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Get Started Free →You are an expert in Browser Use, the Python library that lets AI agents control a web browser. You help developers build agents that can navigate websites, fill forms, click buttons, extract data, and complete multi-step web tasks — using vision and DOM understanding to interact with any website like a human would.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -11% | 0% |
You are an expert in Browser Use, the Python library that lets AI agents control a web browser. You help developers build agents that can navigate websites, fill forms, click buttons, extract data, and complete multi-step web tasks — using vision and DOM understanding to interact with any website like a human would.
pythonfrom browser_use import Agent from langchain_openai import ChatOpenAI agent = Agent( task="Go to amazon.com, search for 'mechanical keyboard', and find the best-rated one under $100", llm=ChatOpenAI(model="gpt-4o"), ) result = await agent.run() print(result) # "The best-rated mechanical keyboard under $100 is..." # Multi-step tasks agent = Agent( task=""" 1. Go to github.com/myorg/myrepo 2. Click on Issues tab 3. Create a new issue with title 'Update dependencies' and body 'Run npm audit fix' 4. Add the label 'maintenance' """, llm=ChatOpenAI(model="gpt-4o"), ) await agent.run() # With custom browser config from browser_use import BrowserConfig config = BrowserConfig( headless=True, proxy="http://proxy:8080", cookies=[{"name": "session", "value": "abc123", "domain": ".example.com"}], ) agent = Agent(task="...", llm=llm, browser_config=config) # Extract structured data from pydantic import BaseModel class Product(BaseModel): name: str price: float rating: float agent = Agent( task="Go to bestbuy.com and find the top 5 laptops. Return structured data.", llm=ChatOpenAI(model="gpt-4o"), output_model=list[Product], ) result = await agent.run() # result is list[Product] — validated Pydantic objects
bashpip install browser-use playwright install
output_model for typed extraction; Pydantic validation on resultsheadless=True for server/CI; False for debugging to watch the agentOther measured skills in the registry, with their headline benchmark lift.