camoufoxvsbrowser-use

MPL-2.0 244 3 7,089

1.0 million (month) Mar 01 2024 0.4.11(2025-01-29 09:33:18 ago)

87,251 30 226 MIT

Nov 01 2024 8.9 million (month) 0.12.6(2026-04-02 07:55:13 ago)

Camoufox is an anti-detect browser built on Firefox, designed specifically for web scraping and browser automation while evading bot detection systems. Unlike Chrome-based anti-detect solutions, Camoufox uses a patched version of Firefox with over 100 modifications to prevent fingerprinting and detection.

Key features include:

Firefox-based stealth Uses Firefox instead of Chrome, which has different fingerprinting characteristics and is less commonly targeted by anti-bot systems. The browser is patched at the C++ level to prevent JavaScript-based fingerprint leaks.
Realistic fingerprints Generates and applies realistic browser fingerprints including screen resolution, WebGL renderer, fonts, and other browser properties that match real user configurations.
Playwright integration Works seamlessly with Playwright for Python, providing the familiar Playwright API while running through the stealth-patched Firefox instance.
Proxy support Built-in proxy support with authentication, including residential proxy rotation.
Geolocation spoofing Can spoof browser geolocation to match proxy IP location for consistent fingerprints.
Human-like behavior Includes utilities for adding human-like mouse movements and typing patterns.

Camoufox is particularly effective against sophisticated anti-bot systems that detect Chrome-based automation tools. By using Firefox as the base, it avoids many Chrome-specific detection vectors while the deep browser patches prevent general fingerprinting techniques.

Browser-use is a Python library that enables AI agents to control web browsers using natural language instructions. It connects large language models (LLMs) to browser automation, allowing you to describe what you want done in plain English instead of writing explicit selectors and interaction code.

Key features include:

Natural language browser control Describe tasks like "go to Amazon and find the cheapest laptop under $500" and the AI agent will navigate, interact with elements, and extract the requested information.
Multi-step task execution Can handle complex workflows that require multiple pages, form filling, clicking, scrolling, and waiting for dynamic content.
Vision support Uses screenshot analysis (multimodal LLMs) to understand page layout and find elements visually, not just through DOM inspection.
Multiple LLM providers Works with OpenAI, Anthropic Claude, Google Gemini, and other LLM providers.
Playwright backend Uses Playwright under the hood for reliable browser automation across Chrome, Firefox, and Safari.
Structured output Can return extracted data in structured formats defined by Pydantic models.

Browser-use represents a new paradigm in web scraping where instead of writing brittle selectors, you describe the extraction task and let the AI figure out how to navigate and extract the data. This is especially useful for scraping diverse sites with varying layouts.

Highlights

anti-detectstealth

ai-powerednatural-languageasync

Example Use

```python from camoufox.sync_api import Camoufox # Launch stealth Firefox browser with Camoufox(headless=True) as browser: page = browser.new_page() # Navigate - the browser appears as a real Firefox user page.goto("https://example.com") # Use standard Playwright API for interaction page.fill("input[name='search']", "web scraping") page.click("button[type='submit']") # Extract data results = page.query_selector_all(".result") for result in results: title = result.query_selector("h3").text_content() print(title) # With proxy and custom fingerprint from camoufox.sync_api import Camoufox with Camoufox( headless=True, proxy={"server": "http://proxy.example.com:8080"}, geoip=True, # auto-match geolocation to proxy IP ) as browser: page = browser.new_page() page.goto("https://example.com") print(page.content()) ```

```python from browser_use import Agent from langchain_openai import ChatOpenAI import asyncio async def main(): # Create an AI agent with a language model agent = Agent( task="Go to reddit.com/r/webscraping, find the top 5 posts " "from today, and extract their titles and scores", llm=ChatOpenAI(model="gpt-4o"), ) # Run the agent - it navigates and extracts automatically result = await agent.run() print(result) # More complex multi-step task agent = Agent( task="Go to example.com/login, log in with user@test.com " "and password 'test123', then navigate to the dashboard " "and extract all notification messages", llm=ChatOpenAI(model="gpt-4o"), ) result = await agent.run() print(result) asyncio.run(main()) ```