Qcrawl: Fast async web crawling and scraping framework for Python (github.com)

Qcrawl, a new asynchronous web crawling and scraping framework for Python, has been unveiled, aiming to simplify the extraction of structured data from web pages. The framework boasts a cross-platform design that allows for easy installation via pip or conda, making it accessible to a wide audience of developers. Its core feature is a high-performance architecture built on asyncio, enabling concurrent crawling that significantly speeds up the data collection process. Additionally, it utilizes a Redis-backed queue for efficient message delivery and includes advanced features such as DNS caching and messagepack serialization for optimized performance. The significance of Qcrawl for the AI/ML community lies in its powerful parsing capabilities using CSS and XPath selectors, alongside a customizable middleware system for request and response handling. Developers can choose from various output formats, including JSON, CSV, and XML, facilitating data integration into machine learning workflows. With flexible queue management options and pluggable downloaders like aiohttp and Camoufox for bypassing bot detection, Qcrawl is poised to become a vital tool for researchers and practitioners seeking to gather large datasets for training AI models effectively and efficiently.