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+#!/usr/bin/env python
+# coding: utf-8
+
+# # Lab 1: Implementing self-editing memory from scratch
+
+# ## Preparation
+# <p style="background-color:#fff6ff; padding:15px; border-width:3px; border-color:#efe6ef; border-style:solid; border-radius:6px"> 💻 &nbsp; 
+# <b>Note: </b>The Letta libraries have been progressing since the filming of this course and you should be careful to use the requirements.txt file to load the revisions that are compatible with these notebooks if you download them and run them in your own evironment.
+# </p>
+
+# <div style="background-color:#fff6ff; padding:13px; border-width:3px; border-color:#efe6ef; border-style:solid; border-radius:6px">
+# <p> 💻 &nbsp; <b>Access <code>requirements.txt</code> and <code>helper.py</code> files:</b> 1) click on the <em>"File"</em> option on the top menu of the notebook and then 2) click on <em>"Open"</em>.
+# 
+# <p> ⬇ &nbsp; <b>Download Notebooks:</b> 1) click on the <em>"File"</em> option on the top menu of the notebook and then 2) click on <em>"Download as"</em> and select <em>"Notebook (.ipynb)"</em>.</p>
+# 
+# <p> 📒 &nbsp; For more help, please see the <em>"Appendix – Tips, Help, and Download"</em> Lesson.</p>
+# </div>
+
+# <p style="background-color:#f7fff8; padding:15px; border-width:3px; border-color:#e0f0e0; border-style:solid; border-radius:6px"> 🚨
+# &nbsp; <b>Different Run Results:</b> The output generated by AI models can vary with each execution due to their dynamic, probabilistic nature. Your results may differ from those shown in the video.</p>
+
+# ## Section 0: Setup OpenAI 
+
+# In[ ]:
+
+
+from helper import get_openai_api_key
+openai_api_key = get_openai_api_key()
+
+
+# In[ ]:
+
+
+from openai import OpenAI
+import os
+
+client = OpenAI(
+    api_key=openai_api_key
+)
+
+
+# ## Section 1: Breaking down the LLM context window
+# ### A simple agent's context window
+
+# In[ ]:
+
+
+model = "gpt-4o-mini"
+
+
+# In[ ]:
+
+
+system_prompt = "You are a chatbot."
+
+
+# In[ ]:
+
+
+# Make the completion request with the tool usage
+chat_completion = client.chat.completions.create(
+    model=model,
+    messages=[
+        # system prompt: always included in the context window 
+        {"role": "system", "content": system_prompt}, 
+        # chat history (evolves over time)
+        {"role": "user", "content": "What is my name?"}, 
+    ]
+)
+chat_completion.choices[0].message.content
+
+
+# ### Adding memory to the context 
+# 
+
+# In[ ]:
+
+
+agent_memory = {"human": "Name: Bob"}
+system_prompt = "You are a chatbot. " \
++ "You have a section of your context called [MEMORY] " \
++ "that contains information relevant to your conversation"
+
+
+# In[ ]:
+
+
+import json
+
+
+chat_completion = client.chat.completions.create(
+    model=model,
+    messages=[
+        # system prompt 
+        {"role": "system", "content": system_prompt + "[MEMORY]\n" + \
+         json.dumps(agent_memory)},
+        # chat history 
+        {"role": "user", "content": "What is my name?"},
+    ],
+)
+chat_completion.choices[0].message.content
+
+
+# ## Section 2: Modifing the memory with tools 
+
+# ### Defining a memory editing tool 
+# 
+
+# In[ ]:
+
+
+agent_memory = {"human": "", "agent": ""}
+
+def core_memory_save(section: str, memory: str): 
+    agent_memory[section] += '\n' 
+    agent_memory[section] += memory 
+
+
+# In[ ]:
+
+
+agent_memory
+
+
+# In[ ]:
+
+
+core_memory_save("human", "The human's name is Charles")
+
+
+# In[ ]:
+
+
+agent_memory
+
+
+# In[ ]:
+
+
+# tool description 
+core_memory_save_description = "Save important information about you," \
++ "the agent or the human you are chatting with."
+
+# arguments into the tool (generated by the LLM)
+# defines what the agent must generate to input into the tool 
+core_memory_save_properties = \
+{
+    # arg 1: section of memory to edit
+    "section": {
+        "type": "string",
+        "enum": ["human", "agent"],
+        "description": "Must be either 'human' " \
+        + "(to save information about the human) or 'agent'" \
+        + "(to save information about yourself)",            
+    },
+    # arg 2: memory to save
+    "memory": {
+        "type": "string",
+        "description": "Memory to save in the section",
+    },
+}
+
+# tool schema (passed to OpenAI)
+core_memory_save_metadata = \
+    {
+        "type": "function",
+        "function": {
+            "name": "core_memory_save",
+            "description": core_memory_save_description,
+            "parameters": {
+                "type": "object",
+                "properties": core_memory_save_properties,
+                "required": ["section", "memory"],
+            },
+        }
+    }
+
+
+# In[ ]:
+
+
+agent_memory = {"human": ""}
+system_prompt = "You are a chatbot. " \
++ "You have a section of your context called [MEMORY] " \
++ "that contains information relevant to your conversation"
+
+chat_completion = client.chat.completions.create(
+    model=model,
+    messages=[
+        # system prompt 
+        {"role": "system", "content": system_prompt}, 
+        # memory 
+        {"role": "system", "content": "[MEMORY]\n" + json.dumps(agent_memory)},
+        # chat history 
+        {"role": "user", "content": "My name is Bob"},
+    ],
+    # tool schemas 
+    tools=[core_memory_save_metadata]
+)
+response = chat_completion.choices[0]
+response
+
+
+# ### Executing the tool 
+# 
+
+# In[ ]:
+
+
+arguments = json.loads(response.message.tool_calls[0].function.arguments)
+arguments
+
+
+# In[ ]:
+
+
+# run the function with the specified arguments 
+core_memory_save(**arguments)
+
+
+# In[ ]:
+
+
+agent_memory
+
+
+# ### Running the next agent step 
+# 
+
+# In[ ]:
+
+
+chat_completion = client.chat.completions.create(
+    model=model,
+    messages=[
+        # system prompt 
+        {"role": "system", "content": system_prompt}, 
+        # memory 
+        {"role": "system", "content": "[MEMORY]\n" + json.dumps(agent_memory)},
+        # chat history 
+        {"role": "user", "content": "what is my name"},
+    ],
+    tools=[core_memory_save_metadata]
+)
+response = chat_completion.choices[0]
+response.message
+
+
+# ## Implementing an agentic loop
+# 
+
+# In[ ]:
+
+
+agent_memory = {"human": ""}
+
+
+# In[ ]:
+
+
+system_prompt_os = system_prompt \
++ "\n. You must either call a tool (core_memory_save) or" \
++ "write a response to the user. " \
++ "Do not take the same actions multiple times!" \
++ "When you learn new information, make sure to always" \
++ "call the core_memory_save tool." 
+
+
+# In[ ]:
+
+
+def agent_step(user_message): 
+    
+    # prefix messages with system prompt and memory
+    messages = [
+        # system prompt 
+        {"role": "system", "content": system_prompt_os}, 
+        # memory
+        {
+            "role": "system", 
+            "content": "[MEMORY]\n" + json.dumps(agent_memory)
+        },
+    ]    
+
+    # append the most recent message
+    messages.append({"role": "user", "content": user_message})
+    
+    # agentic loop 
+    while True: 
+        chat_completion = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            tools=[core_memory_save_metadata]
+        )
+        response = chat_completion.choices[0]
+
+        # update the messages with the agent's response 
+        messages.append(response.message)
+        
+        # if NOT calling a tool (responding to the user), return 
+        if not response.message.tool_calls: 
+            return response.message.content
+
+        # if calling a tool, execute the tool
+        else: 
+            print("TOOL CALL:", response.message.tool_calls[0].function)
+            
+            # parse the arguments from the LLM function call
+            arguments = json.loads(
+                response.message.tool_calls[0].function.arguments
+            )
+
+            # run the function with the specified arguments
+            core_memory_save(**arguments)
+
+            # add the tool call response to the message history 
+            messages.append({
+                "role": "tool", 
+                "tool_call_id": response.message.tool_calls[0].id, 
+                "name": "core_memory_save", 
+                "content": f"Updated memory: {json.dumps(agent_memory)}"
+            })
+
+
+# In[ ]:
+
+
+agent_step("my name is bob.")
+
+
+# In[ ]:
+
+
+# Try some prompts of your own!
+
+
+# In[ ]:
+
+
+
+