| name | scrapling-0-4-8 |
| description | Adaptive web scraping framework for Python providing HTML parsing with CSS/XPath/text/regex selection, HTTP and browser-based fetchers with anti-bot bypass, adaptive element tracking that survives website changes, a Scrapy-like spider crawling system with pause/resume, proxy rotation, and an MCP server for AI integration. Use when building scrapers, crawlers, or data extraction pipelines needing resilience to website structure changes and anti-bot protections. |
Scrapling 0.4.8
Overview
Scrapling is an adaptive web scraping framework for Python that handles everything from a single HTTP request to full-scale concurrent crawls. Its parser learns from website changes and automatically relocates elements when pages update. Its fetchers bypass anti-bot systems like Cloudflare Turnstile out of the box. Its spider framework supports concurrent multi-session crawls with pause/resume and automatic proxy rotation.
Key differentiators:
- Adaptive scraping — elements are relocated after website structure changes using similarity algorithms, no AI required
- Anti-bot bypass — StealthyFetcher handles Cloudflare Turnstile/Interstitial automatically
- Scrapy-like spiders — async crawling framework with concurrency control, checkpointing, and streaming
- MCP server — built-in Model Context Protocol server for AI-assisted scraping
When to Use
- Parsing HTML with CSS/XPath/text/regex selection and DOM traversal
- Fetching websites via HTTP (with TLS impersonation), Playwright browsers, or stealthy anti-bot bypass
- Building concurrent crawlers that follow links, export results, and survive interruptions
- Scraping sites behind Cloudflare or other anti-bot protections
- Integrating web scraping into AI workflows via MCP server
- Rapid prototyping with the interactive shell
Installation / Setup
pip install scrapling
pip install "scrapling[fetchers]"
scrapling install
pip install "scrapling[ai]"
pip install "scrapling[shell]"
pip install "scrapling[all]"
scrapling install
Docker: docker pull pyd4vinci/scrapling (includes all extras and browsers).
Requires Python 3.10+.
Core Concepts
Selector — the core parsing object wrapping an HTML document or element. Supports CSS, XPath, text-based, regex-based, and filter-based selection. Returns Selector (single) or Selectors (list subclass) objects. All text values are TextHandler objects (string subclass with .clean(), .json(), .re() methods).
Response — extends Selector with HTTP metadata: .status, .headers, .cookies, .body (bytes), .url, .meta.
Fetcher classes — Fetcher (HTTP via curl_cffi, TLS impersonation), DynamicFetcher (Playwright Chromium/Chrome for JS rendering), StealthyFetcher (anti-bot bypass with Cloudflare solving). Each has sync and async variants plus session classes for state persistence.
Adaptive feature — save element properties with auto_save=True, then relocate after website changes with adaptive=True. Uses SQLite by default; custom storage backends supported.
Spider system — Scrapy-inspired async crawler with start_urls, parse() callbacks, response.follow(), concurrency limits, pause/resume via checkpoints, streaming mode, multi-session routing, proxy rotation, and blocked-request retry.
Usage Examples
Parse HTML directly:
from scrapling import Selector
page = Selector('<html><body><h1>Hello</h1></body></html>')
title = page.css('h1::text').get()
articles = page.find_all('article')
Fetch with HTTP (browser impersonation):
from scrapling.fetchers import Fetcher
page = Fetcher.get('https://example.com', impersonate='chrome')
print(page.css('title::text').get())
Fetch dynamic content with browser:
from scrapling.fetchers import DynamicFetcher
page = DynamicFetcher.fetch('https://spa.example.com', network_idle=True)
items = page.css('.item')
Bypass anti-bot protections:
from scrapling.fetchers import StealthyFetcher
page = StealthyFetcher.fetch('https://protected-site.com', solve_cloudflare=True)
Adaptive scraping — survive website changes:
from scrapling.fetchers import Fetcher
Fetcher.adaptive = True
page = Fetcher.get('https://example.com')
products = page.css('.product', auto_save=True)
products = page.css('.product', adaptive=True)
Basic spider:
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()}
result = MySpider().start()
result.items.to_json("output.json")
Advanced Topics
Parsing Deep Dive: Selector/Selectors classes, TextHandler, DOM traversal, selector generation → Parsing
Fetchers and Sessions: HTTP fetcher with TLS impersonation, DynamicFetcher with Playwright, StealthyFetcher with anti-bot bypass, session management, proxy rotation → Fetchers
Adaptive Scraping: Save/match phases, auto_save/adaptive arguments, manual save/retrieve/relocate, custom storage backends → Adaptive Scraping
Spider Framework: Spider class, callbacks, concurrency, pause/resume, streaming, multi-session routing, proxy rotation, blocked-request handling, lifecycle hooks → Spiders
CLI and MCP Server: Interactive shell, extract commands (GET/POST/fetch/stealthy-fetch), MCP server setup for Claude/Cursor, MCP tools reference → CLI and MCP
