> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# OpenAI Agents SDK
> Integrate Mem0 with the OpenAI Agents SDK for persistent memory across multi-agent workflows.
Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [OpenAI Agents SDK](https://github.com/openai/openai-agents-python), a lightweight framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.
## Overview
1. Store and retrieve memories from Mem0 within OpenAI agents
2. Multi-agent workflows with shared memory
3. Retrieve relevant memories for past conversations
4. Personalized responses based on user history
## Prerequisites
Before setting up Mem0 with OpenAI Agents SDK, ensure you have:
1. Installed the required packages:
```bash theme={null}
pip install openai-agents mem0ai
```
2. Valid API keys:
* Mem0 API Key
* [OpenAI API Key](https://platform.openai.com/api-keys)
## Basic Integration Example
The following example demonstrates how to create an OpenAI agent with Mem0 memory integration:
```python theme={null}
import os
from agents import Agent, Runner, function_tool
from mem0 import MemoryClient
# Set up environment variables
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
# Initialize Mem0 client
mem0 = MemoryClient()
# Define memory tools for the agent
@function_tool
def search_memory(query: str, user_id: str) -> str:
"""Search through past conversations and memories"""
memories = mem0.search(query, filters={"user_id": user_id}, top_k=3)
if memories and memories.get('results'):
return "\n".join([f"- {mem['memory']}" for mem in memories['results']])
return "No relevant memories found."
@function_tool
def save_memory(content: str, user_id: str) -> str:
"""Save important information to memory"""
mem0.add([{"role": "user", "content": content}], user_id=user_id)
return "Information saved to memory."
# Create agent with memory capabilities
agent = Agent(
name="Personal Assistant",
instructions="""You are a helpful personal assistant with memory capabilities.
Use the search_memory tool to recall past conversations and user preferences.
Use the save_memory tool to store important information about the user.
Always personalize your responses based on available memory.""",
tools=[search_memory, save_memory],
model="gpt-5-mini"
)
def chat_with_agent(user_input: str, user_id: str) -> str:
"""
Handle user input with automatic memory integration.
Args:
user_input: The user's message
user_id: Unique identifier for the user
Returns:
The agent's response
"""
# Run the agent (it will automatically use memory tools when needed)
result = Runner.run_sync(agent, user_input)
return result.final_output
# Example usage
if __name__ == "__main__":
# preferences will be saved in memory (using save_memory tool)
response_1 = chat_with_agent(
"I love Italian food and I'm planning a trip to Rome next month",
user_id="alice"
)
print(response_1)
# memory will be retrieved using search_memory tool to answer the user query
response_2 = chat_with_agent(
"Give me some recommendations for food",
user_id="alice"
)
print(response_2)
```
## Multi-Agent Workflow with Handoffs
Create multiple specialized agents with proper handoffs and shared memory:
```python theme={null}
from agents import Agent, Runner, handoffs, function_tool
# Specialized agents
travel_agent = Agent(
name="Travel Planner",
instructions="""You are a travel planning specialist. Use get_user_context to
understand the user's travel preferences and history before making recommendations.
After providing your response, use store_conversation to save important details.""",
tools=[search_memory, save_memory],
model="gpt-5-mini"
)
health_agent = Agent(
name="Health Advisor",
instructions="""You are a health and wellness advisor. Use get_user_context to
understand the user's health goals and dietary preferences.
After providing advice, use store_conversation to save relevant information.""",
tools=[search_memory, save_memory],
model="gpt-5-mini"
)
# Triage agent with handoffs
triage_agent = Agent(
name="Personal Assistant",
instructions="""You are a helpful personal assistant that routes requests to specialists.
For travel-related questions (trips, hotels, flights, destinations), hand off to the Travel Planner.
For health-related questions (fitness, diet, wellness, exercise), hand off to the Health Advisor.
For general questions, handle them directly using available tools.""",
handoffs=[travel_agent, health_agent],
model="gpt-5-mini"
)
def chat_with_handoffs(user_input: str, user_id: str) -> str:
"""
Handle user input with automatic agent handoffs and memory integration.
Args:
user_input: The user's message
user_id: Unique identifier for the user
Returns:
The agent's response
"""
# Run the triage agent (it will automatically handoff when needed)
result = Runner.run_sync(triage_agent, user_input)
# Store the original conversation in memory
conversation = [
{"role": "user", "content": user_input},
{"role": "assistant", "content": result.final_output}
]
mem0.add(conversation, user_id=user_id)
return result.final_output
# Example usage
response = chat_with_handoffs("Plan a healthy meal for my Italy trip", user_id="alex")
print(response)
```
## Quick Start Chat Interface
Simple interactive chat with memory:
```python theme={null}
def interactive_chat():
"""Interactive chat interface with memory and handoffs"""
user_id = input("Enter your user ID: ") or "demo_user"
print(f"Chat started for user: {user_id}")
print("Type 'quit' to exit\n")
while True:
user_input = input("You: ")
if user_input.lower() == 'quit':
break
response = chat_with_handoffs(user_input, user_id)
print(f"Assistant: {response}\n")
if __name__ == "__main__":
interactive_chat()
```
## Key Features
### 1. Automatic Memory Integration
* **Tool-Based Memory**: Agents use function tools to search and save memories
* **Conversation Storage**: All interactions are automatically stored
* **Context Retrieval**: Agents can access relevant past conversations
### 2. Multi-Agent Memory Sharing
* **Shared Context**: Multiple agents access the same memory store
* **Specialized Agents**: Create domain-specific agents with shared memory
* **Seamless Handoffs**: Agents maintain context across handoffs
### 3. Flexible Memory Operations
* **Retrieve Capabilities**: Retrieve relevant memories from previous conversations
* **User Segmentation**: Organize memories by user ID
* **Memory Management**: Built-in tools for saving and retrieving information
## Configuration Options
Customize memory behavior:
```python theme={null}
# Configure memory search
memories = mem0.search(
query="travel preferences",
filters={"user_id": "alex"},
top_k=5 # Number of memories to retrieve
)
# Add metadata to memories
mem0.add(
messages=[{"role": "user", "content": "I prefer luxury hotels"}],
user_id="alex",
metadata={"category": "travel", "importance": "high"}
)
```
Learn how to integrate Mem0 with OpenAI function calling
Build agents with OpenAI SDK tools