How to integrate Mem0 MCP with Mastra AI

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Introduction

This guide walks you through connecting Mem0 to Mastra AI 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 Mastra AI 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:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Mem0 tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Mem0 tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Mem0 agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

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

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Mem0 through MCP.

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_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 your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session

Create a Tool Router session for Mem0

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["mem0"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Mem0 MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "mem0" for Mem0 access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Mem0 toolkit

Create the Mastra agent

typescript
const agent = new Agent({
    name: "mem0-mastra-agent",
    instructions: "You are an AI agent with Mem0 tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

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

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        mem0: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Mem0 toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

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

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["mem0"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      mem0: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "mem0-mastra-agent",
    instructions: "You are an AI agent with Mem0 tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { mem0: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Mem0 through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows

How to build Mem0 MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Mem0 MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Mem0 MCP with Claude Agent SDK

Google ADK

Google ADK

Use Mem0 MCP with Google ADK

LangChain

LangChain

Use Mem0 MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Mem0 MCP with Vercel AI SDK

LlamaIndex

LlamaIndex

Use Mem0 MCP with LlamaIndex

CrewAI

CrewAI

Use Mem0 MCP with CrewAI

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 Mastra AI?

Yes, you can. Mastra AI 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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