How to Use the Spiritme MCP in LangChain

Q: How do I handle video failure in LangChain?

You can check the job status using getjobstatus. If it reports an error, your agent needs logic to decide if it should retry the process or flag the content creator. The MCP Server tools give you the necessary inputs for that decision.

Q: Can Spiritme and LangChain handle multiple language assets?

Absolutely. You can list all available voices using listvoices and then use getvoice to inspect specific voice parameters, ensuring the correct localized audio is generated for your personalized video.

Q: What data does Spiritme provide regarding video creation in LangChain?

Spiritme provides details on templates (listtemplates) and avatars (getavatar). When running a chain, you'll get the necessary IDs and metadata to build out a comprehensive record of what was created.

Q: What if I need to get details about existing videos with Spiritme and LangChain?

You just call listvideos. This gives you a list of video IDs, which you can then pass directly into the getjobstatus tool to pull specific metadata on any project.

Build multi-step video pipelines with LangChain.

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Create your Vinkius account to connect Spiritme to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Spiritme with LangChain

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Orchestrate Video Generation with MCP Server

The `generate_video` tool executes the core function: creating a personalized, multilingual video using selected avatars and templates. You can build complex chains where calling `list_templates` provides the necessary ID to feed into `get_template`, which then feeds parameters directly into the generation step. It's perfect for multi-step logic. For example, you first use `get_avatar` to verify a presenter's details, and that output determines the inputs used when calling `generate_audio`. This makes your agent decide not just what to do, but in exactly what order.

Manage Assets via LangChain

Need to know what assets are available? Use `list_avatars` and `list_voices` to gather all potential presenters and voices. Your agent can loop through these lists, checking criteria like availability or specific languages before making a decision. If you're building complex logic, your chain might first call `get_avatar` for three different options, then pass those details to a function that determines the best one based on external metrics.

Monitor Job Status with LangChain

`list_videos` gives you an overview of all projects. When a video is running, `get_job_status` tells your agent exactly where it stands—is it queued, processing, or finished? This allows the chain to pause and wait for confirmation before proceeding. If something goes wrong, the state machine can use `check_spiritme_status` to verify connectivity first. This prevents a whole pipeline from crashing because of a temporary network blip.

Setup guide

Set up Spiritme 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 Spiritme 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({
    "spiritme-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 Spiritme 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 Spiritme. 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 Spiritme MCP in LangChain

You can check the job status using `get_job_status`. If it reports an error, your agent needs logic to decide if it should retry the process or flag the content creator. The MCP Server tools give you the necessary inputs for that decision.

Absolutely. You can list all available voices using `list_voices` and then use `get_voice` to inspect specific voice parameters, ensuring the correct localized audio is generated for your personalized video.

Spiritme provides details on templates (`list_templates`) and avatars (`get_avatar`). When running a chain, you'll get the necessary IDs and metadata to build out a comprehensive record of what was created.

Yes. If a video job needs to be scrapped or deleted before completion, you can use the `delete_video` tool. It's simple cleanup logic that fits right into your multi-step workflow.

You just call `list_videos`. This gives you a list of video IDs, which you can then pass directly into the `get_job_status` tool to pull specific metadata on any project.

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