---
name: browser-use-c6f2e1e05672
description: "AI驱动的智能浏览器自动化工具。使用LLM理解页面并自动执行任务，比传统Playwright更智能、更省token。适用于复杂交互、动态页面、需要智能决策的浏览器操作。Chrome浏览器优先。"
allowed-tools: Bash, Exec, Read, Write
author: Daniel Li
---

# browser-use 智能浏览器自动化

## 概述

browser-use 是一个 AI 驱动的浏览器自动化工具，它使用 LLM 来：
- 理解网页内容
- 智能决策下一步操作
- 自动完成任务

与 Playwright 的区别：
| 特性 | Playwright | browser-use |
|------|-----------|-------------|
| 控制方式 | 预编程脚本 | AI智能决策 |
| 适应性 | 页面变化需重写 | 自动适应 |
| Token消耗 | 较低但需调试 | 智能精简 |
| 复杂交互 | 需精确选择器 | 自然语言描述 |
| 维护成本 | 高 | 低 |

## ⚠️ 资源清理原则（强制）

**所有涉及浏览器的 cron 任务完成后，必须自动关闭 Chrome 进程！**

```python
import asyncio
from browser_use import Agent

async def main():
    agent = Agent(task="...", llm=llm)
    result = await agent.run()

    # ⚠️ 任务结束后必须显式关闭浏览器
    if hasattr(agent, 'browser') and agent.browser:
        await agent.browser.close()

    # ⚠️ 推荐在脚本结束时强制清理残留进程
    import subprocess
    subprocess.run(['pkill', '-f', 'chrome'], capture_output=True)

    return result
```

**原因**: 避免内存泄漏和资源占用，防止 Gateway CPU 100% 过载

## 安装状态

✅ 已安装：
- browser-use 0.11.11
- browser-use-sdk 2.0.15

## 快速开始

### 基本用法

```python
import asyncio
from browser_use import Agent
from langchain_openai import ChatOpenAI

async def main():
    agent = Agent(
        task="打开 polymarket.com，查看 Fed 利率市场",
        llm=ChatOpenAI(model="gpt-4o"),
    )
    result = await agent.run()
    print(result)

asyncio.run(main())
```

### 使用自定义 LLM（推荐配置）

**⚠️ 重要**：your-provider API 是 **Anthropic 格式**，不是 OpenAI 格式！必须使用 `ChatAnthropic`。

```python
from browser_use.llm.anthropic.chat import ChatAnthropic

# your-provider API（Anthropic 兼容）✅ 推荐
llm = ChatAnthropic(
    model="claude-sonnet-4-6",
    base_url="https://your-anthropic-proxy.example.com",  # 注意：不加 /v1
    api_key="your-api-key",  # 或从 pass show api/your-provider 获取
)

agent = Agent(
    task="你的任务",
    llm=llm,
)
```

```python
# ❌ 错误用法：不要用 ChatOpenAI + your-provider
# from browser_use.llm.openai.chat import ChatOpenAI  # 这个不行！your-provider 不支持 OpenAI 格式
```

```python
# 如果使用 OpenAI 兼容 API（如 Provider-B），用 ChatOpenAI：
from browser_use.llm.openai.chat import ChatOpenAI
llm = ChatOpenAI(
    model="gpt-4o",
    base_url="https://ai.9w7.cn/v1",
    api_key="your-api-key",
)
```

### 使用 Chrome 浏览器

```python
from browser_use import Agent, BrowserProfile, BrowserSession

# 配置使用 Chrome
profile = BrowserProfile(
    executable_path="/usr/bin/google-chrome-stable",
    headless=True,
    disable_security=True,
)
session = BrowserSession(browser_profile=profile)

agent = Agent(
    task="任务描述",
    llm=llm,
    browser_session=session,
)
```

### 保存/加载登录态

```python
# 保存登录态
await browser_context.save_storage_state(path="polymarket_auth.json")

# 加载登录态
context = BrowserContextConfig(
    storage_state="polymarket_auth.json"
)
```

## 常用配置

### 最小化 Token 消耗

```python
agent = Agent(
    task="任务",
    llm=llm,
    use_vision=False,  # 禁用视觉，减少token
    max_actions_per_step=3,  # 限制每步操作数
    message_compaction=True,  # 消息压缩
)
```

### 调试模式

```python
agent = Agent(
    task="任务",
    llm=llm,
    headless=False,  # 显示浏览器
    slow_mo=1000,  # 慢放，每步延迟1秒
    save_conversation_path="debug_log/",  # 保存日志
)
```

### 提取结构化数据

```python
from pydantic import BaseModel

class MarketData(BaseModel):
    question: str
    yes_price: float
    no_price: float

agent = Agent(
    task="获取 Polymarket Fed 利率市场数据",
    llm=llm,
    output_model_schema=MarketData,
)
result = await agent.run()
# result 将是 MarketData 类型
```

## Polymarket 集成

### 查看市场

```python
agent = Agent(
    task="""
    1. 打开 https://polymarket.com/event/fed-decision-in-march-885
    2. 提取以下信息：
       - 市场问题
       - Yes 价格
       - No 价格
       - 交易量
    3. 返回 JSON 格式数据
    """,
    llm=llm,
)
```

### 执行交易

```python
agent = Agent(
    task="""
    1. 打开 Polymarket
    2. 连接钱包（如果需要）
    3. 导航到 Fed 利率市场
    4. 买入 $0.60 的 No（价格 ≥ 0.85）
    5. 确认交易
    """,
    llm=llm,
    sensitive_data={
        "wallet_address": "0x...",
    }
)
```

## 最佳实践

### 1. 任务描述要清晰

```python
# 好 ✅
task="打开 polymarket.com，找到 Fed 利率市场，提取 Yes/No 价格"

# 差 ❌
task="帮我看看那个市场"
```

### 2. 使用结构化输出

```python
from pydantic import BaseModel

class TradingResult(BaseModel):
    success: bool
    market: str
    action: str  # "buy_yes", "buy_no"
    amount: float
    price: float
    tx_hash: str | None

agent = Agent(
    task="执行交易...",
    output_model_schema=TradingResult,
)
```

### 3. 错误处理

```python
try:
    result = await agent.run()
except Exception as e:
    print(f"任务失败: {e}")
    # 可以使用 browser tool 作为后备
```

### 4. 复用登录态

```python
# 第一次登录后保存
# 后续直接加载，避免重复登录

browser_context = BrowserContextConfig(
    storage_state="~/.playwright-data/polymarket/auth.json"
)
```

## Token 消耗优化

### 对比（相同任务）

| 工具 | 平均 Token | 原因 |
|------|-----------|------|
| browser tool | ~5000-10000 | 每次快照全页面 |
| Playwright | ~1000-2000 | 需多次调试 |
| browser-use | ~2000-4000 | AI精简决策 |

### 优化技巧

1. **禁用视觉**：`use_vision=False`
2. **限制历史**：`max_history_items=10`
3. **压缩消息**：`message_compaction=True`
4. **减少步骤**：`max_actions_per_step=3`
5. **使用 Flash 模式**：`flash_mode=True`（快速模式）

## 完整示例

```python
import asyncio
from browser_use import Agent
from langchain_openai import ChatOpenAI
from pydantic import BaseModel
import os

class MarketInfo(BaseModel):
    question: str
    yes_price: float
    no_price: float
    volume: str

async def check_polymarket():
    # 使用本地 LLM API
    llm = ChatOpenAI(
        model="claude-3-5-sonnet-20241022",
        base_url="https://your-anthropic-proxy.example.com",
        api_key=os.environ.get("XSC_API_KEY"),
    )
    
    agent = Agent(
        task="""
        访问 Polymarket Fed 利率市场：
        https://polymarket.com/event/fed-decision-in-march-885
        
        提取并返回：
        - 市场问题
        - Yes 价格（0-1）
        - No 价格（0-1）
        - 24h 交易量
        """,
        llm=llm,
        output_model_schema=MarketInfo,
        use_vision=False,
        max_actions_per_step=5,
    )
    
    result = await agent.run()
    return result

if __name__ == "__main__":
    asyncio.run(check_polymarket())
```

## 资源

- 官方文档: https://docs.browser-use.com
- GitHub: https://github.com/browser-use/browser-use
- 示例: `~/clawd/skills/browser-use/examples/`

## 快速命令

```bash
# 安装（已完成）
pip install browser-use

# 运行脚本
python3 script.py

# 测试
python3 -c "from browser_use import Agent; print('✅ OK')"
```

---

**记住**: browser-use 让浏览器操作更智能，省去调试选择器的痛苦！🚀


---
Source attribution: aAAaqwq
Original project: https://github.com/aAAaqwq/AGI-Super-Team/blob/a8b45d5c6b9a7382ea4aa3903f3624ac5a1828a3/skills/browser-use/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 AGI Super Team Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: aAAaqwq
Original project: https://github.com/aAAaqwq/AGI-Super-Team/blob/a8b45d5c6b9a7382ea4aa3903f3624ac5a1828a3/skills/browser-use/SKILL.md
Attribution basis: Source repository record

Fields of work: Development & Engineering
Roles: Data Engineer, Frontend Developer, ML / AI Engineer
Specialties: API, Backend, Git, Playwright
Tools: Browser Use Sdk, Langchain_Openai
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 6d6882b989475e061cb1e90026bd8803e857dbf310422195f7edc71ca284afc3