How to Use the Livepeer (Decentralized Video) MCP in OpenAI Agents SDK

Q: Can the agent automatically push live feeds to platforms like Twitch using Livepeer (Decentralized Video) and OpenAI Agents SDK?

Yes, the agent can call createmultistreamtarget to configure external platforms. It then links that target to your active stream to syndicate your content instantly.

Q: How does OpenAI Agents SDK handle transcoding failures on Livepeer (Decentralized Video)?

The agent monitors the job status by polling gettask at set intervals. If a failure occurs, the agent's built-in error handling catches the exception and can trigger a retry.

Build production video agents with the OpenAI Agents SDK to spin up live broadcasts and query metrics on the Livepeer network.

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Connect Livepeer (Decentralized Video) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Livepeer (Decentralized Video) to OpenAI Agents SDK 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 Livepeer (Decentralized Video) with OpenAI Agents SDK

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Launch and terminate streams with OpenAI Agents SDK

Your agent spins up live broadcasts instantly using the `create_stream` tool. When the broadcast ends, the agent cleans up by running `terminate_stream` to prevent runaway compute costs. This setup keeps your production pipeline tight and predictable. You get raw ingest URLs and stream keys back immediately, letting your agent hand off the playback details to downstream services without delay.

Transcode video assets on a decentralized MCP Server

The `request_asset_upload` tool lets your agent request secure upload endpoints for raw video files. Once uploaded, the agent kicks off transcoding jobs via `create_transcode_job` to prepare the files for multi-device playback. Running this through an MCP Server means your OpenAI agent handles the asynchronous polling of `get_task` in the background. It only alerts your main application once the decentralized network finishes processing the video.

Track broadcast performance with agent guardrails

Checking viewer engagement is straightforward when your agent invokes `get_realtime_viewership` to pull current session data. To analyze historical trends, the agent queries `get_viewership_metrics` to break down playback by device and region. OpenAI's built-in guardrails prevent the agent from making unauthorized API calls or querying excessive metrics. You get clean, validated performance data routed straight to your monitoring dashboard.

Setup guide

Set up Livepeer (Decentralized Video) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Livepeer (Decentralized Video) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Livepeer (Decentralized Video) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Livepeer (Decentralized Video) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Livepeer (Decentralized Video) Agent",
            instructions="You have access to Livepeer (Decentralized Video) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Livepeer (Decentralized Video) MCP in OpenAI Agents SDK

You install the SDK using pip and register the server using the streamable HTTP class. The agent auto-discovers all tools like `create_stream` instantly.

Yes, the agent can call `create_multistream_target` to configure external platforms. It then links that target to your active stream to syndicate your content instantly.

The agent monitors the job status by polling `get_task` at set intervals. If a failure occurs, the agent's built-in error handling catches the exception and can trigger a retry.

You limit agent tools by defining specific system prompts or using specialized sub-agents. This ensures a billing agent only reads metrics via `get_usage_metrics` while a production MCP agent manages streams.

All webhook URLs and viewing metrics remain isolated within Vinkius's secure sandboxed environment. Your API keys are injected at the connection level, meaning the agent never exposes your credentials to external players.

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