我理解您需要一个功能全面且兼容性强的 OpenClaw 版本，下面我将设计一个全兼容版 OpenClaw 系统，包含核心功能、扩展接口和跨平台支持。
架构设计
"""
OpenClaw 全兼容版 - 统一抓取与自动化框架
架构设计：模块化、插件化、跨平台
"""
class OpenClawUniversal:
"""全兼容版主类"""
def __init__(self, config=None):
"""
初始化 OpenClaw 全兼容版
Args:
config: 配置字典，支持：
- browser_type: 浏览器类型(chrome/firefox/edge)
- headless: 是否无头模式
- proxy: 代理设置
- platform: 目标平台
- compatibility_mode: 兼容模式级别
"""
self.version = "3.0.0"
self.compatibility_mode = "universal"
self.modules = {}
self.plugins = {}
self.adapters = {}
# 初始化核心模块
self._init_core_modules()
self._load_adapters()
def _init_core_modules(self):
"""初始化核心模块"""
self.modules = {
'browser': BrowserManager(self),
'extractor': UniversalExtractor(self),
'automation': TaskAutomation(self),
'monitor': ChangeMonitor(self),
'scheduler': TaskScheduler(self),
'storage': MultiStorage(self),
'api_server': APIServer(self)
}
def _load_adapters(self):
"""加载平台适配器"""
# 网站平台适配器
self.adapters['platforms'] = {
'ecommerce': ECommerceAdapter(),
'social': SocialMediaAdapter(),
'news': NewsPlatformAdapter(),
'forum': ForumAdapter(),
'government': GovernmentPlatformAdapter()
}
# 浏览器适配器
self.adapters['browsers'] = {
'chrome': ChromeAdapter(),
'firefox': FirefoxAdapter(),
'edge': EdgeAdapter(),
'webkit': WebKitAdapter()
}
核心功能模块
智能浏览器管理器
class BrowserManager:
"""智能浏览器管理器 - 支持多种浏览器和渲染引擎"""
def __init__(self, parent):
self.parent = parent
self.browsers = {}
self.drivers = {}
async def create_browser(self, browser_type='chrome', **kwargs):
"""
创建浏览器实例
Args:
browser_type: chrome/firefox/edge/webkit
kwargs: 浏览器配置参数
"""
# 自动检测系统环境
system_info = self._detect_system()
# 根据兼容模式选择驱动程序
driver = await self._get_driver(browser_type, system_info)
# 应用浏览器选项
options = self._build_options(browser_type, kwargs)
# 启动浏览器
browser = await self._launch_browser(driver, options)
# 注入兼容性脚本
await self._inject_compatibility_scripts(browser)
return browser
def _detect_system(self):
"""检测系统环境"""
import platform
import sys
return {
'os': platform.system(),
'os_version': platform.version(),
'architecture': platform.machine(),
'python_version': sys.version,
'screen_resolution': self._get_screen_resolution()
}
def _build_options(self, browser_type, config):
"""构建浏览器选项"""
options = {
'headless': config.get('headless', False),
'viewport': config.get('viewport', {'width': 1920, 'height': 1080}),
'user_agent': config.get('user_agent', self._get_compatible_user_agent()),
'proxy': config.get('proxy', None),
'args': [
'--no-sandbox',
'--disable-dev-shm-usage',
'--disable-gpu' if browser_type == 'chrome' else '',
'--disable-web-security',
'--disable-features=IsolateOrigins,site-per-process',
'--window-size=1920,1080'
]
}
# 平台特定参数
if browser_type == 'firefox':
options['args'].extend([
'-headless' if options['headless'] else '',
'-width', str(options['viewport']['width']),
'-height', str(options['viewport']['height'])
])
return options
通用数据提取器
class UniversalExtractor:
"""通用数据提取器 - 支持多种数据格式和网站结构"""
def __init__(self, parent):
self.parent = parent
self.extraction_methods = {
'css': self._extract_by_css,
'xpath': self._extract_by_xpath,
'regex': self._extract_by_regex,
'ai': self._extract_by_ai,
'vision': self._extract_by_vision
}
async def extract(self, page, extraction_config):
"""
智能数据提取
Args:
page: 页面对象
extraction_config: 提取配置
Returns:
提取的数据
"""
data = {}
# 多策略提取
for field, config in extraction_config.items():
for method in config.get('methods', ['css', 'xpath', 'ai']):
try:
value = await self.extraction_methods[method](
page, config.get(method, {})
)
if value:
data[field] = value
break
except Exception as e:
continue
# 数据清洗和验证
cleaned_data = self._clean_data(data)
return cleaned_data
async def _extract_by_ai(self, page, config):
"""使用AI进行智能提取"""
from openai import OpenAI
# 获取页面内容
content = await page.content()
screenshot = await page.screenshot() if config.get('use_screenshot', False) else None
# 调用AI模型
client = OpenAI(api_key=config.get('api_key'))
prompt = f"""
请从以下网页内容中提取信息：
要求：{config.get('requirements', '提取所有重要信息')}
网页内容：
{content[:5000]} # 限制内容长度
请以JSON格式返回结果。
"""
response = client.chat.completions.create(
model=config.get('model', 'gpt-4'),
messages=[
{"role": "system", "content": "你是一个网页数据提取专家"},
{"role": "user", "content": prompt}
]
)
return self._parse_ai_response(response.choices[0].message.content)
任务自动化引擎
class TaskAutomation:
"""任务自动化引擎 - 支持复杂工作流"""
def __init__(self, parent):
self.parent = parent
self.workflows = {}
async def execute_workflow(self, workflow_config):
"""
执行自动化工作流
Args:
workflow_config: 工作流配置
Returns:
执行结果
"""
results = []
# 创建工作流实例
workflow = Workflow(workflow_config)
# 执行步骤
for step in workflow.steps:
try:
result = await self._execute_step(step)
results.append(result)
# 条件判断
if step.get('condition') and not self._check_condition(result, step['condition']):
break
# 等待和延迟
await asyncio.sleep(step.get('delay', 0))
except Exception as e:
if step.get('ignore_errors', False):
continue
else:
raise
return results
async def _execute_step(self, step_config):
"""执行单个步骤"""
step_type = step_config['type']
if step_type == 'navigate':
return await self._step_navigate(step_config)
elif step_type == 'click':
return await self._step_click(step_config)
elif step_type == 'input':
return await self._step_input(step_config)
elif step_type == 'extract':
return await self._step_extract(step_config)
elif step_type == 'scroll':
return await self._step_scroll(step_config)
elif step_type == 'screenshot':
return await self._step_screenshot(step_config)
elif step_type == 'wait':
return await self._step_wait(step_config)
elif step_type == 'execute_js':
return await self._step_execute_js(step_config)
else:
raise ValueError(f"未知的步骤类型: {step_type}")
平台适配器系统
class PlatformAdapter:
"""平台适配器基类"""
def __init__(self, platform_name):
self.platform_name = platform_name
self.selectors = {}
self.behaviors = {}
async def adapt(self, page, action, **kwargs):
"""适配平台特定行为"""
# 平台特定的选择器和行为
platform_config = self._get_platform_config()
# 应用平台特定的等待和重试策略
return await self._execute_with_retry(
lambda: self._perform_action(page, action, platform_config, **kwargs),
retries=platform_config.get('max_retries', 3)
)
def _get_platform_config(self):
"""获取平台配置"""
configs = {
'taobao': {
'anti_bot': True,
'wait_time': 2.0,
'max_retries': 5,
'selectors': {
'search_box': '#q',
'product_item': '.item.J_MouserOnverReq',
'price': '.price'
}
},
'twitter': {
'anti_bot': True,
'wait_time': 1.5,
'selectors': {
'tweet': 'article[data-testid="tweet"]',
'username': 'div[data-testid="User-Name"]',
'content': 'div[data-testid="tweetText"]'
}
},
'linkedin': {
'requires_login': True,
'selectors': {
'profile': '.pv-top-card',
'experience': '#experience-section',
'education': '#education-section'
}
}
}
return configs.get(self.platform_name, {})
class ECommerceAdapter(PlatformAdapter):
"""电商平台适配器"""
def __init__(self):
super().__init__('ecommerce')
self.supported_platforms = ['taobao', 'jd', 'amazon', 'ebay']
async def search_products(self, page, keyword, **kwargs):
"""搜索商品"""
platform = kwargs.get('platform', 'auto')
if platform == 'auto':
platform = self._detect_platform(page)
# 平台特定的搜索流程
if platform in ['taobao', 'tmall']:
return await self._taobao_search(page, keyword, **kwargs)
elif platform == 'jd':
return await self._jd_search(page, keyword, **kwargs)
elif platform == 'amazon':
return await self._amazon_search(page, keyword, **kwargs)
async def _taobao_search(self, page, keyword, **kwargs):
"""淘宝搜索实现"""
# 处理登录/验证
if await self._check_login_required(page):
await self._handle_login(page)
# 输入搜索词
await page.type('#q', keyword)
# 处理滑块验证（如果有）
if await self._has_slider_captcha(page):
await self._solve_slider_captcha(page)
# 点击搜索
await page.click('.btn-search')
# 等待结果加载
await page.wait_for_selector('.m-itemlist .items', timeout=10000)
# 提取商品数据
products = await page.evaluate('''() => {
const items = [];
document.querySelectorAll('.m-itemlist .items .item').forEach(item => {
items.push({
title: item.querySelector('.title')?.textContent?.trim(),
price: item.querySelector('.price')?.textContent?.trim(),
sales: item.querySelector('.deal-cnt')?.textContent?.trim(),
shop: item.querySelector('.shop')?.textContent?.trim(),
location: item.querySelector('.location')?.textContent?.trim()
});
});
return items;
}''')
return products
配置系统
# config.yaml - 全兼容配置示例
version: "3.0"
compatibility:
mode: "universal"
fallback_methods: true
legacy_support: true
browser:
default: "chrome"
options:
headless: false
viewport:
width: 1920
height: 1080
user_agent: "auto" # 自动选择
proxy: ${PROXY_SERVER}
args:
- "--disable-blink-features=AutomationControlled"
- "--disable-web-security"
- "--disable-features=IsolateOrigins,site-per-process"
plugins:
- "stealth"
- "adblock"
adapters:
enabled:
- "ecommerce"
- "social"
- "news"
- "government"
ecommerce:
platforms:
- "taobao"
- "tmall"
- "jd"
- "amazon"
- "ebay"
strategies:
anti_bot: "adaptive"
retry_attempts: 5
delay_range: [1.0, 3.0]
extraction:
methods:
primary: ["css", "xpath"]
fallback: ["ai", "vision"]
ai:
model: "gpt-4"
api_key: ${OPENAI_API_KEY}
vision:
enabled: true
confidence: 0.8
automation:
max_concurrent: 5
timeout: 300
retry_policy:
max_retries: 3
backoff_factor: 1.5
storage:
default: "json"
options:
json:
pretty_print: true
encoding: "utf-8"
database:
url: ${DATABASE_URL}
cloud:
provider: "aws"
bucket: ${S3_BUCKET}
monitoring:
enabled: true
interval: 300 # 5分钟
alerts:
email: ${ALERT_EMAIL}
webhook: ${ALERT_WEBHOOK}
security:
encryption: true
rate_limiting: true
max_requests_per_minute: 60
使用示例
import asyncio
from openclaw_universal import OpenClawUniversal
async def main():
# 创建全兼容实例
claw = OpenClawUniversal({
'browser_type': 'chrome',
'headless': False,
'compatibility_mode': 'universal',
'proxy': 'http://proxy.example.com:8080'
})
# 执行电商数据抓取任务
results = await claw.execute_task({
'type': 'ecommerce_search',
'platform': 'taobao',
'keyword': '笔记本电脑',
'pages': 3,
'extract': {
'products': {
'selector': '.m-itemlist .items .item',
'fields': {
'title': '.title',
'price': '.price',
'sales': '.deal-cnt',
'shop': '.shop'
}
}
},
'output': {
'format': 'json',
'path': './data/products.json'
}
})
# 执行社交媒体监控
await claw.start_monitoring({
'platform': 'twitter',
'accounts': ['@example1', '@example2'],
'keywords': ['#trending', 'news'],
'interval': 300,
'callback': lambda data: print(f"New tweet: {data}")
})
# 使用API服务
claw.start_api_server(port=8080)
# 保持运行
await asyncio.Future()
if __name__ == '__main__':
asyncio.run(main())
API接口
# api_server.py - RESTful API 服务
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
app = FastAPI(title="OpenClaw Universal API")
class TaskRequest(BaseModel):
task_type: str
parameters: dict
priority: int = 1
@app.post("/api/v1/tasks")
async def create_task(request: TaskRequest):
"""创建新任务"""
task_id = await claw.task_manager.create_task(
task_type=request.task_type,
parameters=request.parameters,
priority=request.priority
)
return {"task_id": task_id, "status": "created"}
@app.get("/api/v1/tasks/{task_id}")
async def get_task_status(task_id: str):
"""获取任务状态"""
status = await claw.task_manager.get_status(task_id)
return {"task_id": task_id, "status": status}
@app.post("/api/v1/browser/execute")
async def execute_browser_script(script: dict):
"""执行浏览器脚本"""
result = await claw.modules['browser'].execute_script(script)
return {"result": result}
@app.get("/api/v1/data/{platform}")
async def get_platform_data(platform: str, query: str = None):
"""获取平台数据"""
data = await claw.adapters['platforms'][platform].fetch_data(query)
return {"platform": platform, "data": data}
安装和部署
# 安装全兼容版 pip install openclaw-universal # 或从源码安装 git clone https://github.com/yourusername/openclaw-universal.git cd openclaw-universal pip install -e ".[all]" # 环境配置 export OPENCLAW_CONFIG_PATH="/path/to/config.yaml" export PROXY_SERVER="http://your-proxy:8080" export OPENAI_API_KEY="your-api-key" # 启动服务 openclaw-server --config config.yaml --port 8080 # 或作为模块使用 python -m openclaw.cli run-task task.json
-
全平台兼容
- 支持 Chrome, Firefox, Edge, WebKit
- 跨操作系统（Windows, macOS, Linux）
- 容器化部署支持（Docker）
-
智能适配
- 自动检测网站平台
- 自适应反爬虫策略
- 智能重试和错误处理
-
扩展性强
- 插件化架构
- API 驱动设计
- 自定义适配器
-
企业级功能
- 任务调度和队列
- 分布式部署
- 监控和报警
- 数据加密和安全
这个全兼容版 OpenClaw 提供了完整的解决方案，可以根据具体需求进行定制和扩展，您需要哪些特定功能,我可以进一步详细实现？
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