SIGN UPLOG IN
X (Twitter)LinkedInGitHubDiscordYouTube

How to integrate Ambee MCP with LangChain

Connect LangChain to Ambee MCP. Show today's air quality for paris, get wildfire risk forecast for california, and more using natural language, with authentication handled for you.

Get started for freeGet a demo
Ambee logoAmbee
Api Key

Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows.

27 Tools

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 check air quality and pollen levels, fetch weather forecasts, track wildfires and natural disasters, and geocode places 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.

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Ambee with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAIPydantic AIAutoGen

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 environmental data, so your agent can check air quality history, monitor current weather, review pollen conditions, assess fire risk, and track natural disasters on your behalf.

  • Air quality history: Have your agent retrieve historical air quality data for any location using its latitude and longitude.
  • Weather conditions and forecasts: Let the agent check current temperature, pressure, humidity, and wind conditions, or retrieve forecasts for up to 72 hours by coordinates.
  • Pollen monitoring: Direct your agent to find current pollen conditions by place or coordinates and retrieve 48-hour pollen forecasts by place name.
  • Natural disaster tracking: Instruct your agent to retrieve recent or historical earthquake, flood, drought, and volcanic eruption data by country or coordinates.
  • Fire risk and location details: Have your agent assess forecast fire risk and Fire Weather Index conditions, or turn coordinates into a readable address, place name, and postal code.

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

Step by step10 STEPS
1

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
2

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.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters 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/core is the core agent framework
  • dotenv/config loads environment variables
4

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
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Ambee functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
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
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['ambee']
    }
);

const 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
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "ambee-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "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
  • getTools() retrieves all available Ambee tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Ambee related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept 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 invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Ambee and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['ambee']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "ambee-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Ambee related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

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.
TOOLS

Supported Tools

Every Ambee action and event your agent gets out of the box.

Geocode by Place

Tool to transform a place name or address into geographic coordinates.

Get Air Quality by City

Tool to retrieve real-time air quality data for a specific city.

Get Air Quality by Country Code

Retrieve real-time air quality readings from monitoring stations in a country using a 3-letter ISO country code.

Get Air Quality by Latitude and Longitude

Tool to retrieve real-time air quality data for a specific latitude and longitude.

Get Air Quality by Postal Code

Retrieve real-time air quality data for a specific postal code and country.

Get Air Quality Forecast by Latitude and Longitude

Tool to retrieve air quality forecast for a specific latitude and longitude.

Get Air Quality History by Latitude and Longitude

Tool to retrieve historical air quality data for a specific latitude and longitude.

Retrieve latest natural disasters by continent

Tool to retrieve latest natural disaster data for a specific continent.

Get historical natural disasters by date range

Retrieve global historical natural disaster events from a specified starting date, optionally filtering by event type and paginating results.

Get historical natural disasters by country code

Tool to retrieve historical natural disaster data for a country by ISO country code.

Get historical natural disasters by coordinates

Tool to retrieve historical natural disaster data for a specific location by coordinates with date range.

Get latest disasters by country code

Tool to retrieve latest natural disaster data for a specific country using ISO country code.

Get latest natural disasters by coordinates

Tool to retrieve latest natural disaster data (earthquakes, floods, droughts, volcanic eruptions) for a specific location using geographic coordinates.

Get Elevation by Latitude and Longitude

Tool to retrieve elevation statistics (min, max, mean) for a specific latitude and longitude.

Get Elevation by Place

Retrieve elevation statistics (minimum, maximum, and mean) for a location by place name.

Get Fire Risk by Latitude and Longitude

Retrieve weekly fire risk forecasts and Fire Weather Index data for geographic coordinates in North America, covering the next four weeks.

Get ILI Forecast by Latitude and Longitude

Retrieve an influenza-like illness (ILI) risk forecast for latitude and longitude, optionally including pollen and weather details.

Get Pollen Forecast by Place

Tool to retrieve 48-hour pollen forecast for a specific location by place name.

Get Pollen History by Place

Tool to retrieve historical pollen data for a specific place name.

Get Latest Pollen Data by Latitude and Longitude

Tool to retrieve latest pollen data for a specific geographic location using latitude and longitude coordinates.

Get Latest Pollen by Place

Tool to retrieve latest pollen data for a specific place by name.

Get Weather Forecast by Latitude and Longitude

Tool to retrieve weather forecast for a specific latitude and longitude (up to 72 hours).

Get Current Weather by Latitude and Longitude

Tool to retrieve current weather data for a specific latitude and longitude, including temperature, pressure, humidity, and wind conditions.

Get Wildfire Data by Latitude and Longitude

Retrieves real-time wildfire data for a specific geographic location using latitude and longitude coordinates.

Get Wildfire Data by Place

Retrieve recent wildfire data by place name, optionally filtering for satellite-detected or officially reported fires.

Get Wildfire Risk Forecast by Place

Retrieve weekly wildfire risk forecasts for a place in North America over the next four weeks.

Reverse Geocode by Latitude and Longitude

Tool to convert geographic coordinates into human-readable location data (address, place name, postal code).

FRAMEWORKS

How to build Ambee MCP Agent with another framework

Claude Code logo

Claude Code

Use Ambee MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Ambee MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Ambee MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Ambee MCP with Claude Agent SDK

Codex logo

Codex

Use Ambee MCP with Codex

OpenClaw logo

OpenClaw

Use Ambee MCP with OpenClaw

Hermes logo

Hermes

Use Ambee MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Ambee MCP with Atomic Agent

Google ADK logo

Google ADK

Use Ambee MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Ambee MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Ambee MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Ambee MCP with LlamaIndex

CrewAI logo

CrewAI

Use Ambee MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Ambee MCP with Pydantic AI

AutoGen logo

AutoGen

Use Ambee MCP with AutoGen

FAQ

Frequently asked questions

Start with Ambee.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Ambee tool your agent needs.Free to start.

Start building