seleniumvsbrowser-use

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Selenium is a Python package that allows developers to automate web browsers. It provides a way for developers to interact with web browsers programmatically, simulating user interactions such as clicking links, filling out forms, and navigating between pages. Selenium can be used to automate tasks such as web scraping, testing web applications, and automating repetitive tasks on websites.

Selenium is built on top of WebDriver, which is a browser automation API that allows Selenium to interact with web browsers. Selenium supports a wide variety of web browsers, including Chrome, Firefox, Safari, and Internet Explorer.

One of the main advantages of Selenium is that it can be used with many different programming languages, not only Python, and it also supports different platforms.

The package also provide a set of APIs that allows you to interact with web pages, you can locate elements, interact with them, get their properties and interact with javascript, you can use the APIs to automate the browser and interact with web pages in the same way a human user would.

Selenium is widely used in web scraping, web testing, and other automation tasks because it allows developers to automate web browsers in a way that is very similar to how a human user would interact with the browser.

Overall, Selenium is a powerful and versatile tool for automating web browsers and is widely used in web scraping, web testing, and other automation tasks.

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

ai-powerednatural-languageasync

Example Use

```python from selenium import webdriver # Create an instance of the webdriver driver = webdriver.Firefox() # Navigate to a website driver.get("http://www.example.com") # Find an element by its id element = driver.find_element_by_id("example-id") # Interact with the element element.click() # Find an element by its name element = driver.find_element_by_name("example-name") # Fill an input form element.send_keys("example text") # Find and click a button driver.find_element_by_xpath("//button[text()='Search']").click() # Wait for the page to load driver.implicitly_wait(10) # Get the page title print(driver.title) # Close the browser driver.close() ```

```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()) ```

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