ScrapeGraphAI
scrapegraphai.com
An open-source Python library for AI-powered web scraping using LLMs
Description
The scraper for the AI Era · The web scraping API built for the AI era. Extract structured data from any website. No proxies, no selectors, no maintenance needed. · Turn any webpage into structured data with one API call · Use Cases · Model Context Protocol, Skills & CLI · Official integrations
ScrapeGraphAI is a Python library that leverages LLMs and graph logic to automate the creation of scraping pipelines for websites, local documents (XML, HTML, JSON), and other data sources. It aims to simplify web scraping by allowing users to specify the information they need in natural language, and the AI handles the extraction process. The library supports multiple LLMs including GPT, Gemini, Groq, Azure, and local models via Ollama.
Installation
pip install scrapegraphai| Installs into | python, cli |
Not sure where to start — ask an assistant to walk you through:
Features
AI-Powered Structured Data
Dynamic Content Handling
Proxy Rotation & Anti-Bot Bypass
Multi-language SDKs
AI Agent Web Access (MCP)
RAG Pipeline Enhancement
Autonomous Agent Empowerment
Major AI Platform Compatibility
SmartScraper for Structured Data
SmartCrawler for Whole Sites
Use cases
Automated web scraping for data collection
Extracting information from local documents
Market research and data analysis
Content aggregation
Building datasets for machine learning
FAQ
ScrapeGraphAI is an open-source Python library that uses large language models (LLMs) and graph logic to automate the creation of scraping pipelines for websites and various document types including HTML, XML, and local documents. It revolutionizes web scraping by leveraging LLMs to understand and extract web data semantically, eliminating the need for complex CSS selectors or XPath expressions.
Traditional scraping tools rely on fixed patterns and manual configurations to extract data from web pages. In contrast, ScrapeGraphAI adapts to website structure changes using LLMs, reducing the need for constant developer intervention and reducing maintenance burden. This flexibility ensures that scrapers remain functional even when website layouts change.
ScrapeGraphAI's self-healing technology automatically adapts to changes in website structure, ensuring continuous data extraction without requiring manual updates.
No, coding expertise is not required. The natural language interface allows users without coding skills to specify data extraction tasks easily in plain English.
Key features include SmartScraper for intelligent data extraction, SearchScraper for multi-source information gathering, Markdownify for content conversion to Markdown format, AI-powered semantic understanding, structured output, and source attribution.
ScrapeGraphAI supports many LLMs including GPT, Gemini, Groq, Azure, and Hugging Face. It also supports local models that can run on your machine using Ollama.
Absolutely. ScrapeGraphAI can be easily defined as a tool in frameworks like LangGraph and LangChain, enabling AI agents to leverage its scraping capabilities. This allows for seamless integration with modern AI workflows and agent-based systems.
Best practices include clear and specific prompt writing, proper error handling, respecting rate limits, data validation, resource management, and documentation of your scraping tasks.
Getting started involves installing Python 3.7 or later, obtaining an API key from your chosen LLM provider, installing the SDK, setting up your environment variables, running your first scrape, and understanding the basics of the library.
Optimization strategies include writing efficient and clear prompts, effective resource management, implementing parallel processing where applicable, using caching strategies, implementing proper error handling, and monitoring your scraping operations.
ScrapeGraphAI provides several methods for different scenarios: SmartScraper, which is the default method for structured data extraction with AI-powered analysis; Markdownify, which converts web pages to clean markdown format for better readability; SearchScraper, which targets specific information extraction based on search queries; and Crawl, which offers comprehensive site crawling with schema-based data extraction.
Specs
| Type | Framework |
| Section | Automation |
| Pricing | has a free tier (от $17/mo) |
| Platform | Command line |
| Systems | python, cli, api |
| Hosting | hybrid |
| Install | package |
| Installs into | python, cli |
| Who for | Individual |
| Site language | en |
| Vendor | ScrapeGraphAI, Inc. |
| GitHub | ScrapeGraphAI/Scrapegraph-ai |
| ★ Stars | 30 028 |
| Rating | 4.40 (0 reviews) |
| Views | 115 840 |
| Launched | 2024-09-01 |
