How to integrate Manus MCP with LangChain

This guide walks you through connecting Manus to LangChain using the Composio tool router. By the end, you'll have a working Manus agent that can create manus task for competitor research, list pending manus workflow tasks, summarize completed manus automation runs through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Manus account through Composio's Manus MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Manus is an AI-powered task automation and workflow management platform. It helps teams delegate complex work, track progress, and run repeatable workflows.

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Introduction

This guide walks you through connecting Manus to LangChain using the Composio tool router. By the end, you'll have a working Manus agent that can create manus task for competitor research, list pending manus workflow tasks, summarize completed manus automation runs through natural language commands.

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

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

Also integrate Manus with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Manus project to Composio
  • Create a Tool Router MCP session for Manus
  • Initialize an MCP client and retrieve Manus tools
  • Build a LangChain agent that can interact with Manus
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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

The Manus MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Manus account. It provides structured and secure access to your projects and AI tasks, so your agent can create projects, start tasks, review task details, monitor progress, and list existing tasks on your behalf.

  • Project creation: Have your agent create a new Manus project with default instructions whenever you need a fresh workspace.
  • AI task creation: Direct your agent to start custom AI tasks with instructions, attachments, and optional connectors.
  • Task progress monitoring: Let the agent check a task's status, output messages, credit usage, and related details.
  • Task discovery: Instruct your agent to list and filter existing tasks so you can quickly find the work that matters.
  • Webhook verification support: Have your agent retrieve the public key needed to verify that webhook updates came from Manus.

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 step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
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

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Manus functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Manus tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['manus']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Manus tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Manus tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "manus-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Manus MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Manus tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

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

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

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;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Manus and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['manus']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "manus-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Manus related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    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;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Manus through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
TOOLS

Supported Tools

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

Create File

Tool to create a file record and obtain a presigned URL for uploading file content to S3.

Create New Project

Tool to create a new project with default instructions.

Create New Task

Creates a new AI task with custom parameters, attachments, and optional connectors.

Create Webhook

Tool to register a webhook to receive real-time task notifications.

Delete a file

Deletes a file by ID.

Delete task

Permanently delete a task by its ID.

Delete Webhook

Removes a webhook subscription.

Get file details

Retrieves details of a specific file by ID, including its status and metadata.

Get Task

Retrieves details of a specific task by ID, including status, output messages, credit usage, and metadata.

Get Webhook Public Key

Retrieves the RSA public key used for verifying webhook signatures.

List uploaded files

Retrieves a list of the 10 most recently uploaded files.

List Projects

Retrieve a list of all projects in your account with optional pagination.

List tasks

Retrieves a list of tasks with optional filtering and pagination.

Update Task

Updates a task's metadata such as title, sharing settings, and visibility in the task list.

FRAMEWORKS

How to build Manus MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Manus MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Manus MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Manus MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Manus MCP with Claude Cowork

Codex logo

Codex

Use Manus MCP with Codex

OpenClaw logo

OpenClaw

Use Manus MCP with OpenClaw

Hermes logo

Hermes

Use Manus MCP with Hermes

CLI logo

CLI

Use Manus MCP with CLI

Google ADK logo

Google ADK

Use Manus MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Manus MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Manus MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Manus MCP with LlamaIndex

CrewAI logo

CrewAI

Use Manus MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. LangChain 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 Manus tools.

Yes, absolutely. You can configure which Manus 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 Manus data and credentials are handled as safely as possible.

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