ayakashivsscrapegraphai
Ayakashi is a web scraping library for Node.js that allows developers to easily extract structured data from websites. It is built on top of the popular "puppeteer" library and provides a simple and intuitive API for defining and querying the structure of a website.
Features:
- Powerful querying and data models
Ayakashi's way of finding things in the page and using them is done with props and domQL. Directly inspired by the relational database world (and SQL), domQL makes DOM access easy and readable no matter how obscure the page's structure is. Props are the way to package domQL expressions as re-usable structures which can then be passed around to actions or to be used as models for data extraction. - High level builtin actions
Ready made actions so you can focus on what matters. Easily handle infinite scrolling, single page navigation, events and more. Plus, you can always build your own actions, either from scratch or by composing other actions. - Preload code on pages
Need to include a bunch of code, a library you made or a 3rd party module and make it available on a page? Preloaders have you covered.
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 ayakashi = require("ayakashi");
const myAyakashi = ayakashi.init();
// navigate the browser
await myAyakashi.goTo("https://example.com/product");
// parsing HTML
// first by defnining a selector
myAyakashi
.select("productList")
.where({class: {eq: "product-item"}});
// then executing selector on current HTML:
const productList = await myAyakashi.extract("productList");
console.log(productList);
```
```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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