How to integrate Turbot pipes MCP with CrewAI

BOOK A CALLGET STARTED FOR FREE
Framework Integration Gradient
Turbot pipes Logo
CrewAI Logo
divider

Introduction

This guide walks you through connecting Turbot pipes to CrewAI using the Composio tool router. By the end, you'll have a working Turbot pipes agent that can show all recent activity logs for my account, list all workspaces i have access to, retrieve my current organization details, fetch all notification endpoints linked to me through natural language commands.

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

The Turbot pipes MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Turbot pipes account. It provides structured and secure access to your Turbot Pipes platform, so your agent can perform actions like retrieving activity logs, managing workspaces, exploring identities, viewing organizations, and handling notification endpoints on your behalf.

  • Activity monitoring and auditing: Ask your agent to fetch detailed activity logs for your account, helping you track user actions and audit changes across your Turbot Pipes environment.
  • Workspace and organization management: Retrieve and list all organizations and workspaces associated with your account to keep tabs on your team’s collaboration spaces and resources.
  • Identity exploration and avatar retrieval: Let your agent search identities, fetch details by handle, and even grab profile avatars—useful for user management and directory automation.
  • Notification endpoint discovery: Quickly list all user notifiers set up for your account, so you can manage or audit where and how notifications are delivered.
  • Account and connection insights: Access detailed information about the authenticated actor and their connections to maintain visibility and control over account access and linked integrations.

Supported Tools & Triggers

Tools
Get Authenticated ActorTool to retrieve the authenticated actor.
List Actor ActivityTool to list activities for the authenticated actor.
List actor connectionsTool to list connections associated with the authenticated actor.
List Actor OrganizationsTool to list organizations associated with the authenticated actor.
List Actor WorkspacesTool to list workspaces for the authenticated actor.
Start login via EmailTool to start login process by sending a confirmation code to a user's email.
Get IdentityTool to retrieve a specific identity by handle.
Get Identity AvatarTool to retrieve avatar image for an identity.
List IdentitiesTool to list all identities.
List User NotifiersTool to list all notifiers for a user.
Delete User TokenTool to delete a specific user token.
Get User TokenTool to retrieve details of a specific user token.

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 Turbot pipes 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 Turbot pipes 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 Turbot pipes MCP URL

Create a Composio Tool Router session for Turbot pipes

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["turbot_pipes"],
)
url = session.mcp.url
What's happening:
  • You create a Turbot pipes only session through Composio
  • Composio returns an MCP HTTP URL that exposes Turbot pipes 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="Turbot pipes Assistant",
    goal="Help users interact with Turbot pipes through natural language commands",
    backstory=(
        "You are an expert assistant with access to Turbot pipes tools. "
        "You can perform various Turbot pipes 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 Turbot pipes 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 Turbot pipes 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 Turbot pipes 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_turbot_pipes_agent.py

Complete Code

Here's the complete code to get you started with Turbot pipes and CrewAI:

python
# file: crewai_turbot_pipes_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 Turbot pipes session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["turbot_pipes"],
    )
    url = session.mcp.url

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

    # Create Turbot pipes assistant agent
    toolkit_agent = Agent(
        role="Turbot pipes Assistant",
        goal="Help users interact with Turbot pipes through natural language commands",
        backstory=(
            "You are an expert assistant with access to Turbot pipes tools. "
            "You can perform various Turbot pipes 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 Turbot pipes 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 Turbot pipes 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 Turbot pipes through Composio's Tool Router. The agent can perform Turbot pipes 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 Turbot pipes MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Turbot pipes MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Turbot pipes MCP with Claude Agent SDK

Google ADK

Google ADK

Use Turbot pipes MCP with Google ADK

LangChain

LangChain

Use Turbot pipes MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Turbot pipes MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Turbot pipes MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Turbot pipes MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Turbot pipes MCP?

With a standalone Turbot pipes MCP server, the agents and LLMs can only access a fixed set of Turbot pipes tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Turbot pipes 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 Turbot pipes tools.

Can I manage the permissions and scopes for Turbot pipes while using Tool Router?

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

Used by agents from

Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free