How to integrate Pipeline crm MCP with LangChain

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Introduction

This guide walks you through connecting Pipeline crm to LangChain using the Composio tool router. By the end, you'll have a working Pipeline crm agent that can list all leads added this week, get details for deal with id 789, delete company with id 123 through natural language commands.

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

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

Also integrate Pipeline crm with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Pipeline crm project to Composio
  • Create a Tool Router MCP session for Pipeline crm
  • Initialize an MCP client and retrieve Pipeline crm tools
  • Build a LangChain agent that can interact with Pipeline crm
  • 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 Pipeline crm MCP server, and what's possible with it?

The Pipeline crm MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Pipeline crm account. It provides structured and secure access to your sales pipeline data, so your agent can perform actions like tracking leads, managing deals, updating company information, organizing tasks, and streamlining sales workflows on your behalf.

  • Lead and company management: Effortlessly list, retrieve, or delete companies and leads, making it easy to keep your CRM up to date and focused on the right opportunities.
  • Deal tracking and stage management: Let your agent fetch deal details, monitor deal stages, or remove outdated deals to ensure your pipeline always reflects current sales activity.
  • Calendar task organization: Automatically list and retrieve calendar tasks, or pull specific task details by ID to stay on top of follow-ups and scheduled activities.
  • Comprehensive sales workflow automation: Ask your agent to enumerate deal stages, manage associated tasks, and streamline repetitive CRM operations so your team can focus on closing more deals.

Supported Tools & Triggers

Tools
Create Pipeline CRM CompanyCreates a new company record in Pipeline CRM with contact details, address, and social media information.
Create DealTool to create a new deal in Pipeline CRM.
Delete CompanyTool to delete a company by ID in Pipeline CRM.
Delete DealTool to delete a deal by ID.
Delete calendar taskTool to delete a calendar task by ID.
Get Company by IDRetrieves comprehensive details for a specific company by ID from Pipeline CRM.
Get Deal by IDTool to retrieve details for a specific deal by ID in Pipeline CRM.
Get Deal Stage by IDTool to retrieve details for a specific stage by ID in Pipeline CRM.
Get calendar task by IDTool to retrieve details for a specific task by ID in Pipeline CRM.
List CompaniesList companies in Pipeline CRM with optional filtering, sorting, and pagination.
List LeadsTool to list leads in Pipeline CRM.
List Deal StagesTool to list deal stages.
List calendar tasksTool to list calendar tasks.
List UsersList users in Pipeline CRM account with optional pagination.
Update CompanyTool to update an existing company by ID in Pipeline CRM.

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

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 Pipeline crm 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 Pipeline crm tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Pipeline crm
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['pipeline_crm']
)

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "pipeline_crm-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 Pipeline crm MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Pipeline crm 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 Pipeline crm 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 Pipeline crm 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=['pipeline_crm']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "pipeline_crm-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 Pipeline crm 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 Pipeline crm 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.

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FAQ

What are the differences in Tool Router MCP and Pipeline crm MCP?

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

Can I manage the permissions and scopes for Pipeline crm while using Tool Router?

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

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