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botasaurusvsscrapy

MIT 52 5 4,321
35.5 thousand (month) Oct 01 2023 4.0.97(2026-01-06 07:45:54 ago)
61,276 30 640 BSD-3-Clause
Jul 26 2019 3.1 million (month) 2.15.0(2026-04-09 12:02:09 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.

Scrapy is an open-source Python library for web scraping. It allows developers to extract structured data from websites using a simple and consistent interface.

Scrapy provides:

  • A built-in way to follow links and extract data from multiple pages (crawling)
  • Handling common web scraping tasks such as logging in, handling cookies, and handling redirects.

Scrapy is built on top of the Twisted networking engine, which provides a non-blocking way to handle multiple requests at the same time, allowing Scrapy to efficiently scrape large websites.

It also comes with a built-in mechanism for handling common web scraping problems, such as:

  • handling HTTP errors
  • handling broken links

Scrapy also provide these features:

  • Support for storing scraped data in various formats, such as CSV, JSON, and XML.
  • Built-in support for selecting and extracting data using XPath or CSS selectors (through parsel).
  • Built-in support for handling common web scraping problems (like deduplication and url filtering).
  • Ability to easily extend its functionality using middlewares.
  • Ability to easily extend output processing using pipelines.

Highlights


anti-detectstealthlarge-scale
popularcss-selectorsxpath-selectorscommunity-toolsoutput-pipelinesmiddlewaresasyncproductionlarge-scale

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"] ) ```

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