How to Use the Cerebras Inference MCP in OpenAI Agents SDK

Q: Can I track Cerebras Inference usage with OpenAI Agents SDK?

Monitor your performance by calling getmetrics to pull Prometheus data into your dashboard. This tracks exactly how much compute your agent is consuming.

Run Cerebras Inference directly in your OpenAI Agents SDK production pipeline for low-latency text and batch processing.

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Connect Cerebras Inference MCP to OpenAI Agents SDK

Create your Vinkius account to connect Cerebras 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 Cerebras Inference with OpenAI Agents SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Direct OpenAI Agents SDK model management

Call `create_chat_completion` or `create_completion` to generate text through your agent logic. Your Python code handles the response flow while the server hits the wafer-scale hardware for speed. `list_models` and `get_model` allow your agent to verify hardware availability before sending a request. This keeps your agent lifecycle predictable.

Batch operations for production agents

Offload heavy processing to `create_batch` to keep your main agent loop responsive. Use `get_batch` to check on progress without blocking the agent thread. `list_batches` and `cancel_batch` give you control over your queue. You keep your agent throughput high by moving bulk work to the background.

File handling within OpenAI Agents SDK

Upload JSONL files for tuning or batching using `upload_file`. The agent manages the lifecycle of these files through `list_files` and `get_file`. `get_file_content` and `delete_file` ensure you aren't leaving junk on the server. Your agent keeps its workspace clean automatically.

Setup guide

Set up Cerebras 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 Cerebras Inference tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Cerebras 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 Cerebras 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="Cerebras Inference Agent",
            instructions="You have access to Cerebras 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 Cerebras Inference MCP in OpenAI Agents SDK

Use the MCPServerStreamableHttp class to point your agent at the Vinkius endpoint. You initialize this inside your agent constructor to make all fifteen tools instantly available.

Yes, because the tools return structured data that your agent validates before execution. If the inference output violates your schema, the agent catches it immediately.

Monitor your performance by calling `get_metrics` to pull Prometheus data into your dashboard. This tracks exactly how much compute your agent is consuming.

Your JSONL files are encrypted in transit and stored in an ephemeral sandbox. Once you delete them via the server, the data is wiped from the disk.

Your agent code handles the exception based on standard Python async patterns. You can check the status of your task using the batch retrieval tools if the network drops.

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