How to Use the Verbit MCP in LangChain

Q: How does Verbit and LangChain handle multi-step transcription?

Your agent uses a chain that first calls createjob to upload the file. It then cycles through getjob until the status is 'complete.' Finally, it executes gettranscript to get the data.

Q: Does the MCP Server support persistent context when using LangChain?

You'll need to use client.session() to maintain state between job checks or multiple transcript requests within a single workflow run.

Build multi-step reasoning pipelines for transcription with LangChain.

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Connect Verbit MCP to LangChain

Create your Vinkius account to connect Verbit 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 Verbit with LangChain

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LangChain: Full Observability for Media Tasks

The `create_job` tool lets your chain upload a media file immediately. You can track the entire lifecycle of that job, which is key for multi-step pipelines. The agent doesn't just call tools; it knows the sequence. After calling `create_job`, the next step uses `get_job` to check progress before finally executing `get_transcript`. It’s all traceable.

MCP Server: Chaining Media Processing Steps

When building chains, you need tools that feed into each other. Verbit provides exactly this: uploading a file (`create_job`) generates an ID that is immediately passed to the `get_job` tool. This makes state management simple. This mechanism lets your agent handle asynchronous tasks like transcription without needing external polling logic in your code.

LangChain: Handling Asynchronous Transcription

Transcribing media takes time, and the chain must wait. The `get_job` tool is designed for this pattern. Your agent can loop through checks—calling `get_job` repeatedly—until the status indicates completion. Once the job is done, the final step is straightforward: calling `get_transcript` downloads the finished content directly into your workflow.

Setup guide

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

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Common questions about Verbit MCP in LangChain

Your agent uses a chain that first calls `create_job` to upload the file. It then cycles through `get_job` until the status is 'complete.' Finally, it executes `get_transcript` to get the data.

The server handles uploading various types of media files for transcription. You just need to pass the file through the `create_job` tool.

Verbit primarily handles your raw media files and the resulting transcripts. Your AI client interacts with these file uploads and job status updates through the MCP Server.

Yes, since Verbit is an MCP Server, your agent can aggregate tool calls alongside other API interactions, allowing you to observe latency and token use for every step.

You'll need to use `client.session()` to maintain state between job checks or multiple transcript requests within a single workflow run.

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