How to integrate Felt MCP with LangChain

This guide walks you through connecting Felt to LangChain using the Composio tool router. By the end, you'll have a working Felt agent that can add geojson features to an existing map, duplicate a project map for a new client, delete a layer from your city zoning map through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Felt account through Composio's Felt MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Felt is a modern GIS platform for creating, editing, and sharing interactive maps. It streamlines collaboration and mapping workflows for teams working with geospatial data.

15 Tools

Introduction

This guide walks you through connecting Felt to LangChain using the Composio tool router. By the end, you'll have a working Felt agent that can add geojson features to an existing map, duplicate a project map for a new client, delete a layer from your city zoning map through natural language commands.

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

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

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OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Felt MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Felt account. It provides structured and secure access to your maps, projects, and geospatial data, so your agent can perform actions like creating projects, modifying maps, updating map elements, and retrieving user or map details on your behalf.

  • Project and map creation: Instantly have your agent create new Felt projects and initialize interactive maps to kickstart geospatial workflows.
  • Element and layer management: Direct your agent to add, update, or delete map elements and layers—making it easy to modify map content or clean up unwanted data.
  • Map duplication and deletion: Clone existing maps for experimentation or backup, or remove entire maps and projects when they’re no longer needed.
  • Detailed map and user insights: Retrieve comprehensive details about any specific map or your authenticated user profile for streamlined map management and reporting.

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 Felt 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 Felt 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: ['felt']
    }
);

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

Configure the agent with the MCP URL

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

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

Create or Update Elements

Create or update map elements using GeoJSON FeatureCollection format.

Create Project

Create a new Felt project with the specified name and visibility settings.

Delete Element

Tool to delete a specific element from a map.

Delete Layer

Tool to delete a specific layer from a map.

Delete Map

Permanently deletes a map and all its associated data from Felt.

Delete Project

Tool to delete a project and all its contents.

Duplicate Map

Creates a complete copy of a Felt map including all layers, elements, and configuration.

Get Map Details

Retrieves comprehensive details of a specific Felt map including title, URL, layers, elements, basemap settings, access permissions, and timestamps.

Get User Details

Tool to retrieve information about the authenticated user.

List Element Groups

Retrieves all element groups from a Felt map.

List Elements

Lists all elements on a specific map as a GeoJSON FeatureCollection.

List Layers

Tool to list all layers on a specific map.

List Projects

Tool to retrieve a list of projects accessible to the user.

List Sources

List all data sources (external data connections) accessible to the authenticated user.

Update Project

Tool to update an existing project's name or visibility.

FRAMEWORKS

How to build Felt MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Felt MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Felt MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Felt MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Felt MCP with Claude Cowork

Codex logo

Codex

Use Felt MCP with Codex

OpenClaw logo

OpenClaw

Use Felt MCP with OpenClaw

Hermes logo

Hermes

Use Felt MCP with Hermes

CLI logo

CLI

Use Felt MCP with CLI

Google ADK logo

Google ADK

Use Felt MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Felt MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Felt MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Felt MCP with LlamaIndex

CrewAI logo

CrewAI

Use Felt MCP with CrewAI

FAQ

Frequently asked questions

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

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

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