Browser Use — AI Browser Automation

What it is

Browser Use is an open-source Python library that connects large language models to a real browser. It gives AI agents the ability to navigate websites, fill forms, click buttons, extract data, and perform multi-step web tasks. The library supports Claude, GPT, Gemini, and other LLM providers as the reasoning engine behind the browser actions.

Browser Use targets developers building AI agents that need web interaction capabilities, QA engineers automating browser tests with natural language, and teams building web scraping pipelines that adapt to changing page layouts.

How it saves time or tokens

Traditional browser automation (Selenium, Playwright) requires writing explicit selectors and step-by-step scripts that break when pages change. Browser Use delegates the page understanding to an LLM, which reads the DOM and decides what to click, type, or extract. This makes automations more resilient to layout changes and reduces the maintenance burden of selector-based scripts.

The library handles browser state management, screenshot capture for vision models, and action execution automatically, so you write high-level task descriptions instead of low-level browser commands.

How to use

1. Install Browser Use: pip install browser-use. Install a browser backend like Playwright: playwright install chromium.
2. Configure your LLM provider (set API keys for OpenAI, Anthropic, or Google).
3. Define a task in natural language and run the agent. Browser Use opens a browser, interprets the page, and executes the steps.

Example

from browser_use import Agent
from langchain_openai import ChatOpenAI

async def main():
    agent = Agent(
        task='Go to google.com, search for browser-use github, and return the star count',
        llm=ChatOpenAI(model='gpt-4o'),
    )
    result = await agent.run()
    print(result)

The agent opens a browser, navigates to Google, types the search query, clicks the result, reads the star count, and returns it as structured output.

Related on TokRepo

• AI tools for browser automation — Compare browser automation solutions
• AI tools for web scraping — Data extraction tools for the web

Common pitfalls

• Each browser action requires an LLM call, so token costs add up for complex multi-step tasks. Use cheaper models for simple navigation and reserve expensive models for complex reasoning steps.
• Some websites block automated browsers. Use headless mode with caution and respect robots.txt and terms of service.
• Vision-based page understanding (sending screenshots to the LLM) uses more tokens than DOM-text-based approaches. Choose the interaction mode based on your cost sensitivity.