How to Use the Kapwing MCP in LangChain

Q: How do I install the Kapwing integration for LangChain?

Run pip install langchain-mcp-adapters langgraph. Then use MultiServerMCPClient to connect to the Kapwing MCP endpoint and fetch the tools for your agent.

Q: Can LangChain agents poll the Kapwing API for video completion?

Yes. The agent calls createrender to get a job ID, then loops getrenderstatus based on its ReAct reasoning until the download URL is ready.

Build complex video generation pipelines with Kapwing and LangChain.

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Connect Kapwing MCP to LangChain

Create your Vinkius account to connect Kapwing 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 Kapwing with LangChain

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Automate video generation with LangChain

The `create_render` tool takes a JSON project definition containing width, height, and layer data to initiate a media build on Kapwing's servers. You pass your raw text or asset URLs into a LangChain prompt, and your agent structures the exact JSON payload required to start the job. Because this runs through a chain, you track every token and latency metric in LangSmith. If a text layer exceeds the video width, the agent catches the error and regenerates the payload before moving to the next step.

Track render jobs in an MCP Server loop

The `get_render_status` tool retrieves the current progress and final download URL for a specific render ID. Your ReAct agent uses this inside a polling loop, checking the API until the video finishes processing. You don't write custom retry logic. The agent holds the render ID in its scratchpad, waits for the completion signal, and then immediately passes the download URL to your next tool, like a database insertion or a Slack notification step.

Manage media assets across pipelines

The `list_renders` tool returns a paginated list of all media generated by your account. LangChain agents use this to audit past jobs or find specific historical assets without leaving the execution context. You combine this with other integrations. The agent pulls the last 50 renders, filters them by specific dimensions or text layers, and pushes the results into a vector store for future queries.

Setup guide

Set up Kapwing 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 Kapwing 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({
    "kapwing-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 Kapwing transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Kapwing. 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.

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Common questions about Kapwing MCP in LangChain

Run `pip install langchain-mcp-adapters langgraph`. Then use `MultiServerMCPClient` to connect to the Kapwing MCP endpoint and fetch the tools for your agent.

Yes. The agent calls `create_render` to get a job ID, then loops `get_render_status` based on its ReAct reasoning until the download URL is ready.

You define the text content in your LangChain prompt. The agent structures the JSON project definition, including width and height, and sends it to the MCP Server.

Every call to the Kapwing API logs directly to LangSmith. You see the exact JSON payload sent to the tools and the response latency.

The server processes JSON project definitions containing video asset URLs and text layers. Vinkius isolates this execution in an ephemeral V8 sandbox, destroying the environment immediately after the job fires.

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