How to integrate Luma Labs MCP with LangChain

This guide walks you through connecting Luma Labs to LangChain using the Composio tool router. By the end, you'll have a working Luma Labs agent that can generate images, create videos, edit your media, reframe your videos, and manage your files through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Luma Labs account through Composio's Luma Labs MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Luma Labs is an AI media generation platform for creating images, videos, and reusable assets through its Agents API. Use it to turn prompts and files into polished visual content fast.

11 Tools

Introduction

This guide walks you through connecting Luma Labs to LangChain using the Composio tool router. By the end, you'll have a working Luma Labs agent that can generate images, create videos, edit your media, reframe your videos, and manage your files through natural language commands.

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

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

Also integrate Luma Labs with

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 Luma Labs project to Composio
  • Create a Tool Router MCP session for Luma Labs
  • Initialize an MCP client and retrieve Luma Labs tools
  • Build a LangChain agent that can interact with Luma Labs
  • 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 Luma Labs MCP server, and what's possible with it?

The Luma Labs MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Luma Labs account. It provides structured and secure access to your AI generations and reusable files, so your agent can create images and videos, edit media, reframe videos, extract image layers, and manage files on your behalf.

  • AI image creation and editing: Have your agent generate new images from your instructions or submit edits to existing images.
  • Video generation and editing: Direct your agent to create videos with Ray 3.2 or edit existing footage based on your request.
  • Video reframing: Let the agent reframe a video to better suit a different composition or viewing format.
  • Image layer extraction: Instruct your agent to separate an image into reusable layers for further creative work.
  • Generation and file management: Have your agent upload, list, inspect, or delete reusable files, and check generation progress, failures, and finished outputs.

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 Luma Labs 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 Luma Labs 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: ['luma_labs']
    }
);

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

Configure the agent with the MCP URL

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

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

Delete File

Soft-delete a Luma file so new generations cannot reference it.

Edit Image

Submit a paid, asynchronous Luma image-edit job.

Edit Video

Submit a paid, asynchronous Ray 3.

Extract Image Layers

Submit a paid, asynchronous Luma image layer-extraction job.

Generate Image

Submit a paid, asynchronous Luma image generation job.

Generate Video

Submit a paid, asynchronous Ray 3.

Get File

Get a Luma file's metadata and current ingest, moderation, or deletion state.

Get Generation

Get the current state, failure details, and output URLs for a Luma generation.

List Files

List files in the connected Luma account, newest first, with optional state and purpose filters.

Reframe Video

Submit a paid, asynchronous Ray 3.

Upload File

Directly upload file bytes to Luma and return the file ID, metadata, and current upload/ingest state.

FRAMEWORKS

How to build Luma Labs MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Luma Labs MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Luma Labs MCP with Claude Agent SDK

Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

Use Luma Labs MCP with Claude Cowork

Codex logo

Codex

Use Luma Labs MCP with Codex

OpenClaw logo

OpenClaw

Use Luma Labs MCP with OpenClaw

Hermes logo

Hermes

Use Luma Labs MCP with Hermes

CLI logo

CLI

Use Luma Labs MCP with CLI

Google ADK logo

Google ADK

Use Luma Labs MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Luma Labs MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Luma Labs MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Luma Labs MCP with LlamaIndex

CrewAI logo

CrewAI

Use Luma Labs MCP with CrewAI

FAQ

Frequently asked questions

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

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

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