How to Use the Baidu Qianfan MCP in OpenAI Agents SDK

Q: Does Baidu Qianfan integration in OpenAI Agents SDK support async calls?

It does. The MCPServerStreamableHttp client handles all communication asynchronously, so your agent won't hang while waiting for a response.

Q: How do I list Baidu Qianfan models within OpenAI Agents SDK?

Use the listmodels tool. Your agent will receive a structured list of available endpoints, which you can then parse or cache using cacheToolsList.

Directly pipe Baidu Qianfan models into your OpenAI Agents SDK workflow for immediate production deployment.

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Connect Baidu Qianfan MCP to OpenAI Agents SDK

Create your Vinkius account to connect Baidu Qianfan 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 Baidu Qianfan with OpenAI Agents SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Execute model calls in OpenAI Agents SDK

Trigger `chat_completions` and `text_to_image` directly from your agent logic. Your Python code handles the request, and the MCP server maps the Baidu endpoints to your agent's execution flow. Safety is baked in. Since your agent uses built-in guardrails, every request to the Baidu backend is validated against your local constraints before it hits the network.

Manage data and jobs via OpenAI Agents SDK

Pull your `list_datasets` and `list_train_jobs` data to monitor your Baidu pipeline status. You see what's happening without leaving your Python environment. This keeps your agent aware of training progress. It treats Baidu status updates as native state changes, allowing for automated recovery routines.

Generate embeddings with OpenAI Agents SDK

Call `get_embeddings` to turn text into vectors for your RAG pipeline. The response gets injected into your agent's context, ready for immediate vector store indexing. Everything stays inside your async context manager. This ensures your agent maintains high throughput while handling Baidu's vector output.

Setup guide

Set up Baidu Qianfan 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 Baidu Qianfan tools at runtime.
3

Create your Agent

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

Run the agent

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

asyncio.run(main())

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Baidu Qianfan. 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 Baidu Qianfan MCP in OpenAI Agents SDK

Yes. You connect the server using an HTTP transport, and your agents treat the tools like any other local function. It's built for stability, not demos.

It does. The `MCPServerStreamableHttp` client handles all communication asynchronously, so your agent won't hang while waiting for a response.

Use the `list_models` tool. Your agent will receive a structured list of available endpoints, which you can then parse or cache using `cacheToolsList`.

Vinkius manages the endpoint token for you. Your agents interact over a private, ephemeral sandbox that keeps your credentials out of your local code.

Your prompts and dataset metadata are processed only within the scope of your specific request. No long-term storage occurs on the Vinkius infrastructure, and all data is wiped after the session closes.

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