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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
Strumenti/GitHubGitHub/d4vinci/scrapling
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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73.4k7.3k16618h 52m faRevisionato da Kitploit

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Scrapling Poster
Effortless Web Scraping for the Modern Web

D4Vinci%2FScrapling | Trendshift
README بالعربية README en Español README em Português (Brasil) README en Français README auf Deutsch 简体中文版自述文件 日本語のREADME Русская версия README 한국어 README
Tests PyPI version Docker Pulls PyPI package downloads Static Badge OpenClaw Skill
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Supported Python versions

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

Scrapling is an adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl.

Its parser learns from website changes and automatically relocates your elements when pages update. Its fetchers bypass anti-bot systems like Cloudflare Turnstile out of the box. And its spider framework lets you scale up to concurrent, multi-session crawls with pause/resume, automatic proxy rotation, and a crawl speed that adapts to how fast each website responds and backs off when it starts blocking you - all in a few lines of Python. One library, zero compromises.

Blazing fast crawls with real-time stats and streaming. Built by Web Scrapers for Web Scrapers and regular users, there's something for everyone.

from scrapling.fetchers import Fetcher, AsyncFetcher, StealthyFetcher, DynamicFetcher
StealthyFetcher.adaptive = True
p = StealthyFetcher.fetch('https://example.com', headless=True, network_idle=True)  # Fetch website under the radar!
products = p.css('.product', auto_save=True)                                        # Scrape data that survives website design changes!
products = p.css('.product', adaptive=True)                                         # Later, if the website structure changes, pass `adaptive=True` to find them!

Or scale up to full crawls

from scrapling.spiders import Spider, Response

class MySpider(Spider):
  name = "demo"
  start_urls = ["https://example.com/"]

  async def parse(self, response: Response):
      for item in response.css('.product'):
          yield {"title": item.css('h2::text').get()}

MySpider().start()

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