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

Q: Can LangChain handle Luma AI (Generative Video & Creative) credit management?

Yes, you can include a tool call to lm.getcredits before any generation step. This lets your agent verify the balance before attempting to burn compute.

Build complex cinematic pipelines in LangChain. Chain Luma AI video generation tools together to automate your pre-visualization workflow.

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

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

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Setup Luma AI (Generative Video & Creative) with LangChain

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Claude

Perplexity

Automate multi-step video production in LangChain

Connect your video generation logic directly into your agent's reasoning chain. You can feed the output of a prompt into `lm.extend_video` or `lm.interpolate` to maintain visual continuity across complex sequences. Your agent decides exactly when to call these functions based on the current chain state. This eliminates manual trigger steps and keeps your creative pipeline moving without human intervention.

Fine-grained camera control for LangChain agents

Direct your virtual camera with precision using `lm.camera_control` parameters. Your agent can calculate specific pan, tilt, or orbit values to match your project's storyboards exactly. This gives you programmatic control over the frame composition. It's built for developers who need specific cinematic movements rather than random generation.

Monitor Luma AI performance via LangSmith

Track every tool call through your LangSmith traces to debug generation latency. You can watch how your agent uses `lm.get_generation` to poll for results and manage credit usage via `lm.get_credits` in real-time. Visibility is high for every step of the process. You'll see exactly which parameters were sent to the model and how long the render took, letting you optimize your chains for speed.

Setup guide

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

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Luma AI (Generative Video & Creative) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "luma-ai-generative-video-creative-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Luma AI (Generative Video & Creative) transactions"
    })
    print(result["messages"][-1].content)

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

You pass the tool definitions into your agent's execution loop. The agent selects the right tool based on your prompt, allowing for dynamic chaining of generation tasks.

Yes, you can include a tool call to `lm.get_credits` before any generation step. This lets your agent verify the balance before attempting to burn compute.

The server is stateless, but you can store generation IDs in your LangChain memory buffer. This allows the agent to reference past clips when calling `lm.extend_video` later.

All requests are authenticated via your endpoint token at the Vinkius gateway. Your local LangChain environment never touches raw API keys directly.

Your prompts and generated video URLs stay within your secure execution context. Vinkius operates a zero-trust sandbox, ensuring your creative assets aren't logged or exposed to third parties.

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