Skip to content

scrapegraphaivsmechanize

MIT 4 17 23,278
59.6 thousand (month) Jan 15 2024 1.76.0(2026-04-09 09:41:03 ago)
4,440 8 6 MIT
Jul 25 2009 213.1 thousand (month) 2.14.0(2025-01-05 18:30:46 ago)

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.

Mechanize is a Ruby library for automating interaction with websites. It automatically stores and sends cookies, follows redirects, and can submit forms — making it behave like a web browser without needing an actual browser engine.

Key features include:

  • Automatic cookie management Stores cookies received from servers and sends them back on subsequent requests, maintaining session state across multiple pages.
  • Form handling Can find, fill in, and submit HTML forms programmatically. Supports text inputs, selects, checkboxes, radio buttons, and file uploads.
  • Link following Navigate through pages by clicking links using their text content, CSS selectors, or href patterns.
  • History and back/forward Maintains a browsing history, allowing you to go back and forward through visited pages.
  • HTTP authentication Supports basic and digest HTTP authentication.
  • Proxy support Can route requests through HTTP proxies.
  • Redirect handling Automatically follows HTTP redirects (configurable).

Mechanize is one of the oldest and most established web interaction libraries in Ruby. It is best suited for scraping traditional server-rendered websites with forms and multi-page workflows. For JavaScript-heavy sites, a browser automation tool like Selenium or Playwright is recommended instead.

Highlights


ai-poweredpopular
popularproduction

Example Use


```python from scrapegraphai.graphs import SmartScraperGraph from pydantic import BaseModel, Field from typing import List # Define the output schema class Product(BaseModel): name: str = Field(description="Product name") price: float = Field(description="Price in USD") rating: float = Field(description="Customer rating out of 5") class ProductList(BaseModel): products: List[Product] # Create a scraping graph with natural language instruction graph = SmartScraperGraph( prompt="Extract all products with their names, prices, and ratings", source="https://example.com/products", schema=ProductList, config={ "llm": { "model": "openai/gpt-4o", "api_key": "YOUR_API_KEY", }, }, ) # Run the graph result = graph.run() for product in result["products"]: print(f"{product['name']}: ${product['price']} ({product['rating']}/5)") ```
```ruby require 'mechanize' agent = Mechanize.new # Navigate to a page page = agent.get('https://example.com') puts page.title # Find and click a link page = page.link_with(text: 'Products').click # Extract data from the page page.search('.product').each do |product| name = product.at('.name').text price = product.at('.price').text puts "#{name}: #{price}" end # Fill in and submit a login form login_page = agent.get('https://example.com/login') form = login_page.form_with(action: '/login') form['username'] = 'user@example.com' form['password'] = 'password123' dashboard = agent.submit(form) # Cookies are maintained automatically puts dashboard.title # "Dashboard" # Download a file agent.get('https://example.com/report.csv').save('report.csv') ```

Alternatives / Similar


Was this page helpful?