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botasaurusvscurl-cffi

MIT 52 5 4,321
35.5 thousand (month) Oct 01 2023 4.0.97(2026-01-06 07:45:54 ago)
1,751 2 34 MIT
Feb 23 2022 594.9 thousand (month) 0.7.1(2024-07-13 09:07:25 ago)

Botasaurus is an all-in-one Python web scraping framework that combines browser automation, anti-detection, and scaling features into a single package. It aims to simplify the entire web scraping workflow from development to deployment.

Key features include:

  • Anti-detect browser Ships with a stealth-patched browser that passes common bot detection tests. Automatically handles fingerprinting, user agent rotation, and other anti-detection measures.
  • Decorator-based API Uses Python decorators (@browser, @request) to define scraping tasks, making code clean and easy to organize.
  • Built-in parallelism Easy parallel execution of scraping tasks across multiple browser instances with configurable concurrency.
  • Caching Built-in caching layer to avoid re-scraping pages during development and debugging.
  • Profile persistence Can save and reuse browser profiles (cookies, localStorage) across scraping sessions for maintaining login state.
  • Output handling Automatic output to JSON, CSV, or custom formats with built-in data filtering.
  • Web dashboard Includes a web UI for monitoring scraping progress, viewing results, and managing tasks.

Botasaurus is designed for developers who want a batteries-included framework that handles anti-detection automatically, without needing to manually configure stealth settings or manage browser fingerprints.

Curl-cffi is a Python library for implementing curl-impersonate which is a HTTP client that appears as one of popular web browsers like: - Google Chrome - Microsoft Edge - Safari - Firefox Unlike requests and httpx which are native Python libraries, curl-cffi uses cURL and inherits it's powerful features like extensive HTTP protocol support and detection patches for TLS and HTTP fingerprinting.

Using curl-cffi web scrapers can bypass TLS and HTTP fingerprinting.

Highlights


anti-detectstealthlarge-scale
bypasshttp2tls-fingerprinthttp-fingerprintsyncasync

Example Use


```python from botasaurus.browser import browser, Driver from botasaurus.request import request, Request # Browser-based scraping with anti-detection @browser(parallel=3, cache=True) def scrape_products(driver: Driver, url: str): driver.get(url) # Wait for content to load driver.wait_for_element(".product-list") # Extract product data products = [] for el in driver.select_all(".product-card"): products.append({ "name": el.select(".product-name").text, "price": el.select(".product-price").text, "url": el.select("a").get_attribute("href"), }) return products # HTTP-based scraping (no browser needed) @request(parallel=5, cache=True) def scrape_api(req: Request, url: str): response = req.get(url) return response.json() # Run the scraper results = scrape_products( ["https://example.com/page/1", "https://example.com/page/2"] ) ```
curl-cffi can be accessed as low-level curl client as well as an easy high-level HTTP client: ```python from curl_cffi import requests response = requests.get('https://httpbin.org/json') print(response.json()) # or using sessions session = requests.Session() response = session.get('https://httpbin.org/json') # also supports async requests using asyncio import asyncio from curl_cffi.requests import AsyncSession urls = [ "http://httpbin.org/html", "http://httpbin.org/html", "http://httpbin.org/html", ] async with AsyncSession() as s: tasks = [] for url in urls: task = s.get(url) tasks.append(task) # scrape concurrently: responses = await asyncio.gather(*tasks) # also supports websocket connections from curl_cffi.requests import Session, WebSocket def on_message(ws: WebSocket, message): print(message) with Session() as s: ws = s.ws_connect( "wss://api.gemini.com/v1/marketdata/BTCUSD", on_message=on_message, ) ws.run_forever() ```

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