How to Use the AssemblyAI MCP in LangChain

Q: How do I connect the AssemblyAI MCP Server to LangChain?

Install langchain-mcp-adapters via pip. Initialize a MultiServerMCPClient pointing to your Vinkius endpoint, then pass the fetched tools directly into your createagent setup.

Build audio-processing chains where LangChain agents automatically transcribe media and chunk the text for downstream tasks.

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Connect AssemblyAI MCP to LangChain

Create your Vinkius account to connect AssemblyAI 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 AssemblyAI with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain audio into LangChain workflows.

This MCP Server drops AssemblyAI transcription right into your ReAct agent pipelines using the `transcribe_audio` tool. You pass an audio URL. The model kicks off an async speech-to-text job without breaking the chain's execution flow. Once the job finishes, the agent decides what to do next based on intermediate results. It might call `get_transcript` to read the full text, or route the output directly into a vector database for immediate querying.

Granular text extraction.

Dumping a massive block of text usually breaks downstream LLM context windows. This server gives you chunking out of the box. Your agent uses `get_transcript_paragraphs` to pull logical blocks of dialogue. If you need finer control for sentiment analysis or translation chains, `get_transcript_sentences` feeds line-by-line arrays into your next prompt. Every MCP tool call tracks perfectly in LangSmith so you see exactly how many tokens the transcription consumed.

Lifecycle management for media jobs.

Your multi-step pipelines need to clean up after themselves. Agents can check the status of previous runs using `list_transcripts` to find orphaned jobs from interrupted chains. When a temporary audio file finishes processing, the agent fires `delete_transcript` to wipe the record from the provider. You keep your workspace clean and avoid paying for storage you no longer need.

Setup guide

Set up AssemblyAI 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 AssemblyAI 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({
    "assemblyai-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 AssemblyAI 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 AssemblyAI. 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 AssemblyAI MCP in LangChain

Install `langchain-mcp-adapters` via pip. Initialize a `MultiServerMCPClient` pointing to your Vinkius endpoint, then pass the fetched tools directly into your `create_agent` setup.

Yes, because every operation routes through standard tool calls. LangSmith automatically traces the inputs and outputs of functions alongside your usual latency metrics.

The API requires a publicly accessible URL for the media. Your agent needs to upload local files to a temporary bucket first, then pass that link to the transcription tool.

Build a loop into your graph that periodically checks the job status. The agent starts the process, then calls the fetch command on a delay until the payload returns a completed state.

The server processes your media URLs and returns plain text transcriptions. Vinkius runs the adapter in an isolated V8 sandbox, ensuring your API keys and transcription payloads never leak to third parties.

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