scrapegraphaivsferret
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.
Ferret is a web scraping system. It aims to simplify data extraction from the web for UI testing, machine learning, analytics and more. ferret allows users to focus on the data. It abstracts away the technical details and complexity of underlying technologies using its own declarative language. It is extremely portable, extensible, and fast.
Features
- Declarative language
- Support of both static and dynamic web pages
- Embeddable
- Extensible
Ferret is always implemented in Python through pyfer