Skip to content

scrapegraphaivsphpscraper

MIT 4 17 23,278
59.6 thousand (month) Jan 15 2024 1.76.0(2026-04-09 09:41:03 ago)
583 2 28 GPL-3.0-or-later
May 04 2020 104 (month) 3.0.0(2024-04-09 15:34:59 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.

PHPScraper is a universal web-util for PHP. The main goal is to get stuff done instead of getting distracted with selectors, preparing & converting data structures, etc. Instead, you can just go to a website and get the relevant information for your project.

PHPScraper is a minimalistic scraper framework that is built on top of other popular scraping tools.

Features:

  • Direct access to page basic features like: Meta data, Links, Images, Headings, Content, Keywords etc.
  • File downloading.
  • RSS, Sitemap and other feed processing.
  • CSV, XML and JSON file processing.

Highlights


ai-poweredpopular

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)") ```
```javascript // create scraper object $web = new \Spekulatius\PHPScraper\PHPScraper; // go to URL $web->go('https://test-pages.phpscraper.de/content/selectors.html'); // elements can be found using XPath: echo $web->filter("//*[@id='by-id']")->text(); // "Content by ID" // or pre-defined variables covering basic page data: $web->links; // for all links $web->headings; $web->images; $web->contentKeywords; $web->orderedLists; $web->unorderedLists; $web->paragraphs; $web->outline; // basic page outline $web->cleanOutlineWithParagraphs; // basic page outline ```

Alternatives / Similar


Was this page helpful?