How to integrate Runway MCP with LangChain

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

Runway logoRunway
Api Key

Runway is a generative AI media platform for creating and transforming video, images, audio, avatars, voices, and creative workflows. It helps teams build high-quality visual content faster with API-driven media generation.

22 Tools

Introduction

This guide walks you through connecting Runway to LangChain using the Composio tool router. By the end, you'll have a working Runway agent that can generate images, create videos, produce speech, make sound effects, and manage documents through natural language commands.

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

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

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TL;DR

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

The Runway MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Runway account. It provides structured and secure access to your generative media, workflows, knowledge documents, and account usage, so your agent can generate media, transform content, run workflows, manage documents, and monitor activity on your behalf.

  • Image and video generation: Have your agent create images from prompts, generate videos from text, or animate existing images with your chosen Runway model.
  • Media transformation and upscaling: Direct your agent to restyle existing videos or upscale images and videos for higher-quality results.
  • Speech and sound creation: Let the agent synthesize speech with supported voices or generate sound effects from written descriptions.
  • Workflow and knowledge management: Instruct your agent to inspect and run published workflows, then create, review, update, or delete knowledge documents used by avatar workflows.
  • Task, credit, and usage monitoring: Have your agent check generation progress, retrieve completed media, cancel running tasks, and review credit balances, quotas, and daily usage.

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 Runway 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 Runway 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: ['runway']
    }
);

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

Configure the agent with the MCP URL

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

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

Cancel Task

Cancel a running Runway task or delete its task record.

Create Document

Create a Runway knowledge document from markdown or plain text for reuse by avatar workflows.

Create Ephemeral Upload

Create only a temporary Runway media upload slot and return its presigned multipart form details and runway:// URI.

Delete Document

Permanently delete a Runway knowledge document by its ID.

Generate Image

Start a Runway text-to-image or reference-guided image generation task and return its task ID and estimated credit cost.

Generate Sound Effect

Generate an audio effect from a text prompt and return the task ID and estimated credit cost.

Generate Speech

Synthesize speech with Runway using an exact model-specific voice and option contract, then return the task ID and estimated credit cost.

Generate Video from Image

Animate one or more input images with a selected Runway model and return the task ID and estimated credit cost.

Generate Video from Text

Start a Runway text-to-video task with a model-specific typed request and return its task ID and estimated credit cost.

Get Document

Get one Runway knowledge document, including its full content and the avatars that currently use it.

Get Organization

Return the connected Runway organization's credit balance, monthly credit spend cap, exact per-model quotas, and current daily generation usage.

Get Task

Get the current status, cost, output URLs, and failure details for a Runway generation task.

Get Usage

Return daily Runway credit usage by model for an optional UTC date range of up to 90 days.

Get Workflow

Get a published Runway workflow version and its graph so node IDs and output keys can be inspected before running the workflow.

Get Workflow Invocation

Get the current status, completed node output URLs, and node or invocation failure details for a Runway workflow invocation.

List Documents

List Runway knowledge documents one page at a time, with sorting and an opaque continuation cursor.

List Workflows

List the connected account's published Runway workflows and their available versions, ordered as returned by Runway.

Run Workflow

Invoke a published Runway workflow, optionally overriding graph node outputs, and return the created workflow invocation ID.

Transform Video

Transform or restyle an existing video with Runway and return the created task ID and estimated credit cost.

Update Document

Change the name, content, or both for an existing Runway knowledge document.

Upscale Image

Start a Runway image upscaling task and return its task ID and estimated credit cost.

Upscale Video

Start a Runway video upscaling task and return its task ID and estimated credit cost.

FAQ

Frequently asked questions

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

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

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