How to integrate Nango MCP with LangChain

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Introduction

This guide walks you through connecting Nango to LangChain using the Composio tool router. By the end, you'll have a working Nango agent that can list all connected crm accounts, trigger manual sync with salesforce provider, get configuration for all available scripts through natural language commands.

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

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

Also integrate Nango with

TL;DR

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

The Nango MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Nango account. It provides structured and secure access to your integrations, so your agent can perform actions like triggering syncs, managing connections, listing providers, and executing workflow actions across 250+ external APIs on your behalf.

  • Connection management and discovery: Effortlessly list all your existing Nango connections, view metadata, or retrieve connection information without exposing sensitive credentials.
  • Provider information and browsing: Ask your agent to list all available providers or fetch detailed configuration info for a specific provider, making it easy to discover and set up new integrations.
  • Triggering workflow actions: Direct your agent to execute custom workflow actions by specifying the connection, provider, and action identifiers—unlocking advanced automation across connected platforms.
  • Manual sync initiation: Have your agent trigger sync processes for any established connection, ensuring your data stays up-to-date across all integrated services.
  • Script configuration retrieval: Let your agent fetch Nango scripts configuration and triggers, enabling more tailored and automated integration flows.

Supported Tools & Triggers

Tools
Trigger Nango ActionTrigger a Nango action to execute a workflow or operation.
Add ConnectionTool to add a connection with existing credentials to Nango.
List ConnectionsList all Nango connections without credentials.
Create Connect SessionTool to create a new connect session with a 30-minute lifespan for enabling connection creation via Connect UI.
Create IntegrationTool to create a new integration in Nango.
Delete ConnectionTool to delete a specific Nango connection.
Delete IntegrationTool to delete a specific integration by its unique key.
Edit ConnectionTool to edit a connection's tags and metadata.
Get Connection with CredentialsRetrieve a specific connection with its credentials.
Get Environment VariablesTool to retrieve environment variables from the Nango dashboard.
Get IntegrationRetrieve detailed configuration for a specific Nango integration by its unique key.
Proxy GET RequestTool to make a GET request with Nango's Proxy to forward requests to external APIs while managing authentication.
Get Sync StatusTool to get the status of specified sync(s) for a connection or all connections.
List ConnectionsTool to list all connections without credentials.
List IntegrationsTool to retrieve a list of all configured integrations.
Get Provider DetailsRetrieve detailed configuration for a specific Nango provider by its unique key.
List ProvidersTool to retrieve a list of all available providers.
Proxy PUT RequestTool to make a PUT request with the Nango Proxy to forward requests to external APIs while managing authentication.
Reconnect SessionCreate a new connect session to reconnect to a specific integration.
Get Integration Functions ConfigurationRetrieve all integration functions configuration from Nango.
Set Connection MetadataTool to set custom metadata for one or more Nango connections.
Trigger SyncTool to trigger sync process(es) manually.
Update Connection MetadataTool to edit custom metadata for one or multiple connections.
Update IntegrationTool to update an existing integration's configuration.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "nango-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 Nango 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 Nango 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 Nango MCP?

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

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

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

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