How to Use the DeepInfra (Serverless LLM Inference) MCP in OpenAI Agents SDK

Run DeepInfra models inside your OpenAI Agents SDK production pipeline for reliable, cost-effective inference.

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Connect DeepInfra (Serverless LLM Inference) MCP to OpenAI Agents SDK

Create your Vinkius account to connect DeepInfra (Serverless LLM Inference) 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 DeepInfra (Serverless LLM Inference) with OpenAI Agents SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Direct `create_chat_completion` calls

Send your agent's message history to DeepInfra without leaving your Python environment. This keeps your request logic clean while pushing the heavy compute to their serverless infrastructure. Your agent handles the response immediately. You get the exact model output your application needs to progress through its logic flow.

Native inference for custom tasks

Trigger specialized models using `run_native_inference` that standard chat endpoints miss. This tool lets you pull in speech or vision data for complex agent workflows. Everything stays within your established guardrails. You define the flow, and the MCP server handles the handshake with the model provider.

Efficient text embedding generation

Generate vector embeddings for your knowledge base using `create_embedding`. It turns raw text into the numbers your agent uses for semantic retrieval. This happens in real-time during your agent execution. No more batch processing or waiting for external jobs to complete.

Setup guide

Set up DeepInfra (Serverless LLM Inference) 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 DeepInfra (Serverless LLM Inference) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives DeepInfra (Serverless LLM Inference) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate DeepInfra (Serverless LLM Inference) 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="DeepInfra (Serverless LLM Inference) Agent",
            instructions="You have access to DeepInfra (Serverless LLM Inference) 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 DeepInfra (Serverless LLM Inference) MCP in OpenAI Agents SDK

You connect the two by passing the server instance to your agent constructor. The SDK automatically discovers the tools and links them to your agent's execution path.

Not if you cache your tool list. The serverless architecture handles the request offload, and local caching keeps your agent responsive during standard operations.

Yes, you can route specific validation tasks through the server. Since you control the agent logic, you decide when to query the model for compliance checks.

Vinkius handles the underlying transport security. You only need your Vinkius endpoint token to get the communication channel open.

Your prompts and model outputs are processed in a zero-trust sandbox. We don't store your input text, keeping your agent's reasoning process isolated.

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