How to Use the Luma AI (Generative Video & Creative) MCP in AutoGen

Debate your video concepts using AutoGen. Let agents negotiate Luma AI generation parameters to reach the perfect cinematic shot.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Luma AI (Generative Video & Creative) MCP to AutoGen

Create your Vinkius account to connect Luma AI (Generative Video & Creative) to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Luma AI (Generative Video & Creative) with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Negotiate camera paths with AutoGen agents

Set up a creative agent and a technical agent to discuss `lm.camera_control` settings. They can debate whether a dolly or orbit shot better serves the scene's composition. This puts your creative direction through a rigorous validation process. You get the best possible movement parameters by letting the agents iterate on the prompt before execution.

Consensus-based generation in AutoGen

Require agents to agree on the prompt before calling `lm.text_to_video`. One agent can draft the visual description while another reviews it for style consistency. This eliminates single-point-of-failure prompts. You end up with higher-quality video because multiple perspectives vetted the instructions sent to the model.

Handle Luma AI errors with multi-agent logic

Assign a checker agent to monitor `lm.get_generation` status updates. If a generation fails, the agent can automatically suggest a prompt tweak and retry. This creates a self-healing pipeline for your video production. Your main agent doesn't get stuck waiting; the team of agents handles the recovery process.

Setup guide

Set up Luma AI (Generative Video & Creative) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Luma AI (Generative Video & Creative) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Luma AI (Generative Video & Creative)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Luma AI (Generative Video & Creative) data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

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

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Luma AI (Generative Video & Creative)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Luma AI (Generative Video & Creative) data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Luma AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Luma AI (Generative Video & Creative) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Luma AI (Generative Video & Creative) MCP in AutoGen

You configure a group chat where agents propose and critique generation prompts. Once they reach a consensus, the chosen agent executes the tool call.

Yes, you can give one agent the role of 'budget manager' to call `lm.get_credits` before any high-cost generation tasks are approved.

The MCP server is registered as a tool set within your agent's configuration. The agents treat these functions as native capabilities for video creation.

Your API interactions are isolated within the Vinkius MCP environment. Each agent request is authenticated and encrypted to prevent unauthorized access.

All generation prompts and visual outputs are ephemeral. The Vinkius sandbox ensures your creative work remains isolated and secure during the multi-agent deliberation process.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript