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How to integrate Radar MCP with LangChain

Connect LangChain to Radar MCP. Autocomplete address based on partial input, get users currently inside geofence, and more using natural language, with authentication handled for you.

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Radar is a location infrastructure platform providing APIs and SDKs for geofencing, geocoding, and location tracking. It helps developers add precise, scalable location features to any app with minimal effort.

37 Tools

Introduction

This guide walks you through connecting Radar to LangChain using the Composio tool router. By the end, you'll have a working Radar agent that can geocode addresses and IPs, manage geofences and trips, track location updates, search nearby places, and get route directions through natural language commands.

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

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

Also integrate Radar with

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TL;DR

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

The Radar MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Radar account. It provides structured and secure access to advanced location services, so your agent can perform actions like geocoding addresses, managing geofences, tracking trips, searching places, and retrieving location context on your behalf.

  • Address and place autocomplete: Instantly get relevant address or place suggestions based on partial user input, improving data quality and user experience.
  • Precise geocoding and location context: Convert full addresses to latitude/longitude and fetch rich context—including region, geofence, and place details—for any set of coordinates.
  • Geofence management: Retrieve, create, or delete geofences to define dynamic boundaries and monitor activity within specific areas automatically.
  • Trip creation and tracking: Start, fetch, or delete trips to enable real-time location tracking and trip management for devices or users.
  • Live user monitoring in geofences: Effortlessly list all users currently inside a defined geofence, supporting presence-based automation and analytics.

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 Radar 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 Radar 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: ['radar']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Radar 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 Radar tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "radar-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 Radar MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Radar 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 Radar 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 Radar 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: ['radar']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "radar-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 Radar 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 Radar 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 Radar action and event your agent gets out of the box.

Autocomplete Address or Place

Tool to autocomplete partial addresses and place names based on relevance and proximity.

Create Beacon

Tool to create a new beacon in Radar.

Create Trip

Creates a new Radar trip with an external ID and optional origin, destination, mode, and metadata details.

Delete Beacon

Tool to delete a beacon by its Radar ID.

Delete Geofence

Tool to delete a geofence by ID.

Delete Geofence By Tag

Tool to delete a geofence by tag and external ID.

Delete Trip

Tool to delete a trip by its Radar ID or external ID.

Delete User

Delete a Radar user by Radar _id, userId, or deviceId.

Forward Geocode

Geocode a full address into geographic coordinates, with optional layer and country filters.

Get Beacon

Retrieve a beacon by its Radar ID, returning the supported beacon fields.

Get Beacon By Tag

Look up a beacon by tag and external ID, returning the first beacon in the filtered results.

Get Context for Location

Tool to retrieve context for a given location.

Get Geofence

Retrieve a geofence by Radar ID or by tag and external ID, returning the supported geofence fields.

Get Places Settings

Request Radar project Places settings.

Get Route Directions

Tool to get turn-by-turn directions between multiple locations.

Get Route Matrix

Tool to calculate travel distance and duration between multiple origins and destinations for up to 625 routes.

Get Trip

Tool to retrieve a trip by ID or externalId.

Get User

Get a user by Radar _id, userId, or deviceId.

Get Users in Geofence

Retrieve users currently within a geofence identified by its tag and external ID.

IP Geocode

Tool to geocode an IP address to city, state, and country.

List Events

Tool to list events.

List Geofences

List a page of geofences sorted by updated time, with optional filters and pagination cursors.

List Trips

Lists trips sorted by updated time, with optional user and external ID filters and ID-based cursor parameters.

List Users

Tool to list Radar users sorted by update time.

Reverse Geocode

Tool to convert geographic coordinates to structured addresses.

Route Distance

Computes travel distance and duration between one origin and one destination for a selected travel mode.

Search Geofences

Tool to search for geofences near a given location.

Search Places Near Location

Tool to search for places near given coordinates.

Search Users Near Location

Tool to search for users near a location.

Track Location Update

Tool to track a user's location update.

Update Places Settings

Submits a Places settings update for a Radar project with an optional chain metadata enabled flag.

Update Trip

Tool to update a trip.

Update Trip By ID

Tool to update a trip status by Radar _id or external ID.

Upsert Beacon by ID

Tool to create or update a beacon by Radar _id.

Upsert Beacon by Tag

Tool to create or update a beacon by tag and externalId.

Upsert Geofence

Tool to create or update a geofence by tag and externalId.

Upsert Geofence By ID

Tool to create or update a geofence by Radar _id.

FRAMEWORKS

How to build Radar MCP Agent with another framework

Claude Code logo

Claude Code

Use Radar MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Radar MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Radar MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Radar MCP with Claude Agent SDK

Grok Bot logo

Grok Bot

Use Radar MCP with Grok Bot

Codex logo

Codex

Use Radar MCP with Codex

OpenClaw logo

OpenClaw

Use Radar MCP with OpenClaw

Hermes logo

Hermes

Use Radar MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Radar MCP with Atomic Agent

Google ADK logo

Google ADK

Use Radar MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Radar MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Radar MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Radar MCP with LlamaIndex

CrewAI logo

CrewAI

Use Radar MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Radar MCP with Pydantic AI

AutoGen logo

AutoGen

Use Radar MCP with AutoGen

FAQ

Frequently asked questions

With a standalone Radar MCP server, the agents and LLMs can only access a fixed set of Radar tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Radar and many other apps based on the task at hand, all through a single MCP endpoint.

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 Radar tools.

Yes, absolutely. You can configure which Radar 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.

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 Radar data and credentials are handled as safely as possible.

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