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scrapegraphaivsgeziyor

MIT 4 17 23,278
59.6 thousand (month) Jan 15 2024 1.76.0(2026-04-09 09:41:03 ago)
2,772 1 30 MPL-2.0
Jun 06 2019 2026-04-11(2026-04-11 21:30:25 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.

Geziyor is a blazing fast web crawling and web scraping framework. It can be used to crawl websites and extract structured data from them. Geziyor is useful for a wide range of purposes such as data mining, monitoring and automated testing.

Features:

  • JS Rendering
  • 5.000+ Requests/Sec
  • Caching (Memory/Disk/LevelDB)
  • Automatic Data Exporting (JSON, CSV, or custom)
  • Metrics (Prometheus, Expvar, or custom)
  • Limit Concurrency (Global/Per Domain)
  • Request Delays (Constant/Randomized)
  • Cookies, Middlewares, robots.txt
  • Automatic response decoding to UTF-8
  • Proxy management (Single, Round-Robin, Custom)

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)") ```
```go // This example extracts all quotes from quotes.toscrape.com and exports to JSON file. func main() { geziyor.NewGeziyor(&geziyor.Options{ StartURLs: []string{"http://quotes.toscrape.com/"}, ParseFunc: quotesParse, Exporters: []export.Exporter{&export.JSON{}}, }).Start() } func quotesParse(g *geziyor.Geziyor, r *client.Response) { r.HTMLDoc.Find("div.quote").Each(func(i int, s *goquery.Selection) { g.Exports <- map[string]interface{}{ "text": s.Find("span.text").Text(), "author": s.Find("small.author").Text(), } }) if href, ok := r.HTMLDoc.Find("li.next > a").Attr("href"); ok { g.Get(r.JoinURL(href), quotesParse) } } ```

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