Browser Use by quentintou/openclaw-skill-browser-use
npx skills add https://github.com/quentintou/openclaw-skill-browser-use --skill 'Browser Use'用于浏览器自动化的两个互补工具:
| 工具 | 最适合 | 工作原理 |
|---|---|---|
| agent-browser | 逐步控制、网页抓取、表单填写 | CLI 命令,您驱动每个操作 |
| browser-use | 复杂的自主任务 | 自行决定操作的 Python 代理 |
# 导航和检查
agent-browser open "https://example.com"
agent-browser snapshot -i # 获取带有 @refs 的交互元素
# 使用引用进行交互
agent-browser click @e3 # 点击元素
agent-browser fill @e2 "text" # 填写输入框(先清除)
agent-browser press Enter # 按键
# 提取数据
agent-browser get text @e1 # 获取元素文本
agent-browser get attr @e1 href # 获取属性
agent-browser screenshot /tmp/p.png # 截图
# 完成
agent-browser close
# 运行完整的自主浏览任务
browser-use-agent "查找 Notion 的定价并比较套餐"
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
代理将导航、点击、阅读页面并返回结构化结果。
agent-browser open <url> # 导航到 URL
agent-browser back # 后退
agent-browser forward # 前进
agent-browser reload # 重新加载页面
agent-browser close # 关闭浏览器
agent-browser snapshot # 完整的无障碍功能树
agent-browser snapshot -i # 仅交互元素(推荐)
agent-browser snapshot -c # 紧凑输出
agent-browser snapshot -d 3 # 限制深度为 3
agent-browser snapshot -s "#main" # 限定到 CSS 选择器范围
agent-browser snapshot -i --json # 用于解析的 JSON 输出
agent-browser click @e1 # 点击
agent-browser dblclick @e1 # 双击
agent-browser fill @e2 "text" # 清除并输入(用于输入框)
agent-browser type @e2 "text" # 输入而不清除
agent-browser press Enter # 按键
agent-browser press Control+a # 组合键
agent-browser hover @e1 # 悬停
agent-browser check @e1 # 勾选复选框
agent-browser uncheck @e1 # 取消勾选复选框
agent-browser select @e1 "value" # 选择下拉选项
agent-browser scroll down 500 # 滚动页面
agent-browser scrollintoview @e1 # 将元素滚动到视图中
agent-browser drag @e1 @e2 # 拖放
agent-browser upload @e1 file.pdf # 上传文件
agent-browser get text @e1 # 获取元素文本
agent-browser get html @e1 # 获取 innerHTML
agent-browser get value @e1 # 获取输入值
agent-browser get attr @e1 href # 获取属性
agent-browser get title # 页面标题
agent-browser get url # 当前 URL
agent-browser get count ".item" # 统计匹配元素数量
agent-browser wait @e1 # 等待元素
agent-browser wait 2000 # 等待毫秒数
agent-browser wait --text "Done" # 等待文本出现
agent-browser wait --url "/dash" # 等待 URL 模式
agent-browser wait --load networkidle # 等待网络空闲
agent-browser screenshot path.png # 保存截图
agent-browser screenshot --full # 完整页面截图
agent-browser pdf output.pdf # 保存为 PDF
agent-browser record start ./demo.webm # 开始录制
agent-browser record stop # 停止并保存
agent-browser --session s1 open "https://site1.com"
agent-browser --session s2 open "https://site2.com"
agent-browser session list
agent-browser state save auth.json # 保存会话(cookies、存储)
agent-browser state load auth.json # 恢复会话
agent-browser cookies # 获取所有 cookies
agent-browser cookies set name value # 设置 cookie
agent-browser cookies clear # 清除 cookies
agent-browser storage local # 获取所有 localStorage
agent-browser storage local set k v # 设置值
agent-browser tab # 列出标签页
agent-browser tab new [url] # 新建标签页
agent-browser tab 2 # 切换到标签页
agent-browser frame "#iframe" # 切换到 iframe
agent-browser frame main # 返回主框架
agent-browser set viewport 1920 1080
agent-browser set device "iPhone 14"
agent-browser set geo 37.7749 -122.4194
agent-browser set offline on
agent-browser set media dark
agent-browser eval "document.title" # 在页面上下文中运行 JS
适用于您希望代理自行确定浏览步骤的复杂任务:
browser-use-agent "您的任务描述在此"
# 通过以下方式运行:/opt/browser-use/bin/python3 script.py
import asyncio, os
from browser_use import Agent, Browser
from langchain_anthropic import ChatAnthropic
async def run():
browser = Browser()
llm = ChatAnthropic(
model='claude-sonnet-4-20250514',
api_key=os.environ['ANTHROPIC_API_KEY']
)
agent = Agent(
task="在 3 个竞争对手网站上比较定价",
llm=llm,
browser=browser,
)
result = await agent.run(max_steps=15)
await browser.close()
return result
asyncio.run(run())
您可以将 LLM 替换为任何与 langchain 兼容的模型(OpenAI、Anthropic 等)。
# 1. 打开页面
agent-browser open "https://example.com"
# 2. 快照以查看页面内容
agent-browser snapshot -i
# 3. 使用快照中的 @refs 与元素交互
agent-browser fill @e1 "搜索查询"
agent-browser click @e2
# 4. 等待新页面加载
agent-browser wait --load networkidle
# 5. 重新快照(导航后引用会改变!)
agent-browser snapshot -i
# 6. 提取所需内容
agent-browser get text @e5
# 7. 完成后关闭
agent-browser close
snapshot -i — 每次页面加载后引用都会改变fill 而不是 type — fill 会先清除现有文本wait --load networkidleagent-browser closeweb_searchstate save/load--json--session <name>snapshot -i 以获取当前引用wait --load networkidleweb_search 工具state save每周安装次数
–
代码仓库
GitHub 星标数
4
首次出现时间
–
安全审计
Two complementary tools for browser automation:
| Tool | Best for | How it works |
|---|---|---|
| agent-browser | Step-by-step control, scraping, form filling | CLI commands, you drive each action |
| browser-use | Complex autonomous tasks | Python agent that decides actions itself |
# Navigate and inspect
agent-browser open "https://example.com"
agent-browser snapshot -i # Get interactive elements with @refs
# Interact using refs
agent-browser click @e3 # Click element
agent-browser fill @e2 "text" # Fill input (clears first)
agent-browser press Enter # Press key
# Extract data
agent-browser get text @e1 # Get element text
agent-browser get attr @e1 href # Get attribute
agent-browser screenshot /tmp/p.png # Screenshot
# Done
agent-browser close
# Run a full autonomous browsing task
browser-use-agent "Find the pricing for Notion and compare plans"
The agent will navigate, click, read pages, and return a structured result.
agent-browser open <url> # Navigate to URL
agent-browser back # Go back
agent-browser forward # Go forward
agent-browser reload # Reload page
agent-browser close # Close browser
agent-browser snapshot # Full accessibility tree
agent-browser snapshot -i # Interactive elements only (recommended)
agent-browser snapshot -c # Compact output
agent-browser snapshot -d 3 # Limit depth to 3
agent-browser snapshot -s "#main" # Scope to CSS selector
agent-browser snapshot -i --json # JSON output for parsing
agent-browser click @e1 # Click
agent-browser dblclick @e1 # Double-click
agent-browser fill @e2 "text" # Clear and type (use this for inputs)
agent-browser type @e2 "text" # Type without clearing
agent-browser press Enter # Press key
agent-browser press Control+a # Key combination
agent-browser hover @e1 # Hover
agent-browser check @e1 # Check checkbox
agent-browser uncheck @e1 # Uncheck checkbox
agent-browser select @e1 "value" # Select dropdown option
agent-browser scroll down 500 # Scroll page
agent-browser scrollintoview @e1 # Scroll element into view
agent-browser drag @e1 @e2 # Drag and drop
agent-browser upload @e1 file.pdf # Upload files
agent-browser get text @e1 # Get element text
agent-browser get html @e1 # Get innerHTML
agent-browser get value @e1 # Get input value
agent-browser get attr @e1 href # Get attribute
agent-browser get title # Page title
agent-browser get url # Current URL
agent-browser get count ".item" # Count matching elements
agent-browser wait @e1 # Wait for element
agent-browser wait 2000 # Wait milliseconds
agent-browser wait --text "Done" # Wait for text to appear
agent-browser wait --url "/dash" # Wait for URL pattern
agent-browser wait --load networkidle # Wait for network idle
agent-browser screenshot path.png # Save screenshot
agent-browser screenshot --full # Full page screenshot
agent-browser pdf output.pdf # Save as PDF
agent-browser record start ./demo.webm # Start recording
agent-browser record stop # Stop and save
agent-browser --session s1 open "https://site1.com"
agent-browser --session s2 open "https://site2.com"
agent-browser session list
agent-browser state save auth.json # Save session (cookies, storage)
agent-browser state load auth.json # Restore session
agent-browser cookies # Get all cookies
agent-browser cookies set name value # Set cookie
agent-browser cookies clear # Clear cookies
agent-browser storage local # Get all localStorage
agent-browser storage local set k v # Set value
agent-browser tab # List tabs
agent-browser tab new [url] # New tab
agent-browser tab 2 # Switch to tab
agent-browser frame "#iframe" # Switch to iframe
agent-browser frame main # Back to main frame
agent-browser set viewport 1920 1080
agent-browser set device "iPhone 14"
agent-browser set geo 37.7749 -122.4194
agent-browser set offline on
agent-browser set media dark
agent-browser eval "document.title" # Run JS in page context
For complex tasks where you want the agent to figure out the browsing steps:
browser-use-agent "Your task description here"
# Run via: /opt/browser-use/bin/python3 script.py
import asyncio, os
from browser_use import Agent, Browser
from langchain_anthropic import ChatAnthropic
async def run():
browser = Browser()
llm = ChatAnthropic(
model='claude-sonnet-4-20250514',
api_key=os.environ['ANTHROPIC_API_KEY']
)
agent = Agent(
task="Compare pricing on 3 competitor sites",
llm=llm,
browser=browser,
)
result = await agent.run(max_steps=15)
await browser.close()
return result
asyncio.run(run())
You can swap the LLM for any langchain-compatible model (OpenAI, Anthropic, etc).
# 1. Open page
agent-browser open "https://example.com"
# 2. Snapshot to see what's on the page
agent-browser snapshot -i
# 3. Interact with elements using @refs from snapshot
agent-browser fill @e1 "search query"
agent-browser click @e2
# 4. Wait for new page to load
agent-browser wait --load networkidle
# 5. Re-snapshot (refs change after navigation!)
agent-browser snapshot -i
# 6. Extract what you need
agent-browser get text @e5
# 7. Close when done
agent-browser close
snapshot -i after navigation — refs change on every page loadfill not type for inputs — fill clears existing text firstwait --load networkidleagent-browser closeweb_search insteadstate save/load--json when you need machine-parseable outputsnapshot -i to get current refswait --load networkidle after navigationweb_search tool insteadstate save againWeekly Installs
–
Repository
GitHub Stars
4
First Seen
–
Security Audits
Skills CLI 使用指南:AI Agent 技能包管理器安装与管理教程
27,400 周安装
--session <name>