A guide to building long-term compounding knowledge infrastructure. See details on GitHub .

ScrapeGraphAI

ScrapeGraphAI is an LLM-powered scraping library that converts websites and documents into structured data, offering SDKs, pipelines, and integrations for production workflows.

Overview

ScrapeGraphAI is a developer-focused scraping toolkit that leverages large language models and graph-based extraction to transform web pages and local documents (HTML, JSON, Markdown, etc.) into structured data. It provides ready-made pipelines (e.g., SmartScraperGraph), Python and Node.js SDKs, and integrations with popular RAG and LLM frameworks to accelerate data engineering and knowledge ingestion workflows.

Key Features

  • Graph-driven scraping pipelines and prompt-driven extractors for flexible field extraction.
  • Official SDKs for Python and JavaScript, with support for local and cloud LLM backends.
  • Integrations with LangChain, LlamaIndex and other frameworks; usable in production pipelines.
  • Extensible pipeline components for parsing, cleaning, and exporting results to downstream stores.

Use Cases

  • Batch extraction from news, product pages and catalogs for search, analytics, and monitoring.
  • Building knowledge bases for RAG systems by converting web content into searchable documents.
  • Rapid prototyping of extraction tasks with minimal configuration and a few prompts.

Technical Notes

  • Combines LLM reasoning with explicit graph structures to improve extraction accuracy on complex pages.
  • Supports concurrency and distributed scraping for scale and reliability.
  • Open-source under MIT license; examples and tests are included in the repository.

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ScrapeGraphAI
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🌱 Open Source 🛠️ Dev Tools 📦 SDK 🕷️ Browser Automation 💾 Data