# How to integrate Felt MCP with LangChain

```json
{
  "title": "How to integrate Felt MCP with LangChain",
  "toolkit": "Felt",
  "toolkit_slug": "felt",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/felt/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/felt/framework/langchain.md",
  "updated_at": "2026-05-12T10:11:13.566Z"
}
```

## 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.

## Also integrate Felt with

- [OpenAI Agents SDK](https://composio.dev/toolkits/felt/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/felt/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/felt/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/felt/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/felt/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/felt/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/felt/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/felt/framework/cli)
- [Google ADK](https://composio.dev/toolkits/felt/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/felt/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/felt/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/felt/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/felt/framework/crew-ai)

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `FELT_CREATE_OR_UPDATE_ELEMENTS` | Create or Update Elements | Create or update map elements using GeoJSON FeatureCollection format. Creates new elements by default; to update existing elements, include 'felt:id' in the feature's properties. Supports Point, LineString, Polygon, and Multi-type geometries. Returns the created/updated elements with assigned IDs and Felt-specific properties. |
| `FELT_CREATE_PROJECT` | Create Project | Create a new Felt project with the specified name and visibility settings. Projects are organizational containers for grouping related maps within a workspace. |
| `FELT_DELETE_ELEMENT` | Delete Element | Tool to delete a specific element from a map. Use when you have both map and element IDs and need to remove the element permanently. |
| `FELT_DELETE_LAYER` | Delete Layer | Tool to delete a specific layer from a map. Use when you have the map's and layer's IDs and need to remove it permanently. |
| `FELT_DELETE_MAP` | Delete Map | Permanently deletes a map and all its associated data from Felt. WARNING: This action cannot be undone. The map and all its layers, elements, and comments will be permanently removed. Use when you have the map's ID and need to permanently remove it. Returns no content (HTTP 204) on success. |
| `FELT_DELETE_PROJECT` | Delete Project | Tool to delete a project and all its contents. Use when you need to permanently remove a project after confirmation. |
| `FELT_DUPLICATE_MAP` | Duplicate Map | Creates a complete copy of a Felt map including all layers, elements, and configuration. Use when you need to clone an existing map to a new location or create a template-based map. The duplicated map can optionally be placed in a specific project or folder. |
| `FELT_GET_MAP_DETAILS` | Get Map Details | Retrieves comprehensive details of a specific Felt map including title, URL, layers, elements, basemap settings, access permissions, and timestamps. Requires a valid map ID. Use this when you need to: - Get complete map configuration and metadata - Access map layers and elements - Check map permissions and access settings - Retrieve map URLs for sharing |
| `FELT_GET_USER_DETAILS` | Get User Details | Tool to retrieve information about the authenticated user. Use after obtaining a valid token to fetch user profile details. |
| `FELT_LIST_ELEMENT_GROUPS` | List Element Groups | Retrieves all element groups from a Felt map. Element groups are collections of geographic features (points, lines, polygons) organized together. Each group returns a GeoJSON FeatureCollection with the group's elements, along with styling properties like color and symbol. Use this when you need to discover what element groups exist on a map or access grouped geographic data. |
| `FELT_LIST_ELEMENTS` | List Elements | Lists all elements on a specific map as a GeoJSON FeatureCollection. Returns elements that are not in element groups. Use when you need to retrieve the map's direct elements after obtaining a valid map_id. |
| `FELT_LIST_LAYERS` | List Layers | Tool to list all layers on a specific map. Returns all layers present on the map with their complete metadata including status, geometry type, styling, and attributes. Use this when you need to inspect or enumerate the data layers on a map. |
| `FELT_LIST_PROJECTS` | List Projects | Tool to retrieve a list of projects accessible to the user. Use when you need to browse or select from existing projects before proceeding. |
| `FELT_LIST_SOURCES` | List Sources | List all data sources (external data connections) accessible to the authenticated user. Sources represent connections to external data providers like BigQuery, PostgreSQL, S3, Snowflake, etc. Use this to discover available sources before importing data from them into Felt maps. Each source includes sync status, connection type, and access permissions. |
| `FELT_UPDATE_PROJECT` | Update Project | Tool to update an existing project's name or visibility. Use after confirming the project_id. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Felt MCP server is an implementation of the Model Context Protocol that connects your AI agent to Felt. It provides structured and secure access so your agent can perform Felt operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Initialize Composio client

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
```python
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")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

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
```python
# Create Tool Router session for Felt
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['felt']
)

url = session.mcp.url
```

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

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "felt-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)
```

```typescript
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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Felt 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")
```

```typescript
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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
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=['felt']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "felt-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 Felt 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())
```

```typescript
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.

## How to build Felt MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/felt/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/felt/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/felt/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/felt/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/felt/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/felt/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/felt/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/felt/framework/cli)
- [Google ADK](https://composio.dev/toolkits/felt/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/felt/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/felt/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/felt/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/felt/framework/crew-ai)

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- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
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- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
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- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Felt MCP?

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.

### 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 Felt tools.

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

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.

### 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 Felt data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)