How to integrate Mem0 MCP with CrewAI

This guide walks you through connecting Mem0 to CrewAI 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 through natural language commands. This guide will help you understand how to give your CrewAI 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.

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Mem0 is an AI-powered note-taking and knowledge management platform. It helps you organize, search, and generate content from your personal knowledge base.

47 Tools

Introduction

This guide walks you through connecting Mem0 to CrewAI 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 through natural language commands.

This guide will help you understand how to give your CrewAI 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.

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TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Mem0 connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Mem0
  • Build a conversational loop where your agent can execute Mem0 operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Mem0 connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

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.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Mem0 via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Mem0 MCP URL
6

Create a Composio Tool Router session for Mem0

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["mem0"])

url = session.mcp.url
What's happening:
  • You create a Mem0 only session through Composio
  • Composio returns an MCP HTTP URL that exposes Mem0 tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["mem0"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Mem0 through Composio's Tool Router. The agent can perform Mem0 operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
TOOLS

Supported Tools

Every Mem0 action and event your agent gets out of the box.

Add member to project

Adds an existing user to a project (identified by `project_id` within organization `org_id`), assigning a valid system role.

Add new memory records

Stores 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 member

Adds a new member, who must be a registered user, to an organization, assigning them a specific role.

Create a new agent

Creates a new agent with a unique `agent_id` and an optional `name`; additional metadata may be assigned by the system.

Create a new agent run

Creates a new agent run in the mem0.

Create a new application

Creates 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 entry

Creates a new organization entry using the provided name and returns its details.

Create a new user

Creates a new user with the specified unique `user_id` and supports associating `metadata` (not part of the request schema fields).

Create memory entry

Lists/searches existing memory entries with filtering and pagination; critically, this action retrieves memories and does *not* create new ones, despite its name.

Create project

Creates a new project with a given name within an organization that must already exist.

Create webhook

Creates a new webhook for a specific project to receive real-time notifications.

Delete an organization

Permanently deletes an existing organization identified by its unique ID.

Delete memory by id

Permanently deletes a specific memory by its unique ID; ensure the `memory_id` exists as this operation is irreversible.

Delete entity by type and id

Call 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 memories

Deletes all memories matching specified filter criteria.

Delete memory batch with uuids

Deletes a batch of up to 1000 existing memories, identified by their UUIDs, in a single API call.

Delete project

Permanently 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 member

Removes an existing member, specified by email address, from a project, immediately revoking their project-specific access; the user is not removed from the organization.

Delete webhook

Deletes a webhook and stops receiving notifications for the specified webhook ID.

Export data based on filters

Creates a new memory export job with optional entity filters (user_id, agent_id, app_id, run_id).

List organizations

Retrieves 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 organization

Fetches comprehensive details for an organization using its `org_id`; the `org_id` must be valid and for an existing organization.

Get list of entity filters

Retrieves predefined filter definitions for entities (e.

Get entity by id

Fetches detailed information for an existing entity (user, agent, app, or run) identified by its type and unique ID.

Get event status by event ID

Retrieves a single async event by ID to check its current status and results.

Get memories by entity

Tool to retrieve all memories associated with a specific entity (user, agent, app, or run).

Get memory export

Retrieves the status and results of a memory export job by its ID.

Get organization members

Fetches a list of members for a specified, existing organization.

Get project details

Fetches comprehensive details for a specified project within an organization.

Get project members

Retrieves all members for a specified project within an organization.

Get projects

Retrieves all projects for a given organization `org_id` to which the caller has access.

Get project webhooks

Retrieves all webhooks configured for a specific project.

Get user memory stats

Retrieves a summary of the authenticated user's memory activity, including total memories created, search events, and add events.

List entities

Retrieves 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 memories

Searches memories semantically using a natural language query and metadata filters.

Remove a member from the organization

Removes 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 user

Retrieves a paginated list of events for the authenticated user, filterable and paginable via URL query parameters.

Retrieve list of memory events

Retrieves a chronological list of all memory events (e.

Retrieve memory by id

Retrieves a complete memory entry by its unique identifier; `memory_id` must be valid and for an existing memory.

Retrieve memory history by id

Retrieves the complete version history for an existing memory, using its unique `memory_id`, to inspect its evolution or audit changes.

Retrieve memory list

Retrieves a list of memories, supporting pagination and diverse filtering (e.

Search memories with filters

Semantically searches memories using structured filters with an optional natural language query.

Update memory batch with uuid

Updates text for up to 1000 memories in a single batch, using their UUIDs.

Update memory text content

Updates the text content of an existing memory, identified by its `memory_id`.

Update organization member role

Updates the role of an existing member to a new valid role within an existing organization.

Update project

Updates 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 role

Updates the role of a specific member within a designated project, ensuring the new role is valid and recognized by the system.

FAQ

Frequently asked questions

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.

Yes, you can. CrewAI 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.

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.

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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