botasaurusvsfirecrawl

MIT 52 5 4,321

35.5 thousand (month) Oct 01 2023 4.0.97(2026-01-06 07:45:54 ago)

- - - None

Apr 01 2024 0.0.0(2025-03-15 00:00:00 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.

Firecrawl is an AI-powered web scraping API that converts web pages into clean Markdown or structured data, optimized for use with large language models (LLMs) and retrieval-augmented generation (RAG) pipelines. It handles JavaScript rendering, anti-bot bypass, and content extraction automatically.

Firecrawl offers multiple modes:

Scrape Convert a single URL into clean Markdown, HTML, or structured data. Handles JavaScript rendering and anti-bot protections automatically.
Crawl Crawl an entire website starting from a URL, with configurable depth, URL patterns, and page limits. Returns all pages as clean Markdown.
Map Quickly discover all URLs on a website without fully scraping each page. Useful for sitemap generation and crawl planning.
Extract Use LLMs to extract specific structured data from pages based on a schema definition.

Key features:

Clean Markdown output ideal for LLM context windows
Automatic JavaScript rendering with headless browsers
Built-in anti-bot bypass for protected websites
Structured extraction with JSON schemas
Batch crawling with webhook notifications
Python and JavaScript SDKs

Firecrawl is a commercial API service (requires API key, has a free tier) backed by Y Combinator. It has become one of the most popular tools for feeding web content into AI applications and is widely used in the LLM/RAG ecosystem.

Note: while the primary service is an API, the core is open source and can be self-hosted.

Highlights

anti-detectstealthlarge-scale

ai-poweredpopularasync

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"] ) ```

```python from firecrawl import FirecrawlApp app = FirecrawlApp(api_key="YOUR_API_KEY") # Scrape a single page - get clean markdown result = app.scrape_url("https://example.com/blog/article") print(result["markdown"]) # clean markdown content # Extract structured data with a schema result = app.scrape_url( "https://example.com/product/123", params={ "formats": ["extract"], "extract": { "schema": { "type": "object", "properties": { "name": {"type": "string"}, "price": {"type": "number"}, "description": {"type": "string"}, }, } }, }, ) print(result["extract"]) # {"name": "...", "price": 29.99, ...} # Crawl an entire website crawl_result = app.crawl_url( "https://example.com", params={"limit": 100, "scrapeOptions": {"formats": ["markdown"]}}, ) for page in crawl_result["data"]: print(page["metadata"]["title"], page["markdown"][:100]) # Map all URLs on a site map_result = app.map_url("https://example.com") print(f"Found {len(map_result['links'])} URLs") ```

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