Browser-Use — Make Any Website Accessible to AI Agents

Browser-Use is an open-source Python framework that lets AI agents control web browsers autonomously — clicking, typing, scrolling, filling forms, and navigating multi-step workflows from natural language instructions. With 93k+ GitHub stars, it’s the most popular AI browser automation tool. It works with Claude, GPT, Gemini, and local models, and ships with a Claude Code skill for direct integration.

What It Does

Give a browser agent a task in plain English, and it figures out how to complete it:

from browser_use import Agent, Browser, ChatBrowserUse
import asyncio

async def main():
    agent = Agent(
        task="Go to Amazon, search for 'wireless headphones under $50', "
             "sort by best reviews, and save the top 3 results to a file",
        llm=ChatBrowserUse(),
        browser=Browser(),
    )
    await agent.run()

asyncio.run(main())

The agent processes the page (via DOM/HTML extraction, plus optional vision screenshots), reasons about what to do, takes actions, and repeats until the task is done.

How a Normal User Would Use This

Scenario 1: Job Application Automation

You have a resume and want to apply to 20 jobs on LinkedIn:

agent = Agent(
    task="Go to LinkedIn Jobs, search for 'Python developer remote', "
         "apply to the first 20 jobs using Easy Apply. "
         "Use my resume details: [name, email, experience]",
    llm=ChatBrowserUse(),
)

Scenario 2: Price Comparison Shopping

agent = Agent(
    task="Compare prices for 'Sony WH-1000XM5' across Amazon, "
         "Best Buy, and Walmart. Create a markdown table with "
         "price, shipping, and availability for each store.",
    llm=ChatBrowserUse(),
)

Scenario 3: Research Data Collection

agent = Agent(
    task="Go to Google Scholar, search for 'transformer attention efficiency 2026', "
         "collect the titles, authors, and citation counts of the top 20 results, "
         "and save to research_papers.csv",
    llm=ChatBrowserUse(),
)

Scenario 4: CLI Mode (No Code Needed)

# Interactive browser control from your terminal
browser-use open https://example.com
browser-use state              # List all clickable elements
browser-use click 5            # Click element #5
browser-use type "search query"
browser-use screenshot result.png
browser-use close

Supported Models

Open-Source vs Cloud

Claude Code Integration

Install the Browser-Use skill directly:

mkdir -p ~/.claude/skills/browser-use
curl -o ~/.claude/skills/browser-use/SKILL.md \
  https://raw.githubusercontent.com/browser-use/browser-use/main/skills/browser-use/SKILL.md

Now Claude Code can automate browser tasks as part of your coding workflow.

Architecture

Natural Language Task
        ↓
┌──────────────────┐
│   LLM (reasoning) │  Decides what to do next
└────────┬─────────┘
         ↓
┌──────────────────┐
│  Action Pipeline  │  Translates to browser commands
└────────┬─────────┘
         ↓
┌──────────────────┐
│  Chromium (via CDP)  │  Executes: click, type, scroll, screenshot
└────────┬─────────┘
         ↓
┌──────────────────┐
│  Context Manager  │  Extracts DOM + screenshots for next decision
└──────────────────┘
         ↓
    Loop until done

Real-World Use Cases

• Form automation — Job applications, survey completion, account registration across any website.
• E-commerce — Grocery shopping lists, price comparison, checkout automation.
• Research — Web scraping, competitor monitoring, data collection from sites without APIs.
• Administrative — Appointment scheduling, document downloads, account maintenance.
• Testing — QA workflows, multi-step user journey testing, accessibility auditing.
• Social media — Content posting, engagement tracking, analytics collection.

Note: Some use cases (account registration, checkout automation, job applications at scale) may conflict with website Terms of Service. Always verify you have permission before automating interactions with third-party sites.

How LearnAI Team Could Use This

• Web automation lab — Students build browser agents for real tasks (price comparison, data collection) and learn about AI tool use, error recovery, and task decomposition.
• Agent evaluation project — Compare Browser-Use across different LLM backends on the same 100-task benchmark — teaching model selection and cost-performance tradeoffs.
• Ethics discussion — When should AI automate web interactions? CAPTCHA solving, anti-detection, and Terms of Service implications.

Links

• GitHub: browser-use/browser-use (93k+ stars)
• Website: browser-use.com
• Benchmark: browser-use/benchmark
• Claude Code Skill: Available via GitHub raw URL