scrapegraphaivscrawl4ai
ScrapeGraphAI is a Python library that uses large language models (LLMs) to create web scraping pipelines automatically. Instead of writing CSS selectors or XPath expressions, you describe what data you want in natural language and provide a Pydantic schema — the library handles the rest.
Key features include:
- Natural language extraction Describe what you want to extract in plain English (e.g., "Extract all product names and prices") and the LLM figures out how to find and extract the data.
- Pydantic schema output Define the expected output structure using Pydantic models for type-safe, validated extraction results.
- Graph-based pipeline Built on a directed graph architecture where each node performs a specific task (fetching, parsing, extracting, merging). This makes pipelines modular and debuggable.
- Multiple graph types SmartScraperGraph (single page), SearchGraph (search + scrape), SpeechGraph (audio output), and more specialized pipelines.
- Multiple LLM providers Works with OpenAI, Anthropic, Google, Groq, local models via Ollama, and more.
- HTML and JSON support Can extract data from both HTML pages and JSON API responses.
ScrapeGraphAI is particularly useful for rapid prototyping of scrapers and for extracting data from pages with complex or frequently changing layouts where traditional selectors would be brittle.
Crawl4AI is an open-source AI-powered web crawling and data extraction library for Python. It uses large language models (LLMs) to intelligently extract structured data from web pages with minimal code. Unlike traditional scraping frameworks that rely on CSS selectors or XPath, Crawl4AI can understand page content semantically and extract data based on natural language descriptions of what you want.
Key features include:
- LLM-based extraction Define what data you want in plain English and Crawl4AI uses LLMs to find and extract it from the page content. Supports multiple LLM providers including OpenAI, Anthropic, and local models.
- Automatic crawling Built-in crawler with support for JavaScript rendering, parallel crawling, and session management.
- Structured output Returns data in structured formats (JSON, Pydantic models) making it easy to integrate into data pipelines.
- Markdown conversion Can convert web pages to clean markdown format, useful for feeding content to LLMs.
- Chunking strategies Multiple strategies for breaking down large pages into processable chunks for LLM extraction.
- Async support Built on async Python for efficient concurrent crawling and extraction.
Crawl4AI is particularly useful for scraping unstructured content where writing traditional CSS/XPath selectors would be tedious or fragile. It excels at content extraction, article parsing, and data mining from diverse page layouts.