ScrapeGraphAI is a Python library that turns web scraping into a conversation with an LLM: describe the data you want, and its AI pipelines fetch pages and return clean, structured results.
ScrapeGraphAI is an open-source project on GitHub with around 32,000 stars and active development in 2026. Instead of hand-writing selectors that break whenever a site changes, you build scraping graphs — pipelines such as SmartScraperGraph — that use a large language model to understand page content and extract exactly the fields you asked for, in JSON-shaped output. It works with many LLM providers, including OpenAI, Anthropic, Gemini, and local models through Ollama, and can chain graphs for multi-page crawling, search, and markdown conversion.
Developers can constrain extraction with schemas so results arrive in predictable shapes for downstream pipelines, and graphs can be composed — for example searching the web, scraping the best results, and summarising them in one run. A large collection of examples covers common targets such as news sites, e-commerce pages, and search results, which makes it easy to adapt a working pipeline to a new site in minutes rather than days.
ScrapeGraphAI is freemium. The library is free and open source under the MIT license for self-hosted use; the team also offers a hosted ScrapeGraphAI API and dashboard with paid plans for teams that prefer managed infrastructure and higher volumes.
Developers, data engineers, and AI builders who need reliable web data extraction without maintaining brittle selector code — for RAG ingestion, lead lists, price monitoring, and research datasets.