How to integrate Composio search MCP with CrewAI

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Introduction

This guide walks you through connecting Composio search to CrewAI using the Composio tool router. By the end, you'll have a working Composio search agent that can find recent news about electric vehicles, search for top-rated hotels in paris, get latest stock info for apple, show upcoming concerts in san francisco through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Composio search account through Composio's Composio search 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:
  • Get a Composio API key and configure your Composio search connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Composio search
  • Build a conversational loop where your agent can execute Composio search 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 Composio search MCP server, and what's possible with it?

The Composio search MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to the entire Composio Search suite. It provides structured and secure access to powerful web, travel, shopping, news, academic, and financial search tools, so your agent can perform actions like searching the web, finding events, locating places, pulling news, and fetching academic research on your behalf.

  • Comprehensive web and news search: Instantly ask your agent to fetch up-to-date web pages, breaking news, or current events using Google, DuckDuckGo, or news-specific search APIs.
  • Travel and local discovery: Let your agent find nearby hotels, flights, events, or map locations using Google Maps and events search for seamless travel planning and local exploration.
  • E-commerce and product lookup: Have your agent search for products, deals, and reviews across major retailers like Amazon and Walmart to help you shop smarter and faster.
  • Financial and market data retrieval: Direct your agent to pull real-time stock information, financial news, and market trends with just a query—no manual research needed.
  • Academic and scholarly research: Empower your agent to find relevant academic papers, citations, and scholarly articles using Google Scholar and Exa Answer for research-heavy tasks.

Supported Tools & Triggers

Tools
Composio DuckDuckGo SearchThe duckduckgosearch class utilizes the composio duckduckgo search api to perform searches, focusing on web information and details.
Composio Google Events SearchThe eventsearch class enables scraping of google events search queries.
Exa AnswerGet answers with citations using the exa api.
Composio SimilarlinksPerform a search to find similar links and retrieve a list of relevant results.
Composio Finance SearchThe financesearch class utilizes the composio finance search api to conduct financial searches, focusing on financial data and stock information.
Composio Google Maps SearchThe googlemapssearch class performs a location-specific search using the composio goolge maps search api.
Composio Image SearchThe imagesearch class performs an image search using the composio image search api, to target image data and information.
Composio News SearchThe newssearch class performs a news-specific search using the composio news search api.
Composio Scholar SearchScholar api allows you to scrape results from a google scholar search query.
Composio Google SearchPerform a google search using the composio google search api.
Composio Shopping SearchThe shoppingsearch class performs a product search using the composio shopping search api.
Composio LLM SearchThe composio llm search class serves as a gateway to the composio llm search api, allowing users to perform searches across a broad range of content with multiple filtering options.
Composio Trends SearchThe trendssearch class performs a trend search using the google trends search api, to target trend data and information.

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:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Composio search connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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.

Install dependencies

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

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

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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 Composio search MCP URL

Create a Composio Tool Router session for Composio search

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["composio_search"],
)
url = session.mcp.url
What's happening:
  • You create a Composio search only session through Composio
  • Composio returns an MCP HTTP URL that exposes Composio search tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Composio search Assistant",
    goal="Help users interact with Composio search through natural language commands",
    backstory=(
        "You are an expert assistant with access to Composio search tools. "
        "You can perform various Composio search operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Composio search MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Composio search operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Composio search related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_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:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_composio_search_agent.py

Complete Code

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

python
# file: crewai_composio_search_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Composio search session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["composio_search"],
    )
    url = session.mcp.url

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

    # Create Composio search assistant agent
    toolkit_agent = Agent(
        role="Composio search Assistant",
        goal="Help users interact with Composio search through natural language commands",
        backstory=(
            "You are an expert assistant with access to Composio search tools. "
            "You can perform various Composio search operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Composio search operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Composio search related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

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

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Composio search through Composio's Tool Router. The agent can perform Composio search 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

How to build Composio search MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Composio search MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Composio search MCP with Claude Agent SDK

Google ADK

Google ADK

Use Composio search MCP with Google ADK

LangChain

LangChain

Use Composio search MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Composio search MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Composio search MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Composio search MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Composio search MCP?

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

Can I use Tool Router MCP with CrewAI?

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 Composio search tools.

Can I manage the permissions and scopes for Composio search while using Tool Router?

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

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