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Web Unlocker is a scraping API — you send a URL, it returns the fully rendered HTML. This page shows the most common ways to put that to use.

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Scrape a page yourself

The simplest use case: you need the content of a webpage that blocks regular HTTP requests.

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Python

import requests

response = requests.get(
    "https://parsing.webunlocker.gologin.com/v1/scrape",
    params={"url": "https://example.com/product/123"},
    headers={"apikey": "YOUR_API_KEY"}
)

html = response.text
print(html)

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JavaScript

const html = await fetch(
  "https://parsing.webunlocker.gologin.com/v1/scrape?url=" +
    encodeURIComponent("https://example.com/product/123"),
  { headers: { apikey: "YOUR_API_KEY" } }
).then((r) => r.text());

console.log(html);
No browser to launch, no proxy to configure, no Cloudflare to fight. One call, you get the page. Good for:
  • Checking prices on a competitor’s site
  • Pulling job listings, reviews, or product data
  • Monitoring pages for changes
  • One-off data collection scripts

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Feed content into an AI

Web Unlocker returns raw HTML. Pass that into an LLM to extract structured data, summarize content, or answer questions about the page — no parsing logic needed.

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Python + OpenAI

import requests
from openai import OpenAI

html = requests.get(
    "https://parsing.webunlocker.gologin.com/v1/scrape",
    params={"url": "https://news.ycombinator.com"},
    headers={"apikey": "YOUR_API_KEY"}
).text

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": f"Extract the top 5 story titles and their URLs from this HTML:\n\n{html[:8000]}"
        }
    ]
)

print(response.choices[0].message.content)

​
JavaScript + Anthropic

import Anthropic from "@anthropic-ai/sdk";

const html = await fetch(
  "https://parsing.webunlocker.gologin.com/v1/scrape?url=" +
    encodeURIComponent("https://news.ycombinator.com"),
  { headers: { apikey: "YOUR_API_KEY" } }
).then((r) => r.text());

const client = new Anthropic();
const message = await client.messages.create({
  model: "claude-opus-4-5",
  max_tokens: 1024,
  messages: [
    {
      role: "user",
      content: `Extract the top 5 story titles and their URLs from this HTML:\n\n${html.slice(0, 8000)}`,
    },
  ],
});

console.log(message.content[0].text);
Good for:
  • Extracting structured data (prices, names, dates) without writing parsers
  • Summarizing articles or product pages
  • Comparing multiple pages and drawing conclusions
  • Building research or monitoring tools powered by AI

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Use it inside an AI agent

Give your AI agent a scrape tool backed by Web Unlocker. The agent can then browse the web on its own — fetching pages, reading content, and taking action — without you writing any browsing logic. 
import requests
import anthropic

def scrape(url: str) -> str:
    """Fetch a URL and return its rendered HTML."""
    return requests.get(
        "https://parsing.webunlocker.gologin.com/v1/scrape",
        params={"url": url},
        headers={"apikey": "YOUR_API_KEY"}
    ).text

tools = [
    {
        "name": "scrape",
        "description": "Fetch the rendered HTML of any URL, including JavaScript-heavy and protected sites.",
        "input_schema": {
            "type": "object",
            "properties": {
                "url": {"type": "string", "description": "The URL to scrape"}
            },
            "required": ["url"]
        }
    }
]

client = anthropic.Anthropic()
messages = [
    {"role": "user", "content": "What is the current price of the iPhone 16 Pro on apple.com?"}
]

while True:
    response = client.messages.create(
        model="claude-opus-4-5",
        max_tokens=1024,
        tools=tools,
        messages=messages
    )

    if response.stop_reason == "end_turn":
        print(response.content[0].text)
        break

    tool_results = []
    for block in response.content:
        if block.type == "tool_use" and block.name == "scrape":
            html = scrape(block.input["url"])
            tool_results.append({
                "type": "tool_result",
                "tool_use_id": block.id,
                "content": html[:10000]
            })

    messages.append({"role": "assistant", "content": response.content})
    messages.append({"role": "user", "content": tool_results})
The agent decides when to scrape and what URL to fetch. Web Unlocker handles the actual retrieval. Good for:
  • Research agents that browse the web autonomously
  • Price comparison or deal-finding bots
  • Competitive intelligence pipelines
  • Any agent that needs real-time web data

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Scrape many pages at once

Run requests concurrently to collect data from multiple URLs in parallel. 
import requests
from concurrent.futures import ThreadPoolExecutor

API_URL = "https://parsing.webunlocker.gologin.com/v1/scrape"
API_KEY = "YOUR_API_KEY"

urls = [
    "https://example.com/page/1",
    "https://example.com/page/2",
    "https://example.com/page/3",
]

def scrape(url):
    response = requests.get(
        API_URL,
        params={"url": url},
        headers={"apikey": API_KEY}
    )
    return url, response.text

with ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(scrape, urls))

for url, html in results:
    print(f"{url}: {len(html)} chars")
Good for:
  • Scraping entire product catalogs or listing pages
  • Bulk data collection for training datasets
  • Monitoring many pages simultaneously

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Summary

Use caseWhat you doWhat you get
Scrape a single pageGET with URL paramRendered HTML
Feed into an LLMScrape → pass to modelExtracted data or summary
AI agent toolAgent calls on demandReal-time web content
Bulk scrapingConcurrent requestsHTML for each URL
See Quickstart to make your first request.