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requestiumvsbrowser-use

BSD-3-Clause 8 4 1,837
79.9 thousand (month) Dec 28 2012 0.5.1(2026-01-19 16:59:35 ago)
87,251 30 226 MIT
Nov 01 2024 8.9 million (month) 0.12.6(2026-04-02 07:55:13 ago)

Requestium is a Python library that merges the power of Requests, Selenium, and Parsel into a single integrated tool for automatizing web actions.

The library was created for writing web automation scripts that are written using mostly Requests but that are able to seamlessly switch to Selenium for the JavaScript heavy parts of the website, while maintaining the session.

Requestium adds independent improvements to both Requests and Selenium, and every new feature is lazily evaluated, so its useful even if writing scripts that use only Requests or Selenium.

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 requestium import Session, Keys session = Session(webdriver_path='./chromedriver', browser='chrome-headless', default_timeout=15) # then session object can be used like requests and parsel: title = session.get('http://samplesite.com').xpath('//title/text()').extract_first(default='Default Title') # other advance functions like POST requests and proxy settings are also available: s.post('http://www.samplesite.com/sample', data={'field1': 'data1'}) s.proxies.update({'http': 'http://10.11.4.254:3128', 'https': 'https://10.11.4.252:3128'}) # session can also be used like selenium as it exposes all selenium functions. # like typing keys: s.driver.find_element_by_xpath("//input[@class='user_name']").send_keys('James Bond', Keys.ENTER) ```
```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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