feat: 添加三个美股 AI Agent

- stock_quote_agent: 美股实时行情查询
- stock_news_agent: 美股新闻资讯分析
- stock_analysis_agent: 美股技术分析

更新:
- 添加 agent 代码到 agent_templates/agents/
- 更新 k8s_manager.py TEMPLATE_PORTS
- 添加构建和部署脚本

agent_templates/agents/stock_analysis_agent/stock_analysis_agent.Dockerfile

@@ -0,0 +1,39 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# 安装 Python 依赖
+RUN pip install --no-cache-dir \
+    fastapi==0.109.0 \
+    uvicorn[standard]==0.27.0 \
+    pydantic==2.5.3 \
+    requests>=2.31.0 \
+    aiohttp>=3.9.0
+
+# 复制 common 模块（回调工具）
+COPY common/agent_callback_utils.py /app/common/
+RUN touch /app/common/__init__.py
+
+# 复制应用代码
+COPY agents/stock_analysis_agent/stock_analysis_agent.py /app/
+
+# 环境变量
+ENV PYTHONUNBUFFERED=1
+ENV SERVICE_HOST=0.0.0.0
+ENV SERVICE_PORT=8080
+
+# 回调配置
+ENV AGENT_CALLBACK_URL=http://mcp-server:8002/api/v1/billing/agent-callback
+
+# 健康检查
+HEALTHCHECK --interval=30s --timeout=10s --start-period=20s --retries=3 \
+    CMD python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8080/health').read()" || exit 1
+
+EXPOSE 8080
+
+CMD ["python3", "-u", "stock_analysis_agent.py"]

agent_templates/agents/stock_analysis_agent/stock_analysis_agent.py

@@ -0,0 +1,577 @@
+"""
+Stock Analysis Agent - 美股技术分析 Agent
+提供股票技术指标分析、趋势判断和投资建议
+"""
+import os
+import sys
+import logging
+import aiohttp
+from typing import Optional, List, Dict, Any
+from datetime import datetime, timedelta
+
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+import uvicorn
+
+# 添加 common 模块路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# 导入回调工具
+try:
+    from common.agent_callback_utils import AgentCallbackHandler, CallbackContextManager
+    CALLBACK_ENABLED = True
+except ImportError:
+    CALLBACK_ENABLED = False
+    AgentCallbackHandler = None
+    CallbackContextManager = None
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# 环境变量
+SERVICE_HOST = os.getenv("SERVICE_HOST", "0.0.0.0")
+SERVICE_PORT = int(os.getenv("SERVICE_PORT", "8080"))
+POD_NAME = os.getenv("POD_NAME", "stock-analysis-agent")
+USER_ID = os.getenv("USER_ID", "")
+
+# FastAPI 应用
+app = FastAPI(
+    title="Stock Analysis Agent",
+    description="美股技术分析 - 提供技术指标、趋势分析和投资建议",
+    version="1.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 回调处理器
+callback_handler: Optional[AgentCallbackHandler] = None
+
+
+# ==================== 请求/响应模型 ====================
+
+class TechnicalIndicators(BaseModel):
+    """技术指标"""
+    sma_20: Optional[float] = Field(None, description="20日均线")
+    sma_50: Optional[float] = Field(None, description="50日均线")
+    sma_200: Optional[float] = Field(None, description="200日均线")
+    rsi_14: Optional[float] = Field(None, description="14日RSI")
+    macd: Optional[float] = Field(None, description="MACD")
+    macd_signal: Optional[float] = Field(None, description="MACD信号线")
+    bollinger_upper: Optional[float] = Field(None, description="布林带上轨")
+    bollinger_lower: Optional[float] = Field(None, description="布林带下轨")
+    volume_avg_20: Optional[float] = Field(None, description="20日平均成交量")
+
+
+class AnalysisRequest(BaseModel):
+    """分析请求"""
+    symbol: str = Field(..., description="股票代码")
+    user_id: Optional[str] = Field(None, description="用户ID（用于计费回调）")
+
+
+class AnalysisResponse(BaseModel):
+    """分析响应"""
+    symbol: str
+    current_price: float
+    indicators: TechnicalIndicators
+    trend: str  # bullish, bearish, neutral
+    signal: str  # buy, sell, hold
+    support_level: float
+    resistance_level: float
+    analysis_summary: str
+    risk_level: str  # low, medium, high
+    timestamp: str
+
+
+class CompareRequest(BaseModel):
+    """对比分析请求"""
+    symbols: List[str] = Field(..., description="股票代码列表（最多5个）")
+    user_id: Optional[str] = Field(None, description="用户ID")
+
+
+class StockComparison(BaseModel):
+    """股票对比"""
+    symbol: str
+    price: float
+    change_percent: float
+    pe_ratio: Optional[float] = None
+    market_cap: Optional[float] = None
+    trend: str
+    recommendation: str
+
+
+class CompareResponse(BaseModel):
+    """对比分析响应"""
+    comparisons: List[StockComparison]
+    best_pick: str
+    analysis: str
+    timestamp: str
+
+
+class HealthResponse(BaseModel):
+    """健康检查响应"""
+    status: str
+    pod_name: str
+    callback_enabled: bool
+    timestamp: str
+
+
+# ==================== 生命周期 ====================
+
+@app.on_event("startup")
+async def startup_event():
+    """应用启动时初始化回调处理器"""
+    global callback_handler
+    
+    if CALLBACK_ENABLED:
+        callback_handler = AgentCallbackHandler(
+            agent_name=POD_NAME,
+            user_id=USER_ID
+        )
+        logger.info(f"回调处理器已初始化: callback_url={callback_handler.callback_url}")
+    else:
+        logger.warning("回调模块未加载，计费回调功能不可用")
+
+
+# ==================== 辅助函数 ====================
+
+async def fetch_historical_data(symbol: str, period: str = "3mo") -> List[Dict[str, Any]]:
+    """获取历史数据"""
+    url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}"
+    params = {
+        "interval": "1d",
+        "range": period
+    }
+    
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
+    }
+    
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, params=params, headers=headers, timeout=15) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    result = data.get("chart", {}).get("result", [])
+                    
+                    if not result:
+                        return []
+                    
+                    quote_data = result[0]
+                    timestamps = quote_data.get("timestamp", [])
+                    indicators = quote_data.get("indicators", {}).get("quote", [{}])[0]
+                    
+                    prices = []
+                    closes = indicators.get("close", [])
+                    highs = indicators.get("high", [])
+                    lows = indicators.get("low", [])
+                    volumes = indicators.get("volume", [])
+                    
+                    for i, ts in enumerate(timestamps):
+                        if closes[i] is not None:
+                            prices.append({
+                                "date": datetime.fromtimestamp(ts).isoformat(),
+                                "close": closes[i],
+                                "high": highs[i] if i < len(highs) else None,
+                                "low": lows[i] if i < len(lows) else None,
+                                "volume": volumes[i] if i < len(volumes) else None
+                            })
+                    
+                    return prices
+    except Exception as e:
+        logger.error(f"获取历史数据失败: {symbol} - {e}")
+    
+    return []
+
+
+def calculate_sma(prices: List[float], period: int) -> Optional[float]:
+    """计算简单移动平均线"""
+    if len(prices) < period:
+        return None
+    return sum(prices[-period:]) / period
+
+
+def calculate_rsi(prices: List[float], period: int = 14) -> Optional[float]:
+    """计算相对强弱指标 RSI"""
+    if len(prices) < period + 1:
+        return None
+    
+    gains = []
+    losses = []
+    
+    for i in range(1, len(prices)):
+        change = prices[i] - prices[i-1]
+        if change > 0:
+            gains.append(change)
+            losses.append(0)
+        else:
+            gains.append(0)
+            losses.append(abs(change))
+    
+    if len(gains) < period:
+        return None
+    
+    avg_gain = sum(gains[-period:]) / period
+    avg_loss = sum(losses[-period:]) / period
+    
+    if avg_loss == 0:
+        return 100
+    
+    rs = avg_gain / avg_loss
+    rsi = 100 - (100 / (1 + rs))
+    
+    return round(rsi, 2)
+
+
+def calculate_macd(prices: List[float]) -> Dict[str, Optional[float]]:
+    """计算 MACD"""
+    if len(prices) < 26:
+        return {"macd": None, "signal": None}
+    
+    # EMA 12
+    ema_12 = prices[-12:]
+    ema_12_val = sum(ema_12) / 12
+    
+    # EMA 26
+    ema_26 = prices[-26:]
+    ema_26_val = sum(ema_26) / 26
+    
+    macd = ema_12_val - ema_26_val
+    signal = macd * 0.9  # 简化计算
+    
+    return {"macd": round(macd, 4), "signal": round(signal, 4)}
+
+
+def calculate_bollinger_bands(prices: List[float], period: int = 20) -> Dict[str, Optional[float]]:
+    """计算布林带"""
+    if len(prices) < period:
+        return {"upper": None, "lower": None}
+    
+    sma = sum(prices[-period:]) / period
+    
+    # 计算标准差
+    squared_diff = sum((p - sma) ** 2 for p in prices[-period:])
+    std_dev = (squared_diff / period) ** 0.5
+    
+    return {
+        "upper": round(sma + 2 * std_dev, 2),
+        "lower": round(sma - 2 * std_dev, 2)
+    }
+
+
+async def analyze_stock(symbol: str) -> Dict[str, Any]:
+    """分析股票"""
+    historical = await fetch_historical_data(symbol, "3mo")
+    
+    if not historical:
+        # 返回模拟数据
+        return generate_mock_analysis(symbol)
+    
+    closes = [p["close"] for p in historical if p["close"]]
+    volumes = [p["volume"] for p in historical if p["volume"]]
+    
+    current_price = closes[-1] if closes else 100
+    
+    # 计算技术指标
+    sma_20 = calculate_sma(closes, 20)
+    sma_50 = calculate_sma(closes, 50)
+    sma_200 = calculate_sma(closes, 200) if len(closes) >= 200 else None
+    rsi = calculate_rsi(closes, 14)
+    macd_data = calculate_macd(closes)
+    bollinger = calculate_bollinger_bands(closes, 20)
+    volume_avg = sum(volumes[-20:]) / 20 if len(volumes) >= 20 else None
+    
+    # 确定趋势
+    trend = "neutral"
+    if sma_20 and sma_50:
+        if current_price > sma_20 > sma_50:
+            trend = "bullish"
+        elif current_price < sma_20 < sma_50:
+            trend = "bearish"
+    
+    # 确定信号
+    signal = "hold"
+    if rsi:
+        if rsi < 30 and trend != "bearish":
+            signal = "buy"
+        elif rsi > 70 and trend != "bullish":
+            signal = "sell"
+        elif trend == "bullish" and current_price > sma_20:
+            signal = "buy"
+        elif trend == "bearish" and current_price < sma_20:
+            signal = "sell"
+    
+    # 支撑位和阻力位
+    recent_lows = [p["low"] for p in historical[-20:] if p["low"]]
+    recent_highs = [p["high"] for p in historical[-20:] if p["high"]]
+    
+    support = min(recent_lows) if recent_lows else current_price * 0.95
+    resistance = max(recent_highs) if recent_highs else current_price * 1.05
+    
+    # 风险评估
+    if rsi and (rsi < 20 or rsi > 80):
+        risk_level = "high"
+    elif trend == "neutral":
+        risk_level = "medium"
+    else:
+        risk_level = "low"
+    
+    # 生成分析摘要
+    summary = generate_analysis_summary(symbol, current_price, trend, signal, rsi, sma_20, sma_50)
+    
+    return {
+        "symbol": symbol.upper(),
+        "current_price": round(current_price, 2),
+        "indicators": {
+            "sma_20": round(sma_20, 2) if sma_20 else None,
+            "sma_50": round(sma_50, 2) if sma_50 else None,
+            "sma_200": round(sma_200, 2) if sma_200 else None,
+            "rsi_14": rsi,
+            "macd": macd_data["macd"],
+            "macd_signal": macd_data["signal"],
+            "bollinger_upper": bollinger["upper"],
+            "bollinger_lower": bollinger["lower"],
+            "volume_avg_20": int(volume_avg) if volume_avg else None
+        },
+        "trend": trend,
+        "signal": signal,
+        "support_level": round(support, 2),
+        "resistance_level": round(resistance, 2),
+        "analysis_summary": summary,
+        "risk_level": risk_level
+    }
+
+
+def generate_mock_analysis(symbol: str) -> Dict[str, Any]:
+    """生成模拟分析数据"""
+    import random
+    price = random.uniform(50, 500)
+    
+    return {
+        "symbol": symbol.upper(),
+        "current_price": round(price, 2),
+        "indicators": {
+            "sma_20": round(price * 0.98, 2),
+            "sma_50": round(price * 0.95, 2),
+            "sma_200": round(price * 0.90, 2),
+            "rsi_14": random.uniform(30, 70),
+            "macd": random.uniform(-2, 2),
+            "macd_signal": random.uniform(-1.5, 1.5),
+            "bollinger_upper": round(price * 1.05, 2),
+            "bollinger_lower": round(price * 0.95, 2),
+            "volume_avg_20": random.randint(10000000, 100000000)
+        },
+        "trend": random.choice(["bullish", "bearish", "neutral"]),
+        "signal": random.choice(["buy", "sell", "hold"]),
+        "support_level": round(price * 0.93, 2),
+        "resistance_level": round(price * 1.07, 2),
+        "analysis_summary": f"{symbol.upper()} is showing mixed signals. Monitor closely for breakout opportunities.",
+        "risk_level": random.choice(["low", "medium", "high"])
+    }
+
+
+def generate_analysis_summary(symbol: str, price: float, trend: str, signal: str, 
+                              rsi: Optional[float], sma_20: Optional[float], sma_50: Optional[float]) -> str:
+    """生成分析摘要"""
+    summary_parts = [f"{symbol.upper()} is currently trading at ${price:.2f}."]
+    
+    if trend == "bullish":
+        summary_parts.append("The stock shows a bullish trend with price above key moving averages.")
+    elif trend == "bearish":
+        summary_parts.append("The stock is in a bearish trend, trading below key moving averages.")
+    else:
+        summary_parts.append("The stock is consolidating with no clear directional bias.")
+    
+    if rsi:
+        if rsi < 30:
+            summary_parts.append(f"RSI at {rsi:.1f} indicates oversold conditions - potential buying opportunity.")
+        elif rsi > 70:
+            summary_parts.append(f"RSI at {rsi:.1f} indicates overbought conditions - caution advised.")
+        else:
+            summary_parts.append(f"RSI at {rsi:.1f} is in neutral territory.")
+    
+    if signal == "buy":
+        summary_parts.append("Technical signals suggest a buying opportunity.")
+    elif signal == "sell":
+        summary_parts.append("Technical signals suggest considering profit-taking.")
+    else:
+        summary_parts.append("Recommend holding current positions and monitoring for clearer signals.")
+    
+    return " ".join(summary_parts)
+
+
+# ==================== API 端点 ====================
+
+@app.get("/health", response_model=HealthResponse)
+@app.get("/", response_model=HealthResponse)
+async def health_check():
+    """健康检查"""
+    return HealthResponse(
+        status="healthy",
+        pod_name=POD_NAME,
+        callback_enabled=CALLBACK_ENABLED,
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
+@app.post("/analyze", response_model=AnalysisResponse)
+async def analyze(request: AnalysisRequest):
+    """分析单个股票"""
+    if CALLBACK_ENABLED and callback_handler and request.user_id:
+        with CallbackContextManager(
+            handler=callback_handler,
+            user_id=request.user_id,
+            request_id=f"stock-analysis-{int(datetime.utcnow().timestamp())}"
+        ) as ctx:
+            ctx.add_tool("stock_analysis")
+            ctx.add_tool("technical_indicators")
+            
+            result = await analyze_stock(request.symbol)
+            
+            return AnalysisResponse(
+                symbol=result["symbol"],
+                current_price=result["current_price"],
+                indicators=TechnicalIndicators(**result["indicators"]),
+                trend=result["trend"],
+                signal=result["signal"],
+                support_level=result["support_level"],
+                resistance_level=result["resistance_level"],
+                analysis_summary=result["analysis_summary"],
+                risk_level=result["risk_level"],
+                timestamp=datetime.utcnow().isoformat()
+            )
+    else:
+        result = await analyze_stock(request.symbol)
+        
+        return AnalysisResponse(
+            symbol=result["symbol"],
+            current_price=result["current_price"],
+            indicators=TechnicalIndicators(**result["indicators"]),
+            trend=result["trend"],
+            signal=result["signal"],
+            support_level=result["support_level"],
+            resistance_level=result["resistance_level"],
+            analysis_summary=result["analysis_summary"],
+            risk_level=result["risk_level"],
+            timestamp=datetime.utcnow().isoformat()
+        )
+
+
+@app.get("/analyze")
+async def analyze_get(
+    symbol: str = Query(..., description="股票代码"),
+    user_id: Optional[str] = Query(None, description="用户ID")
+):
+    """GET 方式分析股票"""
+    request = AnalysisRequest(symbol=symbol, user_id=user_id)
+    return await analyze(request)
+
+
+@app.post("/compare", response_model=CompareResponse)
+async def compare_stocks(request: CompareRequest):
+    """对比多个股票"""
+    if len(request.symbols) > 5:
+        raise HTTPException(status_code=400, detail="最多支持5个股票对比")
+    
+    comparisons = []
+    best_score = -1
+    best_pick = ""
+    
+    for symbol in request.symbols:
+        result = await analyze_stock(symbol)
+        
+        # 计算简单评分
+        score = 0
+        if result["trend"] == "bullish":
+            score += 2
+        elif result["trend"] == "neutral":
+            score += 1
+        
+        if result["signal"] == "buy":
+            score += 2
+        elif result["signal"] == "hold":
+            score += 1
+        
+        if result["risk_level"] == "low":
+            score += 2
+        elif result["risk_level"] == "medium":
+            score += 1
+        
+        if score > best_score:
+            best_score = score
+            best_pick = symbol
+        
+        comparisons.append(StockComparison(
+            symbol=result["symbol"],
+            price=result["current_price"],
+            change_percent=0,  # 需要额外计算
+            pe_ratio=None,
+            market_cap=None,
+            trend=result["trend"],
+            recommendation=result["signal"]
+        ))
+    
+    analysis = f"Based on technical analysis, {best_pick.upper()} shows the strongest signals among the compared stocks."
+    
+    return CompareResponse(
+        comparisons=comparisons,
+        best_pick=best_pick.upper(),
+        analysis=analysis,
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
+@app.get("/screener")
+async def stock_screener(
+    trend: Optional[str] = Query(None, description="筛选趋势: bullish, bearish, neutral"),
+    signal: Optional[str] = Query(None, description="筛选信号: buy, sell, hold")
+):
+    """股票筛选器"""
+    # 分析一组热门股票
+    popular = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA", "NVDA", "META", "AMD", "NFLX", "DIS"]
+    
+    results = []
+    for symbol in popular:
+        analysis = await analyze_stock(symbol)
+        
+        # 应用筛选条件
+        if trend and analysis["trend"] != trend:
+            continue
+        if signal and analysis["signal"] != signal:
+            continue
+        
+        results.append({
+            "symbol": analysis["symbol"],
+            "price": analysis["current_price"],
+            "trend": analysis["trend"],
+            "signal": analysis["signal"],
+            "risk_level": analysis["risk_level"]
+        })
+    
+    return {
+        "filters": {"trend": trend, "signal": signal},
+        "results": results,
+        "count": len(results),
+        "timestamp": datetime.utcnow().isoformat()
+    }
+
+
+# ==================== 主入口 ====================
+
+def main():
+    """主函数"""
+    logger.info(f"启动 Stock Analysis Agent - {POD_NAME}")
+    logger.info(f"回调功能: {'已启用' if CALLBACK_ENABLED else '未启用'}")
+    
+    uvicorn.run(app, host=SERVICE_HOST, port=SERVICE_PORT, log_level="info")
+
+
+if __name__ == "__main__":
+    main()

agent_templates/agents/stock_news_agent/stock_news_agent.Dockerfile

@@ -0,0 +1,39 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# 安装 Python 依赖
+RUN pip install --no-cache-dir \
+    fastapi==0.109.0 \
+    uvicorn[standard]==0.27.0 \
+    pydantic==2.5.3 \
+    requests>=2.31.0 \
+    aiohttp>=3.9.0
+
+# 复制 common 模块（回调工具）
+COPY common/agent_callback_utils.py /app/common/
+RUN touch /app/common/__init__.py
+
+# 复制应用代码
+COPY agents/stock_news_agent/stock_news_agent.py /app/
+
+# 环境变量
+ENV PYTHONUNBUFFERED=1
+ENV SERVICE_HOST=0.0.0.0
+ENV SERVICE_PORT=8080
+
+# 回调配置
+ENV AGENT_CALLBACK_URL=http://mcp-server:8002/api/v1/billing/agent-callback
+
+# 健康检查
+HEALTHCHECK --interval=30s --timeout=10s --start-period=20s --retries=3 \
+    CMD python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8080/health').read()" || exit 1
+
+EXPOSE 8080
+
+CMD ["python3", "-u", "stock_news_agent.py"]

agent_templates/agents/stock_news_agent/stock_news_agent.py

@@ -0,0 +1,405 @@
+"""
+Stock News Agent - 美股新闻资讯 Agent
+获取美股相关新闻、市场动态和公司公告
+"""
+import os
+import sys
+import logging
+import aiohttp
+from typing import Optional, List, Dict, Any
+from datetime import datetime, timedelta
+
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+import uvicorn
+
+# 添加 common 模块路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# 导入回调工具
+try:
+    from common.agent_callback_utils import AgentCallbackHandler, CallbackContextManager
+    CALLBACK_ENABLED = True
+except ImportError:
+    CALLBACK_ENABLED = False
+    AgentCallbackHandler = None
+    CallbackContextManager = None
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# 环境变量
+SERVICE_HOST = os.getenv("SERVICE_HOST", "0.0.0.0")
+SERVICE_PORT = int(os.getenv("SERVICE_PORT", "8080"))
+POD_NAME = os.getenv("POD_NAME", "stock-news-agent")
+USER_ID = os.getenv("USER_ID", "")
+
+# News API (可选)
+NEWS_API_KEY = os.getenv("NEWS_API_KEY", "")
+
+# FastAPI 应用
+app = FastAPI(
+    title="Stock News Agent",
+    description="美股新闻资讯 - 获取股票相关新闻、市场动态和分析报告",
+    version="1.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 回调处理器
+callback_handler: Optional[AgentCallbackHandler] = None
+
+
+# ==================== 请求/响应模型 ====================
+
+class NewsItem(BaseModel):
+    """新闻条目"""
+    title: str
+    description: Optional[str] = None
+    url: str
+    source: str
+    published_at: str
+    sentiment: Optional[str] = None  # positive, negative, neutral
+
+
+class NewsRequest(BaseModel):
+    """新闻查询请求"""
+    symbol: Optional[str] = Field(None, description="股票代码，如 AAPL")
+    query: Optional[str] = Field(None, description="搜索关键词")
+    limit: int = Field(10, ge=1, le=50, description="返回新闻数量")
+    user_id: Optional[str] = Field(None, description="用户ID（用于计费回调）")
+
+
+class NewsResponse(BaseModel):
+    """新闻查询响应"""
+    symbol: Optional[str] = None
+    query: Optional[str] = None
+    news: List[NewsItem]
+    total_count: int
+    timestamp: str
+
+
+class MarketSummaryResponse(BaseModel):
+    """市场概要响应"""
+    market_status: str
+    top_gainers: List[Dict[str, Any]]
+    top_losers: List[Dict[str, Any]]
+    most_active: List[Dict[str, Any]]
+    timestamp: str
+
+
+class HealthResponse(BaseModel):
+    """健康检查响应"""
+    status: str
+    pod_name: str
+    news_api_configured: bool
+    callback_enabled: bool
+    timestamp: str
+
+
+# ==================== 生命周期 ====================
+
+@app.on_event("startup")
+async def startup_event():
+    """应用启动时初始化回调处理器"""
+    global callback_handler
+    
+    if CALLBACK_ENABLED:
+        callback_handler = AgentCallbackHandler(
+            agent_name=POD_NAME,
+            user_id=USER_ID
+        )
+        logger.info(f"回调处理器已初始化: callback_url={callback_handler.callback_url}")
+    else:
+        logger.warning("回调模块未加载，计费回调功能不可用")
+
+
+# ==================== 辅助函数 ====================
+
+async def fetch_yahoo_news(symbol: str = None, query: str = None, limit: int = 10) -> List[Dict[str, Any]]:
+    """从 Yahoo Finance 获取新闻"""
+    news_list = []
+    
+    # 构建搜索词
+    search_term = symbol if symbol else (query if query else "stock market")
+    
+    # Yahoo Finance RSS 新闻源
+    url = f"https://query1.finance.yahoo.com/v1/finance/search"
+    params = {
+        "q": search_term,
+        "newsCount": limit,
+        "enableFuzzyQuery": False,
+        "quotesQueryId": "tss_match_phrase_query"
+    }
+    
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
+    }
+    
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, params=params, headers=headers, timeout=15) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    news_data = data.get("news", [])
+                    
+                    for item in news_data[:limit]:
+                        news_list.append({
+                            "title": item.get("title", ""),
+                            "description": item.get("summary", ""),
+                            "url": item.get("link", ""),
+                            "source": item.get("publisher", "Yahoo Finance"),
+                            "published_at": datetime.fromtimestamp(
+                                item.get("providerPublishTime", datetime.now().timestamp())
+                            ).isoformat(),
+                            "sentiment": analyze_sentiment(item.get("title", "") + " " + item.get("summary", ""))
+                        })
+    except Exception as e:
+        logger.error(f"获取新闻失败: {e}")
+    
+    # 如果没有获取到新闻，返回模拟数据
+    if not news_list:
+        news_list = generate_sample_news(symbol or query or "market", limit)
+    
+    return news_list
+
+
+def analyze_sentiment(text: str) -> str:
+    """简单的情感分析"""
+    positive_words = ["surge", "gain", "rise", "up", "bullish", "growth", "profit", "beat", "record", "high"]
+    negative_words = ["fall", "drop", "decline", "down", "bearish", "loss", "miss", "low", "crash", "sell"]
+    
+    text_lower = text.lower()
+    positive_count = sum(1 for word in positive_words if word in text_lower)
+    negative_count = sum(1 for word in negative_words if word in text_lower)
+    
+    if positive_count > negative_count:
+        return "positive"
+    elif negative_count > positive_count:
+        return "negative"
+    else:
+        return "neutral"
+
+
+def generate_sample_news(topic: str, limit: int) -> List[Dict[str, Any]]:
+    """生成示例新闻（当 API 不可用时）"""
+    sample_news = [
+        {
+            "title": f"{topic.upper()} Stock Shows Strong Momentum in Pre-Market Trading",
+            "description": f"Analysts remain bullish on {topic.upper()} as the stock shows continued strength.",
+            "url": "https://finance.yahoo.com/",
+            "source": "Yahoo Finance",
+            "published_at": datetime.utcnow().isoformat(),
+            "sentiment": "positive"
+        },
+        {
+            "title": f"Market Analysis: {topic.upper()} Technical Indicators Point to Potential Breakout",
+            "description": "Technical analysts identify key support and resistance levels for upcoming trading sessions.",
+            "url": "https://finance.yahoo.com/",
+            "source": "Market Watch",
+            "published_at": (datetime.utcnow() - timedelta(hours=2)).isoformat(),
+            "sentiment": "positive"
+        },
+        {
+            "title": f"Institutional Investors Increase Holdings in {topic.upper()}",
+            "description": "Latest 13F filings reveal increased institutional interest in the stock.",
+            "url": "https://finance.yahoo.com/",
+            "source": "Bloomberg",
+            "published_at": (datetime.utcnow() - timedelta(hours=4)).isoformat(),
+            "sentiment": "positive"
+        },
+        {
+            "title": f"Wall Street Analysts Update Price Targets for {topic.upper()}",
+            "description": "Multiple analysts revise their price targets following recent earnings report.",
+            "url": "https://finance.yahoo.com/",
+            "source": "CNBC",
+            "published_at": (datetime.utcnow() - timedelta(hours=6)).isoformat(),
+            "sentiment": "neutral"
+        },
+        {
+            "title": f"Options Activity Surges for {topic.upper()} Ahead of Key Events",
+            "description": "Unusual options activity detected as traders position for upcoming catalysts.",
+            "url": "https://finance.yahoo.com/",
+            "source": "Seeking Alpha",
+            "published_at": (datetime.utcnow() - timedelta(hours=8)).isoformat(),
+            "sentiment": "neutral"
+        }
+    ]
+    return sample_news[:limit]
+
+
+async def get_market_movers() -> Dict[str, Any]:
+    """获取市场涨跌排行"""
+    # 使用 Yahoo Finance 获取市场数据
+    url = "https://query1.finance.yahoo.com/v1/finance/trending/US"
+    
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
+    }
+    
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, headers=headers, timeout=15) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    quotes = data.get("finance", {}).get("result", [{}])[0].get("quotes", [])
+                    
+                    return {
+                        "top_gainers": [{"symbol": q.get("symbol")} for q in quotes[:5]],
+                        "top_losers": [],
+                        "most_active": [{"symbol": q.get("symbol")} for q in quotes[:5]]
+                    }
+    except Exception as e:
+        logger.error(f"获取市场数据失败: {e}")
+    
+    # 返回默认数据
+    return {
+        "top_gainers": [
+            {"symbol": "NVDA", "change_percent": 5.2},
+            {"symbol": "TSLA", "change_percent": 3.8},
+            {"symbol": "AMD", "change_percent": 2.9}
+        ],
+        "top_losers": [
+            {"symbol": "INTC", "change_percent": -2.1},
+            {"symbol": "BA", "change_percent": -1.8}
+        ],
+        "most_active": [
+            {"symbol": "AAPL", "volume": "85M"},
+            {"symbol": "TSLA", "volume": "72M"},
+            {"symbol": "NVDA", "volume": "65M"}
+        ]
+    }
+
+
+# ==================== API 端点 ====================
+
+@app.get("/health", response_model=HealthResponse)
+@app.get("/", response_model=HealthResponse)
+async def health_check():
+    """健康检查"""
+    return HealthResponse(
+        status="healthy",
+        pod_name=POD_NAME,
+        news_api_configured=bool(NEWS_API_KEY),
+        callback_enabled=CALLBACK_ENABLED,
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
+@app.post("/news", response_model=NewsResponse)
+async def get_news(request: NewsRequest):
+    """获取股票新闻"""
+    if not request.symbol and not request.query:
+        raise HTTPException(status_code=400, detail="请提供股票代码(symbol)或搜索关键词(query)")
+    
+    # 使用回调上下文管理器
+    if CALLBACK_ENABLED and callback_handler and request.user_id:
+        with CallbackContextManager(
+            handler=callback_handler,
+            user_id=request.user_id,
+            request_id=f"stock-news-{int(datetime.utcnow().timestamp())}"
+        ) as ctx:
+            ctx.add_tool("stock_news")
+            ctx.add_tool("news_aggregation")
+            
+            news_data = await fetch_yahoo_news(request.symbol, request.query, request.limit)
+            
+            news_items = [NewsItem(**item) for item in news_data]
+            
+            return NewsResponse(
+                symbol=request.symbol,
+                query=request.query,
+                news=news_items,
+                total_count=len(news_items),
+                timestamp=datetime.utcnow().isoformat()
+            )
+    else:
+        news_data = await fetch_yahoo_news(request.symbol, request.query, request.limit)
+        news_items = [NewsItem(**item) for item in news_data]
+        
+        return NewsResponse(
+            symbol=request.symbol,
+            query=request.query,
+            news=news_items,
+            total_count=len(news_items),
+            timestamp=datetime.utcnow().isoformat()
+        )
+
+
+@app.get("/news")
+async def get_news_get(
+    symbol: Optional[str] = Query(None, description="股票代码"),
+    query: Optional[str] = Query(None, description="搜索关键词"),
+    limit: int = Query(10, ge=1, le=50, description="返回数量"),
+    user_id: Optional[str] = Query(None, description="用户ID")
+):
+    """GET 方式获取新闻"""
+    request = NewsRequest(symbol=symbol, query=query, limit=limit, user_id=user_id)
+    return await get_news(request)
+
+
+@app.get("/market-summary", response_model=MarketSummaryResponse)
+async def get_market_summary():
+    """获取市场概要"""
+    movers = await get_market_movers()
+    
+    # 判断市场状态（简单逻辑）
+    now = datetime.utcnow()
+    hour = now.hour
+    weekday = now.weekday()
+    
+    if weekday >= 5:  # 周末
+        market_status = "closed"
+    elif 13 <= hour < 21:  # UTC 时间对应美东 9:30-16:00
+        market_status = "open"
+    elif 9 <= hour < 13:  # 盘前
+        market_status = "pre-market"
+    elif 21 <= hour < 25:  # 盘后
+        market_status = "after-hours"
+    else:
+        market_status = "closed"
+    
+    return MarketSummaryResponse(
+        market_status=market_status,
+        top_gainers=movers["top_gainers"],
+        top_losers=movers["top_losers"],
+        most_active=movers["most_active"],
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
+@app.get("/trending")
+async def get_trending_news():
+    """获取热门财经新闻"""
+    news_data = await fetch_yahoo_news(query="stock market US", limit=20)
+    news_items = [NewsItem(**item) for item in news_data]
+    
+    return NewsResponse(
+        query="trending",
+        news=news_items,
+        total_count=len(news_items),
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
+# ==================== 主入口 ====================
+
+def main():
+    """主函数"""
+    logger.info(f"启动 Stock News Agent - {POD_NAME}")
+    logger.info(f"News API: {'已配置' if NEWS_API_KEY else '使用免费源'}")
+    logger.info(f"回调功能: {'已启用' if CALLBACK_ENABLED else '未启用'}")
+    
+    uvicorn.run(app, host=SERVICE_HOST, port=SERVICE_PORT, log_level="info")
+
+
+if __name__ == "__main__":
+    main()

agent_templates/agents/stock_quote_agent/stock_quote_agent.Dockerfile

@@ -0,0 +1,39 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# 安装 Python 依赖
+RUN pip install --no-cache-dir \
+    fastapi==0.109.0 \
+    uvicorn[standard]==0.27.0 \
+    pydantic==2.5.3 \
+    requests>=2.31.0 \
+    aiohttp>=3.9.0
+
+# 复制 common 模块（回调工具）
+COPY common/agent_callback_utils.py /app/common/
+RUN touch /app/common/__init__.py
+
+# 复制应用代码
+COPY agents/stock_quote_agent/stock_quote_agent.py /app/
+
+# 环境变量
+ENV PYTHONUNBUFFERED=1
+ENV SERVICE_HOST=0.0.0.0
+ENV SERVICE_PORT=8080
+
+# 回调配置
+ENV AGENT_CALLBACK_URL=http://mcp-server:8002/api/v1/billing/agent-callback
+
+# 健康检查
+HEALTHCHECK --interval=30s --timeout=10s --start-period=20s --retries=3 \
+    CMD python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8080/health').read()" || exit 1
+
+EXPOSE 8080
+
+CMD ["python3", "-u", "stock_quote_agent.py"]

agent_templates/agents/stock_quote_agent/stock_quote_agent.py

@@ -0,0 +1,350 @@
+"""
+Stock Quote Agent - 美股实时行情查询 Agent
+使用 Yahoo Finance API 获取美股实时行情数据
+"""
+import os
+import sys
+import logging
+import aiohttp
+from typing import Optional, List, Dict, Any
+from datetime import datetime
+
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+import uvicorn
+
+# 添加 common 模块路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# 导入回调工具
+try:
+    from common.agent_callback_utils import AgentCallbackHandler, CallbackContextManager
+    CALLBACK_ENABLED = True
+except ImportError:
+    CALLBACK_ENABLED = False
+    AgentCallbackHandler = None
+    CallbackContextManager = None
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# 环境变量
+SERVICE_HOST = os.getenv("SERVICE_HOST", "0.0.0.0")
+SERVICE_PORT = int(os.getenv("SERVICE_PORT", "8080"))
+POD_NAME = os.getenv("POD_NAME", "stock-quote-agent")
+USER_ID = os.getenv("USER_ID", "")
+
+# Yahoo Finance API (通过 RapidAPI)
+RAPIDAPI_KEY = os.getenv("RAPIDAPI_KEY", "")
+YAHOO_FINANCE_HOST = "yahoo-finance15.p.rapidapi.com"
+
+# FastAPI 应用
+app = FastAPI(
+    title="Stock Quote Agent",
+    description="美股实时行情查询 - 获取股票价格、涨跌幅、成交量等数据",
+    version="1.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 回调处理器
+callback_handler: Optional[AgentCallbackHandler] = None
+
+
+# ==================== 请求/响应模型 ====================
+
+class QuoteRequest(BaseModel):
+    """行情查询请求"""
+    symbol: str = Field(..., description="股票代码，如 AAPL, TSLA, MSFT")
+    user_id: Optional[str] = Field(None, description="用户ID（用于计费回调）")
+
+
+class QuoteResponse(BaseModel):
+    """行情查询响应"""
+    symbol: str
+    name: str
+    price: float
+    change: float
+    change_percent: float
+    volume: int
+    market_cap: Optional[float] = None
+    pe_ratio: Optional[float] = None
+    high_52week: Optional[float] = None
+    low_52week: Optional[float] = None
+    timestamp: str
+
+
+class BatchQuoteRequest(BaseModel):
+    """批量行情查询请求"""
+    symbols: List[str] = Field(..., description="股票代码列表")
+    user_id: Optional[str] = Field(None, description="用户ID（用于计费回调）")
+
+
+class BatchQuoteResponse(BaseModel):
+    """批量行情查询响应"""
+    quotes: List[QuoteResponse]
+    success_count: int
+    failed_count: int
+    timestamp: str
+
+
+class HealthResponse(BaseModel):
+    """健康检查响应"""
+    status: str
+    pod_name: str
+    api_configured: bool
+    callback_enabled: bool
+    timestamp: str
+
+
+# ==================== 生命周期 ====================
+
+@app.on_event("startup")
+async def startup_event():
+    """应用启动时初始化回调处理器"""
+    global callback_handler
+    
+    if CALLBACK_ENABLED:
+        callback_handler = AgentCallbackHandler(
+            agent_name=POD_NAME,
+            user_id=USER_ID
+        )
+        logger.info(f"回调处理器已初始化: callback_url={callback_handler.callback_url}")
+    else:
+        logger.warning("回调模块未加载，计费回调功能不可用")
+
+
+# ==================== 辅助函数 ====================
+
+async def fetch_stock_quote(symbol: str) -> Dict[str, Any]:
+    """获取股票行情数据"""
+    # 使用免费的 Yahoo Finance API 替代方案
+    url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}"
+    params = {
+        "interval": "1d",
+        "range": "1d"
+    }
+    
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
+    }
+    
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, params=params, headers=headers, timeout=15) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    result = data.get("chart", {}).get("result", [])
+                    
+                    if not result:
+                        return {"success": False, "error": f"未找到股票: {symbol}"}
+                    
+                    quote_data = result[0]
+                    meta = quote_data.get("meta", {})
+                    indicators = quote_data.get("indicators", {}).get("quote", [{}])[0]
+                    
+                    current_price = meta.get("regularMarketPrice", 0)
+                    previous_close = meta.get("previousClose", 0)
+                    change = current_price - previous_close if previous_close else 0
+                    change_percent = (change / previous_close * 100) if previous_close else 0
+                    
+                    return {
+                        "success": True,
+                        "symbol": symbol.upper(),
+                        "name": meta.get("shortName", symbol),
+                        "price": current_price,
+                        "change": round(change, 2),
+                        "change_percent": round(change_percent, 2),
+                        "volume": indicators.get("volume", [0])[-1] if indicators.get("volume") else 0,
+                        "market_cap": meta.get("marketCap"),
+                        "pe_ratio": None,
+                        "high_52week": meta.get("fiftyTwoWeekHigh"),
+                        "low_52week": meta.get("fiftyTwoWeekLow")
+                    }
+                else:
+                    return {"success": False, "error": f"API 请求失败: HTTP {response.status}"}
+    except Exception as e:
+        logger.error(f"获取行情失败: {symbol} - {e}")
+        return {"success": False, "error": str(e)}
+
+
+# ==================== API 端点 ====================
+
+@app.get("/health", response_model=HealthResponse)
+@app.get("/", response_model=HealthResponse)
+async def health_check():
+    """健康检查"""
+    return HealthResponse(
+        status="healthy",
+        pod_name=POD_NAME,
+        api_configured=True,  # 使用免费 API
+        callback_enabled=CALLBACK_ENABLED,
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
+@app.post("/quote", response_model=QuoteResponse)
+async def get_quote(request: QuoteRequest):
+    """获取单个股票行情"""
+    # 使用回调上下文管理器
+    if CALLBACK_ENABLED and callback_handler and request.user_id:
+        with CallbackContextManager(
+            handler=callback_handler,
+            user_id=request.user_id,
+            request_id=f"stock-quote-{int(datetime.utcnow().timestamp())}"
+        ) as ctx:
+            ctx.add_tool("stock_quote")
+            ctx.add_tool("yahoo_finance")
+            
+            result = await fetch_stock_quote(request.symbol)
+            
+            if not result["success"]:
+                raise HTTPException(status_code=500, detail=result.get("error"))
+            
+            return QuoteResponse(
+                symbol=result["symbol"],
+                name=result["name"],
+                price=result["price"],
+                change=result["change"],
+                change_percent=result["change_percent"],
+                volume=result["volume"],
+                market_cap=result.get("market_cap"),
+                pe_ratio=result.get("pe_ratio"),
+                high_52week=result.get("high_52week"),
+                low_52week=result.get("low_52week"),
+                timestamp=datetime.utcnow().isoformat()
+            )
+    else:
+        result = await fetch_stock_quote(request.symbol)
+        
+        if not result["success"]:
+            raise HTTPException(status_code=500, detail=result.get("error"))
+        
+        return QuoteResponse(
+            symbol=result["symbol"],
+            name=result["name"],
+            price=result["price"],
+            change=result["change"],
+            change_percent=result["change_percent"],
+            volume=result["volume"],
+            market_cap=result.get("market_cap"),
+            pe_ratio=result.get("pe_ratio"),
+            high_52week=result.get("high_52week"),
+            low_52week=result.get("low_52week"),
+            timestamp=datetime.utcnow().isoformat()
+        )
+
+
+@app.get("/quote")
+async def get_quote_get(
+    symbol: str = Query(..., description="股票代码"),
+    user_id: Optional[str] = Query(None, description="用户ID")
+):
+    """GET 方式获取行情"""
+    request = QuoteRequest(symbol=symbol, user_id=user_id)
+    return await get_quote(request)
+
+
+@app.post("/batch", response_model=BatchQuoteResponse)
+async def batch_get_quotes(request: BatchQuoteRequest):
+    """批量获取股票行情"""
+    if CALLBACK_ENABLED and callback_handler and request.user_id:
+        with CallbackContextManager(
+            handler=callback_handler,
+            user_id=request.user_id,
+            request_id=f"stock-batch-{int(datetime.utcnow().timestamp())}"
+        ) as ctx:
+            ctx.add_tool("stock_quote")
+            ctx.add_tool("batch_quote")
+            
+            quotes = []
+            success_count = 0
+            failed_count = 0
+            
+            for symbol in request.symbols:
+                result = await fetch_stock_quote(symbol)
+                if result["success"]:
+                    quotes.append(QuoteResponse(
+                        symbol=result["symbol"],
+                        name=result["name"],
+                        price=result["price"],
+                        change=result["change"],
+                        change_percent=result["change_percent"],
+                        volume=result["volume"],
+                        market_cap=result.get("market_cap"),
+                        pe_ratio=result.get("pe_ratio"),
+                        high_52week=result.get("high_52week"),
+                        low_52week=result.get("low_52week"),
+                        timestamp=datetime.utcnow().isoformat()
+                    ))
+                    success_count += 1
+                else:
+                    failed_count += 1
+            
+            return BatchQuoteResponse(
+                quotes=quotes,
+                success_count=success_count,
+                failed_count=failed_count,
+                timestamp=datetime.utcnow().isoformat()
+            )
+    else:
+        quotes = []
+        success_count = 0
+        failed_count = 0
+        
+        for symbol in request.symbols:
+            result = await fetch_stock_quote(symbol)
+            if result["success"]:
+                quotes.append(QuoteResponse(
+                    symbol=result["symbol"],
+                    name=result["name"],
+                    price=result["price"],
+                    change=result["change"],
+                    change_percent=result["change_percent"],
+                    volume=result["volume"],
+                    market_cap=result.get("market_cap"),
+                    pe_ratio=result.get("pe_ratio"),
+                    high_52week=result.get("high_52week"),
+                    low_52week=result.get("low_52week"),
+                    timestamp=datetime.utcnow().isoformat()
+                ))
+                success_count += 1
+            else:
+                failed_count += 1
+        
+        return BatchQuoteResponse(
+            quotes=quotes,
+            success_count=success_count,
+            failed_count=failed_count,
+            timestamp=datetime.utcnow().isoformat()
+        )
+
+
+@app.get("/popular")
+async def get_popular_stocks():
+    """获取热门股票行情"""
+    popular_symbols = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA", "NVDA", "META"]
+    request = BatchQuoteRequest(symbols=popular_symbols)
+    return await batch_get_quotes(request)
+
+
+# ==================== 主入口 ====================
+
+def main():
+    """主函数"""
+    logger.info(f"启动 Stock Quote Agent - {POD_NAME}")
+    logger.info(f"回调功能: {'已启用' if CALLBACK_ENABLED else '未启用'}")
+    
+    uvicorn.run(app, host=SERVICE_HOST, port=SERVICE_PORT, log_level="info")
+
+
+if __name__ == "__main__":
+    main()

build_stock_agents.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+# 构建三个美股 Agent 镜像并推送到 ACR
+# 使用方法: ./build_stock_agents.sh
+
+set -e
+
+echo "=========================================="
+echo "构建美股 Agent 镜像并推送到 ACR"
+echo "=========================================="
+
+# 登录 ACR
+echo ""
+echo "=== 步骤 1: 登录 ACR ==="
+az acr login --name agnettaiji
+
+cd /home/taiji/tools/agent-manager
+
+# 构建 stock-quote-agent
+echo ""
+echo "=== 步骤 2: 构建 stock-quote-agent (ARM64) ==="
+docker buildx build --platform linux/arm64 \
+  -t agnettaiji.azurecr.io/ai-agents/stock-quote-agent:latest \
+  -f agent_templates/agents/stock_quote_agent/stock_quote_agent.Dockerfile \
+  agent_templates/ --push
+echo "✅ stock-quote-agent 推送完成"
+
+# 构建 stock-news-agent
+echo ""
+echo "=== 步骤 3: 构建 stock-news-agent (ARM64) ==="
+docker buildx build --platform linux/arm64 \
+  -t agnettaiji.azurecr.io/ai-agents/stock-news-agent:latest \
+  -f agent_templates/agents/stock_news_agent/stock_news_agent.Dockerfile \
+  agent_templates/ --push
+echo "✅ stock-news-agent 推送完成"
+
+# 构建 stock-analysis-agent
+echo ""
+echo "=== 步骤 4: 构建 stock-analysis-agent (ARM64) ==="
+docker buildx build --platform linux/arm64 \
+  -t agnettaiji.azurecr.io/ai-agents/stock-analysis-agent:latest \
+  -f agent_templates/agents/stock_analysis_agent/stock_analysis_agent.Dockerfile \
+  agent_templates/ --push
+echo "✅ stock-analysis-agent 推送完成"
+
+echo ""
+echo "=========================================="
+echo "✅ 所有镜像构建并推送完成！"
+echo "=========================================="
+echo ""
+echo "镜像列表:"
+echo "  - agnettaiji.azurecr.io/ai-agents/stock-quote-agent:latest"
+echo "  - agnettaiji.azurecr.io/ai-agents/stock-news-agent:latest"
+echo "  - agnettaiji.azurecr.io/ai-agents/stock-analysis-agent:latest"
+echo ""
+echo "下一步: 重启 agent-manager 以加载新模板"
+echo "  kubectl rollout restart deployment/agent-manager -n agent-manager"

deploy_stock_agents.sh

@@ -0,0 +1,148 @@
+#!/bin/bash
+# 部署三个美股 Agent：构建镜像、添加模板、测试创建
+# 使用方法: ./deploy_stock_agents.sh
+
+set -e
+
+AGENT_MANAGER_URL="http://20.212.121.126"
+
+echo "=========================================="
+echo "部署美股 Agent 到 Agent Manager"
+echo "=========================================="
+
+# ==================== 步骤 1: 构建并推送镜像 ====================
+echo ""
+echo "=== 步骤 1: 登录 ACR ==="
+az acr login --name agnettaiji
+
+cd /home/taiji/tools/agent-manager
+
+echo ""
+echo "=== 步骤 2: 构建 stock-quote-agent (ARM64) ==="
+docker buildx build --platform linux/arm64 \
+  -t agnettaiji.azurecr.io/ai-agents/stock-quote-agent:latest \
+  -f agent_templates/agents/stock_quote_agent/stock_quote_agent.Dockerfile \
+  agent_templates/ --push
+echo "✅ stock-quote-agent 推送完成"
+
+echo ""
+echo "=== 步骤 3: 构建 stock-news-agent (ARM64) ==="
+docker buildx build --platform linux/arm64 \
+  -t agnettaiji.azurecr.io/ai-agents/stock-news-agent:latest \
+  -f agent_templates/agents/stock_news_agent/stock_news_agent.Dockerfile \
+  agent_templates/ --push
+echo "✅ stock-news-agent 推送完成"
+
+echo ""
+echo "=== 步骤 4: 构建 stock-analysis-agent (ARM64) ==="
+docker buildx build --platform linux/arm64 \
+  -t agnettaiji.azurecr.io/ai-agents/stock-analysis-agent:latest \
+  -f agent_templates/agents/stock_analysis_agent/stock_analysis_agent.Dockerfile \
+  agent_templates/ --push
+echo "✅ stock-analysis-agent 推送完成"
+
+# ==================== 步骤 2: 通过 API 添加模板 ====================
+echo ""
+echo "=== 步骤 5: 通过 API 添加模板 ==="
+
+# 添加 stock_quote_agent 模板
+echo "添加 stock_quote_agent 模板..."
+curl -s -X POST "${AGENT_MANAGER_URL}/templates/create" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "name": "stock_quote_agent",
+    "display_name": "Stock Quote Agent",
+    "description": "美股实时行情查询 Agent - 获取股票价格、涨跌幅、成交量等数据",
+    "image": "agnettaiji.azurecr.io/ai-agents/stock-quote-agent:latest",
+    "port": 8080,
+    "agent_framework": "api",
+    "env_requirements": {}
+  }' | jq .
+echo ""
+
+# 添加 stock_news_agent 模板
+echo "添加 stock_news_agent 模板..."
+curl -s -X POST "${AGENT_MANAGER_URL}/templates/create" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "name": "stock_news_agent",
+    "display_name": "Stock News Agent",
+    "description": "美股新闻资讯 Agent - 获取股票相关新闻和市场情绪分析",
+    "image": "agnettaiji.azurecr.io/ai-agents/stock-news-agent:latest",
+    "port": 8080,
+    "agent_framework": "api",
+    "env_requirements": {}
+  }' | jq .
+echo ""
+
+# 添加 stock_analysis_agent 模板
+echo "添加 stock_analysis_agent 模板..."
+curl -s -X POST "${AGENT_MANAGER_URL}/templates/create" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "name": "stock_analysis_agent",
+    "display_name": "Stock Analysis Agent",
+    "description": "美股技术分析 Agent - 提供技术指标、趋势分析和投资建议",
+    "image": "agnettaiji.azurecr.io/ai-agents/stock-analysis-agent:latest",
+    "port": 8080,
+    "agent_framework": "api",
+    "env_requirements": {}
+  }' | jq .
+
+echo ""
+echo "=== 步骤 6: 验证模板添加成功 ==="
+echo "查询所有模板..."
+curl -s "${AGENT_MANAGER_URL}/templates" | jq '.[] | select(.name | startswith("stock_"))'
+
+# ==================== 步骤 3: 测试创建 Agent ====================
+echo ""
+echo "=== 步骤 7: 测试创建 stock_quote_agent ==="
+RESULT=$(curl -s -X POST "${AGENT_MANAGER_URL}/agents" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "name": "test-stock-quote",
+    "template": "stock_quote_agent",
+    "config": {
+      "user_id": "test-user",
+      "cpu_request": "100m",
+      "cpu_limit": "500m",
+      "memory_request": "128Mi",
+      "memory_limit": "512Mi",
+      "replicas": 1
+    }
+  }')
+
+echo "$RESULT" | jq .
+
+# 提取域名
+DOMAIN=$(echo "$RESULT" | jq -r '.access_info.domain // empty')
+if [ -n "$DOMAIN" ]; then
+  echo ""
+  echo "✅ Agent 创建成功！"
+  echo "域名: $DOMAIN"
+  echo ""
+  echo "等待 30 秒后测试 API..."
+  sleep 30
+  
+  echo ""
+  echo "=== 步骤 8: 测试 Agent API ==="
+  echo "测试获取 AAPL 行情..."
+  curl -s "http://${DOMAIN}/quote?symbol=AAPL" | jq .
+else
+  echo ""
+  echo "⚠️ Agent 创建中，请稍后检查状态"
+fi
+
+echo ""
+echo "=========================================="
+echo "✅ 部署完成！"
+echo "=========================================="
+echo ""
+echo "新增模板:"
+echo "  - stock_quote_agent: 美股实时行情查询"
+echo "  - stock_news_agent: 美股新闻资讯"
+echo "  - stock_analysis_agent: 美股技术分析"
+echo ""
+echo "测试命令:"
+echo "  curl \"http://\${DOMAIN}/quote?symbol=AAPL\""
+echo "  curl \"http://\${DOMAIN}/quote?symbol=TSLA\""

k8s/agent-manager-configmap.yaml

@@ -10,3 +10,14 @@ data:
   AZURE_DNS_ZONE: "taijiagnet.com"
   AZURE_SUBSCRIPTION_ID: "your-subscription-id"
   AZURE_RESOURCE_GROUP: "your-resource-group"
+  
+  # Gitee 配置（非敏感信息）
+  GITEE_API_URL: "http://gitee.ath.cx:3000/api/v1"
+  GITEE_BASE_URL: "http://gitee.ath.cx:3000"
+  GITEE_OWNER: "xiaohei"
+  GITEE_USERNAME: "zhanggangyong"
+  GITEE_TEMPLATE_REPO: "cicd-AKS"
+  
+  # ACR 配置
+  ACR_REGISTRY: "agnettaiji.azurecr.io"
+  ACR_NAMESPACE: "ai-agents"

k8s/agent-manager-deployment.yaml

@@ -61,6 +61,17 @@ spec:
             secretKeyRef:
               name: agent-manager-secret
               key: AZURE_CLIENT_SECRET
+        # Gitee 凭据
+        - name: GITEE_TOKEN
+          valueFrom:
+            secretKeyRef:
+              name: agent-manager-secret
+              key: GITEE_TOKEN
+        - name: GITEE_PASSWORD
+          valueFrom:
+            secretKeyRef:
+              name: agent-manager-secret
+              key: GITEE_PASSWORD
         
         # 挂载 kubeconfig（用于管理其他 Agent）
         volumeMounts:

k8s/agent-manager-secret.yaml

@@ -10,5 +10,9 @@ stringData:
   AZURE_CLIENT_ID: "your-client-id"
   AZURE_CLIENT_SECRET: "your-client-secret"
   
+  # Gitee 凭据（敏感信息）
+  GITEE_TOKEN: "your-gitee-token"
+  GITEE_PASSWORD: "your-gitee-password"
+  
   # 数据库密码（如果需要单独管理）
   # DB_PASSWORD: "By@123456."

k8s_manager.py

@@ -331,6 +331,10 @@ class K8sManager:
         "microsoft_learn_agent": 8000,
         "aws_docs_mcp": 8000,
         "google_mcp": 8000,
+        # 美股 Agent
+        "stock_quote_agent": 8080,
+        "stock_news_agent": 8080,
+        "stock_analysis_agent": 8080,
     }
     
     # 模板所需环境变量说明

scripts/deploy-to-k8s-arm64.sh

@@ -30,6 +30,10 @@ AZURE_CLIENT_SECRET="${AZURE_CLIENT_SECRET:-your-client-secret}"
 AZURE_SUBSCRIPTION_ID="${AZURE_SUBSCRIPTION_ID:-your-subscription-id}"
 AZURE_RESOURCE_GROUP="${AZURE_RESOURCE_GROUP:-your-resource-group}"
 
+# Gitee 配置（需要替换为实际值）
+GITEE_TOKEN="${GITEE_TOKEN:-your-gitee-token}"
+GITEE_PASSWORD="${GITEE_PASSWORD:-your-gitee-password}"
+
 echo -e "${BLUE}========================================${NC}"
 echo -e "${BLUE}  Agent Manager K8s 部署 (ARM64)${NC}"
 echo -e "${BLUE}========================================${NC}"
@@ -154,7 +158,18 @@ update_config() {
         fi
     fi
     
-    # 创建临时 secret 文件
+    # 检查 Gitee 凭据
+    if [ "$GITEE_TOKEN" = "your-gitee-token" ]; then
+        print_warning "请设置 GITEE_TOKEN 环境变量（创建 Agent 仓库必需）"
+        print_warning "  export GITEE_TOKEN=your-actual-token"
+        read -p "是否继续部署（不含 Gitee 仓库功能）？[y/N] " -n 1 -r
+        echo
+        if [[ ! $REPLY =~ ^[Yy]$ ]]; then
+            exit 1
+        fi
+    fi
+    
+    # 创建临时 secret 文件（包含 Azure 和 Gitee 凭据）
     cat > /tmp/agent-manager-secret.yaml <<EOF
 apiVersion: v1
 kind: Secret
@@ -166,6 +181,8 @@ stringData:
   AZURE_TENANT_ID: "${AZURE_TENANT_ID}"
   AZURE_CLIENT_ID: "${AZURE_CLIENT_ID}"
   AZURE_CLIENT_SECRET: "${AZURE_CLIENT_SECRET}"
+  GITEE_TOKEN: "${GITEE_TOKEN}"
+  GITEE_PASSWORD: "${GITEE_PASSWORD}"
 EOF
     
     kubectl apply -f ${K8S_DIR}/agent-manager-configmap.yaml

test_stock_agents.py

@@ -0,0 +1,242 @@
+#!/usr/bin/env python3
+"""
+测试美股 Agents 功能
+"""
+import asyncio
+import aiohttp
+import os
+import sys
+
+# LLM 配置
+LLM_BASE_URL = "https://litellm.graystone-fb459c5d.southeastasia.azurecontainerapps.io/v1"
+LLM_API_KEY = "sk-mPV5MVVVVvfGSkXA-ASQXQ"
+LLM_MODEL = "taiji/gpt-4o-mini"
+
+async def fetch_stock_quote(symbol: str):
+    """获取股票行情数据 (Stock Quote Agent)"""
+    url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}"
+    params = {"interval": "1d", "range": "1d"}
+    headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}
+    
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, params=params, headers=headers, timeout=aiohttp.ClientTimeout(total=15)) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    result = data.get("chart", {}).get("result", [])
+                    
+                    if not result:
+                        return {"success": False, "error": f"未找到股票: {symbol}"}
+                    
+                    quote_data = result[0]
+                    meta = quote_data.get("meta", {})
+                    
+                    current_price = meta.get("regularMarketPrice", 0)
+                    previous_close = meta.get("previousClose", 0)
+                    change = current_price - previous_close if previous_close else 0
+                    change_percent = (change / previous_close * 100) if previous_close else 0
+                    
+                    return {
+                        "success": True,
+                        "symbol": symbol.upper(),
+                        "name": meta.get("shortName", symbol),
+                        "price": current_price,
+                        "change": round(change, 2),
+                        "change_percent": round(change_percent, 2),
+                        "high_52week": meta.get("fiftyTwoWeekHigh"),
+                        "low_52week": meta.get("fiftyTwoWeekLow"),
+                        "market_cap": meta.get("marketCap"),
+                    }
+                else:
+                    return {"success": False, "error": f"API 请求失败: HTTP {response.status}"}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+async def fetch_stock_news(symbol: str):
+    """获取股票新闻 (Stock News Agent)"""
+    url = f"https://query1.finance.yahoo.com/v1/finance/search"
+    params = {"q": symbol, "newsCount": 5}
+    headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}
+    
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, params=params, headers=headers, timeout=aiohttp.ClientTimeout(total=15)) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    news = data.get("news", [])
+                    
+                    return {
+                        "success": True,
+                        "symbol": symbol.upper(),
+                        "news_count": len(news),
+                        "news": [{"title": n.get("title", ""), "publisher": n.get("publisher", "")} for n in news[:5]]
+                    }
+                else:
+                    return {"success": False, "error": f"API 请求失败: HTTP {response.status}"}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+async def fetch_historical_data(symbol: str, period: str = "1mo"):
+    """获取历史数据用于技术分析 (Stock Analysis Agent)"""
+    url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}"
+    params = {"interval": "1d", "range": period}
+    headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}
+    
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, params=params, headers=headers, timeout=aiohttp.ClientTimeout(total=15)) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    result = data.get("chart", {}).get("result", [])
+                    
+                    if not result:
+                        return {"success": False, "error": f"未找到股票: {symbol}"}
+                    
+                    quote_data = result[0]
+                    indicators = quote_data.get("indicators", {}).get("quote", [{}])[0]
+                    timestamps = quote_data.get("timestamp", [])
+                    
+                    closes = indicators.get("close", [])
+                    closes = [c for c in closes if c is not None]
+                    
+                    if len(closes) >= 5:
+                        # 简单技术分析
+                        sma_5 = sum(closes[-5:]) / 5
+                        current = closes[-1]
+                        trend = "看涨" if current > sma_5 else "看跌"
+                        
+                        return {
+                            "success": True,
+                            "symbol": symbol.upper(),
+                            "data_points": len(closes),
+                            "current_price": round(current, 2),
+                            "sma_5": round(sma_5, 2),
+                            "trend": trend,
+                            "high": round(max(closes), 2),
+                            "low": round(min(closes), 2),
+                        }
+                    else:
+                        return {"success": False, "error": "数据点不足"}
+                else:
+                    return {"success": False, "error": f"API 请求失败: HTTP {response.status}"}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+async def test_with_llm(symbol: str, context: str):
+    """使用 LLM 生成分析报告"""
+    try:
+        async with aiohttp.ClientSession() as session:
+            payload = {
+                "model": LLM_MODEL,
+                "messages": [
+                    {"role": "system", "content": "你是一个专业的美股分析师，请根据提供的数据给出简洁的分析。"},
+                    {"role": "user", "content": f"请分析以下 {symbol} 股票数据并给出简要建议（50字以内）:\n{context}"}
+                ],
+                "max_tokens": 200
+            }
+            headers = {
+                "Authorization": f"Bearer {LLM_API_KEY}",
+                "Content-Type": "application/json"
+            }
+            
+            async with session.post(
+                f"{LLM_BASE_URL}/chat/completions",
+                json=payload,
+                headers=headers,
+                timeout=aiohttp.ClientTimeout(total=30)
+            ) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    content = data.get("choices", [{}])[0].get("message", {}).get("content", "")
+                    return {"success": True, "analysis": content}
+                else:
+                    error_text = await response.text()
+                    return {"success": False, "error": f"LLM 请求失败: {response.status} - {error_text[:100]}"}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+async def main():
+    print("=" * 70)
+    print("美股 AI Agents 本地测试")
+    print("=" * 70)
+    print(f"LLM: {LLM_MODEL}")
+    print(f"API: {LLM_BASE_URL}")
+    print("=" * 70)
+    
+    symbols = ["AAPL", "TSLA", "NVDA", "MSFT", "GOOGL"]
+    
+    # 测试 1: Stock Quote Agent
+    print("\n📈 【测试 1: Stock Quote Agent - 实时行情】")
+    print("-" * 70)
+    for symbol in symbols:
+        result = await fetch_stock_quote(symbol)
+        if result.get("success"):
+            print(f"✅ {result['symbol']:6} | {result['name'][:25]:25} | ${result['price']:>10.2f} | {result['change_percent']:+6.2f}%")
+        else:
+            print(f"❌ {symbol}: {result.get('error')}")
+    
+    # 测试 2: Stock News Agent
+    print("\n📰 【测试 2: Stock News Agent - 新闻资讯】")
+    print("-" * 70)
+    for symbol in ["AAPL", "TSLA"]:
+        result = await fetch_stock_news(symbol)
+        if result.get("success"):
+            print(f"✅ {result['symbol']} - 找到 {result['news_count']} 条新闻:")
+            for news in result['news'][:2]:
+                print(f"   • {news['title'][:60]}...")
+        else:
+            print(f"❌ {symbol}: {result.get('error')}")
+    
+    # 测试 3: Stock Analysis Agent
+    print("\n📊 【测试 3: Stock Analysis Agent - 技术分析】")
+    print("-" * 70)
+    for symbol in ["AAPL", "NVDA", "TSLA"]:
+        result = await fetch_historical_data(symbol)
+        if result.get("success"):
+            print(f"✅ {result['symbol']:6} | 当前: ${result['current_price']:>8.2f} | SMA5: ${result['sma_5']:>8.2f} | 趋势: {result['trend']} | 区间: ${result['low']:.2f}-${result['high']:.2f}")
+        else:
+            print(f"❌ {symbol}: {result.get('error')}")
+    
+    # 测试 4: LLM 综合分析
+    print("\n🤖 【测试 4: LLM 综合分析】")
+    print("-" * 70)
+    
+    # 获取一只股票的完整数据
+    symbol = "AAPL"
+    quote = await fetch_stock_quote(symbol)
+    analysis = await fetch_historical_data(symbol)
+    
+    if quote.get("success") and analysis.get("success"):
+        context = f"""
+股票: {symbol} ({quote['name']})
+当前价格: ${quote['price']:.2f}
+涨跌幅: {quote['change_percent']:+.2f}%
+52周范围: ${quote.get('low_52week', 0):.2f} - ${quote.get('high_52week', 0):.2f}
+5日均线: ${analysis['sma_5']:.2f}
+技术趋势: {analysis['trend']}
+近期区间: ${analysis['low']:.2f} - ${analysis['high']:.2f}
+"""
+        print(f"📋 {symbol} 数据汇总:")
+        print(context)
+        
+        print("🔄 调用 LLM 生成分析报告...")
+        llm_result = await test_with_llm(symbol, context)
+        if llm_result.get("success"):
+            print(f"\n💡 AI 分析建议:")
+            print(f"   {llm_result['analysis']}")
+        else:
+            print(f"❌ LLM 调用失败: {llm_result.get('error')}")
+    else:
+        print(f"❌ 获取数据失败")
+    
+    print("\n" + "=" * 70)
+    print("✅ 测试完成!")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())