How to Integrate OpenAI with ZenRows

import os
import requests
from openai import OpenAI

ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

def scrape(url: str) -> str:
    """Scrape any URL with ZenRows and return clean Markdown."""
    response = requests.get(
        "https://api.zenrows.com/v1/",
        params={
            "apikey": ZENROWS_API_KEY,
            "url": url,
            "mode": "auto",
            "response_type": "markdown",
        },
    )
    response.raise_for_status()
    return response.text

# Scrape a JS-rendered, anti-bot-protected page
markdown = scrape("https://www.scrapingcourse.com/antibot-challenge")

# Summarize with GPT through the Responses API
response = client.responses.create(
    model="gpt-5-mini",
    instructions="You are a concise technical writer.",
    input=f"Summarize this page in 2 sentences:\n\n{markdown}",
)

print(response.output_text)

import json
import os
import requests
from openai import OpenAI

ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

def scrape_website(url: str) -> str:
    """Scrape any website with ZenRows and return Markdown content."""
    response = requests.get(
        "https://api.zenrows.com/v1/",
        params={
            "apikey": ZENROWS_API_KEY,
            "url": url,
            "mode": "auto",
            "response_type": "markdown",
        },
    )
    response.raise_for_status()
    return response.text

# Tell GPT what scrape_website does and how to call it
tools = [
    {
        "type": "function",
        "name": "scrape_website",
        "description": (
            "Scrape any public website and return its content as Markdown. "
            "Use this whenever you need up-to-date information from a specific URL."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "url": {
                    "type": "string",
                    "description": "The fully qualified URL of the website to scrape",
                }
            },
            "required": ["url"],
            "additionalProperties": False,
        },
        "strict": True,
    }
]

input_list = [
    {
        "role": "user",
        "content": "What is Hacker News? Visit https://news.ycombinator.com/ and tell me what the product does.",
    }
]

response = client.responses.create(
    model="gpt-5-mini",
    tools=tools,
    input=input_list,
)

# Capture the entire response output (function calls, reasoning items, etc.)
input_list += response.output

# Run the tool for any function_call item the model produced
for item in response.output:
    if item.type == "function_call" and item.name == "scrape_website":
        args = json.loads(item.arguments)
        result = scrape_website(args["url"])
        input_list.append({
            "type": "function_call_output",
            "call_id": item.call_id,
            "output": result,
        })

# Send the tool result back to the model for the final answer
final = client.responses.create(
    model="gpt-5-mini",
    tools=tools,
    input=input_list,
)

print(final.output_text)

import json
import os
import requests
from openai import OpenAI

ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

def scrape_website(url: str) -> str:
    response = requests.get(
        "https://api.zenrows.com/v1/",
        params={
            "apikey": ZENROWS_API_KEY,
            "url": url,
            "mode": "auto",
            "response_type": "markdown",
        },
    )
    response.raise_for_status()
    return response.text

tools = [
    {
        "type": "function",
        "name": "scrape_website",
        "description": (
            "Scrape any public website and return its content as Markdown. "
            "Use this whenever you need up-to-date information from a specific URL."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "url": {
                    "type": "string",
                    "description": "The fully qualified URL of the website to scrape",
                }
            },
            "required": ["url"],
            "additionalProperties": False,
        },
        "strict": True,
    }
]

def ask(question: str) -> str:
    input_list = [{"role": "user", "content": question}]

    response = client.responses.create(
        model="gpt-5-mini",
        tools=tools,
        input=input_list,
    )

    input_list += response.output
    has_tool_call = False

    for item in response.output:
        if item.type == "function_call" and item.name == "scrape_website":
            has_tool_call = True
            args = json.loads(item.arguments)
            result = scrape_website(args["url"])
            input_list.append({
                "type": "function_call_output",
                "call_id": item.call_id,
                "output": result,
            })

    if not has_tool_call:
        return response.output_text

    final = client.responses.create(
        model="gpt-5-mini",
        tools=tools,
        input=input_list,
    )

    return final.output_text

print(ask("What is Hacker News? Visit https://news.ycombinator.com/ and tell me what the product does."))

Hacker News is a minimalist social-news and discussion site run by Y Combinator that surfaces tech-, startup-, and hacker-oriented links and posts. It’s essentially a community-curated news aggregator + forum where users submit items, vote them up or down, and discuss them in threaded comments.

What the product does (quick summary)
- Aggregates links and text posts about programming, startups, science, and related topics.
- Lets registered users submit stories, vote (upvote) items, and comment; votes and recency determine ranking on the front page.
- Provides named formats like "Show HN" (projects/demos) and "Ask HN" (questions) that signal post type to the community.
- Offers sections/filters: new, past/front, comments, ask, show, jobs, and submit.
- ...