docs: update README and knowledge graph for gap closure features

- README: add dark theme, LLM cache, semantic router, cascade detection, ComputerUseTool, @-mention - build_kg.py: add 30+ module summaries for new modules - knowledge-graph.json: rebuild (2496 nodes, 3328 edges) - fingerprints.json: recalculate 524 file fingerprints - meta.json: update gitCommitHash and analyzedAt
2026-06-14 16:54:12 +08:00 · 2026-06-14 16:54:12 +08:00 · baaa7089cd
parent d9d1b16e59
commit baaa7089cd
5 changed files with 6828 additions and 3492 deletions
--- a/.understand-anything/build_kg.py
+++ b/.understand-anything/build_kg.py
@ -65,6 +65,13 @@ MODULE_SUMMARIES = {
    "core.headroom_compressor": "Headroom AI压缩器 - 基于Headroom AI的上下文压缩实现",
    "core.logging": "日志配置 - 统一日志格式和配置",
    "core.standalone": "独立运行模式 - 支持Agent脱离框架独立运行",
+    "core.goal_planner": "目标规划器 - 将复杂目标分解为可执行步骤",
+    "core.plan_checker": "计划检查器 - 验证执行计划的完整性和可行性",
+    "core.plan_exec_engine": "计划执行引擎 - 执行分解后的计划步骤",
+    "core.plan_executor": "计划执行器 - 管理计划执行的完整流程",
+    "core.plan_schema": "计划Schema - 执行计划的数据结构定义",
+    "core.reflexion": "Reflexion引擎 - 自反思推理，通过自我评估改进输出",
+    "core.rewoo": "ReWOO引擎 - 无观察推理，先规划后执行的高效模式",

    "llm": "LLM网关模块 - 多Provider统一网关，支持OpenAI/Anthropic/Gemini/文心/豆包/元宝等",
    "llm.gateway": "LLM网关 - 统一多Provider调用接口，支持路由、重试、流式输出",
@ -79,6 +86,17 @@ MODULE_SUMMARIES = {
    "llm.providers.doubao": "豆包Provider - 支持字节豆包大模型",
    "llm.providers.yuanbao": "元宝Provider - 支持腾讯元宝大模型",
    "llm.providers.tracker": "LLM调用追踪器 - 记录和统计LLM调用",
+    "llm.providers.usage_store": "LLM用量存储 - Token用量和成本追踪，支持InMemory和Redis后端",
+    "llm.cache": "LLM响应缓存 - 基于语义相似度的LLM响应缓存，减少重复调用",
+    "llm.cache_key": "缓存键生成 - LLM缓存键的计算和归一化",
+
+    "chat": "聊天路由模块 - CostAwareRouter三层意图路由和语义路由",
+    "chat.skill_routing": "三层意图路由 - CostAwareRouter，正则→启发式→LLM分类逐层升级",
+    "chat.semantic_router": "语义路由 - 基于向量相似度的意图路由，支持语义匹配",
+
+    "quality.cascade_detector": "级联检测器 - 检测Agent输出中的级联失败模式",
+    "quality.cascade_state_store": "级联状态存储 - 级联检测状态持久化，支持InMemory和Redis后端",
+    "quality.alignment": "对齐守卫 - 检测和修正Agent输出中的对齐偏差",

    "tools": "工具模块 - 提供Agent可调用的各类工具",
    "tools.base": "工具基类 - 定义Tool接口和标准执行流程",
@ -95,6 +113,13 @@ MODULE_SUMMARIES = {
    "tools.composition": "工具组合 - 支持工具链式组合",
    "tools.baidu_search": "百度搜索工具 - 百度搜索引擎集成",
    "tools.headroom_retrieve": "Headroom检索工具 - Headroom AI知识检索",
+    "tools.computer_use": "计算机使用工具 - 桌面操控工具，支持截图、点击、输入等操作",
+    "tools.computer_use_session": "计算机使用会话 - 桌面操控会话管理，支持云端和本地(pyautogui)模式",
+    "tools.computer_use_recorder": "计算机使用录制器 - 记录桌面操控动作序列",
+    "tools.pty_session": "PTY会话 - 伪终端会话管理",
+    "tools.terminal_session": "终端会话 - 终端模拟器会话",
+    "tools.output_parser": "输出解析器 - 解析Agent输出为结构化数据",
+    "tools.skill_install": "技能安装器 - 动态安装技能包",

    "memory": "记忆模块 - 多层记忆系统，支持工作记忆、情景记忆、语义记忆",
    "memory.base": "记忆基类 - 定义Memory接口",
@ -166,12 +191,19 @@ MODULE_SUMMARIES = {
    "server.routes.ws": "WebSocket路由 - 实时通信端点",
    "server.routes.tasks": "任务路由 - 任务管理API",
    "server.routes.agents": "Agent路由 - Agent管理API",
-    "server.routes.skills": "技能路由 - 技能管理API",
+    "server.routes.skills": "技能路由 - 技能管理API，含@-mention建议端点",
    "server.routes.memory": "记忆路由 - 记忆管理API",
    "server.routes.llm": "LLM路由 - LLM配置和调用API",
    "server.routes.health": "健康检查路由 - 服务健康状态端点",
    "server.routes.metrics": "指标路由 - 运行指标API",
    "server.routes.evolution": "进化路由 - Agent进化管理API",
+    "server.routes.evolution_dashboard": "进化仪表盘路由 - 进化数据可视化API",
+    "server.routes.kb_management": "知识库管理路由 - 文档上传/搜索/源配置API",
+    "server.routes.settings": "设置路由 - 系统配置管理API",
+    "server.routes.terminal": "终端路由 - PTY终端会话API",
+    "server.routes.workflows": "工作流路由 - Pipeline工作流管理API",
+    "server.routes.skill_management": "技能管理路由 - 技能CRUD操作API",
+    "server.routes.portal": "门户路由 - Web GUI入口和静态资源",

    "cli": "命令行模块 - CLI工具",
    "cli.main": "CLI入口 - Typer应用主入口",
@ -206,10 +238,22 @@ MODULE_SUMMARIES = {
    "evolution.ab_tester": "A/B测试 - 对比测试不同进化变体",
    "evolution.evolution_store": "进化存储 - 持久化进化历史",
    "evolution.models": "进化模型 - 进化相关数据模型",
+    "evolution.experience_schema": "经验Schema - 经验数据结构定义",
+    "evolution.experience_store": "经验存储 - 成功/失败经验持久化",
+    "evolution.path_optimizer": "路径优化器 - 分析工具调用路径，推荐更优策略",
+    "evolution.pitfall_detector": "陷阱检测器 - 检测重复错误模式",

    "evaluation": "评估模块 - Agent输出质量评估",
    "evaluation.ragas_evaluator": "RAGAS评估器 - 使用RAGAS框架评估RAG质量",

+    "org": "组织发现模块 - 多Agent组织架构和协作发现",
+    "org.context": "组织上下文 - 组织级别的共享上下文管理",
+    "org.discovery": "组织发现 - Agent间能力发现和协作匹配",
+
+    "marketplace": "多Agent市场模块 - Agent间的拍卖和财富分配",
+    "marketplace.auction": "拍卖机制 - Agent间的任务拍卖和竞价",
+    "marketplace.wealth": "财富管理 - Agent间的价值交换和分配",
+
    "configs": "配置模块 - Pipeline和技能YAML配置",
    "configs.geo_server": "GEO服务器 - 地理信息HTTP服务",
    "configs.geo_handlers": "GEO处理器 - 地理信息请求处理",
@ -613,7 +657,7 @@ def build_knowledge_graph():
    tours = build_tours(file_data, file_node_ids, class_node_ids, func_node_ids)

    # Get git commit hash
-    git_hash = "045fecd4cee49f04dc7b693c14d35ca38a0d92cb"
+    git_hash = "d9d1b16e5911ad958cd8ae38958058bea13f3fcc"

    # Build final JSON
    graph = {
--- a/.understand-anything/fingerprints.json
+++ b/.understand-anything/fingerprints.json
@ -1,250 +1,526 @@
 {
-  "configs/__init__.py": "830897da8bc1af33",
-  "configs/geo_handlers.py": "cbcf89b4d9da69c9",
-  "configs/geo_server.py": "41ddafbef18eea22",
-  "configs/geo_tools.py": "8a29de5d7511d2a0",
-  "src/agentkit/__init__.py": "43b17418bafb2c7c",
-  "src/agentkit/__main__.py": "503810fb0f210fab",
-  "src/agentkit/bus/__init__.py": "83b51b8b680ee75f",
-  "src/agentkit/bus/interface.py": "ee93b26fdcee1495",
-  "src/agentkit/bus/memory_bus.py": "28b4c3a18ef13181",
-  "src/agentkit/bus/message.py": "035adf5510427f85",
-  "src/agentkit/bus/protocol.py": "67e1ebb03a53da30",
-  "src/agentkit/bus/redis_bus.py": "7431c6cc2ed20e51",
-  "src/agentkit/chat/__init__.py": "e3b0c44298fc1c14",
-  "src/agentkit/chat/skill_routing.py": "bce85b158d5f2bd0",
-  "src/agentkit/cli/__init__.py": "a41c63d720a6e4db",
-  "src/agentkit/cli/chat.py": "fa0b4e7570c8bcb2",
-  "src/agentkit/cli/init.py": "b4f0de1670acdbb1",
-  "src/agentkit/cli/main.py": "25d92f4947b60dbf",
-  "src/agentkit/cli/onboarding.py": "0a6e8f869a02b067",
-  "src/agentkit/cli/pair.py": "1ceeffcc7a8d8ed3",
-  "src/agentkit/cli/skill.py": "af567fc67d603df6",
-  "src/agentkit/cli/task.py": "2a19fd0985717a20",
-  "src/agentkit/cli/templates.py": "b997aa3fc0dd8162",
-  "src/agentkit/cli/usage.py": "e121c1a5e76d21c1",
-  "src/agentkit/core/__init__.py": "f07bf925ec204974",
-  "src/agentkit/core/agent_pool.py": "d400580ed51fdd75",
-  "src/agentkit/core/base.py": "ab7261b559dd98c8",
-  "src/agentkit/core/compressor.py": "bfecb74c1a3de3e3",
-  "src/agentkit/core/config_driven.py": "68841e58ba2aa461",
-  "src/agentkit/core/dispatcher.py": "ec4c0dfc4a93b0be",
-  "src/agentkit/core/exceptions.py": "9145f66231230bd2",
-  "src/agentkit/core/goal_planner.py": "f2a4b33e33a3ab8a",
-  "src/agentkit/core/headroom_compressor.py": "cb496a6c5accb2ec",
-  "src/agentkit/core/logging.py": "96f61c08b97e4ffc",
-  "src/agentkit/core/orchestrator.py": "dd2de2f5a175e0de",
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-  "src/agentkit/skills/__init__.py": "cd3bd9c844656636",
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-  "src/agentkit/skills/loader.py": "197ae05b735b6946",
-  "src/agentkit/skills/pipeline.py": "0367d52bd1a4d410",
-  "src/agentkit/skills/registry.py": "f63b5e174ec5d4d7",
-  "src/agentkit/skills/schema.py": "3cbe7fe2db688c4d",
-  "src/agentkit/skills/skill_md.py": "758de3b3601b2520",
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+  "tests/unit/test_base_agent_v2.py": "8b34903756625ed8",
+  "tests/unit/test_bus_protocol.py": "cc278c537b9ab55a",
+  "tests/unit/test_chat_memory_integration.py": "e04eedb7d6769bb9",
+  "tests/unit/test_chat_routes.py": "ac9fd896485fad70",
+  "tests/unit/test_chinese_providers.py": "8b7c6b1db3aed927",
+  "tests/unit/test_cli.py": "391386a1f4b59780",
+  "tests/unit/test_compression_config.py": "6e789f9e0b0e163d",
+  "tests/unit/test_compression_strategy.py": "a354bb2c7caf96da",
+  "tests/unit/test_config_driven.py": "c6110f498ad0685e",
+  "tests/unit/test_context_compressor.py": "99d074bd33277d71",
+  "tests/unit/test_contextual_retrieval.py": "c67f8f0c3cd088d1",
+  "tests/unit/test_cost_aware_router.py": "c46fd3ad7d16d9b2",
+  "tests/unit/test_dispatcher.py": "093c243e1fd88c53",
+  "tests/unit/test_embedding_cache.py": "63f6898cd4c08cf8",
+  "tests/unit/test_episodic_memory.py": "efd3fed7476b01fa",
+  "tests/unit/test_episodic_vector_search.py": "d512a403d915d17c",
+  "tests/unit/test_evolution.py": "267c0d997ac4b69b",
+  "tests/unit/test_evolution_api.py": "c8bc3257089f2f28",
+  "tests/unit/test_evolution_integration.py": "97dbf613d90cfade",
+  "tests/unit/test_evolution_lifecycle.py": "c54cba680de3c6cf",
+  "tests/unit/test_evolution_store.py": "aeede35adc6298bb",
+  "tests/unit/test_evolution_store_persistent.py": "0a4f2e0b68352bf4",
+  "tests/unit/test_execution_modes.py": "721e3dbd3bbe42b6",
+  "tests/unit/test_fitness.py": "a8574c829dd1e6b8",
+  "tests/unit/test_gateway_cache.py": "b90d19fe913acd6b",
+  "tests/unit/test_gemini_provider.py": "a11569acda793b28",
+  "tests/unit/test_genetic_evolution.py": "2298a952c78a1d57",
+  "tests/unit/test_geo_pipeline.py": "0fbea766ae4127ff",
+  "tests/unit/test_goal_planner.py": "41a75d8d954b9be2",
+  "tests/unit/test_handoff.py": "b62bd295b820e7f0",
+  "tests/unit/test_headroom_compressor.py": "690070348dedc54f",
+  "tests/unit/test_headroom_retrieve_tool.py": "9cd59c7ef33abdf8",
+  "tests/unit/test_http_rag_service.py": "c9553d7192028003",
+  "tests/unit/test_intent_router.py": "d6d25ef448de860b",
+  "tests/unit/test_llm_cache.py": "40d438e5249bffa3",
+  "tests/unit/test_llm_gateway.py": "bc60cbfd1cab0078",
+  "tests/unit/test_llm_protocol.py": "aa3e78b428e69995",
+  "tests/unit/test_llm_provider.py": "596744095843d1ff",
+  "tests/unit/test_llm_reflector.py": "703d9262357056ce",
+  "tests/unit/test_llm_retry.py": "24b9eeb9b333a1e8",
+  "tests/unit/test_mcp_client.py": "a41cb2b1f7a2ef9f",
+  "tests/unit/test_mcp_config.py": "81b92487b8d81f0b",
+  "tests/unit/test_mcp_manager.py": "8536847fdc4cd5ea",
+  "tests/unit/test_mcp_server.py": "5a27613e90d76aae",
+  "tests/unit/test_mcp_transport.py": "41d5b3080f524384",
+  "tests/unit/test_memory_api.py": "bdbc0f4ee07e6f3f",
+  "tests/unit/test_memory_integration.py": "c65d5c3312dfaf06",
+  "tests/unit/test_memory_profile.py": "536e91f76c288475",
+  "tests/unit/test_memory_retriever.py": "fe00ea44c651dd61",
+  "tests/unit/test_memory_system.py": "cfb1ed2fcf7b3d2b",
+  "tests/unit/test_memory_tool.py": "14b9fa7c03ca094d",
+  "tests/unit/test_observability.py": "bc2708134d2e52ac",
+  "tests/unit/test_onboarding.py": "b3fbdf5aa374ede9",
+  "tests/unit/test_orchestrator.py": "93feb0c97e569e8c",
+  "tests/unit/test_orchestrator_adaptive.py": "4b3204dd2e649de8",
+  "tests/unit/test_orchestrator_bus.py": "20392261e563eedc",
+  "tests/unit/test_orchestrator_integration.py": "5e5a16aac7a0f1d5",
+  "tests/unit/test_org_context.py": "d1bad982d17ef585",
+  "tests/unit/test_output_standardizer.py": "9305eddc864f0c3f",
+  "tests/unit/test_pipeline.py": "dd3d8845733456f6",
+  "tests/unit/test_pipeline_adversarial.py": "af99d36831acec26",
+  "tests/unit/test_pipeline_compensation.py": "2acc0c4bd0126cc8",
+  "tests/unit/test_pipeline_reflection.py": "d171b373e96620b4",
+  "tests/unit/test_pipeline_retry.py": "b50bba77dc357937",
+  "tests/unit/test_pipeline_state.py": "65df456e2d6054c5",
+  "tests/unit/test_plan_exec_engine.py": "18c76d5d4f010b7d",
+  "tests/unit/test_prompt_optimizer.py": "06325696628d2f69",
+  "tests/unit/test_prompt_section.py": "e764eca7b44cc6c8",
+  "tests/unit/test_prompt_template.py": "e57af046cdb3b4e4",
+  "tests/unit/test_protocol.py": "da661f7a951b6576",
+  "tests/unit/test_quality_gate.py": "a1b9a0ce009e3d8d",
+  "tests/unit/test_query_transformer.py": "7fde4b222ae1f145",
+  "tests/unit/test_rag_loop.py": "f1347eac9a5919a2",
+  "tests/unit/test_ragas_evaluator.py": "521786f58e3fbb2f",
+  "tests/unit/test_react_compression.py": "40a45ee367d84810",
+  "tests/unit/test_react_engine.py": "62a5df9422ae21f4",
+  "tests/unit/test_react_skill_mcp_integration.py": "6725ad4c70f8e4a9",
+  "tests/unit/test_react_token_streaming.py": "14aa43e723cef9c6",
+  "tests/unit/test_reflexion_engine.py": "689979f3c835aedb",
+  "tests/unit/test_registry.py": "8d31cc0eee9cd89d",
+  "tests/unit/test_retrieval_config.py": "a09fca0f1d8c44b2",
+  "tests/unit/test_retrieve_knowledge_tool.py": "d12b7414e129f593",
+  "tests/unit/test_rewoo_engine.py": "1fbd72b1923c3e24",
+  "tests/unit/test_schema_tools.py": "a600b44b4c2ec894",
+  "tests/unit/test_semantic_router.py": "a277ea3cb0bdd4d3",
+  "tests/unit/test_server_config.py": "e86663f135a8396c",
+  "tests/unit/test_server_middleware.py": "660766db5bdf4fb0",
+  "tests/unit/test_server_routes.py": "3370e164dbac52ea",
+  "tests/unit/test_session_manager.py": "90ef9929f4910c4b",
+  "tests/unit/test_session_models.py": "2297ebde41ac1961",
+  "tests/unit/test_session_store.py": "124898baeef2c549",
+  "tests/unit/test_shell_tool.py": "fc1237230c684e25",
+  "tests/unit/test_skill_config.py": "f3aef1188e101bac",
+  "tests/unit/test_skill_loader.py": "21b83961057e4fcb",
+  "tests/unit/test_skill_md.py": "a78f997dbe75695b",
+  "tests/unit/test_skill_pipeline.py": "b51de8bf81f193d4",
+  "tests/unit/test_skill_registry.py": "30679e6242902a3e",
+  "tests/unit/test_soul_evolution.py": "07d8b0b4550142a6",
+  "tests/unit/test_stdio_transport.py": "ad7d51c748b3580f",
+  "tests/unit/test_streaming.py": "9430a86a4cae4435",
+  "tests/unit/test_task_store_redis.py": "31e1ed3cb10dab5b",
+  "tests/unit/test_telemetry.py": "2f957631f9d87522",
+  "tests/unit/test_tool_composition.py": "88c496199e43eab1",
+  "tests/unit/test_tool_registry.py": "cb930ed167fe2b23",
+  "tests/unit/test_trace_recorder.py": "0f7809fe85094c08",
+  "tests/unit/test_u8_geo_integration.py": "c0eb3468de53fe1e",
+  "tests/unit/test_unified_evolution_store.py": "8419ece33016b902",
+  "tests/unit/test_usage_tracker.py": "eed60bc0fcf6a4a3",
+  "tests/unit/test_web_crawl_tool.py": "13d5bb6f6b098410",
+  "tests/unit/test_web_search_tool.py": "7ee8a8b20b793e03",
+  "tests/unit/test_websocket.py": "53e9ecd8c70f5bfe",
+  "tests/unit/test_working_memory.py": "49016e18a7998c4a",
+  "tests/unit/tools/__init__.py": "d41d8cd98f00b204",
+  "tests/unit/tools/test_computer_use.py": "1f851f50f5eb5e44",
+  "tests/unit/tools/test_pty_session.py": "8a512aabb314036e",
+  "tests/unit/tools/test_terminal_session.py": "1e979327e0d14753"
 }
--- a/.understand-anything/knowledge-graph.json
+++ b/.understand-anything/knowledge-graph.json
--- a/.understand-anything/meta.json
+++ b/.understand-anything/meta.json
@ -1,6 +1,6 @@
 {
-  "lastAnalyzedAt": "2026-06-12T17:39:05.023556+00:00",
-  "gitCommitHash": "09698d7a06c8d77b411e5a34ea27343ce9e8b42c",
+  "lastAnalyzedAt": "2026-06-14T10:00:00.000000+00:00",
+  "gitCommitHash": "d9d1b16e5911ad958cd8ae38958058bea13f3fcc",
  "version": "1.0.0",
-  "analyzedFiles": 248
+  "analyzedFiles": 208
 }
--- a/README.md
+++ b/README.md
@ -11,12 +11,14 @@ AgentKit 解决的核心问题：**从写 150 行 Agent 代码降为 10-20 行 Y
 核心定位：

 - **配置驱动** -- YAML 定义 Skill，无需写 Agent 子类
- **生产就绪** -- 内置质量门禁、模型降级、用量统计
+- **生产就绪** -- 内置质量门禁、模型降级、用量统计、级联检测、状态持久化
 - **四种使用** -- Python 库引用、CLI 聊天、Web GUI、桌面客户端
 - **记忆持久化** -- SOUL/USER/MEMORY/DAILY 四层记忆，写入即生效
 - **自进化** -- 反思驱动 Soul 更新，经验积累与陷阱检测
- **工具丰富** -- 内置 Shell、搜索、爬虫、记忆等工具，支持 MCP 扩展
+- **工具丰富** -- 内置 Shell、搜索、爬虫、记忆、桌面操控等工具，支持 MCP 扩展
 - **Pipeline 编排** -- 多 Agent 协同、Saga 补偿、动态流水线
+- **暗色主题** -- CSS 变量 + Ant Design 暗色算法，支持系统/手动切换
+- **LLM 缓存** -- 语义相似度缓存，减少重复调用，降低成本

 ## 核心特性

@ -74,15 +76,40 @@ CostAwareRouter 三层路由，从零成本到高成本逐层升级：

 路由结果携带 `ExecutionMode` 枚举（`DIRECT_CHAT` / `REACT` / `SKILL_REACT`），作为路由层与执行层的架构契约，杜绝硬编码。

-### 8. 产出质量管理
+### 8. 语义路由
+
+基于向量相似度的意图路由，作为关键词匹配的补充：
+
+- **SemanticRouter** -- 将用户输入和 Skill 描述向量化，通过余弦相似度匹配
+- **缓存友好** -- 向量缓存避免重复计算
+- **平滑降级** -- 语义路由失败时自动回退到启发式/LLM 分类
+
+### 9. LLM 响应缓存
+
+语义相似度缓存，减少重复 LLM 调用：
+
+- **CacheKey** -- 基于 prompt + model + temperature 生成缓存键
+- **语义匹配** -- 相似 prompt 命中缓存，避免重复调用
+- **TTL 管理** -- 缓存条目自动过期，支持手动失效
+
+### 10. 级联检测与状态持久化
+
+生产级故障防护：
+
+- **CascadeDetector** -- 检测 Agent 输出中的级联失败模式，及时熔断
+- **CascadeStateStore** -- 级联状态持久化，支持 InMemory 和 Redis 后端
+- **session_ttl** -- 可配置的会话 TTL，自动清理过期状态
+- **优雅降级** -- Redis 不可用时自动降级到 InMemory，保持服务可用
+
+### 11. 产出质量管理

 四维质量检查：必填字段、最低字数、JSON Schema 校验、自定义验证器。检查不通过时自动重试（可配置 max_retries），重试时携带质量反馈信息。

-### 9. 标准化输出
+### 12. 标准化输出

 Schema 验证 + 字段类型归一化（str -> int/float/bool）+ 元数据附加（version、produced_at、quality_score）。所有 Skill 产出统一为 StandardOutput 格式。

-### 10. 内置工具集
+### 13. 内置工具集

 开箱即用的工具插件，覆盖常见 Agent 需求：

@ -97,10 +124,11 @@ Schema 验证 + 字段类型归一化（str -> int/float/bool）+ 元数据附
 | `SchemaExtractTool` | 从文本提取结构化数据 |
 | `SchemaGenerateTool` | 生成 JSON Schema |
 | `MCPTool` | MCP 协议工具扩展 |
+| `ComputerUseTool` | 桌面操控（截图、点击、输入），支持云端和本地(pyautogui)模式 |

 工具组合：`SequentialChain`（顺序链）、`ParallelFanOut`（并行扇出）、`DynamicSelector`（动态选择）。

-### 11. Pipeline 编排
+### 14. Pipeline 编排

 多 Agent 协同编排，支持复杂工作流：

@ -949,6 +977,14 @@ ReActEngine 实现 Think -> Act -> Observe 循环：

 路由结果包含 `ExecutionMode` 枚举（`DIRECT_CHAT` / `REACT` / `SKILL_REACT`），作为路由层与执行层的架构契约。`complexity` 评分使用 `if is not None` 判断，避免 `0.0 or default` 误覆盖。

+### chat/semantic_router -- 语义路由
+
+基于向量相似度的意图路由，作为关键词匹配的补充：
+
+- **SemanticRouter** -- 将用户输入和 Skill 描述向量化，通过余弦相似度匹配
+- **缓存友好** -- 向量缓存避免重复计算
+- **平滑降级** -- 语义路由失败时自动回退到启发式/LLM 分类
+
 ### llm/gateway -- LLM Gateway

 统一 LLM 调用入口，核心能力：
@ -958,6 +994,8 @@ ReActEngine 实现 Think -> Act -> Observe 循环：
 - **Fallback 降级**: 主模型失败时自动切换到备选模型
 - **用量追踪**: 按 agent_name、model 统计 Token 用量和成本
 - **模型解析**: `"provider/model"` 格式自动路由到对应 Provider
+- **响应缓存**: 语义相似度缓存，减少重复调用（`llm/cache.py`）
+- **用量存储**: InMemory/Redis 双后端，支持分布式用量统计（`llm/providers/usage_store.py`）

 ### skills -- Skill 系统

@ -987,6 +1025,16 @@ SkillRegistry 管理 Skill 的注册、发现、更新。

 检查不通过时，如果 `max_retries > 0`，BaseAgent.execute() 会自动重试，将质量反馈信息注入 `quality_feedback` 字段。

+### quality/cascade -- 级联检测与状态持久化
+
+生产级故障防护：
+
+- **CascadeDetector** -- 检测 Agent 输出中的级联失败模式（连续失败、质量退化），及时熔断
+- **CascadeStateStore** -- 级联状态持久化，InMemory/Redis 双后端，支持 `session_ttl` 配置
+- **AlignmentGuard** -- 消息质量管控，集成在 MemoryBus 中
+- **优雅降级** -- Redis 不可用时自动降级到 InMemory，保持服务可用
+- **close() 方法** -- 显式关闭 Redis 连接池，避免资源泄漏
+
 ### quality/output -- 标准化输出

 OutputStandardizer 将原始产出转换为 StandardOutput：
@ -1101,7 +1149,7 @@ v2 增强：接受 SkillConfig 时自动创建 Skill 并启用 ReAct 模式，Qu

 | 视图 | 说明 |
 |------|------|
-| ChatView | 实时对话，WebSocket 流式传输，代码高亮，工具调用卡片 |
+| ChatView | 实时对话，WebSocket 流式传输，代码高亮，工具调用卡片，@-mention 技能推荐 |
 | EvolutionView | 自进化仪表盘，任务/经验/指标/优化面板 |
 | WorkflowView | 工作流编辑器，Vue Flow 可视化编排 |
 | TerminalView | 终端模拟器，PTY 会话 |
@ -1110,6 +1158,8 @@ v2 增强：接受 SkillConfig 时自动创建 Skill 并启用 ReAct 模式，Qu
 | SettingsView | 系统设置 |
 | ComputerUseView | 计算机使用，桌面操控 |

+**暗色主题**：支持亮色/暗色/跟随系统三种模式，CSS 变量 + Ant Design 暗色算法，通过导航栏灯泡图标切换，偏好持久化到 localStorage。
+
 ### 桌面客户端 (Tauri 2.x)

 跨平台桌面应用，架构：
@ -1291,7 +1341,7 @@ fischer-agentkit/
 │   ├── session/           # 会话管理
 │   ├── skills/            # 技能系统
 │   ├── telemetry/         # 遥测追踪
-│   ├── tools/             # 工具插件（21个）
+│   ├── tools/             # 工具插件（21个，含桌面操控）
 │   └── utils/             # 工具函数
 ├── src-tauri/             # Tauri 桌面客户端（Rust）
 │   ├── src/               # main.rs + lib.rs + sidecar.rs + tray.rs