nodrivervsbrowser-use

AGPL-3.0 14 2 4,003

321.9 thousand (month) Jan 15 2024 0.48.1(2025-11-09 05:57:23 ago)

87,251 30 226 MIT

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

nodriver is a Python library for browser automation that communicates directly with the browser via the Chrome DevTools Protocol (CDP), without relying on Selenium or chromedriver. It is the successor to undetected-chromedriver, created by the same author, and is designed from the ground up to be undetectable by anti-bot systems.

Key advantages over traditional browser automation:

No chromedriver dependency Communicates directly with Chrome/Chromium via CDP websocket, eliminating the most common detection vector (chromedriver fingerprint).
Undetectable by default Does not set the navigator.webdriver flag, does not inject automation-related JavaScript, and avoids CDP detection patterns that anti-bot systems look for.
Fast and lightweight Without the Selenium/WebDriver protocol overhead, nodriver is significantly faster at launching browsers and executing commands.
Async-first Built entirely on Python's asyncio, enabling efficient concurrent browser automation.
Simple API Clean, Pythonic API that is easier to use than raw CDP or Selenium.

nodriver is particularly useful for scraping websites protected by advanced anti-bot systems like Cloudflare, DataDome, or PerimeterX, where standard Selenium or Playwright setups get detected and blocked.

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-detectcdpasyncfast

ai-powerednatural-languageasync

Example Use

```python import nodriver as uc import asyncio async def main(): # Launch browser - undetected by default browser = await uc.start() # Open a new tab and navigate tab = await browser.get("https://example.com") # Wait for an element and interact with it search_box = await tab.find("input[name='q']") await search_box.send_keys("web scraping") # Click a button button = await tab.find("button[type='submit']") await button.click() # Wait for navigation and extract content await tab.wait_for("div.results") results = await tab.query_selector_all("div.result") for result in results: title = await result.query_selector("h3") print(await title.get_text()) # Take a screenshot await tab.save_screenshot("results.png") browser.stop() asyncio.run(main()) ```

```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()) ```