How to Use the Browse AI MCP in OpenAI Agents SDK

Q: How do I run Browse AI tasks using the OpenAI Agents SDK?

You instantiate the server with MCPServerStreamableHttp and pass it to your agent constructor. Once connected, your agent calls the tools like standard functions.

Q: Can Browse AI and the OpenAI Agents SDK handle bulk jobs?

Yes, you use runbulktask to queue multiple URLs at once. The server handles the concurrent processing and lets you retrieve results via downloadbulkdata.

Q: Does Browse AI integrate with the OpenAI Agents SDK for status checks?

You check task progress by calling gettask with your ID. This returns the status, letting your agent decide whether to wait or move on.

Q: What happens if my Browse AI credits run low in the OpenAI Agents SDK?

You call listcredits to check your current usage. If you hit your limit, your agent code can catch the response and alert you before the next job fails.

Pull live web data into your OpenAI Agents SDK pipeline without writing custom scrapers.

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Connect Browse AI MCP to OpenAI Agents SDK

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

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Trigger scraping tasks from your OpenAI Agents SDK

Launch extraction jobs directly from your agent logic using `run_robot`. You pass the target URL as an input parameter, and the system returns a task ID immediately. This workflow keeps your agent in the loop for every data gathering step. You don't need to leave your code to initiate a crawl.

Validate Browse AI results inside your agent flow

After a task finishes, use `get_task_data` to pull the final JSON output into your Python environment. This ensures your agent processes fresh data exactly when it needs it. `download_bulk_data` helps when you are handling larger sets of records. It returns the raw array directly, letting you feed structured info into your agent's context.

Monitor site changes via this MCP Server

Keep tabs on target pages by calling `list_monitors` within your agent script. It tracks scheduled changes and keeps you updated on site status. Use `list_robots` to see every active worker you have configured. This MCP Server gives your agent full visibility over your entire scrapers library.

Setup guide

Set up Browse 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 Browse AI tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Browse 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 Browse 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="Browse AI Agent",
            instructions="You have access to Browse 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 Browse 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Common questions about Browse AI MCP in OpenAI Agents SDK

You instantiate the server with `MCPServerStreamableHttp` and pass it to your agent constructor. Once connected, your agent calls the tools like standard functions.

Yes, you use `run_bulk_task` to queue multiple URLs at once. The server handles the concurrent processing and lets you retrieve results via `download_bulk_data`.

You check task progress by calling `get_task` with your ID. This returns the status, letting your agent decide whether to wait or move on.

You call `list_credits` to check your current usage. If you hit your limit, your agent code can catch the response and alert you before the next job fails.

Vinkius isolates your session, ensuring only your agent has access to the extracted JSON records. We process your raw web snapshots in a secured environment, keeping your credentials separate from the data payload.

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