How to integrate Mem0 MCP with LlamaIndex

This guide walks you through connecting Mem0 to LlamaIndex 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 LlamaIndex 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.

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Introduction

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

Also integrate Mem0 with

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI CrewAI

TL;DR

Here's what you'll learn:

Set your OpenAI and Composio API keys
Install LlamaIndex and Composio packages
Create a Composio Tool Router session for Mem0
Connect LlamaIndex to the Mem0 MCP server
Build a Mem0-powered agent using LlamaIndex
Interact with Mem0 through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

ReAct Agent: Reasoning and acting pattern for tool-using agents
MCP Tools: Native support for Model Context Protocol
Context Management: Maintain conversation context across interactions
Async Support: Built for async/await patterns

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:

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

Step-by-step Guide

Step by step10 STEPS

Prerequisites

Before you begin, make sure you have:

Python 3.8/Node 16 or higher installed
A Composio account with the API key
An OpenAI API key
A Mem0 account and project
Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Mem0

OpenAI API key (OPENAI_API_KEY)

Go to the OpenAI dashboard
Create an API key if you don't have one
Assign it to OPENAI_API_KEY in .env

Composio API key and user ID

Log into the Composio dashboard
Copy your API key from Settings
- Use this as COMPOSIO_API_KEY
Pick a stable user identifier (email or ID)
- Use this as COMPOSIO_USER_ID

Installing dependencies

npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv

Create a new Typescript project and install the necessary dependencies:

@composio/llamaindex: Composio's LlamaIndex integration
@llamaindex/openai: OpenAI LLM integration
@llamaindex/tools: MCP client for LlamaIndex
@llamaindex/workflow: Workflow framework for LlamaIndex
dotenv: Environment variable management

Set environment variables

bash

OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

Authenticate with OpenAI's GPT-5 model
Connect to Composio's Tool Router
Identify your Composio user session for Mem0 access

Import modules

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

Create a new file called mem0_llamaindex_agent.ts and import the required modules:

Key imports:

dotenv.config loads .env at runtime
readline gives us a simple CLI chat loop
Composio is the main Composio SDK client
mcp connects to an MCP endpoint
createAgent builds a LlamaIndex agent
openai configures the LLM backend

Load environment variables and initialize Composio

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

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

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["mem0"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Mem0 actions." ,
    llm,
    tools,
  });

  return agent;
}

What's happening here:

We create a Composio client using your API key and configure it with the LlamaIndex provider
We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, mem0)
The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
LlamaIndex will connect to this endpoint to dynamically discover and use the available Mem0 tools.
The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

What's happening:

We're creating a direct terminal interface to chat with Mem0
The LLM's responses are streamed to the CLI for faster interaction.
The agent uses context to maintain conversation history
The agent processes the request, selects appropriate Mem0 tools, and returns a result
We extract the answer from the result data structure and display it to the user
You can type 'quit' or 'exit' to stop the chat loop gracefully
Agent responses and any errors are streamed in a clear, readable format

Define the main entry point

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();

What's happening here:

We're orchestrating the entire application flow
The agent gets built with proper error handling
Then we kick off the interactive chat loop so you can start talking to Mem0

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Mem0, then start asking questions.

Complete Code

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

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["mem0"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Mem0 actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();

Conclusion

You've successfully connected Mem0 to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:

Tool Router dynamically exposes Mem0 tools through an MCP endpoint
LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
The agent becomes more capable without increasing prompt size
Async Python provides clean, efficient execution of agent workflows

You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

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.

FRAMEWORKS

How to build Mem0 MCP Agent with another framework

ChatGPT

Use Mem0 MCP with ChatGPT

OpenAI Agents SDK

Use Mem0 MCP with OpenAI Agents SDK

Claude Agent SDK

Use Mem0 MCP with Claude Agent SDK

Claude Code

Use Mem0 MCP with Claude Code

Claude Cowork

Use Mem0 MCP with Claude Cowork

Codex

Use Mem0 MCP with Codex

Cursor

Use Mem0 MCP with Cursor

OpenClaw

Use Mem0 MCP with OpenClaw

Hermes

Use Mem0 MCP with Hermes

CLI

Use Mem0 MCP with CLI

Google ADK

Use Mem0 MCP with Google ADK

LangChain

Use Mem0 MCP with LangChain

Vercel AI SDK

Use Mem0 MCP with Vercel AI SDK

Mastra AI

Use Mem0 MCP with Mastra AI

CrewAI

Use Mem0 MCP with CrewAI

OpenCode

Use Mem0 MCP with OpenCode

VS Code

Use Mem0 MCP with VS Code

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Google Sheets

Oauth2

Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.

Notion

Oauth2Api Key

Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.

Airtable

Oauth2Api Key

Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.

Asana

Oauth2

Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals.

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. LlamaIndex 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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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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