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Scrapling

Scrapling• Pinned

Scrapling: Python web scraping framework for requests, stealth browsers, parsing, and concurrent crawling.

What problem it solves

Web scraping workflows often span HTTP requests, JavaScript rendering, browser interaction, data extraction, and large-scale crawling. Scrapling brings these activities into one Python library and supports use from Python, the terminal, or an AI agent.

Key features

  • Fetch and interact with websites — Fetch static content through HTTP requests or render and interact with dynamic websites through stealth browsers. Persistent sessions, proxy rotation, background API capture, and asynchronous fetchers support varied collection tasks.

    Source: README, scrapling

  • Parse and extract data — Select and extract content with CSS, XPath, text, regex, filters, and related-element matching. Adaptive tracking can relocate elements after website changes, while Markdown output supports clean page conversion.

    Source: README

  • Crawl and export results — Build concurrent spiders with reusable sessions, throttling, retries, pause and resume, streaming, templates, and link extraction. Built-in exporters produce JSON, JSONL, CSV, and XML results.

    Source: README

  • Use from Python, the terminal, or AI agents — The project provides Python APIs, an interactive shell, CLI commands, an MCP server, and an Agent Skill. These interfaces let users browse and extract data through coding tools or AI-agent workflows.

    Source: README, agent-skill, server.json

Quick start

Copied verbatim from the project's README.

from scrapling.fetchers import Fetcher

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

Who it may suit

This project may suit Python developers and information-technology teams collecting structured data from static or dynamic websites, as well as AI-agent users who need web browsing and extraction tools.

Topics to discuss

  • Which combination of HTTP requests, browser automation, and API capture works best for different scraping scenarios?

  • How should adaptive element tracking and selection methods be improved for websites that change frequently?

  • Which spider templates, proxy strategies, and export formats are most useful for community use cases?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/D4Vinci/Scrapling#readme

ReadmeX opened this community for a trending project; it is not run by the project. Maintainers can claim it from the community page.

github.comhttps://github.com/D4Vinci/Scrapling

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