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node-crawlervsscrapegraphai

MIT 30 6 6,790
15.3 thousand (month) Sep 10 2012 2.0.2(2025-05-28 09:36:01 ago)
23,278 17 4 MIT
Jan 15 2024 59.6 thousand (month) 1.76.0(2026-04-09 09:41:03 ago)

node-crawler is a popular web scraping library for Node.js that allows you to easily navigate and extract data from websites. It has a simple API and supports concurrency, making it efficient for scraping large numbers of pages.

Features:

  • Server-side DOM & automatic jQuery insertion with Cheerio (default) or JSDOM,
  • Configurable pool size and retries,
  • Control rate limit,
  • Priority queue of requests,
  • forceUTF8 mode to let crawler deal for you with charset detection and conversion,
  • Compatible with 4.x or newer version.
  • Http2 support
  • Proxy support

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.

Highlights


ai-poweredpopular

Example Use


```javascript const Crawler = require('crawler'); const c = new Crawler({ maxConnections: 10, // This will be called for each crawled page callback: (error, res, done) => { if (error) { console.log(error); } else { const $ = res.$; // $ is Cheerio by default //a lean implementation of core jQuery designed specifically for the server console.log($('title').text()); } done(); } }); // Queue just one URL, with default callback c.queue('http://www.amazon.com'); // Queue a list of URLs c.queue(['http://www.google.com/','http://www.yahoo.com']); // Queue URLs with custom callbacks & parameters c.queue([{ uri: 'http://parishackers.org/', jQuery: false, // The global callback won't be called callback: (error, res, done) => { if (error) { console.log(error); } else { console.log('Grabbed', res.body.length, 'bytes'); } done(); } }]); // Queue some HTML code directly without grabbing (mostly for tests) c.queue([{ html: '

This is a test

' }]); ```
```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)") ```

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