How to Use the Kling AI (Generative Video & Image) MCP in LangChain

Q: How do I use Kling AI (Generative Video & Image) with LangChain to create a video and wait for it?

You'll create an agent that calls the texttovideo tool. In your chain, you then use a loop or a waiting step that repeatedly calls getvideotask with the returned task ID until the status is 'succeed'.

Q: Does the LangChain integration for Kling AI (Generative Video & Image) support image generation?

Absolutely. Your agent can call the texttoimage tool to generate images and getimagetask to poll for the results. You can build chains that create images first, then animate them using imagetovideo.

Build automated video pipelines with LangChain. Chain together prompts, generation, and polling for hands-off asset creation.

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Connect Kling AI (Generative Video & Image) MCP to LangChain

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Setup Kling AI (Generative Video & Image) with LangChain

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Claude

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Build Video Generation Pipelines

Use LangChain to create agents that generate video from start to finish. Your agent can call `text_to_video` with a prompt, grab the task ID, and then automatically poll the `get_video_task` endpoint until the MP4 URL is ready. No manual steps. This isn't just one-off generation. You can build complex chains where an initial agent brainstorms prompts, a second agent calls the Kling AI tools, and a final agent checks the output. Every step is traced in LangSmith, so you see exactly what your agent did and how long it took.

Create Dynamic Product Try-Ons

Build an agent that automates your e-commerce visuals. Feed it a photo of a model and a picture of a shirt, and the agent uses `virtual_try_on` to create the composite image. It gets a task ID back and polls `get_tryon_task` to fetch the final result. You can extend this into a full content workflow. Have one agent use `text_to_image` to generate lifestyle backgrounds, then another agent combines those with your product shots using `image_to_video`. LangChain lets you connect these different MCP tools into a single, automated process.

Generative Content Agents with LangChain

Go beyond simple tool calls. With LangChain, your agent can decide which Kling AI tool to use based on the input. If it gets a text prompt, it uses `text_to_video`. If it gets an image and audio, it might choose `lip_sync_video`. This lets you build more flexible systems. You can create a ReAct agent that attempts to generate a video, checks the status with `list_video_tasks`, and retries with a different model if the first attempt fails. It's about building resilient, multi-step logic around the Kling AI API.

Setup guide

Set up Kling AI (Generative Video & Image) 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 Kling AI (Generative Video & Image) 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({
    "kling-ai-generative-video-image-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 Kling AI (Generative Video & Image) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Kling AI (Generative Video & Image) MCP in LangChain

You'll create an agent that calls the `text_to_video` tool. In your chain, you then use a loop or a waiting step that repeatedly calls `get_video_task` with the returned task ID until the status is 'succeed'.

Yes, that's the point. You can have a chain that pulls product descriptions from a database, uses a model to turn them into video prompts, calls the Kling AI `text_to_video` tool, and then saves the final URL back to your database.

Absolutely. Your agent can call the `text_to_image` tool to generate images and `get_image_task` to poll for the results. You can build chains that create images first, then animate them using `image_to_video`.

LangChain's expression language (LCEL) has built-in error handling. You can add `.with_fallbacks()` to your chain to retry a failed `text_to_video` call or direct the agent to try a different tool if the first one fails.

The server processes the text prompts and image URLs you provide for generation. Vinkius isolates each request in an ephemeral sandbox, and your connection is secured by your single endpoint token. The server doesn't store your input data after the task is complete.

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