How to integrate Manus MCP with CrewAI

This guide walks you through connecting Manus to CrewAI 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 CrewAI 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.

14 Tools

Introduction

This guide walks you through connecting Manus to CrewAI 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 CrewAI 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 ADKLangChainVercel AI SDKMastra AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Manus connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Manus
  • Build a conversational loop where your agent can execute Manus 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 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 step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Manus 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 Manus 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 Manus MCP URL
6

Create a Composio Tool Router session for Manus

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

url = session.mcp.url
What's happening:
  • You create a Manus only session through Composio
  • Composio returns an MCP HTTP URL that exposes Manus 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 Manus 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=["manus"],
)
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 Manus through Composio's Tool Router. The agent can perform Manus 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 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

LangChain logo

LangChain

Use Manus MCP with LangChain

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

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. 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 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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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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