Scrapling

Tracked

An adaptive web scraping framework with AI-powered element selection, stealth capabilities, and MCP server support for AI agent integration.

Author D4Vinci Open Sourced 2024-10-13 Last Commit Unknown

Overview

Scrapling is a high-performance Python web scraping framework that uses AI-powered adaptive element selection to resiliently extract data from web pages. It provides built-in stealth capabilities to bypass bot detection and ships with an MCP server, making it a powerful tool for AI agents that need to interact with web content.

Key Features

  • AI-adaptive element selection that survives page layout changes using intelligent similarity matching
  • Built-in stealth mode with real browser fingerprint simulation to bypass anti-bot protections
  • MCP server integration enabling AI agents to use scraping as a tool
  • High-performance fetching with support for Playwright, Camoufox, and real browser rendering
  • Smart XPath and CSS selector generation with automatic fallback strategies

Use Cases

  • AI agent web data extraction and browser automation workflows
  • Resilient production scraping that adapts to target site changes
  • Anti-detection data collection from protected websites
  • Building MCP-powered agent tools for web interaction

Technical Details

  • Written in Python with support for multiple browser backends (Playwright, Camoufox)
  • Uses Levenshtein distance and adaptive algorithms for element matching across page mutations
  • Ships as both a standalone library and an MCP server for agent integration