AgentSkillsCN

browser-use

基于 Browser-Use 库的 AI 驱动浏览器自动化。利用大语言模型与 Chrome DevTools 协议，打造能够自主导航、与网页交互并提取数据的智能代理。适用场景：浏览器自动化、网页爬虫代理、AI 浏览器代理、Browser-Use、CDP 自动化。

SKILL.md
--- frontmatter
name: browser-use
description: >
  AI-powered browser automation using Browser-Use library. Build agents that can navigate,
  interact with, and extract data from web pages autonomously using LLMs and Chrome DevTools Protocol.
  Use when: browser automation, web scraping agent, AI browser agent, browser-use, CDP automation.

Browser-Use Skill

Build AI agents that autonomously browse the web using LLMs and the Chrome DevTools Protocol.

When to Use

  • •Building AI agents that need to interact with web pages
  • •Automating web tasks (form filling, data extraction, navigation)
  • •Creating autonomous web research agents
  • •Testing web applications with AI-driven exploration
  • •Scraping dynamic content that requires JavaScript execution

Core Concepts

Architecture

code
User Task → LLM (Claude/GPT) → Browser-Use Controller → Chrome CDP → Web Page
                ↑                        ↓
                └──── Page State (HTML/Screenshot) ────┘

Installation

bash
pip install browser-use
playwright install chromium

Basic Agent

python
from browser_use import Agent
from langchain_anthropic import ChatAnthropic

agent = Agent(
    task="Find the latest Python release version on python.org",
    llm=ChatAnthropic(model="claude-sonnet-4-20250514"),
)

result = await agent.run()
print(result)

Advanced Configuration

python
from browser_use import Agent, Browser, BrowserConfig

browser = Browser(
    config=BrowserConfig(
        headless=True,
        disable_security=False,
        extra_chromium_args=["--no-sandbox"],
    )
)

agent = Agent(
    task="Navigate to example.com and extract all links",
    llm=llm,
    browser=browser,
    max_actions_per_step=5,
    use_vision=True,  # Use screenshots for better understanding
)

Key Patterns

1. Multi-Step Task Automation

python
agent = Agent(
    task="""
    1. Go to github.com
    2. Search for 'browser-use'
    3. Click on the first repository result
    4. Extract the star count and description
    """,
    llm=llm,
)

2. Data Extraction

python
agent = Agent(
    task="Go to news.ycombinator.com and extract the top 10 story titles with their URLs",
    llm=llm,
    max_actions_per_step=3,
)

3. Form Interaction

python
agent = Agent(
    task="""
    Go to the contact form at example.com/contact
    Fill in: Name='Test User', Email='test@example.com', Message='Hello'
    Submit the form
    """,
    llm=llm,
)

Best Practices

  1. •Be specific in task descriptions — Clear, step-by-step instructions yield better results
  2. •Use vision mode for complex UIs — use_vision=True sends screenshots to the LLM
  3. •Set reasonable action limits — Prevent infinite loops with max_actions_per_step
  4. •Handle authentication carefully — Never hardcode credentials in task descriptions
  5. •Use headless mode in production — headless=True for server deployments
  6. •Implement error handling — Wrap agent runs in try/except for graceful failures

Common Pitfalls

  • •CAPTCHAs: Browser-Use cannot solve CAPTCHAs; use authenticated sessions instead
  • •Rate limiting: Add delays between actions for sensitive websites
  • •Dynamic content: Use use_vision=True for SPAs with heavy JavaScript
  • •Memory usage: Close browser instances after use to prevent memory leaks

References