scrapegraphaivsgracy
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.
Gracy is an API client library based on httpx that provides an extra stability layer with:
- Retry logic
- Logging
- Connection throttling
- Tracking/Middleware
In web scraping, Gracy can be a convenient tool for creating scraper based API clients.
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)")
```
```python
# 0. Import
import asyncio
from typing import Awaitable
from gracy import BaseEndpoint, Gracy, GracyConfig, LogEvent, LogLevel
# 1. Define your endpoints
class PokeApiEndpoint(BaseEndpoint):
GET_POKEMON = "/pokemon/{NAME}" # 👈 Put placeholders as needed
# 2. Define your Graceful API
class GracefulPokeAPI(Gracy[str]):
class Config: # type: ignore
BASE_URL = "https://pokeapi.co/api/v2/" # 👈 Optional BASE_URL
# 👇 Define settings to apply for every request
SETTINGS = GracyConfig(
log_request=LogEvent(LogLevel.DEBUG),
log_response=LogEvent(LogLevel.INFO, "{URL} took {ELAPSED}"),
parser={
"default": lambda r: r.json()
}
)
async def get_pokemon(self, name: str) -> Awaitable[dict]:
return await self.get(PokeApiEndpoint.GET_POKEMON, {"NAME": name})
# Note: since Gracy is based on httpx we can customized the used client with custom headers etc"
def _create_client(self) -> httpx.AsyncClient:
client = super()._create_client()
client.headers = {"User-Agent": f"My Scraper"}
return client
pokeapi = GracefulPokeAPI()
async def main():
try:
pokemon = await pokeapi.get_pokemon("pikachu")
print(pokemon)
finally:
pokeapi.report_status("rich")
asyncio.run(main())
```