Merge branch 'main' of https://git.yishihui.com/howard/Agent

.gitignore

@@ -64,3 +64,4 @@ output
 examples/**/output*/
 
 frontend/htmlTemplate/mock_data
+frontend/react-template/yarn.lock

agent/core/runner.py

@@ -98,6 +98,12 @@ BUILTIN_TOOLS = [
     "browser_ensure_login_with_cookies",
     "browser_wait_for_user_action",
     "browser_done",
+
+    # 飞书工具
+    "feishu_get_chat_history",
+    "feishu_get_contact_replies",
+    "feishu_send_message_to_contact",
+    "feishu_get_contact_list",
 ]
 
 

agent/llm/__init__.py

@@ -6,5 +6,25 @@ LLM Providers
 
 from .gemini import create_gemini_llm_call
 from .openrouter import create_openrouter_llm_call
+from .usage import TokenUsage, TokenUsageAccumulator, create_usage_from_response
+from .pricing import (
+    ModelPricing,
+    PricingCalculator,
+    get_pricing_calculator,
+    calculate_cost,
+)
 
-__all__ = ["create_gemini_llm_call", "create_openrouter_llm_call"]
+__all__ = [
+    # Providers
+    "create_gemini_llm_call",
+    "create_openrouter_llm_call",
+    # Usage
+    "TokenUsage",
+    "TokenUsageAccumulator",
+    "create_usage_from_response",
+    # Pricing
+    "ModelPricing",
+    "PricingCalculator",
+    "get_pricing_calculator",
+    "calculate_cost",
+]

agent/llm/gemini.py

@@ -12,6 +12,9 @@ import sys
 import httpx
 from typing import List, Dict, Any, Optional
 
+from .usage import TokenUsage
+from .pricing import calculate_cost
+
 
 def _dump_llm_request(endpoint: str, payload: Dict[str, Any], model: str):
     """
@@ -430,18 +433,23 @@ def create_gemini_llm_call(
                             }
                         })
 
-        # 提取 usage
+        # 提取 usage（完整版）
         usage_meta = gemini_resp.get("usageMetadata", {})
-        prompt_tokens = usage_meta.get("promptTokenCount", 0)
-        completion_tokens = usage_meta.get("candidatesTokenCount", 0)
+        usage = TokenUsage.from_gemini(usage_meta)
+
+        # 计算费用
+        cost = calculate_cost(model, usage)
 
         return {
             "content": content,
             "tool_calls": tool_calls,
-            "prompt_tokens": prompt_tokens,
-            "completion_tokens": completion_tokens,
+            "prompt_tokens": usage.input_tokens,
+            "completion_tokens": usage.output_tokens,
+            "reasoning_tokens": usage.reasoning_tokens,
+            "cached_content_tokens": usage.cached_content_tokens,
             "finish_reason": finish_reason,
-            "cost": 0.0
+            "cost": cost,
+            "usage": usage,  # 完整的 TokenUsage 对象
         }
 
     return gemini_llm_call

agent/llm/openrouter.py

@@ -3,6 +3,11 @@ OpenRouter Provider
 
 使用 OpenRouter API 调用各种模型（包括 Claude Sonnet 4.5）
 支持 OpenAI 兼容的 API 格式
+
+OpenRouter 转发多种模型，需要根据实际模型处理不同的 usage 格式：
+- OpenAI 模型: prompt_tokens, completion_tokens, completion_tokens_details.reasoning_tokens
+- Claude 模型: input_tokens, output_tokens, cache_creation_input_tokens, cache_read_input_tokens
+- DeepSeek 模型: prompt_tokens, completion_tokens, reasoning_tokens
 """
 
 import os
@@ -10,6 +15,61 @@ import json
 import httpx
 from typing import List, Dict, Any, Optional
 
+from .usage import TokenUsage, create_usage_from_response
+from .pricing import calculate_cost
+
+
+def _detect_provider_from_model(model: str) -> str:
+    """根据模型名称检测提供商"""
+    model_lower = model.lower()
+    if model_lower.startswith("anthropic/") or "claude" in model_lower:
+        return "anthropic"
+    elif model_lower.startswith("openai/") or model_lower.startswith("gpt") or model_lower.startswith("o1") or model_lower.startswith("o3"):
+        return "openai"
+    elif model_lower.startswith("deepseek/") or "deepseek" in model_lower:
+        return "deepseek"
+    elif model_lower.startswith("google/") or "gemini" in model_lower:
+        return "gemini"
+    else:
+        return "openai"  # 默认使用 OpenAI 格式
+
+
+def _parse_openrouter_usage(usage: Dict[str, Any], model: str) -> TokenUsage:
+    """
+    解析 OpenRouter 返回的 usage
+
+    OpenRouter 会根据底层模型返回不同格式的 usage
+    """
+    provider = _detect_provider_from_model(model)
+
+    # OpenRouter 通常返回 OpenAI 格式，但可能包含额外字段
+    if provider == "anthropic":
+        # Claude 模型可能有缓存字段
+        return TokenUsage(
+            input_tokens=usage.get("prompt_tokens") or usage.get("input_tokens", 0),
+            output_tokens=usage.get("completion_tokens") or usage.get("output_tokens", 0),
+            cache_creation_tokens=usage.get("cache_creation_input_tokens", 0),
+            cache_read_tokens=usage.get("cache_read_input_tokens", 0),
+        )
+    elif provider == "deepseek":
+        # DeepSeek 可能有 reasoning_tokens
+        return TokenUsage(
+            input_tokens=usage.get("prompt_tokens", 0),
+            output_tokens=usage.get("completion_tokens", 0),
+            reasoning_tokens=usage.get("reasoning_tokens", 0),
+        )
+    else:
+        # OpenAI 格式（包括 o1/o3 的 reasoning_tokens）
+        reasoning = 0
+        if details := usage.get("completion_tokens_details"):
+            reasoning = details.get("reasoning_tokens", 0)
+
+        return TokenUsage(
+            input_tokens=usage.get("prompt_tokens", 0),
+            output_tokens=usage.get("completion_tokens", 0),
+            reasoning_tokens=reasoning,
+        )
+
 
 async def openrouter_llm_call(
     messages: List[Dict[str, Any]],
@@ -88,21 +148,24 @@ async def openrouter_llm_call(
     tool_calls = message.get("tool_calls")
     finish_reason = choice.get("finish_reason")  # stop, length, tool_calls, content_filter 等
 
-    # 提取 usage
-    usage = result.get("usage", {})
-    prompt_tokens = usage.get("prompt_tokens", 0)
-    completion_tokens = usage.get("completion_tokens", 0)
+    # 提取 usage（完整版，根据模型类型解析）
+    raw_usage = result.get("usage", {})
+    usage = _parse_openrouter_usage(raw_usage, model)
 
-    # 计算成本（OpenRouter 通常在响应中提供，但这里简化为 0）
-    cost = 0.0
+    # 计算费用
+    cost = calculate_cost(model, usage)
 
     return {
         "content": content,
         "tool_calls": tool_calls,
-        "prompt_tokens": prompt_tokens,
-        "completion_tokens": completion_tokens,
+        "prompt_tokens": usage.input_tokens,
+        "completion_tokens": usage.output_tokens,
+        "reasoning_tokens": usage.reasoning_tokens,
+        "cache_creation_tokens": usage.cache_creation_tokens,
+        "cache_read_tokens": usage.cache_read_tokens,
         "finish_reason": finish_reason,
-        "cost": cost
+        "cost": cost,
+        "usage": usage,  # 完整的 TokenUsage 对象
     }
 
 

agent/llm/pricing.py

@@ -0,0 +1,350 @@
+"""
+LLM 定价计算器
+
+使用策略模式，支持：
+1. YAML 配置文件定义模型价格
+2. 不同 token 类型的差异化定价（input/output/reasoning/cache）
+3. 自动匹配模型（支持通配符）
+4. 费用计算
+
+定价单位：美元 / 1M tokens
+"""
+
+import os
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Dict, Any, Optional, List
+import yaml
+
+from .usage import TokenUsage
+
+
+@dataclass
+class ModelPricing:
+    """
+    单个模型的定价配置
+
+    所有价格单位：美元 / 1M tokens
+    """
+    model: str                          # 模型名称（支持通配符 *）
+    input_price: float = 0.0            # 输入 token 价格
+    output_price: float = 0.0           # 输出 token 价格
+
+    # 可选的差异化定价
+    reasoning_price: Optional[float] = None      # 推理 token 价格（默认 = output_price）
+    cache_creation_price: Optional[float] = None # 缓存创建价格（默认 = input_price * 1.25）
+    cache_read_price: Optional[float] = None     # 缓存读取价格（默认 = input_price * 0.1）
+
+    # 元数据
+    provider: Optional[str] = None      # 提供商
+    description: Optional[str] = None   # 描述
+
+    def get_reasoning_price(self) -> float:
+        """获取推理 token 价格"""
+        return self.reasoning_price if self.reasoning_price is not None else self.output_price
+
+    def get_cache_creation_price(self) -> float:
+        """获取缓存创建价格"""
+        return self.cache_creation_price if self.cache_creation_price is not None else self.input_price * 1.25
+
+    def get_cache_read_price(self) -> float:
+        """获取缓存读取价格"""
+        return self.cache_read_price if self.cache_read_price is not None else self.input_price * 0.1
+
+    def calculate_cost(self, usage: TokenUsage) -> float:
+        """
+        计算费用
+
+        Args:
+            usage: Token 使用量
+
+        Returns:
+            费用（美元）
+        """
+        cost = 0.0
+
+        # 基础输入费用
+        # 如果有缓存，需要分开计算
+        if usage.cache_read_tokens or usage.cache_creation_tokens:
+            # 普通输入 = 总输入 - 缓存读取（缓存读取部分单独计价）
+            regular_input = usage.input_tokens - usage.cache_read_tokens
+            cost += (regular_input / 1_000_000) * self.input_price
+            cost += (usage.cache_read_tokens / 1_000_000) * self.get_cache_read_price()
+            cost += (usage.cache_creation_tokens / 1_000_000) * self.get_cache_creation_price()
+        else:
+            cost += (usage.input_tokens / 1_000_000) * self.input_price
+
+        # 输出费用
+        # 如果有 reasoning tokens，需要分开计算
+        if usage.reasoning_tokens:
+            # 普通输出 = 总输出 - reasoning（reasoning 部分单独计价）
+            regular_output = usage.output_tokens - usage.reasoning_tokens
+            cost += (regular_output / 1_000_000) * self.output_price
+            cost += (usage.reasoning_tokens / 1_000_000) * self.get_reasoning_price()
+        else:
+            cost += (usage.output_tokens / 1_000_000) * self.output_price
+
+        return cost
+
+    def matches(self, model_name: str) -> bool:
+        """
+        检查模型名称是否匹配
+
+        支持通配符：
+        - "gpt-4*" 匹配 "gpt-4", "gpt-4-turbo", "gpt-4o" 等
+        - "claude-3-*" 匹配 "claude-3-opus", "claude-3-sonnet" 等
+        """
+        pattern = self.model.replace("*", ".*")
+        return bool(re.match(f"^{pattern}$", model_name, re.IGNORECASE))
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ModelPricing":
+        """从字典创建"""
+        return cls(
+            model=data["model"],
+            input_price=data.get("input_price", 0.0),
+            output_price=data.get("output_price", 0.0),
+            reasoning_price=data.get("reasoning_price"),
+            cache_creation_price=data.get("cache_creation_price"),
+            cache_read_price=data.get("cache_read_price"),
+            provider=data.get("provider"),
+            description=data.get("description"),
+        )
+
+
+class PricingCalculator:
+    """
+    定价计算器
+
+    从 YAML 配置加载定价表，计算 LLM 调用费用
+    """
+
+    def __init__(self, config_path: Optional[str] = None):
+        """
+        初始化定价计算器
+
+        Args:
+            config_path: 定价配置文件路径，默认查找：
+                1. 环境变量 AGENT_PRICING_CONFIG
+                2. ./pricing.yaml
+                3. ./config/pricing.yaml
+                4. 使用内置默认配置
+        """
+        self._pricing_map: Dict[str, ModelPricing] = {}
+        self._patterns: List[ModelPricing] = []  # 带通配符的定价
+
+        # 加载配置
+        config_path = self._resolve_config_path(config_path)
+        if config_path and Path(config_path).exists():
+            self._load_from_file(config_path)
+        else:
+            self._load_defaults()
+
+    def _resolve_config_path(self, config_path: Optional[str]) -> Optional[str]:
+        """解析配置文件路径"""
+        if config_path:
+            return config_path
+
+        # 检查环境变量
+        if env_path := os.getenv("AGENT_PRICING_CONFIG"):
+            return env_path
+
+        # 获取 agent 包的根目录（agent/llm/pricing.py -> agent/）
+        agent_dir = Path(__file__).parent.parent
+        project_root = agent_dir.parent  # 项目根目录
+
+        # 检查默认位置（按优先级）
+        search_paths = [
+            # 1. 当前工作目录
+            Path("pricing.yaml"),
+            Path("config/pricing.yaml"),
+            # 2. 项目根目录
+            project_root / "pricing.yaml",
+            project_root / "config" / "pricing.yaml",
+            # 3. agent 包目录
+            agent_dir / "pricing.yaml",
+            agent_dir / "config" / "pricing.yaml",
+        ]
+
+        for path in search_paths:
+            if path.exists():
+                print(f"[Pricing] Loaded config from: {path}")
+                return str(path)
+
+        return None
+
+    def _load_from_file(self, config_path: str) -> None:
+        """从 YAML 文件加载配置"""
+        with open(config_path, "r", encoding="utf-8") as f:
+            config = yaml.safe_load(f)
+
+        for item in config.get("models", []):
+            pricing = ModelPricing.from_dict(item)
+            if "*" in pricing.model:
+                self._patterns.append(pricing)
+            else:
+                self._pricing_map[pricing.model.lower()] = pricing
+
+    def _load_defaults(self) -> None:
+        """加载内置默认定价"""
+        defaults = self._get_default_pricing()
+        for item in defaults:
+            pricing = ModelPricing.from_dict(item)
+            if "*" in pricing.model:
+                self._patterns.append(pricing)
+            else:
+                self._pricing_map[pricing.model.lower()] = pricing
+
+    def _get_default_pricing(self) -> List[Dict[str, Any]]:
+        """
+        内置默认定价表
+
+        价格来源：各提供商官网（2024-12 更新）
+        单位：美元 / 1M tokens
+        """
+        return [
+            # ===== OpenAI =====
+            {"model": "gpt-4o", "input_price": 2.50, "output_price": 10.00, "provider": "openai"},
+            {"model": "gpt-4o-mini", "input_price": 0.15, "output_price": 0.60, "provider": "openai"},
+            {"model": "gpt-4-turbo", "input_price": 10.00, "output_price": 30.00, "provider": "openai"},
+            {"model": "gpt-4", "input_price": 30.00, "output_price": 60.00, "provider": "openai"},
+            {"model": "gpt-3.5-turbo", "input_price": 0.50, "output_price": 1.50, "provider": "openai"},
+            # o1/o3 系列（reasoning tokens 单独计价）
+            {"model": "o1", "input_price": 15.00, "output_price": 60.00, "reasoning_price": 60.00, "provider": "openai"},
+            {"model": "o1-mini", "input_price": 3.00, "output_price": 12.00, "reasoning_price": 12.00, "provider": "openai"},
+            {"model": "o1-preview", "input_price": 15.00, "output_price": 60.00, "reasoning_price": 60.00, "provider": "openai"},
+            {"model": "o3-mini", "input_price": 1.10, "output_price": 4.40, "reasoning_price": 4.40, "provider": "openai"},
+
+            # ===== Anthropic Claude =====
+            {"model": "claude-3-5-sonnet-20241022", "input_price": 3.00, "output_price": 15.00, "provider": "anthropic"},
+            {"model": "claude-3-5-haiku-20241022", "input_price": 0.80, "output_price": 4.00, "provider": "anthropic"},
+            {"model": "claude-3-opus-20240229", "input_price": 15.00, "output_price": 75.00, "provider": "anthropic"},
+            {"model": "claude-3-sonnet-20240229", "input_price": 3.00, "output_price": 15.00, "provider": "anthropic"},
+            {"model": "claude-3-haiku-20240307", "input_price": 0.25, "output_price": 1.25, "provider": "anthropic"},
+            # Claude 通配符
+            {"model": "claude-3-5-sonnet*", "input_price": 3.00, "output_price": 15.00, "provider": "anthropic"},
+            {"model": "claude-3-opus*", "input_price": 15.00, "output_price": 75.00, "provider": "anthropic"},
+            {"model": "claude-sonnet-4*", "input_price": 3.00, "output_price": 15.00, "provider": "anthropic"},
+            {"model": "claude-opus-4*", "input_price": 15.00, "output_price": 75.00, "provider": "anthropic"},
+
+            # ===== Google Gemini =====
+            {"model": "gemini-2.0-flash", "input_price": 0.10, "output_price": 0.40, "provider": "google"},
+            {"model": "gemini-2.0-flash-thinking", "input_price": 0.10, "output_price": 0.40, "reasoning_price": 0.40, "provider": "google"},
+            {"model": "gemini-1.5-pro", "input_price": 1.25, "output_price": 5.00, "provider": "google"},
+            {"model": "gemini-1.5-flash", "input_price": 0.075, "output_price": 0.30, "provider": "google"},
+            {"model": "gemini-2.5-pro", "input_price": 1.25, "output_price": 10.00, "reasoning_price": 10.00, "provider": "google"},
+            # Gemini 通配符
+            {"model": "gemini-2.0*", "input_price": 0.10, "output_price": 0.40, "provider": "google"},
+            {"model": "gemini-1.5*", "input_price": 1.25, "output_price": 5.00, "provider": "google"},
+            {"model": "gemini-2.5*", "input_price": 1.25, "output_price": 10.00, "provider": "google"},
+
+            # ===== DeepSeek =====
+            {"model": "deepseek-chat", "input_price": 0.14, "output_price": 0.28, "provider": "deepseek"},
+            {"model": "deepseek-reasoner", "input_price": 0.55, "output_price": 2.19, "reasoning_price": 2.19, "provider": "deepseek"},
+            {"model": "deepseek-r1*", "input_price": 0.55, "output_price": 2.19, "reasoning_price": 2.19, "provider": "deepseek"},
+
+            # ===== OpenRouter 转发（使用原模型价格）=====
+            {"model": "anthropic/claude-3-5-sonnet", "input_price": 3.00, "output_price": 15.00, "provider": "openrouter"},
+            {"model": "anthropic/claude-3-opus", "input_price": 15.00, "output_price": 75.00, "provider": "openrouter"},
+            {"model": "anthropic/claude-sonnet-4*", "input_price": 3.00, "output_price": 15.00, "provider": "openrouter"},
+            {"model": "anthropic/claude-opus-4*", "input_price": 15.00, "output_price": 75.00, "provider": "openrouter"},
+            {"model": "openai/gpt-4o", "input_price": 2.50, "output_price": 10.00, "provider": "openrouter"},
+            {"model": "openai/o1*", "input_price": 15.00, "output_price": 60.00, "reasoning_price": 60.00, "provider": "openrouter"},
+            {"model": "google/gemini*", "input_price": 1.25, "output_price": 5.00, "provider": "openrouter"},
+            {"model": "deepseek/deepseek-r1*", "input_price": 0.55, "output_price": 2.19, "reasoning_price": 2.19, "provider": "openrouter"},
+        ]
+
+    def get_pricing(self, model: str) -> Optional[ModelPricing]:
+        """
+        获取模型定价
+
+        Args:
+            model: 模型名称
+
+        Returns:
+            ModelPricing 或 None（未找到）
+        """
+        model_lower = model.lower()
+
+        # 精确匹配
+        if model_lower in self._pricing_map:
+            return self._pricing_map[model_lower]
+
+        # 通配符匹配
+        for pattern in self._patterns:
+            if pattern.matches(model):
+                return pattern
+
+        return None
+
+    def calculate_cost(
+        self,
+        model: str,
+        usage: TokenUsage,
+        fallback_input_price: float = 1.0,
+        fallback_output_price: float = 2.0
+    ) -> float:
+        """
+        计算费用
+
+        Args:
+            model: 模型名称
+            usage: Token 使用量
+            fallback_input_price: 未找到定价时的默认输入价格
+            fallback_output_price: 未找到定价时的默认输出价格
+
+        Returns:
+            费用（美元）
+        """
+        pricing = self.get_pricing(model)
+
+        if pricing:
+            return pricing.calculate_cost(usage)
+
+        # 使用 fallback 价格
+        fallback = ModelPricing(
+            model=model,
+            input_price=fallback_input_price,
+            output_price=fallback_output_price
+        )
+        return fallback.calculate_cost(usage)
+
+    def add_pricing(self, pricing: ModelPricing) -> None:
+        """动态添加定价"""
+        if "*" in pricing.model:
+            self._patterns.append(pricing)
+        else:
+            self._pricing_map[pricing.model.lower()] = pricing
+
+    def list_models(self) -> List[str]:
+        """列出所有已配置的模型"""
+        models = list(self._pricing_map.keys())
+        models.extend(p.model for p in self._patterns)
+        return sorted(models)
+
+
+# 全局单例
+_calculator: Optional[PricingCalculator] = None
+
+
+def get_pricing_calculator() -> PricingCalculator:
+    """获取全局定价计算器"""
+    global _calculator
+    if _calculator is None:
+        _calculator = PricingCalculator()
+    return _calculator
+
+
+def calculate_cost(model: str, usage: TokenUsage) -> float:
+    """
+    便捷函数：计算费用
+
+    Args:
+        model: 模型名称
+        usage: Token 使用量
+
+    Returns:
+        费用（美元）
+    """
+    return get_pricing_calculator().calculate_cost(model, usage)

agent/llm/usage.py

@@ -0,0 +1,297 @@
+"""
+Token Usage 数据模型和费用计算
+
+支持各种 LLM 提供商的完整 token 统计：
+- 基础 tokens: input/output
+- 思考 tokens: reasoning/thinking (OpenAI o1/o3, DeepSeek R1, Gemini 2.x)
+- 缓存 tokens: cache_creation/cache_read (Claude)
+- 其他: cached_content (Gemini)
+
+设计模式：
+- TokenUsage: 不可变数据类，表示单次调用的 token 使用
+- TokenUsageAccumulator: 累加器，用于统计多次调用
+- PricingCalculator: 策略模式，根据定价表计算费用
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, Any, Optional
+import copy
+
+
+@dataclass(frozen=True)
+class TokenUsage:
+    """
+    Token 使用量（不可变）
+
+    统一所有提供商的 token 统计字段，未使用的字段为 0
+    """
+    # 基础 tokens（所有提供商都有）
+    input_tokens: int = 0           # 输入 tokens (prompt_tokens)
+    output_tokens: int = 0          # 输出 tokens (completion_tokens)
+
+    # 思考/推理 tokens（部分模型）
+    # - OpenAI o1/o3: reasoning_tokens (在 completion_tokens_details 中)
+    # - DeepSeek R1: reasoning_tokens
+    # - Gemini 2.x thinking mode: thoughts_tokens
+    reasoning_tokens: int = 0
+
+    # 缓存相关 tokens（Claude）
+    # - cache_creation_input_tokens: 创建缓存消耗的 tokens
+    # - cache_read_input_tokens: 读取缓存的 tokens（通常更便宜）
+    cache_creation_tokens: int = 0
+    cache_read_tokens: int = 0
+
+    # Gemini 特有
+    cached_content_tokens: int = 0  # cachedContentTokenCount
+
+    @property
+    def total_tokens(self) -> int:
+        """总 tokens（input + output，不含 reasoning）"""
+        return self.input_tokens + self.output_tokens
+
+    @property
+    def total_input_tokens(self) -> int:
+        """
+        总输入 tokens
+
+        对于 Claude 带缓存的情况：
+        实际输入 = input_tokens（已包含 cache_read）
+        计费输入 = input_tokens - cache_read_tokens + cache_creation_tokens
+        """
+        return self.input_tokens
+
+    @property
+    def total_output_tokens(self) -> int:
+        """
+        总输出 tokens
+
+        对于有 reasoning 的模型：
+        output_tokens 通常已包含 reasoning_tokens
+        """
+        return self.output_tokens
+
+    @property
+    def billable_input_tokens(self) -> int:
+        """
+        计费输入 tokens（考虑缓存折扣）
+
+        Claude 缓存定价：
+        - cache_read: 0.1x 价格
+        - cache_creation: 1.25x 价格
+        - 普通 input: 1x 价格
+
+        这里返回等效的全价 tokens 数
+        """
+        # 普通输入 = 总输入 - 缓存读取
+        regular_input = self.input_tokens - self.cache_read_tokens
+        # 等效计费 = 普通输入 + 缓存读取*0.1 + 缓存创建*1.25
+        # 简化：返回原始值，让 PricingCalculator 处理
+        return self.input_tokens
+
+    def __add__(self, other: "TokenUsage") -> "TokenUsage":
+        """支持 + 运算符累加"""
+        if not isinstance(other, TokenUsage):
+            return NotImplemented
+        return TokenUsage(
+            input_tokens=self.input_tokens + other.input_tokens,
+            output_tokens=self.output_tokens + other.output_tokens,
+            reasoning_tokens=self.reasoning_tokens + other.reasoning_tokens,
+            cache_creation_tokens=self.cache_creation_tokens + other.cache_creation_tokens,
+            cache_read_tokens=self.cache_read_tokens + other.cache_read_tokens,
+            cached_content_tokens=self.cached_content_tokens + other.cached_content_tokens,
+        )
+
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典（只包含非零字段）"""
+        result = {
+            "input_tokens": self.input_tokens,
+            "output_tokens": self.output_tokens,
+            "total_tokens": self.total_tokens,
+        }
+        # 只添加非零的可选字段
+        if self.reasoning_tokens:
+            result["reasoning_tokens"] = self.reasoning_tokens
+        if self.cache_creation_tokens:
+            result["cache_creation_tokens"] = self.cache_creation_tokens
+        if self.cache_read_tokens:
+            result["cache_read_tokens"] = self.cache_read_tokens
+        if self.cached_content_tokens:
+            result["cached_content_tokens"] = self.cached_content_tokens
+        return result
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "TokenUsage":
+        """从字典创建（兼容旧格式）"""
+        return cls(
+            input_tokens=data.get("input_tokens") or data.get("prompt_tokens", 0),
+            output_tokens=data.get("output_tokens") or data.get("completion_tokens", 0),
+            reasoning_tokens=data.get("reasoning_tokens", 0),
+            cache_creation_tokens=data.get("cache_creation_tokens", 0),
+            cache_read_tokens=data.get("cache_read_tokens", 0),
+            cached_content_tokens=data.get("cached_content_tokens", 0),
+        )
+
+    @classmethod
+    def from_openai(cls, usage: Dict[str, Any]) -> "TokenUsage":
+        """
+        从 OpenAI 格式创建
+
+        OpenAI 格式：
+        {
+            "prompt_tokens": 100,
+            "completion_tokens": 50,
+            "total_tokens": 150,
+            "completion_tokens_details": {
+                "reasoning_tokens": 20  # o1/o3 模型
+            }
+        }
+        """
+        reasoning = 0
+        if details := usage.get("completion_tokens_details"):
+            reasoning = details.get("reasoning_tokens", 0)
+
+        return cls(
+            input_tokens=usage.get("prompt_tokens", 0),
+            output_tokens=usage.get("completion_tokens", 0),
+            reasoning_tokens=reasoning,
+        )
+
+    @classmethod
+    def from_anthropic(cls, usage: Dict[str, Any]) -> "TokenUsage":
+        """
+        从 Anthropic/Claude 格式创建
+
+        Claude 格式：
+        {
+            "input_tokens": 100,
+            "output_tokens": 50,
+            "cache_creation_input_tokens": 1000,  # 可选
+            "cache_read_input_tokens": 500        # 可选
+        }
+        """
+        return cls(
+            input_tokens=usage.get("input_tokens", 0),
+            output_tokens=usage.get("output_tokens", 0),
+            cache_creation_tokens=usage.get("cache_creation_input_tokens", 0),
+            cache_read_tokens=usage.get("cache_read_input_tokens", 0),
+        )
+
+    @classmethod
+    def from_gemini(cls, usage_metadata: Dict[str, Any]) -> "TokenUsage":
+        """
+        从 Gemini 格式创建
+
+        Gemini 格式：
+        {
+            "promptTokenCount": 100,
+            "candidatesTokenCount": 50,
+            "totalTokenCount": 150,
+            "cachedContentTokenCount": 0,    # 可选
+            "thoughtsTokenCount": 20          # Gemini 2.x thinking mode
+        }
+        """
+        return cls(
+            input_tokens=usage_metadata.get("promptTokenCount", 0),
+            output_tokens=usage_metadata.get("candidatesTokenCount", 0),
+            reasoning_tokens=usage_metadata.get("thoughtsTokenCount", 0),
+            cached_content_tokens=usage_metadata.get("cachedContentTokenCount", 0),
+        )
+
+    @classmethod
+    def from_deepseek(cls, usage: Dict[str, Any]) -> "TokenUsage":
+        """
+        从 DeepSeek 格式创建
+
+        DeepSeek R1 格式（OpenAI 兼容 + 扩展）：
+        {
+            "prompt_tokens": 100,
+            "completion_tokens": 50,
+            "reasoning_tokens": 30,  # DeepSeek R1 特有
+            "total_tokens": 150
+        }
+        """
+        return cls(
+            input_tokens=usage.get("prompt_tokens", 0),
+            output_tokens=usage.get("completion_tokens", 0),
+            reasoning_tokens=usage.get("reasoning_tokens", 0),
+        )
+
+
+class TokenUsageAccumulator:
+    """
+    Token 使用量累加器
+
+    用于在 Trace 级别累计多次 LLM 调用的 token 使用
+    """
+
+    def __init__(self):
+        self._input_tokens: int = 0
+        self._output_tokens: int = 0
+        self._reasoning_tokens: int = 0
+        self._cache_creation_tokens: int = 0
+        self._cache_read_tokens: int = 0
+        self._cached_content_tokens: int = 0
+        self._call_count: int = 0
+
+    def add(self, usage: TokenUsage) -> None:
+        """累加一次调用的 token 使用"""
+        self._input_tokens += usage.input_tokens
+        self._output_tokens += usage.output_tokens
+        self._reasoning_tokens += usage.reasoning_tokens
+        self._cache_creation_tokens += usage.cache_creation_tokens
+        self._cache_read_tokens += usage.cache_read_tokens
+        self._cached_content_tokens += usage.cached_content_tokens
+        self._call_count += 1
+
+    @property
+    def total(self) -> TokenUsage:
+        """获取累计的 TokenUsage"""
+        return TokenUsage(
+            input_tokens=self._input_tokens,
+            output_tokens=self._output_tokens,
+            reasoning_tokens=self._reasoning_tokens,
+            cache_creation_tokens=self._cache_creation_tokens,
+            cache_read_tokens=self._cache_read_tokens,
+            cached_content_tokens=self._cached_content_tokens,
+        )
+
+    @property
+    def call_count(self) -> int:
+        """调用次数"""
+        return self._call_count
+
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典"""
+        result = self.total.to_dict()
+        result["call_count"] = self._call_count
+        return result
+
+
+# 向后兼容的别名
+def create_usage_from_response(
+    provider: str,
+    usage_data: Dict[str, Any]
+) -> TokenUsage:
+    """
+    根据提供商创建 TokenUsage
+
+    Args:
+        provider: 提供商名称 ("openai", "anthropic", "gemini", "deepseek", "openrouter")
+        usage_data: API 返回的 usage 数据
+
+    Returns:
+        TokenUsage 实例
+    """
+    provider = provider.lower()
+
+    if provider in ("openai", "openrouter"):
+        return TokenUsage.from_openai(usage_data)
+    elif provider in ("anthropic", "claude"):
+        return TokenUsage.from_anthropic(usage_data)
+    elif provider == "gemini":
+        return TokenUsage.from_gemini(usage_data)
+    elif provider == "deepseek":
+        return TokenUsage.from_deepseek(usage_data)
+    else:
+        # 默认使用 OpenAI 格式
+        return TokenUsage.from_openai(usage_data)

agent/tools/builtin/__init__.py

@@ -22,6 +22,8 @@ from agent.tools.builtin.sandbox import (sandbox_create_environment, sandbox_run
 # 导入浏览器工具以触发注册
 import agent.tools.builtin.browser  # noqa: F401
 
+import agent.tools.builtin.feishu
+
 __all__ = [
     # 文件操作
     "read_file",

agent/tools/builtin/feishu/__init__.py

@@ -0,0 +1,9 @@
+from agent.tools.builtin.feishu.chat import (feishu_get_chat_history, feishu_get_contact_replies,
+                                         feishu_send_message_to_contact,feishu_get_contact_list)
+
+__all__ = [
+    "feishu_get_chat_history",
+    "feishu_get_contact_replies",
+    "feishu_send_message_to_contact",
+    "feishu_get_contact_list"
+]

agent/tools/builtin/feishu/chat.py

@@ -0,0 +1,486 @@
+import json
+import os
+import base64
+import httpx
+import asyncio
+from typing import Optional, List, Dict, Any, Union
+from .feishu_client import FeishuClient, FeishuDomain
+from agent.tools import tool, ToolResult, ToolContext
+
+# 从环境变量获取飞书配置
+# 也可以在此设置硬编码的默认值，但推荐使用环境变量
+FEISHU_APP_ID = os.getenv("FEISHU_APP_ID", "cli_a90fe317987a9cc9")
+FEISHU_APP_SECRET = os.getenv("FEISHU_APP_SECRET", "nn2dWuXTiRA2N6xodbm4g0qz1AfM2ayi")
+
+CONTACTS_FILE = os.path.join(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", "..")), "config", "feishu_contacts.json")
+CHAT_HISTORY_DIR = os.path.join(os.path.dirname(__file__), "chat_history")
+UNREAD_SUMMARY_FILE = os.path.join(CHAT_HISTORY_DIR, "chat_summary.json")
+
+# ==================== 一、文件内使用的功能函数 ====================
+
+def load_contacts() -> List[Dict[str, Any]]:
+    """读取 contacts.json 中的所有联系人"""
+    if not os.path.exists(CONTACTS_FILE):
+        return []
+    try:
+        with open(CONTACTS_FILE, 'r', encoding='utf-8') as f:
+            return json.load(f)
+    except Exception:
+        return []
+
+def save_contacts(contacts: List[Dict[str, Any]]):
+    """保存联系人信息到 contacts.json"""
+    try:
+        with open(CONTACTS_FILE, 'w', encoding='utf-8') as f:
+            json.dump(contacts, f, ensure_ascii=False, indent=2)
+    except Exception as e:
+        print(f"保存联系人失败: {e}")
+
+def list_contacts_info() -> List[Dict[str, str]]:
+    """
+    1. 列出所有联系人信息
+    读取 contacts.json 中的每一个联系人的 name、description，以字典列表返回
+    """
+    contacts = load_contacts()
+    return [{"name": c.get("name", ""), "description": c.get("description", "")} for c in contacts]
+
+def get_contact_full_info(name: str) -> Optional[Dict[str, Any]]:
+    """
+    2. 根据联系人名称获取联系人完整字典信息
+    从 contacts.json 中读取每一个联系人做名称匹配，返回数据中的所有字段为一个字典对象
+    """
+    contacts = load_contacts()
+    for c in contacts:
+        if c.get("name") == name:
+            return c
+    return None
+
+def get_contact_by_id(id_value: str) -> Optional[Dict[str, Any]]:
+    """根据 chat_id 或 open_id 获取联系人信息"""
+    contacts = load_contacts()
+    for c in contacts:
+        if c.get("chat_id") == id_value or c.get("open_id") == id_value:
+            return c
+    return None
+
+def update_contact_chat_id(name: str, chat_id: str):
+    """
+    3. 更新某一个联系人的 chat_id
+    """
+    contacts = load_contacts()
+    updated = False
+    for c in contacts:
+        if c.get("name") == name:
+            if not c.get("chat_id"):
+                c["chat_id"] = chat_id
+                updated = True
+            break
+    if updated:
+        save_contacts(contacts)
+
+# ==================== 二、聊天记录文件管理 ====================
+
+def _ensure_chat_history_dir():
+    if not os.path.exists(CHAT_HISTORY_DIR):
+        os.makedirs(CHAT_HISTORY_DIR)
+
+def get_chat_file_path(contact_name: str) -> str:
+    _ensure_chat_history_dir()
+    return os.path.join(CHAT_HISTORY_DIR, f"chat_{contact_name}.json")
+
+def load_chat_history(contact_name: str) -> List[Dict[str, Any]]:
+    path = get_chat_file_path(contact_name)
+    if os.path.exists(path):
+        try:
+            with open(path, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        except Exception:
+            return []
+    return []
+
+def save_chat_history(contact_name: str, history: List[Dict[str, Any]]):
+    path = get_chat_file_path(contact_name)
+    try:
+        with open(path, 'w', encoding='utf-8') as f:
+            json.dump(history, f, ensure_ascii=False, indent=2)
+    except Exception as e:
+        print(f"保存聊天记录失败: {e}")
+
+def update_unread_count(contact_name: str, increment: int = 1, reset: bool = False):
+    """更新未读消息摘要"""
+    _ensure_chat_history_dir()
+    summary = {}
+    if os.path.exists(UNREAD_SUMMARY_FILE):
+        try:
+            with open(UNREAD_SUMMARY_FILE, 'r', encoding='utf-8') as f:
+                summary = json.load(f)
+        except Exception:
+            summary = {}
+    
+    if reset:
+        summary[contact_name] = 0
+    else:
+        summary[contact_name] = summary.get(contact_name, 0) + increment
+    
+    try:
+        with open(UNREAD_SUMMARY_FILE, 'w', encoding='utf-8') as f:
+            json.dump(summary, f, ensure_ascii=False, indent=2)
+    except Exception as e:
+        print(f"更新未读摘要失败: {e}")
+
+# ==================== 三、@tool 工具 ====================
+
+@tool(
+    display={
+        "zh": {
+            "name": "获取飞书联系人列表",
+            "params": {}
+        },
+        "en": {
+            "name": "Get Feishu Contact List",
+            "params": {}
+        }
+    }
+)
+async def feishu_get_contact_list(context: Optional[ToolContext] = None) -> ToolResult:
+    """
+    获取所有联系人的名称和描述。
+
+    Args:
+        context: 工具执行上下文（可选）
+    """
+    contacts = list_contacts_info()
+    return ToolResult(
+        title="获取联系人列表成功",
+        output=json.dumps(contacts, ensure_ascii=False, indent=2),
+        metadata={"contacts": contacts}
+    )
+
+@tool(
+    display={
+        "zh": {
+            "name": "给飞书联系人发送消息",
+            "params": {
+                "contact_name": "联系人名称",
+                "content": "消息内容。OpenAI 多模态格式列表 (例如: [{'type': 'text', 'text': '你好'}, {'type': 'image_url', 'image_url': {'url': '...'}}])"
+            }
+        },
+        "en": {
+            "name": "Send Message to Feishu Contact",
+            "params": {
+                "contact_name": "Contact Name",
+                "content": "Message content. OpenAI multimodal list format."
+            }
+        }
+    }
+)
+async def feishu_send_message_to_contact(
+    contact_name: str,
+    content: Any,
+    context: Optional[ToolContext] = None
+) -> ToolResult:
+    """
+    给指定的联系人发送消息。支持发送文本和图片，OpenAI 多模态格式，会自动转换为飞书相应的格式并发起多次发送。
+
+    Args:
+        contact_name: 飞书联系人的名称
+        content: 消息内容。OpenAI 多模态列表格式。
+    """
+    contact = get_contact_full_info(contact_name)
+    if not contact:
+        return ToolResult(title="发送失败", output=f"未找到联系人: {contact_name}", error="Contact not found")
+
+    client = FeishuClient(app_id=FEISHU_APP_ID, app_secret=FEISHU_APP_SECRET)
+    
+    # 确定接收者 ID (优先使用 chat_id，否则使用 open_id)
+    receive_id = contact.get("chat_id") or contact.get("open_id") or contact.get("user_id")
+    if not receive_id:
+        return ToolResult(title="发送失败", output="联系人 ID 信息缺失", error="Receiver ID not found in contacts.json")
+
+    # 如果 content 是字符串，尝试解析为 JSON
+    if isinstance(content, str):
+        try:
+            parsed = json.loads(content)
+            if isinstance(parsed, (list, dict)):
+                content = parsed
+        except (json.JSONDecodeError, TypeError):
+            pass
+
+    try:
+        last_res = None
+        if isinstance(content, str):
+            last_res = client.send_message(to=receive_id, text=content)
+        elif isinstance(content, list):
+            for item in content:
+                item_type = item.get("type")
+                if item_type == "text":
+                    last_res = client.send_message(to=receive_id, text=item.get("text", ""))
+                elif item_type == "image_url":
+                    img_info = item.get("image_url", {})
+                    url = img_info.get("url")
+                    if url.startswith("data:image"):
+                        # 处理 base64 图片
+                        try:
+                            if "," in url:
+                                _, encoded = url.split(",", 1)
+                            else:
+                                encoded = url
+                            image_bytes = base64.b64decode(encoded)
+                            last_res = client.send_image(to=receive_id, image=image_bytes)
+                        except Exception as e:
+                            print(f"解析 base64 图片失败: {e}")
+                    else:
+                        # 处理网络 URL
+                        try:
+                            async with httpx.AsyncClient() as httpx_client:
+                                img_resp = await httpx_client.get(url, timeout=15.0)
+                                img_resp.raise_for_status()
+                                last_res = client.send_image(to=receive_id, image=img_resp.content)
+                        except Exception as e:
+                            print(f"下载图片失败: {e}")
+        elif isinstance(content, dict):
+            # 如果是单块格式也支持一下
+            item_type = content.get("type")
+            if item_type == "text":
+                last_res = client.send_message(to=receive_id, text=content.get("text", ""))
+            elif item_type == "image_url":
+                # ... 逻辑与上面类似，为了简洁这里也可以统一转成 list 处理
+                content = [content]
+                # 此处递归或重写逻辑，这里选择简单地重新判断
+                return await feishu_send_message_to_contact(contact_name, content, context)
+        else:
+            return ToolResult(title="发送失败", output="不支持的内容格式", error="Invalid content format")
+
+        if last_res:
+            # 更新 chat_id
+            update_contact_chat_id(contact_name, last_res.chat_id)
+
+            # [待开启] 发送即记录：为了维护完整的聊天记录，将机器人发出的消息也保存到本地文件
+            try:
+                history = load_chat_history(contact_name)
+                history.append({
+                    "role": "assistant",
+                    "message_id": last_res.message_id,
+                    "content": content if isinstance(content, list) else [{"type": "text", "text": content}]
+                })
+                save_chat_history(contact_name, history)
+                # 机器人回复了，将该联系人的未读计数重置为 0
+                update_unread_count(contact_name, reset=True)
+            except Exception as e:
+                print(f"记录发送的消息失败: {e}")
+
+            return ToolResult(
+                title=f"消息已成功发送至 {contact_name}",
+                output=f"发送成功。消息 ID: {last_res.message_id}",
+                metadata={"message_id": last_res.message_id, "chat_id": last_res.chat_id}
+            )
+        return ToolResult(title="发送失败", output="没有执行成功的发送操作")
+    except Exception as e:
+        return ToolResult(title="发送异常", output=str(e), error=str(e))
+
+@tool(
+    display={
+        "zh": {
+            "name": "获取飞书联系人回复",
+            "params": {
+                "contact_name": "联系人名称",
+                "wait_time_seconds": "可选，如果当前没有新回复，则最多等待指定的秒数。在等待期间会每秒检查一次，一旦有新回复则立即返回。超过时长仍无回复则返回空。"
+            }
+        },
+        "en": {
+            "name": "Get Feishu Contact Replies",
+            "params": {
+                "contact_name": "Contact Name",
+                "wait_time_seconds": "Optional. If there are no new replies, wait up to the specified number of seconds. It will check every second and return immediately if a new reply is detected. If no reply is received after the duration, it returns empty."
+            }
+        }
+    }
+)
+async def feishu_get_contact_replies(
+    contact_name: str,
+    wait_time_seconds: Optional[int] = None,
+    context: Optional[ToolContext] = None
+) -> ToolResult:
+    """
+    获取指定联系人的最新回复消息。
+    返回的数据格式为 OpenAI 多模态消息内容列表。
+    只抓取自上一个机器人消息之后的用户回复。
+
+    Args:
+        contact_name: 飞书联系人的名称
+        wait_time_seconds: 可选的最大轮询等待时间。如果暂时没有新回复，将每秒检查一次直到有回复或超时。
+        context: 工具执行上下文（可选）
+    """
+    contact = get_contact_full_info(contact_name)
+    if not contact:
+        return ToolResult(title="获取失败", output=f"未找到联系人: {contact_name}", error="Contact not found")
+
+    chat_id = contact.get("chat_id")
+    if not chat_id:
+        return ToolResult(title="获取失败", output=f"联系人 {contact_name} 尚未建立会话 (无 chat_id)", error="No chat_id")
+
+    client = FeishuClient(app_id=FEISHU_APP_ID, app_secret=FEISHU_APP_SECRET)
+    
+    try:
+        def get_replies():
+            msg_list_res = client.get_message_list(chat_id=chat_id)
+            if not msg_list_res or "items" not in msg_list_res:
+                return []
+
+            openai_blocks = []
+            # 遍历消息列表 (最新的在前)
+            for msg in msg_list_res["items"]:
+                if msg.get("sender_type") == "app":
+                    # 碰到机器人的消息即停止
+                    break
+                
+                content_blocks = _convert_feishu_msg_to_openai_content(client, msg)
+                openai_blocks.extend(content_blocks)
+
+            # 反转列表以保持时间正序 (旧 -> 新)
+            openai_blocks.reverse()
+            return openai_blocks
+
+        openai_blocks = get_replies()
+        
+        # 如果初始没有获取到回复，且设置了等待时间，则开始轮询
+        if not openai_blocks and wait_time_seconds and wait_time_seconds > 0:
+            for _ in range(int(wait_time_seconds)):
+                await asyncio.sleep(1)
+                openai_blocks = get_replies()
+                if openai_blocks:
+                    break
+
+        return ToolResult(
+            title=f"获取 {contact_name} 回复成功",
+            output=json.dumps(openai_blocks, ensure_ascii=False, indent=2) if openai_blocks else "目前没有新的用户回复",
+            metadata={"replies": openai_blocks}
+        )
+    except Exception as e:
+        return ToolResult(title="获取回复异常", output=str(e), error=str(e))
+
+def _convert_feishu_msg_to_openai_content(client: FeishuClient, msg: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """将单条飞书消息内容转换为 OpenAI 多模态格式块列表"""
+    blocks = []
+    msg_type = msg.get("content_type")
+    raw_content = msg.get("content", "")
+    message_id = msg.get("message_id")
+
+    if msg_type == "text":
+        blocks.append({"type": "text", "text": raw_content})
+    elif msg_type == "image":
+        try:
+            content_dict = json.loads(raw_content)
+            image_key = content_dict.get("image_key")
+            if image_key and message_id:
+                img_bytes = client.download_message_resource(
+                    message_id=message_id,
+                    file_key=image_key,
+                    resource_type="image"
+                )
+                b64_str = base64.b64encode(img_bytes).decode('utf-8')
+                blocks.append({
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/png;base64,{b64_str}"}
+                })
+        except Exception as e:
+            print(f"转换图片消息失败: {e}")
+            blocks.append({"type": "text", "text": "[图片内容获取失败]"})
+    elif msg_type == "post":
+        blocks.append({"type": "text", "text": raw_content})
+    else:
+        blocks.append({"type": "text", "text": f"[{msg_type} 消息]: {raw_content}"})
+    
+    return blocks
+
+@tool(
+    display={
+        "zh": {
+            "name": "获取飞书聊天历史记录",
+            "params": {
+                "contact_name": "联系人名称",
+                "start_time": "起始时间戳 (秒)，可选",
+                "end_time": "结束时间戳 (秒)，可选",
+                "page_size": "分页大小，默认 20",
+                "page_token": "分页令牌，用于加载下一页，可选"
+            }
+        },
+        "en": {
+            "name": "Get Feishu Chat History",
+            "params": {
+                "contact_name": "Contact Name",
+                "start_time": "Start timestamp (seconds), optional",
+                "end_time": "End timestamp (seconds), optional",
+                "page_size": "Page size, default 20",
+                "page_token": "Page token for next page, optional"
+            }
+        }
+    }
+)
+async def feishu_get_chat_history(
+    contact_name: str,
+    start_time: Optional[int] = None,
+    end_time: Optional[int] = None,
+    page_size: int = 20,
+    page_token: Optional[str] = None,
+    context: Optional[ToolContext] = None
+) -> ToolResult:
+    """
+    根据联系人名称获取完整的历史聊天记录。
+    支持通过时间戳进行范围筛选，并支持分页获取。
+    返回的消息按时间倒序排列（最新的在前面）。
+
+    Args:
+        contact_name: 飞书联系人的名称
+        start_time: 筛选起始时间的时间戳（秒），可选
+        end_time: 筛选结束时间的时间戳（秒），可选
+        page_size: 每页消息数量，默认为 20
+        page_token: 分页令牌，用于加载上一页/下一页，可选
+        context: 工具执行上下文（可选）
+    """
+    contact = get_contact_full_info(contact_name)
+    if not contact:
+        return ToolResult(title="获取历史失败", output=f"未找到联系人: {contact_name}", error="Contact not found")
+
+    chat_id = contact.get("chat_id")
+    if not chat_id:
+        return ToolResult(title="获取历史失败", output=f"联系人 {contact_name} 尚未建立会话 (无 chat_id)", error="No chat_id")
+
+    client = FeishuClient(app_id=FEISHU_APP_ID, app_secret=FEISHU_APP_SECRET)
+
+    try:
+        res = client.get_message_list(
+            chat_id=chat_id,
+            start_time=start_time,
+            end_time=end_time,
+            page_size=page_size,
+            page_token=page_token
+        )
+
+        if not res or "items" not in res:
+            return ToolResult(title="获取历史失败", output="请求接口失败或返回为空")
+
+        # 将所有消息转换为 OpenAI 多模态格式
+        formatted_messages = []
+        for msg in res["items"]:
+            formatted_messages.append({
+                "message_id": msg.get("message_id"),
+                "sender_id": msg.get("sender_id"),
+                "sender_type": "assistant" if msg.get("sender_type") == "app" else "user",
+                "create_time": msg.get("create_time"),
+                "content": _convert_feishu_msg_to_openai_content(client, msg)
+            })
+
+        result_data = {
+            "messages": formatted_messages,
+            "page_token": res.get("page_token"),
+            "has_more": res.get("has_more")
+        }
+
+        return ToolResult(
+            title=f"获取 {contact_name} 历史记录成功",
+            output=json.dumps(result_data, ensure_ascii=False, indent=2),
+            metadata=result_data
+        )
+    except Exception as e:
+        return ToolResult(title="获取历史异常", output=str(e), error=str(e))

agent/tools/builtin/feishu/chat_test.py

@@ -0,0 +1,79 @@
+import asyncio
+import json
+import os
+import sys
+
+# 将项目根目录添加到 python 路径，确保可以正确导入 agent 包
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
+if PROJECT_ROOT not in sys.path:
+    sys.path.append(PROJECT_ROOT)
+
+from agent.tools.builtin.feishu.chat import (
+    feishu_get_contact_list,
+    feishu_send_message_to_contact,
+    feishu_get_contact_replies,
+    feishu_get_chat_history
+)
+
+async def feishu_tools():
+    print("开始测试飞书工具...\n")
+
+    # # 1. 测试获取联系人列表
+    # print("--- 测试: feishu_get_contact_list ---")
+    # result_list = await feishu_get_contact_list()
+    # print(f"标题: {result_list.title}")
+    # print(f"输出: {result_list.output}")
+    # print("-" * 30 + "\n")
+    #
+    # # 2. 测试发送消息 (以 '谭景玉' 为例，请确保 contacts.json 中有此人且信息正确)
+    contact_name = "谭景玉"
+    # print(f"--- 测试: feishu_send_message_to_contact (对象: {contact_name}) ---")
+    #
+    # 测试发送纯文本
+    text_content = "干活"
+    print(f"正在发送文本: {text_content}")
+    result_send_text = await feishu_send_message_to_contact(contact_name, text_content)
+    print(f"标题: {result_send_text.title}")
+    print(f"输出: {result_send_text.output}")
+    if result_send_text.error:
+        print(f"错误: {result_send_text.error}")
+
+    # 测试发送多模态消息 (文本 + 图片)
+    # 注意：这里的图片 URL 需要是一个可访问的地址，或者你可以使用 base64 格式
+    # multimodal_content = [
+    #     {"type": "text", "text": "这是一条多模态测试消息："},
+    #     {"type": "image_url", "image_url": {"url": "https://www.baidu.com/img/flexible/logo/pc/result.png"}}
+    # ]
+    # print(f"\n正在发送多模态消息...")
+    # result_send_multi = await feishu_send_message_to_contact(contact_name, multimodal_content)
+    # print(f"标题: {result_send_multi.title}")
+    # # print(f"输出: {result_send_multi.output}")
+    # if result_send_multi.error:
+    #     print(f"错误: {result_send_multi.error}")
+    # print("-" * 30 + "\n")
+
+    # # 3. 测试获取回复
+    # print(f"--- 测试: feishu_get_contact_replies (对象: {contact_name}) ---")
+    # result_replies = await feishu_get_contact_replies(contact_name)
+    # print(f"标题: {result_replies.title}")
+    # print(f"消息详情: {result_replies.output}")
+    # print("-" * 30 + "\n")
+
+    # # 4. 测试获取历史记录
+    # print(f"--- 测试: feishu_get_chat_history (对象: {contact_name}) ---")
+    # result_history = await feishu_get_chat_history(contact_name, page_size=5, page_token="4cXSlmN7uFAnWWU5yfIGMNvUNrBPLlXZREzLcnvUtOcmK2QFKfwEqfbui_UDsR-y8ne0BkzXABiYTAQASh-n7my_3zQp6o3ERRz0bZ4LB5zMvahf8x7OQoso1rjrMaKM")
+    # print(f"标题: {result_history.title}")
+    # print(f"历史记录输出: {result_history.output}")
+    # print("-" * 30 + "\n")
+
+if __name__ == "__main__":
+    # 模拟环境变量 (如果在系统环境变量中已设置，此处可省略)
+    os.environ["FEISHU_APP_ID"] = "cli_a90fe317987a9cc9"
+    os.environ["FEISHU_APP_SECRET"] = "nn2dWuXTiRA2N6xodbm4g0qz1AfM2ayi"
+    
+    try:
+        asyncio.run(feishu_tools())
+    except KeyboardInterrupt:
+        pass
+    except Exception as e:
+        print(f"测试过程中出现异常: {e}")

agent/tools/builtin/feishu/feishu_client.py

@@ -0,0 +1,945 @@
+"""
+飞书消息处理客户端
+基于 OpenClaw 项目的飞书集成代码整理
+
+依赖安装:
+    pip install lark-oapi websocket-client requests
+
+使用示例:
+    client = FeishuClient(app_id="cli_xxx", app_secret="xxx")
+
+    # 发送消息
+    client.send_message(to="ou_xxx", text="Hello!")
+
+    # 监听消息
+    client.start_websocket(on_message=my_handler)
+"""
+
+import json
+import io
+import logging
+import os
+import tempfile
+import threading
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import lark_oapi as lark
+from lark_oapi.api.contact.v3 import GetUserRequest, GetUserResponse
+from lark_oapi.api.im.v1 import (
+    CreateMessageRequest, CreateMessageRequestBody,
+    ReplyMessageRequest, ReplyMessageRequestBody,
+    GetMessageRequest, GetMessageResponse,
+    PatchMessageRequest, PatchMessageRequestBody,
+    CreateImageRequest, CreateImageRequestBody,
+    GetImageRequest,
+    CreateFileRequest, CreateFileRequestBody,
+    GetMessageResourceRequest, GetMessageResourceResponse,
+    ListMessageRequest, ListMessageResponse
+)
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class FeishuDomain(Enum):
+    """飞书域名"""
+    FEISHU = "https://open.feishu.cn"      # 中国版
+    LARK = "https://open.larksuite.com"    # 国际版
+
+
+class ChatType(Enum):
+    """聊天类型"""
+    P2P = "p2p"      # 私聊
+    GROUP = "group"  # 群聊
+
+
+class ReceiveIdType(Enum):
+    """接收者ID类型"""
+    OPEN_ID = "open_id"
+    USER_ID = "user_id"
+    UNION_ID = "union_id"
+    EMAIL = "email"
+    CHAT_ID = "chat_id"
+
+
+@dataclass
+class FeishuMessageEvent:
+    """飞书消息事件"""
+    message_id: str
+    chat_id: str
+    chat_type: ChatType
+    content: str
+    content_type: str  # text, image, file, post, etc.
+    sender_open_id: str
+    sender_user_id: Optional[str] = None
+    sender_name: Optional[str] = None
+    root_id: Optional[str] = None      # 根消息ID（话题）
+    parent_id: Optional[str] = None    # 父消息ID（回复）
+    mentions: List[Dict] = field(default_factory=list)
+    mentioned_bot: bool = False
+
+
+@dataclass
+class SendResult:
+    """发送结果"""
+    message_id: str
+    chat_id: str
+
+
+class FeishuClient:
+    """
+    飞书客户端
+
+    功能:
+    - 发送/接收消息
+    - 上传/下载媒体文件
+    - WebSocket 实时监听
+    """
+
+    def __init__(
+        self,
+        app_id: str,
+        app_secret: str,
+        domain: FeishuDomain = FeishuDomain.FEISHU,
+        encrypt_key: Optional[str] = None,
+        verification_token: Optional[str] = None,
+    ):
+        """
+        初始化飞书客户端
+
+        Args:
+            app_id: 飞书应用 App ID
+            app_secret: 飞书应用 App Secret
+            domain: 飞书域名 (FEISHU 或 LARK)
+            encrypt_key: 事件加密密钥 (可选)
+            verification_token: 事件验证令牌 (可选)
+        """
+        self.app_id = app_id
+        self.app_secret = app_secret
+        self.domain = domain
+        self.encrypt_key = encrypt_key
+        self.verification_token = verification_token
+
+        # 创建 Lark 客户端
+        self.client = lark.Client.builder() \
+            .app_id(app_id) \
+            .app_secret(app_secret) \
+            .domain(domain.value) \
+            .build()
+
+        # 缓存
+        self._bot_open_id: Optional[str] = None
+        self._sender_name_cache: Dict[str, str] = {}
+
+    # ==================== 消息发送 ====================
+
+    def send_message(
+        self,
+        to: str,
+        text: str,
+        reply_to_message_id: Optional[str] = None,
+        receive_id_type: Optional[ReceiveIdType] = None,
+    ) -> SendResult:
+        """
+        发送文本消息
+
+        Args:
+            to: 接收者ID (open_id, user_id, chat_id 等)
+            text: 消息文本
+            reply_to_message_id: 回复的消息ID (可选)
+            receive_id_type: 接收者ID类型 (可选，自动推断)
+
+        Returns:
+            SendResult: 发送结果
+        """
+        if receive_id_type is None:
+            receive_id_type = self._resolve_receive_id_type(to)
+
+        # 构建富文本消息 (支持 Markdown)
+        content = json.dumps({
+            "zh_cn": {
+                "content": [[{"tag": "md", "text": text}]]
+            }
+        })
+
+        if reply_to_message_id:
+            # 回复消息
+            request = ReplyMessageRequest.builder() \
+                .message_id(reply_to_message_id) \
+                .request_body(ReplyMessageRequestBody.builder()
+                    .content(content)
+                    .msg_type("post")
+                    .build()) \
+                .build()
+
+            response = self.client.im.v1.message.reply(request)
+        else:
+            # 新消息
+            request = CreateMessageRequest.builder() \
+                .receive_id_type(receive_id_type.value) \
+                .request_body(CreateMessageRequestBody.builder()
+                    .receive_id(to)
+                    .content(content)
+                    .msg_type("post")
+                    .build()) \
+                .build()
+
+            response = self.client.im.v1.message.create(request)
+
+        if not response.success():
+            raise Exception(f"发送消息失败: {response.msg} (code: {response.code})")
+
+        return SendResult(
+            message_id=response.data.message_id,
+            chat_id=response.data.chat_id
+        )
+
+    def send_card(
+        self,
+        to: str,
+        card: Dict[str, Any],
+        reply_to_message_id: Optional[str] = None,
+        receive_id_type: Optional[ReceiveIdType] = None,
+    ) -> SendResult:
+        """
+        发送卡片消息 (交互式消息)
+
+        Args:
+            to: 接收者ID
+            card: 卡片内容 (JSON 结构)
+            reply_to_message_id: 回复的消息ID (可选)
+            receive_id_type: 接收者ID类型 (可选)
+
+        Returns:
+            SendResult: 发送结果
+        """
+        if receive_id_type is None:
+            receive_id_type = self._resolve_receive_id_type(to)
+
+        content = json.dumps(card)
+
+        if reply_to_message_id:
+            request = ReplyMessageRequest.builder() \
+                .message_id(reply_to_message_id) \
+                .request_body(ReplyMessageRequestBody.builder()
+                    .content(content)
+                    .msg_type("interactive")
+                    .build()) \
+                .build()
+
+            response = self.client.im.v1.message.reply(request)
+        else:
+            request = CreateMessageRequest.builder() \
+                .receive_id_type(receive_id_type.value) \
+                .request_body(CreateMessageRequestBody.builder()
+                    .receive_id(to)
+                    .content(content)
+                    .msg_type("interactive")
+                    .build()) \
+                .build()
+
+            response = self.client.im.v1.message.create(request)
+
+        if not response.success():
+            raise Exception(f"发送卡片失败: {response.msg}")
+
+        return SendResult(
+            message_id=response.data.message_id,
+            chat_id=response.data.chat_id
+        )
+
+    def send_markdown_card(
+        self,
+        to: str,
+        text: str,
+        reply_to_message_id: Optional[str] = None,
+    ) -> SendResult:
+        """
+        发送 Markdown 卡片 (更好的格式渲染)
+
+        Args:
+            to: 接收者ID
+            text: Markdown 文本
+            reply_to_message_id: 回复的消息ID (可选)
+
+        Returns:
+            SendResult: 发送结果
+        """
+        card = {
+            "config": {"wide_screen_mode": True},
+            "elements": [{"tag": "markdown", "content": text}]
+        }
+        return self.send_card(to, card, reply_to_message_id)
+
+    # ==================== 媒体处理 ====================
+
+    def upload_image(
+            self,
+            image: Union[bytes, str],
+            image_type: str = "message"
+    ) -> str:
+        """
+        上传图片
+        """
+        file_obj = None
+
+        try:
+            # 1. 准备文件对象
+            if isinstance(image, str):
+                # 如果是路径，直接打开
+                file_obj = open(image, "rb")
+            else:
+                # 如果是二进制数据，使用内存文件 (避免写磁盘)
+                file_obj = io.BytesIO(image)
+                # 某些 SDK/API 依赖文件名来判断 Content-Type，我们手动给一个名字
+                # 如果知道真实格式更好，不知道则默认 .png 或 .bin
+                file_obj.name = "upload.png"
+
+                # 2. 构建请求
+            # 注意：这里直接传入 file_obj
+            request = CreateImageRequest.builder() \
+                .request_body(CreateImageRequestBody.builder()
+                              .image_type(image_type)
+                              .image(file_obj)
+                              .build()) \
+                .build()
+
+            # 3. 发起请求
+            response = self.client.im.v1.image.create(request)
+
+            if not response.success():
+                raise Exception(f"上传图片失败: {response.msg}")
+
+            return response.data.image_key
+
+        finally:
+            # 4. 显式关闭文件句柄
+            if file_obj and not isinstance(file_obj, io.BytesIO):
+                file_obj.close()
+
+    def download_image(self, image_key: str) -> bytes:
+        """
+        下载图片
+
+        Args:
+            image_key: 图片 key
+
+        Returns:
+            bytes: 图片数据
+        """
+        request = GetImageRequest.builder() \
+            .image_key(image_key) \
+            .build()
+
+        response = self.client.im.v1.image.get(request)
+
+        if not response.success():
+            raise Exception(f"下载图片失败: {response.msg}")
+
+        return response.file.read()
+
+    def send_image(
+        self,
+        to: str,
+        image: Union[bytes, str],
+        reply_to_message_id: Optional[str] = None,
+    ) -> SendResult:
+        """
+        发送图片消息
+
+        Args:
+            to: 接收者ID
+            image: 图片数据或文件路径
+            reply_to_message_id: 回复的消息ID (可选)
+
+        Returns:
+            SendResult: 发送结果
+        """
+        image_key = self.upload_image(image)
+        content = json.dumps({"image_key": image_key})
+
+        receive_id_type = self._resolve_receive_id_type(to)
+
+        if reply_to_message_id:
+            request = ReplyMessageRequest.builder() \
+                .message_id(reply_to_message_id) \
+                .request_body(ReplyMessageRequestBody.builder()
+                    .content(content)
+                    .msg_type("image")
+                    .build()) \
+                .build()
+
+            response = self.client.im.v1.message.reply(request)
+        else:
+            request = CreateMessageRequest.builder() \
+                .receive_id_type(receive_id_type.value) \
+                .request_body(CreateMessageRequestBody.builder()
+                    .receive_id(to)
+                    .content(content)
+                    .msg_type("image")
+                    .build()) \
+                .build()
+
+            response = self.client.im.v1.message.create(request)
+
+        if not response.success():
+            raise Exception(f"发送图片失败: {response.msg}")
+
+        return SendResult(
+            message_id=response.data.message_id,
+            chat_id=response.data.chat_id
+        )
+
+    def upload_file(
+        self,
+        file: Union[bytes, str],
+        file_name: str,
+        file_type: str = "stream",
+    ) -> str:
+        """
+        上传文件
+
+        Args:
+            file: 文件数据或路径
+            file_name: 文件名
+            file_type: 文件类型 (opus/mp4/pdf/doc/xls/ppt/stream)
+
+        Returns:
+            str: file_key
+        """
+        if isinstance(file, str):
+            with open(file, "rb") as f:
+                file_data = f.read()
+            if not file_name:
+                file_name = os.path.basename(file)
+        else:
+            file_data = file
+
+        with tempfile.NamedTemporaryFile(delete=False) as tmp:
+            tmp.write(file_data)
+            tmp_path = tmp.name
+
+        try:
+            request = CreateFileRequest.builder() \
+                .request_body(CreateFileRequestBody.builder()
+                    .file_type(file_type)
+                    .file_name(file_name)
+                    .file(open(tmp_path, "rb"))
+                    .build()) \
+                .build()
+
+            response = self.client.im.v1.file.create(request)
+
+            if not response.success():
+                raise Exception(f"上传文件失败: {response.msg}")
+
+            return response.data.file_key
+        finally:
+            os.unlink(tmp_path)
+
+    def send_file(
+        self,
+        to: str,
+        file: Union[bytes, str],
+        file_name: str,
+        reply_to_message_id: Optional[str] = None,
+    ) -> SendResult:
+        """
+        发送文件消息
+
+        Args:
+            to: 接收者ID
+            file: 文件数据或路径
+            file_name: 文件名
+            reply_to_message_id: 回复的消息ID (可选)
+
+        Returns:
+            SendResult: 发送结果
+        """
+        file_type = self._detect_file_type(file_name)
+        file_key = self.upload_file(file, file_name, file_type)
+        content = json.dumps({"file_key": file_key})
+
+        receive_id_type = self._resolve_receive_id_type(to)
+
+        if reply_to_message_id:
+            request = ReplyMessageRequest.builder() \
+                .message_id(reply_to_message_id) \
+                .request_body(ReplyMessageRequestBody.builder()
+                    .content(content)
+                    .msg_type("file")
+                    .build()) \
+                .build()
+
+            response = self.client.im.v1.message.reply(request)
+        else:
+            request = CreateMessageRequest.builder() \
+                .receive_id_type(receive_id_type.value) \
+                .request_body(CreateMessageRequestBody.builder()
+                    .receive_id(to)
+                    .content(content)
+                    .msg_type("file")
+                    .build()) \
+                .build()
+
+            response = self.client.im.v1.message.create(request)
+
+        if not response.success():
+            raise Exception(f"发送文件失败: {response.msg}")
+
+        return SendResult(
+            message_id=response.data.message_id,
+            chat_id=response.data.chat_id
+        )
+
+    def download_message_resource(
+        self,
+        message_id: str,
+        file_key: str,
+        resource_type: str = "file"
+    ) -> bytes:
+        """
+        下载消息中的资源文件
+
+        Args:
+            message_id: 消息ID
+            file_key: 文件 key
+            resource_type: 资源类型 ("image" 或 "file")
+
+        Returns:
+            bytes: 文件数据
+        """
+        request = GetMessageResourceRequest.builder() \
+            .message_id(message_id) \
+            .file_key(file_key) \
+            .type(resource_type) \
+            .build()
+
+        response = self.client.im.v1.message_resource.get(request)
+
+        if not response.success():
+            raise Exception(f"下载资源失败: {response.msg}")
+
+        return response.file.read()
+
+    # ==================== 消息获取 ====================
+
+    def get_message(self, message_id: str) -> Optional[Dict]:
+        """
+        获取消息详情
+
+        Args:
+            message_id: 消息ID
+
+        Returns:
+            Dict: 消息详情，失败返回 None
+        """
+        request = GetMessageRequest.builder() \
+            .message_id(message_id) \
+            .build()
+
+        response = self.client.im.v1.message.get(request)
+
+        if not response.success():
+            return None
+
+        items = response.data.items
+        if not items:
+            return None
+
+        item = items[0]
+        content = item.body.content if item.body else ""
+
+        # 解析文本内容
+        try:
+            parsed = json.loads(content)
+            if item.msg_type == "text" and "text" in parsed:
+                content = parsed["text"]
+        except:
+            pass
+
+        return {
+            "message_id": item.message_id,
+            "chat_id": item.chat_id,
+            "sender_id": item.sender.id if item.sender else None,
+            "sender_type": item.sender.sender_type if item.sender else None,
+            "content": content,
+            "content_type": item.msg_type,
+            "create_time": item.create_time,
+        }
+
+    def get_message_list(self, chat_id: str, start_time: Optional[Union[str, int]] = None, end_time: Optional[Union[str, int]] = None, page_size: int = 20, page_token: Optional[str] = None) -> Optional[Dict]:
+        """
+        获取消息列表
+
+        Args:
+            chat_id: 会话 ID
+            start_time: 起始时间 (可选)
+            end_time: 结束时间 (可选)
+            page_size: 分页大小 (默认 20)
+            page_token: 分页令牌 (可选)
+
+        Returns:
+            Dict: 包含消息列表和分页信息，失败返回 None
+        """
+        builder = ListMessageRequest.builder() \
+            .container_id_type("chat") \
+            .container_id(chat_id) \
+            .sort_type("ByCreateTimeDesc") \
+            .page_size(page_size)
+
+        if start_time is not None:
+            builder.start_time(str(start_time))
+        if end_time is not None:
+            builder.end_time(str(end_time))
+        if page_token:
+            builder.page_token(page_token)
+
+        request = builder.build()
+
+        # 发起请求
+        response: ListMessageResponse = self.client.im.v1.message.list(request)
+
+        # 处理失败返回
+        if not response.success():
+            logger.error(
+                f"client.im.v1.message.list failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}")
+            return None
+
+        # 构建返回结果
+        messages = []
+        if response.data.items:
+            for item in response.data.items:
+                content = item.body.content if item.body else ""
+                # 解析文本内容
+                try:
+                    parsed = json.loads(content)
+                    if item.msg_type == "text" and "text" in parsed:
+                        content = parsed["text"]
+                except:
+                    pass
+
+                messages.append({
+                    "message_id": item.message_id,
+                    "chat_id": item.chat_id,
+                    "sender_id": item.sender.id if item.sender else None,
+                    "sender_type": item.sender.sender_type if item.sender else None,
+                    "content": content,
+                    "content_type": item.msg_type,
+                    "create_time": item.create_time,
+                })
+
+        return {
+            "items": messages,
+            "page_token": response.data.page_token,
+            "has_more": response.data.has_more
+        }
+
+    # ==================== 用户信息 ====================
+
+    def get_user_info(self, open_id: str) -> Optional[Dict]:
+        """
+        获取用户信息
+
+        Args:
+            open_id: 用户 open_id
+
+        Returns:
+            Dict: 用户信息，失败返回 None
+        """
+        # 检查缓存
+        if open_id in self._sender_name_cache:
+            return {"name": self._sender_name_cache[open_id]}
+
+        request = GetUserRequest.builder() \
+            .user_id(open_id) \
+            .user_id_type("open_id") \
+            .build()
+
+        response = self.client.contact.v3.user.get(request)
+
+        if not response.success():
+            return None
+
+        user = response.data.user
+        name = user.name or user.en_name or user.nickname
+
+        if name:
+            self._sender_name_cache[open_id] = name
+
+        return {
+            "open_id": user.open_id,
+            "user_id": user.user_id,
+            "name": name,
+            "en_name": user.en_name,
+            "nickname": user.nickname,
+            "email": user.email,
+            "mobile": user.mobile,
+            "avatar": user.avatar.avatar_origin if user.avatar else None,
+        }
+
+    # ==================== WebSocket 监听 ====================
+
+    def start_websocket(
+        self,
+        on_message: Callable[[FeishuMessageEvent], None],
+        on_bot_added: Optional[Callable[[str], None]] = None,
+        on_bot_removed: Optional[Callable[[str], None]] = None,
+        blocking: bool = True,
+    ):
+        """
+        启动 WebSocket 监听消息
+
+        Args:
+            on_message: 消息回调函数
+            on_bot_added: 机器人被添加到群的回调 (可选)
+            on_bot_removed: 机器人被移出群的回调 (可选)
+            blocking: 是否阻塞当前线程
+        """
+        # 创建事件处理器
+        # 注意: lark-oapi SDK 的回调函数只接受一个参数 (data)
+        event_handler = lark.EventDispatcherHandler.builder(
+            self.encrypt_key or "",
+            self.verification_token or ""
+        ).register_p2_im_message_receive_v1(
+            lambda data: self._handle_message_event(data, on_message)
+        )
+
+        if on_bot_added:
+            event_handler = event_handler.register_p2_im_chat_member_bot_added_v1(
+                lambda data: on_bot_added(data.event.chat_id)
+            )
+
+        if on_bot_removed:
+            event_handler = event_handler.register_p2_im_chat_member_bot_deleted_v1(
+                lambda data: on_bot_removed(data.event.chat_id)
+            )
+
+        handler = event_handler.build()
+
+        # 创建 WebSocket 客户端
+        ws_client = lark.ws.Client(
+            self.app_id,
+            self.app_secret,
+            event_handler=handler,
+            domain=lark.FEISHU_DOMAIN if self.domain == FeishuDomain.FEISHU else lark.LARK_DOMAIN,
+            log_level=lark.LogLevel.INFO,
+        )
+
+        logger.info("启动飞书 WebSocket 监听...")
+
+        if blocking:
+            ws_client.start()
+        else:
+            thread = threading.Thread(target=ws_client.start, daemon=True)
+            thread.start()
+            return thread
+
+    def _handle_message_event(
+        self,
+        data,
+        callback: Callable[[FeishuMessageEvent], None]
+    ):
+        """处理消息事件"""
+        try:
+            # data 结构: P2ImMessageReceiveV1 对象
+            # data.event 包含实际的事件数据
+            event = data.event
+            msg = event.message
+            sender = event.sender
+
+            # 解析消息内容
+            content = self._parse_message_content(msg.content, msg.message_type)
+
+            # 检查是否 @了机器人
+            mentioned_bot = self._check_bot_mentioned(msg.mentions)
+
+            # 去除 @机器人 的文本
+            if msg.mentions:
+                content = self._strip_bot_mention(content, msg.mentions)
+
+            # 构建事件对象
+            message_event = FeishuMessageEvent(
+                message_id=msg.message_id,
+                chat_id=msg.chat_id,
+                chat_type=ChatType(msg.chat_type),
+                content=content,
+                content_type=msg.message_type,
+                sender_open_id=sender.sender_id.open_id if sender.sender_id else "",
+                sender_user_id=sender.sender_id.user_id if sender.sender_id else None,
+                root_id=msg.root_id,
+                parent_id=msg.parent_id,
+                mentions=[],  # 简化处理
+                mentioned_bot=mentioned_bot,
+            )
+
+            # 尝试获取发送者名称 (可能会失败，不影响主流程)
+            try:
+                if message_event.sender_open_id:
+                    user_info = self.get_user_info(message_event.sender_open_id)
+                    if user_info:
+                        message_event.sender_name = user_info.get("name")
+            except Exception as e:
+                logger.debug(f"获取用户信息失败: {e}")
+
+            callback(message_event)
+
+        except Exception as e:
+            logger.error(f"处理消息事件失败: {e}", exc_info=True)
+
+    # ==================== 辅助方法 ====================
+
+    def _resolve_receive_id_type(self, receive_id: str) -> ReceiveIdType:
+        """推断接收者ID类型"""
+        if receive_id.startswith("ou_"):
+            return ReceiveIdType.OPEN_ID
+        elif receive_id.startswith("on_"):
+            return ReceiveIdType.UNION_ID
+        elif receive_id.startswith("oc_"):
+            return ReceiveIdType.CHAT_ID
+        elif "@" in receive_id:
+            return ReceiveIdType.EMAIL
+        else:
+            return ReceiveIdType.USER_ID
+
+    def _parse_message_content(self, content: str, message_type: str) -> str:
+        """解析消息内容"""
+        try:
+            parsed = json.loads(content)
+            if message_type == "text":
+                return parsed.get("text", "")
+            elif message_type == "post":
+                return self._parse_post_content(parsed)
+            return content
+        except:
+            return content
+
+    def _parse_post_content(self, parsed: Dict) -> str:
+        """解析富文本消息"""
+        title = parsed.get("title", "")
+        content_blocks = parsed.get("content", [])
+
+        text_parts = [title] if title else []
+
+        for paragraph in content_blocks:
+            if isinstance(paragraph, list):
+                for element in paragraph:
+                    if element.get("tag") == "text":
+                        text_parts.append(element.get("text", ""))
+                    elif element.get("tag") == "a":
+                        text_parts.append(element.get("text", element.get("href", "")))
+                    elif element.get("tag") == "at":
+                        text_parts.append(f"@{element.get('user_name', '')}")
+
+        return "\n".join(text_parts).strip() or "[富文本消息]"
+
+    def _check_bot_mentioned(self, mentions: Optional[List]) -> bool:
+        """检查是否 @了机器人"""
+        if not mentions:
+            return False
+
+        if not self._bot_open_id:
+            # 如果没有缓存机器人 open_id，假设有 mention 就是 @了机器人
+            return len(mentions) > 0
+
+        return any(m.id.open_id == self._bot_open_id for m in mentions)
+
+    def _strip_bot_mention(self, text: str, mentions: List) -> str:
+        """去除 @机器人 的文本"""
+        result = text
+        for mention in mentions:
+            name = mention.name if hasattr(mention, 'name') else ""
+            key = mention.key if hasattr(mention, 'key') else ""
+            if name:
+                result = result.replace(f"@{name}", "").strip()
+            if key:
+                result = result.replace(key, "").strip()
+        return result
+
+    def _detect_file_type(self, file_name: str) -> str:
+        """检测文件类型"""
+        ext = os.path.splitext(file_name)[1].lower()
+        type_map = {
+            ".opus": "opus", ".ogg": "opus",
+            ".mp4": "mp4", ".mov": "mp4", ".avi": "mp4",
+            ".pdf": "pdf",
+            ".doc": "doc", ".docx": "doc",
+            ".xls": "xls", ".xlsx": "xls",
+            ".ppt": "ppt", ".pptx": "ppt",
+        }
+        return type_map.get(ext, "stream")
+
+
+# ==================== 使用示例 ====================
+
+if __name__ == "__main__":
+    # 从环境变量获取配置
+    APP_ID = os.getenv("FEISHU_APP_ID", "cli_a90fe317987a9cc9")
+    APP_SECRET = os.getenv("FEISHU_APP_SECRET", "nn2dWuXTiRA2N6xodbm4g0qz1AfM2ayi")
+
+    if not APP_ID or not APP_SECRET:
+        print("请设置环境变量 FEISHU_APP_ID 和 FEISHU_APP_SECRET")
+        exit(1)
+
+    # 创建客户端
+    client = FeishuClient(
+        app_id=APP_ID,
+        app_secret=APP_SECRET,
+        domain=FeishuDomain.FEISHU,
+    )
+
+    # 消息处理回调
+    def handle_message(event: FeishuMessageEvent):
+        print(f"\n收到消息:")
+        print(f"  发送者: {event.sender_name or event.sender_open_id}")
+        print(f"  类型: {event.chat_type.value}")
+        print(f"  内容: {event.content}")
+        print(f"  @机器人: {event.mentioned_bot}")
+
+        # 自动回复示例
+        if event.chat_type == ChatType.P2P or event.mentioned_bot:
+            # 先回复文字
+            reply_text = f"收到你的消息: {event.content}"
+            chat_id = event.chat_id
+            content = event.content
+            content_type = event.content_type # image、text等
+            open_id = event.sender_open_id
+            client.send_message(
+                to=event.chat_id,
+                text=reply_text,
+                reply_to_message_id=event.message_id
+            )
+            print(f"  已回复文字: {reply_text}")
+
+            # 再回复一张图片 (读取当前目录下的 hanli.png)
+            try:
+                image_path = os.path.join(os.path.dirname(__file__) or ".", "hanli.png")
+                if os.path.exists(image_path):
+                    client.send_image(
+                        to=event.chat_id,
+                        image=image_path,
+                    )
+                    print(f"  已回复图片: {image_path}")
+                else:
+                    print(f"  图片不存在: {image_path}")
+            except Exception as e:
+                print(f"  回复图片失败: {e}")
+
+    # 启动 WebSocket 监听
+    print("启动飞书消息监听...")
+    print("按 Ctrl+C 退出")
+
+    try:
+        client.start_websocket(
+            on_message=handle_message,
+            on_bot_added=lambda chat_id: print(f"机器人被添加到群: {chat_id}"),
+            on_bot_removed=lambda chat_id: print(f"机器人被移出群: {chat_id}"),
+            blocking=True
+        )
+
+        # res = client.get_message_list(chat_id='oc_56e85f0e2c97405d176729b62d8f56e5', start_time=0, end_time=1770623620)
+        # print(f"获取消息列表结果: {json.dumps(res, indent=4, ensure_ascii=False)}")
+    except KeyboardInterrupt:
+        print("\n退出")

agent/tools/builtin/feishu/websocket_event.py

@@ -0,0 +1,92 @@
+import os
+import json
+import logging
+import asyncio
+import sys
+from typing import Optional
+
+# 将项目根目录添加到 python 路径，确保可以作为独立脚本运行
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
+if PROJECT_ROOT not in sys.path:
+    sys.path.append(PROJECT_ROOT)
+
+from agent.tools.builtin.feishu.feishu_client import FeishuClient, FeishuMessageEvent, FeishuDomain
+from agent.tools.builtin.feishu.chat import (
+    FEISHU_APP_ID, 
+    FEISHU_APP_SECRET, 
+    get_contact_by_id, 
+    load_chat_history, 
+    save_chat_history, 
+    update_unread_count,
+    _convert_feishu_msg_to_openai_content
+)
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("FeishuWebsocket")
+
+class FeishuMessageListener:
+    def __init__(self):
+        self.client = FeishuClient(
+            app_id=FEISHU_APP_ID,
+            app_secret=FEISHU_APP_SECRET,
+            domain=FeishuDomain.FEISHU
+        )
+
+    def handle_incoming_message(self, event: FeishuMessageEvent):
+        """处理收到的飞书消息事件"""
+        # 1. 识别联系人
+        # 优先使用 sender_open_id 匹配联系人，如果没有则尝试 chat_id
+        contact = get_contact_by_id(event.sender_open_id) or get_contact_by_id(event.chat_id)
+        
+        if not contact:
+            logger.warning(f"收到未知发送者的消息: open_id={event.sender_open_id}, chat_id={event.chat_id}")
+            # 对于未知联系人，我们可以选择忽略，或者记录到 'unknown' 分类
+            contact = {"name": "未知联系人", "open_id": event.sender_open_id}
+
+        contact_name = contact.get("name")
+        logger.info(f"收到来自 [{contact_name}] 的消息: {event.content[:50]}...")
+
+        # 2. 转换为 OpenAI 多模态格式
+        # 构造一个类似 get_message_list 返回的字典对象，以便重用转换逻辑
+        msg_dict = {
+            "message_id": event.message_id,
+            "content_type": event.content_type,
+            "content": event.content, # 对于 text, websocket 传来的已经是解析后的字符串；对于 image 则是原始 JSON 字符串
+            "sender_id": event.sender_open_id,
+            "sender_type": "user" # WebSocket 收到的一般是用户消息，除非是机器人自己的回显（通常会过滤）
+        }
+        
+        openai_content = _convert_feishu_msg_to_openai_content(self.client, msg_dict)
+
+        # 3. 维护聊天记录
+        history = load_chat_history(contact_name)
+        new_message = {
+            "role": "user",
+            "message_id": event.message_id,
+            "timestamp": os.path.getmtime(os.path.join(os.path.dirname(__file__), "chat.py")), # 简单模拟一个时间戳，实际应使用事件时间
+            "content": openai_content
+        }
+        history.append(new_message)
+        save_chat_history(contact_name, history)
+
+        # 4. 更新未读计数
+        update_unread_count(contact_name, increment=1)
+        logger.info(f"已更新 [{contact_name}] 的聊天记录并增加未读计数")
+
+    def start(self):
+        """启动监听"""
+        logger.info("正在启动飞书消息实时监听...")
+        try:
+            self.client.start_websocket(
+                on_message=self.handle_incoming_message,
+                blocking=True
+            )
+        except KeyboardInterrupt:
+            logger.info("监听已停止")
+        except Exception as e:
+            logger.error(f"监听过程中出现错误: {e}")
+
+if __name__ == "__main__":
+    listener = FeishuMessageListener()
+    listener.start()

agent/trace/models.py

@@ -10,6 +10,11 @@ from datetime import datetime
 from typing import Dict, Any, List, Optional, Literal
 import uuid
 
+# 导入 TokenUsage（延迟导入避免循环依赖）
+def _get_token_usage_class():
+    from ..llm.usage import TokenUsage
+    return TokenUsage
+
 
 @dataclass
 class Trace:
@@ -44,6 +49,9 @@ class Trace:
     total_tokens: int = 0        # 总 tokens（向后兼容，= prompt + completion）
     total_prompt_tokens: int = 0      # 总输入 tokens
     total_completion_tokens: int = 0  # 总输出 tokens
+    total_reasoning_tokens: int = 0   # 总推理 tokens（o1/o3, DeepSeek R1, Gemini thinking）
+    total_cache_creation_tokens: int = 0  # 总缓存创建 tokens（Claude）
+    total_cache_read_tokens: int = 0      # 总缓存读取 tokens（Claude）
     total_cost: float = 0.0
     total_duration_ms: int = 0   # 总耗时（毫秒）
 
@@ -97,6 +105,9 @@ class Trace:
             "total_tokens": self.total_tokens,
             "total_prompt_tokens": self.total_prompt_tokens,
             "total_completion_tokens": self.total_completion_tokens,
+            "total_reasoning_tokens": self.total_reasoning_tokens,
+            "total_cache_creation_tokens": self.total_cache_creation_tokens,
+            "total_cache_read_tokens": self.total_cache_read_tokens,
             "total_cost": self.total_cost,
             "total_duration_ms": self.total_duration_ms,
             "last_sequence": self.last_sequence,
@@ -139,6 +150,9 @@ class Message:
     # 元数据
     prompt_tokens: Optional[int] = None  # 输入 tokens
     completion_tokens: Optional[int] = None  # 输出 tokens
+    reasoning_tokens: Optional[int] = None   # 推理 tokens（o1/o3, DeepSeek R1, Gemini thinking）
+    cache_creation_tokens: Optional[int] = None  # 缓存创建 tokens（Claude）
+    cache_read_tokens: Optional[int] = None      # 缓存读取 tokens（Claude）
     cost: Optional[float] = None
     duration_ms: Optional[int] = None
     created_at: datetime = field(default_factory=datetime.now)
@@ -148,9 +162,25 @@ class Message:
 
     @property
     def tokens(self) -> int:
-        """动态计算总 tokens（向后兼容）"""
+        """动态计算总 tokens（向后兼容，input + output）"""
         return (self.prompt_tokens or 0) + (self.completion_tokens or 0)
 
+    @property
+    def all_tokens(self) -> int:
+        """所有 tokens（包括 reasoning）"""
+        return self.tokens + (self.reasoning_tokens or 0)
+
+    def get_usage(self):
+        """获取 TokenUsage 对象"""
+        TokenUsage = _get_token_usage_class()
+        return TokenUsage(
+            input_tokens=self.prompt_tokens or 0,
+            output_tokens=self.completion_tokens or 0,
+            reasoning_tokens=self.reasoning_tokens or 0,
+            cache_creation_tokens=self.cache_creation_tokens or 0,
+            cache_read_tokens=self.cache_read_tokens or 0,
+        )
+
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "Message":
         """从字典创建 Message（处理向后兼容）"""
@@ -174,6 +204,9 @@ class Message:
         tool_call_id: Optional[str] = None,
         prompt_tokens: Optional[int] = None,
         completion_tokens: Optional[int] = None,
+        reasoning_tokens: Optional[int] = None,
+        cache_creation_tokens: Optional[int] = None,
+        cache_read_tokens: Optional[int] = None,
         cost: Optional[float] = None,
         duration_ms: Optional[int] = None,
         finish_reason: Optional[str] = None,
@@ -192,6 +225,9 @@ class Message:
             tool_call_id=tool_call_id,
             prompt_tokens=prompt_tokens,
             completion_tokens=completion_tokens,
+            reasoning_tokens=reasoning_tokens,
+            cache_creation_tokens=cache_creation_tokens,
+            cache_read_tokens=cache_read_tokens,
             cost=cost,
             duration_ms=duration_ms,
             finish_reason=finish_reason,
@@ -261,7 +297,7 @@ class Message:
 
     def to_dict(self) -> Dict[str, Any]:
         """转换为字典"""
-        return {
+        result = {
             "message_id": self.message_id,
             "trace_id": self.trace_id,
             "role": self.role,
@@ -278,6 +314,14 @@ class Message:
             "finish_reason": self.finish_reason,
             "created_at": self.created_at.isoformat() if self.created_at else None,
         }
+        # 只添加非空的可选字段
+        if self.reasoning_tokens:
+            result["reasoning_tokens"] = self.reasoning_tokens
+        if self.cache_creation_tokens:
+            result["cache_creation_tokens"] = self.cache_creation_tokens
+        if self.cache_read_tokens:
+            result["cache_read_tokens"] = self.cache_read_tokens
+        return result
 
 
 # ===== 已弃用：Step 模型（保留用于向后兼容）=====

agent/trace/store.py

@@ -308,7 +308,7 @@ class FileSystemTraceStore:
         # 1. 写入 message 文件
         messages_dir = self._get_messages_dir(trace_id)
         message_file = messages_dir / f"{message.message_id}.json"
-        message_file.write_text(json.dumps(message.to_dict(), indent=2, ensure_ascii=False))
+        message_file.write_text(json.dumps(message.to_dict(), indent=2, ensure_ascii=False), encoding="utf-8")
 
         # 2. 更新 trace 统计
         trace = await self.get_trace(trace_id)
@@ -316,11 +316,18 @@ class FileSystemTraceStore:
             trace.total_messages += 1
             trace.last_sequence = max(trace.last_sequence, message.sequence)
 
-            # 累计 tokens（拆分）
+            # 累计 tokens（完整版）
             if message.prompt_tokens:
                 trace.total_prompt_tokens += message.prompt_tokens
             if message.completion_tokens:
                 trace.total_completion_tokens += message.completion_tokens
+            if message.reasoning_tokens:
+                trace.total_reasoning_tokens += message.reasoning_tokens
+            if message.cache_creation_tokens:
+                trace.total_cache_creation_tokens += message.cache_creation_tokens
+            if message.cache_read_tokens:
+                trace.total_cache_read_tokens += message.cache_read_tokens
+
             # 向后兼容：也更新 total_tokens
             if message.tokens:
                 trace.total_tokens += message.tokens
@@ -332,7 +339,7 @@ class FileSystemTraceStore:
             if message.duration_ms:
                 trace.total_duration_ms += message.duration_ms
 
-            # 更新 Trace（不要传递 trace_id，它已经在方法参数中）
+            # 更新 Trace
             await self.update_trace(
                 trace_id,
                 total_messages=trace.total_messages,
@@ -340,6 +347,9 @@ class FileSystemTraceStore:
                 total_tokens=trace.total_tokens,
                 total_prompt_tokens=trace.total_prompt_tokens,
                 total_completion_tokens=trace.total_completion_tokens,
+                total_reasoning_tokens=trace.total_reasoning_tokens,
+                total_cache_creation_tokens=trace.total_cache_creation_tokens,
+                total_cache_read_tokens=trace.total_cache_read_tokens,
                 total_cost=trace.total_cost,
                 total_duration_ms=trace.total_duration_ms
             )
@@ -555,7 +565,7 @@ class FileSystemTraceStore:
 
         # 追加到 events.jsonl
         events_file = self._get_events_file(trace_id)
-        with events_file.open('a') as f:
+        with events_file.open('a', encoding='utf-8') as f:
             f.write(json.dumps(event, ensure_ascii=False) + '\n')
 
         return event_id

config/feishu_contacts.json

@@ -0,0 +1,28 @@
+[
+    {
+        "name": "谭景玉",
+        "description": "",
+        "open_id": "ou_11fdbd559cc6513ab53ff06d6c63413d",
+        "chat_id": "oc_56e85f0e2c97405d176729b62d8f56e5"
+    },
+    {
+        "name": "王华东",
+        "description": "",
+        "open_id": "ou_82340312cf9d215f49a41b67fa9c02c2"
+    },
+    {
+        "name": "孙若天",
+        "description": "",
+        "open_id": "ou_ede69f28c2617bf80a7574f059879c8d"
+    },
+    {
+        "name": "刘斌",
+        "description": "",
+        "open_id": "ou_50c2307c3531e6293b3d5533d14592e9"
+    },
+    {
+        "name": "关涛",
+        "description": "",
+        "open_id": "ou_90b80ed994fe41b7f038a63cb9182f72"
+    }
+]

config/pricing.yaml

@@ -0,0 +1,216 @@
+# LLM 定价配置
+#
+# 价格单位：美元 / 1M tokens
+# 支持通配符：* 匹配任意字符
+#
+# 字段说明：
+#   model: 模型名称（必填）
+#   input_price: 输入 token 价格（必填）
+#   output_price: 输出 token 价格（必填）
+#   reasoning_price: 推理 token 价格（可选，默认 = output_price）
+#   cache_creation_price: 缓存创建价格（可选，默认 = input_price * 1.25）
+#   cache_read_price: 缓存读取价格（可选，默认 = input_price * 0.1）
+#   provider: 提供商名称（可选，用于分类）
+#   description: 描述（可选）
+#
+# 使用方法：
+#   1. 复制此文件到项目根目录或 config/ 目录
+#   2. 或设置环境变量 AGENT_PRICING_CONFIG 指向配置文件
+#   3. 根据实际使用的模型修改价格
+
+models:
+  # ===== OpenAI =====
+  - model: gpt-4o
+    input_price: 2.50
+    output_price: 10.00
+    provider: openai
+
+  - model: gpt-4o-mini
+    input_price: 0.15
+    output_price: 0.60
+    provider: openai
+
+  - model: gpt-4-turbo
+    input_price: 10.00
+    output_price: 30.00
+    provider: openai
+
+  # o1 系列（有 reasoning tokens）
+  - model: o1
+    input_price: 15.00
+    output_price: 60.00
+    reasoning_price: 60.00  # reasoning tokens 和 output 同价
+    provider: openai
+
+  - model: o1-mini
+    input_price: 3.00
+    output_price: 12.00
+    reasoning_price: 12.00
+    provider: openai
+
+  - model: o3-mini
+    input_price: 1.10
+    output_price: 4.40
+    reasoning_price: 4.40
+    provider: openai
+
+  # ===== Anthropic Claude =====
+  # Claude 支持 prompt caching，缓存价格：
+  #   - cache_creation: 1.25x input_price
+  #   - cache_read: 0.1x input_price
+  - model: claude-3-5-sonnet-20241022
+    input_price: 3.00
+    output_price: 15.00
+    cache_creation_price: 3.75   # 3.00 * 1.25
+    cache_read_price: 0.30       # 3.00 * 0.1
+    provider: anthropic
+
+  - model: claude-3-5-haiku-20241022
+    input_price: 0.80
+    output_price: 4.00
+    provider: anthropic
+
+  - model: claude-3-opus-20240229
+    input_price: 15.00
+    output_price: 75.00
+    provider: anthropic
+
+  # Claude 通配符（匹配新版本）
+  - model: claude-3-5-sonnet*
+    input_price: 3.00
+    output_price: 15.00
+    provider: anthropic
+
+  - model: claude-sonnet-4*
+    input_price: 3.00
+    output_price: 15.00
+    provider: anthropic
+
+  - model: claude-opus-4*
+    input_price: 15.00
+    output_price: 75.00
+    provider: anthropic
+
+  # ===== Google Gemini =====
+  - model: gemini-2.5-pro
+    input_price: 1.25
+    output_price: 10.00
+    reasoning_price: 10.00  # thinking mode
+    provider: google
+
+  - model: gemini-2.0-flash
+    input_price: 0.10
+    output_price: 0.40
+    provider: google
+
+  - model: gemini-2.0-flash-thinking
+    input_price: 0.10
+    output_price: 0.40
+    reasoning_price: 0.40
+    provider: google
+
+  - model: gemini-1.5-pro
+    input_price: 1.25
+    output_price: 5.00
+    provider: google
+
+  - model: gemini-1.5-flash
+    input_price: 0.075
+    output_price: 0.30
+    provider: google
+
+  # Gemini 通配符
+  - model: gemini-2.5*
+    input_price: 1.25
+    output_price: 10.00
+    provider: google
+
+  - model: gemini-2.0*
+    input_price: 0.10
+    output_price: 0.40
+    provider: google
+
+  # ===== DeepSeek =====
+  - model: deepseek-chat
+    input_price: 0.14
+    output_price: 0.28
+    provider: deepseek
+
+  - model: deepseek-reasoner
+    input_price: 0.55
+    output_price: 2.19
+    reasoning_price: 2.19
+    provider: deepseek
+
+  - model: deepseek-r1*
+    input_price: 0.55
+    output_price: 2.19
+    reasoning_price: 2.19
+    provider: deepseek
+
+  # ===== OpenRouter 转发 =====
+  # OpenRouter 使用 provider/model 格式
+  - model: anthropic/claude-sonnet-4.5
+    input_price: 3.00
+    output_price: 15.00
+    provider: openrouter
+
+  - model: anthropic/claude-opus-4.5
+    input_price: 5.00
+    output_price: 25.00
+    provider: openrouter
+
+  - model: anthropic/claude-opus-4.6
+    input_price: 5.00
+    output_price: 25.00
+    provider: openrouter
+
+  - model: anthropic/claude-haiku-4.5
+    input_price: 1.00
+    output_price: 5.00
+    provider: openrouter
+
+  - model: anthropic/claude-sonnet-4
+    input_price: 3.00
+    output_price: 15.00
+    provider: openrouter
+
+  - model: anthropic/claude*
+    input_price: 3.00
+    output_price: 15.00
+    provider: openrouter
+
+  - model: openai/gpt-4o*
+    input_price: 2.50
+    output_price: 10.00
+    provider: openrouter
+
+  - model: openai/o1*
+    input_price: 15.00
+    output_price: 60.00
+    reasoning_price: 60.00
+    provider: openrouter
+
+  - model: google/gemini-3-pro-preview
+    input_price: 2
+    output_price: 12
+    reasoning_price: 12
+    provider: openrouter
+
+  - model: google/gemini-3-flash-preview
+    input_price: 0.50
+    output_price: 3
+    reasoning_price: 3
+    provider: openrouter
+
+  - model: google/gemini*
+    input_price: 0.30
+    output_price: 2.50
+    reasoning_price: 2.50
+    provider: openrouter
+
+  - model: deepseek/deepseek-r1*
+    input_price: 0.55
+    output_price: 2.19
+    reasoning_price: 2.19
+    provider: openrouter

examples/feature_extract/run.py

@@ -105,7 +105,7 @@ async def main():
         system_prompt=system_prompt,
         model="anthropic/claude-sonnet-4.5",  # OpenRouter 模型名称
         temperature=float(prompt.config.get('temperature', 0.3)),
-        max_iterations=200,
+        max_iterations=1000,
         # tools 参数不传入，测试自动加载内置工具
     ):
         # 处理 Trace 对象（整体状态变化）

examples/feature_extract/test.prompt

@@ -9,10 +9,20 @@ $system$
 $user$
 # 任务
 分析一个优质内容的指定特征适合什么样的表示（这可能需要通过广泛的调研、深入的分析确认），然后帮我完成该特征的提取，再验证基于提取出来的特征+文字描述生成出来的内容是否与原内容在指定维度上保持了一致性。如果验证评估失败，要继续探索其他可行方案。
-提取的特征将用于在生成类似内容时作为参考内容（所以要保留重要信息），也会和其他内容的同一维度的特征放在一起聚类发现规律（所以特征表示要尽量精简、不要过于具体），或用于模型训练。
+
+提取的特征将用于在生成类似内容时作为参考内容（所以要保留重要信息），也会和其他内容的同一维度的特征放在一起聚类发现规律（所以特征表示要尽量精简、不要过于具体），或用于模型训练。可以仅提取一种表示方式，也可以提取多种表示方式。
+
+**禁止降级解决**：不允许为了方便而使用效果显著更差的简单方案。你应该拆解子目标逐步完成，或者转交给sub agent解决某一项适合拆分的子任务。
+**禁止平凡表示**：不允许提供自然语言的特征表示，而是应该使用多模态提供超越语言的信息。
 
 # 指定的特征：
 %text%
 
-# 结果保存路径
-examples/feature_extract/output_1/
+# 结果输出
+在指定的输出目录 examples/feature_extract/output_1/
+输出两项核心输出：
+- <feature>_extract_skill.md 提取指定特征的具体方法与工具；要注明是否验证及验证结果
+- ./feature/ 在子目录中输出特征提取的最终结果
+此外，需要保留的过程信息：
+- reduction.jpg 使用特征提取结果和必要的文字说明后重新生成的图像；可以有多张、可以是其他图像格式
+- ./resource/ 在子目录中保存过程中用到的脚本等内容

frontend/react-template/yarn.lock

@@ -1,4483 +0,0 @@
-# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
-# yarn lockfile v1
-
-
-"@alloc/quick-lru@^5.2.0":
-  version "5.2.0"
-  resolved "https://registry.npmmirror.com/@alloc/quick-lru/-/quick-lru-5.2.0.tgz"
-  integrity sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==
-
-"@babel/code-frame@^7.28.6", "@babel/code-frame@^7.29.0":
-  version "7.29.0"
-  resolved "https://registry.npmmirror.com/@babel/code-frame/-/code-frame-7.29.0.tgz"
-  integrity sha512-9NhCeYjq9+3uxgdtp20LSiJXJvN0FeCtNGpJxuMFZ1Kv3cWUNb6DOhJwUvcVCzKGR66cw4njwM6hrJLqgOwbcw==
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-"@rollup/rollup-win32-arm64-msvc@4.57.1":
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-"@rollup/rollup-win32-x64-msvc@4.57.1":
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-
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-  resolved "https://registry.npmmirror.com/@tailwindcss/node/-/node-4.1.18.tgz"
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-
-"@tailwindcss/oxide-android-arm64@4.1.18":
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-
-"@tailwindcss/oxide-darwin-arm64@4.1.18":
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-"@tailwindcss/oxide-darwin-x64@4.1.18":
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-
-"@tailwindcss/oxide-freebsd-x64@4.1.18":
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-"@tailwindcss/oxide-linux-arm-gnueabihf@4.1.18":
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-"@tailwindcss/oxide-linux-arm64-gnu@4.1.18":
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-"@tailwindcss/oxide-linux-arm64-musl@4.1.18":
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-"@tailwindcss/oxide-linux-x64-gnu@4.1.18":
-  version "4.1.18"
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-"@tailwindcss/oxide-linux-x64-musl@4.1.18":
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-  integrity sha512-bhJ2y2OQNlcRwwgOAGMY0xTFStt4/wyU6pvI6LSuZpRgKQwxTec0/3Scu91O8ir7qCR3AuepQKLU/kX99FouqQ==
-
-"@tailwindcss/oxide-wasm32-wasi@4.1.18":
-  version "4.1.18"
-  resolved "https://registry.npmmirror.com/@tailwindcss/oxide-wasm32-wasi/-/oxide-wasm32-wasi-4.1.18.tgz#9464df0e28a499aab1c55e97682be37b3a656c88"
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-  dependencies:
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-    "@tybys/wasm-util" "^0.10.1"
-    tslib "^2.4.0"
-
-"@tailwindcss/oxide-win32-arm64-msvc@4.1.18":
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-
-"@tailwindcss/oxide-win32-x64-msvc@4.1.18":
-  version "4.1.18"
-  resolved "https://registry.npmmirror.com/@tailwindcss/oxide-win32-x64-msvc/-/oxide-win32-x64-msvc-4.1.18.tgz#9c628d04623aa4c3536c508289f58d58ba4b3fb1"
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-
-"@tailwindcss/oxide@4.1.18":
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-    "@tailwindcss/oxide-darwin-arm64" "4.1.18"
-    "@tailwindcss/oxide-darwin-x64" "4.1.18"
-    "@tailwindcss/oxide-freebsd-x64" "4.1.18"
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-    "@tailwindcss/oxide-linux-x64-musl" "4.1.18"
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-    "@tailwindcss/oxide-win32-arm64-msvc" "4.1.18"
-    "@tailwindcss/oxide-win32-x64-msvc" "4.1.18"
-
-"@tailwindcss/postcss@^4.0.0":
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-  dependencies:
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-"@tailwindcss/vite@^4.0.0":
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requirements.txt

@@ -12,3 +12,6 @@ langchain_core>=0.3.0
 fastapi>=0.115.0
 uvicorn[standard]>=0.32.0
 websockets>=13.0
+
+# 飞书
+lark-oapi==1.5.3