How to integrate Ambee MCP with LangChain

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Introduction

This guide walks you through connecting Ambee to LangChain using the Composio tool router. By the end, you'll have a working Ambee agent that can show today's air quality for paris, get wildfire risk forecast for california, retrieve pollen count by my zip code, list recent natural disasters in asia through natural language commands.

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

The Ambee MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ambee account. It provides structured and secure access to hyperlocal environmental data, so your agent can perform actions like retrieving real-time air quality, fetching pollen and weather updates, forecasting wildfire risk, and monitoring natural disasters on your behalf.

  • Hyperlocal air quality monitoring: Instantly get real-time air quality data for any city, postal code, or precise geographic coordinates to stay informed about pollution and AQI levels in your area.
  • Air quality forecasting: Ask your agent to predict air quality trends up to 48 hours in advance for specific locations, helping you plan outdoor activities with health in mind.
  • Wildfire risk assessment: Access weekly wildfire risk forecasts for targeted places, so you can stay aware of environmental hazards before they happen.
  • Disaster and incident tracking: Retrieve the latest natural disaster data by continent, keeping you updated about major environmental events worldwide.
  • Geocoding and location resolution: Effortlessly transform city names or addresses into latitude and longitude coordinates, making location-based queries accurate and seamless for your agent.

Supported Tools & Triggers

Tools
Get Air Quality by CityTool to retrieve real-time air quality data for a specific city.
Get Air Quality by Latitude and LongitudeTool to retrieve real-time air quality data for a specific latitude and longitude.
Get Air Quality Forecast by Latitude and LongitudeTool to retrieve air quality forecast for a specific latitude and longitude.
Get Wildfire Risk Forecast by PlaceTool to retrieve wildfire risk forecast for a specific place.
Geocode by PlaceTool to transform a place name or address into geographic coordinates.
Get Air Quality by CityTool to retrieve real-time air quality data for a specific city.
Get Air Quality by Country CodeTool to retrieve real-time air quality data for a specific country using a 3-letter iso code.
Get Air Quality by Latitude and LongitudeTool to retrieve real-time air quality data for a specific latitude and longitude.
Get Air Quality by Postal CodeTool to retrieve real-time air quality data for a specific postal code and country.
Retrieve latest natural disasters by continentTool to retrieve latest natural disaster data for a specific continent.
Get Elevation by Latitude and LongitudeTool to retrieve elevation statistics (min, max, mean) for a specific latitude and longitude.
Get Elevation by PlaceTool to retrieve elevation or altitude data for a specific location by place name.
Get ILI Forecast by Latitude and LongitudeTool to retrieve 30-day forecast of influenza-like illness (ili) risk using latitude and longitude.
Get Wildfire Data by Latitude and LongitudeTool to retrieve real-time wildfire data for a specific latitude and longitude.
Get Wildfire Data by PlaceTool to retrieve real-time wildfire data for a specific place.
Get Wildfire Risk Forecast by PlaceTool to retrieve wildfire risk forecast for a specific place.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Ambee MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Ambee MCP with Claude Agent SDK

Google ADK

Google ADK

Use Ambee MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Ambee MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Ambee MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Ambee MCP with LlamaIndex

CrewAI

CrewAI

Use Ambee MCP with CrewAI

FAQ

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

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

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

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

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