How to Use the Huddle01 (Web3 Video API) MCP in LangChain

Q: Can I trace Huddle01 (Web3 Video API) tool calls in LangChain using LangSmith?

Yes, every tool call like getroommetadata or getparticipantlist appears in your LangSmith dashboard. You can inspect the exact payload, latency, and token cost of each Web3 video API interaction.

Q: How do I handle room locking inside a LangChain sequence?

Your agent uses createroom and specifies the lock parameter based on the chain's logic. The output room ID then flows to the next node in your LangChain graph to notify participants.

Q: Can LangChain agents handle concurrent session monitoring?

Yes, your agent can poll getlivesessions and use getlivesessionparticipants in a loop to track active callers. This lets you build monitoring bots that react when specific Web3 wallets join a meeting.

Spin up Web3 video rooms and fetch live session metrics inside your LangChain reasoning loops.

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Connect Huddle01 (Web3 Video API) MCP to LangChain

Create your Vinkius account to connect Huddle01 (Web3 Video API) 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 Huddle01 (Web3 Video API) with LangChain

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Automate room creation in LangChain chains

The `create_room` tool provisions a new Huddle01 meeting space directly from your agentic execution path. When your agent detects a scheduled event or an urgent incident, it spins up a room instantly, setting lock configurations based on input parameters. You pass this output directly into subsequent steps of your chain, like sending the room link via chat or logging the ID. This makes decentralized video management a standard step in your LangChain workflows, keeping everything in a single execution trace.

Trace live video metrics with LangSmith

The `get_metrics` tool pulls real-time API usage and active session counts into your LangChain workflow. Your agent monitors these numbers to decide if it needs to scale down active rooms or alert operations about high usage. LangSmith traces every call, showing you the exact inputs and outputs of `get_live_sessions` and `get_live_session_details`. You see how your agent evaluates active rooms and participant counts without guessing what happened inside the chain.

Chain recording retrieval in an MCP Server setup

The `get_recordings` tool retrieves meeting recordings by session ID, letting your agent fetch video files as soon as a call ends. Your LangChain agent receives the recording URL and immediately feeds it into a transcription or summarization step. By combining this with `get_room_sessions` and `get_participant_list`, your pipeline compiles complete post-meeting packages. This MCP Server setup ensures your agent has the exact context of who attended and what was recorded.

Setup guide

Set up Huddle01 (Web3 Video 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 Huddle01 (Web3 Video 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({
    "huddle01-web3-video-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 Huddle01 (Web3 Video API) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Huddle01 (Web3 Video API) MCP in LangChain

You connect the MCP Server using the LangChain MCP adapter to expose the 11 video tools to your agent. The agent calls tools like `create_room` or `get_live_sessions` dynamically based on user prompts.

Yes, every tool call like `get_room_metadata` or `get_participant_list` appears in your LangSmith dashboard. You can inspect the exact payload, latency, and token cost of each Web3 video API interaction.

Your agent uses `create_room` and specifies the lock parameter based on the chain's logic. The output room ID then flows to the next node in your LangChain graph to notify participants.

Yes, your agent can poll `get_live_sessions` and use `get_live_session_participants` in a loop to track active callers. This lets you build monitoring bots that react when specific Web3 wallets join a meeting.

Vinkius runs this MCP Server in a sandboxed environment, keeping your API keys and room metadata isolated. Meeting recordings and participant lists are only accessed when your LangChain agent explicitly invokes the retrieval tools.

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