How to integrate Turbot pipes MCP with LangChain

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Introduction

This guide walks you through connecting Turbot pipes to LangChain 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 LangChain 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 and set up your OpenAI and Composio API keys
  • Connect your Turbot pipes project to Composio
  • Create a Tool Router MCP session for Turbot pipes
  • Initialize an MCP client and retrieve Turbot pipes tools
  • Build a LangChain agent that can interact with Turbot pipes
  • 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 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 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

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

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv loads environment variables

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

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

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

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
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 Turbot pipes tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Turbot pipes
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['turbot_pipes']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Turbot pipes 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 Turbot pipes tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "turbot_pipes-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

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

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Turbot pipes related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['turbot_pipes']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "turbot_pipes-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Turbot pipes related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Turbot pipes 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.

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

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

CrewAI

CrewAI

Use Turbot pipes MCP with CrewAI

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 LangChain?

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 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.

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

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