zep-autogen 0.1.0

Autogen integration for Zep

Zep AutoGen Integration

A dedicated integration package that enables Zep to work seamlessly with Microsoft AutoGen agents, providing persistent conversation memory and intelligent context retrieval.

Installation

pip install zep-autogen

Quick Start

import asyncio
from zep_cloud.client import AsyncZep
from zep_autogen import ZepMemory
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main():
    # Initialize Zep client
    zep_client = AsyncZep(api_key="your-zep-api-key")
    
    # Create Zep memory for your agent
    memory = ZepMemory(
        client=zep_client,
        user_id="user_123",
        thread_id="conversation_456"
    )
    
    # Create AutoGen agent with Zep memory
    agent = AssistantAgent(
        name="MemoryAwareAssistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4.1-mini"),
        memory=[memory]  # Add Zep memory to the agent
    )
    
    # Your agent now has persistent memory across conversations!
    response = await agent.run(task="What's my name again?")
    print(response.messages[-1].content)
    
asyncio.run(main())

Features

• Persistent Memory: Conversations persist across agent sessions
• Intelligent Retrieval: Zep's memory automatically provides relevant context
• AutoGen Compatible: Seamlessly integrates with AutoGen's memory interface
• Async Support: Full async/await support for modern applications
• Type Safety: Fully typed with comprehensive type hints

Configuration

Environment Variables

# Required: Your Zep Cloud API key
export ZEP_API_KEY="your-zep-api-key"

ZepMemory Parameters

• client (AsyncZep): Your Zep client instance
• user_id (str): Unique identifier for the user
• thread_id (str, optional): Thread/conversation identifier
• memory_type (str, optional): Type of memory to use (default: "perpetual")
• max_tokens (int, optional): Maximum tokens to retrieve (default: 4000)

Examples

Basic Usage

See examples/autogen_basic.py for a complete working example.

Multi-Agent with Shared Memory

# Multiple agents can share the same memory context
shared_memory = ZepMemory(
    client=zep_client,
    user_id="team_project",
    thread_id="brainstorm_session"
)

researcher = AssistantAgent(
    name="Researcher",
    model_client=model_client,
    memory=[shared_memory]
)

writer = AssistantAgent(
    name="Writer", 
    model_client=model_client,
    memory=[shared_memory]
)

Requirements

• Python 3.10+
• zep-cloud>=2.15.0
• autogen-agentchat>=0.6.1
• autogen-core>=0.6.1

License

MIT License - see LICENSE for details.

Support

• Zep Documentation
• AutoGen Documentation
• GitHub Issues

Contributing

Contributions are welcome! Please see our Contributing Guide for details.