browser-use
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
Example Use
```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()) ```