How to Integrate Anthropic Claude API with ZenRows

import os
import requests
from anthropic import Anthropic
from dotenv import load_dotenv
load_dotenv()
ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]
client = Anthropic()

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 Claude through the Messages API
message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    system="You are a concise technical writer.",
    messages=[
        {
            "role": "user",
            "content": f"Summarize this page in 2 sentences:\n\n{markdown}",
        }
    ],
)

print(message.content[0].text)

import os
import requests
from anthropic import Anthropic
from dotenv import load_dotenv
load_dotenv()
ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]
client = Anthropic()

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

# JSON schema to tell Claude what scrape_website does and how to call it
tools = [
    {
        "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."
        ),
        "input_schema": {
            "type": "object",
            "properties": {
                "url": {
                    "type": "string",
                    "description": "The fully qualified URL of the website to scrape",
                }
            },
            "required": ["url"],
        },
    }
]

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

    # Send the question to Claude with the scraping tool available
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        tools=tools,
        messages=messages,
    )

    # Keep looping as long as Claude wants to call a tool
    while response.stop_reason == "tool_use":
        # Extract the tool call from Claude's response
        tool_use = next(block for block in response.content if block.type == "tool_use")
        # Execute the scrape and capture the result
        result = scrape_website(**tool_use.input)

        # Append Claude's response and the tool result to the conversation history
        messages.append({"role": "assistant", "content": response.content})
        messages.append(
            {
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "tool_use_id": tool_use.id,
                        "content": result,
                    }
                ],
            }
        )

        # Send the updated conversation history back to Claude to continue
        response = client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=1024,
            tools=tools,
            messages=messages,
        )

    # Return Claude's final text response once no more tool calls are needed
    return next(block.text for block in response.content if block.type == "text")

# Ask Claude a question that requires live web access to answer
print(
    ask(
        "What does the Hugging Face blog cover? "
        "Visit https://huggingface.co/blog and tell me what topics they post about."
    )
)

import os
import requests
from anthropic import Anthropic
from dotenv import load_dotenv

load_dotenv()
ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]
client = Anthropic()

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

tools = [
    {
        "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."
        ),
        "input_schema": {
            "type": "object",
            "properties": {
                "url": {
                    "type": "string",
                    "description": "The fully qualified URL of the website to scrape",
                }
            },
            "required": ["url"],
        },
    }
]

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

    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        tools=tools,
        messages=messages,
    )

    while response.stop_reason == "tool_use":
        tool_use = next(block for block in response.content if block.type == "tool_use")
        result = scrape_website(**tool_use.input)

        messages.append({"role": "assistant", "content": response.content})
        messages.append(
            {
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "tool_use_id": tool_use.id,
                        "content": result,
                    }
                ],
            }
        )

        response = client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=1024,
            tools=tools,
            messages=messages,
        )

    return next(block.text for block in response.content if block.type == "text")

print(
    ask(
        "What does the Hugging Face blog cover? "
        "Visit https://huggingface.co/blog and tell me what topics they post about."
    )
)

The **Hugging Face Blog** (https://huggingface.co/blog) is a community-driven publication covering a wide range of topics related to AI, machine learning, and open-source development. Here's a breakdown of what they post abo

### 🏷️ Main Topic Categories

The blog uses tags to organize content, including:
- **NLP** (Natural Language Processing)
- ...truncated for brevity

### 📝 Recent Article Highlights

Here are some of the specific topics covered in recent posts:
1. **Robotics** – e.g.,...
...truncated for brevity

### 👥 Who Writes It?

The blog is a **community blog**, meaning articles are contributed by a mix of:

- **Hugging Face team members**
- ...truncated for brevity

In summary, the Hugging Face blog is a rich resource for anyone interested in **cutting-edge AI research, practical ML tutorials, model releases, open-source tooling, and industry insights**.