How to integrate Neutrino MCP with LangChain

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Introduction

This guide walks you through connecting Neutrino to LangChain using the Composio tool router. By the end, you'll have a working Neutrino agent that can detect profanity in user-submitted comments, convert 50 usd to eur instantly, geocode address to get latitude and longitude, validate if an email address is deliverable through natural language commands.

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

The Neutrino MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Neutrino account. It provides structured and secure access to Neutrino’s robust suite of APIs, so your agent can validate data, analyze geolocations, assess security risks, convert currencies, and clean content automatically on your behalf.

  • Real-time data validation and analysis: Have your agent validate email addresses, check mobile numbers, and analyze BIN (bank identification numbers) for accuracy and reliability.
  • Geolocation and address intelligence: Ask your agent to geocode addresses to coordinates, or perform reverse geocoding to turn latitude and longitude into real-world locations for smarter workflows.
  • Content safety and cleaning: Let your agent scan text for profanity using the Bad Word Filter or sanitize untrusted HTML to ensure safe, presentable content anywhere it’s needed.
  • Security and risk assessment: Automate reputation checks on hosts and domains, enabling your agent to proactively identify potential threats or block risky sources without manual effort.
  • Currency and unit conversion: Empower your agent to convert between different units or currencies on demand, streamlining financial or scientific operations with ease.

Supported Tools & Triggers

Tools
Bad Word FilterTool to detect bad words and profanity in text.
BIN LookupTool to perform a bin (bank iin) lookup.
Convert ValueTool to perform unit and currency conversions.
Validate and analyze an email addressTool to parse, validate, and clean an email address.
Verify Email AddressTool to verify and analyze the deliverability of an email address.
Geocode AddressTool to geocode an address.
Reverse GeocodeTool to convert geographic coordinates to a physical address.
HLR LookupTool to perform real-time hlr lookup and mobile number validation.
Host ReputationTool to check the reputation of a host against dnsbls.
HTML CleanTool to clean and sanitize untrusted html.
HTML RenderTool to render html content into png or pdf.
Resize ImageTool to resize, crop, and convert images.
IP InfoTool to retrieve ip location and network information.
IP LookupTool to geolocate an ip address and retrieve isp, asn, blocklist, and threat metadata.
IP ProbeTool to analyze an ip address to determine its threat level and associated risk factors.
Phone ValidateTool to validate and lookup phone numbers.
QR CodeTool to generate a qr code image from text or url.
SMS VerifyTool to send a unique security code via sms.
UA LookupTool to parse, validate, and get detailed user-agent information.
URL InfoTool to parse, analyze, and retrieve content from the supplied url.
Verify Security CodeTool to verify a security code generated by the generate security code api.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Neutrino MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Neutrino MCP with Claude Agent SDK

Google ADK

Google ADK

Use Neutrino MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Neutrino MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Neutrino MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Neutrino MCP with LlamaIndex

CrewAI

CrewAI

Use Neutrino MCP with CrewAI

FAQ

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

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

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

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

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