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Scrapling — Adaptive web scraping framework with anti-bot bypass, automatic element relocation, concurrent crawling, proxy rotation, and browser automation for resilient data extraction. | Kitploit
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Dynamic Analysis (Sandboxing)Web SecurityCrawlerAnti-BotFingerprint Spoofing
GitHubd4vinci/scrapling

Scrapling

Adaptive web scraping framework with anti-bot bypass, automatic element relocation, concurrent crawling, proxy rotation, and browser automation for resilient data extraction.

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73.4k7.3k2242 days agoReviewed by Kitploit

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Effortless Web Scraping for the Modern Web

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Supported Python versions

Selection methods · Fetchers · Spiders · Proxy Rotation · CLI · MCP

Scrapling is a Python Web Scraping framework that brings fast HTTP requests, stealth browsers, powerful parsing, and concurrent crawling into one library.

Scrape static pages, render JavaScript, interact with websites, and extract data from the API calls behind them. Build crawlers with reusable sessions, proxy rotation, automatic retries, pause/resume, and built-in data exports.

Use it from Python, the terminal, or your AI agent through its MCP server and Agent Skill. From extracting a few fields to collecting a whole website as clean Markdown, Scrapling covers the full scraping workflow.

What you can do with Scrapling

  • Fetch & interact: Fast HTTP requests, stealth browsers, JavaScript rendering, Cloudflare Turnstile/Interstitial handling, persistent sessions, and background API capture.
  • Parse & extract: CSS, XPath, text and regex search, adaptive element tracking, similar-element matching, and clean Markdown output.
  • Crawl & export: Concurrent spiders, proxy rotation, retries, AutoThrottle, pause/resume, streaming, ready-made templates, and JSON/JSONL/CSV/XML exports.
  • Scrape with AI or from your terminal: An MCP server for AI agents to browse and extract data, an Agent Skill, CLI commands, and an interactive shell.

Start with a single page:

from scrapling.fetchers import Fetcher

page = Fetcher.get('https://quotes.toscrape.com/')
quotes = page.css('.quote .text::text').getall()
print(quotes)

Or scale up with a spider:

from scrapling.spiders import Spider, Response

class QuotesSpider(Spider):
    name = "quotes"
    start_urls = ["https://quotes.toscrape.com/"]

    async def parse(self, response: Response):
        for quote in response.css('.quote'):
            yield {
                "text": quote.css('.text::text').get(),
                "author": quote.css('.author::text').get(),
            }

result = QuotesSpider().start()
print(len(result.items))
result.items.to_json("quotes.json")

This prints 10 and saves the quotes and authors to quotes.json.

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