Autogen Memory Integration

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import os
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import uuid
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from autogen_agentchat import AssistantAgent
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from autogen_agentchat.models import OpenAIChatCompletionClient
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from zep_cloud.client import AsyncZep
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from zep_autogen import ZepMemory
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# Initialize Zep and create memory
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zep_client = AsyncZep(api_key=os.environ.get("ZEP_API_KEY"))
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await zep_client.user.add(user_id="user_123")
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memory = ZepMemory(
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    client=zep_client,
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    user_id="user_123",
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    thread_id=str(uuid.uuid4())
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)
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# Add memory to any Autogen agent
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agent = AssistantAgent(
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    name="Assistant",
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    model_client=OpenAIChatCompletionClient(
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        model="gpt-4o-mini",
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        api_key=os.environ.get("OPENAI_API_KEY")
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    ),
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    system_message="You are a helpful assistant.",
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    memory=[memory]  # ← This gives the agent access to user memory
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)
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from autogen_core.memory import MemoryContent, MemoryMimeType
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# Start a conversation - add user message to memory first
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user_message = "Hi, my name is Alice and I love hiking. What outdoor activities would you recommend for someone like me?"
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await memory.add(MemoryContent(
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    content=user_message,
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    mime_type=MemoryMimeType.TEXT,
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    metadata={"type": "message", "role": "user", "name": "Alice"}
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))
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# Run the agent
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response = await agent.run(task=user_message)
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agent_response = response.messages[-1].content
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print(f"Agent: {agent_response}")
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# Add the agent's response to memory
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await memory.add(MemoryContent(
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    content=agent_response,
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    mime_type=MemoryMimeType.TEXT,
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    metadata={"type": "message", "role": "assistant", "name": "Assistant"}
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))

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from autogen_core.memory import MemoryContent, MemoryMimeType
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# Store additional user information
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await memory.add(MemoryContent(
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    content="Alice prefers weekend outdoor activities and lives near the mountains",
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    mime_type=MemoryMimeType.TEXT,
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    metadata={"type": "message", "role": "user", "name": "Alice"}
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))
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# Store structured data about Alice
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await memory.add(MemoryContent(
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    content='{"user_profile": {"name": "Alice", "interests": ["hiking", "rock climbing"], "experience_level": "beginner", "location": "mountain region"}}',
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    mime_type=MemoryMimeType.JSON,
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    metadata={"type": "data", "role": "assistant", "name": "Assistant"}
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))

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# Query user edges (facts)
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results = await memory.query(
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    query="What does the user like to do?",
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    scope="edges"
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)
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print(f"Thread results: {results}")
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# Query all entities in the user's knowledge graph
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results = await memory.query(
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    query="What do I know about this user's hobbies?",
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    scope="entities"
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)
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print(f"User results: {results}")
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# Query globally across all users (if permitted)
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results = await memory.query(
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    query="Common outdoor activities mentioned",
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    scope="episodes"  # Search across all conversations
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)
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print(f"Global results: {results}")