How to Use the Lambda Labs (GPU Cloud) MCP in OpenAI Agents SDK

Q: How do I get started with Lambda Labs (GPU Cloud) and the OpenAI Agents SDK?

Just install openai-agents and point it to the Vinkius MCP endpoint URL. The SDK automatically discovers all the tools like launchinstance. Set cacheToolsList=True in the constructor for better performance.

Q: What happens if I try to launch an unavailable instance type with Lambda Labs (GPU Cloud) and the OpenAI Agents SDK?

The launchinstance tool will return an error from the Lambda Labs API. Your agent's code should be written to handle this failure, perhaps by trying a different region or instance type.

Q: How do I clean up resources when my agent is done?

Your agent must explicitly call the terminateinstances tool with the correct instance IDs. The OpenAI SDK's tracing helps you confirm that the cleanup step was actually executed by your agent.

Manage Lambda GPU fleets from your OpenAI Agents SDK, with built-in guardrails from this MCP server to prevent costly mistakes.

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Connect Lambda Labs (GPU Cloud) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Lambda Labs (GPU Cloud) 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 Lambda Labs (GPU Cloud) with OpenAI Agents SDK

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Provision GPU Instances On-Demand

The `launch_instance` tool gives your agent the power to spin up new GPU nodes when you need them. It can check `list_instance_types` to find available H100s in a specific region, then provision a machine and inject a public key from `list_ssh_keys` so it's ready for work. Here’s the thing: the OpenAI Agents SDK adds a safety layer. You can define rules that prevent your agent from overspending, like capping the number of concurrent instances or restricting it to certain machine types. It’s control, not just automation.

Monitor and Connect to Instances

Use `get_instance` and `list_instances` to give your agent real-time status on your entire cluster. An agent can poll `get_instance` to wait for a machine to become 'active' before it tries to connect and start a job. This is essential for building reliable workflows. The agent gets the exact IP and SSH details from the API response, so there's no guesswork. This MCP connection makes your agent a true infrastructure manager, with every action traced in your OpenAI dashboard.

Manage Storage and Keys with your MCP Server

This MCP Server exposes tools for managing persistent storage and access credentials. Your agent can call `list_filesystems` to see which shared NAS volumes are available to be mounted to a new instance. It also uses `list_ssh_keys` to manage access. Before launching an instance, the agent can confirm the correct key exists, ensuring you never provision a machine you can't access. It keeps your setup clean and secure.

Setup guide

Set up Lambda Labs (GPU Cloud) 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 Lambda Labs (GPU Cloud) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Lambda Labs (GPU Cloud) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Lambda Labs (GPU Cloud) 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="Lambda Labs (GPU Cloud) Agent",
            instructions="You have access to Lambda Labs (GPU Cloud) 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 Lambda Labs (GPU Cloud) MCP in OpenAI Agents SDK

Just install `openai-agents` and point it to the Vinkius MCP endpoint URL. The SDK automatically discovers all the tools like `launch_instance`. Set `cacheToolsList=True` in the constructor for better performance.

Yes. You can design specialized agents that hand off tasks. For example, one agent could find the cheapest available GPU using `list_instance_types`, while another agent launches it and monitors the job.

The `launch_instance` tool will return an error from the Lambda Labs API. Your agent's code should be written to handle this failure, perhaps by trying a different region or instance type.

Your agent must explicitly call the `terminate_instances` tool with the correct instance IDs. The OpenAI SDK's tracing helps you confirm that the cleanup step was actually executed by your agent.

This server only handles metadata about your Lambda Labs (GPU Cloud) infrastructure. It processes instance IDs, IP addresses, and your public SSH keys. Since every tool call is traced via the OpenAI dashboard, you get a full audit log of what your agent accessed and when.

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