How to integrate Mem0 MCP with Autogen

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Introduction

This guide walks you through connecting Mem0 to AutoGen using the Composio tool router. By the end, you'll have a working Mem0 agent that can store meeting notes from today's call, export all project memories as csv, add new user to our team space, search recent notes mentioning quarterly goals through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Mem0 account through Composio's Mem0 MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Mem0
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Mem0 tools
  • Run a live chat loop where you ask the agent to perform Mem0 operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

What is the Mem0 MCP server, and what's possible with it?

The Mem0 MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mem0 account. It provides structured and secure access to your notes, projects, and organizational knowledge, so your agent can perform actions like searching memories, managing users, adding content, and orchestrating agent runs on your behalf.

  • AI-powered memory search and recall: Let your agent search and retrieve existing memory entries using advanced filters and pagination to surface just the right note or piece of information.
  • Automated content and note creation: Have your agent store new memory records from conversations, meetings, or tasks—ensuring nothing slips through the cracks.
  • Collaboration and organization management: Direct your agent to add members to projects or organizations, assign roles, and keep team structures up to date.
  • Agent and application orchestration: Enable your agent to create new AI agents, initiate agent runs, and manage applications for custom workflows and automation.
  • Structured knowledge export and reporting: Ask your agent to initiate export jobs with specific schemas and filters, so you can back up or analyze your stored knowledge on demand.

Supported Tools & Triggers

Tools
Add member to projectAdds an existing user to a project (identified by `project id` within organization `org id`), assigning a valid system role.
Add new memory recordsStores new memory records from a list of messages, optionally inferring structured content; requires association via `agent id`, `user id`, `app id`, or `run id`.
Add organization memberAdds a new member, who must be a registered user, to an organization, assigning them a specific role.
Create a new agentCreates a new agent with a unique `agent id` and an optional `name`; additional metadata may be assigned by the system.
Create a new agent runCreates a new agent run in the mem0.
Create a new applicationCreates a new application, allowing metadata to be passed in the request body (not an explicit field in this action's request model); ensure `app id` is unique to avoid potential errors or unintended updates.
Create a new organization entryCreates a new organization entry using the provided name and returns its details.
Create a new userCreates a new user with the specified unique `user id` and supports associating `metadata` (not part of the request schema fields).
Create an export job with schemaInitiates an asynchronous job to export memories, structured by a schema provided in the request body and allowing optional filters.
Create memory entryLists/searches existing memory entries with filtering and pagination; critically, this action retrieves memories and does *not* create new ones, despite its name.
Create projectCreates a new project with a given name within an organization that must already exist.
Delete an organizationPermanently deletes an existing organization identified by its unique id.
Delete memory by idPermanently deletes a specific memory by its unique id; ensure the `memory id` exists as this operation is irreversible.
Delete entity by type and idCall to permanently and irreversibly hard-delete an existing entity (user, agent, app, or run) and all its associated data, using its type and id.
Delete memoriesDeletes memories matching specified filter criteria; omitting all filters may result in deleting all memories.
Delete memory batch with uuidsDeletes a batch of up to 1000 existing memories, identified by their uuids, in a single api call.
Delete projectPermanently deletes a specific project and all its associated data from an organization; this action cannot be undone and requires the project to exist within the specified organization.
Delete project memberRemoves an existing member, specified by username, from a project, immediately revoking their project-specific access; the user is not removed from the organization.
Export data based on filtersRetrieves memory export data, optionally filtered by various identifiers (e.
List organizationsRetrieves a summary list of organizations for administrative oversight; returns summary data (names, ids), not exhaustive details, despite 'detailed' in the name.
Fetch details of a specific organizationFetches comprehensive details for an organization using its `org id`; the `org id` must be valid and for an existing organization.
Get list of entity filtersRetrieves predefined filter definitions for entities (e.
Get entity by idFetches detailed information for an existing entity (user, agent, app, or run) identified by its type and unique id.
Get organization membersFetches a list of members for a specified, existing organization.
Get project detailsFetches comprehensive details for a specified project within an organization.
Get project membersRetrieves all members for a specified project within an organization.
Get projectsRetrieves all projects for a given organization `org id` to which the caller has access.
Get user memory statsRetrieves a summary of the authenticated user's memory activity, including total memories created, search events, and add events.
List entitiesRetrieves a list of entities, optionally filtered by organization or project (prefer `org id`/`project id` over deprecated `org name`/`project name`), noting results may be summaries and subject to limits.
Perform semantic search on memoriesSearches memories semantically using a natural language query (required if `only metadata based search` is false) and/or metadata filters.
Remove a member from the organizationRemoves a member, specified by their username, from an existing organization of which they are currently a member.
Retrieve all events for the currently logged in userRetrieves a paginated list of events for the authenticated user, filterable and paginable via url query parameters.
Retrieve entity-specific memoriesRetrieves all memories (e.
Retrieve list of memory eventsRetrieves a chronological list of all memory events (e.
Retrieve memory by idRetrieves a complete memory entry by its unique identifier; `memory id` must be valid and for an existing memory.
Retrieve memory history by idRetrieves the complete version history for an existing memory, using its unique `memory id`, to inspect its evolution or audit changes.
Retrieve memory listRetrieves a list of memories, supporting pagination and diverse filtering (e.
Search memories with filtersSemantically searches memories using a natural language query and mandatory structured filters, offering options to rerank results and select specific fields; any provided `org id` or `project id` must reference a valid existing entity.
Update memory batch with uuidUpdates text for up to 1000 memories in a single batch, using their uuids.
Update memory text contentUpdates the text content of an existing memory, identified by its `memory id`.
Update organization member roleUpdates the role of an existing member to a new valid role within an existing organization.
Update projectUpdates a project by `project id` within an `org id`, modifying only provided fields (name, description, custom instructions, custom categories); list fields are fully replaced (cleared by `[]`), other omitted/null fields remain unchanged.
Update project member roleUpdates the role of a specific member within a designated project, ensuring the new role is valid and recognized by the system.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Mem0 account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Mem0 via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Mem0 connections to use

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Mem0 session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["mem0"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Mem0 tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Mem0 assistant agent with MCP tools
    agent = AssistantAgent(
        name="mem0_assistant",
        description="An AI assistant that helps with Mem0 operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Mem0 tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Mem0 related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Mem0 tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Mem0 and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Mem0 session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["mem0"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Mem0 assistant agent with MCP tools
        agent = AssistantAgent(
            name="mem0_assistant",
            description="An AI assistant that helps with Mem0 operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Mem0 related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Mem0 through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Mem0, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Mem0 MCP Agent with another framework

FAQ

What are the differences in Tool Router MCP and Mem0 MCP?

With a standalone Mem0 MCP server, the agents and LLMs can only access a fixed set of Mem0 tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Mem0 and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Autogen?

Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Mem0 tools.

Can I manage the permissions and scopes for Mem0 while using Tool Router?

Yes, absolutely. You can configure which Mem0 scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Mem0 data and credentials are handled as safely as possible.

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HubSpot
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Letta
glean
HubSpot
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Altera
DataStax
Entelligence
Rolai

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