How to Use the Mistral AI MCP in OpenAI Agents SDK

Q: Can I run parallel batch jobs using Mistral AI and OpenAI Agents SDK?

Yes, you can. Your agent uses createbatch to submit JSONL files, tracks progress with listbatches, and retrieves results asynchronously while the SDK handles agent tracing.

Q: What happens if a Mistral AI batch job fails inside my OpenAI Agents SDK pipeline?

Your agent can poll the status using getbatch and, if a failure is detected, inspect the run or clean up temporary storage with deletefile. The SDK logs the entire sequence on your dashboard for debugging.

Run Mistral AI models directly inside your OpenAI Agents SDK production pipelines with auto-discovered tools via this MCP Server.

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Connect Mistral AI MCP to OpenAI Agents SDK

Create your Vinkius account to connect Mistral AI 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 Mistral AI with OpenAI Agents SDK

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Run Mistral AI chat models directly in OpenAI Agents SDK

The `chat` tool lets your OpenAI Agents SDK setup talk directly to Mistral models like codestral-latest. No custom API wrappers needed. Your agent hits the endpoint, grabs the live model IDs using `list_models`, and routes messages based on active conversations. This setup handles the token handoffs between specialized agents while executing model queries. You get clean trace logs on your OpenAI dashboard for every single prompt sent to the Mistral API.

Offload heavy text processing to asynchronous batch jobs

The `create_batch` tool lets OpenAI Agents SDK pipelines group thousands of evaluation requests into a single non-blocking file upload. Your agent prepares the JSONL inputs, submits the job, and checks the status using `get_batch` without halting the main application loop. When things go sideways or a run is misconfigured, the agent calls `cancel_batch` to stop processing immediately. This keeps your API spend in check while managing massive datasets through automated background workers.

Run content safety checks on agent outputs

The `moderate` tool provides instant safety scores for text passing through your OpenAI Agents SDK pipeline using our MCP setup. Before any generated content gets sent to your end users, the agent runs this check to flag potential policy violations. If the safety score triggers a flag, the agent can dynamically branch to a fallback state or clean up the inputs. You get a reliable safety layer built directly into your production runtime.

Setup guide

Set up Mistral AI 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 Mistral AI tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Mistral AI 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="Mistral AI Agent",
            instructions="You have access to Mistral AI tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mistral AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Mistral AI MCP in OpenAI Agents SDK

You initialize the MCP connection using the MCPServerStreamableHttp class and pass it to your agent constructor. The SDK automatically discovers tools like `chat` and `list_models` without manual schema definitions.

Yes, you can. Your agent uses `create_batch` to submit JSONL files, tracks progress with `list_batches`, and retrieves results asynchronously while the SDK handles agent tracing.

The SDK queries the Vinkius endpoint at startup to map tools like `embeddings` and `moderate` directly to agent-callable functions. Setting cacheToolsList to true keeps this handshake fast.

Your agent can poll the status using `get_batch` and, if a failure is detected, inspect the run or clean up temporary storage with `delete_file`. The SDK logs the entire sequence on your dashboard for debugging.

All chat payloads and JSONL files are routed directly to Mistral's API endpoints through Vinkius's zero-trust sandbox. No data is cached or stored on the proxy, keeping your sensitive enterprise inputs isolated.

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