How to Integrate OpenAI Agents SDK with ZenRows

Here are the three most recent posts on the front page of Hacker News (https://news.ycombinator.com/) as of now:

### 1. [Accelerating Gemma 4: faster inference with multi-token prediction drafters](https://blog.google/innovation-and-ai/technology/developers-tools/multi-token-prediction-gemma-4/)
- **Source:** blog.google
- **Summary:** Google shares an update on improving inference speed for its Gemma 4 AI model...
---
### 2. [Three Inverse Laws of AI](https://susam.net/inverse-laws-of-robotics.html)
- **Source:** susam.net
- **Summary:** A blog discusses three proposed "inverse laws" for artificial intelligence, likely as a counterpoint to Isaac Asimov's classic...
---
### 3. [Computer Use is 45x more expensive than structured APIs](https://reflex.dev/blog/computer-use-is-45x-more-expensive-than-structured-apis/)
- **Source:** reflex.dev
- **Summary:** This post analyzes the cost disparity between general computer use (running code, scripts, unstructured processing) and consuming structured APIs...

import asyncio
import os
from agents import Agent, HostedMCPTool, Runner

ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]

async def main() -> None:
    research_agent = Agent(
        name="Research Agent",
        instructions=(
            "You are a thorough web researcher. When given a topic and a starting URL, "
            "visit the URL, identify the 3 most relevant linked articles, scrape each one, "
            "and produce a synthesized report covering key findings, common themes, and "
            "any contradictions. Cite each source with its URL."
        ),
        tools=[
            HostedMCPTool(
                tool_config={
                    "type": "mcp",
                    "server_label": "zenrows",
                    "server_description": "Web scraping MCP server for accessing live web content.",
                    "server_url": "https://mcp.zenrows.com/mcp",
                    "authorization": ZENROWS_API_KEY,
                    "require_approval": "never",
                }
            )
        ],
        model="gpt-4o",
    )

    result = await Runner.run(
        research_agent,
        "Research recent developments in AI. Start at https://huggingface.co/blog and follow the most relevant links.",
    )

    print(result.final_output)

asyncio.run(main())

import asyncio
import os
from agents import Agent, HostedMCPTool, Runner
from pydantic import BaseModel

ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]

class CompanyInfo(BaseModel):
    name: str
    industry: str
    description: str

async def main() -> None:
    agent = Agent(
        name="Company Researcher",
        instructions="Visit the URL the user provides and extract structured company information from the page.",
        tools=[
            HostedMCPTool(
                tool_config={
                    "type": "mcp",
                    "server_label": "zenrows",
                    "server_description": "Web scraping MCP server for accessing live web content.",
                    "server_url": "https://mcp.zenrows.com/mcp",
                    "authorization": ZENROWS_API_KEY,
                    "require_approval": "never",
                }
            )
        ],
        output_type=CompanyInfo,
        model="gpt-4o-mini",
    )

    result = await Runner.run(
        agent,
        "Extract company information from https://www.zillow.com/",
    )

    company: CompanyInfo = result.final_output
    print(company.model_dump_json(indent=2))

asyncio.run(main())

{
  "name": "Zillow (Zillow Group)",
  "industry": "Real estate / Online real estate marketplace",
  "description": "Zillow is an online real estate marketplace and services company offering home listings for sale and rent, rental-management and agent-finding tools, home value estimates (Zestimates), mortgage products through Zillow Home Loans, and affiliated brands such as Trulia, StreetEasy, and HotPads."
}

import os
from agents import Agent, HostedMCPTool

ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]

zenrows_tool = HostedMCPTool(
    tool_config={
        "type": "mcp",
        "server_label": "zenrows",
        "server_description": "Web scraping MCP server for accessing live web content.",
        "server_url": "https://mcp.zenrows.com/mcp",
        "authorization": ZENROWS_API_KEY,
        "require_approval": "never",
    }
)

writer_agent = Agent(
    name="Writer Agent",
    instructions="Take research findings and produce a polished article.",
    model="gpt-4o-mini",
)

researcher_agent = Agent(
    name="Researcher Agent",
    instructions=(
        "Gather research from the web on the user's topic. "
        "Once you have enough material, hand off to the Writer Agent."
    ),
    tools=[zenrows_tool],
    handoffs=[writer_agent],
    model="gpt-4o",
)