings(): _openai_shared._use_responses_by_default = True +@pytest.fixture(autouse=True) +def clear_default_runner(): + set_default_agent_runner(None) + + # This fixture will run after all tests end @pytest.fixture(autouse=True, scope="session") def shutdown_trace_provider(): yield - GLOBAL_TRACE_PROVIDER.shutdown() + get_trace_provider().shutdown() @pytest.fixture(autouse=True) diff --git a/tests/fake_model.py b/tests/fake_model.py index 52d3a3b2b..6c1377e6d 100644 --- a/tests/fake_model.py +++ b/tests/fake_model.py @@ -3,9 +3,10 @@ from collections.abc import AsyncIterator from typing import Any -from openai.types.responses import Response, ResponseCompletedEvent +from openai.types.responses import Response, ResponseCompletedEvent, ResponseUsage +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails -from agents.agent_output import AgentOutputSchema +from agents.agent_output import AgentOutputSchemaBase from agents.handoffs import Handoff from agents.items import ( ModelResponse, @@ -33,6 +34,10 @@ def __init__( ) self.tracing_enabled = tracing_enabled self.last_turn_args: dict[str, Any] = {} + self.hardcoded_usage: Usage | None = None + + def set_hardcoded_usage(self, usage: Usage): + self.hardcoded_usage = usage def set_next_output(self, output: list[TResponseOutputItem] | Exception): self.turn_outputs.append(output) @@ -51,11 +56,12 @@ async def get_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, *, previous_response_id: str | None, + prompt: Any | None, ) -> ModelResponse: self.last_turn_args = { "system_instructions": system_instructions, @@ -83,7 +89,7 @@ async def get_response( return ModelResponse( output=output, - usage=Usage(), + usage=self.hardcoded_usage or Usage(), response_id=None, ) @@ -93,11 +99,12 @@ async def stream_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, *, previous_response_id: str | None, + prompt: Any | None, ) -> AsyncIterator[TResponseStreamEvent]: self.last_turn_args = { "system_instructions": system_instructions, @@ -123,11 +130,16 @@ async def stream_response( yield ResponseCompletedEvent( type="response.completed", - response=get_response_obj(output), + response=get_response_obj(output, usage=self.hardcoded_usage), + sequence_number=0, ) -def get_response_obj(output: list[TResponseOutputItem], response_id: str | None = None) -> Response: +def get_response_obj( + output: list[TResponseOutputItem], + response_id: str | None = None, + usage: Usage | None = None, +) -> Response: return Response( id=response_id or "123", created_at=123, @@ -138,4 +150,11 @@ def get_response_obj(output: list[TResponseOutputItem], response_id: str | None tools=[], top_p=None, parallel_tool_calls=False, + usage=ResponseUsage( + input_tokens=usage.input_tokens if usage else 0, + output_tokens=usage.output_tokens if usage else 0, + total_tokens=usage.total_tokens if usage else 0, + input_tokens_details=InputTokensDetails(cached_tokens=0), + output_tokens_details=OutputTokensDetails(reasoning_tokens=0), + ), ) diff --git a/tests/fastapi/__init__.py b/tests/fastapi/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/fastapi/streaming_app.py b/tests/fastapi/streaming_app.py new file mode 100644 index 000000000..b93ccf3f3 --- /dev/null +++ b/tests/fastapi/streaming_app.py @@ -0,0 +1,30 @@ +from collections.abc import AsyncIterator + +from fastapi import FastAPI +from starlette.responses import StreamingResponse + +from agents import Agent, Runner, RunResultStreaming + +agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", +) + + +app = FastAPI() + + +@app.post("/stream") +async def stream(): + result = Runner.run_streamed(agent, input="Tell me a joke") + stream_handler = StreamHandler(result) + return StreamingResponse(stream_handler.stream_events(), media_type="application/x-ndjson") + + +class StreamHandler: + def __init__(self, result: RunResultStreaming): + self.result = result + + async def stream_events(self) -> AsyncIterator[str]: + async for event in self.result.stream_events(): + yield f"{event.type}\n\n" diff --git a/tests/fastapi/test_streaming_context.py b/tests/fastapi/test_streaming_context.py new file mode 100644 index 000000000..ee13045e4 --- /dev/null +++ b/tests/fastapi/test_streaming_context.py @@ -0,0 +1,29 @@ +import pytest +from httpx import ASGITransport, AsyncClient +from inline_snapshot import snapshot + +from ..fake_model import FakeModel +from ..test_responses import get_text_message +from .streaming_app import agent, app + + +@pytest.mark.asyncio +async def test_streaming_context(): + """This ensures that FastAPI streaming works. The context for this test is that the Runner + method was called in one async context, and the streaming was ended in another context, + leading to a tracing error because the context was closed in the wrong context. This test + ensures that this actually works. + """ + model = FakeModel() + agent.model = model + model.set_next_output([get_text_message("done")]) + + transport = ASGITransport(app) + async with AsyncClient(transport=transport, base_url="http://test") as ac: + async with ac.stream("POST", "/stream") as r: + assert r.status_code == 200 + body = (await r.aread()).decode("utf-8") + lines = [line for line in body.splitlines() if line] + assert lines == snapshot( + ["agent_updated_stream_event", "raw_response_event", "run_item_stream_event"] + ) diff --git a/tests/mcp/helpers.py b/tests/mcp/helpers.py index 8ff153c18..31d43c228 100644 --- a/tests/mcp/helpers.py +++ b/tests/mcp/helpers.py @@ -1,11 +1,14 @@ +import asyncio import json import shutil from typing import Any from mcp import Tool as MCPTool -from mcp.types import CallToolResult, TextContent +from mcp.types import CallToolResult, GetPromptResult, ListPromptsResult, PromptMessage, TextContent from agents.mcp import MCPServer +from agents.mcp.server import _MCPServerWithClientSession +from agents.mcp.util import ToolFilter tee = shutil.which("tee") or "" assert tee, "tee not found" @@ -28,11 +31,41 @@ async def __aexit__(self, exc_type, exc_val, exc_tb): pass +class _TestFilterServer(_MCPServerWithClientSession): + """Minimal implementation of _MCPServerWithClientSession for testing tool filtering""" + + def __init__(self, tool_filter: ToolFilter, server_name: str): + # Initialize parent class properly to avoid type errors + super().__init__( + cache_tools_list=False, + client_session_timeout_seconds=None, + tool_filter=tool_filter, + ) + self._server_name: str = server_name + # Override some attributes for test isolation + self.session = None + self._cleanup_lock = asyncio.Lock() + + def create_streams(self): + raise NotImplementedError("Not needed for filtering tests") + + @property + def name(self) -> str: + return self._server_name + + class FakeMCPServer(MCPServer): - def __init__(self, tools: list[MCPTool] | None = None): + def __init__( + self, + tools: list[MCPTool] | None = None, + tool_filter: ToolFilter = None, + server_name: str = "fake_mcp_server", + ): self.tools: list[MCPTool] = tools or [] self.tool_calls: list[str] = [] self.tool_results: list[str] = [] + self.tool_filter = tool_filter + self._server_name = server_name def add_tool(self, name: str, input_schema: dict[str, Any]): self.tools.append(MCPTool(name=name, inputSchema=input_schema)) @@ -43,8 +76,16 @@ async def connect(self): async def cleanup(self): pass - async def list_tools(self): - return self.tools + async def list_tools(self, run_context=None, agent=None): + tools = self.tools + + # Apply tool filtering using the REAL implementation + if self.tool_filter is not None: + # Use the real _MCPServerWithClientSession filtering logic + filter_server = _TestFilterServer(self.tool_filter, self.name) + tools = await filter_server._apply_tool_filter(tools, run_context, agent) + + return tools async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> CallToolResult: self.tool_calls.append(tool_name) @@ -53,6 +94,18 @@ async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None) -> C content=[TextContent(text=self.tool_results[-1], type="text")], ) + async def list_prompts(self, run_context=None, agent=None) -> ListPromptsResult: + """Return empty list of prompts for fake server""" + return ListPromptsResult(prompts=[]) + + async def get_prompt( + self, name: str, arguments: dict[str, Any] | None = None + ) -> GetPromptResult: + """Return a simple prompt result for fake server""" + content = f"Fake prompt content for {name}" + message = PromptMessage(role="user", content=TextContent(type="text", text=content)) + return GetPromptResult(description=f"Fake prompt: {name}", messages=[message]) + @property def name(self) -> str: - return "fake_mcp_server" + return self._server_name diff --git a/tests/mcp/test_caching.py b/tests/mcp/test_caching.py index cac409e6e..f31cdf951 100644 --- a/tests/mcp/test_caching.py +++ b/tests/mcp/test_caching.py @@ -3,7 +3,9 @@ import pytest from mcp.types import ListToolsResult, Tool as MCPTool +from agents import Agent from agents.mcp import MCPServerStdio +from agents.run_context import RunContextWrapper from .helpers import DummyStreamsContextManager, tee @@ -33,25 +35,29 @@ async def test_server_caching_works( mock_list_tools.return_value = ListToolsResult(tools=tools) async with server: + # Create test context and agent + run_context = RunContextWrapper(context=None) + agent = Agent(name="test_agent", instructions="Test agent") + # Call list_tools() multiple times - tools = await server.list_tools() - assert tools == tools + result_tools = await server.list_tools(run_context, agent) + assert result_tools == tools assert mock_list_tools.call_count == 1, "list_tools() should have been called once" # Call list_tools() again, should return the cached value - tools = await server.list_tools() - assert tools == tools + result_tools = await server.list_tools(run_context, agent) + assert result_tools == tools assert mock_list_tools.call_count == 1, "list_tools() should not have been called again" # Invalidate the cache and call list_tools() again server.invalidate_tools_cache() - tools = await server.list_tools() - assert tools == tools + result_tools = await server.list_tools(run_context, agent) + assert result_tools == tools assert mock_list_tools.call_count == 2, "list_tools() should be called again" # Without invalidating the cache, calling list_tools() again should return the cached value - tools = await server.list_tools() - assert tools == tools + result_tools = await server.list_tools(run_context, agent) + assert result_tools == tools diff --git a/tests/mcp/test_mcp_tracing.py b/tests/mcp/test_mcp_tracing.py index b71954b5b..54575dcb5 100644 --- a/tests/mcp/test_mcp_tracing.py +++ b/tests/mcp/test_mcp_tracing.py @@ -44,6 +44,10 @@ async def test_mcp_tracing(): { "workflow_name": "Agent workflow", "children": [ + { + "type": "mcp_tools", + "data": {"server": "fake_mcp_server", "result": ["test_tool_1"]}, + }, { "type": "agent", "data": { @@ -53,10 +57,6 @@ async def test_mcp_tracing(): "output_type": "str", }, "children": [ - { - "type": "mcp_tools", - "data": {"server": "fake_mcp_server", "result": ["test_tool_1"]}, - }, { "type": "function", "data": { @@ -66,8 +66,12 @@ async def test_mcp_tracing(): "mcp_data": {"server": "fake_mcp_server"}, }, }, + { + "type": "mcp_tools", + "data": {"server": "fake_mcp_server", "result": ["test_tool_1"]}, + }, ], - } + }, ], } ] @@ -100,6 +104,13 @@ async def test_mcp_tracing(): { "workflow_name": "Agent workflow", "children": [ + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2"], + }, + }, { "type": "agent", "data": { @@ -109,13 +120,6 @@ async def test_mcp_tracing(): "output_type": "str", }, "children": [ - { - "type": "mcp_tools", - "data": { - "server": "fake_mcp_server", - "result": ["test_tool_1", "test_tool_2"], - }, - }, { "type": "function", "data": { @@ -133,8 +137,15 @@ async def test_mcp_tracing(): "mcp_data": {"server": "fake_mcp_server"}, }, }, + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2"], + }, + }, ], - } + }, ], } ] @@ -165,6 +176,13 @@ async def test_mcp_tracing(): { "workflow_name": "Agent workflow", "children": [ + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2", "test_tool_3"], + }, + }, { "type": "agent", "data": { @@ -174,13 +192,6 @@ async def test_mcp_tracing(): "output_type": "str", }, "children": [ - { - "type": "mcp_tools", - "data": { - "server": "fake_mcp_server", - "result": ["test_tool_1", "test_tool_2", "test_tool_3"], - }, - }, { "type": "function", "data": { @@ -190,8 +201,15 @@ async def test_mcp_tracing(): "mcp_data": {"server": "fake_mcp_server"}, }, }, + { + "type": "mcp_tools", + "data": { + "server": "fake_mcp_server", + "result": ["test_tool_1", "test_tool_2", "test_tool_3"], + }, + }, ], - } + }, ], } ] diff --git a/tests/mcp/test_mcp_util.py b/tests/mcp/test_mcp_util.py index 74356a16d..3230e63dd 100644 --- a/tests/mcp/test_mcp_util.py +++ b/tests/mcp/test_mcp_util.py @@ -57,7 +57,10 @@ async def test_get_all_function_tools(): server3.add_tool(names[4], schemas[4]) servers: list[MCPServer] = [server1, server2, server3] - tools = await MCPUtil.get_all_function_tools(servers, convert_schemas_to_strict=False) + run_context = RunContextWrapper(context=None) + agent = Agent(name="test_agent", instructions="Test agent") + + tools = await MCPUtil.get_all_function_tools(servers, False, run_context, agent) assert len(tools) == 5 assert all(tool.name in names for tool in tools) @@ -70,7 +73,7 @@ async def test_get_all_function_tools(): assert tool.name == names[idx] # Also make sure it works with strict schemas - tools = await MCPUtil.get_all_function_tools(servers, convert_schemas_to_strict=True) + tools = await MCPUtil.get_all_function_tools(servers, True, run_context, agent) assert len(tools) == 5 assert all(tool.name in names for tool in tools) @@ -144,7 +147,8 @@ async def test_agent_convert_schemas_true(): agent = Agent( name="test_agent", mcp_servers=[server], mcp_config={"convert_schemas_to_strict": True} ) - tools = await agent.get_mcp_tools() + run_context = RunContextWrapper(context=None) + tools = await agent.get_mcp_tools(run_context) foo_tool = next(tool for tool in tools if tool.name == "foo") assert isinstance(foo_tool, FunctionTool) @@ -208,7 +212,8 @@ async def test_agent_convert_schemas_false(): agent = Agent( name="test_agent", mcp_servers=[server], mcp_config={"convert_schemas_to_strict": False} ) - tools = await agent.get_mcp_tools() + run_context = RunContextWrapper(context=None) + tools = await agent.get_mcp_tools(run_context) foo_tool = next(tool for tool in tools if tool.name == "foo") assert isinstance(foo_tool, FunctionTool) @@ -245,7 +250,8 @@ async def test_agent_convert_schemas_unset(): server.add_tool("bar", non_strict_schema) server.add_tool("baz", possible_to_convert_schema) agent = Agent(name="test_agent", mcp_servers=[server]) - tools = await agent.get_mcp_tools() + run_context = RunContextWrapper(context=None) + tools = await agent.get_mcp_tools(run_context) foo_tool = next(tool for tool in tools if tool.name == "foo") assert isinstance(foo_tool, FunctionTool) @@ -279,7 +285,9 @@ async def test_util_adds_properties(): server = FakeMCPServer() server.add_tool("test_tool", schema) - tools = await MCPUtil.get_all_function_tools([server], convert_schemas_to_strict=False) + run_context = RunContextWrapper(context=None) + agent = Agent(name="test_agent", instructions="Test agent") + tools = await MCPUtil.get_all_function_tools([server], False, run_context, agent) tool = next(tool for tool in tools if tool.name == "test_tool") assert isinstance(tool, FunctionTool) diff --git a/tests/mcp/test_prompt_server.py b/tests/mcp/test_prompt_server.py new file mode 100644 index 000000000..15afe28e4 --- /dev/null +++ b/tests/mcp/test_prompt_server.py @@ -0,0 +1,301 @@ +from typing import Any + +import pytest + +from agents import Agent, Runner +from agents.mcp import MCPServer + +from ..fake_model import FakeModel +from ..test_responses import get_text_message + + +class FakeMCPPromptServer(MCPServer): + """Fake MCP server for testing prompt functionality""" + + def __init__(self, server_name: str = "fake_prompt_server"): + self.prompts: list[Any] = [] + self.prompt_results: dict[str, str] = {} + self._server_name = server_name + + def add_prompt(self, name: str, description: str, arguments: dict[str, Any] | None = None): + """Add a prompt to the fake server""" + from mcp.types import Prompt + + prompt = Prompt(name=name, description=description, arguments=[]) + self.prompts.append(prompt) + + def set_prompt_result(self, name: str, result: str): + """Set the result that should be returned for a prompt""" + self.prompt_results[name] = result + + async def connect(self): + pass + + async def cleanup(self): + pass + + async def list_prompts(self, run_context=None, agent=None): + """List available prompts""" + from mcp.types import ListPromptsResult + + return ListPromptsResult(prompts=self.prompts) + + async def get_prompt(self, name: str, arguments: dict[str, Any] | None = None): + """Get a prompt with arguments""" + from mcp.types import GetPromptResult, PromptMessage, TextContent + + if name not in self.prompt_results: + raise ValueError(f"Prompt '{name}' not found") + + content = self.prompt_results[name] + + # If it's a format string, try to format it with arguments + if arguments and "{" in content: + try: + content = content.format(**arguments) + except KeyError: + pass # Use original content if formatting fails + + message = PromptMessage(role="user", content=TextContent(type="text", text=content)) + + return GetPromptResult(description=f"Generated prompt for {name}", messages=[message]) + + async def list_tools(self, run_context=None, agent=None): + return [] + + async def call_tool(self, tool_name: str, arguments: dict[str, Any] | None = None): + raise NotImplementedError("This fake server doesn't support tools") + + @property + def name(self) -> str: + return self._server_name + + +@pytest.mark.asyncio +async def test_list_prompts(): + """Test listing available prompts""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + + result = await server.list_prompts() + + assert len(result.prompts) == 1 + assert result.prompts[0].name == "generate_code_review_instructions" + assert "code review" in result.prompts[0].description + + +@pytest.mark.asyncio +async def test_get_prompt_without_arguments(): + """Test getting a prompt without arguments""" + server = FakeMCPPromptServer() + server.add_prompt("simple_prompt", "A simple prompt") + server.set_prompt_result("simple_prompt", "You are a helpful assistant.") + + result = await server.get_prompt("simple_prompt") + + assert len(result.messages) == 1 + assert result.messages[0].content.text == "You are a helpful assistant." + + +@pytest.mark.asyncio +async def test_get_prompt_with_arguments(): + """Test getting a prompt with arguments""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + server.set_prompt_result( + "generate_code_review_instructions", + "You are a senior {language} code review specialist. Focus on {focus}.", + ) + + result = await server.get_prompt( + "generate_code_review_instructions", + {"focus": "security vulnerabilities", "language": "python"}, + ) + + assert len(result.messages) == 1 + expected_text = ( + "You are a senior python code review specialist. Focus on security vulnerabilities." + ) + assert result.messages[0].content.text == expected_text + + +@pytest.mark.asyncio +async def test_get_prompt_not_found(): + """Test getting a prompt that doesn't exist""" + server = FakeMCPPromptServer() + + with pytest.raises(ValueError, match="Prompt 'nonexistent' not found"): + await server.get_prompt("nonexistent") + + +@pytest.mark.asyncio +async def test_agent_with_prompt_instructions(): + """Test using prompt-generated instructions with an agent""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + server.set_prompt_result( + "generate_code_review_instructions", + "You are a code reviewer. Analyze the provided code for security issues.", + ) + + # Get instructions from prompt + prompt_result = await server.get_prompt("generate_code_review_instructions") + instructions = prompt_result.messages[0].content.text + + # Create agent with prompt-generated instructions + model = FakeModel() + agent = Agent(name="prompt_agent", instructions=instructions, model=model, mcp_servers=[server]) + + # Mock model response + model.add_multiple_turn_outputs( + [[get_text_message("Code analysis complete. Found security vulnerability.")]] + ) + + # Run the agent + result = await Runner.run(agent, input="Review this code: def unsafe_exec(cmd): os.system(cmd)") + + assert "Code analysis complete" in result.final_output + assert ( + agent.instructions + == "You are a code reviewer. Analyze the provided code for security issues." + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("streaming", [False, True]) +async def test_agent_with_prompt_instructions_streaming(streaming: bool): + """Test using prompt-generated instructions with streaming and non-streaming""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + server.set_prompt_result( + "generate_code_review_instructions", + "You are a {language} code reviewer focusing on {focus}.", + ) + + # Get instructions from prompt with arguments + prompt_result = await server.get_prompt( + "generate_code_review_instructions", {"language": "Python", "focus": "security"} + ) + instructions = prompt_result.messages[0].content.text + + # Create agent + model = FakeModel() + agent = Agent( + name="streaming_prompt_agent", instructions=instructions, model=model, mcp_servers=[server] + ) + + model.add_multiple_turn_outputs([[get_text_message("Security analysis complete.")]]) + + if streaming: + streaming_result = Runner.run_streamed(agent, input="Review code") + async for _ in streaming_result.stream_events(): + pass + final_result = streaming_result.final_output + else: + result = await Runner.run(agent, input="Review code") + final_result = result.final_output + + assert "Security analysis complete" in final_result + assert agent.instructions == "You are a Python code reviewer focusing on security." + + +@pytest.mark.asyncio +async def test_multiple_prompts(): + """Test server with multiple prompts""" + server = FakeMCPPromptServer() + + # Add multiple prompts + server.add_prompt( + "generate_code_review_instructions", "Generate agent instructions for code review tasks" + ) + server.add_prompt( + "generate_testing_instructions", "Generate agent instructions for testing tasks" + ) + + server.set_prompt_result("generate_code_review_instructions", "You are a code reviewer.") + server.set_prompt_result("generate_testing_instructions", "You are a test engineer.") + + # Test listing prompts + prompts_result = await server.list_prompts() + assert len(prompts_result.prompts) == 2 + + prompt_names = [p.name for p in prompts_result.prompts] + assert "generate_code_review_instructions" in prompt_names + assert "generate_testing_instructions" in prompt_names + + # Test getting each prompt + review_result = await server.get_prompt("generate_code_review_instructions") + assert review_result.messages[0].content.text == "You are a code reviewer." + + testing_result = await server.get_prompt("generate_testing_instructions") + assert testing_result.messages[0].content.text == "You are a test engineer." + + +@pytest.mark.asyncio +async def test_prompt_with_complex_arguments(): + """Test prompt with complex argument formatting""" + server = FakeMCPPromptServer() + server.add_prompt( + "generate_detailed_instructions", "Generate detailed instructions with multiple parameters" + ) + server.set_prompt_result( + "generate_detailed_instructions", + "You are a {role} specialist. Your focus is on {focus}. " + + "You work with {language} code. Your experience level is {level}.", + ) + + arguments = { + "role": "security", + "focus": "vulnerability detection", + "language": "Python", + "level": "senior", + } + + result = await server.get_prompt("generate_detailed_instructions", arguments) + + expected = ( + "You are a security specialist. Your focus is on vulnerability detection. " + "You work with Python code. Your experience level is senior." + ) + assert result.messages[0].content.text == expected + + +@pytest.mark.asyncio +async def test_prompt_with_missing_arguments(): + """Test prompt with missing arguments in format string""" + server = FakeMCPPromptServer() + server.add_prompt("incomplete_prompt", "Prompt with missing arguments") + server.set_prompt_result("incomplete_prompt", "You are a {role} working on {task}.") + + # Only provide one of the required arguments + result = await server.get_prompt("incomplete_prompt", {"role": "developer"}) + + # Should return the original string since formatting fails + assert result.messages[0].content.text == "You are a {role} working on {task}." + + +@pytest.mark.asyncio +async def test_prompt_server_cleanup(): + """Test that prompt server cleanup works correctly""" + server = FakeMCPPromptServer() + server.add_prompt("test_prompt", "Test prompt") + server.set_prompt_result("test_prompt", "Test result") + + # Test that server works before cleanup + result = await server.get_prompt("test_prompt") + assert result.messages[0].content.text == "Test result" + + # Cleanup should not raise any errors + await server.cleanup() + + # Server should still work after cleanup (in this fake implementation) + result = await server.get_prompt("test_prompt") + assert result.messages[0].content.text == "Test result" diff --git a/tests/mcp/test_server_errors.py b/tests/mcp/test_server_errors.py index bdca7ce62..9e0455115 100644 --- a/tests/mcp/test_server_errors.py +++ b/tests/mcp/test_server_errors.py @@ -1,12 +1,14 @@ import pytest +from agents import Agent from agents.exceptions import UserError from agents.mcp.server import _MCPServerWithClientSession +from agents.run_context import RunContextWrapper class CrashingClientSessionServer(_MCPServerWithClientSession): def __init__(self): - super().__init__(cache_tools_list=False) + super().__init__(cache_tools_list=False, client_session_timeout_seconds=5) self.cleanup_called = False def create_streams(self): @@ -35,8 +37,11 @@ async def test_server_errors_cause_error_and_cleanup_called(): async def test_not_calling_connect_causes_error(): server = CrashingClientSessionServer() + run_context = RunContextWrapper(context=None) + agent = Agent(name="test_agent", instructions="Test agent") + with pytest.raises(UserError): - await server.list_tools() + await server.list_tools(run_context, agent) with pytest.raises(UserError): await server.call_tool("foo", {}) diff --git a/tests/mcp/test_tool_filtering.py b/tests/mcp/test_tool_filtering.py new file mode 100644 index 000000000..0127df806 --- /dev/null +++ b/tests/mcp/test_tool_filtering.py @@ -0,0 +1,246 @@ +""" +Tool filtering tests use FakeMCPServer instead of real MCPServer implementations to avoid +external dependencies (processes, network connections) and ensure fast, reliable unit tests. +FakeMCPServer delegates filtering logic to the real _MCPServerWithClientSession implementation. +""" + +import asyncio + +import pytest +from mcp import Tool as MCPTool + +from agents import Agent +from agents.mcp import ToolFilterContext, create_static_tool_filter +from agents.run_context import RunContextWrapper + +from .helpers import FakeMCPServer + + +def create_test_agent(name: str = "test_agent") -> Agent: + """Create a test agent for filtering tests.""" + return Agent(name=name, instructions="Test agent") + + +def create_test_context() -> RunContextWrapper: + """Create a test run context for filtering tests.""" + return RunContextWrapper(context=None) + + +# === Static Tool Filtering Tests === + + +@pytest.mark.asyncio +async def test_static_tool_filtering(): + """Test all static tool filtering scenarios: allowed, blocked, both, none, etc.""" + server = FakeMCPServer(server_name="test_server") + server.add_tool("tool1", {}) + server.add_tool("tool2", {}) + server.add_tool("tool3", {}) + server.add_tool("tool4", {}) + + # Create test context and agent for all calls + run_context = create_test_context() + agent = create_test_agent() + + # Test allowed_tool_names only + server.tool_filter = {"allowed_tool_names": ["tool1", "tool2"]} + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"tool1", "tool2"} + + # Test blocked_tool_names only + server.tool_filter = {"blocked_tool_names": ["tool3", "tool4"]} + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"tool1", "tool2"} + + # Test both filters together (allowed first, then blocked) + server.tool_filter = { + "allowed_tool_names": ["tool1", "tool2", "tool3"], + "blocked_tool_names": ["tool3"], + } + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"tool1", "tool2"} + + # Test no filter + server.tool_filter = None + tools = await server.list_tools(run_context, agent) + assert len(tools) == 4 + + # Test helper function + server.tool_filter = create_static_tool_filter( + allowed_tool_names=["tool1", "tool2"], blocked_tool_names=["tool2"] + ) + tools = await server.list_tools(run_context, agent) + assert len(tools) == 1 + assert tools[0].name == "tool1" + + +# === Dynamic Tool Filtering Core Tests === + + +@pytest.mark.asyncio +async def test_dynamic_filter_sync_and_async(): + """Test both synchronous and asynchronous dynamic filters""" + server = FakeMCPServer(server_name="test_server") + server.add_tool("allowed_tool", {}) + server.add_tool("blocked_tool", {}) + server.add_tool("restricted_tool", {}) + + # Create test context and agent + run_context = create_test_context() + agent = create_test_agent() + + # Test sync filter + def sync_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + return tool.name.startswith("allowed") + + server.tool_filter = sync_filter + tools = await server.list_tools(run_context, agent) + assert len(tools) == 1 + assert tools[0].name == "allowed_tool" + + # Test async filter + async def async_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + await asyncio.sleep(0.001) # Simulate async operation + return "restricted" not in tool.name + + server.tool_filter = async_filter + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"allowed_tool", "blocked_tool"} + + +@pytest.mark.asyncio +async def test_dynamic_filter_context_handling(): + """Test dynamic filters with context access""" + server = FakeMCPServer(server_name="test_server") + server.add_tool("admin_tool", {}) + server.add_tool("user_tool", {}) + server.add_tool("guest_tool", {}) + + # Test context-independent filter + def context_independent_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + return not tool.name.startswith("admin") + + server.tool_filter = context_independent_filter + run_context = create_test_context() + agent = create_test_agent() + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"user_tool", "guest_tool"} + + # Test context-dependent filter (needs context) + def context_dependent_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + assert context is not None + assert context.run_context is not None + assert context.agent is not None + assert context.server_name == "test_server" + + # Only admin tools for agents with "admin" in name + if "admin" in context.agent.name.lower(): + return True + else: + return not tool.name.startswith("admin") + + server.tool_filter = context_dependent_filter + + # Should work with context + run_context = RunContextWrapper(context=None) + regular_agent = create_test_agent("regular_user") + tools = await server.list_tools(run_context, regular_agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"user_tool", "guest_tool"} + + admin_agent = create_test_agent("admin_user") + tools = await server.list_tools(run_context, admin_agent) + assert len(tools) == 3 + + +@pytest.mark.asyncio +async def test_dynamic_filter_error_handling(): + """Test error handling in dynamic filters""" + server = FakeMCPServer(server_name="test_server") + server.add_tool("good_tool", {}) + server.add_tool("error_tool", {}) + server.add_tool("another_good_tool", {}) + + def error_prone_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + if tool.name == "error_tool": + raise ValueError("Simulated filter error") + return True + + server.tool_filter = error_prone_filter + + # Test with server call + run_context = create_test_context() + agent = create_test_agent() + tools = await server.list_tools(run_context, agent) + assert len(tools) == 2 + assert {t.name for t in tools} == {"good_tool", "another_good_tool"} + + +# === Integration Tests === + + +@pytest.mark.asyncio +async def test_agent_dynamic_filtering_integration(): + """Test dynamic filtering integration with Agent methods""" + server = FakeMCPServer() + server.add_tool("file_read", {"type": "object", "properties": {"path": {"type": "string"}}}) + server.add_tool( + "file_write", + { + "type": "object", + "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, + }, + ) + server.add_tool( + "database_query", {"type": "object", "properties": {"query": {"type": "string"}}} + ) + server.add_tool( + "network_request", {"type": "object", "properties": {"url": {"type": "string"}}} + ) + + # Role-based filter for comprehensive testing + async def role_based_filter(context: ToolFilterContext, tool: MCPTool) -> bool: + # Simulate async permission check + await asyncio.sleep(0.001) + + agent_name = context.agent.name.lower() + if "admin" in agent_name: + return True + elif "readonly" in agent_name: + return "read" in tool.name or "query" in tool.name + else: + return tool.name.startswith("file_") + + server.tool_filter = role_based_filter + + # Test admin agent + admin_agent = Agent(name="admin_user", instructions="Admin", mcp_servers=[server]) + run_context = RunContextWrapper(context=None) + admin_tools = await admin_agent.get_mcp_tools(run_context) + assert len(admin_tools) == 4 + + # Test readonly agent + readonly_agent = Agent(name="readonly_viewer", instructions="Read-only", mcp_servers=[server]) + readonly_tools = await readonly_agent.get_mcp_tools(run_context) + assert len(readonly_tools) == 2 + assert {t.name for t in readonly_tools} == {"file_read", "database_query"} + + # Test regular agent + regular_agent = Agent(name="regular_user", instructions="Regular", mcp_servers=[server]) + regular_tools = await regular_agent.get_mcp_tools(run_context) + assert len(regular_tools) == 2 + assert {t.name for t in regular_tools} == {"file_read", "file_write"} + + # Test get_all_tools method + all_tools = await regular_agent.get_all_tools(run_context) + mcp_tool_names = { + t.name + for t in all_tools + if t.name in {"file_read", "file_write", "database_query", "network_request"} + } + assert mcp_tool_names == {"file_read", "file_write"} diff --git a/tests/model_settings/test_serialization.py b/tests/model_settings/test_serialization.py new file mode 100644 index 000000000..6e8c65180 --- /dev/null +++ b/tests/model_settings/test_serialization.py @@ -0,0 +1,176 @@ +import json +from dataclasses import fields + +from openai.types.shared import Reasoning +from pydantic import TypeAdapter +from pydantic_core import to_json + +from agents.model_settings import MCPToolChoice, ModelSettings + + +def verify_serialization(model_settings: ModelSettings) -> None: + """Verify that ModelSettings can be serialized to a JSON string.""" + json_dict = model_settings.to_json_dict() + json_string = json.dumps(json_dict) + assert json_string is not None + + +def test_basic_serialization() -> None: + """Tests whether ModelSettings can be serialized to a JSON string.""" + + # First, lets create a ModelSettings instance + model_settings = ModelSettings( + temperature=0.5, + top_p=0.9, + max_tokens=100, + ) + + # Now, lets serialize the ModelSettings instance to a JSON string + verify_serialization(model_settings) + + +def test_mcp_tool_choice_serialization() -> None: + """Tests whether ModelSettings with MCPToolChoice can be serialized to a JSON string.""" + # First, lets create a ModelSettings instance + model_settings = ModelSettings( + temperature=0.5, + tool_choice=MCPToolChoice(server_label="mcp", name="mcp_tool"), + ) + # Now, lets serialize the ModelSettings instance to a JSON string + verify_serialization(model_settings) + + +def test_all_fields_serialization() -> None: + """Tests whether ModelSettings can be serialized to a JSON string.""" + + # First, lets create a ModelSettings instance + model_settings = ModelSettings( + temperature=0.5, + top_p=0.9, + frequency_penalty=0.0, + presence_penalty=0.0, + tool_choice="auto", + parallel_tool_calls=True, + truncation="auto", + max_tokens=100, + reasoning=Reasoning(), + metadata={"foo": "bar"}, + store=False, + include_usage=False, + response_include=["reasoning.encrypted_content"], + extra_query={"foo": "bar"}, + extra_body={"foo": "bar"}, + extra_headers={"foo": "bar"}, + extra_args={"custom_param": "value", "another_param": 42}, + ) + + # Verify that every single field is set to a non-None value + for field in fields(model_settings): + assert getattr(model_settings, field.name) is not None, ( + f"You must set the {field.name} field" + ) + + # Now, lets serialize the ModelSettings instance to a JSON string + verify_serialization(model_settings) + + +def test_extra_args_serialization() -> None: + """Test that extra_args are properly serialized.""" + model_settings = ModelSettings( + temperature=0.5, + extra_args={"custom_param": "value", "another_param": 42, "nested": {"key": "value"}}, + ) + + json_dict = model_settings.to_json_dict() + assert json_dict["extra_args"] == { + "custom_param": "value", + "another_param": 42, + "nested": {"key": "value"}, + } + + # Verify serialization works + verify_serialization(model_settings) + + +def test_extra_args_resolve() -> None: + """Test that extra_args are properly merged in the resolve method.""" + base_settings = ModelSettings( + temperature=0.5, extra_args={"param1": "base_value", "param2": "base_only"} + ) + + override_settings = ModelSettings( + top_p=0.9, extra_args={"param1": "override_value", "param3": "override_only"} + ) + + resolved = base_settings.resolve(override_settings) + + # Check that regular fields are properly resolved + assert resolved.temperature == 0.5 # from base + assert resolved.top_p == 0.9 # from override + + # Check that extra_args are properly merged + expected_extra_args = { + "param1": "override_value", # override wins + "param2": "base_only", # from base + "param3": "override_only", # from override + } + assert resolved.extra_args == expected_extra_args + + +def test_extra_args_resolve_with_none() -> None: + """Test that resolve works properly when one side has None extra_args.""" + # Base with extra_args, override with None + base_settings = ModelSettings(extra_args={"param1": "value1"}) + override_settings = ModelSettings(temperature=0.8) + + resolved = base_settings.resolve(override_settings) + assert resolved.extra_args == {"param1": "value1"} + assert resolved.temperature == 0.8 + + # Base with None, override with extra_args + base_settings = ModelSettings(temperature=0.5) + override_settings = ModelSettings(extra_args={"param2": "value2"}) + + resolved = base_settings.resolve(override_settings) + assert resolved.extra_args == {"param2": "value2"} + assert resolved.temperature == 0.5 + + +def test_extra_args_resolve_both_none() -> None: + """Test that resolve works when both sides have None extra_args.""" + base_settings = ModelSettings(temperature=0.5) + override_settings = ModelSettings(top_p=0.9) + + resolved = base_settings.resolve(override_settings) + assert resolved.extra_args is None + assert resolved.temperature == 0.5 + assert resolved.top_p == 0.9 + + +def test_pydantic_serialization() -> None: + """Tests whether ModelSettings can be serialized with Pydantic.""" + + # First, lets create a ModelSettings instance + model_settings = ModelSettings( + temperature=0.5, + top_p=0.9, + frequency_penalty=0.0, + presence_penalty=0.0, + tool_choice="auto", + parallel_tool_calls=True, + truncation="auto", + max_tokens=100, + reasoning=Reasoning(), + metadata={"foo": "bar"}, + store=False, + include_usage=False, + extra_query={"foo": "bar"}, + extra_body={"foo": "bar"}, + extra_headers={"foo": "bar"}, + extra_args={"custom_param": "value", "another_param": 42}, + ) + + json = to_json(model_settings) + deserialized = TypeAdapter(ModelSettings).validate_json(json) + + assert model_settings == deserialized diff --git a/tests/models/__init__.py b/tests/models/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/models/conftest.py b/tests/models/conftest.py new file mode 100644 index 000000000..79d85d8b4 --- /dev/null +++ b/tests/models/conftest.py @@ -0,0 +1,11 @@ +import os +import sys + + +# Skip voice tests on Python 3.9 +def pytest_ignore_collect(collection_path, config): + if sys.version_info[:2] == (3, 9): + this_dir = os.path.dirname(__file__) + + if str(collection_path).startswith(this_dir): + return True diff --git a/tests/models/test_kwargs_functionality.py b/tests/models/test_kwargs_functionality.py new file mode 100644 index 000000000..210610a02 --- /dev/null +++ b/tests/models/test_kwargs_functionality.py @@ -0,0 +1,177 @@ +import litellm +import pytest +from litellm.types.utils import Choices, Message, ModelResponse, Usage +from openai.types.chat.chat_completion import ChatCompletion, Choice +from openai.types.chat.chat_completion_message import ChatCompletionMessage +from openai.types.completion_usage import CompletionUsage + +from agents.extensions.models.litellm_model import LitellmModel +from agents.model_settings import ModelSettings +from agents.models.interface import ModelTracing +from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_litellm_kwargs_forwarded(monkeypatch): + """ + Test that kwargs from ModelSettings are forwarded to litellm.acompletion. + """ + captured: dict[str, object] = {} + + async def fake_acompletion(model, messages=None, **kwargs): + captured.update(kwargs) + msg = Message(role="assistant", content="test response") + choice = Choices(index=0, message=msg) + return ModelResponse(choices=[choice], usage=Usage(0, 0, 0)) + + monkeypatch.setattr(litellm, "acompletion", fake_acompletion) + + settings = ModelSettings( + temperature=0.5, + extra_args={ + "custom_param": "custom_value", + "seed": 42, + "stop": ["END"], + "logit_bias": {123: -100}, + }, + ) + model = LitellmModel(model="test-model") + + await model.get_response( + system_instructions=None, + input="test input", + model_settings=settings, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + # Verify that all kwargs were passed through + assert captured["custom_param"] == "custom_value" + assert captured["seed"] == 42 + assert captured["stop"] == ["END"] + assert captured["logit_bias"] == {123: -100} + + # Verify regular parameters are still passed + assert captured["temperature"] == 0.5 + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_openai_chatcompletions_kwargs_forwarded(monkeypatch): + """ + Test that kwargs from ModelSettings are forwarded to OpenAI chat completions API. + """ + captured: dict[str, object] = {} + + class MockChatCompletions: + async def create(self, **kwargs): + captured.update(kwargs) + msg = ChatCompletionMessage(role="assistant", content="test response") + choice = Choice(index=0, message=msg, finish_reason="stop") + return ChatCompletion( + id="test-id", + created=0, + model="gpt-4", + object="chat.completion", + choices=[choice], + usage=CompletionUsage(completion_tokens=5, prompt_tokens=10, total_tokens=15), + ) + + class MockChat: + def __init__(self): + self.completions = MockChatCompletions() + + class MockClient: + def __init__(self): + self.chat = MockChat() + self.base_url = "https://api.openai.com/v1" + + settings = ModelSettings( + temperature=0.7, + extra_args={ + "seed": 123, + "logit_bias": {456: 10}, + "stop": ["STOP", "END"], + "user": "test-user", + }, + ) + + mock_client = MockClient() + model = OpenAIChatCompletionsModel(model="gpt-4", openai_client=mock_client) # type: ignore + + await model.get_response( + system_instructions="Test system", + input="test input", + model_settings=settings, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + # Verify that all kwargs were passed through + assert captured["seed"] == 123 + assert captured["logit_bias"] == {456: 10} + assert captured["stop"] == ["STOP", "END"] + assert captured["user"] == "test-user" + + # Verify regular parameters are still passed + assert captured["temperature"] == 0.7 + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_empty_kwargs_handling(monkeypatch): + """ + Test that empty or None kwargs are handled gracefully. + """ + captured: dict[str, object] = {} + + async def fake_acompletion(model, messages=None, **kwargs): + captured.update(kwargs) + msg = Message(role="assistant", content="test response") + choice = Choices(index=0, message=msg) + return ModelResponse(choices=[choice], usage=Usage(0, 0, 0)) + + monkeypatch.setattr(litellm, "acompletion", fake_acompletion) + + # Test with None kwargs + settings_none = ModelSettings(temperature=0.5, extra_args=None) + model = LitellmModel(model="test-model") + + await model.get_response( + system_instructions=None, + input="test input", + model_settings=settings_none, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + # Should work without error and include regular parameters + assert captured["temperature"] == 0.5 + + # Test with empty dict + captured.clear() + settings_empty = ModelSettings(temperature=0.3, extra_args={}) + + await model.get_response( + system_instructions=None, + input="test input", + model_settings=settings_empty, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + # Should work without error and include regular parameters + assert captured["temperature"] == 0.3 diff --git a/tests/models/test_litellm_chatcompletions_stream.py b/tests/models/test_litellm_chatcompletions_stream.py new file mode 100644 index 000000000..cd342e444 --- /dev/null +++ b/tests/models/test_litellm_chatcompletions_stream.py @@ -0,0 +1,301 @@ +from collections.abc import AsyncIterator + +import pytest +from openai.types.chat.chat_completion_chunk import ( + ChatCompletionChunk, + Choice, + ChoiceDelta, + ChoiceDeltaToolCall, + ChoiceDeltaToolCallFunction, +) +from openai.types.completion_usage import ( + CompletionTokensDetails, + CompletionUsage, + PromptTokensDetails, +) +from openai.types.responses import ( + Response, + ResponseFunctionToolCall, + ResponseOutputMessage, + ResponseOutputRefusal, + ResponseOutputText, +) + +from agents.extensions.models.litellm_model import LitellmModel +from agents.extensions.models.litellm_provider import LitellmProvider +from agents.model_settings import ModelSettings +from agents.models.interface import ModelTracing + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_yields_events_for_text_content(monkeypatch) -> None: + """ + Validate that `stream_response` emits the correct sequence of events when + streaming a simple assistant message consisting of plain text content. + We simulate two chunks of text returned from the chat completion stream. + """ + # Create two chunks that will be emitted by the fake stream. + chunk1 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(content="He"))], + ) + # Mark last chunk with usage so stream_response knows this is final. + chunk2 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(content="llo"))], + usage=CompletionUsage( + completion_tokens=5, + prompt_tokens=7, + total_tokens=12, + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=2), + prompt_tokens_details=PromptTokensDetails(cached_tokens=6), + ), + ) + + async def fake_stream() -> AsyncIterator[ChatCompletionChunk]: + for c in (chunk1, chunk2): + yield c + + # Patch _fetch_response to inject our fake stream + async def patched_fetch_response(self, *args, **kwargs): + # `_fetch_response` is expected to return a Response skeleton and the async stream + resp = Response( + id="resp-id", + created_at=0, + model="fake-model", + object="response", + output=[], + tool_choice="none", + tools=[], + parallel_tool_calls=False, + ) + return resp, fake_stream() + + monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response) + model = LitellmProvider().get_model("gpt-4") + output_events = [] + async for event in model.stream_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ): + output_events.append(event) + # We expect a response.created, then a response.output_item.added, content part added, + # two content delta events (for "He" and "llo"), a content part done, the assistant message + # output_item.done, and finally response.completed. + # There should be 8 events in total. + assert len(output_events) == 8 + # First event indicates creation. + assert output_events[0].type == "response.created" + # The output item added and content part added events should mark the assistant message. + assert output_events[1].type == "response.output_item.added" + assert output_events[2].type == "response.content_part.added" + # Two text delta events. + assert output_events[3].type == "response.output_text.delta" + assert output_events[3].delta == "He" + assert output_events[4].type == "response.output_text.delta" + assert output_events[4].delta == "llo" + # After streaming, the content part and item should be marked done. + assert output_events[5].type == "response.content_part.done" + assert output_events[6].type == "response.output_item.done" + # Last event indicates completion of the stream. + assert output_events[7].type == "response.completed" + # The completed response should have one output message with full text. + completed_resp = output_events[7].response + assert isinstance(completed_resp.output[0], ResponseOutputMessage) + assert isinstance(completed_resp.output[0].content[0], ResponseOutputText) + assert completed_resp.output[0].content[0].text == "Hello" + + assert completed_resp.usage, "usage should not be None" + assert completed_resp.usage.input_tokens == 7 + assert completed_resp.usage.output_tokens == 5 + assert completed_resp.usage.total_tokens == 12 + assert completed_resp.usage.input_tokens_details.cached_tokens == 6 + assert completed_resp.usage.output_tokens_details.reasoning_tokens == 2 + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_yields_events_for_refusal_content(monkeypatch) -> None: + """ + Validate that when the model streams a refusal string instead of normal content, + `stream_response` emits the appropriate sequence of events including + `response.refusal.delta` events for each chunk of the refusal message and + constructs a completed assistant message with a `ResponseOutputRefusal` part. + """ + # Simulate refusal text coming in two pieces, like content but using the `refusal` + # field on the delta rather than `content`. + chunk1 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(refusal="No"))], + ) + chunk2 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(refusal="Thanks"))], + usage=CompletionUsage(completion_tokens=2, prompt_tokens=2, total_tokens=4), + ) + + async def fake_stream() -> AsyncIterator[ChatCompletionChunk]: + for c in (chunk1, chunk2): + yield c + + async def patched_fetch_response(self, *args, **kwargs): + resp = Response( + id="resp-id", + created_at=0, + model="fake-model", + object="response", + output=[], + tool_choice="none", + tools=[], + parallel_tool_calls=False, + ) + return resp, fake_stream() + + monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response) + model = LitellmProvider().get_model("gpt-4") + output_events = [] + async for event in model.stream_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ): + output_events.append(event) + # Expect sequence similar to text: created, output_item.added, content part added, + # two refusal delta events, content part done, output_item.done, completed. + assert len(output_events) == 8 + assert output_events[0].type == "response.created" + assert output_events[1].type == "response.output_item.added" + assert output_events[2].type == "response.content_part.added" + assert output_events[3].type == "response.refusal.delta" + assert output_events[3].delta == "No" + assert output_events[4].type == "response.refusal.delta" + assert output_events[4].delta == "Thanks" + assert output_events[5].type == "response.content_part.done" + assert output_events[6].type == "response.output_item.done" + assert output_events[7].type == "response.completed" + completed_resp = output_events[7].response + assert isinstance(completed_resp.output[0], ResponseOutputMessage) + refusal_part = completed_resp.output[0].content[0] + assert isinstance(refusal_part, ResponseOutputRefusal) + assert refusal_part.refusal == "NoThanks" + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_yields_events_for_tool_call(monkeypatch) -> None: + """ + Validate that `stream_response` emits the correct sequence of events when + the model is streaming a function/tool call instead of plain text. + The function call will be split across two chunks. + """ + # Simulate a single tool call whose ID stays constant and function name/args built over chunks. + tool_call_delta1 = ChoiceDeltaToolCall( + index=0, + id="tool-id", + function=ChoiceDeltaToolCallFunction(name="my_", arguments="arg1"), + type="function", + ) + tool_call_delta2 = ChoiceDeltaToolCall( + index=0, + id="tool-id", + function=ChoiceDeltaToolCallFunction(name="func", arguments="arg2"), + type="function", + ) + chunk1 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))], + ) + chunk2 = ChatCompletionChunk( + id="chunk-id", + created=1, + model="fake", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))], + usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2), + ) + + async def fake_stream() -> AsyncIterator[ChatCompletionChunk]: + for c in (chunk1, chunk2): + yield c + + async def patched_fetch_response(self, *args, **kwargs): + resp = Response( + id="resp-id", + created_at=0, + model="fake-model", + object="response", + output=[], + tool_choice="none", + tools=[], + parallel_tool_calls=False, + ) + return resp, fake_stream() + + monkeypatch.setattr(LitellmModel, "_fetch_response", patched_fetch_response) + model = LitellmProvider().get_model("gpt-4") + output_events = [] + async for event in model.stream_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ): + output_events.append(event) + # Sequence should be: response.created, then after loop we expect function call-related events: + # one response.output_item.added for function call, a response.function_call_arguments.delta, + # a response.output_item.done, and finally response.completed. + assert output_events[0].type == "response.created" + # The next three events are about the tool call. + assert output_events[1].type == "response.output_item.added" + # The added item should be a ResponseFunctionToolCall. + added_fn = output_events[1].item + assert isinstance(added_fn, ResponseFunctionToolCall) + assert added_fn.name == "my_func" # Name should be concatenation of both chunks. + assert added_fn.arguments == "arg1arg2" + assert output_events[2].type == "response.function_call_arguments.delta" + assert output_events[2].delta == "arg1arg2" + assert output_events[3].type == "response.output_item.done" + assert output_events[4].type == "response.completed" + assert output_events[2].delta == "arg1arg2" + assert output_events[3].type == "response.output_item.done" + assert output_events[4].type == "response.completed" + assert added_fn.name == "my_func" # Name should be concatenation of both chunks. + assert added_fn.arguments == "arg1arg2" + assert output_events[2].type == "response.function_call_arguments.delta" + assert output_events[2].delta == "arg1arg2" + assert output_events[3].type == "response.output_item.done" + assert output_events[4].type == "response.completed" diff --git a/tests/models/test_litellm_extra_body.py b/tests/models/test_litellm_extra_body.py new file mode 100644 index 000000000..3c83b0607 --- /dev/null +++ b/tests/models/test_litellm_extra_body.py @@ -0,0 +1,44 @@ +import litellm +import pytest +from litellm.types.utils import Choices, Message, ModelResponse, Usage + +from agents.extensions.models.litellm_model import LitellmModel +from agents.model_settings import ModelSettings +from agents.models.interface import ModelTracing + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_extra_body_is_forwarded(monkeypatch): + """ + Forward `extra_body` entries into litellm.acompletion kwargs. + + This ensures that user-provided parameters (e.g. cached_content) + arrive alongside default arguments. + """ + captured: dict[str, object] = {} + + async def fake_acompletion(model, messages=None, **kwargs): + captured.update(kwargs) + msg = Message(role="assistant", content="ok") + choice = Choices(index=0, message=msg) + return ModelResponse(choices=[choice], usage=Usage(0, 0, 0)) + + monkeypatch.setattr(litellm, "acompletion", fake_acompletion) + settings = ModelSettings( + temperature=0.1, extra_body={"cached_content": "some_cache", "foo": 123} + ) + model = LitellmModel(model="test-model") + + await model.get_response( + system_instructions=None, + input=[], + model_settings=settings, + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + + assert {"cached_content": "some_cache", "foo": 123}.items() <= captured.items() diff --git a/tests/models/test_map.py b/tests/models/test_map.py new file mode 100644 index 000000000..b1a129667 --- /dev/null +++ b/tests/models/test_map.py @@ -0,0 +1,21 @@ +from agents import Agent, OpenAIResponsesModel, RunConfig +from agents.extensions.models.litellm_model import LitellmModel +from agents.run import AgentRunner + + +def test_no_prefix_is_openai(): + agent = Agent(model="gpt-4o", instructions="", name="test") + model = AgentRunner._get_model(agent, RunConfig()) + assert isinstance(model, OpenAIResponsesModel) + + +def openai_prefix_is_openai(): + agent = Agent(model="openai/gpt-4o", instructions="", name="test") + model = AgentRunner._get_model(agent, RunConfig()) + assert isinstance(model, OpenAIResponsesModel) + + +def test_litellm_prefix_is_litellm(): + agent = Agent(model="litellm/foo/bar", instructions="", name="test") + model = AgentRunner._get_model(agent, RunConfig()) + assert isinstance(model, LitellmModel) diff --git a/tests/realtime/__init__.py b/tests/realtime/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/realtime/test_agent.py b/tests/realtime/test_agent.py new file mode 100644 index 000000000..aae8bc47c --- /dev/null +++ b/tests/realtime/test_agent.py @@ -0,0 +1,25 @@ +import pytest + +from agents import RunContextWrapper +from agents.realtime.agent import RealtimeAgent + + +def test_can_initialize_realtime_agent(): + agent = RealtimeAgent(name="test", instructions="Hello") + assert agent.name == "test" + assert agent.instructions == "Hello" + + +@pytest.mark.asyncio +async def test_dynamic_instructions(): + agent = RealtimeAgent(name="test") + assert agent.instructions is None + + def _instructions(ctx, agt) -> str: + assert ctx.context is None + assert agt == agent + return "Dynamic" + + agent = RealtimeAgent(name="test", instructions=_instructions) + instructions = await agent.get_system_prompt(RunContextWrapper(context=None)) + assert instructions == "Dynamic" diff --git a/tests/realtime/test_model_events.py b/tests/realtime/test_model_events.py new file mode 100644 index 000000000..b8696cc29 --- /dev/null +++ b/tests/realtime/test_model_events.py @@ -0,0 +1,12 @@ +from typing import get_args + +from agents.realtime.model_events import RealtimeModelEvent + + +def test_all_events_have_type() -> None: + """Test that all events have a type.""" + events = get_args(RealtimeModelEvent) + assert len(events) > 0 + for event in events: + assert event.type is not None + assert isinstance(event.type, str) diff --git a/tests/realtime/test_openai_realtime.py b/tests/realtime/test_openai_realtime.py new file mode 100644 index 000000000..9fe2f9acb --- /dev/null +++ b/tests/realtime/test_openai_realtime.py @@ -0,0 +1,385 @@ +from datetime import datetime +from typing import Any +from unittest.mock import AsyncMock, Mock, patch + +import pytest +import websockets + +from agents.exceptions import UserError +from agents.realtime.model_events import ( + RealtimeModelAudioEvent, + RealtimeModelErrorEvent, + RealtimeModelToolCallEvent, +) +from agents.realtime.openai_realtime import OpenAIRealtimeWebSocketModel + + +class TestOpenAIRealtimeWebSocketModel: + """Test suite for OpenAIRealtimeWebSocketModel connection and event handling.""" + + @pytest.fixture + def model(self): + """Create a fresh model instance for each test.""" + return OpenAIRealtimeWebSocketModel() + + @pytest.fixture + def mock_websocket(self): + """Create a mock websocket connection.""" + mock_ws = AsyncMock() + mock_ws.send = AsyncMock() + mock_ws.close = AsyncMock() + return mock_ws + + +class TestConnectionLifecycle(TestOpenAIRealtimeWebSocketModel): + """Test connection establishment, configuration, and error handling.""" + + @pytest.mark.asyncio + async def test_connect_missing_api_key_raises_error(self, model): + """Test that missing API key raises UserError.""" + config: dict[str, Any] = {"initial_model_settings": {}} + + with patch.dict("os.environ", {}, clear=True): + with pytest.raises(UserError, match="API key is required"): + await model.connect(config) + + @pytest.mark.asyncio + async def test_connect_with_string_api_key(self, model, mock_websocket): + """Test successful connection with string API key.""" + config = { + "api_key": "test-api-key-123", + "initial_model_settings": {"model_name": "gpt-4o-realtime-preview"}, + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket) as mock_connect: + with patch("asyncio.create_task") as mock_create_task: + # Mock create_task to return a mock task and properly handle the coroutine + mock_task = AsyncMock() + + def mock_create_task_func(coro): + # Properly close the coroutine to avoid RuntimeWarning + coro.close() + return mock_task + + mock_create_task.side_effect = mock_create_task_func + + await model.connect(config) + + # Verify WebSocket connection called with correct parameters + mock_connect.assert_called_once() + call_args = mock_connect.call_args + assert ( + call_args[0][0] + == "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview" + ) + assert ( + call_args[1]["additional_headers"]["Authorization"] == "Bearer test-api-key-123" + ) + assert call_args[1]["additional_headers"]["OpenAI-Beta"] == "realtime=v1" + + # Verify task was created for message listening + mock_create_task.assert_called_once() + + # Verify internal state + assert model._websocket == mock_websocket + assert model._websocket_task is not None + assert model.model == "gpt-4o-realtime-preview" + + @pytest.mark.asyncio + async def test_connect_with_callable_api_key(self, model, mock_websocket): + """Test connection with callable API key provider.""" + + def get_api_key(): + return "callable-api-key" + + config = {"api_key": get_api_key} + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + # Mock create_task to return a mock task and properly handle the coroutine + mock_task = AsyncMock() + + def mock_create_task_func(coro): + # Properly close the coroutine to avoid RuntimeWarning + coro.close() + return mock_task + + mock_create_task.side_effect = mock_create_task_func + + await model.connect(config) + # Should succeed with callable API key + assert model._websocket == mock_websocket + + @pytest.mark.asyncio + async def test_connect_with_async_callable_api_key(self, model, mock_websocket): + """Test connection with async callable API key provider.""" + + async def get_api_key(): + return "async-api-key" + + config = {"api_key": get_api_key} + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + # Mock create_task to return a mock task and properly handle the coroutine + mock_task = AsyncMock() + + def mock_create_task_func(coro): + # Properly close the coroutine to avoid RuntimeWarning + coro.close() + return mock_task + + mock_create_task.side_effect = mock_create_task_func + + await model.connect(config) + assert model._websocket == mock_websocket + + @pytest.mark.asyncio + async def test_connect_websocket_failure_propagates(self, model): + """Test that WebSocket connection failures are properly propagated.""" + config = {"api_key": "test-key"} + + with patch( + "websockets.connect", side_effect=websockets.exceptions.ConnectionClosed(None, None) + ): + with pytest.raises(websockets.exceptions.ConnectionClosed): + await model.connect(config) + + # Verify internal state remains clean after failure + assert model._websocket is None + assert model._websocket_task is None + + @pytest.mark.asyncio + async def test_connect_already_connected_assertion(self, model, mock_websocket): + """Test that connecting when already connected raises assertion error.""" + model._websocket = mock_websocket # Simulate already connected + + config = {"api_key": "test-key"} + + with pytest.raises(AssertionError, match="Already connected"): + await model.connect(config) + + +class TestEventHandlingRobustness(TestOpenAIRealtimeWebSocketModel): + """Test event parsing, validation, and error handling robustness.""" + + @pytest.mark.asyncio + async def test_handle_malformed_json_logs_error_continues(self, model): + """Test that malformed JSON is logged as error but doesn't crash.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Malformed JSON should not crash the handler + with patch("agents.realtime.openai_realtime.logger") as mock_logger: + await model._handle_ws_event("invalid json {") + + # Should log error but not crash + mock_logger.error.assert_called_once() + assert "Invalid event" in mock_logger.error.call_args[0][0] + + # Should not emit any events to listeners + mock_listener.on_event.assert_not_called() + + @pytest.mark.asyncio + async def test_handle_invalid_event_schema_logs_error(self, model): + """Test that events with invalid schema are logged but don't crash.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + invalid_event = {"type": "response.audio.delta"} # Missing required fields + + with patch("agents.realtime.openai_realtime.logger") as mock_logger: + await model._handle_ws_event(invalid_event) + + # Should log validation error + mock_logger.error.assert_called_once() + error_msg = mock_logger.error.call_args[0][0] + assert "Invalid event" in error_msg + + # Should not emit events to listeners + mock_listener.on_event.assert_not_called() + + @pytest.mark.asyncio + async def test_handle_unknown_event_type_ignored(self, model): + """Test that unknown event types are ignored gracefully.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Create a well-formed but unknown event type + unknown_event = {"type": "unknown.event.type", "data": "some data"} + + # Should not raise error or log anything for unknown types + with patch("agents.realtime.openai_realtime.logger"): + await model._handle_ws_event(unknown_event) + + # Should not log errors for unknown events (they're just ignored) + # This will depend on the TypeAdapter validation behavior + # If it fails validation, it should log; if it passes but type is + # unknown, it should be ignored + pass + + @pytest.mark.asyncio + async def test_handle_audio_delta_event_success(self, model): + """Test successful handling of audio delta events.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Valid audio delta event (minimal required fields for OpenAI spec) + audio_event = { + "type": "response.audio.delta", + "event_id": "event_123", + "response_id": "resp_123", + "item_id": "item_456", + "output_index": 0, + "content_index": 0, + "delta": "dGVzdCBhdWRpbw==", # base64 encoded "test audio" + } + + with patch("agents.realtime.openai_realtime.datetime") as mock_datetime: + mock_now = datetime(2024, 1, 1, 12, 0, 0) + mock_datetime.now.return_value = mock_now + + await model._handle_ws_event(audio_event) + + # Should emit audio event to listeners + mock_listener.on_event.assert_called_once() + emitted_event = mock_listener.on_event.call_args[0][0] + assert isinstance(emitted_event, RealtimeModelAudioEvent) + assert emitted_event.response_id == "resp_123" + assert emitted_event.data == b"test audio" # decoded from base64 + + # Should update internal audio tracking state + assert model._current_item_id == "item_456" + assert model._current_audio_content_index == 0 + assert model._audio_start_time == mock_now + + @pytest.mark.asyncio + async def test_handle_error_event_success(self, model): + """Test successful handling of error events.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + error_event = { + "type": "error", + "event_id": "event_456", + "error": { + "type": "invalid_request_error", + "code": "invalid_api_key", + "message": "Invalid API key provided", + }, + } + + await model._handle_ws_event(error_event) + + # Should emit error event to listeners + mock_listener.on_event.assert_called_once() + emitted_event = mock_listener.on_event.call_args[0][0] + assert isinstance(emitted_event, RealtimeModelErrorEvent) + + @pytest.mark.asyncio + async def test_handle_tool_call_event_success(self, model): + """Test successful handling of function call events.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Test response.output_item.done with function_call + tool_call_event = { + "type": "response.output_item.done", + "event_id": "event_789", + "response_id": "resp_789", + "output_index": 0, + "item": { + "id": "call_123", + "type": "function_call", + "status": "completed", + "name": "get_weather", + "arguments": '{"location": "San Francisco"}', + }, + } + + await model._handle_ws_event(tool_call_event) + + # Should emit both item updated and tool call events + assert mock_listener.on_event.call_count == 2 + + # First should be item updated, second should be tool call + calls = mock_listener.on_event.call_args_list + tool_call_emitted = calls[1][0][0] + assert isinstance(tool_call_emitted, RealtimeModelToolCallEvent) + assert tool_call_emitted.name == "get_weather" + assert tool_call_emitted.arguments == '{"location": "San Francisco"}' + assert tool_call_emitted.call_id == "call_123" + + @pytest.mark.asyncio + async def test_audio_timing_calculation_accuracy(self, model): + """Test that audio timing calculations are accurate for interruption handling.""" + mock_listener = AsyncMock() + model.add_listener(mock_listener) + + # Send multiple audio deltas to test cumulative timing + audio_deltas = [ + { + "type": "response.audio.delta", + "event_id": "event_1", + "response_id": "resp_1", + "item_id": "item_1", + "output_index": 0, + "content_index": 0, + "delta": "dGVzdA==", # 4 bytes -> "test" + }, + { + "type": "response.audio.delta", + "event_id": "event_2", + "response_id": "resp_1", + "item_id": "item_1", + "output_index": 0, + "content_index": 0, + "delta": "bW9yZQ==", # 4 bytes -> "more" + }, + ] + + for event in audio_deltas: + await model._handle_ws_event(event) + + # Should accumulate audio length: 8 bytes / 24 / 2 = ~0.167ms per byte + # Total: 8 bytes / 24 / 2 = 0.167ms + expected_length = 8 / 24 / 2 + assert abs(model._audio_length_ms - expected_length) < 0.001 + + def test_calculate_audio_length_ms_pure_function(self, model): + """Test the pure audio length calculation function.""" + # Test various audio buffer sizes + assert model._calculate_audio_length_ms(b"test") == 4 / 24 / 2 # 4 bytes + assert model._calculate_audio_length_ms(b"") == 0 # empty + assert model._calculate_audio_length_ms(b"a" * 48) == 1.0 # exactly 1ms worth + + @pytest.mark.asyncio + async def test_handle_audio_delta_state_management(self, model): + """Test that _handle_audio_delta properly manages internal state.""" + # Create mock parsed event + mock_parsed = Mock() + mock_parsed.content_index = 5 + mock_parsed.item_id = "test_item" + mock_parsed.delta = "dGVzdA==" # "test" in base64 + mock_parsed.response_id = "resp_123" + + with patch("agents.realtime.openai_realtime.datetime") as mock_datetime: + mock_now = datetime(2024, 1, 1, 12, 0, 0) + mock_datetime.now.return_value = mock_now + + await model._handle_audio_delta(mock_parsed) + + # Check state was updated correctly + assert model._current_audio_content_index == 5 + assert model._current_item_id == "test_item" + assert model._audio_start_time == mock_now + assert model._audio_length_ms == 4 / 24 / 2 # 4 bytes diff --git a/tests/realtime/test_runner.py b/tests/realtime/test_runner.py new file mode 100644 index 000000000..aabdff140 --- /dev/null +++ b/tests/realtime/test_runner.py @@ -0,0 +1,224 @@ +from unittest.mock import AsyncMock, Mock, patch + +import pytest +from inline_snapshot import snapshot + +from agents.realtime.agent import RealtimeAgent +from agents.realtime.config import RealtimeRunConfig, RealtimeSessionModelSettings +from agents.realtime.model import RealtimeModel, RealtimeModelConfig +from agents.realtime.runner import RealtimeRunner +from agents.realtime.session import RealtimeSession + + +class MockRealtimeModel(RealtimeModel): + async def connect(self, options=None): + pass + + def add_listener(self, listener): + pass + + def remove_listener(self, listener): + pass + + async def send_event(self, event): + pass + + async def send_message(self, message, other_event_data=None): + pass + + async def send_audio(self, audio, commit=False): + pass + + async def send_tool_output(self, tool_call, output, start_response=True): + pass + + async def interrupt(self): + pass + + async def close(self): + pass + + +@pytest.fixture +def mock_agent(): + agent = Mock(spec=RealtimeAgent) + agent.get_system_prompt = AsyncMock(return_value="Test instructions") + agent.get_all_tools = AsyncMock(return_value=[{"type": "function", "name": "test_tool"}]) + return agent + + +@pytest.fixture +def mock_model(): + return MockRealtimeModel() + + +@pytest.mark.asyncio +async def test_run_creates_session_with_no_settings(mock_agent, mock_model): + """Test that run() creates a session correctly if no settings are provided""" + runner = RealtimeRunner(mock_agent, model=mock_model) + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + session = await runner.run() + + # Verify session was created with correct parameters + mock_session_class.assert_called_once() + call_args = mock_session_class.call_args + + assert call_args[1]["model"] == mock_model + assert call_args[1]["agent"] == mock_agent + assert call_args[1]["context"] is None + + # Verify model_config contains expected settings from agent + model_config = call_args[1]["model_config"] + assert model_config == snapshot( + { + "initial_model_settings": { + "instructions": "Test instructions", + "tools": [{"type": "function", "name": "test_tool"}], + } + } + ) + + assert session == mock_session + + +@pytest.mark.asyncio +async def test_run_creates_session_with_settings_only_in_init(mock_agent, mock_model): + """Test that it creates a session with the right settings if they are provided only in init""" + config = RealtimeRunConfig( + model_settings=RealtimeSessionModelSettings(model_name="gpt-4o-realtime", voice="nova") + ) + runner = RealtimeRunner(mock_agent, model=mock_model, config=config) + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + _ = await runner.run() + + # Verify session was created with config overrides + call_args = mock_session_class.call_args + model_config = call_args[1]["model_config"] + + # Should have agent settings plus config overrides + assert model_config == snapshot( + { + "initial_model_settings": { + "instructions": "Test instructions", + "tools": [{"type": "function", "name": "test_tool"}], + "model_name": "gpt-4o-realtime", + "voice": "nova", + } + } + ) + + +@pytest.mark.asyncio +async def test_run_creates_session_with_settings_in_both_init_and_run_overrides( + mock_agent, mock_model +): + """Test settings in both init and run() - init should override run()""" + init_config = RealtimeRunConfig( + model_settings=RealtimeSessionModelSettings(model_name="gpt-4o-realtime", voice="nova") + ) + runner = RealtimeRunner(mock_agent, model=mock_model, config=init_config) + + run_model_config: RealtimeModelConfig = { + "initial_model_settings": RealtimeSessionModelSettings( + voice="alloy", input_audio_format="pcm16" + ) + } + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + _ = await runner.run(model_config=run_model_config) + + # Verify run() settings override init settings + call_args = mock_session_class.call_args + model_config = call_args[1]["model_config"] + + # Should have agent settings, then init config, then run config overrides + assert model_config == snapshot( + { + "initial_model_settings": { + "voice": "nova", + "input_audio_format": "pcm16", + "instructions": "Test instructions", + "tools": [{"type": "function", "name": "test_tool"}], + "model_name": "gpt-4o-realtime", + } + } + ) + + +@pytest.mark.asyncio +async def test_run_creates_session_with_settings_only_in_run(mock_agent, mock_model): + """Test settings provided only in run()""" + runner = RealtimeRunner(mock_agent, model=mock_model) + + run_model_config: RealtimeModelConfig = { + "initial_model_settings": RealtimeSessionModelSettings( + model_name="gpt-4o-realtime-preview", voice="shimmer", modalities=["text", "audio"] + ) + } + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + _ = await runner.run(model_config=run_model_config) + + # Verify run() settings are applied + call_args = mock_session_class.call_args + model_config = call_args[1]["model_config"] + + # Should have agent settings plus run() settings + assert model_config == snapshot( + { + "initial_model_settings": { + "model_name": "gpt-4o-realtime-preview", + "voice": "shimmer", + "modalities": ["text", "audio"], + "instructions": "Test instructions", + "tools": [{"type": "function", "name": "test_tool"}], + } + } + ) + + +@pytest.mark.asyncio +async def test_run_with_context_parameter(mock_agent, mock_model): + """Test that context parameter is passed through to session""" + runner = RealtimeRunner(mock_agent, model=mock_model) + test_context = {"user_id": "test123"} + + with patch("agents.realtime.runner.RealtimeSession") as mock_session_class: + mock_session = Mock(spec=RealtimeSession) + mock_session_class.return_value = mock_session + + await runner.run(context=test_context) + + call_args = mock_session_class.call_args + assert call_args[1]["context"] == test_context + + +@pytest.mark.asyncio +async def test_get_model_settings_with_none_values(mock_model): + """Test _get_model_settings handles None values from agent properly""" + agent = Mock(spec=RealtimeAgent) + agent.get_system_prompt = AsyncMock(return_value=None) + agent.get_all_tools = AsyncMock(return_value=None) + + runner = RealtimeRunner(agent, model=mock_model) + + with patch("agents.realtime.runner.RealtimeSession"): + await runner.run() + + # Should not crash and agent methods should be called + agent.get_system_prompt.assert_called_once() + agent.get_all_tools.assert_called_once() diff --git a/tests/realtime/test_session.py b/tests/realtime/test_session.py new file mode 100644 index 000000000..6003f9443 --- /dev/null +++ b/tests/realtime/test_session.py @@ -0,0 +1,1159 @@ +from typing import cast +from unittest.mock import AsyncMock, Mock + +import pytest + +from agents.guardrail import GuardrailFunctionOutput, OutputGuardrail +from agents.handoffs import Handoff +from agents.realtime.agent import RealtimeAgent +from agents.realtime.config import RealtimeRunConfig +from agents.realtime.events import ( + RealtimeAgentEndEvent, + RealtimeAgentStartEvent, + RealtimeAudio, + RealtimeAudioEnd, + RealtimeAudioInterrupted, + RealtimeError, + RealtimeGuardrailTripped, + RealtimeHistoryAdded, + RealtimeHistoryUpdated, + RealtimeRawModelEvent, + RealtimeToolEnd, + RealtimeToolStart, +) +from agents.realtime.items import ( + AssistantMessageItem, + AssistantText, + InputAudio, + InputText, + RealtimeItem, + UserMessageItem, +) +from agents.realtime.model import RealtimeModel +from agents.realtime.model_events import ( + RealtimeModelAudioDoneEvent, + RealtimeModelAudioEvent, + RealtimeModelAudioInterruptedEvent, + RealtimeModelConnectionStatusEvent, + RealtimeModelErrorEvent, + RealtimeModelInputAudioTranscriptionCompletedEvent, + RealtimeModelItemDeletedEvent, + RealtimeModelItemUpdatedEvent, + RealtimeModelOtherEvent, + RealtimeModelToolCallEvent, + RealtimeModelTranscriptDeltaEvent, + RealtimeModelTurnEndedEvent, + RealtimeModelTurnStartedEvent, +) +from agents.realtime.session import RealtimeSession +from agents.tool import FunctionTool +from agents.tool_context import ToolContext + + +class MockRealtimeModel(RealtimeModel): + def __init__(self): + super().__init__() + self.listeners = [] + self.connect_called = False + self.close_called = False + self.sent_messages = [] + self.sent_audio = [] + self.sent_tool_outputs = [] + self.interrupts_called = 0 + + async def connect(self, options=None): + self.connect_called = True + + def add_listener(self, listener): + self.listeners.append(listener) + + def remove_listener(self, listener): + if listener in self.listeners: + self.listeners.remove(listener) + + async def send_event(self, event): + pass + + async def send_message(self, message, other_event_data=None): + self.sent_messages.append(message) + + async def send_audio(self, audio, commit=False): + self.sent_audio.append((audio, commit)) + + async def send_tool_output(self, tool_call, output, start_response=True): + self.sent_tool_outputs.append((tool_call, output, start_response)) + + async def interrupt(self): + self.interrupts_called += 1 + + async def close(self): + self.close_called = True + + +@pytest.fixture +def mock_agent(): + agent = Mock(spec=RealtimeAgent) + agent.get_all_tools = AsyncMock(return_value=[]) + return agent + + +@pytest.fixture +def mock_model(): + return MockRealtimeModel() + + +@pytest.fixture +def mock_function_tool(): + tool = Mock(spec=FunctionTool) + tool.name = "test_function" + tool.on_invoke_tool = AsyncMock(return_value="function_result") + return tool + + +@pytest.fixture +def mock_handoff(): + handoff = Mock(spec=Handoff) + handoff.name = "test_handoff" + return handoff + + +class TestEventHandling: + """Test suite for event handling and transformation in RealtimeSession.on_event""" + + @pytest.mark.asyncio + async def test_error_event_transformation(self, mock_model, mock_agent): + """Test that error events are properly transformed and queued""" + session = RealtimeSession(mock_model, mock_agent, None) + + error_event = RealtimeModelErrorEvent(error="Test error") + + await session.on_event(error_event) + + # Check that events were queued + assert session._event_queue.qsize() == 2 + + # First event should be raw model event + raw_event = await session._event_queue.get() + assert isinstance(raw_event, RealtimeRawModelEvent) + assert raw_event.data == error_event + + # Second event should be transformed error event + error_session_event = await session._event_queue.get() + assert isinstance(error_session_event, RealtimeError) + assert error_session_event.error == "Test error" + + @pytest.mark.asyncio + async def test_audio_events_transformation(self, mock_model, mock_agent): + """Test that audio-related events are properly transformed""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Test audio event + audio_event = RealtimeModelAudioEvent(data=b"audio_data", response_id="resp_1") + await session.on_event(audio_event) + + # Test audio interrupted event + interrupted_event = RealtimeModelAudioInterruptedEvent() + await session.on_event(interrupted_event) + + # Test audio done event + done_event = RealtimeModelAudioDoneEvent() + await session.on_event(done_event) + + # Should have 6 events total (2 per event: raw + transformed) + assert session._event_queue.qsize() == 6 + + # Check audio event transformation + await session._event_queue.get() # raw event + audio_session_event = await session._event_queue.get() + assert isinstance(audio_session_event, RealtimeAudio) + assert audio_session_event.audio == audio_event + + # Check audio interrupted transformation + await session._event_queue.get() # raw event + interrupted_session_event = await session._event_queue.get() + assert isinstance(interrupted_session_event, RealtimeAudioInterrupted) + + # Check audio done transformation + await session._event_queue.get() # raw event + done_session_event = await session._event_queue.get() + assert isinstance(done_session_event, RealtimeAudioEnd) + + @pytest.mark.asyncio + async def test_turn_events_transformation(self, mock_model, mock_agent): + """Test that turn start/end events are properly transformed""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Test turn started event + turn_started = RealtimeModelTurnStartedEvent() + await session.on_event(turn_started) + + # Test turn ended event + turn_ended = RealtimeModelTurnEndedEvent() + await session.on_event(turn_ended) + + # Should have 4 events total (2 per event: raw + transformed) + assert session._event_queue.qsize() == 4 + + # Check turn started transformation + await session._event_queue.get() # raw event + start_session_event = await session._event_queue.get() + assert isinstance(start_session_event, RealtimeAgentStartEvent) + assert start_session_event.agent == mock_agent + + # Check turn ended transformation + await session._event_queue.get() # raw event + end_session_event = await session._event_queue.get() + assert isinstance(end_session_event, RealtimeAgentEndEvent) + assert end_session_event.agent == mock_agent + + @pytest.mark.asyncio + async def test_transcription_completed_event_updates_history(self, mock_model, mock_agent): + """Test that transcription completed events update history and emit events""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Set up initial history with an audio message + initial_item = UserMessageItem( + item_id="item_1", role="user", content=[InputAudio(transcript=None)] + ) + session._history = [initial_item] + + # Create transcription completed event + transcription_event = RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id="item_1", transcript="Hello world" + ) + + await session.on_event(transcription_event) + + # Check that history was updated + assert len(session._history) == 1 + updated_item = session._history[0] + assert updated_item.content[0].transcript == "Hello world" # type: ignore + assert updated_item.status == "completed" # type: ignore + + # Should have 2 events: raw + history updated + assert session._event_queue.qsize() == 2 + + await session._event_queue.get() # raw event + history_event = await session._event_queue.get() + assert isinstance(history_event, RealtimeHistoryUpdated) + assert len(history_event.history) == 1 + + @pytest.mark.asyncio + async def test_item_updated_event_adds_new_item(self, mock_model, mock_agent): + """Test that item_updated events add new items to history""" + session = RealtimeSession(mock_model, mock_agent, None) + + new_item = AssistantMessageItem( + item_id="new_item", role="assistant", content=[AssistantText(text="Hello")] + ) + + item_updated_event = RealtimeModelItemUpdatedEvent(item=new_item) + + await session.on_event(item_updated_event) + + # Check that item was added to history + assert len(session._history) == 1 + assert session._history[0] == new_item + + # Should have 2 events: raw + history added + assert session._event_queue.qsize() == 2 + + await session._event_queue.get() # raw event + history_event = await session._event_queue.get() + assert isinstance(history_event, RealtimeHistoryAdded) + assert history_event.item == new_item + + @pytest.mark.asyncio + async def test_item_updated_event_updates_existing_item(self, mock_model, mock_agent): + """Test that item_updated events update existing items in history""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Set up initial history + initial_item = AssistantMessageItem( + item_id="existing_item", role="assistant", content=[AssistantText(text="Initial")] + ) + session._history = [initial_item] + + # Create updated version + updated_item = AssistantMessageItem( + item_id="existing_item", role="assistant", content=[AssistantText(text="Updated")] + ) + + item_updated_event = RealtimeModelItemUpdatedEvent(item=updated_item) + + await session.on_event(item_updated_event) + + # Check that item was updated + assert len(session._history) == 1 + updated_item = cast(AssistantMessageItem, session._history[0]) + assert cast(AssistantText, updated_item.content[0]).text == "Updated" + + # Should have 2 events: raw + history updated (not added) + assert session._event_queue.qsize() == 2 + + await session._event_queue.get() # raw event + history_event = await session._event_queue.get() + assert isinstance(history_event, RealtimeHistoryUpdated) + + @pytest.mark.asyncio + async def test_item_deleted_event_removes_item(self, mock_model, mock_agent): + """Test that item_deleted events remove items from history""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Set up initial history with multiple items + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + item2 = AssistantMessageItem( + item_id="item_2", role="assistant", content=[AssistantText(text="Second")] + ) + session._history = [item1, item2] + + # Delete first item + delete_event = RealtimeModelItemDeletedEvent(item_id="item_1") + + await session.on_event(delete_event) + + # Check that item was removed + assert len(session._history) == 1 + assert session._history[0].item_id == "item_2" + + # Should have 2 events: raw + history updated + assert session._event_queue.qsize() == 2 + + await session._event_queue.get() # raw event + history_event = await session._event_queue.get() + assert isinstance(history_event, RealtimeHistoryUpdated) + assert len(history_event.history) == 1 + + @pytest.mark.asyncio + async def test_ignored_events_only_generate_raw_events(self, mock_model, mock_agent): + """Test that ignored events (transcript_delta, connection_status, other) only generate raw + events""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Test transcript delta (should be ignored per TODO comment) + transcript_event = RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="hello", response_id="resp_1" + ) + await session.on_event(transcript_event) + + # Test connection status (should be ignored) + connection_event = RealtimeModelConnectionStatusEvent(status="connected") + await session.on_event(connection_event) + + # Test other event (should be ignored) + other_event = RealtimeModelOtherEvent(data={"custom": "data"}) + await session.on_event(other_event) + + # Should only have 3 raw events (no transformed events) + assert session._event_queue.qsize() == 3 + + for _ in range(3): + event = await session._event_queue.get() + assert isinstance(event, RealtimeRawModelEvent) + + @pytest.mark.asyncio + async def test_function_call_event_triggers_tool_handling(self, mock_model, mock_agent): + """Test that function_call events trigger tool call handling""" + session = RealtimeSession(mock_model, mock_agent, None) + + # Create function call event + function_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_123", arguments='{"param": "value"}' + ) + + # We'll test the detailed tool handling in a separate test class + # Here we just verify that it gets to the handler + with pytest.MonkeyPatch().context() as m: + handle_tool_call_mock = AsyncMock() + m.setattr(session, "_handle_tool_call", handle_tool_call_mock) + + await session.on_event(function_call_event) + + # Should have called the tool handler + handle_tool_call_mock.assert_called_once_with(function_call_event) + + # Should still have raw event + assert session._event_queue.qsize() == 1 + raw_event = await session._event_queue.get() + assert isinstance(raw_event, RealtimeRawModelEvent) + assert raw_event.data == function_call_event + + +class TestHistoryManagement: + """Test suite for history management and audio transcription in + RealtimeSession._get_new_history""" + + def test_merge_transcript_into_existing_audio_message(self): + """Test merging audio transcript into existing placeholder input_audio message""" + # Create initial history with audio message without transcript + initial_item = UserMessageItem( + item_id="item_1", + role="user", + content=[ + InputText(text="Before audio"), + InputAudio(transcript=None, audio="audio_data"), + InputText(text="After audio"), + ], + ) + old_history = [initial_item] + + # Create transcription completed event + transcription_event = RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id="item_1", transcript="Hello world" + ) + + # Apply the history update + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), transcription_event + ) + + # Verify the transcript was merged + assert len(new_history) == 1 + updated_item = cast(UserMessageItem, new_history[0]) + assert updated_item.item_id == "item_1" + assert hasattr(updated_item, "status") and updated_item.status == "completed" + assert len(updated_item.content) == 3 + + # Check that audio content got transcript but other content unchanged + assert cast(InputText, updated_item.content[0]).text == "Before audio" + assert cast(InputAudio, updated_item.content[1]).transcript == "Hello world" + # Should preserve audio data + assert cast(InputAudio, updated_item.content[1]).audio == "audio_data" + assert cast(InputText, updated_item.content[2]).text == "After audio" + + def test_merge_transcript_preserves_other_items(self): + """Test that merging transcript preserves other items in history""" + # Create history with multiple items + item1 = UserMessageItem( + item_id="item_1", role="user", content=[InputText(text="First message")] + ) + item2 = UserMessageItem( + item_id="item_2", role="user", content=[InputAudio(transcript=None)] + ) + item3 = AssistantMessageItem( + item_id="item_3", role="assistant", content=[AssistantText(text="Third message")] + ) + old_history = [item1, item2, item3] + + # Create transcription event for item_2 + transcription_event = RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id="item_2", transcript="Transcribed audio" + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), transcription_event + ) + + # Should have same number of items + assert len(new_history) == 3 + + # First and third items should be unchanged + assert new_history[0] == item1 + assert new_history[2] == item3 + + # Second item should have transcript + updated_item2 = cast(UserMessageItem, new_history[1]) + assert updated_item2.item_id == "item_2" + assert cast(InputAudio, updated_item2.content[0]).transcript == "Transcribed audio" + assert hasattr(updated_item2, "status") and updated_item2.status == "completed" + + def test_merge_transcript_only_affects_matching_audio_content(self): + """Test that transcript merge only affects audio content, not text content""" + # Create item with mixed content including multiple audio items + item = UserMessageItem( + item_id="item_1", + role="user", + content=[ + InputText(text="Text content"), + InputAudio(transcript=None, audio="audio1"), + InputAudio(transcript="existing", audio="audio2"), + InputText(text="More text"), + ], + ) + old_history = [item] + + transcription_event = RealtimeModelInputAudioTranscriptionCompletedEvent( + item_id="item_1", transcript="New transcript" + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), transcription_event + ) + + updated_item = cast(UserMessageItem, new_history[0]) + + # Text content should be unchanged + assert cast(InputText, updated_item.content[0]).text == "Text content" + assert cast(InputText, updated_item.content[3]).text == "More text" + + # All audio content should have the new transcript (current implementation overwrites all) + assert cast(InputAudio, updated_item.content[1]).transcript == "New transcript" + assert ( + cast(InputAudio, updated_item.content[2]).transcript == "New transcript" + ) # Implementation overwrites existing + + def test_update_existing_item_by_id(self): + """Test updating an existing item by item_id""" + # Create initial history + original_item = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="Original")] + ) + old_history = [original_item] + + # Create updated version of same item + updated_item = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="Updated")] + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), updated_item + ) + + # Should have same number of items + assert len(new_history) == 1 + + # Item should be updated + result_item = cast(AssistantMessageItem, new_history[0]) + assert result_item.item_id == "item_1" + assert cast(AssistantText, result_item.content[0]).text == "Updated" + + def test_update_existing_item_preserves_order(self): + """Test that updating existing item preserves its position in history""" + # Create history with multiple items + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + item2 = AssistantMessageItem( + item_id="item_2", role="assistant", content=[AssistantText(text="Second")] + ) + item3 = AssistantMessageItem( + item_id="item_3", role="assistant", content=[AssistantText(text="Third")] + ) + old_history = [item1, item2, item3] + + # Update middle item + updated_item2 = AssistantMessageItem( + item_id="item_2", role="assistant", content=[AssistantText(text="Updated Second")] + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), updated_item2 + ) + + # Should have same number of items in same order + assert len(new_history) == 3 + assert new_history[0].item_id == "item_1" + assert new_history[1].item_id == "item_2" + assert new_history[2].item_id == "item_3" + + # Middle item should be updated + updated_result = cast(AssistantMessageItem, new_history[1]) + assert cast(AssistantText, updated_result.content[0]).text == "Updated Second" + + # Other items should be unchanged + item1_result = cast(AssistantMessageItem, new_history[0]) + item3_result = cast(AssistantMessageItem, new_history[2]) + assert cast(AssistantText, item1_result.content[0]).text == "First" + assert cast(AssistantText, item3_result.content[0]).text == "Third" + + def test_insert_new_item_after_previous_item(self): + """Test inserting new item after specified previous_item_id""" + # Create initial history + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + item3 = AssistantMessageItem( + item_id="item_3", role="assistant", content=[AssistantText(text="Third")] + ) + old_history = [item1, item3] + + # Create new item to insert between them + new_item = AssistantMessageItem( + item_id="item_2", + previous_item_id="item_1", + role="assistant", + content=[AssistantText(text="Second")], + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), new_item + ) + + # Should have one more item + assert len(new_history) == 3 + + # Items should be in correct order + assert new_history[0].item_id == "item_1" + assert new_history[1].item_id == "item_2" + assert new_history[2].item_id == "item_3" + + # Content should be correct + item2_result = cast(AssistantMessageItem, new_history[1]) + assert cast(AssistantText, item2_result.content[0]).text == "Second" + + def test_insert_new_item_after_nonexistent_previous_item(self): + """Test that item with nonexistent previous_item_id gets added to end""" + # Create initial history + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + old_history = [item1] + + # Create new item with nonexistent previous_item_id + new_item = AssistantMessageItem( + item_id="item_2", + previous_item_id="nonexistent", + role="assistant", + content=[AssistantText(text="Second")], + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), new_item + ) + + # Should add to end when previous_item_id not found + assert len(new_history) == 2 + assert new_history[0].item_id == "item_1" + assert new_history[1].item_id == "item_2" + + def test_add_new_item_to_end_when_no_previous_item_id(self): + """Test adding new item to end when no previous_item_id is specified""" + # Create initial history + item1 = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + old_history = [item1] + + # Create new item without previous_item_id + new_item = AssistantMessageItem( + item_id="item_2", role="assistant", content=[AssistantText(text="Second")] + ) + + new_history = RealtimeSession._get_new_history( + cast(list[RealtimeItem], old_history), new_item + ) + + # Should add to end + assert len(new_history) == 2 + assert new_history[0].item_id == "item_1" + assert new_history[1].item_id == "item_2" + + def test_add_first_item_to_empty_history(self): + """Test adding first item to empty history""" + old_history: list[RealtimeItem] = [] + + new_item = AssistantMessageItem( + item_id="item_1", role="assistant", content=[AssistantText(text="First")] + ) + + new_history = RealtimeSession._get_new_history(old_history, new_item) + + assert len(new_history) == 1 + assert new_history[0].item_id == "item_1" + + def test_complex_insertion_scenario(self): + """Test complex scenario with multiple insertions and updates""" + # Start with items A and C + itemA = AssistantMessageItem( + item_id="A", role="assistant", content=[AssistantText(text="A")] + ) + itemC = AssistantMessageItem( + item_id="C", role="assistant", content=[AssistantText(text="C")] + ) + history: list[RealtimeItem] = [itemA, itemC] + + # Insert B after A + itemB = AssistantMessageItem( + item_id="B", previous_item_id="A", role="assistant", content=[AssistantText(text="B")] + ) + history = RealtimeSession._get_new_history(history, itemB) + + # Should be A, B, C + assert len(history) == 3 + assert [item.item_id for item in history] == ["A", "B", "C"] + + # Insert D after B + itemD = AssistantMessageItem( + item_id="D", previous_item_id="B", role="assistant", content=[AssistantText(text="D")] + ) + history = RealtimeSession._get_new_history(history, itemD) + + # Should be A, B, D, C + assert len(history) == 4 + assert [item.item_id for item in history] == ["A", "B", "D", "C"] + + # Update B + updated_itemB = AssistantMessageItem( + item_id="B", role="assistant", content=[AssistantText(text="Updated B")] + ) + history = RealtimeSession._get_new_history(history, updated_itemB) + + # Should still be A, B, D, C but B is updated + assert len(history) == 4 + assert [item.item_id for item in history] == ["A", "B", "D", "C"] + itemB_result = cast(AssistantMessageItem, history[1]) + assert cast(AssistantText, itemB_result.content[0]).text == "Updated B" + + +# Test 3: Tool call execution flow (_handle_tool_call method) +class TestToolCallExecution: + """Test suite for tool call execution flow in RealtimeSession._handle_tool_call""" + + @pytest.mark.asyncio + async def test_function_tool_execution_success( + self, mock_model, mock_agent, mock_function_tool + ): + """Test successful function tool execution""" + # Set up agent to return our mock tool + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + # Create function call event + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_123", arguments='{"param": "value"}' + ) + + await session._handle_tool_call(tool_call_event) + + # Verify the flow + mock_agent.get_all_tools.assert_called_once() + mock_function_tool.on_invoke_tool.assert_called_once() + + # Check the tool context was created correctly + call_args = mock_function_tool.on_invoke_tool.call_args + tool_context = call_args[0][0] + assert isinstance(tool_context, ToolContext) + assert call_args[0][1] == '{"param": "value"}' + + # Verify tool output was sent to model + assert len(mock_model.sent_tool_outputs) == 1 + sent_call, sent_output, start_response = mock_model.sent_tool_outputs[0] + assert sent_call == tool_call_event + assert sent_output == "function_result" + assert start_response is True + + # Verify events were queued + assert session._event_queue.qsize() == 2 + + # Check tool start event + tool_start_event = await session._event_queue.get() + assert isinstance(tool_start_event, RealtimeToolStart) + assert tool_start_event.tool == mock_function_tool + assert tool_start_event.agent == mock_agent + + # Check tool end event + tool_end_event = await session._event_queue.get() + assert isinstance(tool_end_event, RealtimeToolEnd) + assert tool_end_event.tool == mock_function_tool + assert tool_end_event.output == "function_result" + assert tool_end_event.agent == mock_agent + + @pytest.mark.asyncio + async def test_function_tool_with_multiple_tools_available(self, mock_model, mock_agent): + """Test function tool execution when multiple tools are available""" + # Create multiple mock tools + tool1 = Mock(spec=FunctionTool) + tool1.name = "tool_one" + tool1.on_invoke_tool = AsyncMock(return_value="result_one") + + tool2 = Mock(spec=FunctionTool) + tool2.name = "tool_two" + tool2.on_invoke_tool = AsyncMock(return_value="result_two") + + handoff = Mock(spec=Handoff) + handoff.name = "handoff_tool" + + # Set up agent to return all tools + mock_agent.get_all_tools.return_value = [tool1, tool2, handoff] + + session = RealtimeSession(mock_model, mock_agent, None) + + # Call tool_two + tool_call_event = RealtimeModelToolCallEvent( + name="tool_two", call_id="call_456", arguments='{"test": "data"}' + ) + + await session._handle_tool_call(tool_call_event) + + # Only tool2 should have been called + tool1.on_invoke_tool.assert_not_called() + tool2.on_invoke_tool.assert_called_once() + + # Verify correct result was sent + sent_call, sent_output, _ = mock_model.sent_tool_outputs[0] + assert sent_output == "result_two" + + @pytest.mark.asyncio + async def test_handoff_tool_handling_todo(self, mock_model, mock_agent, mock_handoff): + """Test that handoff tools are recognized but not yet implemented""" + # Set up agent to return handoff tool + mock_agent.get_all_tools.return_value = [mock_handoff] + + session = RealtimeSession(mock_model, mock_agent, None) + + tool_call_event = RealtimeModelToolCallEvent( + name="test_handoff", call_id="call_789", arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Should not have sent any tool outputs (handoffs not implemented) + assert len(mock_model.sent_tool_outputs) == 0 + + # Should not have queued any events (handoffs not implemented) + assert session._event_queue.qsize() == 0 + + @pytest.mark.asyncio + async def test_unknown_tool_handling_todo(self, mock_model, mock_agent, mock_function_tool): + """Test that unknown tools are handled gracefully (TODO: add error handling)""" + # Set up agent to return different tool than what's called + mock_function_tool.name = "known_tool" + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + # Call unknown tool + tool_call_event = RealtimeModelToolCallEvent( + name="unknown_tool", call_id="call_unknown", arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Should not have called any tools + mock_function_tool.on_invoke_tool.assert_not_called() + + # Should not have sent any tool outputs + assert len(mock_model.sent_tool_outputs) == 0 + + # Should not have queued any events + assert session._event_queue.qsize() == 0 + + @pytest.mark.asyncio + async def test_function_tool_exception_handling( + self, mock_model, mock_agent, mock_function_tool + ): + """Test that exceptions in function tools are handled (currently they propagate)""" + # Set up tool to raise exception + mock_function_tool.on_invoke_tool.side_effect = ValueError("Tool error") + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_error", arguments="{}" + ) + + # Currently exceptions propagate (no error handling implemented) + with pytest.raises(ValueError, match="Tool error"): + await session._handle_tool_call(tool_call_event) + + # Tool start event should have been queued before the error + assert session._event_queue.qsize() == 1 + tool_start_event = await session._event_queue.get() + assert isinstance(tool_start_event, RealtimeToolStart) + + # But no tool output should have been sent and no end event queued + assert len(mock_model.sent_tool_outputs) == 0 + + @pytest.mark.asyncio + async def test_tool_call_with_complex_arguments( + self, mock_model, mock_agent, mock_function_tool + ): + """Test tool call with complex JSON arguments""" + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + # Complex arguments + complex_args = '{"nested": {"data": [1, 2, 3]}, "bool": true, "null": null}' + + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_complex", arguments=complex_args + ) + + await session._handle_tool_call(tool_call_event) + + # Verify arguments were passed correctly + call_args = mock_function_tool.on_invoke_tool.call_args + assert call_args[0][1] == complex_args + + @pytest.mark.asyncio + async def test_tool_call_with_custom_call_id(self, mock_model, mock_agent, mock_function_tool): + """Test that tool context receives correct call_id""" + mock_agent.get_all_tools.return_value = [mock_function_tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + custom_call_id = "custom_call_id_12345" + + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id=custom_call_id, arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Verify tool context was created with correct call_id + call_args = mock_function_tool.on_invoke_tool.call_args + tool_context = call_args[0][0] + # The call_id is used internally in ToolContext.from_agent_context + # We can't directly access it, but we can verify the context was created + assert isinstance(tool_context, ToolContext) + + @pytest.mark.asyncio + async def test_tool_result_conversion_to_string(self, mock_model, mock_agent): + """Test that tool results are converted to strings for model output""" + # Create tool that returns non-string result + tool = Mock(spec=FunctionTool) + tool.name = "test_function" + tool.on_invoke_tool = AsyncMock(return_value={"result": "data", "count": 42}) + + mock_agent.get_all_tools.return_value = [tool] + + session = RealtimeSession(mock_model, mock_agent, None) + + tool_call_event = RealtimeModelToolCallEvent( + name="test_function", call_id="call_conversion", arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Verify result was converted to string + sent_call, sent_output, _ = mock_model.sent_tool_outputs[0] + assert isinstance(sent_output, str) + assert sent_output == "{'result': 'data', 'count': 42}" + + @pytest.mark.asyncio + async def test_mixed_tool_types_filtering(self, mock_model, mock_agent): + """Test that function tools and handoffs are properly separated""" + # Create mixed tools + func_tool1 = Mock(spec=FunctionTool) + func_tool1.name = "func1" + func_tool1.on_invoke_tool = AsyncMock(return_value="result1") + + handoff1 = Mock(spec=Handoff) + handoff1.name = "handoff1" + + func_tool2 = Mock(spec=FunctionTool) + func_tool2.name = "func2" + func_tool2.on_invoke_tool = AsyncMock(return_value="result2") + + handoff2 = Mock(spec=Handoff) + handoff2.name = "handoff2" + + # Add some other object that's neither (should be ignored) + other_tool = Mock() + other_tool.name = "other" + + all_tools = [func_tool1, handoff1, func_tool2, handoff2, other_tool] + mock_agent.get_all_tools.return_value = all_tools + + session = RealtimeSession(mock_model, mock_agent, None) + + # Call a function tool + tool_call_event = RealtimeModelToolCallEvent( + name="func2", call_id="call_filtering", arguments="{}" + ) + + await session._handle_tool_call(tool_call_event) + + # Only func2 should have been called + func_tool1.on_invoke_tool.assert_not_called() + func_tool2.on_invoke_tool.assert_called_once() + + # Verify result + sent_call, sent_output, _ = mock_model.sent_tool_outputs[0] + assert sent_output == "result2" + + +class TestGuardrailFunctionality: + """Test suite for output guardrail functionality in RealtimeSession""" + + @pytest.fixture + def triggered_guardrail(self): + """Creates a guardrail that always triggers""" + def guardrail_func(context, agent, output): + return GuardrailFunctionOutput( + output_info={"reason": "test trigger"}, + tripwire_triggered=True + ) + return OutputGuardrail(guardrail_function=guardrail_func, name="triggered_guardrail") + + @pytest.fixture + def safe_guardrail(self): + """Creates a guardrail that never triggers""" + def guardrail_func(context, agent, output): + return GuardrailFunctionOutput( + output_info={"reason": "safe content"}, + tripwire_triggered=False + ) + return OutputGuardrail(guardrail_function=guardrail_func, name="safe_guardrail") + + @pytest.mark.asyncio + async def test_transcript_delta_triggers_guardrail_at_threshold( + self, mock_model, mock_agent, triggered_guardrail + ): + """Test that guardrails run when transcript delta reaches debounce threshold""" + run_config: RealtimeRunConfig = { + "output_guardrails": [triggered_guardrail], + "guardrails_settings": {"debounce_text_length": 10} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + # Send transcript delta that exceeds threshold (10 chars) + transcript_event = RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="this is more than ten characters", response_id="resp_1" + ) + + await session.on_event(transcript_event) + + # Should have triggered guardrail and interrupted + assert session._interrupted_by_guardrail is True + assert mock_model.interrupts_called == 1 + assert len(mock_model.sent_messages) == 1 + assert "triggered_guardrail" in mock_model.sent_messages[0] + + # Should have emitted guardrail_tripped event + events = [] + while not session._event_queue.empty(): + events.append(await session._event_queue.get()) + + guardrail_events = [e for e in events if isinstance(e, RealtimeGuardrailTripped)] + assert len(guardrail_events) == 1 + assert guardrail_events[0].message == "this is more than ten characters" + + @pytest.mark.asyncio + async def test_transcript_delta_multiple_thresholds_same_item( + self, mock_model, mock_agent, triggered_guardrail + ): + """Test guardrails run at 1x, 2x, 3x thresholds for same item_id""" + run_config: RealtimeRunConfig = { + "output_guardrails": [triggered_guardrail], + "guardrails_settings": {"debounce_text_length": 5} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + # First delta - reaches 1x threshold (5 chars) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="12345", response_id="resp_1" + )) + + # Second delta - reaches 2x threshold (10 chars total) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="67890", response_id="resp_1" + )) + + # Should only trigger once due to interrupted_by_guardrail flag + assert mock_model.interrupts_called == 1 + assert len(mock_model.sent_messages) == 1 + + @pytest.mark.asyncio + async def test_transcript_delta_different_items_tracked_separately( + self, mock_model, mock_agent, safe_guardrail + ): + """Test that different item_ids are tracked separately for debouncing""" + run_config: RealtimeRunConfig = { + "output_guardrails": [safe_guardrail], + "guardrails_settings": {"debounce_text_length": 10} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + # Add text to item_1 (8 chars - below threshold) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="12345678", response_id="resp_1" + )) + + # Add text to item_2 (8 chars - below threshold) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_2", delta="abcdefgh", response_id="resp_2" + )) + + # Neither should trigger guardrails yet + assert mock_model.interrupts_called == 0 + + # Add more text to item_1 (total 12 chars - above threshold) + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="90ab", response_id="resp_1" + )) + + # item_1 should have triggered guardrail run (but not interrupted since safe) + assert session._item_guardrail_run_counts["item_1"] == 1 + assert ( + "item_2" not in session._item_guardrail_run_counts + or session._item_guardrail_run_counts["item_2"] == 0 + ) + + @pytest.mark.asyncio + async def test_turn_ended_clears_guardrail_state( + self, mock_model, mock_agent, triggered_guardrail + ): + """Test that turn_ended event clears guardrail state for next turn""" + run_config: RealtimeRunConfig = { + "output_guardrails": [triggered_guardrail], + "guardrails_settings": {"debounce_text_length": 5} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + # Trigger guardrail + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="trigger", response_id="resp_1" + )) + + assert session._interrupted_by_guardrail is True + assert len(session._item_transcripts) == 1 + + # End turn + await session.on_event(RealtimeModelTurnEndedEvent()) + + # State should be cleared + assert session._interrupted_by_guardrail is False + assert len(session._item_transcripts) == 0 + assert len(session._item_guardrail_run_counts) == 0 + + @pytest.mark.asyncio + async def test_multiple_guardrails_all_triggered( + self, mock_model, mock_agent + ): + """Test that all triggered guardrails are included in the event""" + def create_triggered_guardrail(name): + def guardrail_func(context, agent, output): + return GuardrailFunctionOutput( + output_info={"name": name}, + tripwire_triggered=True + ) + return OutputGuardrail(guardrail_function=guardrail_func, name=name) + + guardrail1 = create_triggered_guardrail("guardrail_1") + guardrail2 = create_triggered_guardrail("guardrail_2") + + run_config: RealtimeRunConfig = { + "output_guardrails": [guardrail1, guardrail2], + "guardrails_settings": {"debounce_text_length": 5} + } + + session = RealtimeSession(mock_model, mock_agent, None, run_config=run_config) + + await session.on_event(RealtimeModelTranscriptDeltaEvent( + item_id="item_1", delta="trigger", response_id="resp_1" + )) + + # Should have interrupted and sent message with both guardrail names + assert mock_model.interrupts_called == 1 + assert len(mock_model.sent_messages) == 1 + message = mock_model.sent_messages[0] + assert "guardrail_1" in message and "guardrail_2" in message + + # Should have emitted event with both guardrail results + events = [] + while not session._event_queue.empty(): + events.append(await session._event_queue.get()) + + guardrail_events = [e for e in events if isinstance(e, RealtimeGuardrailTripped)] + assert len(guardrail_events) == 1 + assert len(guardrail_events[0].guardrail_results) == 2 diff --git a/tests/realtime/test_tracing.py b/tests/realtime/test_tracing.py new file mode 100644 index 000000000..456ae125f --- /dev/null +++ b/tests/realtime/test_tracing.py @@ -0,0 +1,257 @@ +from unittest.mock import AsyncMock, patch + +import pytest + +from agents.realtime.openai_realtime import OpenAIRealtimeWebSocketModel + + +class TestRealtimeTracingIntegration: + """Test tracing configuration and session.update integration.""" + + @pytest.fixture + def model(self): + """Create a fresh model instance for each test.""" + return OpenAIRealtimeWebSocketModel() + + @pytest.fixture + def mock_websocket(self): + """Create a mock websocket connection.""" + mock_ws = AsyncMock() + mock_ws.send = AsyncMock() + mock_ws.close = AsyncMock() + return mock_ws + + @pytest.mark.asyncio + async def test_tracing_config_storage_and_defaults(self, model, mock_websocket): + """Test that tracing config is stored correctly and defaults to 'auto'.""" + # Test with explicit tracing config + config_with_tracing = { + "api_key": "test-key", + "initial_model_settings": { + "tracing": { + "workflow_name": "test_workflow", + "group_id": "group_123", + "metadata": {"version": "1.0"}, + } + }, + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_task = AsyncMock() + mock_create_task.return_value = mock_task + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model.connect(config_with_tracing) + + # Should store the tracing config + assert model._tracing_config == { + "workflow_name": "test_workflow", + "group_id": "group_123", + "metadata": {"version": "1.0"}, + } + + # Test without tracing config - should default to "auto" + model2 = OpenAIRealtimeWebSocketModel() + config_no_tracing = { + "api_key": "test-key", + "initial_model_settings": {}, + } + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model2.connect(config_no_tracing) # type: ignore[arg-type] + assert model2._tracing_config == "auto" + + @pytest.mark.asyncio + async def test_send_tracing_config_on_session_created(self, model, mock_websocket): + """Test that tracing config is sent when session.created event is received.""" + config = { + "api_key": "test-key", + "initial_model_settings": { + "tracing": {"workflow_name": "test_workflow", "group_id": "group_123"} + }, + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_task = AsyncMock() + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model.connect(config) + + # Simulate session.created event + session_created_event = { + "type": "session.created", + "event_id": "event_123", + "session": {"id": "session_456"}, + } + + with patch.object(model, "send_event") as mock_send_event: + await model._handle_ws_event(session_created_event) + + # Should send session.update with tracing config + mock_send_event.assert_called_once_with( + { + "type": "session.update", + "other_data": { + "session": { + "tracing": { + "workflow_name": "test_workflow", + "group_id": "group_123", + } + } + }, + } + ) + + @pytest.mark.asyncio + async def test_send_tracing_config_auto_mode(self, model, mock_websocket): + """Test that 'auto' tracing config is sent correctly.""" + config = { + "api_key": "test-key", + "initial_model_settings": {}, # No tracing config - defaults to "auto" + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_task = AsyncMock() + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model.connect(config) + + session_created_event = { + "type": "session.created", + "event_id": "event_123", + "session": {"id": "session_456"}, + } + + with patch.object(model, "send_event") as mock_send_event: + await model._handle_ws_event(session_created_event) + + # Should send session.update with "auto" + mock_send_event.assert_called_once_with( + {"type": "session.update", "other_data": {"session": {"tracing": "auto"}}} + ) + + @pytest.mark.asyncio + async def test_tracing_config_none_skips_session_update(self, model, mock_websocket): + """Test that None tracing config skips sending session.update.""" + # Manually set tracing config to None (this would happen if explicitly set) + model._tracing_config = None + + session_created_event = { + "type": "session.created", + "event_id": "event_123", + "session": {"id": "session_456"}, + } + + with patch.object(model, "send_event") as mock_send_event: + await model._handle_ws_event(session_created_event) + + # Should not send any session.update + mock_send_event.assert_not_called() + + @pytest.mark.asyncio + async def test_tracing_config_with_metadata_serialization(self, model, mock_websocket): + """Test that complex metadata in tracing config is handled correctly.""" + complex_metadata = { + "user_id": "user_123", + "session_type": "demo", + "features": ["audio", "tools"], + "config": {"timeout": 30, "retries": 3}, + } + + config = { + "api_key": "test-key", + "initial_model_settings": { + "tracing": {"workflow_name": "complex_workflow", "metadata": complex_metadata} + }, + } + + async def async_websocket(*args, **kwargs): + return mock_websocket + + with patch("websockets.connect", side_effect=async_websocket): + with patch("asyncio.create_task") as mock_create_task: + mock_task = AsyncMock() + mock_create_task.side_effect = lambda coro: (coro.close(), mock_task)[1] + + await model.connect(config) + + session_created_event = { + "type": "session.created", + "event_id": "event_123", + "session": {"id": "session_456"}, + } + + with patch.object(model, "send_event") as mock_send_event: + await model._handle_ws_event(session_created_event) + + # Should send session.update with complete tracing config including metadata + expected_call = { + "type": "session.update", + "other_data": { + "session": { + "tracing": { + "workflow_name": "complex_workflow", + "metadata": complex_metadata, + } + } + }, + } + mock_send_event.assert_called_once_with(expected_call) + + @pytest.mark.asyncio + async def test_tracing_disabled_prevents_tracing(self, mock_websocket): + """Test that tracing_disabled=True prevents tracing configuration.""" + from agents.realtime.agent import RealtimeAgent + from agents.realtime.runner import RealtimeRunner + + # Create a test agent and runner with tracing disabled + agent = RealtimeAgent(name="test_agent", instructions="test") + + runner = RealtimeRunner( + starting_agent=agent, + config={"tracing_disabled": True} + ) + + # Test the _get_model_settings method directly since that's where the logic is + model_settings = await runner._get_model_settings( + agent=agent, + disable_tracing=True, # This should come from config["tracing_disabled"] + initial_settings=None, + overrides=None + ) + + # When tracing is disabled, model settings should have tracing=None + assert model_settings["tracing"] is None + + # Also test that the runner passes disable_tracing=True correctly + with patch.object(runner, '_get_model_settings') as mock_get_settings: + mock_get_settings.return_value = {"tracing": None} + + with patch('agents.realtime.session.RealtimeSession') as mock_session_class: + mock_session = AsyncMock() + mock_session_class.return_value = mock_session + + await runner.run() + + # Verify that _get_model_settings was called with disable_tracing=True + mock_get_settings.assert_called_once_with( + agent=agent, + disable_tracing=True, + initial_settings=None, + overrides=None + ) diff --git a/tests/test_agent_config.py b/tests/test_agent_config.py index 44339dad3..a985fd60d 100644 --- a/tests/test_agent_config.py +++ b/tests/test_agent_config.py @@ -1,7 +1,8 @@ import pytest from pydantic import BaseModel -from agents import Agent, Handoff, RunContextWrapper, Runner, handoff +from agents import Agent, AgentOutputSchema, Handoff, RunContextWrapper, handoff +from agents.run import AgentRunner @pytest.mark.asyncio @@ -42,7 +43,7 @@ async def test_handoff_with_agents(): handoffs=[agent_1, agent_2], ) - handoffs = Runner._get_handoffs(agent_3) + handoffs = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(None)) assert len(handoffs) == 2 assert handoffs[0].agent_name == "agent_1" @@ -77,7 +78,7 @@ async def test_handoff_with_handoff_obj(): ], ) - handoffs = Runner._get_handoffs(agent_3) + handoffs = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(None)) assert len(handoffs) == 2 assert handoffs[0].agent_name == "agent_1" @@ -111,7 +112,7 @@ async def test_handoff_with_handoff_obj_and_agent(): handoffs=[handoff(agent_1), agent_2], ) - handoffs = Runner._get_handoffs(agent_3) + handoffs = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(None)) assert len(handoffs) == 2 assert handoffs[0].agent_name == "agent_1" @@ -159,9 +160,10 @@ async def test_agent_final_output(): output_type=Foo, ) - schema = Runner._get_output_schema(agent) + schema = AgentRunner._get_output_schema(agent) + assert isinstance(schema, AgentOutputSchema) assert schema is not None assert schema.output_type == Foo - assert schema.strict_json_schema is True + assert schema.is_strict_json_schema() is True assert schema.json_schema() is not None assert not schema.is_plain_text() diff --git a/tests/test_agent_prompt.py b/tests/test_agent_prompt.py new file mode 100644 index 000000000..010717d66 --- /dev/null +++ b/tests/test_agent_prompt.py @@ -0,0 +1,97 @@ +import pytest + +from agents import Agent, Prompt, RunContextWrapper, Runner + +from .fake_model import FakeModel +from .test_responses import get_text_message + + +class PromptCaptureFakeModel(FakeModel): + """Subclass of FakeModel that records the prompt passed to the model.""" + + def __init__(self): + super().__init__() + self.last_prompt = None + + async def get_response( + self, + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + tracing, + *, + previous_response_id, + prompt, + ): + # Record the prompt that the agent resolved and passed in. + self.last_prompt = prompt + return await super().get_response( + system_instructions, + input, + model_settings, + tools, + output_schema, + handoffs, + tracing, + previous_response_id=previous_response_id, + prompt=prompt, + ) + + +@pytest.mark.asyncio +async def test_static_prompt_is_resolved_correctly(): + static_prompt: Prompt = { + "id": "my_prompt", + "version": "1", + "variables": {"some_var": "some_value"}, + } + + agent = Agent(name="test", prompt=static_prompt) + context_wrapper = RunContextWrapper(context=None) + + resolved = await agent.get_prompt(context_wrapper) + + assert resolved == { + "id": "my_prompt", + "version": "1", + "variables": {"some_var": "some_value"}, + } + + +@pytest.mark.asyncio +async def test_dynamic_prompt_is_resolved_correctly(): + dynamic_prompt_value: Prompt = {"id": "dyn_prompt", "version": "2"} + + def dynamic_prompt_fn(_data): + return dynamic_prompt_value + + agent = Agent(name="test", prompt=dynamic_prompt_fn) + context_wrapper = RunContextWrapper(context=None) + + resolved = await agent.get_prompt(context_wrapper) + + assert resolved == {"id": "dyn_prompt", "version": "2", "variables": None} + + +@pytest.mark.asyncio +async def test_prompt_is_passed_to_model(): + static_prompt: Prompt = {"id": "model_prompt"} + + model = PromptCaptureFakeModel() + agent = Agent(name="test", model=model, prompt=static_prompt) + + # Ensure the model returns a simple message so the run completes in one turn. + model.set_next_output([get_text_message("done")]) + + await Runner.run(agent, input="hello") + + # The model should have received the prompt resolved by the agent. + expected_prompt = { + "id": "model_prompt", + "version": None, + "variables": None, + } + assert model.last_prompt == expected_prompt diff --git a/tests/test_agent_runner.py b/tests/test_agent_runner.py index 14a278a90..77eb8019e 100644 --- a/tests/test_agent_runner.py +++ b/tests/test_agent_runner.py @@ -745,3 +745,38 @@ async def test_previous_response_id_passed_between_runs_streamed_multi_turn(): pass assert model.last_turn_args.get("previous_response_id") == "resp-stream-test" + + +@pytest.mark.asyncio +async def test_dynamic_tool_addition_run() -> None: + """Test that tools can be added to an agent during a run.""" + model = FakeModel() + + executed: dict[str, bool] = {"called": False} + + agent = Agent(name="test", model=model, tool_use_behavior="run_llm_again") + + @function_tool(name_override="tool2") + def tool2() -> str: + executed["called"] = True + return "result2" + + @function_tool(name_override="add_tool") + async def add_tool() -> str: + agent.tools.append(tool2) + return "added" + + agent.tools.append(add_tool) + + model.add_multiple_turn_outputs( + [ + [get_function_tool_call("add_tool", json.dumps({}))], + [get_function_tool_call("tool2", json.dumps({}))], + [get_text_message("done")], + ] + ) + + result = await Runner.run(agent, input="start") + + assert executed["called"] is True + assert result.final_output == "done" diff --git a/tests/test_agent_runner_streamed.py b/tests/test_agent_runner_streamed.py index 87a76a706..31fe2979b 100644 --- a/tests/test_agent_runner_streamed.py +++ b/tests/test_agent_runner_streamed.py @@ -18,6 +18,7 @@ RunContextWrapper, Runner, UserError, + function_tool, handoff, ) from agents.items import RunItem @@ -684,3 +685,39 @@ async def test_streaming_events(): assert len(agent_data) == 2, "should have 2 agent updated events" assert agent_data[0].new_agent == agent_2, "should have started with agent_2" assert agent_data[1].new_agent == agent_1, "should have handed off to agent_1" + + +@pytest.mark.asyncio +async def test_dynamic_tool_addition_run_streamed() -> None: + model = FakeModel() + + executed: dict[str, bool] = {"called": False} + + agent = Agent(name="test", model=model, tool_use_behavior="run_llm_again") + + @function_tool(name_override="tool2") + def tool2() -> str: + executed["called"] = True + return "result2" + + @function_tool(name_override="add_tool") + async def add_tool() -> str: + agent.tools.append(tool2) + return "added" + + agent.tools.append(add_tool) + + model.add_multiple_turn_outputs( + [ + [get_function_tool_call("add_tool", json.dumps({}))], + [get_function_tool_call("tool2", json.dumps({}))], + [get_text_message("done")], + ] + ) + + result = Runner.run_streamed(agent, input="start") + async for _ in result.stream_events(): + pass + + assert executed["called"] is True + assert result.final_output == "done" diff --git a/tests/test_cancel_streaming.py b/tests/test_cancel_streaming.py new file mode 100644 index 000000000..3417a3c5d --- /dev/null +++ b/tests/test_cancel_streaming.py @@ -0,0 +1,116 @@ +import json + +import pytest + +from agents import Agent, Runner + +from .fake_model import FakeModel +from .test_responses import get_function_tool, get_function_tool_call, get_text_message + + +@pytest.mark.asyncio +async def test_simple_streaming_with_cancel(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + num_events = 0 + stop_after = 1 # There are two that the model gives back. + + async for _event in result.stream_events(): + num_events += 1 + if num_events == stop_after: + result.cancel() + + assert num_events == 1, f"Expected {stop_after} visible events, but got {num_events}" + + +@pytest.mark.asyncio +async def test_multiple_events_streaming_with_cancel(): + model = FakeModel() + agent = Agent( + name="Joker", + model=model, + tools=[get_function_tool("foo", "tool_result")], + ) + + model.add_multiple_turn_outputs( + [ + # First turn: a message and tool call + [ + get_text_message("a_message"), + get_function_tool_call("foo", json.dumps({"a": "b"})), + ], + # Second turn: text message + [get_text_message("done")], + ] + ) + + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + num_events = 0 + stop_after = 2 + + async for _ in result.stream_events(): + num_events += 1 + if num_events == stop_after: + result.cancel() + + assert num_events == stop_after, f"Expected {stop_after} visible events, but got {num_events}" + + +@pytest.mark.asyncio +async def test_cancel_prevents_further_events(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + events = [] + async for event in result.stream_events(): + events.append(event) + result.cancel() + break # Cancel after first event + # Try to get more events after cancel + more_events = [e async for e in result.stream_events()] + assert len(events) == 1 + assert more_events == [], "No events should be yielded after cancel()" + + +@pytest.mark.asyncio +async def test_cancel_is_idempotent(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + events = [] + async for event in result.stream_events(): + events.append(event) + result.cancel() + result.cancel() # Call cancel again + break + # Should not raise or misbehave + assert len(events) == 1 + + +@pytest.mark.asyncio +async def test_cancel_before_streaming(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + result.cancel() # Cancel before streaming + events = [e async for e in result.stream_events()] + assert events == [], "No events should be yielded if cancel() is called before streaming." + + +@pytest.mark.asyncio +async def test_cancel_cleans_up_resources(): + model = FakeModel() + agent = Agent(name="Joker", model=model) + result = Runner.run_streamed(agent, input="Please tell me 5 jokes.") + # Start streaming, then cancel + async for _ in result.stream_events(): + result.cancel() + break + # After cancel, queues should be empty and is_complete True + assert result.is_complete, "Result should be marked complete after cancel." + assert result._event_queue.empty(), "Event queue should be empty after cancel." + assert result._input_guardrail_queue.empty(), ( + "Input guardrail queue should be empty after cancel." + ) diff --git a/tests/test_computer_action.py b/tests/test_computer_action.py index 70dcabd59..a306b1841 100644 --- a/tests/test_computer_action.py +++ b/tests/test_computer_action.py @@ -18,6 +18,7 @@ ActionScroll, ActionType, ActionWait, + PendingSafetyCheck, ResponseComputerToolCall, ) @@ -31,8 +32,9 @@ RunContextWrapper, RunHooks, ) -from agents._run_impl import ComputerAction, ToolRunComputerAction +from agents._run_impl import ComputerAction, RunImpl, ToolRunComputerAction from agents.items import ToolCallOutputItem +from agents.tool import ComputerToolSafetyCheckData class LoggingComputer(Computer): @@ -309,3 +311,44 @@ async def test_execute_invokes_hooks_and_returns_tool_call_output() -> None: assert raw["output"]["type"] == "computer_screenshot" assert "image_url" in raw["output"] assert raw["output"]["image_url"].endswith("xyz") + + +@pytest.mark.asyncio +async def test_pending_safety_check_acknowledged() -> None: + """Safety checks should be acknowledged via the callback.""" + + computer = LoggingComputer(screenshot_return="img") + called: list[ComputerToolSafetyCheckData] = [] + + def on_sc(data: ComputerToolSafetyCheckData) -> bool: + called.append(data) + return True + + tool = ComputerTool(computer=computer, on_safety_check=on_sc) + safety = PendingSafetyCheck(id="sc", code="c", message="m") + tool_call = ResponseComputerToolCall( + id="t1", + type="computer_call", + action=ActionClick(type="click", x=1, y=1, button="left"), + call_id="t1", + pending_safety_checks=[safety], + status="completed", + ) + run_action = ToolRunComputerAction(tool_call=tool_call, computer_tool=tool) + agent = Agent(name="a", tools=[tool]) + ctx = RunContextWrapper(context=None) + + results = await RunImpl.execute_computer_actions( + agent=agent, + actions=[run_action], + hooks=RunHooks[Any](), + context_wrapper=ctx, + config=RunConfig(), + ) + + assert len(results) == 1 + raw = results[0].raw_item + assert isinstance(raw, dict) + assert raw.get("acknowledged_safety_checks") == [{"id": "sc", "code": "c", "message": "m"}] + assert len(called) == 1 + assert called[0].safety_check.id == "sc" diff --git a/tests/test_extra_headers.py b/tests/test_extra_headers.py new file mode 100644 index 000000000..a6af30077 --- /dev/null +++ b/tests/test_extra_headers.py @@ -0,0 +1,100 @@ +import pytest +from openai.types.chat.chat_completion import ChatCompletion, Choice +from openai.types.chat.chat_completion_message import ChatCompletionMessage +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails + +from agents import ModelSettings, ModelTracing, OpenAIChatCompletionsModel, OpenAIResponsesModel + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_extra_headers_passed_to_openai_responses_model(): + """ + Ensure extra_headers in ModelSettings is passed to the OpenAIResponsesModel client. + """ + called_kwargs = {} + + class DummyResponses: + async def create(self, **kwargs): + nonlocal called_kwargs + called_kwargs = kwargs + + class DummyResponse: + id = "dummy" + output = [] + usage = type( + "Usage", + (), + { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "input_tokens_details": InputTokensDetails(cached_tokens=0), + "output_tokens_details": OutputTokensDetails(reasoning_tokens=0), + }, + )() + + return DummyResponse() + + class DummyClient: + def __init__(self): + self.responses = DummyResponses() + + model = OpenAIResponsesModel(model="gpt-4", openai_client=DummyClient()) # type: ignore + extra_headers = {"X-Test-Header": "test-value"} + await model.get_response( + system_instructions=None, + input="hi", + model_settings=ModelSettings(extra_headers=extra_headers), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + assert "extra_headers" in called_kwargs + assert called_kwargs["extra_headers"]["X-Test-Header"] == "test-value" + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_extra_headers_passed_to_openai_client(): + """ + Ensure extra_headers in ModelSettings is passed to the OpenAI client. + """ + called_kwargs = {} + + class DummyCompletions: + async def create(self, **kwargs): + nonlocal called_kwargs + called_kwargs = kwargs + msg = ChatCompletionMessage(role="assistant", content="Hello") + choice = Choice(index=0, finish_reason="stop", message=msg) + return ChatCompletion( + id="resp-id", + created=0, + model="fake", + object="chat.completion", + choices=[choice], + usage=None, + ) + + class DummyClient: + def __init__(self): + self.chat = type("_Chat", (), {"completions": DummyCompletions()})() + self.base_url = "https://api.openai.com" + + model = OpenAIChatCompletionsModel(model="gpt-4", openai_client=DummyClient()) # type: ignore + extra_headers = {"X-Test-Header": "test-value"} + await model.get_response( + system_instructions=None, + input="hi", + model_settings=ModelSettings(extra_headers=extra_headers), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + ) + assert "extra_headers" in called_kwargs + assert called_kwargs["extra_headers"]["X-Test-Header"] == "test-value" diff --git a/tests/test_function_schema.py b/tests/test_function_schema.py index ef1e9c222..54e7e2f9e 100644 --- a/tests/test_function_schema.py +++ b/tests/test_function_schema.py @@ -99,7 +99,7 @@ def varargs_function(x: int, *numbers: float, flag: bool = False, **kwargs: Any) def test_varargs_function(): """Test a function that uses *args and **kwargs.""" - func_schema = function_schema(varargs_function) + func_schema = function_schema(varargs_function, strict_json_schema=False) # Check JSON schema structure assert isinstance(func_schema.params_json_schema, dict) assert func_schema.params_json_schema.get("title") == "varargs_function_args" @@ -439,3 +439,15 @@ def func_with_mapping(test_one: Mapping[str, int]) -> str: with pytest.raises(UserError): function_schema(func_with_mapping) + + +def test_name_override_without_docstring() -> None: + """name_override should be used even when not parsing docstrings.""" + + def foo(x: int) -> int: + return x + + fs = function_schema(foo, use_docstring_info=False, name_override="custom") + + assert fs.name == "custom" + assert fs.params_json_schema.get("title") == "custom_args" diff --git a/tests/test_function_tool.py b/tests/test_function_tool.py index 0a57aea87..9b02a2c42 100644 --- a/tests/test_function_tool.py +++ b/tests/test_function_tool.py @@ -5,8 +5,16 @@ from pydantic import BaseModel from typing_extensions import TypedDict -from agents import FunctionTool, ModelBehaviorError, RunContextWrapper, function_tool +from agents import ( + Agent, + AgentBase, + FunctionTool, + ModelBehaviorError, + RunContextWrapper, + function_tool, +) from agents.tool import default_tool_error_function +from agents.tool_context import ToolContext def argless_function() -> str: @@ -18,11 +26,11 @@ async def test_argless_function(): tool = function_tool(argless_function) assert tool.name == "argless_function" - result = await tool.on_invoke_tool(RunContextWrapper(None), "") + result = await tool.on_invoke_tool(ToolContext(context=None, tool_call_id="1"), "") assert result == "ok" -def argless_with_context(ctx: RunContextWrapper[str]) -> str: +def argless_with_context(ctx: ToolContext[str]) -> str: return "ok" @@ -31,11 +39,11 @@ async def test_argless_with_context(): tool = function_tool(argless_with_context) assert tool.name == "argless_with_context" - result = await tool.on_invoke_tool(RunContextWrapper(None), "") + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), "") assert result == "ok" # Extra JSON should not raise an error - result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1}') + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"a": 1}') assert result == "ok" @@ -48,15 +56,15 @@ async def test_simple_function(): tool = function_tool(simple_function, failure_error_function=None) assert tool.name == "simple_function" - result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1}') + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"a": 1}') assert result == 6 - result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1, "b": 2}') + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"a": 1, "b": 2}') assert result == 3 # Missing required argument should raise an error with pytest.raises(ModelBehaviorError): - await tool.on_invoke_tool(RunContextWrapper(None), "") + await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), "") class Foo(BaseModel): @@ -84,7 +92,7 @@ async def test_complex_args_function(): "bar": Bar(x="hello", y=10), } ) - result = await tool.on_invoke_tool(RunContextWrapper(None), valid_json) + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), valid_json) assert result == "6 hello10 hello" valid_json = json.dumps( @@ -93,7 +101,7 @@ async def test_complex_args_function(): "bar": Bar(x="hello", y=10), } ) - result = await tool.on_invoke_tool(RunContextWrapper(None), valid_json) + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), valid_json) assert result == "3 hello10 hello" valid_json = json.dumps( @@ -103,12 +111,12 @@ async def test_complex_args_function(): "baz": "world", } ) - result = await tool.on_invoke_tool(RunContextWrapper(None), valid_json) + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), valid_json) assert result == "3 hello10 world" # Missing required argument should raise an error with pytest.raises(ModelBehaviorError): - await tool.on_invoke_tool(RunContextWrapper(None), '{"foo": {"a": 1}}') + await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"foo": {"a": 1}}') def test_function_config_overrides(): @@ -168,7 +176,7 @@ async def run_function(ctx: RunContextWrapper[Any], args: str) -> str: assert tool.params_json_schema[key] == value assert tool.strict_json_schema - result = await tool.on_invoke_tool(RunContextWrapper(None), '{"data": "hello"}') + result = await tool.on_invoke_tool(ToolContext(None, tool_call_id="1"), '{"data": "hello"}') assert result == "hello_done" tool_not_strict = FunctionTool( @@ -183,7 +191,7 @@ async def run_function(ctx: RunContextWrapper[Any], args: str) -> str: assert "additionalProperties" not in tool_not_strict.params_json_schema result = await tool_not_strict.on_invoke_tool( - RunContextWrapper(None), '{"data": "hello", "bar": "baz"}' + ToolContext(None, tool_call_id="1"), '{"data": "hello", "bar": "baz"}' ) assert result == "hello_done" @@ -194,7 +202,7 @@ def my_func(a: int, b: int = 5): raise ValueError("test") tool = function_tool(my_func) - ctx = RunContextWrapper(None) + ctx = ToolContext(None, tool_call_id="1") result = await tool.on_invoke_tool(ctx, "") assert "Invalid JSON" in str(result) @@ -218,7 +226,7 @@ def custom_sync_error_function(ctx: RunContextWrapper[Any], error: Exception) -> return f"error_{error.__class__.__name__}" tool = function_tool(my_func, failure_error_function=custom_sync_error_function) - ctx = RunContextWrapper(None) + ctx = ToolContext(None, tool_call_id="1") result = await tool.on_invoke_tool(ctx, "") assert result == "error_ModelBehaviorError" @@ -242,7 +250,7 @@ def custom_sync_error_function(ctx: RunContextWrapper[Any], error: Exception) -> return f"error_{error.__class__.__name__}" tool = function_tool(my_func, failure_error_function=custom_sync_error_function) - ctx = RunContextWrapper(None) + ctx = ToolContext(None, tool_call_id="1") result = await tool.on_invoke_tool(ctx, "") assert result == "error_ModelBehaviorError" @@ -255,3 +263,44 @@ def custom_sync_error_function(ctx: RunContextWrapper[Any], error: Exception) -> result = await tool.on_invoke_tool(ctx, '{"a": 1, "b": 2}') assert result == "error_ValueError" + + +class BoolCtx(BaseModel): + enable_tools: bool + + +@pytest.mark.asyncio +async def test_is_enabled_bool_and_callable(): + @function_tool(is_enabled=False) + def disabled_tool(): + return "nope" + + async def cond_enabled(ctx: RunContextWrapper[BoolCtx], agent: AgentBase) -> bool: + return ctx.context.enable_tools + + @function_tool(is_enabled=cond_enabled) + def another_tool(): + return "hi" + + async def third_tool_on_invoke_tool(ctx: RunContextWrapper[Any], args: str) -> str: + return "third" + + third_tool = FunctionTool( + name="third_tool", + description="third tool", + on_invoke_tool=third_tool_on_invoke_tool, + is_enabled=lambda ctx, agent: ctx.context.enable_tools, + params_json_schema={}, + ) + + agent = Agent(name="t", tools=[disabled_tool, another_tool, third_tool]) + context_1 = RunContextWrapper(BoolCtx(enable_tools=False)) + context_2 = RunContextWrapper(BoolCtx(enable_tools=True)) + + tools_with_ctx = await agent.get_all_tools(context_1) + assert tools_with_ctx == [] + + tools_with_ctx = await agent.get_all_tools(context_2) + assert len(tools_with_ctx) == 2 + assert tools_with_ctx[0].name == "another_tool" + assert tools_with_ctx[1].name == "third_tool" diff --git a/tests/test_function_tool_decorator.py b/tests/test_function_tool_decorator.py index 3b52788fb..d334d8f84 100644 --- a/tests/test_function_tool_decorator.py +++ b/tests/test_function_tool_decorator.py @@ -7,6 +7,7 @@ from agents import function_tool from agents.run_context import RunContextWrapper +from agents.tool_context import ToolContext class DummyContext: @@ -14,8 +15,8 @@ def __init__(self): self.data = "something" -def ctx_wrapper() -> RunContextWrapper[DummyContext]: - return RunContextWrapper(DummyContext()) +def ctx_wrapper() -> ToolContext[DummyContext]: + return ToolContext(context=DummyContext(), tool_call_id="1") @function_tool @@ -44,7 +45,7 @@ async def test_sync_no_context_with_args_invocation(): @function_tool -def sync_with_context(ctx: RunContextWrapper[DummyContext], name: str) -> str: +def sync_with_context(ctx: ToolContext[DummyContext], name: str) -> str: return f"{name}_{ctx.context.data}" @@ -71,7 +72,7 @@ async def test_async_no_context_invocation(): @function_tool -async def async_with_context(ctx: RunContextWrapper[DummyContext], prefix: str, num: int) -> str: +async def async_with_context(ctx: ToolContext[DummyContext], prefix: str, num: int) -> str: await asyncio.sleep(0) return f"{prefix}-{num}-{ctx.context.data}" diff --git a/tests/test_handoff_tool.py b/tests/test_handoff_tool.py index a2a06208f..0f7fc2166 100644 --- a/tests/test_handoff_tool.py +++ b/tests/test_handoff_tool.py @@ -1,3 +1,4 @@ +import json from typing import Any import pytest @@ -11,10 +12,10 @@ MessageOutputItem, ModelBehaviorError, RunContextWrapper, - Runner, UserError, handoff, ) +from agents.run import AgentRunner def message_item(content: str, agent: Agent[Any]) -> MessageOutputItem: @@ -37,16 +38,17 @@ def get_len(data: HandoffInputData) -> int: return input_len + pre_handoff_len + new_items_len -def test_single_handoff_setup(): +@pytest.mark.asyncio +async def test_single_handoff_setup(): agent_1 = Agent(name="test_1") agent_2 = Agent(name="test_2", handoffs=[agent_1]) assert not agent_1.handoffs assert agent_2.handoffs == [agent_1] - assert not Runner._get_handoffs(agent_1) + assert not (await AgentRunner._get_handoffs(agent_1, RunContextWrapper(agent_1))) - handoff_objects = Runner._get_handoffs(agent_2) + handoff_objects = await AgentRunner._get_handoffs(agent_2, RunContextWrapper(agent_2)) assert len(handoff_objects) == 1 obj = handoff_objects[0] assert obj.tool_name == Handoff.default_tool_name(agent_1) @@ -54,7 +56,8 @@ def test_single_handoff_setup(): assert obj.agent_name == agent_1.name -def test_multiple_handoffs_setup(): +@pytest.mark.asyncio +async def test_multiple_handoffs_setup(): agent_1 = Agent(name="test_1") agent_2 = Agent(name="test_2") agent_3 = Agent(name="test_3", handoffs=[agent_1, agent_2]) @@ -63,7 +66,7 @@ def test_multiple_handoffs_setup(): assert not agent_1.handoffs assert not agent_2.handoffs - handoff_objects = Runner._get_handoffs(agent_3) + handoff_objects = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(agent_3)) assert len(handoff_objects) == 2 assert handoff_objects[0].tool_name == Handoff.default_tool_name(agent_1) assert handoff_objects[1].tool_name == Handoff.default_tool_name(agent_2) @@ -75,7 +78,8 @@ def test_multiple_handoffs_setup(): assert handoff_objects[1].agent_name == agent_2.name -def test_custom_handoff_setup(): +@pytest.mark.asyncio +async def test_custom_handoff_setup(): agent_1 = Agent(name="test_1") agent_2 = Agent(name="test_2") agent_3 = Agent( @@ -94,7 +98,7 @@ def test_custom_handoff_setup(): assert not agent_1.handoffs assert not agent_2.handoffs - handoff_objects = Runner._get_handoffs(agent_3) + handoff_objects = await AgentRunner._get_handoffs(agent_3, RunContextWrapper(agent_3)) assert len(handoff_objects) == 2 first_handoff = handoff_objects[0] @@ -276,3 +280,93 @@ def test_handoff_input_schema_is_strict(): "additionalProperties" in obj.input_json_schema and not obj.input_json_schema["additionalProperties"] ), "Input schema should be strict and have additionalProperties=False" + + +def test_get_transfer_message_is_valid_json() -> None: + agent = Agent(name="foo") + obj = handoff(agent) + transfer = obj.get_transfer_message(agent) + assert json.loads(transfer) == {"assistant": agent.name} + + +def test_handoff_is_enabled_bool(): + """Test that handoff respects is_enabled boolean parameter.""" + agent = Agent(name="test") + + # Test enabled handoff (default) + handoff_enabled = handoff(agent) + assert handoff_enabled.is_enabled is True + + # Test explicitly enabled handoff + handoff_explicit_enabled = handoff(agent, is_enabled=True) + assert handoff_explicit_enabled.is_enabled is True + + # Test disabled handoff + handoff_disabled = handoff(agent, is_enabled=False) + assert handoff_disabled.is_enabled is False + + +@pytest.mark.asyncio +async def test_handoff_is_enabled_callable(): + """Test that handoff respects is_enabled callable parameter.""" + agent = Agent(name="test") + + # Test callable that returns True + def always_enabled(ctx: RunContextWrapper[Any], agent: Agent[Any]) -> bool: + return True + + handoff_callable_enabled = handoff(agent, is_enabled=always_enabled) + assert callable(handoff_callable_enabled.is_enabled) + result = handoff_callable_enabled.is_enabled(RunContextWrapper(agent), agent) + assert result is True + + # Test callable that returns False + def always_disabled(ctx: RunContextWrapper[Any], agent: Agent[Any]) -> bool: + return False + + handoff_callable_disabled = handoff(agent, is_enabled=always_disabled) + assert callable(handoff_callable_disabled.is_enabled) + result = handoff_callable_disabled.is_enabled(RunContextWrapper(agent), agent) + assert result is False + + # Test async callable + async def async_enabled(ctx: RunContextWrapper[Any], agent: Agent[Any]) -> bool: + return True + + handoff_async_enabled = handoff(agent, is_enabled=async_enabled) + assert callable(handoff_async_enabled.is_enabled) + result = await handoff_async_enabled.is_enabled(RunContextWrapper(agent), agent) # type: ignore + assert result is True + + +@pytest.mark.asyncio +async def test_handoff_is_enabled_filtering_integration(): + """Integration test that disabled handoffs are filtered out by the runner.""" + + # Set up agents + agent_1 = Agent(name="agent_1") + agent_2 = Agent(name="agent_2") + agent_3 = Agent(name="agent_3") + + # Create main agent with mixed enabled/disabled handoffs + main_agent = Agent( + name="main_agent", + handoffs=[ + handoff(agent_1, is_enabled=True), # enabled + handoff(agent_2, is_enabled=False), # disabled + handoff(agent_3, is_enabled=lambda ctx, agent: True), # enabled callable + ], + ) + + context_wrapper = RunContextWrapper(main_agent) + + # Get filtered handoffs using the runner's method + filtered_handoffs = await AgentRunner._get_handoffs(main_agent, context_wrapper) + + # Should only have 2 handoffs (agent_1 and agent_3), agent_2 should be filtered out + assert len(filtered_handoffs) == 2 + + # Check that the correct agents are present + agent_names = {h.agent_name for h in filtered_handoffs} + assert agent_names == {"agent_1", "agent_3"} + assert "agent_2" not in agent_names diff --git a/tests/test_items_helpers.py b/tests/test_items_helpers.py index 5dba21d88..f711f21e1 100644 --- a/tests/test_items_helpers.py +++ b/tests/test_items_helpers.py @@ -11,7 +11,10 @@ ) from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall from openai.types.responses.response_function_tool_call_param import ResponseFunctionToolCallParam -from openai.types.responses.response_function_web_search import ResponseFunctionWebSearch +from openai.types.responses.response_function_web_search import ( + ActionSearch, + ResponseFunctionWebSearch, +) from openai.types.responses.response_function_web_search_param import ResponseFunctionWebSearchParam from openai.types.responses.response_output_message import ResponseOutputMessage from openai.types.responses.response_output_message_param import ResponseOutputMessageParam @@ -225,7 +228,12 @@ def test_to_input_items_for_file_search_call() -> None: def test_to_input_items_for_web_search_call() -> None: """A web search tool call output should produce the same dict as a web search input.""" - ws_call = ResponseFunctionWebSearch(id="w1", status="completed", type="web_search_call") + ws_call = ResponseFunctionWebSearch( + id="w1", + action=ActionSearch(type="search", query="query"), + status="completed", + type="web_search_call", + ) resp = ModelResponse(output=[ws_call], usage=Usage(), response_id=None) input_items = resp.to_input_items() assert isinstance(input_items, list) and len(input_items) == 1 @@ -233,6 +241,7 @@ def test_to_input_items_for_web_search_call() -> None: "id": "w1", "status": "completed", "type": "web_search_call", + "action": {"type": "search", "query": "query"}, } assert input_items[0] == expected diff --git a/tests/test_openai_chatcompletions.py b/tests/test_openai_chatcompletions.py index 92d65fda1..a6909b195 100644 --- a/tests/test_openai_chatcompletions.py +++ b/tests/test_openai_chatcompletions.py @@ -13,7 +13,10 @@ ChatCompletionMessageToolCall, Function, ) -from openai.types.completion_usage import CompletionUsage +from openai.types.completion_usage import ( + CompletionUsage, + PromptTokensDetails, +) from openai.types.responses import ( Response, ResponseFunctionToolCall, @@ -30,8 +33,8 @@ OpenAIProvider, generation_span, ) +from agents.models.chatcmpl_helpers import ChatCmplHelpers from agents.models.fake_id import FAKE_RESPONSES_ID -from agents.models.openai_chatcompletions import _Converter @pytest.mark.allow_call_model_methods @@ -51,7 +54,13 @@ async def test_get_response_with_text_message(monkeypatch) -> None: model="fake", object="chat.completion", choices=[choice], - usage=CompletionUsage(completion_tokens=5, prompt_tokens=7, total_tokens=12), + usage=CompletionUsage( + completion_tokens=5, + prompt_tokens=7, + total_tokens=12, + # completion_tokens_details left blank to test default + prompt_tokens_details=PromptTokensDetails(cached_tokens=3), + ), ) async def patched_fetch_response(self, *args, **kwargs): @@ -68,6 +77,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ) # Should have produced exactly one output message with one text part assert isinstance(resp, ModelResponse) @@ -81,6 +91,8 @@ async def patched_fetch_response(self, *args, **kwargs): assert resp.usage.input_tokens == 7 assert resp.usage.output_tokens == 5 assert resp.usage.total_tokens == 12 + assert resp.usage.input_tokens_details.cached_tokens == 3 + assert resp.usage.output_tokens_details.reasoning_tokens == 0 assert resp.response_id is None @@ -117,6 +129,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ) assert len(resp.output) == 1 assert isinstance(resp.output[0], ResponseOutputMessage) @@ -127,6 +140,8 @@ async def patched_fetch_response(self, *args, **kwargs): assert resp.usage.requests == 0 assert resp.usage.input_tokens == 0 assert resp.usage.output_tokens == 0 + assert resp.usage.input_tokens_details.cached_tokens == 0 + assert resp.usage.output_tokens_details.reasoning_tokens == 0 @pytest.mark.allow_call_model_methods @@ -167,6 +182,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ) # Expect a message item followed by a function tool call item. assert len(resp.output) == 2 @@ -178,6 +194,41 @@ async def patched_fetch_response(self, *args, **kwargs): assert fn_call_item.arguments == "{'x':1}" +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_get_response_with_no_message(monkeypatch) -> None: + """If the model returns no message, get_response should return an empty output.""" + msg = ChatCompletionMessage(role="assistant", content="ignored") + choice = Choice(index=0, finish_reason="content_filter", message=msg) + choice.message = None # type: ignore[assignment] + chat = ChatCompletion( + id="resp-id", + created=0, + model="fake", + object="chat.completion", + choices=[choice], + usage=None, + ) + + async def patched_fetch_response(self, *args, **kwargs): + return chat + + monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response) + model = OpenAIProvider(use_responses=False).get_model("gpt-4") + resp: ModelResponse = await model.get_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ) + assert resp.output == [] + + @pytest.mark.asyncio async def test_fetch_response_non_stream(monkeypatch) -> None: """ @@ -301,32 +352,32 @@ def test_store_param(): model_settings = ModelSettings() client = AsyncOpenAI() - assert _Converter.get_store_param(client, model_settings) is True, ( + assert ChatCmplHelpers.get_store_param(client, model_settings) is True, ( "Should default to True for OpenAI API calls" ) model_settings = ModelSettings(store=False) - assert _Converter.get_store_param(client, model_settings) is False, ( + assert ChatCmplHelpers.get_store_param(client, model_settings) is False, ( "Should respect explicitly set store=False" ) model_settings = ModelSettings(store=True) - assert _Converter.get_store_param(client, model_settings) is True, ( + assert ChatCmplHelpers.get_store_param(client, model_settings) is True, ( "Should respect explicitly set store=True" ) client = AsyncOpenAI(base_url="http://www.notopenai.com") model_settings = ModelSettings() - assert _Converter.get_store_param(client, model_settings) is None, ( + assert ChatCmplHelpers.get_store_param(client, model_settings) is None, ( "Should default to None for non-OpenAI API calls" ) model_settings = ModelSettings(store=False) - assert _Converter.get_store_param(client, model_settings) is False, ( + assert ChatCmplHelpers.get_store_param(client, model_settings) is False, ( "Should respect explicitly set store=False" ) model_settings = ModelSettings(store=True) - assert _Converter.get_store_param(client, model_settings) is True, ( + assert ChatCmplHelpers.get_store_param(client, model_settings) is True, ( "Should respect explicitly set store=True" ) diff --git a/tests/test_openai_chatcompletions_converter.py b/tests/test_openai_chatcompletions_converter.py index 73acb8ab6..bcfca5495 100644 --- a/tests/test_openai_chatcompletions_converter.py +++ b/tests/test_openai_chatcompletions_converter.py @@ -4,7 +4,7 @@ # See LICENSE file in the project root for full license information. """ -Unit tests for the internal `_Converter` class defined in +Unit tests for the internal `Converter` class defined in `agents.models.openai_chatcompletions`. The converter is responsible for translating between internal "item" structures (e.g., `ResponseOutputMessage` and related types from `openai.types.responses`) and the ChatCompletion message @@ -12,10 +12,10 @@ These tests exercise both conversion directions: -- `_Converter.message_to_output_items` turns a `ChatCompletionMessage` (as +- `Converter.message_to_output_items` turns a `ChatCompletionMessage` (as returned by the OpenAI API) into a list of `ResponseOutputItem` instances. -- `_Converter.items_to_messages` takes in either a simple string prompt, or a +- `Converter.items_to_messages` takes in either a simple string prompt, or a list of input/output items such as `ResponseOutputMessage` and `ResponseFunctionToolCallParam` dicts, and constructs a list of `ChatCompletionMessageParam` dicts suitable for sending back to the API. @@ -41,8 +41,8 @@ from agents.agent_output import AgentOutputSchema from agents.exceptions import UserError from agents.items import TResponseInputItem +from agents.models.chatcmpl_converter import Converter from agents.models.fake_id import FAKE_RESPONSES_ID -from agents.models.openai_chatcompletions import _Converter def test_message_to_output_items_with_text_only(): @@ -51,7 +51,7 @@ def test_message_to_output_items_with_text_only(): into a single ResponseOutputMessage containing one ResponseOutputText. """ msg = ChatCompletionMessage(role="assistant", content="Hello") - items = _Converter.message_to_output_items(msg) + items = Converter.message_to_output_items(msg) # Expect exactly one output item (the message) assert len(items) == 1 message_item = cast(ResponseOutputMessage, items[0]) @@ -72,7 +72,7 @@ def test_message_to_output_items_with_refusal(): with a ResponseOutputRefusal content part. """ msg = ChatCompletionMessage(role="assistant", refusal="I'm sorry") - items = _Converter.message_to_output_items(msg) + items = Converter.message_to_output_items(msg) assert len(items) == 1 message_item = cast(ResponseOutputMessage, items[0]) assert len(message_item.content) == 1 @@ -93,7 +93,7 @@ def test_message_to_output_items_with_tool_call(): function=Function(name="myfn", arguments='{"x":1}'), ) msg = ChatCompletionMessage(role="assistant", content="Hi", tool_calls=[tool_call]) - items = _Converter.message_to_output_items(msg) + items = Converter.message_to_output_items(msg) # Should produce a message item followed by one function tool call item assert len(items) == 2 message_item = cast(ResponseOutputMessage, items[0]) @@ -111,7 +111,7 @@ def test_items_to_messages_with_string_user_content(): A simple string as the items argument should be converted into a user message param dict with the same content. """ - result = _Converter.items_to_messages("Ask me anything") + result = Converter.items_to_messages("Ask me anything") assert isinstance(result, list) assert len(result) == 1 msg = result[0] @@ -130,7 +130,7 @@ def test_items_to_messages_with_easy_input_message(): "content": "How are you?", } ] - messages = _Converter.items_to_messages(items) + messages = Converter.items_to_messages(items) assert len(messages) == 1 out = messages[0] assert out["role"] == "user" @@ -174,7 +174,7 @@ def test_items_to_messages_with_output_message_and_function_call(): resp_msg.model_dump(), # type:ignore func_item, ] - messages = _Converter.items_to_messages(items) + messages = Converter.items_to_messages(items) # Should return a single assistant message assert len(messages) == 1 assistant = messages[0] @@ -197,16 +197,16 @@ def test_items_to_messages_with_output_message_and_function_call(): def test_convert_tool_choice_handles_standard_and_named_options() -> None: """ - The `_Converter.convert_tool_choice` method should return NOT_GIVEN + The `Converter.convert_tool_choice` method should return NOT_GIVEN if no choice is provided, pass through values like "auto", "required", or "none" unchanged, and translate any other string into a function selection dict. """ - assert _Converter.convert_tool_choice(None).__class__.__name__ == "NotGiven" - assert _Converter.convert_tool_choice("auto") == "auto" - assert _Converter.convert_tool_choice("required") == "required" - assert _Converter.convert_tool_choice("none") == "none" - tool_choice_dict = _Converter.convert_tool_choice("mytool") + assert Converter.convert_tool_choice(None).__class__.__name__ == "NotGiven" + assert Converter.convert_tool_choice("auto") == "auto" + assert Converter.convert_tool_choice("required") == "required" + assert Converter.convert_tool_choice("none") == "none" + tool_choice_dict = Converter.convert_tool_choice("mytool") assert isinstance(tool_choice_dict, dict) assert tool_choice_dict["type"] == "function" assert tool_choice_dict["function"]["name"] == "mytool" @@ -214,25 +214,25 @@ def test_convert_tool_choice_handles_standard_and_named_options() -> None: def test_convert_response_format_returns_not_given_for_plain_text_and_dict_for_schemas() -> None: """ - The `_Converter.convert_response_format` method should return NOT_GIVEN + The `Converter.convert_response_format` method should return NOT_GIVEN when no output schema is provided or if the output schema indicates plain text. For structured output schemas, it should return a dict with type `json_schema` and include the generated JSON schema and strict flag from the provided `AgentOutputSchema`. """ # when output is plain text (schema None or output_type str), do not include response_format - assert _Converter.convert_response_format(None).__class__.__name__ == "NotGiven" + assert Converter.convert_response_format(None).__class__.__name__ == "NotGiven" assert ( - _Converter.convert_response_format(AgentOutputSchema(str)).__class__.__name__ == "NotGiven" + Converter.convert_response_format(AgentOutputSchema(str)).__class__.__name__ == "NotGiven" ) # For e.g. integer output, we expect a response_format dict schema = AgentOutputSchema(int) - resp_format = _Converter.convert_response_format(schema) + resp_format = Converter.convert_response_format(schema) assert isinstance(resp_format, dict) assert resp_format["type"] == "json_schema" assert resp_format["json_schema"]["name"] == "final_output" assert "strict" in resp_format["json_schema"] - assert resp_format["json_schema"]["strict"] == schema.strict_json_schema + assert resp_format["json_schema"]["strict"] == schema.is_strict_json_schema() assert "schema" in resp_format["json_schema"] assert resp_format["json_schema"]["schema"] == schema.json_schema() @@ -247,7 +247,7 @@ def test_items_to_messages_with_function_output_item(): "call_id": "somecall", "output": '{"foo": "bar"}', } - messages = _Converter.items_to_messages([func_output_item]) + messages = Converter.items_to_messages([func_output_item]) assert len(messages) == 1 tool_msg = messages[0] assert tool_msg["role"] == "tool" @@ -266,16 +266,16 @@ def test_extract_all_and_text_content_for_strings_and_lists(): should filter to only the textual parts. """ prompt = "just text" - assert _Converter.extract_all_content(prompt) == prompt - assert _Converter.extract_text_content(prompt) == prompt + assert Converter.extract_all_content(prompt) == prompt + assert Converter.extract_text_content(prompt) == prompt text1: ResponseInputTextParam = {"type": "input_text", "text": "one"} text2: ResponseInputTextParam = {"type": "input_text", "text": "two"} - all_parts = _Converter.extract_all_content([text1, text2]) + all_parts = Converter.extract_all_content([text1, text2]) assert isinstance(all_parts, list) assert len(all_parts) == 2 assert all_parts[0]["type"] == "text" and all_parts[0]["text"] == "one" assert all_parts[1]["type"] == "text" and all_parts[1]["text"] == "two" - text_parts = _Converter.extract_text_content([text1, text2]) + text_parts = Converter.extract_text_content([text1, text2]) assert isinstance(text_parts, list) assert all(p["type"] == "text" for p in text_parts) assert [p["text"] for p in text_parts] == ["one", "two"] @@ -288,12 +288,12 @@ def test_items_to_messages_handles_system_and_developer_roles(): `message` typed dicts. """ sys_items: list[TResponseInputItem] = [{"role": "system", "content": "setup"}] - sys_msgs = _Converter.items_to_messages(sys_items) + sys_msgs = Converter.items_to_messages(sys_items) assert len(sys_msgs) == 1 assert sys_msgs[0]["role"] == "system" assert sys_msgs[0]["content"] == "setup" dev_items: list[TResponseInputItem] = [{"role": "developer", "content": "debug"}] - dev_msgs = _Converter.items_to_messages(dev_items) + dev_msgs = Converter.items_to_messages(dev_items) assert len(dev_msgs) == 1 assert dev_msgs[0]["role"] == "developer" assert dev_msgs[0]["content"] == "debug" @@ -301,7 +301,7 @@ def test_items_to_messages_handles_system_and_developer_roles(): def test_maybe_input_message_allows_message_typed_dict(): """ - The `_Converter.maybe_input_message` should recognize a dict with + The `Converter.maybe_input_message` should recognize a dict with "type": "message" and a supported role as an input message. Ensure that such dicts are passed through by `items_to_messages`. """ @@ -311,9 +311,9 @@ def test_maybe_input_message_allows_message_typed_dict(): "role": "user", "content": "hi", } - assert _Converter.maybe_input_message(message_dict) is not None + assert Converter.maybe_input_message(message_dict) is not None # items_to_messages should process this correctly - msgs = _Converter.items_to_messages([message_dict]) + msgs = Converter.items_to_messages([message_dict]) assert len(msgs) == 1 assert msgs[0]["role"] == "user" assert msgs[0]["content"] == "hi" @@ -331,7 +331,7 @@ def test_tool_call_conversion(): type="function_call", ) - messages = _Converter.items_to_messages([function_call]) + messages = Converter.items_to_messages([function_call]) assert len(messages) == 1 tool_msg = messages[0] assert tool_msg["role"] == "assistant" @@ -348,7 +348,7 @@ def test_tool_call_conversion(): @pytest.mark.parametrize("role", ["user", "system", "developer"]) def test_input_message_with_all_roles(role: str): """ - The `_Converter.maybe_input_message` should recognize a dict with + The `Converter.maybe_input_message` should recognize a dict with "type": "message" and a supported role as an input message. Ensure that such dicts are passed through by `items_to_messages`. """ @@ -359,9 +359,9 @@ def test_input_message_with_all_roles(role: str): "role": casted_role, "content": "hi", } - assert _Converter.maybe_input_message(message_dict) is not None + assert Converter.maybe_input_message(message_dict) is not None # items_to_messages should process this correctly - msgs = _Converter.items_to_messages([message_dict]) + msgs = Converter.items_to_messages([message_dict]) assert len(msgs) == 1 assert msgs[0]["role"] == casted_role assert msgs[0]["content"] == "hi" @@ -372,7 +372,7 @@ def test_item_reference_errors(): Test that item references are converted correctly. """ with pytest.raises(UserError): - _Converter.items_to_messages( + Converter.items_to_messages( [ { "type": "item_reference", @@ -392,14 +392,14 @@ def test_unknown_object_errors(): """ with pytest.raises(UserError, match="Unhandled item type or structure"): # Purposely ignore the type error - _Converter.items_to_messages([TestObject()]) # type: ignore + Converter.items_to_messages([TestObject()]) # type: ignore def test_assistant_messages_in_history(): """ Test that assistant messages are added to the history. """ - messages = _Converter.items_to_messages( + messages = Converter.items_to_messages( [ { "role": "user", diff --git a/tests/test_openai_chatcompletions_stream.py b/tests/test_openai_chatcompletions_stream.py index b82f24303..49e7bc2f4 100644 --- a/tests/test_openai_chatcompletions_stream.py +++ b/tests/test_openai_chatcompletions_stream.py @@ -8,7 +8,11 @@ ChoiceDeltaToolCall, ChoiceDeltaToolCallFunction, ) -from openai.types.completion_usage import CompletionUsage +from openai.types.completion_usage import ( + CompletionTokensDetails, + CompletionUsage, + PromptTokensDetails, +) from openai.types.responses import ( Response, ResponseFunctionToolCall, @@ -46,7 +50,13 @@ async def test_stream_response_yields_events_for_text_content(monkeypatch) -> No model="fake", object="chat.completion.chunk", choices=[Choice(index=0, delta=ChoiceDelta(content="llo"))], - usage=CompletionUsage(completion_tokens=5, prompt_tokens=7, total_tokens=12), + usage=CompletionUsage( + completion_tokens=5, + prompt_tokens=7, + total_tokens=12, + prompt_tokens_details=PromptTokensDetails(cached_tokens=2), + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=3), + ), ) async def fake_stream() -> AsyncIterator[ChatCompletionChunk]: @@ -80,6 +90,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ): output_events.append(event) # We expect a response.created, then a response.output_item.added, content part added, @@ -112,6 +123,8 @@ async def patched_fetch_response(self, *args, **kwargs): assert completed_resp.usage.input_tokens == 7 assert completed_resp.usage.output_tokens == 5 assert completed_resp.usage.total_tokens == 12 + assert completed_resp.usage.input_tokens_details.cached_tokens == 2 + assert completed_resp.usage.output_tokens_details.reasoning_tokens == 3 @pytest.mark.allow_call_model_methods @@ -170,6 +183,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ): output_events.append(event) # Expect sequence similar to text: created, output_item.added, content part added, @@ -258,6 +272,7 @@ async def patched_fetch_response(self, *args, **kwargs): handoffs=[], tracing=ModelTracing.DISABLED, previous_response_id=None, + prompt=None, ): output_events.append(event) # Sequence should be: response.created, then after loop we expect function call-related events: diff --git a/tests/test_openai_responses_converter.py b/tests/test_openai_responses_converter.py index 34cbac5c5..8e4866656 100644 --- a/tests/test_openai_responses_converter.py +++ b/tests/test_openai_responses_converter.py @@ -92,7 +92,7 @@ class OutModel(BaseModel): assert inner.get("name") == "final_output" assert isinstance(inner.get("schema"), dict) # Should include a strict flag matching the schema's strictness setting. - assert inner.get("strict") == out_schema.strict_json_schema + assert inner.get("strict") == out_schema.is_strict_json_schema() def test_convert_tools_basic_types_and_includes(): diff --git a/tests/test_output_tool.py b/tests/test_output_tool.py index 86c4b3b5a..e98fd3c55 100644 --- a/tests/test_output_tool.py +++ b/tests/test_output_tool.py @@ -1,17 +1,25 @@ import json +from typing import Any import pytest from pydantic import BaseModel from typing_extensions import TypedDict -from agents import Agent, AgentOutputSchema, ModelBehaviorError, Runner, UserError +from agents import ( + Agent, + AgentOutputSchema, + AgentOutputSchemaBase, + ModelBehaviorError, + UserError, +) from agents.agent_output import _WRAPPER_DICT_KEY +from agents.run import AgentRunner from agents.util import _json def test_plain_text_output(): agent = Agent(name="test") - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert not output_schema, "Shouldn't have an output tool config without an output type" agent = Agent(name="test", output_type=str) @@ -24,9 +32,10 @@ class Foo(BaseModel): def test_structured_output_pydantic(): agent = Agent(name="test", output_type=Foo) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" + assert isinstance(output_schema, AgentOutputSchema) assert output_schema.output_type == Foo, "Should have the correct output type" assert not output_schema._is_wrapped, "Pydantic objects should not be wrapped" for key, value in Foo.model_json_schema().items(): @@ -43,8 +52,9 @@ class Bar(TypedDict): def test_structured_output_typed_dict(): agent = Agent(name="test", output_type=Bar) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" + assert isinstance(output_schema, AgentOutputSchema) assert output_schema.output_type == Bar, "Should have the correct output type" assert not output_schema._is_wrapped, "TypedDicts should not be wrapped" @@ -55,8 +65,9 @@ def test_structured_output_typed_dict(): def test_structured_output_list(): agent = Agent(name="test", output_type=list[str]) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" + assert isinstance(output_schema, AgentOutputSchema) assert output_schema.output_type == list[str], "Should have the correct output type" assert output_schema._is_wrapped, "Lists should be wrapped" @@ -68,14 +79,14 @@ def test_structured_output_list(): def test_bad_json_raises_error(mocker): agent = Agent(name="test", output_type=Foo) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" with pytest.raises(ModelBehaviorError): output_schema.validate_json("not valid json") agent = Agent(name="test", output_type=list[str]) - output_schema = Runner._get_output_schema(agent) + output_schema = AgentRunner._get_output_schema(agent) assert output_schema, "Should have an output tool config with a structured output type" mock_validate_json = mocker.patch.object(_json, "validate_json") @@ -98,7 +109,7 @@ def test_plain_text_obj_doesnt_produce_schema(): def test_structured_output_is_strict(): output_wrapper = AgentOutputSchema(output_type=Foo) - assert output_wrapper.strict_json_schema + assert output_wrapper.is_strict_json_schema() for key, value in Foo.model_json_schema().items(): assert output_wrapper.json_schema()[key] == value @@ -110,6 +121,48 @@ def test_structured_output_is_strict(): def test_setting_strict_false_works(): output_wrapper = AgentOutputSchema(output_type=Foo, strict_json_schema=False) - assert not output_wrapper.strict_json_schema + assert not output_wrapper.is_strict_json_schema() assert output_wrapper.json_schema() == Foo.model_json_schema() assert output_wrapper.json_schema() == Foo.model_json_schema() + + +_CUSTOM_OUTPUT_SCHEMA_JSON_SCHEMA = { + "type": "object", + "properties": { + "foo": {"type": "string"}, + }, + "required": ["foo"], +} + + +class CustomOutputSchema(AgentOutputSchemaBase): + def is_plain_text(self) -> bool: + return False + + def name(self) -> str: + return "FooBarBaz" + + def json_schema(self) -> dict[str, Any]: + return _CUSTOM_OUTPUT_SCHEMA_JSON_SCHEMA + + def is_strict_json_schema(self) -> bool: + return False + + def validate_json(self, json_str: str) -> Any: + return ["some", "output"] + + +def test_custom_output_schema(): + custom_output_schema = CustomOutputSchema() + agent = Agent(name="test", output_type=custom_output_schema) + output_schema = AgentRunner._get_output_schema(agent) + + assert output_schema, "Should have an output tool config with a structured output type" + assert isinstance(output_schema, CustomOutputSchema) + assert output_schema.json_schema() == _CUSTOM_OUTPUT_SCHEMA_JSON_SCHEMA + assert not output_schema.is_strict_json_schema() + assert not output_schema.is_plain_text() + + json_str = json.dumps({"foo": "bar"}) + validated = output_schema.validate_json(json_str) + assert validated == ["some", "output"] diff --git a/tests/test_reasoning_content.py b/tests/test_reasoning_content.py new file mode 100644 index 000000000..69e9a7d0c --- /dev/null +++ b/tests/test_reasoning_content.py @@ -0,0 +1,285 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator +from typing import Any, cast + +import pytest +from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessage +from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta +from openai.types.completion_usage import ( + CompletionTokensDetails, + CompletionUsage, + PromptTokensDetails, +) +from openai.types.responses import ( + Response, + ResponseOutputMessage, + ResponseOutputText, + ResponseReasoningItem, +) + +from agents.model_settings import ModelSettings +from agents.models.interface import ModelTracing +from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel +from agents.models.openai_provider import OpenAIProvider + + +# Helper functions to create test objects consistently +def create_content_delta(content: str) -> dict[str, Any]: + """Create a delta dictionary with regular content""" + return {"content": content, "role": None, "function_call": None, "tool_calls": None} + + +def create_reasoning_delta(content: str) -> dict[str, Any]: + """Create a delta dictionary with reasoning content. The Only difference is reasoning_content""" + return { + "content": None, + "role": None, + "function_call": None, + "tool_calls": None, + "reasoning_content": content, + } + + +def create_chunk(delta: dict[str, Any], include_usage: bool = False) -> ChatCompletionChunk: + """Create a ChatCompletionChunk with the given delta""" + # Create a ChoiceDelta object from the dictionary + delta_obj = ChoiceDelta( + content=delta.get("content"), + role=delta.get("role"), + function_call=delta.get("function_call"), + tool_calls=delta.get("tool_calls"), + ) + + # Add reasoning_content attribute dynamically if present in the delta + if "reasoning_content" in delta: + # Use direct assignment for the reasoning_content attribute + delta_obj_any = cast(Any, delta_obj) + delta_obj_any.reasoning_content = delta["reasoning_content"] + + # Create the chunk + chunk = ChatCompletionChunk( + id="chunk-id", + created=1, + model="deepseek is usually expected", + object="chat.completion.chunk", + choices=[Choice(index=0, delta=delta_obj)], + ) + + if include_usage: + chunk.usage = CompletionUsage( + completion_tokens=4, + prompt_tokens=2, + total_tokens=6, + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=2), + prompt_tokens_details=PromptTokensDetails(cached_tokens=0), + ) + + return chunk + + +async def create_fake_stream( + chunks: list[ChatCompletionChunk], +) -> AsyncIterator[ChatCompletionChunk]: + for chunk in chunks: + yield chunk + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_yields_events_for_reasoning_content(monkeypatch) -> None: + """ + Validate that when a model streams reasoning content, + `stream_response` emits the appropriate sequence of events including + `response.reasoning_summary_text.delta` events for each chunk of the reasoning content and + constructs a completed response with a `ResponseReasoningItem` part. + """ + # Create test chunks + chunks = [ + # Reasoning content chunks + create_chunk(create_reasoning_delta("Let me think")), + create_chunk(create_reasoning_delta(" about this")), + # Regular content chunks + create_chunk(create_content_delta("The answer")), + create_chunk(create_content_delta(" is 42"), include_usage=True), + ] + + async def patched_fetch_response(self, *args, **kwargs): + resp = Response( + id="resp-id", + created_at=0, + model="fake-model", + object="response", + output=[], + tool_choice="none", + tools=[], + parallel_tool_calls=False, + ) + return resp, create_fake_stream(chunks) + + monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response) + model = OpenAIProvider(use_responses=False).get_model("gpt-4") + output_events = [] + async for event in model.stream_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ): + output_events.append(event) + + # verify reasoning content events were emitted + reasoning_delta_events = [ + e for e in output_events if e.type == "response.reasoning_summary_text.delta" + ] + assert len(reasoning_delta_events) == 2 + assert reasoning_delta_events[0].delta == "Let me think" + assert reasoning_delta_events[1].delta == " about this" + + # verify regular content events were emitted + content_delta_events = [e for e in output_events if e.type == "response.output_text.delta"] + assert len(content_delta_events) == 2 + assert content_delta_events[0].delta == "The answer" + assert content_delta_events[1].delta == " is 42" + + # verify the final response contains both types of content + response_event = output_events[-1] + assert response_event.type == "response.completed" + assert len(response_event.response.output) == 2 + + # first item should be reasoning + assert isinstance(response_event.response.output[0], ResponseReasoningItem) + assert response_event.response.output[0].summary[0].text == "Let me think about this" + + # second item should be message with text + assert isinstance(response_event.response.output[1], ResponseOutputMessage) + assert isinstance(response_event.response.output[1].content[0], ResponseOutputText) + assert response_event.response.output[1].content[0].text == "The answer is 42" + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_get_response_with_reasoning_content(monkeypatch) -> None: + """ + Test that when a model returns reasoning content in addition to regular content, + `get_response` properly includes both in the response output. + """ + # create a message with reasoning content + msg = ChatCompletionMessage( + role="assistant", + content="The answer is 42", + ) + # Use dynamic attribute for reasoning_content + # We need to cast to Any to avoid mypy errors since reasoning_content is not a defined attribute + msg_with_reasoning = cast(Any, msg) + msg_with_reasoning.reasoning_content = "Let me think about this question carefully" + + # create a choice with the message + mock_choice = { + "index": 0, + "finish_reason": "stop", + "message": msg_with_reasoning, + "delta": None, + } + + chat = ChatCompletion( + id="resp-id", + created=0, + model="deepseek is expected", + object="chat.completion", + choices=[mock_choice], # type: ignore[list-item] + usage=CompletionUsage( + completion_tokens=10, + prompt_tokens=5, + total_tokens=15, + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=6), + prompt_tokens_details=PromptTokensDetails(cached_tokens=0), + ), + ) + + async def patched_fetch_response(self, *args, **kwargs): + return chat + + monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response) + model = OpenAIProvider(use_responses=False).get_model("gpt-4") + resp = await model.get_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ) + + # should have produced a reasoning item and a message with text content + assert len(resp.output) == 2 + + # first output should be the reasoning item + assert isinstance(resp.output[0], ResponseReasoningItem) + assert resp.output[0].summary[0].text == "Let me think about this question carefully" + + # second output should be the message with text content + assert isinstance(resp.output[1], ResponseOutputMessage) + assert isinstance(resp.output[1].content[0], ResponseOutputText) + assert resp.output[1].content[0].text == "The answer is 42" + + +@pytest.mark.allow_call_model_methods +@pytest.mark.asyncio +async def test_stream_response_with_empty_reasoning_content(monkeypatch) -> None: + """ + Test that when a model streams empty reasoning content, + the response still processes correctly without errors. + """ + # create test chunks with empty reasoning content + chunks = [ + create_chunk(create_reasoning_delta("")), + create_chunk(create_content_delta("The answer is 42"), include_usage=True), + ] + + async def patched_fetch_response(self, *args, **kwargs): + resp = Response( + id="resp-id", + created_at=0, + model="fake-model", + object="response", + output=[], + tool_choice="none", + tools=[], + parallel_tool_calls=False, + ) + return resp, create_fake_stream(chunks) + + monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response) + model = OpenAIProvider(use_responses=False).get_model("gpt-4") + output_events = [] + async for event in model.stream_response( + system_instructions=None, + input="", + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + ): + output_events.append(event) + + # verify the final response contains the content + response_event = output_events[-1] + assert response_event.type == "response.completed" + + # should only have the message, not an empty reasoning item + assert len(response_event.response.output) == 1 + assert isinstance(response_event.response.output[0], ResponseOutputMessage) + assert isinstance(response_event.response.output[0].content[0], ResponseOutputText) + assert response_event.response.output[0].content[0].text == "The answer is 42" diff --git a/tests/test_repl.py b/tests/test_repl.py new file mode 100644 index 000000000..7ba2011be --- /dev/null +++ b/tests/test_repl.py @@ -0,0 +1,28 @@ +import pytest + +from agents import Agent, run_demo_loop + +from .fake_model import FakeModel +from .test_responses import get_text_input_item, get_text_message + + +@pytest.mark.asyncio +async def test_run_demo_loop_conversation(monkeypatch, capsys): + model = FakeModel() + model.add_multiple_turn_outputs([[get_text_message("hello")], [get_text_message("good")]]) + + agent = Agent(name="test", model=model) + + inputs = iter(["Hi", "How are you?", "quit"]) + monkeypatch.setattr("builtins.input", lambda _=" > ": next(inputs)) + + await run_demo_loop(agent, stream=False) + + output = capsys.readouterr().out + assert "hello" in output + assert "good" in output + assert model.last_turn_args["input"] == [ + get_text_input_item("Hi"), + get_text_message("hello").model_dump(exclude_unset=True), + get_text_input_item("How are you?"), + ] diff --git a/tests/test_responses.py b/tests/test_responses.py index 6b91bf8c6..74212f61b 100644 --- a/tests/test_responses.py +++ b/tests/test_responses.py @@ -49,10 +49,12 @@ def _foo() -> str: ) -def get_function_tool_call(name: str, arguments: str | None = None) -> ResponseOutputItem: +def get_function_tool_call( + name: str, arguments: str | None = None, call_id: str | None = None +) -> ResponseOutputItem: return ResponseFunctionToolCall( id="1", - call_id="2", + call_id=call_id or "2", type="function_call", name=name, arguments=arguments or "", diff --git a/tests/test_responses_tracing.py b/tests/test_responses_tracing.py index 0bc97a953..fe63e8ecb 100644 --- a/tests/test_responses_tracing.py +++ b/tests/test_responses_tracing.py @@ -1,7 +1,10 @@ +from typing import Optional + import pytest from inline_snapshot import snapshot from openai import AsyncOpenAI from openai.types.responses import ResponseCompletedEvent +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails from agents import ModelSettings, ModelTracing, OpenAIResponsesModel, trace from agents.tracing.span_data import ResponseSpanData @@ -16,10 +19,25 @@ def is_disabled(self): class DummyUsage: - def __init__(self, input_tokens=1, output_tokens=1, total_tokens=2): + def __init__( + self, + input_tokens: int = 1, + input_tokens_details: Optional[InputTokensDetails] = None, + output_tokens: int = 1, + output_tokens_details: Optional[OutputTokensDetails] = None, + total_tokens: int = 2, + ): self.input_tokens = input_tokens self.output_tokens = output_tokens self.total_tokens = total_tokens + self.input_tokens_details = ( + input_tokens_details if input_tokens_details else InputTokensDetails(cached_tokens=0) + ) + self.output_tokens_details = ( + output_tokens_details + if output_tokens_details + else OutputTokensDetails(reasoning_tokens=0) + ) class DummyResponse: @@ -32,6 +50,7 @@ def __aiter__(self): yield ResponseCompletedEvent( type="response.completed", response=fake_model.get_response_obj(self.output), + sequence_number=0, ) @@ -52,6 +71,7 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): return DummyResponse() @@ -96,6 +116,7 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): return DummyResponse() @@ -138,6 +159,7 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): return DummyResponse() @@ -177,12 +199,14 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): class DummyStream: async def __aiter__(self): yield ResponseCompletedEvent( type="response.completed", response=fake_model.get_response_obj([], "dummy-id-123"), + sequence_number=0, ) return DummyStream() @@ -229,12 +253,14 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): class DummyStream: async def __aiter__(self): yield ResponseCompletedEvent( type="response.completed", response=fake_model.get_response_obj([], "dummy-id-123"), + sequence_number=0, ) return DummyStream() @@ -280,12 +306,14 @@ async def dummy_fetch_response( handoffs, prev_response_id, stream, + prompt, ): class DummyStream: async def __aiter__(self): yield ResponseCompletedEvent( type="response.completed", response=fake_model.get_response_obj([], "dummy-id-123"), + sequence_number=0, ) return DummyStream() diff --git a/tests/test_result_cast.py b/tests/test_result_cast.py index ec17e3275..c621e7352 100644 --- a/tests/test_result_cast.py +++ b/tests/test_result_cast.py @@ -3,7 +3,7 @@ import pytest from pydantic import BaseModel -from agents import Agent, RunResult +from agents import Agent, RunContextWrapper, RunResult def create_run_result(final_output: Any) -> RunResult: @@ -15,6 +15,7 @@ def create_run_result(final_output: Any) -> RunResult: input_guardrail_results=[], output_guardrail_results=[], _last_agent=Agent(name="test"), + context_wrapper=RunContextWrapper(context=None), ) diff --git a/tests/test_run.py b/tests/test_run.py new file mode 100644 index 000000000..66cfee1f1 --- /dev/null +++ b/tests/test_run.py @@ -0,0 +1,26 @@ +from __future__ import annotations + +from unittest import mock + +import pytest + +from agents import Agent, Runner +from agents.run import AgentRunner, set_default_agent_runner + +from .fake_model import FakeModel + + +@pytest.mark.asyncio +async def test_static_run_methods_call_into_default_runner() -> None: + runner = mock.Mock(spec=AgentRunner) + set_default_agent_runner(runner) + + agent = Agent(name="test", model=FakeModel()) + await Runner.run(agent, input="test") + runner.run.assert_called_once() + + Runner.run_streamed(agent, input="test") + runner.run_streamed.assert_called_once() + + Runner.run_sync(agent, input="test") + runner.run_sync.assert_called_once() diff --git a/tests/test_run_config.py b/tests/test_run_config.py index 51835ab66..e19899006 100644 --- a/tests/test_run_config.py +++ b/tests/test_run_config.py @@ -60,7 +60,7 @@ async def test_run_config_model_name_override_takes_precedence() -> None: async def test_run_config_model_override_object_takes_precedence() -> None: """ When a concrete Model instance is set on the RunConfig, then that instance should be - returned by Runner._get_model regardless of the agent's model. + returned by AgentRunner._get_model regardless of the agent's model. """ fake_model = FakeModel(initial_output=[get_text_message("override-object")]) agent = Agent(name="test", model="agent-model") diff --git a/tests/test_run_error_details.py b/tests/test_run_error_details.py new file mode 100644 index 000000000..104b248fc --- /dev/null +++ b/tests/test_run_error_details.py @@ -0,0 +1,48 @@ +import json + +import pytest + +from agents import Agent, MaxTurnsExceeded, RunErrorDetails, Runner + +from .fake_model import FakeModel +from .test_responses import get_function_tool, get_function_tool_call, get_text_message + + +@pytest.mark.asyncio +async def test_run_error_includes_data(): + model = FakeModel() + agent = Agent(name="test", model=model, tools=[get_function_tool("foo", "res")]) + model.add_multiple_turn_outputs( + [ + [get_text_message("1"), get_function_tool_call("foo", json.dumps({"a": "b"}))], + [get_text_message("done")], + ] + ) + with pytest.raises(MaxTurnsExceeded) as exc: + await Runner.run(agent, input="hello", max_turns=1) + data = exc.value.run_data + assert isinstance(data, RunErrorDetails) + assert data.last_agent == agent + assert len(data.raw_responses) == 1 + assert len(data.new_items) > 0 + + +@pytest.mark.asyncio +async def test_streamed_run_error_includes_data(): + model = FakeModel() + agent = Agent(name="test", model=model, tools=[get_function_tool("foo", "res")]) + model.add_multiple_turn_outputs( + [ + [get_text_message("1"), get_function_tool_call("foo", json.dumps({"a": "b"}))], + [get_text_message("done")], + ] + ) + result = Runner.run_streamed(agent, input="hello", max_turns=1) + with pytest.raises(MaxTurnsExceeded) as exc: + async for _ in result.stream_events(): + pass + data = exc.value.run_data + assert isinstance(data, RunErrorDetails) + assert data.last_agent == agent + assert len(data.raw_responses) == 1 + assert len(data.new_items) > 0 diff --git a/tests/test_run_step_execution.py b/tests/test_run_step_execution.py index 6ae25fbd5..4cf9ae832 100644 --- a/tests/test_run_step_execution.py +++ b/tests/test_run_step_execution.py @@ -1,5 +1,6 @@ from __future__ import annotations +import json from typing import Any import pytest @@ -13,7 +14,6 @@ RunContextWrapper, RunHooks, RunItem, - Runner, ToolCallItem, ToolCallOutputItem, TResponseInputItem, @@ -26,6 +26,9 @@ RunImpl, SingleStepResult, ) +from agents.run import AgentRunner +from agents.tool import function_tool +from agents.tool_context import ToolContext from .test_responses import ( get_final_output_message, @@ -158,6 +161,42 @@ async def test_multiple_tool_calls(): assert isinstance(result.next_step, NextStepRunAgain) +@pytest.mark.asyncio +async def test_multiple_tool_calls_with_tool_context(): + async def _fake_tool(context: ToolContext[str], value: str) -> str: + return f"{value}-{context.tool_call_id}" + + tool = function_tool(_fake_tool, name_override="fake_tool", failure_error_function=None) + + agent = Agent( + name="test", + tools=[tool], + ) + response = ModelResponse( + output=[ + get_function_tool_call("fake_tool", json.dumps({"value": "123"}), call_id="1"), + get_function_tool_call("fake_tool", json.dumps({"value": "456"}), call_id="2"), + ], + usage=Usage(), + response_id=None, + ) + + result = await get_execute_result(agent, response) + assert result.original_input == "hello" + + # 4 items: new message, 2 tool calls, 2 tool call outputs + assert len(result.generated_items) == 4 + assert isinstance(result.next_step, NextStepRunAgain) + + items = result.generated_items + assert_item_is_function_tool_call(items[0], "fake_tool", json.dumps({"value": "123"})) + assert_item_is_function_tool_call(items[1], "fake_tool", json.dumps({"value": "456"})) + assert_item_is_function_tool_call_output(items[2], "123-1") + assert_item_is_function_tool_call_output(items[3], "456-2") + + assert isinstance(result.next_step, NextStepRunAgain) + + @pytest.mark.asyncio async def test_handoff_output_leads_to_handoff_next_step(): agent_1 = Agent(name="test_1") @@ -285,12 +324,12 @@ async def get_execute_result( context_wrapper: RunContextWrapper[Any] | None = None, run_config: RunConfig | None = None, ) -> SingleStepResult: - output_schema = Runner._get_output_schema(agent) - handoffs = Runner._get_handoffs(agent) + output_schema = AgentRunner._get_output_schema(agent) + handoffs = await AgentRunner._get_handoffs(agent, context_wrapper or RunContextWrapper(None)) processed_response = RunImpl.process_model_response( agent=agent, - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(context_wrapper or RunContextWrapper(None)), response=response, output_schema=output_schema, handoffs=handoffs, diff --git a/tests/test_run_step_processing.py b/tests/test_run_step_processing.py index 2ea98f06a..27d36afa8 100644 --- a/tests/test_run_step_processing.py +++ b/tests/test_run_step_processing.py @@ -7,6 +7,7 @@ ResponseFunctionWebSearch, ) from openai.types.responses.response_computer_tool_call import ActionClick +from openai.types.responses.response_function_web_search import ActionSearch from openai.types.responses.response_reasoning_item import ResponseReasoningItem, Summary from pydantic import BaseModel @@ -19,11 +20,11 @@ ModelResponse, ReasoningItem, RunContextWrapper, - Runner, ToolCallItem, Usage, ) from agents._run_impl import RunImpl +from agents.run import AgentRunner from .test_responses import ( get_final_output_message, @@ -34,6 +35,10 @@ ) +def _dummy_ctx() -> RunContextWrapper[None]: + return RunContextWrapper(context=None) + + def test_empty_response(): agent = Agent(name="test") response = ModelResponse( @@ -83,7 +88,7 @@ async def test_single_tool_call(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) assert not result.handoffs assert result.functions and len(result.functions) == 1 @@ -111,7 +116,7 @@ async def test_missing_tool_call_raises_error(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) @@ -140,7 +145,7 @@ async def test_multiple_tool_calls(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) assert not result.handoffs assert result.functions and len(result.functions) == 2 @@ -169,7 +174,7 @@ async def test_handoffs_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await agent_3.get_all_tools(), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) assert not result.handoffs, "Shouldn't have a handoff here" @@ -182,8 +187,8 @@ async def test_handoffs_parsed_correctly(): agent=agent_3, response=response, output_schema=None, - handoffs=Runner._get_handoffs(agent_3), - all_tools=await agent_3.get_all_tools(), + handoffs=await AgentRunner._get_handoffs(agent_3, _dummy_ctx()), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) assert len(result.handoffs) == 1, "Should have a handoff here" handoff = result.handoffs[0] @@ -212,8 +217,8 @@ async def test_missing_handoff_fails(): agent=agent_3, response=response, output_schema=None, - handoffs=Runner._get_handoffs(agent_3), - all_tools=await agent_3.get_all_tools(), + handoffs=await AgentRunner._get_handoffs(agent_3, _dummy_ctx()), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) @@ -235,8 +240,8 @@ async def test_multiple_handoffs_doesnt_error(): agent=agent_3, response=response, output_schema=None, - handoffs=Runner._get_handoffs(agent_3), - all_tools=await agent_3.get_all_tools(), + handoffs=await AgentRunner._get_handoffs(agent_3, _dummy_ctx()), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) assert len(result.handoffs) == 2, "Should have multiple handoffs here" @@ -260,9 +265,9 @@ async def test_final_output_parsed_correctly(): RunImpl.process_model_response( agent=agent, response=response, - output_schema=Runner._get_output_schema(agent), + output_schema=AgentRunner._get_output_schema(agent), handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) @@ -288,7 +293,7 @@ async def test_file_search_tool_call_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) # The final item should be a ToolCallItem for the file search call assert any( @@ -302,7 +307,12 @@ async def test_file_search_tool_call_parsed_correctly(): @pytest.mark.asyncio async def test_function_web_search_tool_call_parsed_correctly(): agent = Agent(name="test") - web_search_call = ResponseFunctionWebSearch(id="w1", status="completed", type="web_search_call") + web_search_call = ResponseFunctionWebSearch( + id="w1", + action=ActionSearch(type="search", query="query"), + status="completed", + type="web_search_call", + ) response = ModelResponse( output=[get_text_message("hello"), web_search_call], usage=Usage(), @@ -313,7 +323,7 @@ async def test_function_web_search_tool_call_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) assert any( isinstance(item, ToolCallItem) and item.raw_item is web_search_call @@ -340,7 +350,7 @@ async def test_reasoning_item_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await Agent(name="test").get_all_tools(), + all_tools=await Agent(name="test").get_all_tools(_dummy_ctx()), ) assert any( isinstance(item, ReasoningItem) and item.raw_item is reasoning for item in result.new_items @@ -409,7 +419,7 @@ async def test_computer_tool_call_without_computer_tool_raises_error(): response=response, output_schema=None, handoffs=[], - all_tools=await Agent(name="test").get_all_tools(), + all_tools=await Agent(name="test").get_all_tools(_dummy_ctx()), ) @@ -437,7 +447,7 @@ async def test_computer_tool_call_with_computer_tool_parsed_correctly(): response=response, output_schema=None, handoffs=[], - all_tools=await agent.get_all_tools(), + all_tools=await agent.get_all_tools(_dummy_ctx()), ) assert any( isinstance(item, ToolCallItem) and item.raw_item is computer_call @@ -467,8 +477,8 @@ async def test_tool_and_handoff_parsed_correctly(): agent=agent_3, response=response, output_schema=None, - handoffs=Runner._get_handoffs(agent_3), - all_tools=await agent_3.get_all_tools(), + handoffs=await AgentRunner._get_handoffs(agent_3, _dummy_ctx()), + all_tools=await agent_3.get_all_tools(_dummy_ctx()), ) assert result.functions and len(result.functions) == 1 assert len(result.handoffs) == 1, "Should have a handoff here" diff --git a/tests/test_session.py b/tests/test_session.py new file mode 100644 index 000000000..032f2bb38 --- /dev/null +++ b/tests/test_session.py @@ -0,0 +1,400 @@ +"""Tests for session memory functionality.""" + +import asyncio +import tempfile +from pathlib import Path + +import pytest + +from agents import Agent, Runner, SQLiteSession, TResponseInputItem +from agents.exceptions import UserError + +from .fake_model import FakeModel +from .test_responses import get_text_message + + +# Helper functions for parametrized testing of different Runner methods +def _run_sync_wrapper(agent, input_data, **kwargs): + """Wrapper for run_sync that properly sets up an event loop.""" + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + return Runner.run_sync(agent, input_data, **kwargs) + finally: + loop.close() + + +async def run_agent_async(runner_method: str, agent, input_data, **kwargs): + """Helper function to run agent with different methods.""" + if runner_method == "run": + return await Runner.run(agent, input_data, **kwargs) + elif runner_method == "run_sync": + # For run_sync, we need to run it in a thread with its own event loop + return await asyncio.to_thread(_run_sync_wrapper, agent, input_data, **kwargs) + elif runner_method == "run_streamed": + result = Runner.run_streamed(agent, input_data, **kwargs) + # For streaming, we first try to get at least one event to trigger any early exceptions + # If there's an exception in setup (like memory validation), it will be raised here + try: + first_event = None + async for event in result.stream_events(): + if first_event is None: + first_event = event + # Continue consuming all events + pass + except Exception: + # If an exception occurs during streaming, we let it propagate up + raise + return result + else: + raise ValueError(f"Unknown runner method: {runner_method}") + + +# Parametrized tests for different runner methods +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_basic_functionality_parametrized(runner_method): + """Test basic session memory functionality with SQLite backend across all runner methods.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_memory.db" + session_id = "test_session_123" + session = SQLiteSession(session_id, db_path) + + model = FakeModel() + agent = Agent(name="test", model=model) + + # First turn + model.set_next_output([get_text_message("San Francisco")]) + result1 = await run_agent_async( + runner_method, + agent, + "What city is the Golden Gate Bridge in?", + session=session, + ) + assert result1.final_output == "San Francisco" + + # Second turn - should have conversation history + model.set_next_output([get_text_message("California")]) + result2 = await run_agent_async( + runner_method, + agent, + "What state is it in?", + session=session, + ) + assert result2.final_output == "California" + + # Verify that the input to the second turn includes the previous conversation + # The model should have received the full conversation history + last_input = model.last_turn_args["input"] + assert len(last_input) > 1 # Should have more than just the current message + + session.close() + + +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_with_explicit_instance_parametrized(runner_method): + """Test session memory with an explicit SQLiteSession instance across all runner methods.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_memory.db" + session_id = "test_session_456" + session = SQLiteSession(session_id, db_path) + + model = FakeModel() + agent = Agent(name="test", model=model) + + # First turn + model.set_next_output([get_text_message("Hello")]) + result1 = await run_agent_async(runner_method, agent, "Hi there", session=session) + assert result1.final_output == "Hello" + + # Second turn + model.set_next_output([get_text_message("I remember you said hi")]) + result2 = await run_agent_async( + runner_method, + agent, + "Do you remember what I said?", + session=session, + ) + assert result2.final_output == "I remember you said hi" + + session.close() + + +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_disabled_parametrized(runner_method): + """Test that session memory is disabled when session=None across all runner methods.""" + model = FakeModel() + agent = Agent(name="test", model=model) + + # First turn (no session parameters = disabled) + model.set_next_output([get_text_message("Hello")]) + result1 = await run_agent_async(runner_method, agent, "Hi there") + assert result1.final_output == "Hello" + + # Second turn - should NOT have conversation history + model.set_next_output([get_text_message("I don't remember")]) + result2 = await run_agent_async(runner_method, agent, "Do you remember what I said?") + assert result2.final_output == "I don't remember" + + # Verify that the input to the second turn is just the current message + last_input = model.last_turn_args["input"] + assert len(last_input) == 1 # Should only have the current message + + +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_different_sessions_parametrized(runner_method): + """Test that different session IDs maintain separate conversation histories across all runner + methods.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_memory.db" + + model = FakeModel() + agent = Agent(name="test", model=model) + + # Session 1 + session_id_1 = "session_1" + session_1 = SQLiteSession(session_id_1, db_path) + + model.set_next_output([get_text_message("I like cats")]) + result1 = await run_agent_async(runner_method, agent, "I like cats", session=session_1) + assert result1.final_output == "I like cats" + + # Session 2 - different session + session_id_2 = "session_2" + session_2 = SQLiteSession(session_id_2, db_path) + + model.set_next_output([get_text_message("I like dogs")]) + result2 = await run_agent_async(runner_method, agent, "I like dogs", session=session_2) + assert result2.final_output == "I like dogs" + + # Back to Session 1 - should remember cats, not dogs + model.set_next_output([get_text_message("Yes, you mentioned cats")]) + result3 = await run_agent_async( + runner_method, + agent, + "What did I say I like?", + session=session_1, + ) + assert result3.final_output == "Yes, you mentioned cats" + + session_1.close() + session_2.close() + + +@pytest.mark.asyncio +async def test_sqlite_session_memory_direct(): + """Test SQLiteSession class directly.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_direct.db" + session_id = "direct_test" + session = SQLiteSession(session_id, db_path) + + # Test adding and retrieving items + items: list[TResponseInputItem] = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + ] + + await session.add_items(items) + retrieved = await session.get_items() + + assert len(retrieved) == 2 + assert retrieved[0].get("role") == "user" + assert retrieved[0].get("content") == "Hello" + assert retrieved[1].get("role") == "assistant" + assert retrieved[1].get("content") == "Hi there!" + + # Test clearing session + await session.clear_session() + retrieved_after_clear = await session.get_items() + assert len(retrieved_after_clear) == 0 + + session.close() + + +@pytest.mark.asyncio +async def test_sqlite_session_memory_pop_item(): + """Test SQLiteSession pop_item functionality.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_pop.db" + session_id = "pop_test" + session = SQLiteSession(session_id, db_path) + + # Test popping from empty session + popped = await session.pop_item() + assert popped is None + + # Add items + items: list[TResponseInputItem] = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + {"role": "user", "content": "How are you?"}, + ] + + await session.add_items(items) + + # Verify all items are there + retrieved = await session.get_items() + assert len(retrieved) == 3 + + # Pop the most recent item + popped = await session.pop_item() + assert popped is not None + assert popped.get("role") == "user" + assert popped.get("content") == "How are you?" + + # Verify item was removed + retrieved_after_pop = await session.get_items() + assert len(retrieved_after_pop) == 2 + assert retrieved_after_pop[-1].get("content") == "Hi there!" + + # Pop another item + popped2 = await session.pop_item() + assert popped2 is not None + assert popped2.get("role") == "assistant" + assert popped2.get("content") == "Hi there!" + + # Pop the last item + popped3 = await session.pop_item() + assert popped3 is not None + assert popped3.get("role") == "user" + assert popped3.get("content") == "Hello" + + # Try to pop from empty session again + popped4 = await session.pop_item() + assert popped4 is None + + # Verify session is empty + final_items = await session.get_items() + assert len(final_items) == 0 + + session.close() + + +@pytest.mark.asyncio +async def test_session_memory_pop_different_sessions(): + """Test that pop_item only affects the specified session.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_pop_sessions.db" + + session_1_id = "session_1" + session_2_id = "session_2" + session_1 = SQLiteSession(session_1_id, db_path) + session_2 = SQLiteSession(session_2_id, db_path) + + # Add items to both sessions + items_1: list[TResponseInputItem] = [ + {"role": "user", "content": "Session 1 message"}, + ] + items_2: list[TResponseInputItem] = [ + {"role": "user", "content": "Session 2 message 1"}, + {"role": "user", "content": "Session 2 message 2"}, + ] + + await session_1.add_items(items_1) + await session_2.add_items(items_2) + + # Pop from session 2 + popped = await session_2.pop_item() + assert popped is not None + assert popped.get("content") == "Session 2 message 2" + + # Verify session 1 is unaffected + session_1_items = await session_1.get_items() + assert len(session_1_items) == 1 + assert session_1_items[0].get("content") == "Session 1 message" + + # Verify session 2 has one item left + session_2_items = await session_2.get_items() + assert len(session_2_items) == 1 + assert session_2_items[0].get("content") == "Session 2 message 1" + + session_1.close() + session_2.close() + + +@pytest.mark.asyncio +async def test_sqlite_session_get_items_with_limit(): + """Test SQLiteSession get_items with limit parameter.""" + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_count.db" + session_id = "count_test" + session = SQLiteSession(session_id, db_path) + + # Add multiple items + items: list[TResponseInputItem] = [ + {"role": "user", "content": "Message 1"}, + {"role": "assistant", "content": "Response 1"}, + {"role": "user", "content": "Message 2"}, + {"role": "assistant", "content": "Response 2"}, + {"role": "user", "content": "Message 3"}, + {"role": "assistant", "content": "Response 3"}, + ] + + await session.add_items(items) + + # Test getting all items (default behavior) + all_items = await session.get_items() + assert len(all_items) == 6 + assert all_items[0].get("content") == "Message 1" + assert all_items[-1].get("content") == "Response 3" + + # Test getting latest 2 items + latest_2 = await session.get_items(limit=2) + assert len(latest_2) == 2 + assert latest_2[0].get("content") == "Message 3" + assert latest_2[1].get("content") == "Response 3" + + # Test getting latest 4 items + latest_4 = await session.get_items(limit=4) + assert len(latest_4) == 4 + assert latest_4[0].get("content") == "Message 2" + assert latest_4[1].get("content") == "Response 2" + assert latest_4[2].get("content") == "Message 3" + assert latest_4[3].get("content") == "Response 3" + + # Test getting more items than available + latest_10 = await session.get_items(limit=10) + assert len(latest_10) == 6 # Should return all available items + assert latest_10[0].get("content") == "Message 1" + assert latest_10[-1].get("content") == "Response 3" + + # Test getting 0 items + latest_0 = await session.get_items(limit=0) + assert len(latest_0) == 0 + + session.close() + + +@pytest.mark.parametrize("runner_method", ["run", "run_sync", "run_streamed"]) +@pytest.mark.asyncio +async def test_session_memory_rejects_both_session_and_list_input(runner_method): + """Test that passing both a session and list input raises a UserError across all runner + methods. + """ + with tempfile.TemporaryDirectory() as temp_dir: + db_path = Path(temp_dir) / "test_validation.db" + session_id = "test_validation_parametrized" + session = SQLiteSession(session_id, db_path) + + model = FakeModel() + agent = Agent(name="test", model=model) + + # Test that providing both a session and a list input raises a UserError + model.set_next_output([get_text_message("This shouldn't run")]) + + list_input = [ + {"role": "user", "content": "Test message"}, + ] + + with pytest.raises(UserError) as exc_info: + await run_agent_async(runner_method, agent, list_input, session=session) + + # Verify the error message explains the issue + assert "Cannot provide both a session and a list of input items" in str(exc_info.value) + assert "manually manage conversation history" in str(exc_info.value) + + session.close() diff --git a/tests/test_tool_converter.py b/tests/test_tool_converter.py index 1b6ebcf93..918de0153 100644 --- a/tests/test_tool_converter.py +++ b/tests/test_tool_converter.py @@ -3,7 +3,7 @@ from agents import Agent, Handoff, function_tool, handoff from agents.exceptions import UserError -from agents.models.openai_chatcompletions import ToolConverter +from agents.models.chatcmpl_converter import Converter from agents.tool import FileSearchTool, WebSearchTool @@ -15,7 +15,7 @@ def test_to_openai_with_function_tool(): some_function(a="foo", b=[1, 2, 3]) tool = function_tool(some_function) - result = ToolConverter.to_openai(tool) + result = Converter.tool_to_openai(tool) assert result["type"] == "function" assert result["function"]["name"] == "some_function" @@ -34,7 +34,7 @@ class Foo(BaseModel): def test_convert_handoff_tool(): agent = Agent(name="test_1", handoff_description="test_2") handoff_obj = handoff(agent=agent) - result = ToolConverter.convert_handoff_tool(handoff_obj) + result = Converter.convert_handoff_tool(handoff_obj) assert result["type"] == "function" assert result["function"]["name"] == Handoff.default_tool_name(agent) @@ -48,7 +48,7 @@ def test_convert_handoff_tool(): def test_tool_converter_hosted_tools_errors(): with pytest.raises(UserError): - ToolConverter.to_openai(WebSearchTool()) + Converter.tool_to_openai(WebSearchTool()) with pytest.raises(UserError): - ToolConverter.to_openai(FileSearchTool(vector_store_ids=["abc"], max_num_results=1)) + Converter.tool_to_openai(FileSearchTool(vector_store_ids=["abc"], max_num_results=1)) diff --git a/tests/test_tracing_errors_streamed.py b/tests/test_tracing_errors_streamed.py index 416793e70..40efef3fa 100644 --- a/tests/test_tracing_errors_streamed.py +++ b/tests/test_tracing_errors_streamed.py @@ -168,10 +168,6 @@ async def test_tool_call_error(): "children": [ { "type": "agent", - "error": { - "message": "Error in agent run", - "data": {"error": "Invalid JSON input for tool foo: bad_json"}, - }, "data": { "name": "test_agent", "handoffs": [], diff --git a/tests/test_usage.py b/tests/test_usage.py new file mode 100644 index 000000000..405f99ddf --- /dev/null +++ b/tests/test_usage.py @@ -0,0 +1,52 @@ +from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails + +from agents.usage import Usage + + +def test_usage_add_aggregates_all_fields(): + u1 = Usage( + requests=1, + input_tokens=10, + input_tokens_details=InputTokensDetails(cached_tokens=3), + output_tokens=20, + output_tokens_details=OutputTokensDetails(reasoning_tokens=5), + total_tokens=30, + ) + u2 = Usage( + requests=2, + input_tokens=7, + input_tokens_details=InputTokensDetails(cached_tokens=4), + output_tokens=8, + output_tokens_details=OutputTokensDetails(reasoning_tokens=6), + total_tokens=15, + ) + + u1.add(u2) + + assert u1.requests == 3 + assert u1.input_tokens == 17 + assert u1.output_tokens == 28 + assert u1.total_tokens == 45 + assert u1.input_tokens_details.cached_tokens == 7 + assert u1.output_tokens_details.reasoning_tokens == 11 + + +def test_usage_add_aggregates_with_none_values(): + u1 = Usage() + u2 = Usage( + requests=2, + input_tokens=7, + input_tokens_details=InputTokensDetails(cached_tokens=4), + output_tokens=8, + output_tokens_details=OutputTokensDetails(reasoning_tokens=6), + total_tokens=15, + ) + + u1.add(u2) + + assert u1.requests == 2 + assert u1.input_tokens == 7 + assert u1.output_tokens == 8 + assert u1.total_tokens == 15 + assert u1.input_tokens_details.cached_tokens == 4 + assert u1.output_tokens_details.reasoning_tokens == 6 diff --git a/tests/test_visualization.py b/tests/test_visualization.py index 6aa867743..8bce897e9 100644 --- a/tests/test_visualization.py +++ b/tests/test_visualization.py @@ -134,3 +134,18 @@ def test_draw_graph(mock_agent): '"Handoff1" [label="Handoff1", shape=box, style=filled, style=rounded, ' "fillcolor=lightyellow, width=1.5, height=0.8];" in graph.source ) + + +def test_cycle_detection(): + agent_a = Agent(name="A") + agent_b = Agent(name="B") + agent_a.handoffs.append(agent_b) + agent_b.handoffs.append(agent_a) + + nodes = get_all_nodes(agent_a) + edges = get_all_edges(agent_a) + + assert nodes.count('"A" [label="A"') == 1 + assert nodes.count('"B" [label="B"') == 1 + assert '"A" -> "B"' in edges + assert '"B" -> "A"' in edges diff --git a/tests/voice/conftest.py b/tests/voice/conftest.py index 7eeb9cec0..79d85d8b4 100644 --- a/tests/voice/conftest.py +++ b/tests/voice/conftest.py @@ -1,14 +1,11 @@ import os import sys -import pytest - -def pytest_collection_modifyitems(config, items): +# Skip voice tests on Python 3.9 +def pytest_ignore_collect(collection_path, config): if sys.version_info[:2] == (3, 9): this_dir = os.path.dirname(__file__) - skip_marker = pytest.mark.skip(reason="Skipped on Python 3.9") - for item in items: - if item.fspath.dirname.startswith(this_dir): - item.add_marker(skip_marker) + if str(collection_path).startswith(this_dir): + return True diff --git a/tests/voice/test_workflow.py b/tests/voice/test_workflow.py index 72a3370d3..e99810be1 100644 --- a/tests/voice/test_workflow.py +++ b/tests/voice/test_workflow.py @@ -2,6 +2,7 @@ import json from collections.abc import AsyncIterator +from typing import Any import pytest from inline_snapshot import snapshot @@ -9,7 +10,7 @@ from openai.types.responses.response_text_delta_event import ResponseTextDeltaEvent from agents import Agent, Model, ModelSettings, ModelTracing, Tool -from agents.agent_output import AgentOutputSchema +from agents.agent_output import AgentOutputSchemaBase from agents.handoffs import Handoff from agents.items import ( ModelResponse, @@ -18,11 +19,12 @@ TResponseStreamEvent, ) +from ..fake_model import get_response_obj +from ..test_responses import get_function_tool, get_function_tool_call, get_text_message + try: from agents.voice import SingleAgentVoiceWorkflow - from ..fake_model import get_response_obj - from ..test_responses import get_function_tool, get_function_tool_call, get_text_message except ImportError: pass @@ -48,11 +50,12 @@ async def get_response( input: str | list[TResponseInputItem], model_settings: ModelSettings, tools: list[Tool], - output_schema: AgentOutputSchema | None, + output_schema: AgentOutputSchemaBase | None, handoffs: list[Handoff], tracing: ModelTracing, *, previous_response_id: str | None, + prompt: Any | None, ) -> ModelResponse: raise NotImplementedError("Not implemented") @@ -62,11 +65,12 @@ async def stream_response( input: str | list[TResponseInputItem], model_settings: 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