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ScrapeGraphAI

AI-powered web scraping tool that uses LLMs and graph-based pipelines to extract structured data from websites, PDFs, and XML

★ 28,138 GitHub MIT web-scrapingaipythonllmdata-extractionrag Automation Tools

Overview

ScrapeGraphAI is an open-source Python library that leverages Large Language Models (LLMs) and graph-based scraping pipelines to automatically extract structured data from websites, PDFs, XML, and more. With over 28,000 GitHub stars, it is one of the most popular AI-powered scraping frameworks available today. The library uses a modular graph architecture where each node represents a specific operation — from fetching pages to extracting data using LLM prompts. You can configure multiple LLM backends including GPT, Gemini, Ollama, and Hugging Face models. It supports SmartScraperGraph for single-page scraping, SearchGraph for multi-page extraction, and SpeechGraph for voice-enabled scraping. ScrapeGraphAI comes with Docker support, a REST API, and works headlessly with Browserbase or Selenium. It's perfect for developers building RAG pipelines, AI agents that need web data, or anyone who needs reliable, LLM-powered web scraping without writing complex selectors or regex patterns.

Requirements

Min vCPU
2
Min RAM
2048 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
4096 MB
Rec Disk
20 GB

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Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  scrapegraph-ai:
    image: mcp/scrapegraph:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/scrapegraph-ai:/data

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Guides & articles

How to Use ScrapeGraphAI: A Practical Tutorial with Docker

Step-by-step tutorial for ScrapeGraphAI with Docker: scrape websites using natural language, extract structured data, and avoid common pitfalls.

Jul 7, 2026

ScrapeGraphAI Guide: LLM-Powered Web Scraping Made Simple

Learn how ScrapeGraphAI uses LLMs and graph-based pipelines to revolutionize web scraping — no more CSS selectors or brittle regex patterns.

Jul 7, 2026