AutoScraper is a Python web scraper that learns scraping rules from sample data to automatically extract similar content from web pages.
The tool addresses the problem of writing custom parsing logic for each website by using a learning approach: you provide a URL or HTML content along with sample data you want to extract, and AutoScraper identifies the patterns in how that data is structured. It then applies those learned rules to new pages to find and return similar elements. The scraper can extract text, URLs, or values from HTML tags, and supports both finding similar results across pages and retrieving exact matches in a specified order.
AutoScraper suits developers who need to quickly prototype scrapers without writing complex parsing code, particularly for sites with consistent structure. It works well for extracting data from multiple pages once you've trained it on examples from one page. The tool is lightweight and fast, making it practical for integration into larger applications like Flask APIs. If you need sophisticated handling of dynamic content or complex JavaScript-rendered pages, you may need additional tools, though the README does not explicitly compare AutoScraper to alternatives.
Development activity shows consistent engagement with the project. The maintainer actively responds to issues raised by users. The codebase receives updates addressing bugs and adding improvements based on user feedback. Documentation includes practical tutorials demonstrating real-world usage patterns, including integration examples with web frameworks.
