docs: 添加美股 AI Agent 文档

- stock_quote_agent: 添加 chat 功能，可分析行情数据并给出投资建议
- stock_news_agent: 添加 chat 功能，可解读新闻并分析市场影响
- stock_analysis_agent: 添加 chat 功能，可进行技术分析并提供交易信号

Chat 端点使用方法:
POST /chat
{
  "message": "请帮我分析 AAPL 行情",
  "api_key": "your-llm-api-key"
}

LLM 配置支持环境变量:
- LLM_BASE_URL: LLM 服务地址
- LLM_API_KEY: API 密钥
- LLM_MODEL: 模型名称

agent_code_generator.py

@@ -37,10 +37,10 @@ class AgentCodeGenerator:
             "ACR_LOGIN_SERVER": "agnettaiji.azurecr.io",
             "ACR_USERNAME": "agnettaiji",
             "ACR_PASSWORD": "hDpX5t34N5ZmnKdtqyjYL5co/SnXJrmD20CRpGpWaG+ACRCw2wGM",
-            "AZ_CLIENT_ID": "fb306798-2cfe-4ac9-ba48-eab7bc71bcfe",
-            "AZ_CLIENT_SECRET": "cVK8Q~xlfBwm2_t2TC24yrTukWV4F3G~eIjBBa0D",
+            "AZ_CLIENT_ID": "f2dd1cb2-02f6-4efb-bc72-d148f6e01545",
+            "AZ_CLIENT_SECRET": "UVU8Q~Hcrf5KeLi2RvUXB2rcuKFEjRCCrf_JrbwA",
             "AZ_TENANT_ID": "263c3ff6-1be5-4141-8308-b188464fb297",
-            "AZ_SUBSCRIPTION_ID": "c6c47e4c-f5f4-49f8-b26f-7728862c17d6",
+            "AZ_SUBSCRIPTION_ID": "45d7a360-af09-40fc-9afc-56dc475245ec",
             "AZ_RG": "taiji-ai-pda",
             "AZ_AKS": "taiji-ai-pda",
             "AZURE_DNS_ZONE": "taijiagnet.com"
@@ -382,21 +382,15 @@ async def {func_name}({params_str}) -> str:
             auth = tool.get("auth", {})
             request_params = tool.get("request_params", {})
             timeout = tool.get("timeout", 30)
+            generated_code = tool.get("generated_code", "")
             
-            # 构建参数
-            params = []
-            params_doc = []
+            # 构建参数信息（用于 TOOL_LIST）
             properties = {}
             required_params = []
-            
-            # 支持两种格式：
-            # 1. {"properties": {"symbol": {...}}}
-            # 2. {"symbol": {...}} (直接参数格式)
             param_props = request_params
             if request_params and request_params.get("properties"):
                 param_props = request_params["properties"]
             elif request_params and not any(k in request_params for k in ["type", "required", "description"]):
-                # 直接参数格式
                 param_props = request_params
             else:
                 param_props = {}
@@ -405,93 +399,48 @@ async def {func_name}({params_str}) -> str:
                 for p_name, p_info in param_props.items():
                     if not isinstance(p_info, dict):
                         continue
-                    p_type = self._json_type_to_python(p_info.get("type", "string"))
                     p_desc = p_info.get("description", "")
                     is_required = p_info.get("required", False)
-                    default = p_info.get("default")
-                    
                     if is_required:
-                        params.append(f"{p_name}: {p_type}")
                         required_params.append(p_name)
-                    else:
-                        default_val = f'"{default}"' if isinstance(default, str) else (default if default is not None else "None")
-                        params.append(f"{p_name}: Optional[{p_type}] = {default_val}")
-                    
-                    params_doc.append(f"        {p_name}: {p_desc}")
                     properties[p_name] = {"type": p_info.get("type", "string"), "description": p_desc}
             
-            params_str = ", ".join(params) if params else ""
-            params_doc_str = "\n".join(params_doc) if params_doc else "        无参数"
-            
-            # 生成认证代码
-            auth_headers = self._get_auth_headers_code(auth)
-            
-            # 构建参数字典代码
-            params_dict_code = ""
-            if param_props:
-                params_dict_code = "params = {"
-                for p_name in param_props.keys():
-                    if isinstance(param_props[p_name], dict):
-                        params_dict_code += f'"{p_name}": {p_name}, '
-                params_dict_code = params_dict_code.rstrip(", ") + "}"
-            else:
-                params_dict_code = "params = {}"
-            
-            # API Key in query
-            if auth and auth.get("type") == "api_key" and auth.get("in") == "query":
-                key_name = auth.get("name", "apikey")
-                params_dict_code += f'\n        params["{key_name}"] = os.getenv("TOOL_API_KEY", "")'
-            
-            # 生成函数代码
-            func_code = f'''
-@server.tool()
-async def {func_name}({params_str}) -> str:
-    """
-    {desc}
-    
-    Args:
-{params_doc_str}
-    
-    Returns:
-        API 响应结果 (JSON 格式)
-    """
-    import httpx
-    
-    url = "{url}"
-    {auth_headers}
-    {params_dict_code}
-    
-    try:
-        async with httpx.AsyncClient(timeout={timeout}) as client:
-            response = await client.request(
-                method="{method}",
-                url=url,
-                headers=headers,
-                params={{k: v for k, v in params.items() if v is not None}}
-            )
-            
-            if response.status_code == 200:
-                return json.dumps({{
-                    "success": True,
-                    "data": response.json() if response.headers.get("content-type", "").startswith("application/json") else response.text
-                }}, ensure_ascii=False, indent=2)
-            else:
-                return json.dumps({{
-                    "success": False,
-                    "status_code": response.status_code,
-                    "error": response.text[:500]
-                }}, ensure_ascii=False)
+            # 如果有 AI 生成的代码，使用它；否则使用模板
+            if generated_code:
+                # 从 AI 生成的代码中提取函数并添加 @server.tool() 装饰器
+                import re
+                # 移除开头的 docstring 和 import 语句
+                code_lines = generated_code.split('\n')
+                func_start = -1
+                for i, line in enumerate(code_lines):
+                    if line.strip().startswith('async def '):
+                        func_start = i
+                        break
                 
-    except Exception as e:
-        # 使用 AI Agent 作为后备
-        result = await get_agent().run(f"请帮我处理这个请求: {params}")
-        return json.dumps({{
-            "success": True,
-            "source": "ai_agent",
-            "result": result.output
-        }}, ensure_ascii=False, indent=2)
+                if func_start >= 0:
+                    # 提取函数代码
+                    func_code_lines = code_lines[func_start:]
+                    func_body = '\n'.join(func_code_lines)
+                    
+                    # 添加 @server.tool() 装饰器
+                    func_code = f'''
+@server.tool()
+{func_body}
 '''
-            tool_functions.append(func_code)
+                    tool_functions.append(func_code)
+                else:
+                    # 无法解析，使用原始代码
+                    logger.warning(f"无法解析 AI 生成的代码: {name}")
+                    func_code = self._generate_fallback_tool_code(
+                        func_name, desc, url, method, auth, param_props, timeout
+                    )
+                    tool_functions.append(func_code)
+            else:
+                # 使用模板生成代码
+                func_code = self._generate_fallback_tool_code(
+                    func_name, desc, url, method, auth, param_props, timeout
+                )
+                tool_functions.append(func_code)
             tool_map_entries.append(f"    '{func_name}': {func_name},")
             
             tool_list_entries.append(f'''    {{
@@ -522,6 +471,7 @@ import json
 import os
 from typing import Optional
 
+import httpx
 from mcp.server.fastmcp import FastMCP
 from pydantic_ai import Agent
 
@@ -603,6 +553,106 @@ if __name__ == '__main__':
         
         return "headers = {}"
     
+    def _generate_fallback_tool_code(
+        self,
+        func_name: str,
+        desc: str,
+        url: str,
+        method: str,
+        auth: Dict,
+        param_props: Dict,
+        timeout: int
+    ) -> str:
+        """生成后备工具代码（当没有 AI 生成代码时使用）"""
+        # 构建参数
+        params = []
+        params_doc = []
+        
+        if param_props:
+            for p_name, p_info in param_props.items():
+                if not isinstance(p_info, dict):
+                    continue
+                p_type = self._json_type_to_python(p_info.get("type", "string"))
+                p_desc = p_info.get("description", "")
+                is_required = p_info.get("required", False)
+                default = p_info.get("default")
+                
+                if is_required:
+                    params.append(f"{p_name}: {p_type}")
+                else:
+                    default_val = f'"{default}"' if isinstance(default, str) else (default if default is not None else "None")
+                    params.append(f"{p_name}: Optional[{p_type}] = {default_val}")
+                
+                params_doc.append(f"        {p_name}: {p_desc}")
+        
+        params_str = ", ".join(params) if params else ""
+        params_doc_str = "\n".join(params_doc) if params_doc else "        无参数"
+        
+        # 生成认证代码
+        auth_headers = self._get_auth_headers_code(auth)
+        
+        # 构建参数字典代码
+        params_dict_code = ""
+        if param_props:
+            params_dict_code = "params = {"
+            for p_name in param_props.keys():
+                if isinstance(param_props[p_name], dict):
+                    params_dict_code += f'"{p_name}": {p_name}, '
+            params_dict_code = params_dict_code.rstrip(", ") + "}"
+        else:
+            params_dict_code = "params = {}"
+        
+        # API Key in query
+        if auth and auth.get("type") == "api_key" and auth.get("in") == "query":
+            key_name = auth.get("name", "apikey")
+            params_dict_code += f'\n        params["{key_name}"] = os.getenv("TOOL_API_KEY", "")'
+        
+        return f'''
+@server.tool()
+async def {func_name}({params_str}) -> str:
+    """
+    {desc}
+    
+    Args:
+{params_doc_str}
+    
+    Returns:
+        API 响应结果 (JSON 格式)
+    """
+    import httpx
+    
+    api_url = "{url}"
+    {auth_headers}
+    {params_dict_code}
+    
+    try:
+        async with httpx.AsyncClient(timeout={timeout}) as client:
+            response = await client.request(
+                method="{method}",
+                url=api_url,
+                headers=headers,
+                params={{k: v for k, v in params.items() if v is not None}}
+            )
+            
+            if response.status_code == 200:
+                return json.dumps({{
+                    "success": True,
+                    "data": response.json() if response.headers.get("content-type", "").startswith("application/json") else response.text
+                }}, ensure_ascii=False, indent=2)
+            else:
+                return json.dumps({{
+                    "success": False,
+                    "status_code": response.status_code,
+                    "error": response.text[:500]
+                }}, ensure_ascii=False)
+                
+    except Exception as e:
+        return json.dumps({{
+            "success": False,
+            "error": str(e)
+        }}, ensure_ascii=False, indent=2)
+'''
+    
     def _generate_system_prompt(
         self,
         agent_name: str,
@@ -1081,8 +1131,15 @@ def get_tools_description() -> str:
     \"\"\"生成工具描述供 LLM 使用\"\"\"
     tools_desc = []
     for t in TOOL_LIST:
-        params = t.get("parameters", {{}})
-        param_desc = ", ".join([f"{{k}}: {{v.get('type', 'string')}}" for k, v in params.items()])
+        # 支持 inputSchema.properties 或 parameters 格式
+        schema = t.get("inputSchema", {{}})
+        params = schema.get("properties", t.get("parameters", {{}}))
+        required = schema.get("required", [])
+        param_parts = []
+        for k, v in params.items():
+            req_mark = "*" if k in required else ""
+            param_parts.append(f"{{k}}{{req_mark}}: {{v.get('type', 'string')}} ({{v.get('description', '')}})")
+        param_desc = ", ".join(param_parts)
         tools_desc.append(f"- {{t['name']}}: {{t['description']}}\\n  参数: {{param_desc or '无'}}")
     return "\\n".join(tools_desc)
 

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 +1,703 @@
+"""
+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, Header, Request
+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", "")
+
+# LLM 配置
+LLM_BASE_URL = os.getenv("LLM_BASE_URL", "https://litellm.graystone-fb459c5d.southeastasia.azurecontainerapps.io/v1")
+LLM_MODEL = os.getenv("LLM_MODEL", "taiji/gpt-4o-mini")
+
+# 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
+
+
+class ChatRequest(BaseModel):
+    """Chat 请求"""
+    message: str = Field(..., description="用户消息")
+    user_id: Optional[str] = Field(None, description="用户ID")
+
+
+class ChatResponse(BaseModel):
+    """Chat 响应"""
+    response: str
+    data: Optional[Dict[str, Any]] = None
+    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()
+    }
+
+
+# ==================== Chat 功能 ====================
+
+async def chat_with_llm(message: str, context: str, api_key: str) -> str:
+    """调用 LLM 生成响应"""
+    try:
+        async with aiohttp.ClientSession() as session:
+            payload = {
+                "model": LLM_MODEL,
+                "messages": [
+                    {
+                        "role": "system", 
+                        "content": """你是一个专业的美股技术分析师。你可以：
+1. 分析股票技术指标（SMA, RSI, MACD, 布林带等）
+2. 判断股票趋势（看涨/看跌/中性）
+3. 提供买卖信号和投资建议
+4. 评估风险等级
+
+请根据提供的技术分析数据，用简洁专业的语言回答用户问题。注意：投资有风险，建议仅供参考。"""
+                    },
+                    {
+                        "role": "user", 
+                        "content": f"技术分析数据:\n{context}\n\n用户问题: {message}"
+                    }
+                ],
+                "max_tokens": 600,
+                "temperature": 0.7
+            }
+            headers = {
+                "Authorization": f"Bearer {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()
+                    return data.get("choices", [{}])[0].get("message", {}).get("content", "抱歉，无法生成回复")
+                else:
+                    error = await response.text()
+                    logger.error(f"LLM 请求失败: {response.status} - {error}")
+                    return f"LLM 服务错误: {response.status}"
+    except Exception as e:
+        logger.error(f"LLM 调用失败: {e}")
+        return f"调用失败: {str(e)}"
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(
+    request: ChatRequest,
+    api_key: Optional[str] = Header(None, alias="api-key"),
+    authorization: Optional[str] = Header(None)
+):
+    """智能对话 - 获取技术分析并提供投资建议
+    
+    api_key 通过请求头传递:
+    - api-key: your-api-key
+    - 或 Authorization: Bearer your-api-key
+    """
+    # 从 Header 获取 api_key
+    if not api_key and authorization:
+        if authorization.startswith("Bearer "):
+            api_key = authorization[7:]
+        else:
+            api_key = authorization
+    
+    if not api_key:
+        raise HTTPException(status_code=401, detail="请在请求头中提供 api-key 或 Authorization")
+    
+    # 从消息中提取股票代码
+    import re
+    symbols = re.findall(r'\b([A-Z]{1,5})\b', request.message.upper())
+    common_words = {"I", "A", "THE", "IS", "IT", "TO", "OF", "AND", "FOR", "IN", "ON", "AT", "BY", "BUY", "SELL"}
+    symbols = [s for s in symbols if s not in common_words][:3]
+    
+    if not symbols:
+        symbols = ["AAPL"]  # 默认分析苹果
+    
+    # 获取技术分析数据
+    analysis_data = []
+    for symbol in symbols:
+        analysis = await analyze_stock(symbol)
+        if analysis:
+            analysis_data.append(analysis)
+    
+    # 构建上下文
+    if analysis_data:
+        context = "\n".join([
+            f"{a['symbol']}: 价格${a['current_price']:.2f}, 趋势:{a['trend']}, "
+            f"信号:{a['signal']}, RSI:{a['indicators'].get('rsi_14', 'N/A')}, "
+            f"风险:{a['risk_level']}"
+            for a in analysis_data
+        ])
+    else:
+        context = "暂无技术分析数据"
+    
+    # 调用 LLM 生成回复
+    llm_response = await chat_with_llm(request.message, context, api_key)
+    
+    return ChatResponse(
+        response=llm_response,
+        data={"analysis": analysis_data, "symbols": symbols},
+        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 +1,527 @@
+"""
+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, Header, Request
+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", "")
+
+# LLM 配置
+LLM_BASE_URL = os.getenv("LLM_BASE_URL", "https://litellm.graystone-fb459c5d.southeastasia.azurecontainerapps.io/v1")
+LLM_MODEL = os.getenv("LLM_MODEL", "taiji/gpt-4o-mini")
+
+# 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
+
+
+class ChatRequest(BaseModel):
+    """Chat 请求"""
+    message: str = Field(..., description="用户消息")
+    user_id: Optional[str] = Field(None, description="用户ID")
+
+
+class ChatResponse(BaseModel):
+    """Chat 响应"""
+    response: str
+    data: Optional[Dict[str, Any]] = None
+    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()
+    )
+
+
+# ==================== Chat 功能 ====================
+
+async def chat_with_llm(message: str, context: str, api_key: str) -> str:
+    """调用 LLM 生成响应"""
+    try:
+        async with aiohttp.ClientSession() as session:
+            payload = {
+                "model": LLM_MODEL,
+                "messages": [
+                    {
+                        "role": "system", 
+                        "content": """你是一个专业的美股新闻分析师。你可以：
+1. 获取和分析股票相关新闻
+2. 解读市场动态和公司公告
+3. 提供新闻情感分析和市场影响评估
+
+请根据提供的新闻数据，用简洁专业的语言回答用户问题。"""
+                    },
+                    {
+                        "role": "user", 
+                        "content": f"最新新闻:\n{context}\n\n用户问题: {message}"
+                    }
+                ],
+                "max_tokens": 500,
+                "temperature": 0.7
+            }
+            headers = {
+                "Authorization": f"Bearer {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()
+                    return data.get("choices", [{}])[0].get("message", {}).get("content", "抱歉，无法生成回复")
+                else:
+                    error = await response.text()
+                    logger.error(f"LLM 请求失败: {response.status} - {error}")
+                    return f"LLM 服务错误: {response.status}"
+    except Exception as e:
+        logger.error(f"LLM 调用失败: {e}")
+        return f"调用失败: {str(e)}"
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(
+    request: ChatRequest,
+    api_key: Optional[str] = Header(None, alias="api-key"),
+    authorization: Optional[str] = Header(None)
+):
+    """智能对话 - 获取新闻并提供分析
+    
+    api_key 通过请求头传递:
+    - api-key: your-api-key
+    - 或 Authorization: Bearer your-api-key
+    """
+    # 从 Header 获取 api_key
+    if not api_key and authorization:
+        if authorization.startswith("Bearer "):
+            api_key = authorization[7:]
+        else:
+            api_key = authorization
+    
+    if not api_key:
+        raise HTTPException(status_code=401, detail="请在请求头中提供 api-key 或 Authorization")
+    
+    # 从消息中提取关键词
+    import re
+    symbols = re.findall(r'\b([A-Z]{1,5})\b', request.message.upper())
+    common_words = {"I", "A", "THE", "IS", "IT", "TO", "OF", "AND", "FOR", "IN", "ON", "AT", "BY", "NEWS", "WHAT"}
+    symbols = [s for s in symbols if s not in common_words][:3]
+    
+    # 获取相关新闻
+    news_data = []
+    if symbols:
+        for symbol in symbols:
+            data = await fetch_yahoo_news(symbol=symbol, limit=3)
+            news_data.extend(data)
+    else:
+        news_data = await fetch_yahoo_news(query="stock market", limit=5)
+    
+    # 构建上下文
+    if news_data:
+        context = "\n".join([
+            f"- {item['title']} ({item['source']}, {item['published_at'][:10]})"
+            for item in news_data[:5]
+        ])
+    else:
+        context = "暂无相关新闻"
+    
+    # 调用 LLM 生成回复
+    llm_response = await chat_with_llm(request.message, context, api_key)
+    
+    return ChatResponse(
+        response=llm_response,
+        data={"news_count": len(news_data), "symbols": symbols},
+        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 +1,480 @@
+"""
+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, Header, Request
+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"
+
+# LLM 配置
+LLM_BASE_URL = os.getenv("LLM_BASE_URL", "https://litellm.graystone-fb459c5d.southeastasia.azurecontainerapps.io/v1")
+LLM_MODEL = os.getenv("LLM_MODEL", "taiji/gpt-4o-mini")
+
+# 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
+
+
+class ChatRequest(BaseModel):
+    """Chat 请求"""
+    message: str = Field(..., description="用户消息")
+    user_id: Optional[str] = Field(None, description="用户ID")
+
+
+class ChatResponse(BaseModel):
+    """Chat 响应"""
+    response: str
+    data: Optional[Dict[str, Any]] = None
+    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)
+
+
+async def chat_with_llm(message: str, context: str, api_key: str) -> str:
+    """调用 LLM 生成响应"""
+    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助手。你可以：
+1. 查询股票实时行情（价格、涨跌幅、成交量）
+2. 分析股票数据并给出建议
+3. 解答关于美股市场的问题
+
+请根据提供的股票数据，用简洁专业的语言回答用户问题。"""
+                    },
+                    {
+                        "role": "user", 
+                        "content": f"当前股票数据:\n{context}\n\n用户问题: {message}"
+                    }
+                ],
+                "max_tokens": 500,
+                "temperature": 0.7
+            }
+            headers = {
+                "Authorization": f"Bearer {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()
+                    return data.get("choices", [{}])[0].get("message", {}).get("content", "抱歉，无法生成回复")
+                else:
+                    error = await response.text()
+                    logger.error(f"LLM 请求失败: {response.status} - {error}")
+                    return f"LLM 服务错误: {response.status}"
+    except Exception as e:
+        logger.error(f"LLM 调用失败: {e}")
+        return f"调用失败: {str(e)}"
+
+
+def extract_symbols_from_message(message: str) -> List[str]:
+    """从消息中提取股票代码"""
+    import re
+    # 匹配常见美股代码格式（1-5个大写字母）
+    symbols = re.findall(r'\b([A-Z]{1,5})\b', message.upper())
+    # 过滤常见词汇
+    common_words = {"I", "A", "THE", "IS", "IT", "TO", "OF", "AND", "FOR", "IN", "ON", "AT", "BY"}
+    return [s for s in symbols if s not in common_words][:5]  # 最多5个
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(
+    request: ChatRequest,
+    api_key: Optional[str] = Header(None, alias="api-key"),
+    authorization: Optional[str] = Header(None)
+):
+    """智能对话 - 支持自然语言查询股票信息
+    
+    api_key 通过请求头传递:
+    - api-key: your-api-key
+    - 或 Authorization: Bearer your-api-key
+    """
+    # 从 Header 获取 api_key
+    if not api_key and authorization:
+        if authorization.startswith("Bearer "):
+            api_key = authorization[7:]
+        else:
+            api_key = authorization
+    
+    if not api_key:
+        raise HTTPException(status_code=401, detail="请在请求头中提供 api-key 或 Authorization")
+    
+    # 从消息中提取股票代码
+    symbols = extract_symbols_from_message(request.message)
+    
+    # 如果没有提取到，默认查询热门股票
+    if not symbols:
+        symbols = ["AAPL", "TSLA", "NVDA"]
+    
+    # 获取股票数据
+    stock_data = []
+    for symbol in symbols:
+        result = await fetch_stock_quote(symbol)
+        if result.get("success"):
+            stock_data.append(result)
+    
+    # 构建上下文
+    if stock_data:
+        context = "\n".join([
+            f"{d['symbol']} ({d['name']}): ${d['price']:.2f}, 涨跌: {d['change_percent']:+.2f}%, "
+            f"成交量: {d['volume']:,}, 52周范围: ${d.get('low_52week', 0):.2f}-${d.get('high_52week', 0):.2f}"
+            for d in stock_data
+        ])
+    else:
+        context = "暂无股票数据"
+    
+    # 调用 LLM 生成回复
+    llm_response = await chat_with_llm(request.message, context, api_key)
+    
+    return ChatResponse(
+        response=llm_response,
+        data={"stocks": stock_data, "symbols_detected": symbols},
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
+# ==================== 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()

app.py

@@ -389,8 +389,8 @@ async def create_agent(request: CreateAgentRequest, db: Session = Depends(get_db
             config_data=config_data
         )
         
-        # 步骤3: 获取服务端口
-        service_port = k8s_manager.TEMPLATE_PORTS.get(request.template)
+        # 步骤3: 获取服务端口（从数据库动态获取）
+        service_port = template_manager.get_port(request.template)
         
         # 步骤4: 创建 LoadBalancer Service（AKS 会自动分配外网 IP）
         service_info = None

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\""

docs/STOCK_AGENTS_DOC.md

@@ -0,0 +1,563 @@
+# 美股 AI Agent 文档
+
+本项目包含 **三个独立的美股 AI Agent 服务**，均通过 **HTTP API** 对外提供能力，支持智能对话分析。
+
+- **Stock Quote Agent**：美股实时行情查询与分析
+- **Stock News Agent**：美股新闻资讯获取与解读
+- **Stock Analysis Agent**：美股技术分析与投资建议
+
+---
+
+## 认证方式
+
+所有 Chat API 需要在请求头中提供 API Key，支持两种方式：
+
+| 方式 | Header | 示例 |
+|------|--------|------|
+| api-key | `api-key` | `api-key: sk-xxxxx` |
+| Bearer Token | `Authorization` | `Authorization: Bearer sk-xxxxx` |
+
+> ⚠️ 未提供认证信息将返回 `401 Unauthorized`
+
+---
+
+## Agent 1：Stock Quote Agent
+
+### 功能概览
+
+提供美股 **实时行情查询** 能力，支持自然语言交互，返回股票价格、涨跌幅、成交量等数据。
+
+支持能力：
+
+- 实时股票行情查询
+- 多股票批量查询
+- 热门股票行情
+- **AI 智能对话分析**
+
+---
+
+### 1️⃣ /chat — 智能对话
+
+#### 功能说明
+
+通过自然语言与 AI 交互，自动识别股票代码并返回行情数据及投资建议。
+
+---
+
+#### REST API 调用
+
+```
+POST /chat
+Content-Type: application/json
+api-key: your-api-key
+```
+
+```json
+{
+  "message": "AAPL 和 TSLA 今天表现如何？"
+}
+```
+
+---
+
+#### 参数说明
+
+| 参数 | 类型 | 必需 | 默认值 | 说明 |
+|------|------|------|--------|------|
+| message | string | ✅ | - | 用户消息（支持自然语言） |
+| user_id | string | ❌ | null | 用户ID（用于计费回调） |
+
+**Header 参数：**
+
+| 参数 | 类型 | 必需 | 说明 |
+|------|------|------|------|
+| api-key | string | ⚠ | API Key（二选一） |
+| Authorization | string | ⚠ | Bearer Token（二选一） |
+
+---
+
+#### 返回结果
+
+```json
+{
+  "response": "今天AAPL的股价为$274.43，涨跌幅为0.00%。TSLA的股价为$398.78...",
+  "data": {
+    "stocks": [
+      {
+        "success": true,
+        "symbol": "AAPL",
+        "name": "Apple Inc.",
+        "price": 274.43,
+        "change": 0,
+        "change_percent": 0,
+        "volume": 5212932,
+        "high_52week": 288.62,
+        "low_52week": 169.21
+      },
+      {
+        "success": true,
+        "symbol": "TSLA",
+        "name": "Tesla, Inc.",
+        "price": 398.78,
+        "change": 0,
+        "change_percent": 0,
+        "volume": 7867089,
+        "high_52week": 498.83,
+        "low_52week": 214.25
+      }
+    ],
+    "symbols_detected": ["AAPL", "TSLA"]
+  },
+  "timestamp": "2026-02-05T15:30:13.347583"
+}
+```
+
+---
+
+### 2️⃣ /quote — 单股行情查询
+
+#### REST API 调用
+
+```
+GET /quote?symbol=AAPL
+```
+
+或
+
+```
+POST /quote
+Content-Type: application/json
+```
+
+```json
+{
+  "symbol": "AAPL"
+}
+```
+
+---
+
+#### 参数说明
+
+| 参数 | 类型 | 必需 | 说明 |
+|------|------|------|------|
+| symbol | string | ✅ | 股票代码（如 AAPL, TSLA） |
+
+---
+
+#### 返回结果
+
+```json
+{
+  "symbol": "AAPL",
+  "name": "Apple Inc.",
+  "price": 274.43,
+  "change": 2.31,
+  "change_percent": 0.85,
+  "volume": 52129320,
+  "market_cap": 4200000000000,
+  "high_52week": 288.62,
+  "low_52week": 169.21,
+  "timestamp": "2026-02-05T15:30:00.000000"
+}
+```
+
+---
+
+### 3️⃣ /batch — 批量行情查询
+
+```
+POST /batch
+Content-Type: application/json
+```
+
+```json
+{
+  "symbols": ["AAPL", "TSLA", "NVDA", "MSFT", "GOOGL"]
+}
+```
+
+---
+
+### 4️⃣ /popular — 热门股票行情
+
+```
+GET /popular
+```
+
+返回 AAPL, MSFT, GOOGL, AMZN, TSLA, NVDA, META 等热门股票行情。
+
+---
+
+## Agent 2：Stock News Agent
+
+### 功能概览
+
+提供美股 **新闻资讯获取与分析** 能力，支持按股票代码或关键词搜索新闻。
+
+支持能力：
+
+- 股票相关新闻查询
+- 市场动态获取
+- 热门财经新闻
+- **AI 新闻解读与影响分析**
+
+---
+
+### 1️⃣ /chat — 智能对话
+
+#### 功能说明
+
+通过自然语言获取股票新闻并进行 AI 分析解读。
+
+---
+
+#### REST API 调用
+
+```
+POST /chat
+Content-Type: application/json
+api-key: your-api-key
+```
+
+```json
+{
+  "message": "NVDA 最近有什么重要新闻？"
+}
+```
+
+---
+
+#### 返回结果
+
+```json
+{
+  "response": "最近关于NVDA的新闻显示出其股票在盘前交易中表现强劲，市场对其AI芯片业务的前景保持乐观...",
+  "data": {
+    "news_count": 5,
+    "symbols": ["NVDA"]
+  },
+  "timestamp": "2026-02-05T15:31:29.298428"
+}
+```
+
+---
+
+### 2️⃣ /news — 获取新闻
+
+```
+POST /news
+Content-Type: application/json
+```
+
+```json
+{
+  "symbol": "AAPL",
+  "limit": 10
+}
+```
+
+---
+
+#### 参数说明
+
+| 参数 | 类型 | 必需 | 默认值 | 说明 |
+|------|------|------|--------|------|
+| symbol | string | ⚠ | null | 股票代码 |
+| query | string | ⚠ | null | 搜索关键词 |
+| limit | integer | ❌ | 10 | 返回新闻数量（1-50） |
+
+> `symbol` 与 `query` 二选一
+
+---
+
+### 3️⃣ /market — 市场动态
+
+```
+GET /market
+```
+
+返回市场涨跌排行、活跃股票等信息。
+
+---
+
+### 4️⃣ /trending — 热门新闻
+
+```
+GET /trending
+```
+
+返回当前热门财经新闻。
+
+---
+
+## Agent 3：Stock Analysis Agent
+
+### 功能概览
+
+提供美股 **技术分析** 能力，计算技术指标并给出交易信号与投资建议。
+
+支持能力：
+
+- 技术指标计算（SMA, RSI, MACD, 布林带）
+- 趋势判断（看涨/看跌/中性）
+- 买卖信号生成
+- 支撑位/阻力位计算
+- **AI 综合分析与投资建议**
+
+---
+
+### 1️⃣ /chat — 智能对话
+
+#### 功能说明
+
+通过自然语言获取股票技术分析并由 AI 提供投资建议。
+
+---
+
+#### REST API 调用
+
+```
+POST /chat
+Content-Type: application/json
+Authorization: Bearer your-api-key
+```
+
+```json
+{
+  "message": "帮我分析 AAPL，现在适合买入吗？"
+}
+```
+
+---
+
+#### 返回结果
+
+```json
+{
+  "response": "根据技术分析数据，AAPL当前价格为$274.31，趋势为中性，信号为持有。RSI值为66.49，接近超买区域...",
+  "data": {
+    "analysis": [
+      {
+        "symbol": "AAPL",
+        "current_price": 274.31,
+        "indicators": {
+          "sma_20": 259.12,
+          "sma_50": 268.63,
+          "sma_200": null,
+          "rsi_14": 66.49,
+          "macd": -0.9012,
+          "macd_signal": -0.8111,
+          "bollinger_upper": 275.39,
+          "bollinger_lower": 242.84
+        },
+        "trend": "neutral",
+        "signal": "hold",
+        "support_level": 243.42,
+        "resistance_level": 279.5,
+        "risk_level": "medium"
+      }
+    ],
+    "symbols": ["AAPL"]
+  },
+  "timestamp": "2026-02-05T15:58:50.578048"
+}
+```
+
+---
+
+### 2️⃣ /analyze — 技术分析
+
+```
+POST /analyze
+Content-Type: application/json
+```
+
+```json
+{
+  "symbol": "AAPL"
+}
+```
+
+---
+
+#### 返回字段说明
+
+| 字段 | 类型 | 说明 |
+|------|------|------|
+| current_price | float | 当前价格 |
+| sma_20 | float | 20日简单移动平均线 |
+| sma_50 | float | 50日简单移动平均线 |
+| sma_200 | float | 200日简单移动平均线 |
+| rsi_14 | float | 14日相对强弱指数 |
+| macd | float | MACD 值 |
+| macd_signal | float | MACD 信号线 |
+| bollinger_upper | float | 布林带上轨 |
+| bollinger_lower | float | 布林带下轨 |
+| trend | string | 趋势：bullish / bearish / neutral |
+| signal | string | 信号：buy / sell / hold |
+| support_level | float | 支撑位 |
+| resistance_level | float | 阻力位 |
+| risk_level | string | 风险等级：low / medium / high |
+
+---
+
+### 3️⃣ /compare — 多股对比
+
+```
+POST /compare
+Content-Type: application/json
+```
+
+```json
+{
+  "symbols": ["AAPL", "MSFT", "GOOGL"]
+}
+```
+
+---
+
+### 4️⃣ /screen — 股票筛选
+
+```
+GET /screen?trend=bullish&signal=buy
+```
+
+根据技术指标筛选符合条件的股票。
+
+---
+
+## 统一错误格式
+
+**成功：**
+
+```json
+{
+  "response": "AI 分析结果...",
+  "data": {},
+  "timestamp": "2026-02-05T15:30:00.000000"
+}
+```
+
+**认证失败（401）：**
+
+```json
+{
+  "detail": "请在请求头中提供 api-key 或 Authorization"
+}
+```
+
+**请求错误（400）：**
+
+```json
+{
+  "detail": "错误描述"
+}
+```
+
+**服务器错误（500）：**
+
+```json
+{
+  "detail": "Internal Server Error"
+}
+```
+
+---
+
+## 调用示例
+
+### cURL 示例
+
+**使用 api-key Header：**
+
+```bash
+curl -X POST "http://test-stock-quote.taijiagnet.com/chat" \
+  -H "Content-Type: application/json" \
+  -H "api-key: sk-mPV5MVVVVvfGSkXA-ASQXQ" \
+  -d '{"message": "AAPL 和 TSLA 今天表现如何？"}'
+```
+
+**使用 Authorization Bearer：**
+
+```bash
+curl -X POST "http://test-stock-analysis.taijiagnet.com/chat" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-mPV5MVVVVvfGSkXA-ASQXQ" \
+  -d '{"message": "帮我分析 NVDA，现在适合买入吗？"}'
+```
+
+---
+
+### Python 示例
+
+```python
+import requests
+
+API_KEY = "sk-mPV5MVVVVvfGSkXA-ASQXQ"
+
+# Stock Quote Agent
+response = requests.post(
+    "http://test-stock-quote.taijiagnet.com/chat",
+    headers={
+        "Content-Type": "application/json",
+        "api-key": API_KEY
+    },
+    json={"message": "AAPL 现在多少钱？"}
+)
+print(response.json())
+
+# Stock News Agent
+response = requests.post(
+    "http://test-stock-news.taijiagnet.com/chat",
+    headers={
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {API_KEY}"
+    },
+    json={"message": "TSLA 最近有什么新闻？"}
+)
+print(response.json())
+
+# Stock Analysis Agent
+response = requests.post(
+    "http://test-stock-analysis.taijiagnet.com/chat",
+    headers={
+        "Content-Type": "application/json",
+        "api-key": API_KEY
+    },
+    json={"message": "帮我技术分析 NVDA"}
+)
+print(response.json())
+```
+
+---
+
+## 部署信息
+
+| Agent | 模板名称 | 镜像 | 端口 |
+|-------|----------|------|------|
+| Stock Quote | stock_quote_agent | agnettaiji.azurecr.io/ai-agents/stock-quote-agent:latest | 8080 |
+| Stock News | stock_news_agent | agnettaiji.azurecr.io/ai-agents/stock-news-agent:latest | 8080 |
+| Stock Analysis | stock_analysis_agent | agnettaiji.azurecr.io/ai-agents/stock-analysis-agent:latest | 8080 |
+
+---
+
+## 环境变量配置
+
+| 变量名 | 说明 | 默认值 |
+|--------|------|--------|
+| LLM_BASE_URL | LLM 服务地址 | https://litellm.graystone-fb459c5d.southeastasia.azurecontainerapps.io/v1 |
+| LLM_MODEL | 模型名称 | taiji/gpt-4o-mini |
+| SERVICE_HOST | 服务监听地址 | 0.0.0.0 |
+| SERVICE_PORT | 服务端口 | 8080 |
+
+---
+
+## 免责声明
+
+> ⚠️ **投资有风险，入市需谨慎。** 本 Agent 提供的分析和建议仅供参考，不构成任何投资建议。用户应自行判断并承担投资风险。
+
+---
+
+*文档版本：v1.0.0*  
+*更新日期：2026-02-05*

external_tool_api.py

@@ -844,6 +844,8 @@ async def create_agent_with_tools(request: CreateAgentWithToolsRequest):
         tools_config = []
         for tool in tools:
             config = tool.get("config", {})
+            # 获取 AI 生成的工具代码
+            tool_code = tool_storage.get_tool_code(tool.get("tool_ref_id", ""))
             tools_config.append({
                 "name": tool["name"],
                 "description": tool.get("description", ""),
@@ -852,7 +854,8 @@ async def create_agent_with_tools(request: CreateAgentWithToolsRequest):
                 "auth": config.get("auth"),
                 "request_params": config.get("request_params"),
                 "request_body": config.get("request_body"),
-                "timeout": config.get("timeout", 30)
+                "timeout": config.get("timeout", 30),
+                "generated_code": tool_code  # 传递 AI 生成的代码
             })
         
         # 生成唯一后缀，确保不同用户创建同名 Agent 不会冲突
@@ -902,7 +905,7 @@ async def create_agent_with_tools(request: CreateAgentWithToolsRequest):
             "ACR_USERNAME": "agnettaiji",
             "ACR_PASSWORD": "hDpX5t34N5ZmnKdtqyjYL5co/SnXJrmD20CRpGpWaG+ACRCw2wGM",
             "AZ_CLIENT_ID": "f2dd1cb2-02f6-4efb-bc72-d148f6e01545",
-            "AZ_CLIENT_SECRET": "S0J8Q~DE.DEu29nreaBn2EbeuGOg7GEIkonRMbYj",
+            "AZ_CLIENT_SECRET": "UVU8Q~Hcrf5KeLi2RvUXB2rcuKFEjRCCrf_JrbwA",
             "AZ_TENANT_ID": "263c3ff6-1be5-4141-8308-b188464fb297",
             "AZ_SUBSCRIPTION_ID": "45d7a360-af09-40fc-9afc-56dc475245ec",
             "AZ_RG": "taiji-ai-pda",

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."

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())

tool_generator_api.py

@@ -225,10 +225,10 @@ async def generate_agent(request: GenerateAgentRequest, background_tasks: Backgr
             "ACR_LOGIN_SERVER": "agnettaiji.azurecr.io",
             "ACR_USERNAME": "agnettaiji",
             "ACR_PASSWORD": "hDpX5t34N5ZmnKdtqyjYL5co/SnXJrmD20CRpGpWaG+ACRCw2wGM",
-            "AZ_CLIENT_ID": "fb306798-2cfe-4ac9-ba48-eab7bc71bcfe",
-            "AZ_CLIENT_SECRET": "cVK8Q~xlfBwm2_t2TC24yrTukWV4F3G~eIjBBa0D",
+            "AZ_CLIENT_ID": "f2dd1cb2-02f6-4efb-bc72-d148f6e01545",
+            "AZ_CLIENT_SECRET": "UVU8Q~Hcrf5KeLi2RvUXB2rcuKFEjRCCrf_JrbwA",
             "AZ_TENANT_ID": "263c3ff6-1be5-4141-8308-b188464fb297",
-            "AZ_SUBSCRIPTION_ID": "c6c47e4c-f5f4-49f8-b26f-7728862c17d6",
+            "AZ_SUBSCRIPTION_ID": "45d7a360-af09-40fc-9afc-56dc475245ec",
             "AZ_RG": "taiji-ai-pda",
             "AZ_AKS": "taiji-ai-pda",
             "AZURE_DNS_ZONE": "taijiagnet.com"