How to integrate Runway MCP with Autogen

This guide walks you through connecting Runway to AutoGen 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 AutoGen 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 AutoGen 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 AutoGen 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
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Runway
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Runway tools
  • Run a live chat loop where you ask the agent to perform Runway operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Runway account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Runway via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Runway connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Runway session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["runway"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Runway tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Runway assistant agent with MCP tools
    agent = AssistantAgent(
        name="runway_assistant",
        description="An AI assistant that helps with Runway operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Runway tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Runway related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Runway tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Runway and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Runway session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["runway"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Runway assistant agent with MCP tools
        agent = AssistantAgent(
            name="runway_assistant",
            description="An AI assistant that helps with Runway operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Runway related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Runway through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Runway, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
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. Autogen 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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