# 安装Playwrightnpm install playwrightnpx playwright install
# 安装MCP相关依赖pip install mcp-client playwright-async
# 克隆Playwright MCP仓库git clone https://github.com/your-repo/playwright-mcp.gitcd playwright-mcp

# config.pyimport osfrom mcp import ClientSession, StdioServerParametersfrom mcp.client.stdio import stdio_client
class PlaywrightMCPConfig:    def __init__(self):        self.browser_type = "chromium"# chromium, firefox, webkit        self.headless = False        self.timeout = 30000        self.llm_api_key = os.getenv("LLM_API_KEY")            def get_server_params(self):        return StdioServerParameters(            command="node",            args=["path/to/playwright-mcp-server.js"]        )

import asynciofrom mcp.client.stdio import stdio_clientfrom mcp import ClientSessionfrom config import PlaywrightMCPConfig
class PlaywrightMCPClient:    def __init__(self, config: PlaywrightMCPConfig):        self.config = config        self.session = None            asyncdef connect(self):        server_params = self.config.get_server_params()        asyncwith stdio_client(server_params) as (read, write):            asyncwith ClientSession(read, write) as session:                self.session = session                # 初始化会话                await session.initialize()                return self

class WebAutomationWorkflow:    def __init__(self, mcp_client):        self.client = mcp_client            asyncdef navigate_to_page(self, url: str):        """导航到指定页面"""        result = await self.client.session.call_tool(            "navigate",            {"url": url}        )        return result            asyncdef fill_form(self, selector: str, value: str):        """填写表单"""        result = await self.client.session.call_tool(            "fill",            {"selector": selector, "value": value}        )        return result            asyncdef click_element(self, selector: str):        """点击元素"""        result = await self.client.session.call_tool(            "click",            {"selector": selector}        )        return result            asyncdef extract_text(self, selector: str):        """提取文本内容"""        result = await self.client.session.call_tool(            "get_text",            {"selector": selector}        )        return result

import openaifrom typing import List, Dict, Any
class LLMTaskPlanner:    def __init__(self, api_key: str):        self.client = openai.OpenAI(api_key=api_key)            def plan_workflow(self, user_request: str) -> List[Dict[str, Any]]:        """使用LLM解析用户请求并生成工作流步骤"""                prompt = f"""        根据以下用户请求，生成一个详细的Playwright自动化工作流。        用户请求: {user_request}                请以JSON格式返回步骤列表，每个步骤包含:        - action: 操作类型 (navigate, click, fill, extract, wait, etc.)        - parameters: 操作参数        - description: 步骤描述                只返回JSON格式的结果。        """                response = self.client.chat.completions.create(            model="gpt-4",            messages=[{"role": "user", "content": prompt}],            temperature=0.1        )                return self._parse_response(response.choices[0].message.content)        def _parse_response(self, response: str) -> List[Dict[str, Any]]:        """解析LLM响应为结构化工作流"""        import json        try:            # 清理响应并提取JSON            cleaned_response = response.strip()            if"```json"in cleaned_response:                cleaned_response = cleaned_response.split("```json")[1].split("```")[0]            elif"```"in cleaned_response:                cleaned_response = cleaned_response.split("```")[1]                            return json.loads(cleaned_response)        except Exception as e:            print(f"解析LLM响应失败: {e}")            return []

class IntelligentWorkflowExecutor:    def __init__(self, mcp_client, llm_planner):        self.mcp_client = mcp_client        self.planner = llm_planner        self.automation = WebAutomationWorkflow(mcp_client)            asyncdef execute_user_request(self, user_request: str):        """执行用户自然语言请求的完整工作流"""                print(f"处理用户请求: {user_request}")                # 1. 使用LLM规划工作流        workflow_steps = self.planner.plan_workflow(user_request)        print(f"生成的工作流步骤: {len(workflow_steps)}步")                # 2. 执行工作流        results = []        for i, step in enumerate(workflow_steps, 1):            print(f"执行步骤 {i}: {step['description']}")                        try:                result = await self._execute_step(step)                results.append({                    "step": i,                    "description": step["description"],                    "result": result,                    "status": "success"                })            except Exception as e:                results.append({                    "step": i,                    "description": step["description"],                    "error": str(e),                    "status": "failed"                })                print(f"步骤 {i} 执行失败: {e}")                break                        return results        asyncdef _execute_step(self, step: Dict[str, Any]):        """执行单个工作流步骤"""        action = step["action"]        params = step["parameters"]                if action == "navigate":            returnawait self.automation.navigate_to_page(params["url"])        elif action == "click":            returnawait self.automation.click_element(params["selector"])        elif action == "fill":            returnawait self.automation.fill_form(params["selector"], params["value"])        elif action == "extract":            returnawait self.automation.extract_text(params["selector"])        elif action == "wait":            await asyncio.sleep(params.get("seconds", 2))            return"等待完成"        else:            raise ValueError(f"未知操作: {action}")

class AdaptiveAIAgent:    def __init__(self, mcp_client, llm_planner, executor):        self.mcp_client = mcp_client        self.planner = llm_planner        self.executor = executor        self.conversation_history = []            asyncdef process_request(self, user_input: str, context: Dict = None):        """处理用户输入并执行相应操作"""                # 添加上下文到对话历史        self.conversation_history.append({"role": "user", "content": user_input})                # 分析用户意图        intent = await self._analyze_intent(user_input, context)                if intent["type"] == "automation":            # 执行自动化工作流            results = await self.executor.execute_user_request(user_input)                        # 生成自然语言总结            summary = await self._generate_summary(user_input, results)                        self.conversation_history.append({                "role": "assistant",                 "content": summary            })                        return {                "type": "automation",                "results": results,                "summary": summary            }                    elif intent["type"] == "query":            # 处理查询请求            response = await self._handle_query(user_input)            return {                "type": "query",                "response": response            }        asyncdef _analyze_intent(self, user_input: str, context: Dict) -> Dict:        """使用LLM分析用户意图"""        # 简化的意图分析实现        automation_keywords = ["打开", "点击", "填写", "导航", "提取", "自动化"]                if any(keyword in user_input for keyword in automation_keywords):            return {"type": "automation", "confidence": 0.9}        else:            return {"type": "query", "confidence": 0.7}        asyncdef _generate_summary(self, request: str, results: List) -> str:        """生成工作流执行总结"""        success_steps = [r for r in results if r["status"] == "success"]                returnf"""        已完成您的要求: {request}                执行统计:        - 总步骤数: {len(results)}        - 成功步骤: {len(success_steps)}        - 失败步骤: {len(results) - len(success_steps)}                {'所有步骤均成功完成!' if len(success_steps) == len(results) else '部分步骤执行失败，请检查错误信息。'}        """

class EcommerceDataAgent:    def __init__(self, base_agent):        self.agent = base_agent            asyncdef collect_product_data(self, product_url: str, data_points: List[str]):        """采集电商产品数据"""                workflow_request = f"""        请执行以下电商数据采集任务:        1. 导航到产品页面: {product_url}        2. 提取产品标题        3. 提取产品价格        4. 提取产品评分        5. 提取产品描述        6. 提取客户评论数量        """                # 执行数据采集        results = await self.agent.process_request(workflow_request)                # 数据清洗和结构化        structured_data = await self._structure_product_data(results)                return structured_data        asyncdef _structure_product_data(self, raw_results: Dict) -> Dict:        """将采集的数据结构化"""        # 实现数据解析和结构化逻辑        structured = {}                for result in raw_results.get("results", []):            if"result"in result and result["result"]:                # 解析提取的数据                text_content = result["result"].get("content", "")                # 根据步骤描述识别数据类型                if"标题"in result["description"]:                    structured["title"] = self._clean_text(text_content)                elif"价格"in result["description"]:                    structured["price"] = self._extract_price(text_content)                elif"评分"in result["description"]:                    structured["rating"] = self._extract_rating(text_content)                            return structured        def _clean_text(self, text: str) -> str:        """清理文本数据"""        return text.strip() if text else""        def _extract_price(self, text: str) -> float:        """提取价格信息"""        import re        matches = re.findall(r'[\d.,]+', text)        return float(matches[0].replace(',', '')) if matches else0.0

class RobustWorkflowExecutor(IntelligentWorkflowExecutor):    asyncdef execute_with_retry(self, user_request: str, max_retries: int = 3):        """带重试机制的工作流执行"""                for attempt in range(max_retries):            try:                results = await self.execute_user_request(user_request)                                # 检查是否有失败步骤                failed_steps = [r for r in results if r["status"] == "failed"]                ifnot failed_steps:                    return results                                    print(f"第 {attempt + 1} 次尝试，{len(failed_steps)} 个步骤失败")                                # 最后一次尝试仍然失败，抛出异常                if attempt == max_retries - 1:                    raise Exception(f"工作流执行失败，{len(failed_steps)} 个步骤未完成")                                except Exception as e:                print(f"第 {attempt + 1} 次尝试失败: {e}")                if attempt == max_retries - 1:                    raise                                await asyncio.sleep(2)  # 重试前等待                    return []        asyncdef _execute_step_with_fallback(self, step: Dict):        """带备用方案的步骤执行"""        try:            returnawait self._execute_step(step)        except Exception as e:            print(f"步骤执行失败: {e}，尝试备用方案")                        # 实现备用执行逻辑            if step["action"] == "click":                # 尝试不同的选择器                returnawait self._try_alternative_selectors(step)            elif step["action"] == "extract":                # 尝试不同的数据提取方法                returnawait self._try_alternative_extraction(step)            else:                raise

import timefrom dataclasses import dataclassfrom typing import List
@dataclassclass PerformanceMetrics:    total_steps: int    successful_steps: int    failed_steps: int    total_time: float    average_step_time: float
class PerformanceMonitor:    def __init__(self):        self.metrics_history: List[PerformanceMetrics] = []            def start_execution(self):        self.start_time = time.time()        self.step_times = []            def record_step(self, success: bool, step_time: float):        self.step_times.append(step_time)            def end_execution(self, total_steps: int, successful_steps: int):        total_time = time.time() - self.start_time        avg_time = sum(self.step_times) / len(self.step_times) if self.step_times else0                metrics = PerformanceMetrics(            total_steps=total_steps,            successful_steps=successful_steps,            failed_steps=total_steps - successful_steps,            total_time=total_time,            average_step_time=avg_time        )                self.metrics_history.append(metrics)        return metrics

class TestingAutomationAgent:    def __init__(self, base_agent):        self.agent = base_agent            async def run_e2e_test(self, test_scenario: str):        """执行端到端测试"""        test_request = f"""        执行以下端到端测试场景:        {test_scenario}                包括:        1. 导航到测试页面        2. 执行测试步骤        3. 验证预期结果        4. 生成测试报告        """                return await self.agent.process_request(test_request)

class MonitoringAgent:    def __init__(self, base_agent, alert_thresholds: Dict):        self.agent = base_agent        self.thresholds = alert_thresholds            asyncdef monitor_website(self, url: str, check_interval: int = 3600):        """定期监控网站状态"""        whileTrue:            try:                status = await self._check_website_status(url)                                ifnot status["is_healthy"]:                    await self._send_alert(f"网站异常: {status['issues']}")                                except Exception as e:                await self._send_alert(f"监控检查失败: {e}")                            await asyncio.sleep(check_interval)        asyncdef _check_website_status(self, url: str) -> Dict:        """检查网站健康状态"""        check_request = f"""        检查网站健康状况:        1. 访问 {url}        2. 检查页面加载时间        3. 验证关键功能是否正常        4. 检查错误信息        """                results = await self.agent.process_request(check_request)        return self._analyze_health_status(results)