How to Use the Apify MCP in OpenAI Agents SDK

Run web scrapers directly from your OpenAI Agents SDK production pipeline to pull structured data into your agent memory.

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Connect Apify MCP to OpenAI Agents SDK

Create your Vinkius account to connect Apify 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 Apify with OpenAI Agents SDK

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Trigger scraping tasks from OpenAI Agents SDK

Your agent starts specific extraction jobs by calling `run_actor`. It handles the execution lifecycle without leaving your Python environment. This keeps your agent logic clean. You don't need external webhooks or polling scripts.

Fetch dataset items into your agent

Once a job finishes, the agent uses `get_dataset_results` to bring the raw data into its context window. It turns unstructured web content into usable facts. Your production agent processes this information immediately. It skips the manual export step entirely.

Monitor execution status via MCP Server

The agent checks `get_run_details` to verify if a job succeeded or hit a timeout. It manages its own error handling based on these responses. This MCP Server provides the feedback loop needed for reliable automation. Your agent knows exactly when data is ready for analysis.

Setup guide

Set up Apify 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 Apify tools at runtime.
3

Create your Agent

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

Run the agent

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

Use the MCPServerStreamableHttp class to point your agent at the Vinkius endpoint. Pass the server instance into your Agent constructor to let the SDK auto-discover your tools.

Yes, you can instruct your agent to process the JSON returned by `get_dataset_results`. It treats the output as a standard data stream for your logic.

The server handles async requests natively. You should use an async context manager in your Python code to ensure your agent stays responsive during long crawls.

The tool returns a detailed error object. Your agent can catch this and decide whether to retry or alert you via your standard logging channels.

Your dataset records reside in your private storage account. The server only transmits the specific results your agent requests via the encrypted tunnel.

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