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MartinEBravoMartinEBravo
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Merge branch 'main' of https://github.com/openai/openai-agents-python into feat/draw_graph
2 parents 9d04671 + 4f8cbfa commit 48fad9e

20 files changed

+1233
-36
lines changed

‎pyproject.toml

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@@ -13,6 +13,7 @@ dependencies = [
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"typing-extensions>=4.12.2, <5",
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"requests>=2.0, <3",
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"types-requests>=2.0, <3",
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"mcp; python_version >= '3.10'",
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]
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classifiers = [
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"Typing :: Typed",

‎src/agents/_run_impl.py

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@@ -344,6 +344,7 @@ def process_model_response(
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cls,
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*,
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agent: Agent[Any],
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all_tools: list[Tool],
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response: ModelResponse,
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output_schema: AgentOutputSchema | None,
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handoffs: list[Handoff],
@@ -355,8 +356,8 @@ def process_model_response(
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computer_actions = []
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handoff_map = {handoff.tool_name: handoff for handoff in handoffs}
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function_map = {tool.name: tool for tool in agent.tools if isinstance(tool, FunctionTool)}
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computer_tool = next((tool for tool in agent.tools if isinstance(tool, ComputerTool)), None)
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function_map = {tool.name: tool for tool in all_tools if isinstance(tool, FunctionTool)}
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computer_tool = next((tool for tool in all_tools if isinstance(tool, ComputerTool)), None)
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for output in response.output:
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if isinstance(output, ResponseOutputMessage):

‎src/agents/agent.py

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@@ -12,6 +12,7 @@
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from .handoffs import Handoff
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from .items import ItemHelpers
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from .logger import logger
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from .mcp import MCPUtil
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from .model_settings import ModelSettings
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from .models.interface import Model
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from .run_context import RunContextWrapper, TContext
@@ -21,6 +22,7 @@
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if TYPE_CHECKING:
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from .lifecycle import AgentHooks
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from .mcp import MCPServer
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from .result import RunResult
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@@ -107,6 +109,16 @@ class Agent(Generic[TContext]):
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tools: list[Tool] = field(default_factory=list)
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"""A list of tools that the agent can use."""
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mcp_servers: list[MCPServer] = field(default_factory=list)
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"""A list of [Model Context Protocol](https://modelcontextprotocol.io/) servers that
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the agent can use. Every time the agent runs, it will include tools from these servers in the
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list of available tools.
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NOTE: You are expected to manage the lifecycle of these servers. Specifically, you must call
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`server.connect()` before passing it to the agent, and `server.cleanup()` when the server is no
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longer needed.
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"""
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input_guardrails: list[InputGuardrail[TContext]] = field(default_factory=list)
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"""A list of checks that run in parallel to the agent's execution, before generating a
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response. Runs only if the agent is the first agent in the chain.
@@ -205,3 +217,11 @@ async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> s
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logger.error(f"Instructions must be a string or a function, got {self.instructions}")
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207219
return None
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async def get_mcp_tools(self) -> list[Tool]:
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"""Fetches the available tools from the MCP servers."""
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return await MCPUtil.get_all_function_tools(self.mcp_servers)
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async def get_all_tools(self) -> list[Tool]:
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"""All agent tools, including MCP tools and function tools."""
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return await MCPUtil.get_all_function_tools(self.mcp_servers) + self.tools

‎src/agents/mcp/__init__.py

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@@ -0,0 +1,21 @@
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try:
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from .server import (
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MCPServer,
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MCPServerSse,
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MCPServerSseParams,
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MCPServerStdio,
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MCPServerStdioParams,
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)
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except ImportError:
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pass
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from .util import MCPUtil
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__all__ = [
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"MCPServer",
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"MCPServerSse",
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"MCPServerSseParams",
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"MCPServerStdio",
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"MCPServerStdioParams",
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"MCPUtil",
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]

‎src/agents/mcp/mcp_util.py

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import functools
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import json
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from typing import Any
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from mcp.types import Tool as MCPTool
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from .. import _debug
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from ..exceptions import AgentsException, ModelBehaviorError, UserError
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from ..logger import logger
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from ..run_context import RunContextWrapper
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from ..tool import FunctionTool, Tool
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from .server import MCPServer
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class MCPUtil:
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"""Set of utilities for interop between MCP and Agents SDK tools."""
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@classmethod
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async def get_all_function_tools(cls, servers: list[MCPServer]) -> list[Tool]:
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"""Get all function tools from a list of MCP servers."""
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tools = []
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tool_names: set[str] = set()
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for server in servers:
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server_tools = await cls.get_function_tools(server)
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server_tool_names = {tool.name for tool in server_tools}
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if len(server_tool_names & tool_names) > 0:
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raise UserError(
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f"Duplicate tool names found across MCP servers: "
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f"{server_tool_names & tool_names}"
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)
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tool_names.update(server_tool_names)
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tools.extend(server_tools)
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return tools
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@classmethod
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async def get_function_tools(cls, server: MCPServer) -> list[Tool]:
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"""Get all function tools from a single MCP server."""
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tools = await server.list_tools()
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return [cls.to_function_tool(tool, server) for tool in tools]
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@classmethod
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def to_function_tool(cls, tool: MCPTool, server: MCPServer) -> FunctionTool:
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"""Convert an MCP tool to an Agents SDK function tool."""
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invoke_func = functools.partial(cls.invoke_mcp_tool, server, tool)
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return FunctionTool(
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name=tool.name,
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description=tool.description or "",
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params_json_schema=tool.inputSchema,
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on_invoke_tool=invoke_func,
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strict_json_schema=False,
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)
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@classmethod
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async def invoke_mcp_tool(
56+
cls, server: MCPServer, tool: MCPTool, context: RunContextWrapper[Any], input_json: str
57+
) -> str:
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"""Invoke an MCP tool and return the result as a string."""
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try:
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json_data: dict[str, Any] = json.loads(input_json) if input_json else {}
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except Exception as e:
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if _debug.DONT_LOG_TOOL_DATA:
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logger.debug(f"Invalid JSON input for tool {tool.name}")
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else:
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logger.debug(f"Invalid JSON input for tool {tool.name}: {input_json}")
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raise ModelBehaviorError(
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f"Invalid JSON input for tool {tool.name}: {input_json}"
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) from e
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if _debug.DONT_LOG_TOOL_DATA:
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logger.debug(f"Invoking MCP tool {tool.name}")
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else:
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logger.debug(f"Invoking MCP tool {tool.name} with input {input_json}")
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try:
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result = await server.call_tool(tool.name, json_data)
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except Exception as e:
78+
logger.error(f"Error invoking MCP tool {tool.name}: {e}")
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raise AgentsException(f"Error invoking MCP tool {tool.name}: {e}") from e
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81+
if _debug.DONT_LOG_TOOL_DATA:
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logger.debug(f"MCP tool {tool.name} completed.")
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else:
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logger.debug(f"MCP tool {tool.name} returned {result}")
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# The MCP tool result is a list of content items, whereas OpenAI tool outputs are a single
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# string. We'll try to convert.
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if len(result.content) == 1:
89+
return result.content[0].model_dump_json()
90+
elif len(result.content) > 1:
91+
return json.dumps([item.model_dump() for item in result.content])
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else:
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logger.error(f"Errored MCP tool result: {result}")
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return "Error running tool."

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