How to integrate Centralstationcrm MCP with LangChain

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Introduction

This guide walks you through connecting Centralstationcrm to LangChain using the Composio tool router. By the end, you'll have a working Centralstationcrm agent that can add new company to crm contacts, log a sales opportunity for a client, count total people in my crm, record a birthday for an existing contact through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Centralstationcrm account through Composio's Centralstationcrm 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 Centralstationcrm project to Composio
  • Create a Tool Router MCP session for Centralstationcrm
  • Initialize an MCP client and retrieve Centralstationcrm tools
  • Build a LangChain agent that can interact with Centralstationcrm
  • 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 Centralstationcrm MCP server, and what's possible with it?

The Centralstationcrm MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Centralstationcrm account. It provides structured and secure access to your customer relationship data, so your agent can perform actions like managing contacts, creating deals, updating company records, and tracking key interactions on your behalf.

  • Automated contact management: Quickly add new people to your CRM, update their details, and ensure your contact database stays current without manual entry.
  • Company and organization creation: Effortlessly create new company records so you can keep your account-based selling and organization tracking up-to-date.
  • Deal tracking and creation: Instantly log new sales opportunities by creating deals linked to your contacts or companies, helping your team stay on top of the pipeline.
  • Detailed relationship enrichment: Add addresses, assistants, avatars, and contact details to people in your CRM, making every customer profile richer and more actionable.
  • Milestone and history recording: Record important life events or milestones (like birthdays or anniversaries) for each person to boost relationship management and personalized outreach.

Supported Tools & Triggers

Tools
Check ConnectionTool to verify the connection status of the centralstationcrm api key.
Count PeopleTool to retrieve the total number of people in the account.
Create CompanyTool to create a new company record.
Create DealTool to create a new deal record.
Create PersonTool to create a new person record.
Create Person AddressTool to create a new address for a specific person.
Create Person AssistantTool to create a new assistant (assi) entry for a specific person.
Create Person AvatarTool to create a new avatar for a specific person.
Create Person Contact DetailTool to create a new contact detail for a specific person.
Create Person Historic EventTool to create a new historic event for a specific person.
Delete CompanyTool to delete a company record by id.
Delete personTool to delete a person record by id.
Delete Person AddressTool to delete a person's address by its id.
Delete Person AssiTool to delete an assi entry of a person.
Delete Person AvatarTool to delete a person's avatar by its id.
Delete Person Contact DetailTool to delete a contact detail of a person.
Delete Person Historic EventTool to delete a historic event of a person by its id.
Get API User MaildropTool to retrieve the current api user's maildrop for people and companies.
Get CompanyTool to retrieve details of a specific company by id.
Get DealTool to retrieve details of a specific deal by its id.
Get DealsTool to retrieve a paginated list of all deals.
Get PersonTool to retrieve details of a specific person by id.
Get Person AddressTool to retrieve a specific address of a person by address id.
Get Person AddressesTool to retrieve all addresses for a specific person.
Get Person AssiTool to retrieve a specific assi entry of a person by id.
Get Person AssisTool to retrieve all assistant entries for a specific person.
Get Person AvatarTool to retrieve a specific avatar of a person by avatar id.
Get Person AvatarsTool to retrieve all avatars for a specific person.
Get Person Contact DetailTool to retrieve a specific contact detail by id for a person.
Get Person Custom FieldsTool to retrieve all custom fields for a specific person.
Get Person Historic EventTool to retrieve a specific historic event of a person by id.
Get Person Historic EventsTool to retrieve all historic events for a specific person.
Get PersonsTool to retrieve a paginated list of all people.
Merge PersonTool to merge another person into an existing person by id.
Search RecordsSearch records
Search PeopleTool to retrieve people matching search criteria.
Stats PeopleTool to retrieve key statistics about people.
Update CompanyTool to update an existing company by id.
Update PersonTool to update an existing person by id.
Update Person AddressTool to update a specific address of a person.
Update Person AssiTool to update an assi entry of a person.
Update Person Contact DetailTool to update a specific contact detail of a person by id.
Update Person Historic EventTool to update a historic event of a person by id.

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 Centralstationcrm 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 Centralstationcrm tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "centralstationcrm-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 Centralstationcrm 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 Centralstationcrm 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 Centralstationcrm MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Centralstationcrm MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Centralstationcrm MCP with Claude Agent SDK

Google ADK

Google ADK

Use Centralstationcrm MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Centralstationcrm MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Centralstationcrm MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Centralstationcrm MCP with LlamaIndex

CrewAI

CrewAI

Use Centralstationcrm MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Centralstationcrm MCP?

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

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

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

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