How to Use the Fliki (AI Text-to-Video & Speech API) MCP in LangChain

Q: How do I handle video generation timeouts in LangChain?

You should use a polling loop with the getvideo tool. Since video rendering is asynchronous, your agent can check the status every thirty seconds until the download link is returned.

Q: How do I select a specific voice in a Fliki (AI Text-to-Video & Speech API) LangChain run?

First, have your agent call listvoices to find the correct voice ID. Then, pass that ID into the voice parameter of the generatevideo tool during the next step of your chain.

Generate and track video renders directly inside your LangChain agentic pipelines.

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Connect Fliki (AI Text-to-Video & Speech API) MCP to LangChain

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Setup Fliki (AI Text-to-Video & Speech API) with LangChain

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Claude

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Run sequential video pipelines with LangChain

The `generate_video` tool lets your agent kick off video creation directly from a structured text script. In a LangChain pipeline, the output of your script generation chain feeds directly into this tool, starting the render without manual copying. You can pass voice IDs and aspect ratios dynamically. This means your agent can analyze a target platform like YouTube or TikTok, select the right layout, and trigger the generation automatically.

Track rendering status in your agent chains

The `get_video` tool retrieves the current rendering status and final download URL for your generated files. Because video rendering takes time, your agent can use this tool inside a loop or a delayed step to check when the asset is ready. LangChain's observability tools let you trace these status checks in real-time. You see exactly when the rendering finishes and when the download link becomes active.

Discover and select voices with this MCP Server

The `list_voices` tool gives your agent a list of all available AI voices. Your agent can query this list to find the perfect voice profile based on the script's tone or target audience. By combining this with other tools in your chain, you can filter voices by language or gender before passing them to the generator. This keeps your automated production runs consistent.

Setup guide

Set up Fliki (AI Text-to-Video & Speech API) 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 Fliki (AI Text-to-Video & Speech API) 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({
    "fliki-ai-text-to-video-speech-api-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 Fliki (AI Text-to-Video & Speech API) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Fliki (AI Text-to-Video & Speech API) MCP in LangChain

You should use a polling loop with the `get_video` tool. Since video rendering is asynchronous, your agent can check the status every thirty seconds until the download link is returned.

Yes. This MCP Server exposes the tools directly to your agent. You can pass these tools to a LangChain agent using the adapter, allowing the model to decide when to list voices or generate videos.

First, have your agent call `list_voices` to find the correct voice ID. Then, pass that ID into the voice parameter of the `generate_video` tool during the next step of your chain.

It lets you build fully automated content pipelines. Instead of manually writing scripts and pasting them into a web UI, your agent writes, refines, and renders the video in a single run.

Your script text and voice configurations are sent directly to the API via secure endpoints. Vinkius runs this MCP Server in an isolated sandbox, meaning your API tokens and video assets are never logged or stored on our servers.

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