ScrapeGraphAI

ScrapeGraphAI extracts structured data from websites, HTML, and PDFs using natural-language prompts and LLMs, offered as an open-source Python library and a cloud API with scrape, extract, search, crawl, and monitor operations.

ScrapeGraphAI reframes web scraping around natural language. Rather than writing and maintaining XPath or CSS selectors, you describe the data you want and the AI extracts structured output from websites, raw HTML, or PDFs. Because the model interprets page content, scrapers are more resilient to layout changes that would break traditional scripts, reducing the ongoing maintenance burden. The project ships as both an open-source Python library, which has amassed north of 20,000 GitHub stars, and a managed cloud API for teams that prefer not to run infrastructure. It offers operations like scrape, extract, search, crawl, and monitor, supports multiple LLM backends, and returns clean, structured formats suited to feeding AI pipelines and RAG systems. Pricing is credit-based, from a free allotment up to higher tiers, with different operations costing different credit amounts (for example markdown scrapes are cheap while extraction and prompted search cost more). This usage model is flexible but requires estimating volume to control costs. ScrapeGraphAI is a strong pick for developers building LLM-ready data pipelines who value adaptability over the fine-grained control of hand-coded scrapers.