How can I preview the output without fully committing my prompt?

Video generation actions using the Runway engines are directly invoked through asynchronous background tasks. Instead of an exact real-time preview, you can start small 5-second generations or iterate closely on your prompt texts until they produce the final aesthetic you want.

Does this integration process video rendering in real-time?

No. Video rendering is asynchronous. When you submit a prompt or an image, the AI will dispatch a background task to Runway's servers and receive a 'task_id'. You will need to ask the AI to verify the status of this task later, usually after a few minutes, to fetch the final video URL.

Can I use Gen-4 models for free?

No. Runway requires credits for all generations, and Gen-4 Turbo consumes significantly more credits than earlier models or standard actions. You must maintain sufficient active credits or a paid subscription on your Runway account for the AI to successfully execute the rendering jobs.

Runway ML MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: Runway ML
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Runway ML through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Runway ML Assistant",
            instructions=(
                "You help users interact with Runway ML. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Runway ML"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Runway ML MCP Server

Connect your AI to Runway ML, the pioneer in applied AI research shaping the next era of art, entertainment and human creativity. This powerful integration empowers you to tap directly into Runway's cutting-edge Gen-3 Alpha and Gen-4 video generation models right from your conversational workspace. Produce stunning, realistic, or highly stylized video clips simply by typing out your vision or providing a reference image.

The OpenAI Agents SDK auto-discovers all 10 tools from Runway ML through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Runway ML, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Text-to-Video Generation — Write detailed prompts to synthesize entirely new, imaginative scenes using gen3_turbo, gen4_turbo, or the standard text_to_video tooling.
Image-to-Video Animation — Bring still images to life using image_to_video or precisely guide the motion of a starting image with a textual director prompt using image_text_to_video.
Advanced Interpolation — Seamlessly blend two distinct keyframe images into one smooth transitional motion clip (interpolate).
Complete Task Management — Maintain full control over costly generation pipelines. Easily check job status or output URLs (get_task, list_tasks), cancel ongoing renders to save credits (cancel_task), and audit your organization's billing usage (get_organization).

The Runway ML MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Runway ML to OpenAI Agents SDK via MCP

Follow these steps to integrate the Runway ML MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from Runway ML

Why Use OpenAI Agents SDK with the Runway ML MCP Server

OpenAI Agents SDK provides unique advantages when paired with Runway ML through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Runway ML + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Runway ML MCP Server delivers measurable value.

Automated workflows: build agents that query Runway ML, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries Runway ML, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Runway ML tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Runway ML to resolve tickets, look up records, and update statuses without human intervention

Runway ML MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect Runway ML to OpenAI Agents SDK via MCP:

cancel_task

This action is irreversible. Cancels a running generation task

gen3_turbo

Quick 5-second video generation using Gen-3 Alpha Turbo

gen4_turbo

High-quality video generation using Gen-4 Turbo

get_organization

Retrieves Runway ML organization and credit details

get_task

Look for SUCCEEDED status and output URL. Retrieves the status and output of a generation task

image_text_to_video

Generates video from both an image and a text prompt

image_to_video

Specify source image URL, model, and duration. Animates a still image into a video

interpolate

Creates smooth motion between two keyframe images

list_tasks

Lists recent generation tasks

text_to_video

Specify prompt, model, and duration (5 or 10). Returns a task ID. Generates a video from a text prompt

Example Prompts for Runway ML in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Runway ML immediately.

"Create a 5 second cinematic video showing a sunset over an alien planet using Runway Gen-3 Turbo."

"Take this reference image URL and animate it with Gen-3 Turbo to make the camera slowly pan backwards."

"List all my ongoing tasks on Runway to see if the video has finished rendering."

Troubleshooting Runway ML MCP Server with OpenAI Agents SDK

Common issues when connecting Runway ML to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Runway ML + OpenAI Agents SDK FAQ

Common questions about integrating Runway ML MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.