{"repo": "chaineasy-ai/alice-agent", "files": {"alice-facade-cmd/src/main/java/org/cland/alice/facade/cmd/AliceCliLauncher.java": {"8ebb2c40d3906a86e5a1eaa8ea381b21d0df9bd9": {"t": 1785745680.1727407, "n": 24154, "h": "c44e229fbcac68d6", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.Map;\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.core.agent.AgentConfig;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.facade.cmd.chat.JLineChatSession;\nimport org.cland.alice.facade.cmd.config.AliceConfigStore;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印 → 成功退出\n        return EXIT_SUCCESS;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 工具列表命令 — 不走 Agent 执行\n      if (config.listTools()) {\n        return handleListTools(config);\n      }\n\n      // 3. 配置管理命令 — 不走 Agent 执行\n      if (config.configAction() != null) {\n        return handleConfig(config);\n      }\n\n      // 3a. Resume 命令 — 走 Agent 执行（通过 ExecutionCoordinator）\n      if (config.resumeMode()) {\n        // resume 命令可能带 --list 标志，直接列表展示\n        if (config.resumeList()) {\n          return handleResumeList(config);\n        }\n        // 否则进入 Agent 执行流程\n      }\n\n      // 4. 初始化 ModelProvider\n      String configDefaultModel = initializeModelProvider();\n\n      // 4a. 如果用户未通过 --model 指定，且 model.json 中配置了 default_model，则使用它\n      String effectiveModel =\n          (configDefaultModel != null && config.model().equals(RunConfig.DEFAULT_MODEL))\n              ? configDefaultModel\n              : config.model();\n      if (configDefaultModel != null) {\n        loadedDefaultModel = configDefaultModel;\n      }\n\n      // 4. 创建渲染器（将有效 model 回传给 config 上下文）\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 5. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer, effectiveModel);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = SnowflakeIdGenerator.generateSessionId();\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : SnowflakeIdGenerator.generateSessionId();\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ResumeSessionCmd resume -> {\n        System.out.println(\"Resuming session: \" + resume.sessionId());\n        if (resume.snapshotId() != null) {\n          System.out.println(\"  Snapshot: \" + resume.snapshotId());\n        }\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = SnowflakeIdGenerator.generateSessionId();\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 工具列表\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice tools} 子命令，列出 ToolRegistry 中的已注册工具。\n   *\n   * @param config 运行配置\n   * @return 退出码\n   */\n  private static int handleListTools(RunConfig config) {\n    try {\n      var registry = org.cland.alice.tool.gateway.ToolRegistryHolder.INSTANCE;\n      var tools = registry.allTools();\n\n      if (tools.isEmpty()) {\n        System.out.println(\"No tools registered.\");\n        System.out.println(\"Use 'alice run --skill <toolset>' or configure MCP to load tools.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Registered tools (\" + tools.size() + \"):\");\n      System.out.println();\n\n      if (config.toolDetail()) {\n        for (var meta : tools) {\n          System.out.println(\"  ── \" + meta.name() + \" ──\");\n          System.out.println(\"     Description: \" + meta.description());\n          if (meta.inputSchema() != null && !meta.inputSchema().isEmpty()) {\n            System.out.println(\"     Parameters:  \" + meta.inputSchema());\n          }\n          System.out.println();\n        }\n      } else {\n        for (var meta : tools) {\n          System.out.println(\"  ⚡ \" + meta.name() + \" — \" + meta.description());\n        }\n        System.out.println();\n        System.out.println(\"Use 'alice tools --detail' to see parameter schemas.\");\n      }\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing tools: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // Resume 会话列表\n  // ========================================================================\n\n  /** 处理 {@code alice resume --list} 子命令，列出 WAL 中可恢复的会话。 */\n  private static int handleResumeList(RunConfig config) {\n    try {\n      // 扫描 ~/.alice/wal 目录下的会话\n      var walDir = java.nio.file.Paths.get(System.getProperty(\"user.home\"), \".alice\", \"wal\");\n      if (!java.nio.file.Files.isDirectory(walDir)) {\n        System.out.println(\"No WAL storage found at: \" + walDir);\n        System.out.println(\"No sessions available.\");\n        return EXIT_SUCCESS;\n      }\n\n      var sessions = new java.util.ArrayList<String>();\n      try (var stream = java.nio.file.Files.newDirectoryStream(walDir, \"*.wal.jsonl\")) {\n        for (var path : stream) {\n          String fileName = path.getFileName().toString();\n          String sessionId = fileName.substring(0, fileName.length() - \".wal.jsonl\".length());\n          sessions.add(sessionId);\n        }\n      }\n\n      if (sessions.isEmpty()) {\n        System.out.println(\"No sessions available.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Available sessions (\" + sessions.size() + \"):\");\n      System.out.println();\n      for (var sid : sessions) {\n        var cpFile = walDir.resolve(sid + \".checkpoint.json\");\n        String info = \"  📁 \" + sid;\n        if (java.nio.file.Files.exists(cpFile)) {\n          info += \" (checkpoint available)\";\n        }\n        System.out.println(info);\n      }\n      System.out.println();\n      System.out.println(\"Use 'alice resume --session-id <id>' to restore a session.\");\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing sessions: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // 配置管理\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice config} 子命令。\n   *\n   * <p>支持:\n   *\n   * <ul>\n   *   <li>{@code alice config} — 显示当前配置概览\n   *   <li>{@code alice config get &lt;key&gt;} — 获取单个配置项\n   *   <li>{@code alice config set &lt;key&gt; &lt;value&gt;} — 设置配置项（当前仅打印模拟）\n   * </ul>\n   */\n  /** 全局配置存储实例（延迟初始化） */\n  private static volatile AliceConfigStore configStore;\n\n  /** 获取或创建配置存储实例。 */\n  private static AliceConfigStore configStore() {\n    if (configStore == null) {\n      synchronized (AliceCliLauncher.class) {\n        if (configStore == null) {\n          configStore = new AliceConfigStore();\n        }\n      }\n    }\n    return configStore;\n  }\n\n  private static int handleConfig(RunConfig config) {\n    try {\n      String action = config.configAction();\n      String key = config.configKey();\n      String value = config.configValue();\n      AliceConfigStore store = configStore();\n\n      if (\"set\".equals(action)) {\n        if (key == null || value == null) {\n          System.err.println(\"Usage: alice config set <key> <value>\");\n          return EXIT_PARAM_ERROR;\n        }\n        store.set(key, value);\n        System.out.println(\n            \"Config '\" + key + \"' set to '\" + value + \"' (persisted to ~/.alice/config.json)\");\n        return EXIT_SUCCESS;\n      }\n\n      if (\"get\".equals(action)) {\n        if (key == null) {\n          System.err.println(\"Usage: alice config get <key>\");\n          return EXIT_PARAM_ERROR;\n        }\n        printConfigValue(key, store);\n        return EXIT_SUCCESS;\n      }\n\n      // \"show\" — 显示全部配置\n      printAllConfig(store);\n      return EXIT_SUCCESS;\n\n    } catch (Exception e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  /**\n   * 获取配置值的实际值（优先级：环境变量 > 配置文件 > 内建默认值）。\n   *\n   * @param key 点分隔的键名\n   * @param store 配置存储\n   * @return 值，或 {@code null} 表示未设置\n   */\n  private static String resolveConfigValue(String key, AliceConfigStore store) {\n    // 1. 环境变量（最高优先级）\n    String envName = configKeyToEnv(key);\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        return envVal;\n      }\n    }\n    // 2. 配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      return stored;\n    }\n    // 3. 内建默认值\n    return switch (key) {\n      case \"default.model\" -> AgentConfig.DEFAULT_MODEL;\n      case \"agent.max_iterations\" -> String.valueOf(AgentConfig.DEFAULT_MAX_ITERATIONS);\n      default -> null;\n    };\n  }\n\n  /** 将配置键名映射为环境变量名。 */\n  private static String configKeyToEnv(String key) {\n    return switch (key) {\n      case \"openai.api_key\", \"providers.openai.api_key\" -> \"OPENAI_API_KEY\";\n      case \"anthropic.api_key\", \"providers.anthropic.api_key\" -> \"ANTHROPIC_API_KEY\";\n      default -> null;\n    };\n  }\n\n  /** 获取并打印单个配置项的值及其来源。 */\n  private static void printConfigValue(String key, AliceConfigStore store) {\n    String envName = configKeyToEnv(key);\n\n    // 检查环境变量\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        System.out.println(\n            key\n                + \" = \"\n                + envVal.substring(0, Math.min(8, envVal.length()))\n                + \"... (from env \"\n                + envName\n                + \")\");\n        return;\n      }\n    }\n\n    // 检查配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      String display =\n          key.contains(\"api_key\")\n              ? stored.substring(0, Math.min(8, stored.length())) + \"...\"\n              : stored;\n      System.out.println(key + \" = \" + display + \" (from ~/.alice/config.json)\");\n      return;\n    }\n\n    // 检查内建默认值\n    switch (key) {\n      case \"default.model\" -> {\n        if (loadedDefaultModel != null) {\n          System.out.println(\n              \"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n        } else {\n          System.out.println(\n              \"default.model = \" + AgentConfig.DEFAULT_MODEL + \" (built-in default)\");\n        }\n      }\n      case \"agent.max_iterations\" ->\n          System.out.println(\n              \"agent.max_iterations = \"\n                  + AgentConfig.DEFAULT_MAX_ITERATIONS\n                  + \" (built-in default)\");\n      default -> System.out.println(key + \" = (not set)\");\n    }\n  }\n\n  /** 打印所有配置项。 */\n  private static void printAllConfig(AliceConfigStore store) {\n    System.out.println(\"=== Alice Agent Configuration ===\");\n    System.out.println();\n\n    // 读取 providers 嵌套结构\n    var all = store.getAll();\n    System.out.println(\"-- Model Providers --\");\n    @SuppressWarnings(\"unchecked\")\n    var providers = (Map<String, Object>) all.get(\"providers\");\n    if (providers != null && !providers.isEmpty()) {\n      for (var entry : providers.entrySet()) {\n        String providerName = entry.getKey();\n        System.out.println(\"  [\" + providerName + \"]\");\n        @SuppressWarnings(\"unchecked\")\n        var providerConfig = (Map<String, Object>) entry.getValue();\n        if (providerConfig != null) {\n          for (var pEntry : providerConfig.entrySet()) {\n            String display =\n                pEntry.getKey().contains(\"api_key\")\n                    ? pEntry\n                            .getValue()\n                            .toString()\n                            .substring(0, Math.min(8, pEntry.getValue().toString().length()))\n                        + \"...\"\n                    : pEntry.getValue().toString();\n            System.out.println(\"    \" + pEntry.getKey() + \" = \" + display);\n          }\n        }\n      }\n    } else {\n      // 回退到扁平键\n      printConfigValue(\"openai.api_key\", store);\n      printConfigValue(\"anthropic.api_key\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"-- Agent Defaults --\");\n    // 从嵌套或扁平读取\n    String model = store.get(\"default.model\");\n    if (model == null) model = store.get(\"default_model\");\n    if (model != null) {\n      System.out.println(\"default.model = \" + model + \" (from ~/.alice/config.json)\");\n    } else if (loadedDefaultModel != null) {\n      System.out.println(\"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n    } else {\n      printConfigValue(\"default.model\", store);\n    }\n\n    String iterStr = store.get(\"agent.max_iterations\");\n    if (iterStr == null) iterStr = store.get(\"max_iterations\");\n    if (iterStr != null) {\n      System.out.println(\"agent.max_iterations = \" + iterStr + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"agent.max_iterations\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"Config file: ~/.alice/config.json\");\n    System.out.println(\"Use 'alice config get <key>' or 'alice config set <key> <value>'\");\n    System.out.println(\n        \"Known keys: openai.api_key, anthropic.api_key, default.model, agent.max_iterations\");\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  /** 从 ~/.alice/model.json 加载的默认模型 ID，供 config 命令和 run 流程使用。 */\n  private static String loadedDefaultModel;\n\n  /** 从 ~/.alice/model.json 加载的默认模型配置对象（新格式），含 enable_thinking / reasoning_effort。 */\n  private static ModelConfigLoader.DefaultModelConfig loadedDefaultModelConfig;\n\n  /**\n   * 初始化模型提供器。\n   *\n   * @return model.json 中配置的默认模型 ID，未设置则返回 {@code null}\n   */\n  private static String initializeModelProvider() {\n    String configDefaultModel = null;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n\n      // 1. 加载 ~/.alice/model.json 配置文件\n      try {\n        ModelConfigLoader configLoader = new ModelConfigLoader();\n        configLoader.load();\n        configLoader.registerTo(provider);\n        configDefaultModel = configLoader.getDefaultModel();\n        loadedDefaultModelConfig = configLoader.getDefaultModelConfig();\n        logger.info(\n            \"Loaded {} provider(s) from ~/.alice/model.json\", configLoader.getProviders().size());\n        if (configDefaultModel != null) {\n          logger.info(\"Default model from model.json: {}\", configDefaultModel);\n        }\n        if (loadedDefaultModelConfig != null) {\n          logger.info(\n              \"Default model config: enable_thinking={}, reasoning_effort={}\",\n              loadedDefaultModelConfig.enableThinking(),\n              loadedDefaultModelConfig.reasoningEffort());\n        }\n      } catch (Exception e) {\n        logger.debug(\"No model config found, using env vars: {}\", e.getMessage());\n      }\n\n      // 2. 注册内置模型枚举\n      provider.registerBuiltinModels();\n\n      // 3. 从环境变量注册供应商（优先级低于配置文件）\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        if (provider.getSupplier(\"gpt-4o-mini\") == null) {\n          provider.registerSupplier(new OpenAiSupplier(openAiKey));\n          logger.info(\"OpenAI supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        if (provider.getSupplier(\"claude-3-5-sonnet-latest\") == null) {\n          provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n          logger.info(\"Anthropic Claude supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n\n      // 4. DeepSeek (OpenAI-compatible) 从环境变量注册\n      String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n      if (deepseekKey != null && !deepseekKey.isEmpty()) {\n        if (provider.getSupplier(\"deepseek-v4-flash\") == null) {\n          provider.registerSupplier(\n              new OpenAiSupplier(\n                  \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n          logger.info(\"DeepSeek supplier registered from env var (OpenAI-compatible)\");\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n    return configDefaultModel;\n  }\n}\n"}, "542dfef86a8f56af43d54230584fe2edf9c80f6a": {"t": 1785755689.6241956, "n": 24154, "h": "c44e229fbcac68d6", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.Map;\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.core.agent.AgentConfig;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.facade.cmd.chat.JLineChatSession;\nimport org.cland.alice.facade.cmd.config.AliceConfigStore;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印 → 成功退出\n        return EXIT_SUCCESS;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 工具列表命令 — 不走 Agent 执行\n      if (config.listTools()) {\n        return handleListTools(config);\n      }\n\n      // 3. 配置管理命令 — 不走 Agent 执行\n      if (config.configAction() != null) {\n        return handleConfig(config);\n      }\n\n      // 3a. Resume 命令 — 走 Agent 执行（通过 ExecutionCoordinator）\n      if (config.resumeMode()) {\n        // resume 命令可能带 --list 标志，直接列表展示\n        if (config.resumeList()) {\n          return handleResumeList(config);\n        }\n        // 否则进入 Agent 执行流程\n      }\n\n      // 4. 初始化 ModelProvider\n      String configDefaultModel = initializeModelProvider();\n\n      // 4a. 如果用户未通过 --model 指定，且 model.json 中配置了 default_model，则使用它\n      String effectiveModel =\n          (configDefaultModel != null && config.model().equals(RunConfig.DEFAULT_MODEL))\n              ? configDefaultModel\n              : config.model();\n      if (configDefaultModel != null) {\n        loadedDefaultModel = configDefaultModel;\n      }\n\n      // 4. 创建渲染器（将有效 model 回传给 config 上下文）\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 5. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer, effectiveModel);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = SnowflakeIdGenerator.generateSessionId();\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : SnowflakeIdGenerator.generateSessionId();\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ResumeSessionCmd resume -> {\n        System.out.println(\"Resuming session: \" + resume.sessionId());\n        if (resume.snapshotId() != null) {\n          System.out.println(\"  Snapshot: \" + resume.snapshotId());\n        }\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = SnowflakeIdGenerator.generateSessionId();\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 工具列表\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice tools} 子命令，列出 ToolRegistry 中的已注册工具。\n   *\n   * @param config 运行配置\n   * @return 退出码\n   */\n  private static int handleListTools(RunConfig config) {\n    try {\n      var registry = org.cland.alice.tool.gateway.ToolRegistryHolder.INSTANCE;\n      var tools = registry.allTools();\n\n      if (tools.isEmpty()) {\n        System.out.println(\"No tools registered.\");\n        System.out.println(\"Use 'alice run --skill <toolset>' or configure MCP to load tools.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Registered tools (\" + tools.size() + \"):\");\n      System.out.println();\n\n      if (config.toolDetail()) {\n        for (var meta : tools) {\n          System.out.println(\"  ── \" + meta.name() + \" ──\");\n          System.out.println(\"     Description: \" + meta.description());\n          if (meta.inputSchema() != null && !meta.inputSchema().isEmpty()) {\n            System.out.println(\"     Parameters:  \" + meta.inputSchema());\n          }\n          System.out.println();\n        }\n      } else {\n        for (var meta : tools) {\n          System.out.println(\"  ⚡ \" + meta.name() + \" — \" + meta.description());\n        }\n        System.out.println();\n        System.out.println(\"Use 'alice tools --detail' to see parameter schemas.\");\n      }\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing tools: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // Resume 会话列表\n  // ========================================================================\n\n  /** 处理 {@code alice resume --list} 子命令，列出 WAL 中可恢复的会话。 */\n  private static int handleResumeList(RunConfig config) {\n    try {\n      // 扫描 ~/.alice/wal 目录下的会话\n      var walDir = java.nio.file.Paths.get(System.getProperty(\"user.home\"), \".alice\", \"wal\");\n      if (!java.nio.file.Files.isDirectory(walDir)) {\n        System.out.println(\"No WAL storage found at: \" + walDir);\n        System.out.println(\"No sessions available.\");\n        return EXIT_SUCCESS;\n      }\n\n      var sessions = new java.util.ArrayList<String>();\n      try (var stream = java.nio.file.Files.newDirectoryStream(walDir, \"*.wal.jsonl\")) {\n        for (var path : stream) {\n          String fileName = path.getFileName().toString();\n          String sessionId = fileName.substring(0, fileName.length() - \".wal.jsonl\".length());\n          sessions.add(sessionId);\n        }\n      }\n\n      if (sessions.isEmpty()) {\n        System.out.println(\"No sessions available.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Available sessions (\" + sessions.size() + \"):\");\n      System.out.println();\n      for (var sid : sessions) {\n        var cpFile = walDir.resolve(sid + \".checkpoint.json\");\n        String info = \"  📁 \" + sid;\n        if (java.nio.file.Files.exists(cpFile)) {\n          info += \" (checkpoint available)\";\n        }\n        System.out.println(info);\n      }\n      System.out.println();\n      System.out.println(\"Use 'alice resume --session-id <id>' to restore a session.\");\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing sessions: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // 配置管理\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice config} 子命令。\n   *\n   * <p>支持:\n   *\n   * <ul>\n   *   <li>{@code alice config} — 显示当前配置概览\n   *   <li>{@code alice config get &lt;key&gt;} — 获取单个配置项\n   *   <li>{@code alice config set &lt;key&gt; &lt;value&gt;} — 设置配置项（当前仅打印模拟）\n   * </ul>\n   */\n  /** 全局配置存储实例（延迟初始化） */\n  private static volatile AliceConfigStore configStore;\n\n  /** 获取或创建配置存储实例。 */\n  private static AliceConfigStore configStore() {\n    if (configStore == null) {\n      synchronized (AliceCliLauncher.class) {\n        if (configStore == null) {\n          configStore = new AliceConfigStore();\n        }\n      }\n    }\n    return configStore;\n  }\n\n  private static int handleConfig(RunConfig config) {\n    try {\n      String action = config.configAction();\n      String key = config.configKey();\n      String value = config.configValue();\n      AliceConfigStore store = configStore();\n\n      if (\"set\".equals(action)) {\n        if (key == null || value == null) {\n          System.err.println(\"Usage: alice config set <key> <value>\");\n          return EXIT_PARAM_ERROR;\n        }\n        store.set(key, value);\n        System.out.println(\n            \"Config '\" + key + \"' set to '\" + value + \"' (persisted to ~/.alice/config.json)\");\n        return EXIT_SUCCESS;\n      }\n\n      if (\"get\".equals(action)) {\n        if (key == null) {\n          System.err.println(\"Usage: alice config get <key>\");\n          return EXIT_PARAM_ERROR;\n        }\n        printConfigValue(key, store);\n        return EXIT_SUCCESS;\n      }\n\n      // \"show\" — 显示全部配置\n      printAllConfig(store);\n      return EXIT_SUCCESS;\n\n    } catch (Exception e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  /**\n   * 获取配置值的实际值（优先级：环境变量 > 配置文件 > 内建默认值）。\n   *\n   * @param key 点分隔的键名\n   * @param store 配置存储\n   * @return 值，或 {@code null} 表示未设置\n   */\n  private static String resolveConfigValue(String key, AliceConfigStore store) {\n    // 1. 环境变量（最高优先级）\n    String envName = configKeyToEnv(key);\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        return envVal;\n      }\n    }\n    // 2. 配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      return stored;\n    }\n    // 3. 内建默认值\n    return switch (key) {\n      case \"default.model\" -> AgentConfig.DEFAULT_MODEL;\n      case \"agent.max_iterations\" -> String.valueOf(AgentConfig.DEFAULT_MAX_ITERATIONS);\n      default -> null;\n    };\n  }\n\n  /** 将配置键名映射为环境变量名。 */\n  private static String configKeyToEnv(String key) {\n    return switch (key) {\n      case \"openai.api_key\", \"providers.openai.api_key\" -> \"OPENAI_API_KEY\";\n      case \"anthropic.api_key\", \"providers.anthropic.api_key\" -> \"ANTHROPIC_API_KEY\";\n      default -> null;\n    };\n  }\n\n  /** 获取并打印单个配置项的值及其来源。 */\n  private static void printConfigValue(String key, AliceConfigStore store) {\n    String envName = configKeyToEnv(key);\n\n    // 检查环境变量\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        System.out.println(\n            key\n                + \" = \"\n                + envVal.substring(0, Math.min(8, envVal.length()))\n                + \"... (from env \"\n                + envName\n                + \")\");\n        return;\n      }\n    }\n\n    // 检查配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      String display =\n          key.contains(\"api_key\")\n              ? stored.substring(0, Math.min(8, stored.length())) + \"...\"\n              : stored;\n      System.out.println(key + \" = \" + display + \" (from ~/.alice/config.json)\");\n      return;\n    }\n\n    // 检查内建默认值\n    switch (key) {\n      case \"default.model\" -> {\n        if (loadedDefaultModel != null) {\n          System.out.println(\n              \"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n        } else {\n          System.out.println(\n              \"default.model = \" + AgentConfig.DEFAULT_MODEL + \" (built-in default)\");\n        }\n      }\n      case \"agent.max_iterations\" ->\n          System.out.println(\n              \"agent.max_iterations = \"\n                  + AgentConfig.DEFAULT_MAX_ITERATIONS\n                  + \" (built-in default)\");\n      default -> System.out.println(key + \" = (not set)\");\n    }\n  }\n\n  /** 打印所有配置项。 */\n  private static void printAllConfig(AliceConfigStore store) {\n    System.out.println(\"=== Alice Agent Configuration ===\");\n    System.out.println();\n\n    // 读取 providers 嵌套结构\n    var all = store.getAll();\n    System.out.println(\"-- Model Providers --\");\n    @SuppressWarnings(\"unchecked\")\n    var providers = (Map<String, Object>) all.get(\"providers\");\n    if (providers != null && !providers.isEmpty()) {\n      for (var entry : providers.entrySet()) {\n        String providerName = entry.getKey();\n        System.out.println(\"  [\" + providerName + \"]\");\n        @SuppressWarnings(\"unchecked\")\n        var providerConfig = (Map<String, Object>) entry.getValue();\n        if (providerConfig != null) {\n          for (var pEntry : providerConfig.entrySet()) {\n            String display =\n                pEntry.getKey().contains(\"api_key\")\n                    ? pEntry\n                            .getValue()\n                            .toString()\n                            .substring(0, Math.min(8, pEntry.getValue().toString().length()))\n                        + \"...\"\n                    : pEntry.getValue().toString();\n            System.out.println(\"    \" + pEntry.getKey() + \" = \" + display);\n          }\n        }\n      }\n    } else {\n      // 回退到扁平键\n      printConfigValue(\"openai.api_key\", store);\n      printConfigValue(\"anthropic.api_key\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"-- Agent Defaults --\");\n    // 从嵌套或扁平读取\n    String model = store.get(\"default.model\");\n    if (model == null) model = store.get(\"default_model\");\n    if (model != null) {\n      System.out.println(\"default.model = \" + model + \" (from ~/.alice/config.json)\");\n    } else if (loadedDefaultModel != null) {\n      System.out.println(\"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n    } else {\n      printConfigValue(\"default.model\", store);\n    }\n\n    String iterStr = store.get(\"agent.max_iterations\");\n    if (iterStr == null) iterStr = store.get(\"max_iterations\");\n    if (iterStr != null) {\n      System.out.println(\"agent.max_iterations = \" + iterStr + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"agent.max_iterations\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"Config file: ~/.alice/config.json\");\n    System.out.println(\"Use 'alice config get <key>' or 'alice config set <key> <value>'\");\n    System.out.println(\n        \"Known keys: openai.api_key, anthropic.api_key, default.model, agent.max_iterations\");\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  /** 从 ~/.alice/model.json 加载的默认模型 ID，供 config 命令和 run 流程使用。 */\n  private static String loadedDefaultModel;\n\n  /** 从 ~/.alice/model.json 加载的默认模型配置对象（新格式），含 enable_thinking / reasoning_effort。 */\n  private static ModelConfigLoader.DefaultModelConfig loadedDefaultModelConfig;\n\n  /**\n   * 初始化模型提供器。\n   *\n   * @return model.json 中配置的默认模型 ID，未设置则返回 {@code null}\n   */\n  private static String initializeModelProvider() {\n    String configDefaultModel = null;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n\n      // 1. 加载 ~/.alice/model.json 配置文件\n      try {\n        ModelConfigLoader configLoader = new ModelConfigLoader();\n        configLoader.load();\n        configLoader.registerTo(provider);\n        configDefaultModel = configLoader.getDefaultModel();\n        loadedDefaultModelConfig = configLoader.getDefaultModelConfig();\n        logger.info(\n            \"Loaded {} provider(s) from ~/.alice/model.json\", configLoader.getProviders().size());\n        if (configDefaultModel != null) {\n          logger.info(\"Default model from model.json: {}\", configDefaultModel);\n        }\n        if (loadedDefaultModelConfig != null) {\n          logger.info(\n              \"Default model config: enable_thinking={}, reasoning_effort={}\",\n              loadedDefaultModelConfig.enableThinking(),\n              loadedDefaultModelConfig.reasoningEffort());\n        }\n      } catch (Exception e) {\n        logger.debug(\"No model config found, using env vars: {}\", e.getMessage());\n      }\n\n      // 2. 注册内置模型枚举\n      provider.registerBuiltinModels();\n\n      // 3. 从环境变量注册供应商（优先级低于配置文件）\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        if (provider.getSupplier(\"gpt-4o-mini\") == null) {\n          provider.registerSupplier(new OpenAiSupplier(openAiKey));\n          logger.info(\"OpenAI supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        if (provider.getSupplier(\"claude-3-5-sonnet-latest\") == null) {\n          provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n          logger.info(\"Anthropic Claude supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n\n      // 4. DeepSeek (OpenAI-compatible) 从环境变量注册\n      String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n      if (deepseekKey != null && !deepseekKey.isEmpty()) {\n        if (provider.getSupplier(\"deepseek-v4-flash\") == null) {\n          provider.registerSupplier(\n              new OpenAiSupplier(\n                  \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n          logger.info(\"DeepSeek supplier registered from env var (OpenAI-compatible)\");\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n    return configDefaultModel;\n  }\n}\n"}, "2bbe5ee1d8031e29c337a8ef11da323abd34bd40": {"t": 1785755691.3378158, "n": 23656, "h": "d19894fb0d903ef4", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.Map;\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.core.agent.AgentConfig;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.facade.cmd.chat.JLineChatSession;\nimport org.cland.alice.facade.cmd.config.AliceConfigStore;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印 → 成功退出\n        return EXIT_SUCCESS;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 工具列表命令 — 不走 Agent 执行\n      if (config.listTools()) {\n        return handleListTools(config);\n      }\n\n      // 3. 配置管理命令 — 不走 Agent 执行\n      if (config.configAction() != null) {\n        return handleConfig(config);\n      }\n\n      // 3a. Resume 命令 — 走 Agent 执行（通过 ExecutionCoordinator）\n      if (config.resumeMode()) {\n        // resume 命令可能带 --list 标志，直接列表展示\n        if (config.resumeList()) {\n          return handleResumeList(config);\n        }\n        // 否则进入 Agent 执行流程\n      }\n\n      // 4. 初始化 ModelProvider\n      String configDefaultModel = initializeModelProvider();\n\n      // 4a. 如果用户未通过 --model 指定，且 model.json 中配置了 default_model，则使用它\n      String effectiveModel =\n          (configDefaultModel != null && config.model().equals(RunConfig.DEFAULT_MODEL))\n              ? configDefaultModel\n              : config.model();\n      if (configDefaultModel != null) {\n        loadedDefaultModel = configDefaultModel;\n      }\n\n      // 4. 创建渲染器（将有效 model 回传给 config 上下文）\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 5. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer, effectiveModel);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = SnowflakeIdGenerator.generateSessionId();\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : SnowflakeIdGenerator.generateSessionId();\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ResumeSessionCmd resume -> {\n        System.out.println(\"Resuming session: \" + resume.sessionId());\n        if (resume.snapshotId() != null) {\n          System.out.println(\"  Snapshot: \" + resume.snapshotId());\n        }\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = SnowflakeIdGenerator.generateSessionId();\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 工具列表\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice tools} 子命令，列出 ToolRegistry 中的已注册工具。\n   *\n   * @param config 运行配置\n   * @return 退出码\n   */\n  private static int handleListTools(RunConfig config) {\n    try {\n      var registry = org.cland.alice.tool.gateway.ToolRegistryHolder.INSTANCE;\n      var tools = registry.allTools();\n\n      if (tools.isEmpty()) {\n        System.out.println(\"No tools registered.\");\n        System.out.println(\"Use 'alice run --skill <toolset>' or configure MCP to load tools.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Registered tools (\" + tools.size() + \"):\");\n      System.out.println();\n\n      if (config.toolDetail()) {\n        for (var meta : tools) {\n          System.out.println(\"  ── \" + meta.name() + \" ──\");\n          System.out.println(\"     Description: \" + meta.description());\n          if (meta.inputSchema() != null && !meta.inputSchema().isEmpty()) {\n            System.out.println(\"     Parameters:  \" + meta.inputSchema());\n          }\n          System.out.println();\n        }\n      } else {\n        for (var meta : tools) {\n          System.out.println(\"  ⚡ \" + meta.name() + \" — \" + meta.description());\n        }\n        System.out.println();\n        System.out.println(\"Use 'alice tools --detail' to see parameter schemas.\");\n      }\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing tools: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // Resume 会话列表\n  // ========================================================================\n\n  /** 处理 {@code alice resume --list} 子命令，列出 WAL 中可恢复的会话。 */\n  private static int handleResumeList(RunConfig config) {\n    try {\n      // 扫描 ~/.alice/wal 目录下的会话\n      var walDir = java.nio.file.Paths.get(System.getProperty(\"user.home\"), \".alice\", \"wal\");\n      if (!java.nio.file.Files.isDirectory(walDir)) {\n        System.out.println(\"No WAL storage found at: \" + walDir);\n        System.out.println(\"No sessions available.\");\n        return EXIT_SUCCESS;\n      }\n\n      var sessions = new java.util.ArrayList<String>();\n      try (var stream = java.nio.file.Files.newDirectoryStream(walDir, \"*.wal.jsonl\")) {\n        for (var path : stream) {\n          String fileName = path.getFileName().toString();\n          String sessionId = fileName.substring(0, fileName.length() - \".wal.jsonl\".length());\n          sessions.add(sessionId);\n        }\n      }\n\n      if (sessions.isEmpty()) {\n        System.out.println(\"No sessions available.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Available sessions (\" + sessions.size() + \"):\");\n      System.out.println();\n      for (var sid : sessions) {\n        var cpFile = walDir.resolve(sid + \".checkpoint.json\");\n        String info = \"  📁 \" + sid;\n        if (java.nio.file.Files.exists(cpFile)) {\n          info += \" (checkpoint available)\";\n        }\n        System.out.println(info);\n      }\n      System.out.println();\n      System.out.println(\"Use 'alice resume --session-id <id>' to restore a session.\");\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing sessions: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // 配置管理\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice config} 子命令。\n   *\n   * <p>支持:\n   *\n   * <ul>\n   *   <li>{@code alice config} — 显示当前配置概览\n   *   <li>{@code alice config get &lt;key&gt;} — 获取单个配置项\n   *   <li>{@code alice config set &lt;key&gt; &lt;value&gt;} — 设置配置项（当前仅打印模拟）\n   * </ul>\n   */\n  /** 全局配置存储实例（延迟初始化） */\n  private static volatile AliceConfigStore configStore;\n\n  /** 获取或创建配置存储实例。 */\n  private static AliceConfigStore configStore() {\n    if (configStore == null) {\n      synchronized (AliceCliLauncher.class) {\n        if (configStore == null) {\n          configStore = new AliceConfigStore();\n        }\n      }\n    }\n    return configStore;\n  }\n\n  private static int handleConfig(RunConfig config) {\n    try {\n      String action = config.configAction();\n      String key = config.configKey();\n      String value = config.configValue();\n      AliceConfigStore store = configStore();\n\n      if (\"set\".equals(action)) {\n        if (key == null || value == null) {\n          System.err.println(\"Usage: alice config set <key> <value>\");\n          return EXIT_PARAM_ERROR;\n        }\n        store.set(key, value);\n        System.out.println(\n            \"Config '\" + key + \"' set to '\" + value + \"' (persisted to ~/.alice/config.json)\");\n        return EXIT_SUCCESS;\n      }\n\n      if (\"get\".equals(action)) {\n        if (key == null) {\n          System.err.println(\"Usage: alice config get <key>\");\n          return EXIT_PARAM_ERROR;\n        }\n        printConfigValue(key, store);\n        return EXIT_SUCCESS;\n      }\n\n      // \"show\" — 显示全部配置\n      printAllConfig(store);\n      return EXIT_SUCCESS;\n\n    } catch (Exception e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  /**\n   * 获取配置值的实际值（优先级：环境变量 > 配置文件 > 内建默认值）。\n   *\n   * @param key 点分隔的键名\n   * @param store 配置存储\n   * @return 值，或 {@code null} 表示未设置\n   */\n  private static String resolveConfigValue(String key, AliceConfigStore store) {\n    // 1. 环境变量（最高优先级）\n    String envName = configKeyToEnv(key);\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        return envVal;\n      }\n    }\n    // 2. 配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      return stored;\n    }\n    // 3. 内建默认值\n    return switch (key) {\n      case \"default.model\" -> AgentConfig.DEFAULT_MODEL;\n      case \"agent.max_iterations\" -> String.valueOf(AgentConfig.DEFAULT_MAX_ITERATIONS);\n      default -> null;\n    };\n  }\n\n  /** 将配置键名映射为环境变量名。 */\n  private static String configKeyToEnv(String key) {\n    return switch (key) {\n      case \"openai.api_key\", \"providers.openai.api_key\" -> \"OPENAI_API_KEY\";\n      case \"anthropic.api_key\", \"providers.anthropic.api_key\" -> \"ANTHROPIC_API_KEY\";\n      default -> null;\n    };\n  }\n\n  /** 获取并打印单个配置项的值及其来源。 */\n  private static void printConfigValue(String key, AliceConfigStore store) {\n    String envName = configKeyToEnv(key);\n\n    // 检查环境变量\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        System.out.println(\n            key\n                + \" = \"\n                + envVal.substring(0, Math.min(8, envVal.length()))\n                + \"... (from env \"\n                + envName\n                + \")\");\n        return;\n      }\n    }\n\n    // 检查配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      String display =\n          key.contains(\"api_key\")\n              ? stored.substring(0, Math.min(8, stored.length())) + \"...\"\n              : stored;\n      System.out.println(key + \" = \" + display + \" (from ~/.alice/config.json)\");\n      return;\n    }\n\n    // 检查内建默认值\n    switch (key) {\n      case \"default.model\" -> {\n        if (loadedDefaultModel != null) {\n          System.out.println(\n              \"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n        } else {\n          System.out.println(\n              \"default.model = \" + AgentConfig.DEFAULT_MODEL + \" (built-in default)\");\n        }\n      }\n      case \"agent.max_iterations\" ->\n          System.out.println(\n              \"agent.max_iterations = \"\n                  + AgentConfig.DEFAULT_MAX_ITERATIONS\n                  + \" (built-in default)\");\n      default -> System.out.println(key + \" = (not set)\");\n    }\n  }\n\n  /** 打印所有配置项。 */\n  private static void printAllConfig(AliceConfigStore store) {\n    System.out.println(\"=== Alice Agent Configuration ===\");\n    System.out.println();\n\n    // 读取 providers 嵌套结构\n    var all = store.getAll();\n    System.out.println(\"-- Model Providers --\");\n    @SuppressWarnings(\"unchecked\")\n    var providers = (Map<String, Object>) all.get(\"providers\");\n    if (providers != null && !providers.isEmpty()) {\n      for (var entry : providers.entrySet()) {\n        String providerName = entry.getKey();\n        System.out.println(\"  [\" + providerName + \"]\");\n        @SuppressWarnings(\"unchecked\")\n        var providerConfig = (Map<String, Object>) entry.getValue();\n        if (providerConfig != null) {\n          for (var pEntry : providerConfig.entrySet()) {\n            String display =\n                pEntry.getKey().contains(\"api_key\")\n                    ? pEntry\n                            .getValue()\n                            .toString()\n                            .substring(0, Math.min(8, pEntry.getValue().toString().length()))\n                        + \"...\"\n                    : pEntry.getValue().toString();\n            System.out.println(\"    \" + pEntry.getKey() + \" = \" + display);\n          }\n        }\n      }\n    } else {\n      // 回退到扁平键\n      printConfigValue(\"openai.api_key\", store);\n      printConfigValue(\"anthropic.api_key\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"-- Agent Defaults --\");\n    // 从嵌套或扁平读取\n    String model = store.get(\"default.model\");\n    if (model == null) model = store.get(\"default_model\");\n    if (model != null) {\n      System.out.println(\"default.model = \" + model + \" (from ~/.alice/config.json)\");\n    } else if (loadedDefaultModel != null) {\n      System.out.println(\"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n    } else {\n      printConfigValue(\"default.model\", store);\n    }\n\n    String iterStr = store.get(\"agent.max_iterations\");\n    if (iterStr == null) iterStr = store.get(\"max_iterations\");\n    if (iterStr != null) {\n      System.out.println(\"agent.max_iterations = \" + iterStr + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"agent.max_iterations\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"Config file: ~/.alice/config.json\");\n    System.out.println(\"Use 'alice config get <key>' or 'alice config set <key> <value>'\");\n    System.out.println(\n        \"Known keys: openai.api_key, anthropic.api_key, default.model, agent.max_iterations\");\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  /** 从 ~/.alice/model.json 加载的默认模型 ID，供 config 命令和 run 流程使用。 */\n  private static String loadedDefaultModel;\n\n  /**\n   * 初始化模型提供器。\n   *\n   * @return model.json 中配置的默认模型 ID，未设置则返回 {@code null}\n   */\n  private static String initializeModelProvider() {\n    String configDefaultModel = null;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n\n      // 1. 加载 ~/.alice/model.json 配置文件\n      try {\n        ModelConfigLoader configLoader = new ModelConfigLoader();\n        configLoader.load();\n        configLoader.registerTo(provider);\n        configDefaultModel = configLoader.getDefaultModel();\n        logger.info(\n            \"Loaded {} model provider(s) from ~/.alice/model.json\",\n            configLoader.getProviders().size());\n        if (configDefaultModel != null) {\n          logger.info(\"Default model from model.json: {}\", configDefaultModel);\n        }\n      } catch (Exception e) {\n        logger.debug(\"No model config found, using env vars: {}\", e.getMessage());\n      }\n\n      // 2. 注册内置模型枚举\n      provider.registerBuiltinModels();\n\n      // 3. 从环境变量注册供应商（优先级低于配置文件）\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        if (provider.getSupplier(\"gpt-4o-mini\") == null) {\n          provider.registerSupplier(new OpenAiSupplier(openAiKey));\n          logger.info(\"OpenAI supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        if (provider.getSupplier(\"claude-3-5-sonnet-latest\") == null) {\n          provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n          logger.info(\"Anthropic Claude supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n\n      // 4. DeepSeek (OpenAI-compatible) 从环境变量注册\n      String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n      if (deepseekKey != null && !deepseekKey.isEmpty()) {\n        if (provider.getSupplier(\"deepseek-v4-flash\") == null) {\n          provider.registerSupplier(\n              new OpenAiSupplier(\n                  \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n          logger.info(\"DeepSeek supplier registered from env var (OpenAI-compatible)\");\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n    return configDefaultModel;\n  }\n}\n"}, "3e58892dd30fad4915a4efd6b5901a09ac3ccae5": {"t": 1785755692.903427, "n": 23608, "h": "8e0fb5edc52116d6", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.Map;\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.core.agent.AgentConfig;\nimport org.cland.alice.facade.cmd.chat.JLineChatSession;\nimport org.cland.alice.facade.cmd.config.AliceConfigStore;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印 → 成功退出\n        return EXIT_SUCCESS;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 工具列表命令 — 不走 Agent 执行\n      if (config.listTools()) {\n        return handleListTools(config);\n      }\n\n      // 3. 配置管理命令 — 不走 Agent 执行\n      if (config.configAction() != null) {\n        return handleConfig(config);\n      }\n\n      // 3a. Resume 命令 — 走 Agent 执行（通过 ExecutionCoordinator）\n      if (config.resumeMode()) {\n        // resume 命令可能带 --list 标志，直接列表展示\n        if (config.resumeList()) {\n          return handleResumeList(config);\n        }\n        // 否则进入 Agent 执行流程\n      }\n\n      // 4. 初始化 ModelProvider\n      String configDefaultModel = initializeModelProvider();\n\n      // 4a. 如果用户未通过 --model 指定，且 model.json 中配置了 default_model，则使用它\n      String effectiveModel =\n          (configDefaultModel != null && config.model().equals(RunConfig.DEFAULT_MODEL))\n              ? configDefaultModel\n              : config.model();\n      if (configDefaultModel != null) {\n        loadedDefaultModel = configDefaultModel;\n      }\n\n      // 4. 创建渲染器（将有效 model 回传给 config 上下文）\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 5. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer, effectiveModel);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ResumeSessionCmd resume -> {\n        System.out.println(\"Resuming session: \" + resume.sessionId());\n        if (resume.snapshotId() != null) {\n          System.out.println(\"  Snapshot: \" + resume.snapshotId());\n        }\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = UUID.randomUUID().toString().substring(0, 8);\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 工具列表\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice tools} 子命令，列出 ToolRegistry 中的已注册工具。\n   *\n   * @param config 运行配置\n   * @return 退出码\n   */\n  private static int handleListTools(RunConfig config) {\n    try {\n      var registry = org.cland.alice.tool.gateway.ToolRegistryHolder.INSTANCE;\n      var tools = registry.allTools();\n\n      if (tools.isEmpty()) {\n        System.out.println(\"No tools registered.\");\n        System.out.println(\"Use 'alice run --skill <toolset>' or configure MCP to load tools.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Registered tools (\" + tools.size() + \"):\");\n      System.out.println();\n\n      if (config.toolDetail()) {\n        for (var meta : tools) {\n          System.out.println(\"  ── \" + meta.name() + \" ──\");\n          System.out.println(\"     Description: \" + meta.description());\n          if (meta.inputSchema() != null && !meta.inputSchema().isEmpty()) {\n            System.out.println(\"     Parameters:  \" + meta.inputSchema());\n          }\n          System.out.println();\n        }\n      } else {\n        for (var meta : tools) {\n          System.out.println(\"  ⚡ \" + meta.name() + \" — \" + meta.description());\n        }\n        System.out.println();\n        System.out.println(\"Use 'alice tools --detail' to see parameter schemas.\");\n      }\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing tools: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // Resume 会话列表\n  // ========================================================================\n\n  /** 处理 {@code alice resume --list} 子命令，列出 WAL 中可恢复的会话。 */\n  private static int handleResumeList(RunConfig config) {\n    try {\n      // 扫描 ~/.alice/wal 目录下的会话\n      var walDir = java.nio.file.Paths.get(System.getProperty(\"user.home\"), \".alice\", \"wal\");\n      if (!java.nio.file.Files.isDirectory(walDir)) {\n        System.out.println(\"No WAL storage found at: \" + walDir);\n        System.out.println(\"No sessions available.\");\n        return EXIT_SUCCESS;\n      }\n\n      var sessions = new java.util.ArrayList<String>();\n      try (var stream = java.nio.file.Files.newDirectoryStream(walDir, \"*.wal.jsonl\")) {\n        for (var path : stream) {\n          String fileName = path.getFileName().toString();\n          String sessionId = fileName.substring(0, fileName.length() - \".wal.jsonl\".length());\n          sessions.add(sessionId);\n        }\n      }\n\n      if (sessions.isEmpty()) {\n        System.out.println(\"No sessions available.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Available sessions (\" + sessions.size() + \"):\");\n      System.out.println();\n      for (var sid : sessions) {\n        var cpFile = walDir.resolve(sid + \".checkpoint.json\");\n        String info = \"  📁 \" + sid;\n        if (java.nio.file.Files.exists(cpFile)) {\n          info += \" (checkpoint available)\";\n        }\n        System.out.println(info);\n      }\n      System.out.println();\n      System.out.println(\"Use 'alice resume --session-id <id>' to restore a session.\");\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing sessions: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // 配置管理\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice config} 子命令。\n   *\n   * <p>支持:\n   *\n   * <ul>\n   *   <li>{@code alice config} — 显示当前配置概览\n   *   <li>{@code alice config get &lt;key&gt;} — 获取单个配置项\n   *   <li>{@code alice config set &lt;key&gt; &lt;value&gt;} — 设置配置项（当前仅打印模拟）\n   * </ul>\n   */\n  /** 全局配置存储实例（延迟初始化） */\n  private static volatile AliceConfigStore configStore;\n\n  /** 获取或创建配置存储实例。 */\n  private static AliceConfigStore configStore() {\n    if (configStore == null) {\n      synchronized (AliceCliLauncher.class) {\n        if (configStore == null) {\n          configStore = new AliceConfigStore();\n        }\n      }\n    }\n    return configStore;\n  }\n\n  private static int handleConfig(RunConfig config) {\n    try {\n      String action = config.configAction();\n      String key = config.configKey();\n      String value = config.configValue();\n      AliceConfigStore store = configStore();\n\n      if (\"set\".equals(action)) {\n        if (key == null || value == null) {\n          System.err.println(\"Usage: alice config set <key> <value>\");\n          return EXIT_PARAM_ERROR;\n        }\n        store.set(key, value);\n        System.out.println(\n            \"Config '\" + key + \"' set to '\" + value + \"' (persisted to ~/.alice/config.json)\");\n        return EXIT_SUCCESS;\n      }\n\n      if (\"get\".equals(action)) {\n        if (key == null) {\n          System.err.println(\"Usage: alice config get <key>\");\n          return EXIT_PARAM_ERROR;\n        }\n        printConfigValue(key, store);\n        return EXIT_SUCCESS;\n      }\n\n      // \"show\" — 显示全部配置\n      printAllConfig(store);\n      return EXIT_SUCCESS;\n\n    } catch (Exception e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  /**\n   * 获取配置值的实际值（优先级：环境变量 > 配置文件 > 内建默认值）。\n   *\n   * @param key 点分隔的键名\n   * @param store 配置存储\n   * @return 值，或 {@code null} 表示未设置\n   */\n  private static String resolveConfigValue(String key, AliceConfigStore store) {\n    // 1. 环境变量（最高优先级）\n    String envName = configKeyToEnv(key);\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        return envVal;\n      }\n    }\n    // 2. 配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      return stored;\n    }\n    // 3. 内建默认值\n    return switch (key) {\n      case \"default.model\" -> AgentConfig.DEFAULT_MODEL;\n      case \"agent.max_iterations\" -> String.valueOf(AgentConfig.DEFAULT_MAX_ITERATIONS);\n      default -> null;\n    };\n  }\n\n  /** 将配置键名映射为环境变量名。 */\n  private static String configKeyToEnv(String key) {\n    return switch (key) {\n      case \"openai.api_key\", \"providers.openai.api_key\" -> \"OPENAI_API_KEY\";\n      case \"anthropic.api_key\", \"providers.anthropic.api_key\" -> \"ANTHROPIC_API_KEY\";\n      default -> null;\n    };\n  }\n\n  /** 获取并打印单个配置项的值及其来源。 */\n  private static void printConfigValue(String key, AliceConfigStore store) {\n    String envName = configKeyToEnv(key);\n\n    // 检查环境变量\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        System.out.println(\n            key\n                + \" = \"\n                + envVal.substring(0, Math.min(8, envVal.length()))\n                + \"... (from env \"\n                + envName\n                + \")\");\n        return;\n      }\n    }\n\n    // 检查配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      String display =\n          key.contains(\"api_key\")\n              ? stored.substring(0, Math.min(8, stored.length())) + \"...\"\n              : stored;\n      System.out.println(key + \" = \" + display + \" (from ~/.alice/config.json)\");\n      return;\n    }\n\n    // 检查内建默认值\n    switch (key) {\n      case \"default.model\" -> {\n        if (loadedDefaultModel != null) {\n          System.out.println(\n              \"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n        } else {\n          System.out.println(\n              \"default.model = \" + AgentConfig.DEFAULT_MODEL + \" (built-in default)\");\n        }\n      }\n      case \"agent.max_iterations\" ->\n          System.out.println(\n              \"agent.max_iterations = \"\n                  + AgentConfig.DEFAULT_MAX_ITERATIONS\n                  + \" (built-in default)\");\n      default -> System.out.println(key + \" = (not set)\");\n    }\n  }\n\n  /** 打印所有配置项。 */\n  private static void printAllConfig(AliceConfigStore store) {\n    System.out.println(\"=== Alice Agent Configuration ===\");\n    System.out.println();\n\n    // 读取 providers 嵌套结构\n    var all = store.getAll();\n    System.out.println(\"-- Model Providers --\");\n    @SuppressWarnings(\"unchecked\")\n    var providers = (Map<String, Object>) all.get(\"providers\");\n    if (providers != null && !providers.isEmpty()) {\n      for (var entry : providers.entrySet()) {\n        String providerName = entry.getKey();\n        System.out.println(\"  [\" + providerName + \"]\");\n        @SuppressWarnings(\"unchecked\")\n        var providerConfig = (Map<String, Object>) entry.getValue();\n        if (providerConfig != null) {\n          for (var pEntry : providerConfig.entrySet()) {\n            String display =\n                pEntry.getKey().contains(\"api_key\")\n                    ? pEntry\n                            .getValue()\n                            .toString()\n                            .substring(0, Math.min(8, pEntry.getValue().toString().length()))\n                        + \"...\"\n                    : pEntry.getValue().toString();\n            System.out.println(\"    \" + pEntry.getKey() + \" = \" + display);\n          }\n        }\n      }\n    } else {\n      // 回退到扁平键\n      printConfigValue(\"openai.api_key\", store);\n      printConfigValue(\"anthropic.api_key\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"-- Agent Defaults --\");\n    // 从嵌套或扁平读取\n    String model = store.get(\"default.model\");\n    if (model == null) model = store.get(\"default_model\");\n    if (model != null) {\n      System.out.println(\"default.model = \" + model + \" (from ~/.alice/config.json)\");\n    } else if (loadedDefaultModel != null) {\n      System.out.println(\"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n    } else {\n      printConfigValue(\"default.model\", store);\n    }\n\n    String iterStr = store.get(\"agent.max_iterations\");\n    if (iterStr == null) iterStr = store.get(\"max_iterations\");\n    if (iterStr != null) {\n      System.out.println(\"agent.max_iterations = \" + iterStr + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"agent.max_iterations\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"Config file: ~/.alice/config.json\");\n    System.out.println(\"Use 'alice config get <key>' or 'alice config set <key> <value>'\");\n    System.out.println(\n        \"Known keys: openai.api_key, anthropic.api_key, default.model, agent.max_iterations\");\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  /** 从 ~/.alice/model.json 加载的默认模型 ID，供 config 命令和 run 流程使用。 */\n  private static String loadedDefaultModel;\n\n  /**\n   * 初始化模型提供器。\n   *\n   * @return model.json 中配置的默认模型 ID，未设置则返回 {@code null}\n   */\n  private static String initializeModelProvider() {\n    String configDefaultModel = null;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n\n      // 1. 加载 ~/.alice/model.json 配置文件\n      try {\n        ModelConfigLoader configLoader = new ModelConfigLoader();\n        configLoader.load();\n        configLoader.registerTo(provider);\n        configDefaultModel = configLoader.getDefaultModel();\n        logger.info(\n            \"Loaded {} model provider(s) from ~/.alice/model.json\",\n            configLoader.getProviders().size());\n        if (configDefaultModel != null) {\n          logger.info(\"Default model from model.json: {}\", configDefaultModel);\n        }\n      } catch (Exception e) {\n        logger.debug(\"No model config found, using env vars: {}\", e.getMessage());\n      }\n\n      // 2. 注册内置模型枚举\n      provider.registerBuiltinModels();\n\n      // 3. 从环境变量注册供应商（优先级低于配置文件）\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        if (provider.getSupplier(\"gpt-4o-mini\") == null) {\n          provider.registerSupplier(new OpenAiSupplier(openAiKey));\n          logger.info(\"OpenAI supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        if (provider.getSupplier(\"claude-3-5-sonnet-latest\") == null) {\n          provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n          logger.info(\"Anthropic Claude supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n\n      // 4. DeepSeek (OpenAI-compatible) 从环境变量注册\n      String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n      if (deepseekKey != null && !deepseekKey.isEmpty()) {\n        if (provider.getSupplier(\"deepseek-v4-flash\") == null) {\n          provider.registerSupplier(\n              new OpenAiSupplier(\n                  \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n          logger.info(\"DeepSeek supplier registered from env var (OpenAI-compatible)\");\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n    return configDefaultModel;\n  }\n}\n"}, "2bb6b3f7e483c34be142c1c9ed1daba4cd65067f": {"t": 1785755694.60479, "n": 21220, "h": "deff7556657ad355", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.Map;\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.core.agent.AgentConfig;\nimport org.cland.alice.facade.cmd.chat.JLineChatSession;\nimport org.cland.alice.facade.cmd.config.AliceConfigStore;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印 → 成功退出\n        return EXIT_SUCCESS;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 工具列表命令 — 不走 Agent 执行\n      if (config.listTools()) {\n        return handleListTools(config);\n      }\n\n      // 3. 配置管理命令 — 不走 Agent 执行\n      if (config.configAction() != null) {\n        return handleConfig(config);\n      }\n\n      // 4. 初始化 ModelProvider\n      String configDefaultModel = initializeModelProvider();\n\n      // 4a. 如果用户未通过 --model 指定，且 model.json 中配置了 default_model，则使用它\n      String effectiveModel =\n          (configDefaultModel != null && config.model().equals(RunConfig.DEFAULT_MODEL))\n              ? configDefaultModel\n              : config.model();\n      if (configDefaultModel != null) {\n        loadedDefaultModel = configDefaultModel;\n      }\n\n      // 4. 创建渲染器（将有效 model 回传给 config 上下文）\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 5. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer, effectiveModel);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = UUID.randomUUID().toString().substring(0, 8);\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 工具列表\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice tools} 子命令，列出 ToolRegistry 中的已注册工具。\n   *\n   * @param config 运行配置\n   * @return 退出码\n   */\n  private static int handleListTools(RunConfig config) {\n    try {\n      var registry = org.cland.alice.tool.gateway.ToolRegistryHolder.INSTANCE;\n      var tools = registry.allTools();\n\n      if (tools.isEmpty()) {\n        System.out.println(\"No tools registered.\");\n        System.out.println(\"Use 'alice run --skill <toolset>' or configure MCP to load tools.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Registered tools (\" + tools.size() + \"):\");\n      System.out.println();\n\n      if (config.toolDetail()) {\n        for (var meta : tools) {\n          System.out.println(\"  ── \" + meta.name() + \" ──\");\n          System.out.println(\"     Description: \" + meta.description());\n          if (meta.inputSchema() != null && !meta.inputSchema().isEmpty()) {\n            System.out.println(\"     Parameters:  \" + meta.inputSchema());\n          }\n          System.out.println();\n        }\n      } else {\n        for (var meta : tools) {\n          System.out.println(\"  ⚡ \" + meta.name() + \" — \" + meta.description());\n        }\n        System.out.println();\n        System.out.println(\"Use 'alice tools --detail' to see parameter schemas.\");\n      }\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing tools: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // 配置管理\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice config} 子命令。\n   *\n   * <p>支持:\n   *\n   * <ul>\n   *   <li>{@code alice config} — 显示当前配置概览\n   *   <li>{@code alice config get &lt;key&gt;} — 获取单个配置项\n   *   <li>{@code alice config set &lt;key&gt; &lt;value&gt;} — 设置配置项（当前仅打印模拟）\n   * </ul>\n   */\n  /** 全局配置存储实例（延迟初始化） */\n  private static volatile AliceConfigStore configStore;\n\n  /** 获取或创建配置存储实例。 */\n  private static AliceConfigStore configStore() {\n    if (configStore == null) {\n      synchronized (AliceCliLauncher.class) {\n        if (configStore == null) {\n          configStore = new AliceConfigStore();\n        }\n      }\n    }\n    return configStore;\n  }\n\n  private static int handleConfig(RunConfig config) {\n    try {\n      String action = config.configAction();\n      String key = config.configKey();\n      String value = config.configValue();\n      AliceConfigStore store = configStore();\n\n      if (\"set\".equals(action)) {\n        if (key == null || value == null) {\n          System.err.println(\"Usage: alice config set <key> <value>\");\n          return EXIT_PARAM_ERROR;\n        }\n        store.set(key, value);\n        System.out.println(\n            \"Config '\" + key + \"' set to '\" + value + \"' (persisted to ~/.alice/config.json)\");\n        return EXIT_SUCCESS;\n      }\n\n      if (\"get\".equals(action)) {\n        if (key == null) {\n          System.err.println(\"Usage: alice config get <key>\");\n          return EXIT_PARAM_ERROR;\n        }\n        printConfigValue(key, store);\n        return EXIT_SUCCESS;\n      }\n\n      // \"show\" — 显示全部配置\n      printAllConfig(store);\n      return EXIT_SUCCESS;\n\n    } catch (Exception e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  /**\n   * 获取配置值的实际值（优先级：环境变量 > 配置文件 > 内建默认值）。\n   *\n   * @param key 点分隔的键名\n   * @param store 配置存储\n   * @return 值，或 {@code null} 表示未设置\n   */\n  private static String resolveConfigValue(String key, AliceConfigStore store) {\n    // 1. 环境变量（最高优先级）\n    String envName = configKeyToEnv(key);\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        return envVal;\n      }\n    }\n    // 2. 配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      return stored;\n    }\n    // 3. 内建默认值\n    return switch (key) {\n      case \"default.model\" -> AgentConfig.DEFAULT_MODEL;\n      case \"agent.max_iterations\" -> String.valueOf(AgentConfig.DEFAULT_MAX_ITERATIONS);\n      default -> null;\n    };\n  }\n\n  /** 将配置键名映射为环境变量名。 */\n  private static String configKeyToEnv(String key) {\n    return switch (key) {\n      case \"openai.api_key\", \"providers.openai.api_key\" -> \"OPENAI_API_KEY\";\n      case \"anthropic.api_key\", \"providers.anthropic.api_key\" -> \"ANTHROPIC_API_KEY\";\n      default -> null;\n    };\n  }\n\n  /** 获取并打印单个配置项的值及其来源。 */\n  private static void printConfigValue(String key, AliceConfigStore store) {\n    String envName = configKeyToEnv(key);\n\n    // 检查环境变量\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        System.out.println(\n            key\n                + \" = \"\n                + envVal.substring(0, Math.min(8, envVal.length()))\n                + \"... (from env \"\n                + envName\n                + \")\");\n        return;\n      }\n    }\n\n    // 检查配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      String display =\n          key.contains(\"api_key\")\n              ? stored.substring(0, Math.min(8, stored.length())) + \"...\"\n              : stored;\n      System.out.println(key + \" = \" + display + \" (from ~/.alice/config.json)\");\n      return;\n    }\n\n    // 检查内建默认值\n    switch (key) {\n      case \"default.model\" -> {\n        if (loadedDefaultModel != null) {\n          System.out.println(\n              \"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n        } else {\n          System.out.println(\n              \"default.model = \" + AgentConfig.DEFAULT_MODEL + \" (built-in default)\");\n        }\n      }\n      case \"agent.max_iterations\" ->\n          System.out.println(\n              \"agent.max_iterations = \"\n                  + AgentConfig.DEFAULT_MAX_ITERATIONS\n                  + \" (built-in default)\");\n      default -> System.out.println(key + \" = (not set)\");\n    }\n  }\n\n  /** 打印所有配置项。 */\n  private static void printAllConfig(AliceConfigStore store) {\n    System.out.println(\"=== Alice Agent Configuration ===\");\n    System.out.println();\n\n    // 读取 providers 嵌套结构\n    var all = store.getAll();\n    System.out.println(\"-- Model Providers --\");\n    @SuppressWarnings(\"unchecked\")\n    var providers = (Map<String, Object>) all.get(\"providers\");\n    if (providers != null && !providers.isEmpty()) {\n      for (var entry : providers.entrySet()) {\n        String providerName = entry.getKey();\n        System.out.println(\"  [\" + providerName + \"]\");\n        @SuppressWarnings(\"unchecked\")\n        var providerConfig = (Map<String, Object>) entry.getValue();\n        if (providerConfig != null) {\n          for (var pEntry : providerConfig.entrySet()) {\n            String display =\n                pEntry.getKey().contains(\"api_key\")\n                    ? pEntry\n                            .getValue()\n                            .toString()\n                            .substring(0, Math.min(8, pEntry.getValue().toString().length()))\n                        + \"...\"\n                    : pEntry.getValue().toString();\n            System.out.println(\"    \" + pEntry.getKey() + \" = \" + display);\n          }\n        }\n      }\n    } else {\n      // 回退到扁平键\n      printConfigValue(\"openai.api_key\", store);\n      printConfigValue(\"anthropic.api_key\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"-- Agent Defaults --\");\n    // 从嵌套或扁平读取\n    String model = store.get(\"default.model\");\n    if (model == null) model = store.get(\"default_model\");\n    if (model != null) {\n      System.out.println(\"default.model = \" + model + \" (from ~/.alice/config.json)\");\n    } else if (loadedDefaultModel != null) {\n      System.out.println(\"default.model = \" + loadedDefaultModel + \" (from ~/.alice/model.json)\");\n    } else {\n      printConfigValue(\"default.model\", store);\n    }\n\n    String iterStr = store.get(\"agent.max_iterations\");\n    if (iterStr == null) iterStr = store.get(\"max_iterations\");\n    if (iterStr != null) {\n      System.out.println(\"agent.max_iterations = \" + iterStr + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"agent.max_iterations\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"Config file: ~/.alice/config.json\");\n    System.out.println(\"Use 'alice config get <key>' or 'alice config set <key> <value>'\");\n    System.out.println(\n        \"Known keys: openai.api_key, anthropic.api_key, default.model, agent.max_iterations\");\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  /** 从 ~/.alice/model.json 加载的默认模型 ID，供 config 命令和 run 流程使用。 */\n  private static String loadedDefaultModel;\n\n  /**\n   * 初始化模型提供器。\n   *\n   * @return model.json 中配置的默认模型 ID，未设置则返回 {@code null}\n   */\n  private static String initializeModelProvider() {\n    String configDefaultModel = null;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n\n      // 1. 加载 ~/.alice/model.json 配置文件\n      try {\n        ModelConfigLoader configLoader = new ModelConfigLoader();\n        configLoader.load();\n        configLoader.registerTo(provider);\n        configDefaultModel = configLoader.getDefaultModel();\n        logger.info(\n            \"Loaded {} model provider(s) from ~/.alice/model.json\",\n            configLoader.getProviders().size());\n        if (configDefaultModel != null) {\n          logger.info(\"Default model from model.json: {}\", configDefaultModel);\n        }\n      } catch (Exception e) {\n        logger.debug(\"No model config found, using env vars: {}\", e.getMessage());\n      }\n\n      // 2. 注册内置模型枚举\n      provider.registerBuiltinModels();\n\n      // 3. 从环境变量注册供应商（优先级低于配置文件）\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        if (provider.getSupplier(\"gpt-4o-mini\") == null) {\n          provider.registerSupplier(new OpenAiSupplier(openAiKey));\n          logger.info(\"OpenAI supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        if (provider.getSupplier(\"claude-3-5-sonnet-latest\") == null) {\n          provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n          logger.info(\"Anthropic Claude supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n\n      // 4. DeepSeek (OpenAI-compatible) 从环境变量注册\n      String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n      if (deepseekKey != null && !deepseekKey.isEmpty()) {\n        if (provider.getSupplier(\"deepseek-v4-flash\") == null) {\n          provider.registerSupplier(\n              new OpenAiSupplier(\n                  \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n          logger.info(\"DeepSeek supplier registered from env var (OpenAI-compatible)\");\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n    return configDefaultModel;\n  }\n}\n"}, "c2c7eac01472b99309c1f867fa8a080907dba673": {"t": 1785755696.0762913, "n": 19994, "h": "41c6911f05cd4f82", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.Map;\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.core.agent.AgentConfig;\nimport org.cland.alice.facade.cmd.chat.JLineChatSession;\nimport org.cland.alice.facade.cmd.config.AliceConfigStore;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印 → 成功退出\n        return EXIT_SUCCESS;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 工具列表命令 — 不走 Agent 执行\n      if (config.listTools()) {\n        return handleListTools(config);\n      }\n\n      // 3. 配置管理命令 — 不走 Agent 执行\n      if (config.configAction() != null) {\n        return handleConfig(config);\n      }\n\n      // 4. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 4. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 5. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = UUID.randomUUID().toString().substring(0, 8);\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 工具列表\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice tools} 子命令，列出 ToolRegistry 中的已注册工具。\n   *\n   * @param config 运行配置\n   * @return 退出码\n   */\n  private static int handleListTools(RunConfig config) {\n    try {\n      var registry = org.cland.alice.tool.gateway.ToolRegistryHolder.INSTANCE;\n      var tools = registry.allTools();\n\n      if (tools.isEmpty()) {\n        System.out.println(\"No tools registered.\");\n        System.out.println(\"Use 'alice run --skill <toolset>' or configure MCP to load tools.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Registered tools (\" + tools.size() + \"):\");\n      System.out.println();\n\n      if (config.toolDetail()) {\n        for (var meta : tools) {\n          System.out.println(\"  ── \" + meta.name() + \" ──\");\n          System.out.println(\"     Description: \" + meta.description());\n          if (meta.inputSchema() != null && !meta.inputSchema().isEmpty()) {\n            System.out.println(\"     Parameters:  \" + meta.inputSchema());\n          }\n          System.out.println();\n        }\n      } else {\n        for (var meta : tools) {\n          System.out.println(\"  ⚡ \" + meta.name() + \" — \" + meta.description());\n        }\n        System.out.println();\n        System.out.println(\"Use 'alice tools --detail' to see parameter schemas.\");\n      }\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing tools: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // 配置管理\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice config} 子命令。\n   *\n   * <p>支持:\n   *\n   * <ul>\n   *   <li>{@code alice config} — 显示当前配置概览\n   *   <li>{@code alice config get &lt;key&gt;} — 获取单个配置项\n   *   <li>{@code alice config set &lt;key&gt; &lt;value&gt;} — 设置配置项（当前仅打印模拟）\n   * </ul>\n   */\n  /** 全局配置存储实例（延迟初始化） */\n  private static volatile AliceConfigStore configStore;\n\n  /** 获取或创建配置存储实例。 */\n  private static AliceConfigStore configStore() {\n    if (configStore == null) {\n      synchronized (AliceCliLauncher.class) {\n        if (configStore == null) {\n          configStore = new AliceConfigStore();\n        }\n      }\n    }\n    return configStore;\n  }\n\n  private static int handleConfig(RunConfig config) {\n    try {\n      String action = config.configAction();\n      String key = config.configKey();\n      String value = config.configValue();\n      AliceConfigStore store = configStore();\n\n      if (\"set\".equals(action)) {\n        if (key == null || value == null) {\n          System.err.println(\"Usage: alice config set <key> <value>\");\n          return EXIT_PARAM_ERROR;\n        }\n        store.set(key, value);\n        System.out.println(\n            \"Config '\" + key + \"' set to '\" + value + \"' (persisted to ~/.alice/config.json)\");\n        return EXIT_SUCCESS;\n      }\n\n      if (\"get\".equals(action)) {\n        if (key == null) {\n          System.err.println(\"Usage: alice config get <key>\");\n          return EXIT_PARAM_ERROR;\n        }\n        printConfigValue(key, store);\n        return EXIT_SUCCESS;\n      }\n\n      // \"show\" — 显示全部配置\n      printAllConfig(store);\n      return EXIT_SUCCESS;\n\n    } catch (Exception e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  /**\n   * 获取配置值的实际值（优先级：环境变量 > 配置文件 > 内建默认值）。\n   *\n   * @param key 点分隔的键名\n   * @param store 配置存储\n   * @return 值，或 {@code null} 表示未设置\n   */\n  private static String resolveConfigValue(String key, AliceConfigStore store) {\n    // 1. 环境变量（最高优先级）\n    String envName = configKeyToEnv(key);\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        return envVal;\n      }\n    }\n    // 2. 配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      return stored;\n    }\n    // 3. 内建默认值\n    return switch (key) {\n      case \"default.model\" -> AgentConfig.DEFAULT_MODEL;\n      case \"agent.max_iterations\" -> String.valueOf(AgentConfig.DEFAULT_MAX_ITERATIONS);\n      default -> null;\n    };\n  }\n\n  /** 将配置键名映射为环境变量名。 */\n  private static String configKeyToEnv(String key) {\n    return switch (key) {\n      case \"openai.api_key\", \"providers.openai.api_key\" -> \"OPENAI_API_KEY\";\n      case \"anthropic.api_key\", \"providers.anthropic.api_key\" -> \"ANTHROPIC_API_KEY\";\n      default -> null;\n    };\n  }\n\n  /** 获取并打印单个配置项的值及其来源。 */\n  private static void printConfigValue(String key, AliceConfigStore store) {\n    String envName = configKeyToEnv(key);\n\n    // 检查环境变量\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        System.out.println(\n            key\n                + \" = \"\n                + envVal.substring(0, Math.min(8, envVal.length()))\n                + \"... (from env \"\n                + envName\n                + \")\");\n        return;\n      }\n    }\n\n    // 检查配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      String display =\n          key.contains(\"api_key\")\n              ? stored.substring(0, Math.min(8, stored.length())) + \"...\"\n              : stored;\n      System.out.println(key + \" = \" + display + \" (from ~/.alice/config.json)\");\n      return;\n    }\n\n    // 检查内建默认值\n    switch (key) {\n      case \"default.model\" ->\n          System.out.println(\n              \"default.model = \" + AgentConfig.DEFAULT_MODEL + \" (built-in default)\");\n      case \"agent.max_iterations\" ->\n          System.out.println(\n              \"agent.max_iterations = \"\n                  + AgentConfig.DEFAULT_MAX_ITERATIONS\n                  + \" (built-in default)\");\n      default -> System.out.println(key + \" = (not set)\");\n    }\n  }\n\n  /** 打印所有配置项。 */\n  private static void printAllConfig(AliceConfigStore store) {\n    System.out.println(\"=== Alice Agent Configuration ===\");\n    System.out.println();\n\n    // 读取 providers 嵌套结构\n    var all = store.getAll();\n    System.out.println(\"-- Model Providers --\");\n    @SuppressWarnings(\"unchecked\")\n    var providers = (Map<String, Object>) all.get(\"providers\");\n    if (providers != null && !providers.isEmpty()) {\n      for (var entry : providers.entrySet()) {\n        String providerName = entry.getKey();\n        System.out.println(\"  [\" + providerName + \"]\");\n        @SuppressWarnings(\"unchecked\")\n        var providerConfig = (Map<String, Object>) entry.getValue();\n        if (providerConfig != null) {\n          for (var pEntry : providerConfig.entrySet()) {\n            String display =\n                pEntry.getKey().contains(\"api_key\")\n                    ? pEntry\n                            .getValue()\n                            .toString()\n                            .substring(0, Math.min(8, pEntry.getValue().toString().length()))\n                        + \"...\"\n                    : pEntry.getValue().toString();\n            System.out.println(\"    \" + pEntry.getKey() + \" = \" + display);\n          }\n        }\n      }\n    } else {\n      // 回退到扁平键\n      printConfigValue(\"openai.api_key\", store);\n      printConfigValue(\"anthropic.api_key\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"-- Agent Defaults --\");\n    // 从嵌套或扁平读取\n    String model = store.get(\"default.model\");\n    if (model == null) model = store.get(\"default_model\");\n    if (model != null) {\n      System.out.println(\"default.model = \" + model + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"default.model\", store);\n    }\n\n    String iterStr = store.get(\"agent.max_iterations\");\n    if (iterStr == null) iterStr = store.get(\"max_iterations\");\n    if (iterStr != null) {\n      System.out.println(\"agent.max_iterations = \" + iterStr + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"agent.max_iterations\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"Config file: ~/.alice/config.json\");\n    System.out.println(\"Use 'alice config get <key>' or 'alice config set <key> <value>'\");\n    System.out.println(\n        \"Known keys: openai.api_key, anthropic.api_key, default.model, agent.max_iterations\");\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n\n      // 1. 加载 ~/.alice/model.json 配置文件\n      try {\n        ModelConfigLoader configLoader = new ModelConfigLoader();\n        configLoader.load();\n        configLoader.registerTo(provider);\n        logger.info(\n            \"Loaded {} model provider(s) from ~/.alice/model.json\",\n            configLoader.getProviders().size());\n      } catch (Exception e) {\n        logger.debug(\"No model config found, using env vars: {}\", e.getMessage());\n      }\n\n      // 2. 注册内置模型枚举\n      provider.registerBuiltinModels();\n\n      // 3. 从环境变量注册供应商（优先级低于配置文件）\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        if (provider.getSupplier(\"gpt-4o-mini\") == null) {\n          provider.registerSupplier(new OpenAiSupplier(openAiKey));\n          logger.info(\"OpenAI supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        if (provider.getSupplier(\"claude-3-5-sonnet-latest\") == null) {\n          provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n          logger.info(\"Anthropic Claude supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n\n      // 4. DeepSeek (OpenAI-compatible) 从环境变量注册\n      String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n      if (deepseekKey != null && !deepseekKey.isEmpty()) {\n        if (provider.getSupplier(\"deepseek-v4-flash\") == null) {\n          provider.registerSupplier(\n              new OpenAiSupplier(\n                  \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n          logger.info(\"DeepSeek supplier registered from env var (OpenAI-compatible)\");\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "ea266cc69fdfde12ecbe7c3df37743f6c42372fc": {"t": 1785755697.7079556, "n": 19990, "h": "e64cc7fa904ee99b", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.Map;\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.core.agent.AgentConfig;\nimport org.cland.alice.facade.cmd.chat.JLineChatSession;\nimport org.cland.alice.facade.cmd.config.AliceConfigStore;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印 → 成功退出\n        return EXIT_SUCCESS;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 工具列表命令 — 不走 Agent 执行\n      if (config.listTools()) {\n        return handleListTools(config);\n      }\n\n      // 3. 配置管理命令 — 不走 Agent 执行\n      if (config.configAction() != null) {\n        return handleConfig(config);\n      }\n\n      // 4. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 4. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 5. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = UUID.randomUUID().toString().substring(0, 8);\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 工具列表\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice tools} 子命令，列出 ToolRegistry 中的已注册工具。\n   *\n   * @param config 运行配置\n   * @return 退出码\n   */\n  private static int handleListTools(RunConfig config) {\n    try {\n      var registry = org.cland.alice.tool.gateway.ToolRegistryHolder.INSTANCE;\n      var tools = registry.allTools();\n\n      if (tools.isEmpty()) {\n        System.out.println(\"No tools registered.\");\n        System.out.println(\"Use 'alice run --skill <toolset>' or configure MCP to load tools.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Registered tools (\" + tools.size() + \"):\");\n      System.out.println();\n\n      if (config.toolDetail()) {\n        for (var meta : tools) {\n          System.out.println(\"  ── \" + meta.name() + \" ──\");\n          System.out.println(\"     Description: \" + meta.description());\n          if (meta.inputSchema() != null && !meta.inputSchema().isEmpty()) {\n            System.out.println(\"     Parameters:  \" + meta.inputSchema());\n          }\n          System.out.println();\n        }\n      } else {\n        for (var meta : tools) {\n          System.out.println(\"  ⚡ \" + meta.name() + \" — \" + meta.description());\n        }\n        System.out.println();\n        System.out.println(\"Use 'alice tools --detail' to see parameter schemas.\");\n      }\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing tools: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // 配置管理\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice config} 子命令。\n   *\n   * <p>支持:\n   *\n   * <ul>\n   *   <li>{@code alice config} — 显示当前配置概览\n   *   <li>{@code alice config get &lt;key&gt;} — 获取单个配置项\n   *   <li>{@code alice config set &lt;key&gt; &lt;value&gt;} — 设置配置项（当前仅打印模拟）\n   * </ul>\n   */\n  /** 全局配置存储实例（延迟初始化） */\n  private static volatile AliceConfigStore configStore;\n\n  /** 获取或创建配置存储实例。 */\n  private static AliceConfigStore configStore() {\n    if (configStore == null) {\n      synchronized (AliceCliLauncher.class) {\n        if (configStore == null) {\n          configStore = new AliceConfigStore();\n        }\n      }\n    }\n    return configStore;\n  }\n\n  private static int handleConfig(RunConfig config) {\n    try {\n      String action = config.configAction();\n      String key = config.configKey();\n      String value = config.configValue();\n      AliceConfigStore store = configStore();\n\n      if (\"set\".equals(action)) {\n        if (key == null || value == null) {\n          System.err.println(\"Usage: alice config set <key> <value>\");\n          return EXIT_PARAM_ERROR;\n        }\n        store.set(key, value);\n        System.out.println(\n            \"Config '\" + key + \"' set to '\" + value + \"' (persisted to ~/.alice/config.json)\");\n        return EXIT_SUCCESS;\n      }\n\n      if (\"get\".equals(action)) {\n        if (key == null) {\n          System.err.println(\"Usage: alice config get <key>\");\n          return EXIT_PARAM_ERROR;\n        }\n        printConfigValue(key, store);\n        return EXIT_SUCCESS;\n      }\n\n      // \"show\" — 显示全部配置\n      printAllConfig(store);\n      return EXIT_SUCCESS;\n\n    } catch (Exception e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  /**\n   * 获取配置值的实际值（优先级：环境变量 > 配置文件 > 内建默认值）。\n   *\n   * @param key 点分隔的键名\n   * @param store 配置存储\n   * @return 值，或 {@code null} 表示未设置\n   */\n  private static String resolveConfigValue(String key, AliceConfigStore store) {\n    // 1. 环境变量（最高优先级）\n    String envName = configKeyToEnv(key);\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        return envVal;\n      }\n    }\n    // 2. 配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      return stored;\n    }\n    // 3. 内建默认值\n    return switch (key) {\n      case \"default.model\" -> AgentConfig.DEFAULT_MODEL;\n      case \"agent.max_iterations\" -> String.valueOf(AgentConfig.DEFAULT_MAX_ITERATIONS);\n      default -> null;\n    };\n  }\n\n  /** 将配置键名映射为环境变量名。 */\n  private static String configKeyToEnv(String key) {\n    return switch (key) {\n      case \"openai.api_key\", \"providers.openai.api_key\" -> \"OPENAI_API_KEY\";\n      case \"anthropic.api_key\", \"providers.anthropic.api_key\" -> \"ANTHROPIC_API_KEY\";\n      default -> null;\n    };\n  }\n\n  /** 获取并打印单个配置项的值及其来源。 */\n  private static void printConfigValue(String key, AliceConfigStore store) {\n    String envName = configKeyToEnv(key);\n\n    // 检查环境变量\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        System.out.println(\n            key\n                + \" = \"\n                + envVal.substring(0, Math.min(8, envVal.length()))\n                + \"... (from env \"\n                + envName\n                + \")\");\n        return;\n      }\n    }\n\n    // 检查配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      String display =\n          key.contains(\"api_key\")\n              ? stored.substring(0, Math.min(8, stored.length())) + \"...\"\n              : stored;\n      System.out.println(key + \" = \" + display + \" (from ~/.alice/config.json)\");\n      return;\n    }\n\n    // 检查内建默认值\n    switch (key) {\n      case \"default.model\" ->\n          System.out.println(\n              \"default.model = \" + AgentConfig.DEFAULT_MODEL + \" (built-in default)\");\n      case \"agent.max_iterations\" ->\n          System.out.println(\n              \"agent.max_iterations = \"\n                  + AgentConfig.DEFAULT_MAX_ITERATIONS\n                  + \" (built-in default)\");\n      default -> System.out.println(key + \" = (not set)\");\n    }\n  }\n\n  /** 打印所有配置项。 */\n  private static void printAllConfig(AliceConfigStore store) {\n    System.out.println(\"=== Alice Agent Configuration ===\");\n    System.out.println();\n\n    // 读取 providers 嵌套结构\n    var all = store.getAll();\n    System.out.println(\"-- Model Providers --\");\n    @SuppressWarnings(\"unchecked\")\n    var providers = (Map<String, Object>) all.get(\"providers\");\n    if (providers != null && !providers.isEmpty()) {\n      for (var entry : providers.entrySet()) {\n        String providerName = entry.getKey();\n        System.out.println(\"  [\" + providerName + \"]\");\n        @SuppressWarnings(\"unchecked\")\n        var providerConfig = (Map<String, Object>) entry.getValue();\n        if (providerConfig != null) {\n          for (var pEntry : providerConfig.entrySet()) {\n            String display =\n                pEntry.getKey().contains(\"api_key\")\n                    ? pEntry\n                            .getValue()\n                            .toString()\n                            .substring(0, Math.min(8, pEntry.getValue().toString().length()))\n                        + \"...\"\n                    : pEntry.getValue().toString();\n            System.out.println(\"    \" + pEntry.getKey() + \" = \" + display);\n          }\n        }\n      }\n    } else {\n      // 回退到扁平键\n      printConfigValue(\"openai.api_key\", store);\n      printConfigValue(\"anthropic.api_key\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"-- Agent Defaults --\");\n    // 从嵌套或扁平读取\n    String model = store.get(\"default.model\");\n    if (model == null) model = store.get(\"default_model\");\n    if (model != null) {\n      System.out.println(\"default.model = \" + model + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"default.model\", store);\n    }\n\n    String iterStr = store.get(\"agent.max_iterations\");\n    if (iterStr == null) iterStr = store.get(\"max_iterations\");\n    if (iterStr != null) {\n      System.out.println(\"agent.max_iterations = \" + iterStr + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"agent.max_iterations\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"Config file: ~/.alice/config.json\");\n    System.out.println(\"Use 'alice config get <key>' or 'alice config set <key> <value>'\");\n    System.out.println(\n        \"Known keys: openai.api_key, anthropic.api_key, default.model, agent.max_iterations\");\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n\n      // 1. 加载 ~/.alice/model.json 配置文件\n      try {\n        ModelConfigLoader configLoader = new ModelConfigLoader();\n        configLoader.load();\n        configLoader.registerTo(provider);\n        logger.info(\n            \"Loaded {} model provider(s) from ~/.alice/model.json\",\n            configLoader.getProviders().size());\n      } catch (Exception e) {\n        logger.debug(\"No model config found, using env vars: {}\", e.getMessage());\n      }\n\n      // 2. 注册内置模型枚举\n      provider.registerBuiltinModels();\n\n      // 3. 从环境变量注册供应商（优先级低于配置文件）\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        if (provider.getSupplier(\"gpt-4o-mini\") == null) {\n          provider.registerSupplier(new OpenAiSupplier(openAiKey));\n          logger.info(\"OpenAI supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        if (provider.getSupplier(\"claude-3-5-sonnet-latest\") == null) {\n          provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n          logger.info(\"Anthropic Claude supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n\n      // 4. DeepSeek (OpenAI-compatible) 从环境变量注册\n      String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n      if (deepseekKey != null && !deepseekKey.isEmpty()) {\n        if (provider.getSupplier(\"deepseek-chat\") == null) {\n          provider.registerSupplier(\n              new OpenAiSupplier(\n                  \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n          logger.info(\"DeepSeek supplier registered from env var (OpenAI-compatible)\");\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "89b2479f36a21903ca0de8000a61a4e11bf70224": {"t": 1785755699.5411308, "n": 19933, "h": "ebc9581e91bf157d", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.Map;\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.core.agent.AgentConfig;\nimport org.cland.alice.facade.cmd.config.AliceConfigStore;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印 → 成功退出\n        return EXIT_SUCCESS;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 工具列表命令 — 不走 Agent 执行\n      if (config.listTools()) {\n        return handleListTools(config);\n      }\n\n      // 3. 配置管理命令 — 不走 Agent 执行\n      if (config.configAction() != null) {\n        return handleConfig(config);\n      }\n\n      // 4. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 4. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 5. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = UUID.randomUUID().toString().substring(0, 8);\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 工具列表\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice tools} 子命令，列出 ToolRegistry 中的已注册工具。\n   *\n   * @param config 运行配置\n   * @return 退出码\n   */\n  private static int handleListTools(RunConfig config) {\n    try {\n      var registry = org.cland.alice.tool.gateway.ToolRegistryHolder.INSTANCE;\n      var tools = registry.allTools();\n\n      if (tools.isEmpty()) {\n        System.out.println(\"No tools registered.\");\n        System.out.println(\"Use 'alice run --skill <toolset>' or configure MCP to load tools.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Registered tools (\" + tools.size() + \"):\");\n      System.out.println();\n\n      if (config.toolDetail()) {\n        for (var meta : tools) {\n          System.out.println(\"  ── \" + meta.name() + \" ──\");\n          System.out.println(\"     Description: \" + meta.description());\n          if (meta.inputSchema() != null && !meta.inputSchema().isEmpty()) {\n            System.out.println(\"     Parameters:  \" + meta.inputSchema());\n          }\n          System.out.println();\n        }\n      } else {\n        for (var meta : tools) {\n          System.out.println(\"  ⚡ \" + meta.name() + \" — \" + meta.description());\n        }\n        System.out.println();\n        System.out.println(\"Use 'alice tools --detail' to see parameter schemas.\");\n      }\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing tools: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // 配置管理\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice config} 子命令。\n   *\n   * <p>支持:\n   *\n   * <ul>\n   *   <li>{@code alice config} — 显示当前配置概览\n   *   <li>{@code alice config get &lt;key&gt;} — 获取单个配置项\n   *   <li>{@code alice config set &lt;key&gt; &lt;value&gt;} — 设置配置项（当前仅打印模拟）\n   * </ul>\n   */\n  /** 全局配置存储实例（延迟初始化） */\n  private static volatile AliceConfigStore configStore;\n\n  /** 获取或创建配置存储实例。 */\n  private static AliceConfigStore configStore() {\n    if (configStore == null) {\n      synchronized (AliceCliLauncher.class) {\n        if (configStore == null) {\n          configStore = new AliceConfigStore();\n        }\n      }\n    }\n    return configStore;\n  }\n\n  private static int handleConfig(RunConfig config) {\n    try {\n      String action = config.configAction();\n      String key = config.configKey();\n      String value = config.configValue();\n      AliceConfigStore store = configStore();\n\n      if (\"set\".equals(action)) {\n        if (key == null || value == null) {\n          System.err.println(\"Usage: alice config set <key> <value>\");\n          return EXIT_PARAM_ERROR;\n        }\n        store.set(key, value);\n        System.out.println(\n            \"Config '\" + key + \"' set to '\" + value + \"' (persisted to ~/.alice/config.json)\");\n        return EXIT_SUCCESS;\n      }\n\n      if (\"get\".equals(action)) {\n        if (key == null) {\n          System.err.println(\"Usage: alice config get <key>\");\n          return EXIT_PARAM_ERROR;\n        }\n        printConfigValue(key, store);\n        return EXIT_SUCCESS;\n      }\n\n      // \"show\" — 显示全部配置\n      printAllConfig(store);\n      return EXIT_SUCCESS;\n\n    } catch (Exception e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  /**\n   * 获取配置值的实际值（优先级：环境变量 > 配置文件 > 内建默认值）。\n   *\n   * @param key 点分隔的键名\n   * @param store 配置存储\n   * @return 值，或 {@code null} 表示未设置\n   */\n  private static String resolveConfigValue(String key, AliceConfigStore store) {\n    // 1. 环境变量（最高优先级）\n    String envName = configKeyToEnv(key);\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        return envVal;\n      }\n    }\n    // 2. 配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      return stored;\n    }\n    // 3. 内建默认值\n    return switch (key) {\n      case \"default.model\" -> AgentConfig.DEFAULT_MODEL;\n      case \"agent.max_iterations\" -> String.valueOf(AgentConfig.DEFAULT_MAX_ITERATIONS);\n      default -> null;\n    };\n  }\n\n  /** 将配置键名映射为环境变量名。 */\n  private static String configKeyToEnv(String key) {\n    return switch (key) {\n      case \"openai.api_key\", \"providers.openai.api_key\" -> \"OPENAI_API_KEY\";\n      case \"anthropic.api_key\", \"providers.anthropic.api_key\" -> \"ANTHROPIC_API_KEY\";\n      default -> null;\n    };\n  }\n\n  /** 获取并打印单个配置项的值及其来源。 */\n  private static void printConfigValue(String key, AliceConfigStore store) {\n    String envName = configKeyToEnv(key);\n\n    // 检查环境变量\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        System.out.println(\n            key\n                + \" = \"\n                + envVal.substring(0, Math.min(8, envVal.length()))\n                + \"... (from env \"\n                + envName\n                + \")\");\n        return;\n      }\n    }\n\n    // 检查配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      String display =\n          key.contains(\"api_key\")\n              ? stored.substring(0, Math.min(8, stored.length())) + \"...\"\n              : stored;\n      System.out.println(key + \" = \" + display + \" (from ~/.alice/config.json)\");\n      return;\n    }\n\n    // 检查内建默认值\n    switch (key) {\n      case \"default.model\" ->\n          System.out.println(\n              \"default.model = \" + AgentConfig.DEFAULT_MODEL + \" (built-in default)\");\n      case \"agent.max_iterations\" ->\n          System.out.println(\n              \"agent.max_iterations = \"\n                  + AgentConfig.DEFAULT_MAX_ITERATIONS\n                  + \" (built-in default)\");\n      default -> System.out.println(key + \" = (not set)\");\n    }\n  }\n\n  /** 打印所有配置项。 */\n  private static void printAllConfig(AliceConfigStore store) {\n    System.out.println(\"=== Alice Agent Configuration ===\");\n    System.out.println();\n\n    // 读取 providers 嵌套结构\n    var all = store.getAll();\n    System.out.println(\"-- Model Providers --\");\n    @SuppressWarnings(\"unchecked\")\n    var providers = (Map<String, Object>) all.get(\"providers\");\n    if (providers != null && !providers.isEmpty()) {\n      for (var entry : providers.entrySet()) {\n        String providerName = entry.getKey();\n        System.out.println(\"  [\" + providerName + \"]\");\n        @SuppressWarnings(\"unchecked\")\n        var providerConfig = (Map<String, Object>) entry.getValue();\n        if (providerConfig != null) {\n          for (var pEntry : providerConfig.entrySet()) {\n            String display =\n                pEntry.getKey().contains(\"api_key\")\n                    ? pEntry\n                            .getValue()\n                            .toString()\n                            .substring(0, Math.min(8, pEntry.getValue().toString().length()))\n                        + \"...\"\n                    : pEntry.getValue().toString();\n            System.out.println(\"    \" + pEntry.getKey() + \" = \" + display);\n          }\n        }\n      }\n    } else {\n      // 回退到扁平键\n      printConfigValue(\"openai.api_key\", store);\n      printConfigValue(\"anthropic.api_key\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"-- Agent Defaults --\");\n    // 从嵌套或扁平读取\n    String model = store.get(\"default.model\");\n    if (model == null) model = store.get(\"default_model\");\n    if (model != null) {\n      System.out.println(\"default.model = \" + model + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"default.model\", store);\n    }\n\n    String iterStr = store.get(\"agent.max_iterations\");\n    if (iterStr == null) iterStr = store.get(\"max_iterations\");\n    if (iterStr != null) {\n      System.out.println(\"agent.max_iterations = \" + iterStr + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"agent.max_iterations\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"Config file: ~/.alice/config.json\");\n    System.out.println(\"Use 'alice config get <key>' or 'alice config set <key> <value>'\");\n    System.out.println(\n        \"Known keys: openai.api_key, anthropic.api_key, default.model, agent.max_iterations\");\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n\n      // 1. 加载 ~/.alice/model.json 配置文件\n      try {\n        ModelConfigLoader configLoader = new ModelConfigLoader();\n        configLoader.load();\n        configLoader.registerTo(provider);\n        logger.info(\n            \"Loaded {} model provider(s) from ~/.alice/model.json\",\n            configLoader.getProviders().size());\n      } catch (Exception e) {\n        logger.debug(\"No model config found, using env vars: {}\", e.getMessage());\n      }\n\n      // 2. 注册内置模型枚举\n      provider.registerBuiltinModels();\n\n      // 3. 从环境变量注册供应商（优先级低于配置文件）\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        if (provider.getSupplier(\"gpt-4o-mini\") == null) {\n          provider.registerSupplier(new OpenAiSupplier(openAiKey));\n          logger.info(\"OpenAI supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        if (provider.getSupplier(\"claude-3-5-sonnet-latest\") == null) {\n          provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n          logger.info(\"Anthropic Claude supplier registered from env var\");\n        }\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n\n      // 4. DeepSeek (OpenAI-compatible) 从环境变量注册\n      String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n      if (deepseekKey != null && !deepseekKey.isEmpty()) {\n        if (provider.getSupplier(\"deepseek-chat\") == null) {\n          provider.registerSupplier(\n              new OpenAiSupplier(\n                  \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n          logger.info(\"DeepSeek supplier registered from env var (OpenAI-compatible)\");\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "a4225676e3fe9533f6466874bfd10308b7f7f6fc": {"t": 1785755700.958288, "n": 18692, "h": "3b9a2afbe1a7e437", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.Map;\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.core.agent.AgentConfig;\nimport org.cland.alice.facade.cmd.config.AliceConfigStore;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印 → 成功退出\n        return EXIT_SUCCESS;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 工具列表命令 — 不走 Agent 执行\n      if (config.listTools()) {\n        return handleListTools(config);\n      }\n\n      // 3. 配置管理命令 — 不走 Agent 执行\n      if (config.configAction() != null) {\n        return handleConfig(config);\n      }\n\n      // 4. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 4. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 5. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = UUID.randomUUID().toString().substring(0, 8);\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 工具列表\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice tools} 子命令，列出 ToolRegistry 中的已注册工具。\n   *\n   * @param config 运行配置\n   * @return 退出码\n   */\n  private static int handleListTools(RunConfig config) {\n    try {\n      var registry = org.cland.alice.tool.gateway.ToolRegistryHolder.INSTANCE;\n      var tools = registry.allTools();\n\n      if (tools.isEmpty()) {\n        System.out.println(\"No tools registered.\");\n        System.out.println(\"Use 'alice run --skill <toolset>' or configure MCP to load tools.\");\n        return EXIT_SUCCESS;\n      }\n\n      System.out.println(\"Registered tools (\" + tools.size() + \"):\");\n      System.out.println();\n\n      if (config.toolDetail()) {\n        for (var meta : tools) {\n          System.out.println(\"  ── \" + meta.name() + \" ──\");\n          System.out.println(\"     Description: \" + meta.description());\n          if (meta.inputSchema() != null && !meta.inputSchema().isEmpty()) {\n            System.out.println(\"     Parameters:  \" + meta.inputSchema());\n          }\n          System.out.println();\n        }\n      } else {\n        for (var meta : tools) {\n          System.out.println(\"  ⚡ \" + meta.name() + \" — \" + meta.description());\n        }\n        System.out.println();\n        System.out.println(\"Use 'alice tools --detail' to see parameter schemas.\");\n      }\n\n      return EXIT_SUCCESS;\n    } catch (Exception e) {\n      System.err.println(\"Error listing tools: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  // ========================================================================\n  // 配置管理\n  // ========================================================================\n\n  /**\n   * 处理 {@code alice config} 子命令。\n   *\n   * <p>支持:\n   *\n   * <ul>\n   *   <li>{@code alice config} — 显示当前配置概览\n   *   <li>{@code alice config get &lt;key&gt;} — 获取单个配置项\n   *   <li>{@code alice config set &lt;key&gt; &lt;value&gt;} — 设置配置项（当前仅打印模拟）\n   * </ul>\n   */\n  /** 全局配置存储实例（延迟初始化） */\n  private static volatile AliceConfigStore configStore;\n\n  /** 获取或创建配置存储实例。 */\n  private static AliceConfigStore configStore() {\n    if (configStore == null) {\n      synchronized (AliceCliLauncher.class) {\n        if (configStore == null) {\n          configStore = new AliceConfigStore();\n        }\n      }\n    }\n    return configStore;\n  }\n\n  private static int handleConfig(RunConfig config) {\n    try {\n      String action = config.configAction();\n      String key = config.configKey();\n      String value = config.configValue();\n      AliceConfigStore store = configStore();\n\n      if (\"set\".equals(action)) {\n        if (key == null || value == null) {\n          System.err.println(\"Usage: alice config set <key> <value>\");\n          return EXIT_PARAM_ERROR;\n        }\n        store.set(key, value);\n        System.out.println(\n            \"Config '\" + key + \"' set to '\" + value + \"' (persisted to ~/.alice/config.json)\");\n        return EXIT_SUCCESS;\n      }\n\n      if (\"get\".equals(action)) {\n        if (key == null) {\n          System.err.println(\"Usage: alice config get <key>\");\n          return EXIT_PARAM_ERROR;\n        }\n        printConfigValue(key, store);\n        return EXIT_SUCCESS;\n      }\n\n      // \"show\" — 显示全部配置\n      printAllConfig(store);\n      return EXIT_SUCCESS;\n\n    } catch (Exception e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return EXIT_RUNTIME_ERROR;\n    }\n  }\n\n  /**\n   * 获取配置值的实际值（优先级：环境变量 > 配置文件 > 内建默认值）。\n   *\n   * @param key 点分隔的键名\n   * @param store 配置存储\n   * @return 值，或 {@code null} 表示未设置\n   */\n  private static String resolveConfigValue(String key, AliceConfigStore store) {\n    // 1. 环境变量（最高优先级）\n    String envName = configKeyToEnv(key);\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        return envVal;\n      }\n    }\n    // 2. 配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      return stored;\n    }\n    // 3. 内建默认值\n    return switch (key) {\n      case \"default.model\" -> AgentConfig.DEFAULT_MODEL;\n      case \"agent.max_iterations\" -> String.valueOf(AgentConfig.DEFAULT_MAX_ITERATIONS);\n      default -> null;\n    };\n  }\n\n  /** 将配置键名映射为环境变量名。 */\n  private static String configKeyToEnv(String key) {\n    return switch (key) {\n      case \"openai.api_key\", \"providers.openai.api_key\" -> \"OPENAI_API_KEY\";\n      case \"anthropic.api_key\", \"providers.anthropic.api_key\" -> \"ANTHROPIC_API_KEY\";\n      default -> null;\n    };\n  }\n\n  /** 获取并打印单个配置项的值及其来源。 */\n  private static void printConfigValue(String key, AliceConfigStore store) {\n    String envName = configKeyToEnv(key);\n\n    // 检查环境变量\n    if (envName != null) {\n      String envVal = System.getenv(envName);\n      if (envVal != null && !envVal.isEmpty()) {\n        System.out.println(\n            key\n                + \" = \"\n                + envVal.substring(0, Math.min(8, envVal.length()))\n                + \"... (from env \"\n                + envName\n                + \")\");\n        return;\n      }\n    }\n\n    // 检查配置文件\n    String stored = store.get(key);\n    if (stored != null) {\n      String display =\n          key.contains(\"api_key\")\n              ? stored.substring(0, Math.min(8, stored.length())) + \"...\"\n              : stored;\n      System.out.println(key + \" = \" + display + \" (from ~/.alice/config.json)\");\n      return;\n    }\n\n    // 检查内建默认值\n    switch (key) {\n      case \"default.model\" ->\n          System.out.println(\n              \"default.model = \" + AgentConfig.DEFAULT_MODEL + \" (built-in default)\");\n      case \"agent.max_iterations\" ->\n          System.out.println(\n              \"agent.max_iterations = \"\n                  + AgentConfig.DEFAULT_MAX_ITERATIONS\n                  + \" (built-in default)\");\n      default -> System.out.println(key + \" = (not set)\");\n    }\n  }\n\n  /** 打印所有配置项。 */\n  private static void printAllConfig(AliceConfigStore store) {\n    System.out.println(\"=== Alice Agent Configuration ===\");\n    System.out.println();\n\n    // 读取 providers 嵌套结构\n    var all = store.getAll();\n    System.out.println(\"-- Model Providers --\");\n    @SuppressWarnings(\"unchecked\")\n    var providers = (Map<String, Object>) all.get(\"providers\");\n    if (providers != null && !providers.isEmpty()) {\n      for (var entry : providers.entrySet()) {\n        String providerName = entry.getKey();\n        System.out.println(\"  [\" + providerName + \"]\");\n        @SuppressWarnings(\"unchecked\")\n        var providerConfig = (Map<String, Object>) entry.getValue();\n        if (providerConfig != null) {\n          for (var pEntry : providerConfig.entrySet()) {\n            String display =\n                pEntry.getKey().contains(\"api_key\")\n                    ? pEntry\n                            .getValue()\n                            .toString()\n                            .substring(0, Math.min(8, pEntry.getValue().toString().length()))\n                        + \"...\"\n                    : pEntry.getValue().toString();\n            System.out.println(\"    \" + pEntry.getKey() + \" = \" + display);\n          }\n        }\n      }\n    } else {\n      // 回退到扁平键\n      printConfigValue(\"openai.api_key\", store);\n      printConfigValue(\"anthropic.api_key\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"-- Agent Defaults --\");\n    // 从嵌套或扁平读取\n    String model = store.get(\"default.model\");\n    if (model == null) model = store.get(\"default_model\");\n    if (model != null) {\n      System.out.println(\"default.model = \" + model + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"default.model\", store);\n    }\n\n    String iterStr = store.get(\"agent.max_iterations\");\n    if (iterStr == null) iterStr = store.get(\"max_iterations\");\n    if (iterStr != null) {\n      System.out.println(\"agent.max_iterations = \" + iterStr + \" (from ~/.alice/config.json)\");\n    } else {\n      printConfigValue(\"agent.max_iterations\", store);\n    }\n\n    System.out.println();\n    System.out.println(\"Config file: ~/.alice/config.json\");\n    System.out.println(\"Use 'alice config get <key>' or 'alice config set <key> <value>'\");\n    System.out.println(\n        \"Known keys: openai.api_key, anthropic.api_key, default.model, agent.max_iterations\");\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      provider.registerBuiltinModels();\n\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        provider.registerSupplier(new OpenAiSupplier(openAiKey));\n        logger.info(\"OpenAI supplier registered\");\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n        logger.info(\"Anthropic Claude supplier registered\");\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "7d35985d4f861780abf7f36eb3f2fa62bea9c786": {"t": 1785755702.4121075, "n": 10043, "h": "0b7481e2f7d6ac20", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.agent.subagent.SubAgentManager;\nimport org.cland.alice.agent.subagent.SubAgentRecord;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.ClaudeSupplier;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印\n        return EXIT_PARAM_ERROR;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 3. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 4. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SpawnSubAgentCmd spawn -> {\n        System.out.println(\"Spawning sub-agent: \" + spawn.goal());\n        // 创建 SubAgentManager 并生成子 Agent\n        String subSessionId = UUID.randomUUID().toString().substring(0, 8);\n        SubAgentManager mgr = new SubAgentManager(subSessionId);\n        SubAgentRecord record = mgr.spawnSubAgent(spawn.goal(), spawn.model());\n        System.out.println(\"Sub-agent \" + record.id() + \" spawned with goal: \" + spawn.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ConnectSubAgentCmd connect -> {\n        System.out.println(\n            \"Connecting to ACP agent: \" + connect.name() + \" at \" + connect.acpEndpoint());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ListSubAgentsCmd list -> {\n        System.out.println(\"Listing sub-agents (not yet wired to a SubAgentManager)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.CancelSubAgentCmd cancel -> {\n        System.out.println(\"Cancel sub-agent: \" + cancel.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.GetSubAgentResultsCmd results -> {\n        System.out.println(\n            \"Get results for sub-agent: \" + results.subAgentId() + \" (not yet wired)\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.SendToSubAgentCmd send -> {\n        System.out.println(\n            \"Send message to sub-agent \" + send.subAgentId() + \": \" + send.message());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.PromptSubAgentCmd prompt -> {\n        System.out.println(\"Prompt sub-agent \" + prompt.subAgentId() + \": \" + prompt.prompt());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      provider.registerBuiltinModels();\n\n      String openAiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (openAiKey != null && !openAiKey.isEmpty()) {\n        provider.registerSupplier(new OpenAiSupplier(openAiKey));\n        logger.info(\"OpenAI supplier registered\");\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n\n      String anthropicKey = System.getenv(\"ANTHROPIC_API_KEY\");\n      if (anthropicKey != null && !anthropicKey.isEmpty()) {\n        provider.registerSupplier(new ClaudeSupplier(anthropicKey));\n        logger.info(\"Anthropic Claude supplier registered\");\n      } else {\n        logger.warn(\"ANTHROPIC_API_KEY not set. LLM calls via Anthropic will be unavailable.\");\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "0b23bcc1514cdbac79d0b5dcebfa9d5682211679": {"t": 1785755703.6172955, "n": 7692, "h": "689a2073074b3dd3", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印\n        return EXIT_PARAM_ERROR;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 3. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 4. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.RoutineTimeCmd.RegisterRoutineCmd routine -> {\n        System.out.println(\"Registering routine: \" + routine.cronExpression());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      provider.registerBuiltinModels();\n\n      String apiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (apiKey != null && !apiKey.isEmpty()) {\n        provider.registerSupplier(new OpenAiSupplier(apiKey));\n        logger.info(\"OpenAI supplier registered\");\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "6ac7c7c409fdeda5f270fc91665793da3d5507a0": {"t": 1785755705.1294005, "n": 7488, "h": "f674b38f33eccf69", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印\n        return EXIT_PARAM_ERROR;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 3. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 4. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId =\n        cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      provider.registerBuiltinModels();\n\n      String apiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (apiKey != null && !apiKey.isEmpty()) {\n        provider.registerSupplier(new OpenAiSupplier(apiKey));\n        logger.info(\"OpenAI supplier registered\");\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "cdaaead10134ed1474525a45acdd5b6fc2ff12da": {"t": 1785755706.4558299, "n": 7480, "h": "8cc072b8b4c6bef7", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印\n        return EXIT_PARAM_ERROR;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 3. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 4. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    return dispatchCommand(cmd);\n  }\n\n  /**\n   * 分发已解析的 {@link AgentCommand}。\n   *\n   * <p>由 {@link JLineChatSession} 等已预先解析的调用方使用，避免重复解析。\n   *\n   * @param cmd 已解析的 AgentCommand\n   * @return 退出码\n   */\n  public static int dispatchCommand(AgentCommand cmd) {\n    if (cmd == null) {\n      System.err.println(\"Cannot dispatch null command\");\n      return EXIT_PARAM_ERROR;\n    }\n\n    String sessionId = cmd.sessionId() != null ? cmd.sessionId() : UUID.randomUUID().toString().substring(0, 8);\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      provider.registerBuiltinModels();\n\n      String apiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (apiKey != null && !apiKey.isEmpty()) {\n        provider.registerSupplier(new OpenAiSupplier(apiKey));\n        logger.info(\"OpenAI supplier registered\");\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "54c104cd19d2e340e51ebaa92cae552fe6b2fc43": {"t": 1785755707.7977746, "n": 6990, "h": "8d369b6724a60229", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印\n        return EXIT_PARAM_ERROR;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 3. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 4. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ClearContextCmd clear -> {\n        System.out.println(\"Clearing context (session=\" + clear.sessionId() + \")\");\n        System.out.println(\"上下文已清除\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.ViewContextCmd view -> {\n        System.out.println(\"Viewing context (session=\" + view.sessionId() + \")\");\n        System.out.println(\"── 上下文状态 ──\");\n        System.out.println(\"会话 ID: \" + view.sessionId());\n        System.out.println(\"Token 占用: N/A (Memory 模块待集成)\");\n        System.out.println(\"消息滑动窗口: N/A\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ControlCmd.CompactContextCmd compact -> {\n        System.out.println(\"Compacting context (session=\" + compact.sessionId() + \")\");\n        System.out.println(\"上下文压缩请求已提交（待 Memory 模块提供总结接口）\");\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      provider.registerBuiltinModels();\n\n      String apiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (apiKey != null && !apiKey.isEmpty()) {\n        provider.registerSupplier(new OpenAiSupplier(apiKey));\n        logger.info(\"OpenAI supplier registered\");\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "ecba2ea793e0640c9d3e7fb067725c450ccae401": {"t": 1785755709.3734043, "n": 6086, "h": "e83e27b3c34155e2", "c": "package org.cland.alice.facade.cmd;\n\nimport java.util.UUID;\nimport org.cland.alice.agent.command.AgentCommand;\nimport org.cland.alice.agent.command.CapabilityCmd;\nimport org.cland.alice.agent.command.ControlCmd;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>对于 {@code run} 子命令，驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>对于其他子命令（预留），通过 {@link AgentCommand} 抽象层路由\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印\n        return EXIT_PARAM_ERROR;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 3. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 4. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  /**\n   * 将用户输入解析为 {@link AgentCommand} 并分发。\n   *\n   * <p>用于交互式 CLI 会话（chat 模式），根据 AgentCommand 类型路由：\n   *\n   * <ul>\n   *   <li>{@code AcquireGoalCmd} / {@code ExecuteRawCmd} → Agent 核心执行\n   *   <li>{@code SwitchModelCmd} → 模型切换\n   *   <li>{@code RegisterSkillCmd} / {@code UpdateRulesCmd} / {@code ReloadKernelCmd} → 能力装载\n   *   <li>{@code ResetSessionCmd} / {@code InterruptCmd} → 生命周期控制\n   * </ul>\n   *\n   * @param input 用户原始输入\n   * @return 退出码\n   */\n  public static int dispatchCommand(String input) {\n    String sessionId = UUID.randomUUID().toString().substring(0, 8);\n    String traceId = UUID.randomUUID().toString().substring(0, 12);\n\n    AgentCommand cmd = AgentCommand.parse(input, sessionId, traceId);\n    if (cmd == null) {\n      System.err.println(\"Unrecognized command: \" + input);\n      return EXIT_PARAM_ERROR;\n    }\n\n    logger.info(\n        \"Dispatching AgentCommand: {} (session={})\", cmd.getClass().getSimpleName(), sessionId);\n\n    return switch (cmd) {\n      case org.cland.alice.agent.command.ExecutionCmd.AcquireGoalCmd run -> {\n        System.out.println(\"Executing goal: \" + run.goal());\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.ExecutionCmd.ExecuteRawCmd exec -> {\n        System.out.println(\"Executing raw: \" + exec.command());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.RegisterSkillCmd skill -> {\n        System.out.println(\"Registering skill: \" + skill.skillRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.UpdateRulesCmd rules -> {\n        System.out.println(\"Updating rules: \" + rules.rulesRef());\n        yield EXIT_SUCCESS;\n      }\n      case CapabilityCmd.ReloadKernelCmd reload -> {\n        System.out.println(\"Reloading kernel...\");\n        yield EXIT_SUCCESS;\n      }\n      case org.cland.alice.agent.command.AlignmentCmd.SwitchModelCmd model -> {\n        System.out.println(\"Switching model to: \" + model.modelId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.ResetSessionCmd reset -> {\n        System.out.println(\"Resetting session: \" + reset.sessionId());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.FeedbackCmd fb -> {\n        System.out.println(\"Feedback received: \" + fb.message());\n        yield EXIT_SUCCESS;\n      }\n      case ControlCmd.InterruptCmd interrupt -> {\n        System.out.println(\"Interrupted: \" + interrupt.cause());\n        yield EXIT_SUCCESS;\n      }\n      case null, default -> {\n        System.err.println(\"Unknown command type\");\n        yield EXIT_PARAM_ERROR;\n      }\n    };\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      provider.registerBuiltinModels();\n\n      String apiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (apiKey != null && !apiKey.isEmpty()) {\n        provider.registerSupplier(new OpenAiSupplier(apiKey));\n        logger.info(\"OpenAI supplier registered\");\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "cf772f464ef785e85743100f561e6292b64c024a": {"t": 1785755710.593858, "n": 3315, "h": "c52ff15a51e74a09", "c": "package org.cland.alice.facade.cmd;\n\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Alice CLI 模块的入口点。\n *\n * <p>对应设计文档中 {@code AliceCliLauncher} 的角色：\n *\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）\n *   <li>初始化核心环境（ModelProvider 等）\n *   <li>驱动 {@link ExecutionCoordinator} 执行任务\n *   <li>映射退出码并退出 JVM\n * </ul>\n *\n * <p>退出码映射（符合设计文档）：\n *\n * <ul>\n *   <li>{@code 0} — 任务执行成功\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）\n *   <li>{@code 2} — 命令参数错误\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n  private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n  // 退出码常量\n  public static final int EXIT_SUCCESS = 0;\n  public static final int EXIT_RUNTIME_ERROR = 1;\n  public static final int EXIT_PARAM_ERROR = 2;\n  public static final int EXIT_INTERRUPTED = 130;\n\n  private AliceCliLauncher() {\n    // 工具类，不可实例化\n  }\n\n  /**\n   * CLI 主入口点。\n   *\n   * @param args 命令行参数\n   */\n  public static void main(String[] args) {\n    // 注册 JVM 关闭钩子处理 Ctrl+C\n    Runtime.getRuntime()\n        .addShutdownHook(\n            new Thread(\n                () -> {\n                  System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n                }));\n\n    int exitCode = run(args);\n    System.exit(exitCode);\n  }\n\n  /**\n   * CLI 运行逻辑（可测试的主逻辑）。\n   *\n   * @param args 命令行参数\n   * @return 退出码\n   */\n  public static int run(String[] args) {\n    try {\n      // 1. 解析参数\n      CommandParser parser = new CommandParser();\n      RunConfig config = parser.parse(args);\n\n      if (config == null) {\n        // parse 返回 null 表示帮助/版本信息已打印\n        return EXIT_PARAM_ERROR;\n      }\n\n      logger.info(\"RunConfig: {}\", config);\n\n      // 2. 初始化 ModelProvider\n      initializeModelProvider();\n\n      // 3. 创建渲染器\n      OutputRenderer renderer = OutputRenderer.create(config);\n\n      // 4. 执行任务\n      ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n      return coordinator.execute();\n\n    } catch (ParseException e) {\n      System.err.println(\"Error: \" + e.getMessage());\n      return e.exitCode();\n    }\n  }\n\n  // ========================================================================\n  // 初始化\n  // ========================================================================\n\n  /**\n   * 初始化模型提供器。\n   *\n   * <p>注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n   */\n  private static void initializeModelProvider() {\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      provider.registerBuiltinModels();\n\n      String apiKey = System.getenv(\"OPENAI_API_KEY\");\n      if (apiKey != null && !apiKey.isEmpty()) {\n        provider.registerSupplier(new OpenAiSupplier(apiKey));\n        logger.info(\"OpenAI supplier registered\");\n      } else {\n        logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n      }\n    } catch (Exception e) {\n      logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n    }\n  }\n}\n"}, "74837030dfba4e7d4c820989eed8c4dd79acc3aa": {"t": 1785755712.0336196, "n": 3576, "h": "e2b9982ec4dc3189", "c": "package org.cland.alice.facade.cmd;\n\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\n\n/**\n * Alice CLI 模块的入口点。\n * <p>\n * 对应设计文档中 {@code AliceCliLauncher} 的角色：\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）</li>\n *   <li>初始化核心环境（ModelProvider 等）</li>\n *   <li>驱动 {@link ExecutionCoordinator} 执行任务</li>\n *   <li>映射退出码并退出 JVM</li>\n * </ul>\n *\n * <p>\n * 退出码映射（符合设计文档）：\n * <ul>\n *   <li>{@code 0} — 任务执行成功</li>\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）</li>\n *   <li>{@code 2} — 命令参数错误</li>\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）</li>\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n    private static final Logger logger = LoggerFactory.getLogger(AliceCliLauncher.class);\n\n    // 退出码常量\n    public static final int EXIT_SUCCESS = 0;\n    public static final int EXIT_RUNTIME_ERROR = 1;\n    public static final int EXIT_PARAM_ERROR = 2;\n    public static final int EXIT_INTERRUPTED = 130;\n\n    private AliceCliLauncher() {\n        // 工具类，不可实例化\n    }\n\n    /**\n     * CLI 主入口点。\n     *\n     * @param args 命令行参数\n     */\n    public static void main(String[] args) {\n        // 注册 JVM 关闭钩子处理 Ctrl+C\n        Runtime.getRuntime().addShutdownHook(new Thread(() -> {\n            System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n        }));\n\n        int exitCode = run(args);\n        System.exit(exitCode);\n    }\n\n    /**\n     * CLI 运行逻辑（可测试的主逻辑）。\n     *\n     * @param args 命令行参数\n     * @return 退出码\n     */\n    public static int run(String[] args) {\n        try {\n            // 1. 解析参数\n            CommandParser parser = new CommandParser();\n            RunConfig config = parser.parse(args);\n\n            if (config == null) {\n                // parse 返回 null 表示帮助/版本信息已打印\n                return EXIT_PARAM_ERROR;\n            }\n\n        logger.info(\"RunConfig: {}\", config);\n\n            // 2. 初始化 ModelProvider\n            initializeModelProvider();\n\n            // 3. 创建渲染器\n            OutputRenderer renderer = OutputRenderer.create(config);\n\n            // 4. 执行任务\n            ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n            return coordinator.execute();\n\n        } catch (ParseException e) {\n            System.err.println(\"Error: \" + e.getMessage());\n            return e.exitCode();\n        }\n    }\n\n    // ========================================================================\n    // 初始化\n    // ========================================================================\n\n    /**\n     * 初始化模型提供器。\n     * <p>\n     * 注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n     */\n    private static void initializeModelProvider() {\n        try {\n            ModelProvider provider = ModelProvider.getInstance();\n            provider.registerBuiltinModels();\n\n            String apiKey = System.getenv(\"OPENAI_API_KEY\");\n            if (apiKey != null && !apiKey.isEmpty()) {\n                provider.registerSupplier(new OpenAiSupplier(apiKey));\n                logger.info(\"OpenAI supplier registered\");\n            } else {\n                logger.warn(\"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n            }\n        } catch (Exception e) {\n        logger.warn(\"ModelProvider initialization failed (some features may be unavailable)\", e);\n        }\n    }\n}\n"}, "b72ce13b6595188b8c47f91d0409c1cb187b621f": {"t": 1785755713.2476056, "n": 3680, "h": "143badcb424dc7cd", "c": "package org.cland.alice.facade.cmd;\n\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\n\n/**\n * Alice CLI 模块的入口点。\n * <p>\n * 对应设计文档中 {@code AliceCliLauncher} 的角色：\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）</li>\n *   <li>初始化核心环境（ModelProvider 等）</li>\n *   <li>驱动 {@link ExecutionCoordinator} 执行任务</li>\n *   <li>映射退出码并退出 JVM</li>\n * </ul>\n *\n * <p>\n * 退出码映射（符合设计文档）：\n * <ul>\n *   <li>{@code 0} — 任务执行成功</li>\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）</li>\n *   <li>{@code 2} — 命令参数错误</li>\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）</li>\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n    private static final System.Logger logger = System.getLogger(AliceCliLauncher.class.getName());\n\n    // 退出码常量\n    public static final int EXIT_SUCCESS = 0;\n    public static final int EXIT_RUNTIME_ERROR = 1;\n    public static final int EXIT_PARAM_ERROR = 2;\n    public static final int EXIT_INTERRUPTED = 130;\n\n    private AliceCliLauncher() {\n        // 工具类，不可实例化\n    }\n\n    /**\n     * CLI 主入口点。\n     *\n     * @param args 命令行参数\n     */\n    public static void main(String[] args) {\n        // 注册 JVM 关闭钩子处理 Ctrl+C\n        Runtime.getRuntime().addShutdownHook(new Thread(() -> {\n            System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n        }));\n\n        int exitCode = run(args);\n        System.exit(exitCode);\n    }\n\n    /**\n     * CLI 运行逻辑（可测试的主逻辑）。\n     *\n     * @param args 命令行参数\n     * @return 退出码\n     */\n    public static int run(String[] args) {\n        try {\n            // 1. 解析参数\n            CommandParser parser = new CommandParser();\n            RunConfig config = parser.parse(args);\n\n            if (config == null) {\n                // parse 返回 null 表示帮助/版本信息已打印\n                return EXIT_PARAM_ERROR;\n            }\n\n            logger.log(System.Logger.Level.INFO, \"RunConfig: {0}\", config);\n\n            // 2. 初始化 ModelProvider\n            initializeModelProvider();\n\n            // 3. 创建渲染器\n            OutputRenderer renderer = OutputRenderer.create(config);\n\n            // 4. 执行任务\n            ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n            return coordinator.execute();\n\n        } catch (ParseException e) {\n            System.err.println(\"Error: \" + e.getMessage());\n            return e.exitCode();\n        }\n    }\n\n    // ========================================================================\n    // 初始化\n    // ========================================================================\n\n    /**\n     * 初始化模型提供器。\n     * <p>\n     * 注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n     */\n    private static void initializeModelProvider() {\n        try {\n            ModelProvider provider = ModelProvider.getInstance();\n            provider.registerBuiltinModels();\n\n            String apiKey = System.getenv(\"OPENAI_API_KEY\");\n            if (apiKey != null && !apiKey.isEmpty()) {\n                provider.registerSupplier(new OpenAiSupplier(apiKey));\n                logger.log(System.Logger.Level.INFO, \"OpenAI supplier registered\");\n            } else {\n                logger.log(System.Logger.Level.WARNING,\n                    \"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n            }\n        } catch (Exception e) {\n            logger.log(System.Logger.Level.WARNING,\n                \"ModelProvider initialization failed (some features may be unavailable)\", e);\n        }\n    }\n}\n"}, "ee70bd0a0bab774568fba363216d26f0e925e276": {"t": 1785755714.5662138, "n": 3673, "h": "835a33a2e85f446d", "c": "package org.cland.alice.facade.cmd;\n\nimport org.cland.alice.facade.cmd.config.CommandParser;\nimport org.cland.alice.facade.cmd.config.CommandParser.ParseException;\nimport org.cland.alice.facade.cmd.config.RunConfig;\nimport org.cland.alice.facade.cmd.render.OutputRenderer;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.model.supplier.OpenAiSupplier;\n\n/**\n * Alice CLI 模块的入口点。\n * <p>\n * 对应设计文档中 {@code AliceCliLauncher} 的角色：\n * <ul>\n *   <li>解析 CLI 命令参数（委托给 {@link CommandParser}）</li>\n *   <li>初始化核心环境（ModelProvider 等）</li>\n *   <li>驱动 {@link ExecutionCoordinator} 执行任务</li>\n *   <li>映射退出码并退出 JVM</li>\n * </ul>\n *\n * <p>\n * 退出码映射（符合设计文档）：\n * <ul>\n *   <li>{@code 0} — 任务执行成功</li>\n *   <li>{@code 1} — 运行时错误（Agent 无法完成目标）</li>\n *   <li>{@code 2} — 命令参数错误</li>\n *   <li>{@code 130} — 用户手动中断（Ctrl+C）</li>\n * </ul>\n */\npublic final class AliceCliLauncher {\n\n    private static final System.Logger logger = System.getLogger(AliceCliLauncher.class.getName());\n\n    // 退出码常量\n    public static final int EXIT_SUCCESS = 0;\n    public static final int EXIT_RUNTIME_ERROR = 1;\n    public static final int EXIT_PARAM_ERROR = 2;\n    public static final int EXIT_INTERRUPTED = 130;\n\n    private AliceCliLauncher() {\n        // 工具类，不可实例化\n    }\n\n    /**\n     * CLI 主入口点。\n     *\n     * @param args 命令行参数\n     */\n    public static void main(String[] args) {\n        // 注册 JVM 关闭钩子处理 Ctrl+C\n        Runtime.getRuntime().addShutdownHook(new Thread(() -> {\n            System.err.println(\"\\nReceived shutdown signal. Exiting...\");\n        }));\n\n        int exitCode = run(args);\n        System.exit(exitCode);\n    }\n\n    /**\n     * CLI 运行逻辑（可测试的主逻辑）。\n     *\n     * @param args 命令行参数\n     * @return 退出码\n     */\n    static int run(String[] args) {\n        try {\n            // 1. 解析参数\n            CommandParser parser = new CommandParser();\n            RunConfig config = parser.parse(args);\n\n            if (config == null) {\n                // parse 返回 null 表示帮助/版本信息已打印\n                return EXIT_PARAM_ERROR;\n            }\n\n            logger.log(System.Logger.Level.INFO, \"RunConfig: {0}\", config);\n\n            // 2. 初始化 ModelProvider\n            initializeModelProvider();\n\n            // 3. 创建渲染器\n            OutputRenderer renderer = OutputRenderer.create(config);\n\n            // 4. 执行任务\n            ExecutionCoordinator coordinator = new ExecutionCoordinator(config, renderer);\n            return coordinator.execute();\n\n        } catch (ParseException e) {\n            System.err.println(\"Error: \" + e.getMessage());\n            return e.exitCode();\n        }\n    }\n\n    // ========================================================================\n    // 初始化\n    // ========================================================================\n\n    /**\n     * 初始化模型提供器。\n     * <p>\n     * 注册内置模型，并从环境变量中读取 API Key 注册外部供应商。\n     */\n    private static void initializeModelProvider() {\n        try {\n            ModelProvider provider = ModelProvider.getInstance();\n            provider.registerBuiltinModels();\n\n            String apiKey = System.getenv(\"OPENAI_API_KEY\");\n            if (apiKey != null && !apiKey.isEmpty()) {\n                provider.registerSupplier(new OpenAiSupplier(apiKey));\n                logger.log(System.Logger.Level.INFO, \"OpenAI supplier registered\");\n            } else {\n                logger.log(System.Logger.Level.WARNING,\n                    \"OPENAI_API_KEY not set. Set it via environment variable to enable LLM calls.\");\n            }\n        } catch (Exception e) {\n            logger.log(System.Logger.Level.WARNING,\n                \"ModelProvider initialization failed (some features may be unavailable)\", e);\n        }\n    }\n}\n"}}, "alice-core-agent/src/main/java/org/cland/alice/core/agent/Agent.java": {"8ebb2c40d3906a86e5a1eaa8ea381b21d0df9bd9": {"t": 1785745700.2901363, "n": 23299, "h": "10f7e7960c0ed5a8", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.Plan;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.core.planner.strategy.FastPathStrategy;\nimport org.cland.alice.core.planner.strategy.SlowPathStrategy;\nimport org.cland.alice.core.planner.strategy.StrategySelector;\nimport org.cland.alice.core.planner.tree.ThinkingTree;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.cland.alice.tool.gateway.ToolRegistryHolder;\nimport org.cland.alice.tool.gateway.builtin.BuiltinTools;\nimport org.cland.alice.tool.gateway.engine.ToolDiscovery;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent implements AgentFacade {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  /** 最近一次 LLM 调用的推理/思考过程，供前端渲染 */\n  private String lastReasoning;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, String sessionId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = sessionId != null ? sessionId : SnowflakeIdGenerator.generateSessionId();\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this(agentId, null, config);\n  }\n\n  // ========== 属性 ==========\n\n  @Override\n  public String agentId() {\n    return agentId;\n  }\n\n  @Override\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  /**\n   * 注入静态规划（SOP）函数，重建 {@link PlannerService} 以支持 SOP 匹配。\n   *\n   * <p>如果当前已有 PlannerService，会复用其 StrategySelector 和现有的 SOP 函数。 该函数通常来自 {@code alice-memory-vault}\n   * 的 {@code StaticPlanner::plan}。\n   *\n   * @param staticPlannerFn 接收上下文 Map 返回 Plan 的函数（null 表示无 SOP）\n   */\n  public Agent withStaticPlanner(\n      java.util.function.Function<Map<String, Object>, Plan> staticPlannerFn) {\n    if (this.plannerService != null) {\n      this.plannerService =\n          PlannerService.builder()\n              .strategySelector(this.plannerService.strategySelector())\n              .staticPlannerFn(staticPlannerFn)\n              .build();\n    }\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  @Override\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  @Override\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  @Override\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                // 提取推理内容供前端渲染\n                lastReasoning =\n                    ctx.containsKey(\"__llm_reasoning\") ? ctx.get(\"__llm_reasoning\").toString() : \"\";\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  /**\n   * 获取最近一次 LLM 调用的推理内容（reasoning_content），供 TUI 渲染。\n   *\n   * @return 推理文本，若无则返回空字符串\n   */\n  /**\n   * 获取 Agent 执行器，用于注册事件监听等。\n   *\n   * @return AgentExecutor 实例\n   */\n  public AgentExecutor getExecutor() {\n    return executor;\n  }\n\n  public String getLastReasoning() {\n    return lastReasoning != null ? lastReasoning : \"\";\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(this.sessionId, config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  @Override\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  @Override\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  @Override\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  /** 取消当前 PPAO 执行。可用于 TUI ESC / CLI Ctrl+C 中断。 */\n  public void cancel() {\n    executor.cancel();\n    logger.info(\"[Agent] Execution cancelled\");\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n\n  // ================================================================\n  // 静态工厂方法 — 供 TUI/CLI 外观模块使用，避免外观直接依赖子模块\n  // ================================================================\n\n  /**\n   * 初始化 ModelProvider：从 {@code ~/.alice/model.json} 加载配置，注册提供商和内置模型枚举。\n   *\n   * <p>此方法负责：\n   *\n   * <ul>\n   *   <li>加载 {@code ~/.alice/model.json} 配置文件\n   *   <li>将配置中的提供商注册到 {@link ModelProvider}\n   *   <li>注册内置模型枚举\n   *   <li>如果环境变量中存在 DEEPSEEK_API_KEY，注册 DeepSeek 供应商\n   * </ul>\n   *\n   * @return 默认模型 ID（配置中指定），若无则返回 {@code null}\n   */\n  public static String initModelProvider() {\n    ModelConfigLoader configLoader = new ModelConfigLoader();\n    try {\n      configLoader.load();\n      configLoader.registerTo(ModelProvider.getInstance());\n      logger.info(\"[Agent] Loaded {} model(s) from config\", configLoader.getModelPool().size());\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config, using defaults: {}\", e.getMessage());\n    }\n\n    // 注册内置模型枚举\n    ModelProvider.getInstance().registerBuiltinModels();\n\n    // 确定默认模型\n    String defaultModel = configLoader.getDefaultModel();\n    if (defaultModel == null || defaultModel.isBlank()) {\n      defaultModel = \"gpt-4o-mini\";\n      logger.info(\n          \"[Agent] No default_model in ~/.alice/model.json, using built-in: {}\", defaultModel);\n    } else {\n      logger.info(\"[Agent] Using default model from config: {}\", defaultModel);\n    }\n\n    // 注册 DeepSeek 供应商（如果环境变量存在且尚未注册）\n    String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n    if (deepseekKey != null && !deepseekKey.isEmpty()) {\n      if (ModelProvider.getInstance().getSupplier(\"deepseek-v4-flash\") == null) {\n        ModelProvider.getInstance()\n            .registerSupplier(\n                new org.cland.alice.model.supplier.OpenAiSupplier(\n                    \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n        logger.info(\"[Agent] Registered DeepSeek supplier via OpenAiSupplier (OpenAI-compatible)\");\n      }\n    }\n\n    return defaultModel;\n  }\n\n  /**\n   * 创建完全初始化的 Agent 实例，自动装配所有子模块（PlannerService、ToolRegistry 等）。\n   *\n   * <p>外观模块可以直接使用此工厂方法创建 Agent，无需直接依赖 planner/tool-gateway/model 模块。\n   *\n   * @param config Agent 配置\n   * @return 已装配所有子模块的 Agent 实例\n   */\n  public static Agent createDefault(AgentConfig config) {\n    Agent agent = new Agent(config);\n\n    // 1. 初始化工具注册中心并发现内置工具\n    ToolRegistry toolRegistry = ToolRegistryHolder.INSTANCE.registry();\n    try {\n      int count = new ToolDiscovery(toolRegistry).scanAndRegister(List.of(new BuiltinTools()));\n      logger.info(\"[Agent] Registered {} builtin tool(s)\", count);\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to discover builtin tools\", e);\n    }\n    agent.withToolRegistry(toolRegistry);\n\n    // 2. 确定双路径模型：\n    //    - 推理/慢路径 (System 2)：使用 config.defaultModelId()\n    //    - 指令/快路径 (System 1)：从 model.json 的 planner.instruction_model_id 读取，\n    //      未设置则回退到 defaultModelId()\n    String reasoningModelId = config.defaultModelId();\n    String instructionModelId = reasoningModelId;\n    org.cland.alice.model.ModelConfigLoader.PlannerConfig plannerCfg = null;\n    try {\n      var configLoader = new org.cland.alice.model.ModelConfigLoader();\n      configLoader.load();\n      plannerCfg = configLoader.getPlannerConfig();\n      if (plannerCfg != null) {\n        if (plannerCfg.instructionModelId() != null) {\n          instructionModelId = plannerCfg.instructionModelId();\n        }\n        if (plannerCfg.reasoningModelId() != null) {\n          reasoningModelId = plannerCfg.reasoningModelId();\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config: {}\", e.getMessage());\n    }\n\n    // 3. 初始化 PlannerService（双路径规划引擎）\n    var plannerSupplier =\n        DefaultPlannerModelSupplier.builder()\n            .provider(ModelProvider.getInstance())\n            .instructionModelId(instructionModelId)\n            .reasoningModelId(reasoningModelId)\n            .plannerConfig(plannerCfg)\n            .build();\n    var fastPath = new FastPathStrategy(plannerSupplier);\n    var thinkingTree = new ThinkingTree(Map.of());\n    var slowPath =\n        SlowPathStrategy.builder()\n            .tree(thinkingTree)\n            .modelSupplier(plannerSupplier)\n            .mctsIterations(10)\n            .build();\n    var selector = StrategySelector.builder().fastPath(fastPath).slowPath(slowPath).build();\n    // SOP 静态规划器（可选）可通过 withPlannerService() 注入。\n    // 调用者（如 alice-bootstrap 或 facade 模块）若有 alice-memory-vault 访问权限，\n    // 可创建 StaticPlanner + SopRegistry 并通过 PlannerService.builder().staticPlannerFn() 注入。\n    var planner = PlannerService.builder().strategySelector(selector).build();\n    agent.withPlannerService(planner);\n\n    logger.info(\n        \"[Agent] Default Agent created: reasoningModel={}, instructionModel={}\",\n        config.defaultModelId(),\n        instructionModelId);\n\n    return agent;\n  }\n}\n"}, "799ea32095e6bad0d1a694bdcc629958685c6397": {"t": 1785755689.045456, "n": 23147, "h": "b1ca170cf4dde6df", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.Plan;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.core.planner.strategy.FastPathStrategy;\nimport org.cland.alice.core.planner.strategy.SlowPathStrategy;\nimport org.cland.alice.core.planner.strategy.StrategySelector;\nimport org.cland.alice.core.planner.tree.ThinkingTree;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.cland.alice.tool.gateway.ToolRegistryHolder;\nimport org.cland.alice.tool.gateway.builtin.BuiltinTools;\nimport org.cland.alice.tool.gateway.engine.ToolDiscovery;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent implements AgentFacade {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  /** 最近一次 LLM 调用的推理/思考过程，供前端渲染 */\n  private String lastReasoning;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, String sessionId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = sessionId != null ? sessionId : SnowflakeIdGenerator.generateSessionId();\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this(agentId, null, config);\n  }\n\n  // ========== 属性 ==========\n\n  @Override\n  public String agentId() {\n    return agentId;\n  }\n\n  @Override\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  /**\n   * 注入静态规划（SOP）函数，重建 {@link PlannerService} 以支持 SOP 匹配。\n   *\n   * <p>如果当前已有 PlannerService，会复用其 StrategySelector 和现有的 SOP 函数。 该函数通常来自 {@code alice-memory-vault}\n   * 的 {@code StaticPlanner::plan}。\n   *\n   * @param staticPlannerFn 接收上下文 Map 返回 Plan 的函数（null 表示无 SOP）\n   */\n  public Agent withStaticPlanner(\n      java.util.function.Function<Map<String, Object>, Plan> staticPlannerFn) {\n    if (this.plannerService != null) {\n      this.plannerService =\n          PlannerService.builder()\n              .strategySelector(this.plannerService.strategySelector())\n              .staticPlannerFn(staticPlannerFn)\n              .build();\n    }\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  @Override\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  @Override\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  @Override\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                // 提取推理内容供前端渲染\n                lastReasoning =\n                    ctx.containsKey(\"__llm_reasoning\") ? ctx.get(\"__llm_reasoning\").toString() : \"\";\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  /**\n   * 获取最近一次 LLM 调用的推理内容（reasoning_content），供 TUI 渲染。\n   *\n   * @return 推理文本，若无则返回空字符串\n   */\n  /**\n   * 获取 Agent 执行器，用于注册事件监听等。\n   *\n   * @return AgentExecutor 实例\n   */\n  public AgentExecutor getExecutor() {\n    return executor;\n  }\n\n  public String getLastReasoning() {\n    return lastReasoning != null ? lastReasoning : \"\";\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(this.sessionId, config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  @Override\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  @Override\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  @Override\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n\n  // ================================================================\n  // 静态工厂方法 — 供 TUI/CLI 外观模块使用，避免外观直接依赖子模块\n  // ================================================================\n\n  /**\n   * 初始化 ModelProvider：从 {@code ~/.alice/model.json} 加载配置，注册提供商和内置模型枚举。\n   *\n   * <p>此方法负责：\n   *\n   * <ul>\n   *   <li>加载 {@code ~/.alice/model.json} 配置文件\n   *   <li>将配置中的提供商注册到 {@link ModelProvider}\n   *   <li>注册内置模型枚举\n   *   <li>如果环境变量中存在 DEEPSEEK_API_KEY，注册 DeepSeek 供应商\n   * </ul>\n   *\n   * @return 默认模型 ID（配置中指定），若无则返回 {@code null}\n   */\n  public static String initModelProvider() {\n    ModelConfigLoader configLoader = new ModelConfigLoader();\n    try {\n      configLoader.load();\n      configLoader.registerTo(ModelProvider.getInstance());\n      logger.info(\"[Agent] Loaded {} model(s) from config\", configLoader.getModelPool().size());\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config, using defaults: {}\", e.getMessage());\n    }\n\n    // 注册内置模型枚举\n    ModelProvider.getInstance().registerBuiltinModels();\n\n    // 确定默认模型\n    String defaultModel = configLoader.getDefaultModel();\n    if (defaultModel == null || defaultModel.isBlank()) {\n      defaultModel = \"gpt-4o-mini\";\n      logger.info(\n          \"[Agent] No default_model in ~/.alice/model.json, using built-in: {}\", defaultModel);\n    } else {\n      logger.info(\"[Agent] Using default model from config: {}\", defaultModel);\n    }\n\n    // 注册 DeepSeek 供应商（如果环境变量存在且尚未注册）\n    String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n    if (deepseekKey != null && !deepseekKey.isEmpty()) {\n      if (ModelProvider.getInstance().getSupplier(\"deepseek-v4-flash\") == null) {\n        ModelProvider.getInstance()\n            .registerSupplier(\n                new org.cland.alice.model.supplier.OpenAiSupplier(\n                    \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n        logger.info(\"[Agent] Registered DeepSeek supplier via OpenAiSupplier (OpenAI-compatible)\");\n      }\n    }\n\n    return defaultModel;\n  }\n\n  /**\n   * 创建完全初始化的 Agent 实例，自动装配所有子模块（PlannerService、ToolRegistry 等）。\n   *\n   * <p>外观模块可以直接使用此工厂方法创建 Agent，无需直接依赖 planner/tool-gateway/model 模块。\n   *\n   * @param config Agent 配置\n   * @return 已装配所有子模块的 Agent 实例\n   */\n  public static Agent createDefault(AgentConfig config) {\n    Agent agent = new Agent(config);\n\n    // 1. 初始化工具注册中心并发现内置工具\n    ToolRegistry toolRegistry = ToolRegistryHolder.INSTANCE.registry();\n    try {\n      int count = new ToolDiscovery(toolRegistry).scanAndRegister(List.of(new BuiltinTools()));\n      logger.info(\"[Agent] Registered {} builtin tool(s)\", count);\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to discover builtin tools\", e);\n    }\n    agent.withToolRegistry(toolRegistry);\n\n    // 2. 确定双路径模型：\n    //    - 推理/慢路径 (System 2)：使用 config.defaultModelId()\n    //    - 指令/快路径 (System 1)：从 model.json 的 planner.instruction_model_id 读取，\n    //      未设置则回退到 defaultModelId()\n    String reasoningModelId = config.defaultModelId();\n    String instructionModelId = reasoningModelId;\n    org.cland.alice.model.ModelConfigLoader.PlannerConfig plannerCfg = null;\n    try {\n      var configLoader = new org.cland.alice.model.ModelConfigLoader();\n      configLoader.load();\n      plannerCfg = configLoader.getPlannerConfig();\n      if (plannerCfg != null) {\n        if (plannerCfg.instructionModelId() != null) {\n          instructionModelId = plannerCfg.instructionModelId();\n        }\n        if (plannerCfg.reasoningModelId() != null) {\n          reasoningModelId = plannerCfg.reasoningModelId();\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config: {}\", e.getMessage());\n    }\n\n    // 3. 初始化 PlannerService（双路径规划引擎）\n    var plannerSupplier =\n        DefaultPlannerModelSupplier.builder()\n            .provider(ModelProvider.getInstance())\n            .instructionModelId(instructionModelId)\n            .reasoningModelId(reasoningModelId)\n            .plannerConfig(plannerCfg)\n            .build();\n    var fastPath = new FastPathStrategy(plannerSupplier);\n    var thinkingTree = new ThinkingTree(Map.of());\n    var slowPath =\n        SlowPathStrategy.builder()\n            .tree(thinkingTree)\n            .modelSupplier(plannerSupplier)\n            .mctsIterations(10)\n            .build();\n    var selector = StrategySelector.builder().fastPath(fastPath).slowPath(slowPath).build();\n    // SOP 静态规划器（可选）可通过 withPlannerService() 注入。\n    // 调用者（如 alice-bootstrap 或 facade 模块）若有 alice-memory-vault 访问权限，\n    // 可创建 StaticPlanner + SopRegistry 并通过 PlannerService.builder().staticPlannerFn() 注入。\n    var planner = PlannerService.builder().strategySelector(selector).build();\n    agent.withPlannerService(planner);\n\n    logger.info(\n        \"[Agent] Default Agent created: reasoningModel={}, instructionModel={}\",\n        config.defaultModelId(),\n        instructionModelId);\n\n    return agent;\n  }\n}\n"}, "42915a7f3e5187792481e9a3b8034547a906d6a3": {"t": 1785755690.421439, "n": 23028, "h": "c95e96f46a9a599e", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.Plan;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.core.planner.strategy.FastPathStrategy;\nimport org.cland.alice.core.planner.strategy.SlowPathStrategy;\nimport org.cland.alice.core.planner.strategy.StrategySelector;\nimport org.cland.alice.core.planner.tree.ThinkingTree;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.cland.alice.tool.gateway.ToolRegistryHolder;\nimport org.cland.alice.tool.gateway.builtin.BuiltinTools;\nimport org.cland.alice.tool.gateway.engine.ToolDiscovery;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  /** 最近一次 LLM 调用的推理/思考过程，供前端渲染 */\n  private String lastReasoning;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, String sessionId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = sessionId != null ? sessionId : SnowflakeIdGenerator.generateSessionId();\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this(agentId, null, config);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  /**\n   * 注入静态规划（SOP）函数，重建 {@link PlannerService} 以支持 SOP 匹配。\n   *\n   * <p>如果当前已有 PlannerService，会复用其 StrategySelector 和现有的 SOP 函数。 该函数通常来自 {@code alice-memory-vault}\n   * 的 {@code StaticPlanner::plan}。\n   *\n   * @param staticPlannerFn 接收上下文 Map 返回 Plan 的函数（null 表示无 SOP）\n   */\n  public Agent withStaticPlanner(\n      java.util.function.Function<Map<String, Object>, Plan> staticPlannerFn) {\n    if (this.plannerService != null) {\n      this.plannerService =\n          PlannerService.builder()\n              .strategySelector(this.plannerService.strategySelector())\n              .staticPlannerFn(staticPlannerFn)\n              .build();\n    }\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                // 提取推理内容供前端渲染\n                lastReasoning =\n                    ctx.containsKey(\"__llm_reasoning\") ? ctx.get(\"__llm_reasoning\").toString() : \"\";\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  /**\n   * 获取最近一次 LLM 调用的推理内容（reasoning_content），供 TUI 渲染。\n   *\n   * @return 推理文本，若无则返回空字符串\n   */\n  /**\n   * 获取 Agent 执行器，用于注册事件监听等。\n   *\n   * @return AgentExecutor 实例\n   */\n  public AgentExecutor getExecutor() {\n    return executor;\n  }\n\n  public String getLastReasoning() {\n    return lastReasoning != null ? lastReasoning : \"\";\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(this.sessionId, config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n\n  // ================================================================\n  // 静态工厂方法 — 供 TUI/CLI 外观模块使用，避免外观直接依赖子模块\n  // ================================================================\n\n  /**\n   * 初始化 ModelProvider：从 {@code ~/.alice/model.json} 加载配置，注册提供商和内置模型枚举。\n   *\n   * <p>此方法负责：\n   *\n   * <ul>\n   *   <li>加载 {@code ~/.alice/model.json} 配置文件\n   *   <li>将配置中的提供商注册到 {@link ModelProvider}\n   *   <li>注册内置模型枚举\n   *   <li>如果环境变量中存在 DEEPSEEK_API_KEY，注册 DeepSeek 供应商\n   * </ul>\n   *\n   * @return 默认模型 ID（配置中指定），若无则返回 {@code null}\n   */\n  public static String initModelProvider() {\n    ModelConfigLoader configLoader = new ModelConfigLoader();\n    try {\n      configLoader.load();\n      configLoader.registerTo(ModelProvider.getInstance());\n      logger.info(\"[Agent] Loaded {} model(s) from config\", configLoader.getModelPool().size());\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config, using defaults: {}\", e.getMessage());\n    }\n\n    // 注册内置模型枚举\n    ModelProvider.getInstance().registerBuiltinModels();\n\n    // 确定默认模型\n    String defaultModel = configLoader.getDefaultModel();\n    if (defaultModel == null || defaultModel.isBlank()) {\n      defaultModel = \"gpt-4o-mini\";\n      logger.info(\n          \"[Agent] No default_model in ~/.alice/model.json, using built-in: {}\", defaultModel);\n    } else {\n      logger.info(\"[Agent] Using default model from config: {}\", defaultModel);\n    }\n\n    // 注册 DeepSeek 供应商（如果环境变量存在且尚未注册）\n    String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n    if (deepseekKey != null && !deepseekKey.isEmpty()) {\n      if (ModelProvider.getInstance().getSupplier(\"deepseek-v4-flash\") == null) {\n        ModelProvider.getInstance()\n            .registerSupplier(\n                new org.cland.alice.model.supplier.OpenAiSupplier(\n                    \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n        logger.info(\"[Agent] Registered DeepSeek supplier via OpenAiSupplier (OpenAI-compatible)\");\n      }\n    }\n\n    return defaultModel;\n  }\n\n  /**\n   * 创建完全初始化的 Agent 实例，自动装配所有子模块（PlannerService、ToolRegistry 等）。\n   *\n   * <p>外观模块可以直接使用此工厂方法创建 Agent，无需直接依赖 planner/tool-gateway/model 模块。\n   *\n   * @param config Agent 配置\n   * @return 已装配所有子模块的 Agent 实例\n   */\n  public static Agent createDefault(AgentConfig config) {\n    Agent agent = new Agent(config);\n\n    // 1. 初始化工具注册中心并发现内置工具\n    ToolRegistry toolRegistry = ToolRegistryHolder.INSTANCE.registry();\n    try {\n      int count = new ToolDiscovery(toolRegistry).scanAndRegister(List.of(new BuiltinTools()));\n      logger.info(\"[Agent] Registered {} builtin tool(s)\", count);\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to discover builtin tools\", e);\n    }\n    agent.withToolRegistry(toolRegistry);\n\n    // 2. 确定双路径模型：\n    //    - 推理/慢路径 (System 2)：使用 config.defaultModelId()\n    //    - 指令/快路径 (System 1)：从 model.json 的 planner.instruction_model_id 读取，\n    //      未设置则回退到 defaultModelId()\n    String reasoningModelId = config.defaultModelId();\n    String instructionModelId = reasoningModelId;\n    org.cland.alice.model.ModelConfigLoader.PlannerConfig plannerCfg = null;\n    try {\n      var configLoader = new org.cland.alice.model.ModelConfigLoader();\n      configLoader.load();\n      plannerCfg = configLoader.getPlannerConfig();\n      if (plannerCfg != null) {\n        if (plannerCfg.instructionModelId() != null) {\n          instructionModelId = plannerCfg.instructionModelId();\n        }\n        if (plannerCfg.reasoningModelId() != null) {\n          reasoningModelId = plannerCfg.reasoningModelId();\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config: {}\", e.getMessage());\n    }\n\n    // 3. 初始化 PlannerService（双路径规划引擎）\n    var plannerSupplier =\n        DefaultPlannerModelSupplier.builder()\n            .provider(ModelProvider.getInstance())\n            .instructionModelId(instructionModelId)\n            .reasoningModelId(reasoningModelId)\n            .plannerConfig(plannerCfg)\n            .build();\n    var fastPath = new FastPathStrategy(plannerSupplier);\n    var thinkingTree = new ThinkingTree(Map.of());\n    var slowPath =\n        SlowPathStrategy.builder()\n            .tree(thinkingTree)\n            .modelSupplier(plannerSupplier)\n            .mctsIterations(10)\n            .build();\n    var selector = StrategySelector.builder().fastPath(fastPath).slowPath(slowPath).build();\n    // SOP 静态规划器（可选）可通过 withPlannerService() 注入。\n    // 调用者（如 alice-bootstrap 或 facade 模块）若有 alice-memory-vault 访问权限，\n    // 可创建 StaticPlanner + SopRegistry 并通过 PlannerService.builder().staticPlannerFn() 注入。\n    var planner = PlannerService.builder().strategySelector(selector).build();\n    agent.withPlannerService(planner);\n\n    logger.info(\n        \"[Agent] Default Agent created: reasoningModel={}, instructionModel={}\",\n        config.defaultModelId(),\n        instructionModelId);\n\n    return agent;\n  }\n}\n"}, "542dfef86a8f56af43d54230584fe2edf9c80f6a": {"t": 1785755691.7581124, "n": 22353, "h": "d1b6d3faedae46ee", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.core.planner.sop.SopRegistry;\nimport org.cland.alice.core.planner.sop.StaticPlanner;\nimport org.cland.alice.core.planner.strategy.FastPathStrategy;\nimport org.cland.alice.core.planner.strategy.SlowPathStrategy;\nimport org.cland.alice.core.planner.strategy.StrategySelector;\nimport org.cland.alice.core.planner.tree.ThinkingTree;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.cland.alice.tool.gateway.ToolRegistryHolder;\nimport org.cland.alice.tool.gateway.builtin.BuiltinTools;\nimport org.cland.alice.tool.gateway.engine.ToolDiscovery;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  /** 最近一次 LLM 调用的推理/思考过程，供前端渲染 */\n  private String lastReasoning;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, String sessionId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = sessionId != null ? sessionId : SnowflakeIdGenerator.generateSessionId();\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this(agentId, null, config);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                // 提取推理内容供前端渲染\n                lastReasoning =\n                    ctx.containsKey(\"__llm_reasoning\") ? ctx.get(\"__llm_reasoning\").toString() : \"\";\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  /**\n   * 获取最近一次 LLM 调用的推理内容（reasoning_content），供 TUI 渲染。\n   *\n   * @return 推理文本，若无则返回空字符串\n   */\n  /**\n   * 获取 Agent 执行器，用于注册事件监听等。\n   *\n   * @return AgentExecutor 实例\n   */\n  public AgentExecutor getExecutor() {\n    return executor;\n  }\n\n  public String getLastReasoning() {\n    return lastReasoning != null ? lastReasoning : \"\";\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(this.sessionId, config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n\n  // ================================================================\n  // 静态工厂方法 — 供 TUI/CLI 外观模块使用，避免外观直接依赖子模块\n  // ================================================================\n\n  /**\n   * 初始化 ModelProvider：从 {@code ~/.alice/model.json} 加载配置，注册提供商和内置模型枚举。\n   *\n   * <p>此方法负责：\n   *\n   * <ul>\n   *   <li>加载 {@code ~/.alice/model.json} 配置文件\n   *   <li>将配置中的提供商注册到 {@link ModelProvider}\n   *   <li>注册内置模型枚举\n   *   <li>如果环境变量中存在 DEEPSEEK_API_KEY，注册 DeepSeek 供应商\n   * </ul>\n   *\n   * @return 默认模型 ID（配置中指定），若无则返回 {@code null}\n   */\n  public static String initModelProvider() {\n    ModelConfigLoader configLoader = new ModelConfigLoader();\n    try {\n      configLoader.load();\n      configLoader.registerTo(ModelProvider.getInstance());\n      logger.info(\"[Agent] Loaded {} model(s) from config\", configLoader.getModelPool().size());\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config, using defaults: {}\", e.getMessage());\n    }\n\n    // 注册内置模型枚举\n    ModelProvider.getInstance().registerBuiltinModels();\n\n    // 确定默认模型\n    String defaultModel = configLoader.getDefaultModel();\n    if (defaultModel == null || defaultModel.isBlank()) {\n      defaultModel = \"gpt-4o-mini\";\n      logger.info(\n          \"[Agent] No default_model in ~/.alice/model.json, using built-in: {}\", defaultModel);\n    } else {\n      logger.info(\"[Agent] Using default model from config: {}\", defaultModel);\n    }\n\n    // 注册 DeepSeek 供应商（如果环境变量存在且尚未注册）\n    String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n    if (deepseekKey != null && !deepseekKey.isEmpty()) {\n      if (ModelProvider.getInstance().getSupplier(\"deepseek-v4-flash\") == null) {\n        ModelProvider.getInstance()\n            .registerSupplier(\n                new org.cland.alice.model.supplier.OpenAiSupplier(\n                    \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n        logger.info(\"[Agent] Registered DeepSeek supplier via OpenAiSupplier (OpenAI-compatible)\");\n      }\n    }\n\n    return defaultModel;\n  }\n\n  /**\n   * 创建完全初始化的 Agent 实例，自动装配所有子模块（PlannerService、ToolRegistry 等）。\n   *\n   * <p>外观模块可以直接使用此工厂方法创建 Agent，无需直接依赖 planner/tool-gateway/model 模块。\n   *\n   * @param config Agent 配置\n   * @return 已装配所有子模块的 Agent 实例\n   */\n  public static Agent createDefault(AgentConfig config) {\n    Agent agent = new Agent(config);\n\n    // 1. 初始化工具注册中心并发现内置工具\n    ToolRegistry toolRegistry = ToolRegistryHolder.INSTANCE.registry();\n    try {\n      int count = new ToolDiscovery(toolRegistry).scanAndRegister(List.of(new BuiltinTools()));\n      logger.info(\"[Agent] Registered {} builtin tool(s)\", count);\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to discover builtin tools\", e);\n    }\n    agent.withToolRegistry(toolRegistry);\n\n    // 2. 确定双路径模型：\n    //    - 推理/慢路径 (System 2)：使用 config.defaultModelId()\n    //    - 指令/快路径 (System 1)：从 model.json 的 planner.instruction_model_id 读取，\n    //      未设置则回退到 defaultModelId()\n    String reasoningModelId = config.defaultModelId();\n    String instructionModelId = reasoningModelId;\n    org.cland.alice.model.ModelConfigLoader.PlannerConfig plannerCfg = null;\n    try {\n      var configLoader = new org.cland.alice.model.ModelConfigLoader();\n      configLoader.load();\n      plannerCfg = configLoader.getPlannerConfig();\n      if (plannerCfg != null) {\n        if (plannerCfg.instructionModelId() != null) {\n          instructionModelId = plannerCfg.instructionModelId();\n        }\n        if (plannerCfg.reasoningModelId() != null) {\n          reasoningModelId = plannerCfg.reasoningModelId();\n        }\n      }\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config: {}\", e.getMessage());\n    }\n\n    // 3. 初始化 PlannerService（双路径规划引擎）\n    var plannerSupplier =\n        DefaultPlannerModelSupplier.builder()\n            .provider(ModelProvider.getInstance())\n            .instructionModelId(instructionModelId)\n            .reasoningModelId(reasoningModelId)\n            .plannerConfig(plannerCfg)\n            .build();\n    var fastPath = new FastPathStrategy(plannerSupplier);\n    var thinkingTree = new ThinkingTree(Map.of());\n    var slowPath =\n        SlowPathStrategy.builder()\n            .tree(thinkingTree)\n            .modelSupplier(plannerSupplier)\n            .mctsIterations(10)\n            .build();\n    var selector = StrategySelector.builder().fastPath(fastPath).slowPath(slowPath).build();\n    var sopRegistry = new SopRegistry();\n    var staticPlanner = new StaticPlanner(sopRegistry);\n    var planner =\n        PlannerService.builder().strategySelector(selector).staticPlanner(staticPlanner).build();\n    agent.withPlannerService(planner);\n\n    logger.info(\n        \"[Agent] Default Agent created: reasoningModel={}, instructionModel={}\",\n        config.defaultModelId(),\n        instructionModelId);\n\n    return agent;\n  }\n}\n"}, "244f833a17dd9e983dda744e688a93601dac1197": {"t": 1785755693.408358, "n": 22095, "h": "1098c21e0e6402ee", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.core.planner.sop.SopRegistry;\nimport org.cland.alice.core.planner.sop.StaticPlanner;\nimport org.cland.alice.core.planner.strategy.FastPathStrategy;\nimport org.cland.alice.core.planner.strategy.SlowPathStrategy;\nimport org.cland.alice.core.planner.strategy.StrategySelector;\nimport org.cland.alice.core.planner.tree.ThinkingTree;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.cland.alice.tool.gateway.ToolRegistryHolder;\nimport org.cland.alice.tool.gateway.builtin.BuiltinTools;\nimport org.cland.alice.tool.gateway.engine.ToolDiscovery;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  /** 最近一次 LLM 调用的推理/思考过程，供前端渲染 */\n  private String lastReasoning;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, String sessionId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = sessionId != null ? sessionId : SnowflakeIdGenerator.generateSessionId();\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this(agentId, null, config);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                // 提取推理内容供前端渲染\n                lastReasoning =\n                    ctx.containsKey(\"__llm_reasoning\") ? ctx.get(\"__llm_reasoning\").toString() : \"\";\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  /**\n   * 获取最近一次 LLM 调用的推理内容（reasoning_content），供 TUI 渲染。\n   *\n   * @return 推理文本，若无则返回空字符串\n   */\n  /**\n   * 获取 Agent 执行器，用于注册事件监听等。\n   *\n   * @return AgentExecutor 实例\n   */\n  public AgentExecutor getExecutor() {\n    return executor;\n  }\n\n  public String getLastReasoning() {\n    return lastReasoning != null ? lastReasoning : \"\";\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(this.sessionId, config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n\n  // ================================================================\n  // 静态工厂方法 — 供 TUI/CLI 外观模块使用，避免外观直接依赖子模块\n  // ================================================================\n\n  /**\n   * 初始化 ModelProvider：从 {@code ~/.alice/model.json} 加载配置，注册提供商和内置模型枚举。\n   *\n   * <p>此方法负责：\n   *\n   * <ul>\n   *   <li>加载 {@code ~/.alice/model.json} 配置文件\n   *   <li>将配置中的提供商注册到 {@link ModelProvider}\n   *   <li>注册内置模型枚举\n   *   <li>如果环境变量中存在 DEEPSEEK_API_KEY，注册 DeepSeek 供应商\n   * </ul>\n   *\n   * @return 默认模型 ID（配置中指定），若无则返回 {@code null}\n   */\n  public static String initModelProvider() {\n    ModelConfigLoader configLoader = new ModelConfigLoader();\n    try {\n      configLoader.load();\n      configLoader.registerTo(ModelProvider.getInstance());\n      logger.info(\n          \"[Agent] Loaded {} model provider(s) from config\", configLoader.getProviders().size());\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config, using defaults: {}\", e.getMessage());\n    }\n\n    // 注册内置模型枚举\n    ModelProvider.getInstance().registerBuiltinModels();\n\n    // 确定默认模型\n    String defaultModel = configLoader.getDefaultModel();\n    if (defaultModel == null || defaultModel.isBlank()) {\n      defaultModel = \"gpt-4o-mini\";\n      logger.info(\n          \"[Agent] No default_model in ~/.alice/model.json, using built-in: {}\", defaultModel);\n    } else {\n      logger.info(\"[Agent] Using default model from config: {}\", defaultModel);\n    }\n\n    // 注册 DeepSeek 供应商（如果环境变量存在且尚未注册）\n    String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n    if (deepseekKey != null && !deepseekKey.isEmpty()) {\n      if (ModelProvider.getInstance().getSupplier(\"deepseek-v4-flash\") == null) {\n        ModelProvider.getInstance()\n            .registerSupplier(\n                new org.cland.alice.model.supplier.OpenAiSupplier(\n                    \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n        logger.info(\"[Agent] Registered DeepSeek supplier via OpenAiSupplier (OpenAI-compatible)\");\n      }\n    }\n\n    return defaultModel;\n  }\n\n  /**\n   * 创建完全初始化的 Agent 实例，自动装配所有子模块（PlannerService、ToolRegistry 等）。\n   *\n   * <p>外观模块可以直接使用此工厂方法创建 Agent，无需直接依赖 planner/tool-gateway/model 模块。\n   *\n   * @param config Agent 配置\n   * @return 已装配所有子模块的 Agent 实例\n   */\n  public static Agent createDefault(AgentConfig config) {\n    Agent agent = new Agent(config);\n\n    // 1. 初始化工具注册中心并发现内置工具\n    ToolRegistry toolRegistry = ToolRegistryHolder.INSTANCE.registry();\n    try {\n      int count = new ToolDiscovery(toolRegistry).scanAndRegister(List.of(new BuiltinTools()));\n      logger.info(\"[Agent] Registered {} builtin tool(s)\", count);\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to discover builtin tools\", e);\n    }\n    agent.withToolRegistry(toolRegistry);\n\n    // 2. 确定双路径模型：\n    //    - 推理/慢路径 (System 2)：使用 config.defaultModelId()\n    //    - 指令/快路径 (System 1)：从 model.json 的 instruction_model 读取，\n    //      未设置则回退到 defaultModelId()\n    String instructionModelId = config.defaultModelId();\n    try {\n      var configLoader = new org.cland.alice.model.ModelConfigLoader();\n      configLoader.load();\n      String configuredInstruction = configLoader.getInstructionModel();\n      if (configuredInstruction != null && !configuredInstruction.isBlank()) {\n        instructionModelId = configuredInstruction;\n      }\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config for instruction model: {}\", e.getMessage());\n    }\n\n    // 3. 初始化 PlannerService（双路径规划引擎）\n    var plannerSupplier =\n        DefaultPlannerModelSupplier.builder()\n            .provider(ModelProvider.getInstance())\n            .instructionModelId(instructionModelId)\n            .reasoningModelId(config.defaultModelId())\n            .build();\n    var fastPath = new FastPathStrategy(plannerSupplier);\n    var thinkingTree = new ThinkingTree(Map.of());\n    var slowPath =\n        SlowPathStrategy.builder()\n            .tree(thinkingTree)\n            .modelSupplier(plannerSupplier)\n            .mctsIterations(10)\n            .build();\n    var selector = StrategySelector.builder().fastPath(fastPath).slowPath(slowPath).build();\n    var sopRegistry = new SopRegistry();\n    var staticPlanner = new StaticPlanner(sopRegistry);\n    var planner =\n        PlannerService.builder().strategySelector(selector).staticPlanner(staticPlanner).build();\n    agent.withPlannerService(planner);\n\n    logger.info(\n        \"[Agent] Default Agent created: reasoningModel={}, instructionModel={}\",\n        config.defaultModelId(),\n        instructionModelId);\n\n    return agent;\n  }\n}\n"}, "f19ac8fe18e0fa7bc3f0c607030d04ee4c9298ff": {"t": 1785755694.95558, "n": 21332, "h": "1d50b31620fcb63d", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.core.planner.sop.SopRegistry;\nimport org.cland.alice.core.planner.sop.StaticPlanner;\nimport org.cland.alice.core.planner.strategy.FastPathStrategy;\nimport org.cland.alice.core.planner.strategy.SlowPathStrategy;\nimport org.cland.alice.core.planner.strategy.StrategySelector;\nimport org.cland.alice.core.planner.tree.ThinkingTree;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelConfigLoader;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.cland.alice.tool.gateway.ToolRegistryHolder;\nimport org.cland.alice.tool.gateway.builtin.BuiltinTools;\nimport org.cland.alice.tool.gateway.engine.ToolDiscovery;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  /** 最近一次 LLM 调用的推理/思考过程，供前端渲染 */\n  private String lastReasoning;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, String sessionId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = sessionId != null ? sessionId : SnowflakeIdGenerator.generateSessionId();\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this(agentId, null, config);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                // 提取推理内容供前端渲染\n                lastReasoning =\n                    ctx.containsKey(\"__llm_reasoning\") ? ctx.get(\"__llm_reasoning\").toString() : \"\";\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  /**\n   * 获取最近一次 LLM 调用的推理内容（reasoning_content），供 TUI 渲染。\n   *\n   * @return 推理文本，若无则返回空字符串\n   */\n  /**\n   * 获取 Agent 执行器，用于注册事件监听等。\n   *\n   * @return AgentExecutor 实例\n   */\n  public AgentExecutor getExecutor() {\n    return executor;\n  }\n\n  public String getLastReasoning() {\n    return lastReasoning != null ? lastReasoning : \"\";\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(this.sessionId, config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n\n  // ================================================================\n  // 静态工厂方法 — 供 TUI/CLI 外观模块使用，避免外观直接依赖子模块\n  // ================================================================\n\n  /**\n   * 初始化 ModelProvider：从 {@code ~/.alice/model.json} 加载配置，注册提供商和内置模型枚举。\n   *\n   * <p>此方法负责：\n   *\n   * <ul>\n   *   <li>加载 {@code ~/.alice/model.json} 配置文件\n   *   <li>将配置中的提供商注册到 {@link ModelProvider}\n   *   <li>注册内置模型枚举\n   *   <li>如果环境变量中存在 DEEPSEEK_API_KEY，注册 DeepSeek 供应商\n   * </ul>\n   *\n   * @return 默认模型 ID（配置中指定），若无则返回 {@code null}\n   */\n  public static String initModelProvider() {\n    ModelConfigLoader configLoader = new ModelConfigLoader();\n    try {\n      configLoader.load();\n      configLoader.registerTo(ModelProvider.getInstance());\n      logger.info(\n          \"[Agent] Loaded {} model provider(s) from config\", configLoader.getProviders().size());\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to load model config, using defaults: {}\", e.getMessage());\n    }\n\n    // 注册内置模型枚举\n    ModelProvider.getInstance().registerBuiltinModels();\n\n    // 确定默认模型\n    String defaultModel = configLoader.getDefaultModel();\n    if (defaultModel == null || defaultModel.isBlank()) {\n      defaultModel = \"gpt-4o-mini\";\n      logger.info(\n          \"[Agent] No default_model in ~/.alice/model.json, using built-in: {}\", defaultModel);\n    } else {\n      logger.info(\"[Agent] Using default model from config: {}\", defaultModel);\n    }\n\n    // 注册 DeepSeek 供应商（如果环境变量存在且尚未注册）\n    String deepseekKey = System.getenv(\"DEEPSEEK_API_KEY\");\n    if (deepseekKey != null && !deepseekKey.isEmpty()) {\n      if (ModelProvider.getInstance().getSupplier(\"deepseek-v4-flash\") == null) {\n        ModelProvider.getInstance()\n            .registerSupplier(\n                new org.cland.alice.model.supplier.OpenAiSupplier(\n                    \"deepseek\", deepseekKey, \"https://api.deepseek.com/v1/chat/completions\"));\n        logger.info(\"[Agent] Registered DeepSeek supplier via OpenAiSupplier (OpenAI-compatible)\");\n      }\n    }\n\n    return defaultModel;\n  }\n\n  /**\n   * 创建完全初始化的 Agent 实例，自动装配所有子模块（PlannerService、ToolRegistry 等）。\n   *\n   * <p>外观模块可以直接使用此工厂方法创建 Agent，无需直接依赖 planner/tool-gateway/model 模块。\n   *\n   * @param config Agent 配置\n   * @return 已装配所有子模块的 Agent 实例\n   */\n  public static Agent createDefault(AgentConfig config) {\n    Agent agent = new Agent(config);\n\n    // 1. 初始化工具注册中心并发现内置工具\n    ToolRegistry toolRegistry = ToolRegistryHolder.INSTANCE.registry();\n    try {\n      int count = new ToolDiscovery(toolRegistry).scanAndRegister(List.of(new BuiltinTools()));\n      logger.info(\"[Agent] Registered {} builtin tool(s)\", count);\n    } catch (Exception e) {\n      logger.warn(\"[Agent] Failed to discover builtin tools\", e);\n    }\n    agent.withToolRegistry(toolRegistry);\n\n    // 2. 初始化 PlannerService（双路径规划引擎）\n    var plannerSupplier =\n        DefaultPlannerModelSupplier.builder()\n            .provider(ModelProvider.getInstance())\n            .instructionModelId(config.defaultModelId())\n            .reasoningModelId(config.defaultModelId())\n            .build();\n    var fastPath = new FastPathStrategy(plannerSupplier);\n    var thinkingTree = new ThinkingTree(Map.of());\n    var slowPath =\n        SlowPathStrategy.builder()\n            .tree(thinkingTree)\n            .modelSupplier(plannerSupplier)\n            .mctsIterations(10)\n            .build();\n    var selector = StrategySelector.builder().fastPath(fastPath).slowPath(slowPath).build();\n    var sopRegistry = new SopRegistry();\n    var staticPlanner = new StaticPlanner(sopRegistry);\n    var planner =\n        PlannerService.builder().strategySelector(selector).staticPlanner(staticPlanner).build();\n    agent.withPlannerService(planner);\n\n    logger.info(\"[Agent] Default Agent created: model={}\", config.defaultModelId());\n\n    return agent;\n  }\n}\n"}, "fe30f878fd5a8725f8d36a221c40b053b2e0476f": {"t": 1785755696.5573947, "n": 16941, "h": "d40d1a4c6d7cfe7d", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  /** 最近一次 LLM 调用的推理/思考过程，供前端渲染 */\n  private String lastReasoning;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, String sessionId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = sessionId != null ? sessionId : SnowflakeIdGenerator.generateSessionId();\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this(agentId, null, config);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                // 提取推理内容供前端渲染\n                lastReasoning =\n                    ctx.containsKey(\"__llm_reasoning\") ? ctx.get(\"__llm_reasoning\").toString() : \"\";\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  /**\n   * 获取最近一次 LLM 调用的推理内容（reasoning_content），供 TUI 渲染。\n   *\n   * @return 推理文本，若无则返回空字符串\n   */\n  /**\n   * 获取 Agent 执行器，用于注册事件监听等。\n   *\n   * @return AgentExecutor 实例\n   */\n  public AgentExecutor getExecutor() {\n    return executor;\n  }\n\n  public String getLastReasoning() {\n    return lastReasoning != null ? lastReasoning : \"\";\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(this.sessionId, config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "a7847d0d4e799b6888fe11fe04bf188620590956": {"t": 1785755698.0833604, "n": 16800, "h": "ede7d36f35a7a061", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  /** 最近一次 LLM 调用的推理/思考过程，供前端渲染 */\n  private String lastReasoning;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, String sessionId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = sessionId != null ? sessionId : SnowflakeIdGenerator.generateSessionId();\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this(agentId, null, config);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                // 提取推理内容供前端渲染\n                lastReasoning =\n                    ctx.containsKey(\"__llm_reasoning\") ? ctx.get(\"__llm_reasoning\").toString() : \"\";\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  /**\n   * 获取最近一次 LLM 调用的推理内容（reasoning_content），供 TUI 渲染。\n   *\n   * @return 推理文本，若无则返回空字符串\n   */\n  public String getLastReasoning() {\n    return lastReasoning != null ? lastReasoning : \"\";\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(this.sessionId, config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "2bbe5ee1d8031e29c337a8ef11da323abd34bd40": {"t": 1785755699.6652026, "n": 16373, "h": "368977a91796e194", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.SnowflakeIdGenerator;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, String sessionId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = sessionId != null ? sessionId : SnowflakeIdGenerator.generateSessionId();\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this(agentId, null, config);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(this.sessionId, config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "cba99d96dd0d1b5a42c3db426295c398120108ad": {"t": 1785755701.109385, "n": 16160, "h": "54ff046eb9acefa2", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.memory.AgentSession;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.agent.wal.RawMessage;\nimport org.cland.alice.core.agent.wal.WalSession;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "2bb6b3f7e483c34be142c1c9ed1daba4cd65067f": {"t": 1785755702.7085521, "n": 16147, "h": "7619886731957ad7", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.agent.AgentSession;\nimport org.cland.alice.memory.wal.RawMessage;\nimport org.cland.alice.memory.wal.WalSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              try {\n                String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n                if (result == null) {\n                  // 回退：直接调用 LLM\n                  result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n              } catch (Exception e) {\n                errorRef.set(e);\n              } finally {\n                latch.countDown();\n              }\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "8168b802097323564028ecfcb924fc8c81ab1a63": {"t": 1785755704.3939183, "n": 16000, "h": "67b6a42e30ebc110", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Future;\nimport io.vertx.core.Vertx;\nimport java.time.Instant;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.UUID;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.agent.AgentSession;\nimport org.cland.alice.memory.wal.RawMessage;\nimport org.cland.alice.memory.wal.WalSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this.agentId = agentId != null ? agentId : UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "067678919a34b6ebe282810b9857313209380ef5": {"t": 1785755705.64542, "n": 15970, "h": "141344a2122a3a20", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.Map;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport java.util.stream.Collectors;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.agent.AgentSession;\nimport org.cland.alice.memory.wal.RawMessage;\nimport org.cland.alice.memory.wal.WalSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.CallStatus;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  /** 注入 {@link WalSession}，启用 WAL 双轨制持久化与上下文压缩能力。 */\n  public Agent withWal(WalSession wal) {\n    this.executor.withWal(wal);\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * <p>完整流程：\n   *\n   * <ol>\n   *   <li>从 WAL 获取该 session 所有消息（排除已存在的 compact + system）\n   *   <li>组装为对话文本，调用 LLM 总结为一段紧凑摘要\n   *   <li>以 role {@code compact} 写入 WAL\n   *   <li>写 Checkpoint 标记旧消息可被 WalCompactor 清理\n   * </ol>\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n\n    WalSession wal = executor.wal();\n    if (wal == null) {\n      return \"上下文压缩失败：WAL 未注入，无法获取历史消息\";\n    }\n\n    // 1. 获取该 session 所有消息\n    java.util.List<RawMessage> allMessages = wal.getAllMessages(sessionId);\n    if (allMessages.isEmpty()) {\n      return \"没有历史消息需要压缩\";\n    }\n\n    // 2. 选出可压缩的消息（排除 system + 已存在的 compact）\n    java.util.List<RawMessage> compressible =\n        allMessages.stream()\n            .filter(m -> !\"compact\".equals(m.role()))\n            .filter(m -> !\"system\".equals(m.role()))\n            .collect(Collectors.toList());\n    if (compressible.isEmpty()) {\n      return \"没有可压缩的消息（全部已为 compact 或 system）\";\n    }\n\n    // 3. 组装 LLM 总结 prompt\n    StringBuilder dialogBuilder = new StringBuilder();\n    dialogBuilder.append(\"请将以下对话提炼为一段紧凑的中文摘要，保留关键事实、决策和工具调用结果。\\n\\n\");\n    for (RawMessage msg : compressible) {\n      String roleLabel =\n          switch (msg.role()) {\n            case \"user\" -> \"用户\";\n            case \"assistant\" -> \"助手\";\n            case \"tool\" -> \"工具结果\";\n            default -> msg.role();\n          };\n      dialogBuilder.append(\"【\").append(roleLabel).append(\"】\");\n      if (msg.content() != null) {\n        dialogBuilder.append(\" \").append(msg.content());\n      }\n      if (msg.toolCalls() != null && !msg.toolCalls().isEmpty()) {\n        String toolCallStr =\n            msg.toolCalls().stream()\n                .map(tc -> tc.function().name() + \"(\" + tc.function().arguments() + \")\")\n                .collect(Collectors.joining(\", \"));\n        dialogBuilder.append(\" [调用工具: \").append(toolCallStr).append(\"]\");\n      }\n      dialogBuilder.append(\"\\n\");\n    }\n\n    // 4. 调用 LLM 总结\n    String summaryContent;\n    try {\n      ModelProvider provider = ModelProvider.getInstance();\n      Call result = provider.dispatch(config.defaultModelId(), dialogBuilder.toString());\n      if (result.status() == CallStatus.FINISHED && result.result() != null) {\n        summaryContent = result.result().content();\n      } else {\n        logger.warn(\"LLM compact summary failed: status={}\", result.status());\n        summaryContent = \"[压缩摘要生成失败] \" + compressible.size() + \" 条历史消息\";\n      }\n    } catch (Exception e) {\n      logger.error(\"LLM compact summary error\", e);\n      summaryContent = \"[压缩摘要生成异常] \" + compressible.size() + \" 条历史消息: \" + e.getMessage();\n    }\n\n    // 5. 以 compact role 写入 WAL\n    wal.compact(sessionId, summaryContent);\n\n    // 6. 写时间戳到 longTermMemory\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, java.time.Instant.now().toString());\n    }\n\n    logger.info(\n        \"Context compacted: {} messages → 1 compact summary (session={})\",\n        compressible.size(),\n        sessionId);\n    return \"上下文压缩完成：将 \" + compressible.size() + \" 条历史消息提炼为 1 条摘要\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "7d35985d4f861780abf7f36eb3f2fa62bea9c786": {"t": 1785755706.950339, "n": 12962, "h": "a374724a8302b899", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.Map;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.agent.AgentSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  public Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n    // 如果有 WAL，先写入（持久化短期记忆到长期记忆作为 checkpoint 的替代）\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, java.time.Instant.now().toString());\n    }\n    // TODO: 触发 LLM 总结（需等待 Memory 模块提供总结接口）\n    return \"上下文压缩完成（释放 Token: N/A，待 Memory 模块提供总结接口）\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "4ce49cdd19b4e7104c64117ccee3c297b2997aac": {"t": 1785755708.3939693, "n": 12955, "h": "1c09a89e77dcc4c9", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.Map;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.agent.AgentSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final String sessionId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.sessionId = java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(ctx.sessionId());\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n    // 如果有 WAL，先写入（持久化短期记忆到长期记忆作为 checkpoint 的替代）\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, java.time.Instant.now().toString());\n    }\n    // TODO: 触发 LLM 总结（需等待 Memory 模块提供总结接口）\n    return \"上下文压缩完成（释放 Token: N/A，待 Memory 模块提供总结接口）\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "e99821cb1d30ed6f81c5c3bfea208c0fb9cb4642": {"t": 1785755709.737224, "n": 12811, "h": "e7b6e8ab42f35415", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.Map;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.agent.AgentSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(sessionId);\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \").append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet()).append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n    // 如果有 WAL，先写入（持久化短期记忆到长期记忆作为 checkpoint 的替代）\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, java.time.Instant.now().toString());\n    }\n    // TODO: 触发 LLM 总结（需等待 Memory 模块提供总结接口）\n    return \"上下文压缩完成（释放 Token: N/A，待 Memory 模块提供总结接口）\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "6ac7c7c409fdeda5f270fc91665793da3d5507a0": {"t": 1785755711.068583, "n": 12832, "h": "e947a9192a0145f6", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.Map;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.agent.AgentSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(agentId);\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \")\n        .append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet())\n        .append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(agentId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n    // 如果有 WAL，先写入（持久化短期记忆到长期记忆作为 checkpoint 的替代）\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + agentId, java.time.Instant.now().toString());\n    }\n    // TODO: 触发 LLM 总结（需等待 Memory 模块提供总结接口）\n    return \"上下文压缩完成（释放 Token: N/A，待 Memory 模块提供总结接口）\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\n        \"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "54c104cd19d2e340e51ebaa92cae552fe6b2fc43": {"t": 1785755712.30239, "n": 12811, "h": "e7b6e8ab42f35415", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.Map;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.agent.AgentSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  // ========== 上下文管理与查询接口 ==========\n\n  /**\n   * 获取当前全量 Context 状态（Token 占用、消息滑动窗口、变量快照）。\n   *\n   * <p>支持 {@code /context} 命令查询。返回的字符串以 Markdown 格式呈现。\n   *\n   * @return 格式化后的上下文状态信息\n   */\n  public String getActiveContext() {\n    AgentContext ctx = new AgentContext();\n    StringBuilder sb = new StringBuilder();\n    sb.append(\"── 上下文状态 ──\\n\");\n    sb.append(\"| 属性 | 值 |\\n\");\n    sb.append(\"|---|---|\\n\");\n    sb.append(\"| 会话 ID | \").append(ctx.sessionId()).append(\" |\\n\");\n    sb.append(\"| Agent ID | \").append(agentId).append(\" |\\n\");\n    sb.append(\"| 默认模型 | \").append(config.defaultModelId()).append(\" |\\n\");\n    sb.append(\"| 最大迭代 | \").append(config.maxIterations()).append(\" |\\n\");\n\n    if (memory != null) {\n      try {\n        String shortTerm = memory.getShortTerm(sessionId);\n        int msgCount = shortTerm.isEmpty() ? 0 : shortTerm.split(\"\\n\").length;\n        sb.append(\"| 消息条数 | \").append(msgCount).append(\" |\\n\");\n        sb.append(\"| Token 占用 | N/A (Token 计数器待集成) |\\n\");\n      } catch (Exception e) {\n        sb.append(\"| Memory 状态 | 异常: \").append(e.getMessage()).append(\" |\\n\");\n      }\n    } else {\n      sb.append(\"| Token 占用 | N/A (Memory 未注入) |\\n\");\n      sb.append(\"| 消息滑动窗口 | N/A |\\n\");\n    }\n\n    sb.append(\"| 变量快照 | \").append(ctx.asMap().isEmpty() ? \"空\" : ctx.asMap().keySet()).append(\" |\\n\");\n    return sb.toString();\n  }\n\n  /**\n   * 清空短期记忆（保留 System Prompt / Rules），重置 Token 计数器。\n   *\n   * <p>支持 {@code /clear} 命令执行。\n   */\n  public void clearMemory() {\n    logger.info(\"Clearing memory for agent {}\", agentId);\n    if (memory != null) {\n      memory.clearSession(sessionId);\n    }\n  }\n\n  /**\n   * 将历史对话写入 WAL（如果启用了 WAL），提炼历史为 Summary 事实快照，释放 Context Window。\n   *\n   * <p>支持 {@code /compact} 命令执行。\n   *\n   * @return 压缩结果信息\n   */\n  public String compactContext() {\n    logger.info(\"Compacting context for agent {}\", agentId);\n    // 如果有 WAL，先写入（持久化短期记忆到长期记忆作为 checkpoint 的替代）\n    if (memory != null) {\n      memory.putLongTerm(\"__last_compact_ts_\" + sessionId, java.time.Instant.now().toString());\n    }\n    // TODO: 触发 LLM 总结（需等待 Memory 模块提供总结接口）\n    return \"上下文压缩完成（释放 Token: N/A，待 Memory 模块提供总结接口）\";\n  }\n\n  /**\n   * 动态切换 LLM 引擎。\n   *\n   * <p>支持 {@code /model} 命令执行。切换后同步刷新 Verification 模块的审计敏感度。\n   *\n   * @param modelId 目标模型标识\n   */\n  public void switchModel(String modelId) {\n    logger.info(\"Switching model from {} to {} for agent {}\", config.defaultModelId(), modelId, agentId);\n    // 更新配置中的默认模型\n    // TODO: 当 AgentConfig 支持动态修改时，更新 defaultModelId\n  }\n\n  /**\n   * 注入人类反馈到 Context。\n   *\n   * <p>支持 {@code /feedback} 命令执行。\n   *\n   * @param feedback 用户的反馈内容\n   */\n  public void injectFeedback(String feedback) {\n    logger.info(\"Injecting feedback for agent {}: {}\", agentId, feedback);\n    // 将反馈注入到 Agent 上下文\n    AgentContext ctx = new AgentContext();\n    ctx.put(\"lastFeedback\", feedback);\n    // TODO: 解除 Agent 的 HITL 挂起状态（待 AgentExecutor 暴露 HumanInTheLoop 接口）\n    if (executor != null) {\n      executor.resumeWithFeedback(feedback);\n    }\n  }\n\n  /**\n   * 获取最后一条反馈（如果有）。\n   *\n   * @return 反馈内容，或 {@code null}\n   */\n  public String feedback() {\n    // TODO: 从 AgentContext 或 Memory 中提取最后一条反馈\n    return null;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "ecba2ea793e0640c9d3e7fb067725c450ccae401": {"t": 1785755713.5827382, "n": 9663, "h": "39ea945538102bf6", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.Map;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.agent.AgentSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "e1f1e0b38c7a15c8be8e1eeb149fbf0adaa200e7": {"t": 1785755714.9592817, "n": 9657, "h": "14db3c272149cbbd", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.Map;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.AgentSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "25dd4a984cba6c0d1dfee910461b9abb0dbcff7b": {"t": 1785755716.4490776, "n": 9719, "h": "84f075d83fae6de2", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.Map;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.lifecycle.Action;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.core.planner.PlannerService;\nimport org.cland.alice.env.adapter.EnvEvent;\nimport org.cland.alice.guardrail.Verificator;\nimport org.cland.alice.memory.AgentSession;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\nimport org.cland.alice.tool.gateway.ToolRegistry;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。\n *\n * <p>Agent 同时承担了 {@code AgentCore} 的角色，作为 PPAO 循环的核心协调者， 持有所有子模块（规划器、安全校验、工具注册中心、记忆、环境适配器）的引用。\n *\n * <p>生命周期状态机：\n *\n * <pre>\n *   START -> PERCEIVING -> PLANNING -> VERIFYING_PRE -> ACTING (Micro-ReAct)\n *       -> OBSERVING -> VERIFYING_POST -> REFLECTING -> (loop|FINISH)\n * </pre>\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 子模块引用（原 AgentCore 字段） ==========\n\n  private PlannerService plannerService;\n  private Verificator guardrail;\n  private ToolRegistry toolRegistry;\n  private AgentSession memory;\n  private EnvEvent envAdapter;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, this);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  // ========== 依赖注入（原 AgentCore 的 with* 方法） ==========\n\n  /** 注入 {@link PlannerService} — 规划器引擎。 */\n  public Agent withPlannerService(PlannerService plannerService) {\n    this.plannerService = plannerService;\n    return this;\n  }\n\n  public Agent withGuardrail(Verificator guardrail) {\n    this.guardrail = guardrail;\n    return this;\n  }\n\n  public Agent withToolRegistry(ToolRegistry toolRegistry) {\n    this.toolRegistry = toolRegistry;\n    return this;\n  }\n\n  public Agent withMemory(AgentSession memory) {\n    this.memory = memory;\n    return this;\n  }\n\n  public Agent withEnvAdapter(EnvEvent envAdapter) {\n    this.envAdapter = envAdapter;\n    return this;\n  }\n\n  // ========== 子模块 Getters（原 AgentCore getters） ==========\n\n  public PlannerService plannerService() {\n    return plannerService;\n  }\n\n  public Verificator guardrail() {\n    return guardrail;\n  }\n\n  public ToolRegistry toolRegistry() {\n    return toolRegistry;\n  }\n\n  public AgentSession memory() {\n    return memory;\n  }\n\n  public EnvEvent envAdapter() {\n    return envAdapter;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  // ========== 验证钩子（原 AgentCore 方法，供 AgentExecutor 调用） ==========\n\n  /**\n   * Pre-Verify: 在执行 Action 前拦截检查安全性和策略合规性。\n   *\n   * @param action 待验证的 Action\n   * @return true 表示通过，false 表示被拦截\n   */\n  public boolean verifyPre(Action action) {\n    if (!config.preVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Pre-verify action: {}\", action);\n    return guardrail.intercept(\n        Map.of(\n            \"type\", action.type().name(),\n            \"target\", action.target() != null ? action.target() : \"\",\n            \"actionId\", action.actionId()));\n  }\n\n  /**\n   * Post-Verify: 执行完成后审计观测结果。\n   *\n   * @param stepResult 当前步骤的结果\n   * @return true 表示通过，false 表示需要 Revision\n   */\n  public boolean verifyPost(StepResult stepResult) {\n    if (!config.postVerifyEnabled() || guardrail == null) {\n      return true;\n    }\n    logger.debug(\"Post-verify result: {}\", stepResult);\n    return guardrail.audit(stepResult);\n  }\n\n  /** 判断 PPAO 循环是否需要终止。 */\n  public boolean shouldFinish(AgentContext context, StepResult result) {\n    if (result instanceof StepResult.Finish) {\n      return true;\n    }\n    if (result instanceof StepResult.Failure) {\n      return true;\n    }\n    if (context.currentPhase() == AgentContext.Phase.FINISH) {\n      return true;\n    }\n    if (context.isMaxIterationsReached()) {\n      logger.warn(\"Agent {} reached max iterations ({})\", agentId, config.maxIterations());\n      return true;\n    }\n    return false;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "cf772f464ef785e85743100f561e6292b64c024a": {"t": 1785755718.0845606, "n": 6530, "h": "ac7276097558da8b", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n *\n * <p>基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。 通过 ModelProvider 与底层模型交互，通过 AgentExecutor\n * 驱动响应式执行循环。\n *\n * <p>使用示例：\n *\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n  private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n  private final String agentId;\n  private final AgentCore agentCore;\n  private final AgentConfig config;\n  private final Vertx vertx;\n  private final AgentExecutor executor;\n\n  // ========== 构造 ==========\n\n  public Agent() {\n    this(null, AgentConfig.defaults());\n  }\n\n  public Agent(String agentId) {\n    this(agentId, AgentConfig.defaults());\n  }\n\n  public Agent(AgentConfig config) {\n    this(null, config);\n  }\n\n  private Agent(String agentId, AgentConfig config) {\n    this.agentId =\n        agentId != null ? agentId : java.util.UUID.randomUUID().toString().substring(0, 8);\n    this.config = config;\n    this.agentCore = new AgentCore(this.agentId, config);\n    this.vertx = Vertx.vertx();\n    this.executor = new AgentExecutor(vertx, agentCore);\n  }\n\n  // ========== 属性 ==========\n\n  public String agentId() {\n    return agentId;\n  }\n\n  public AgentCore agentCore() {\n    return agentCore;\n  }\n\n  public AgentConfig config() {\n    return config;\n  }\n\n  // ========== 同步 API ==========\n\n  /** 使用默认上下文运行 Agent（同步阻塞）。 */\n  public void run() {\n    run(new AgentContext());\n  }\n\n  /**\n   * 使用指定上下文运行 Agent（同步阻塞）。\n   *\n   * <p>内部执行 PPAO 循环，等待结果返回。\n   */\n  public void run(AgentContext context) {\n    String prompt = context.containsKey(\"prompt\") ? context.get(\"prompt\").toString() : \"Hello!\";\n\n    String modelId =\n        context.containsKey(\"model\") ? context.get(\"model\").toString() : config.defaultModelId();\n\n    logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              resultRef.set(ctx);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n              context.put(\"error\", err.getMessage());\n              context.put(\"status\", \"FATAL_ERROR\");\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      context.put(\"error\", \"Interrupted: \" + e.getMessage());\n    }\n\n    AgentContext resultCtx = resultRef.get();\n    if (resultCtx != null) {\n      context.putAll(resultCtx.asMap());\n    }\n\n    logger.info(\"Agent {} completed\");\n  }\n\n  /**\n   * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n   *\n   * <p>对应设计文档中 Agent 的 ask() 方法。\n   *\n   * @param prompt 用户输入的提示词\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt) {\n    return ask(prompt, config.defaultModelId());\n  }\n\n  /**\n   * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n   *\n   * <p>使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer， 否则回退到直接调用 ModelProvider。\n   *\n   * @param prompt 用户输入的提示词\n   * @param modelId 目标模型 ID\n   * @return 模型返回的响应文本\n   */\n  public String ask(String prompt, String modelId) {\n    logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    context.put(\"model\", modelId);\n\n    // 同步执行 PPAO 循环\n    CountDownLatch latch = new CountDownLatch(1);\n    AtomicReference<String> resultRef = new AtomicReference<>();\n    AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n    executor\n        .execute(prompt, context)\n        .onSuccess(\n            ctx -> {\n              String result = ctx.containsKey(\"result\") ? ctx.get(\"result\").toString() : null;\n              if (result == null) {\n                // 回退：直接调用 LLM\n                result = callLlmDirect(prompt, modelId);\n              }\n              resultRef.set(result);\n              latch.countDown();\n            })\n        .onFailure(\n            err -> {\n              errorRef.set(err);\n              latch.countDown();\n            });\n\n    try {\n      latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n    } catch (InterruptedException e) {\n      Thread.currentThread().interrupt();\n      throw new RuntimeException(\"Agent ask interrupted\", e);\n    }\n\n    if (errorRef.get() != null) {\n      throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n    }\n\n    String result = resultRef.get();\n    if (result == null) {\n      throw new RuntimeException(\"Agent ask returned null result\");\n    }\n\n    logger.info(\"Agent {} response length={}\", agentId, result.length());\n    return result;\n  }\n\n  // ========== 异步 API ==========\n\n  /**\n   * 异步执行 PPAO 循环。\n   *\n   * @param prompt 用户输入\n   * @return 异步结果（io.vertx.core.Future）\n   */\n  public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n    AgentContext context = new AgentContext(config.maxIterations());\n    context.put(\"prompt\", prompt);\n    return executor.execute(prompt, context);\n  }\n\n  /** 获取底层的 Vertx 实例，便于调用方集成。 */\n  public Vertx vertx() {\n    return vertx;\n  }\n\n  /** 关闭 Agent 释放资源。 */\n  public void close() {\n    vertx.close();\n  }\n\n  // ========== 辅助 ==========\n\n  /** 直接调用 LLM（回退逻辑） */\n  private String callLlmDirect(String prompt, String modelId) {\n    logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n    ModelProvider provider = ModelProvider.getInstance();\n    Call result = provider.dispatch(modelId, prompt);\n\n    if (result.result() == null) {\n      throw new RuntimeException(\"Agent call failed: \" + result.status());\n    }\n\n    logger.info(\n        \"Agent {} direct LLM response status={}, tokens={}\",\n        agentId,\n        result.status(),\n        result.metrics().tokenUsage() != null\n            ? result.metrics().tokenUsage().totalTokens()\n            : \"N/A\");\n\n    return result.result().content();\n  }\n}\n"}, "74837030dfba4e7d4c820989eed8c4dd79acc3aa": {"t": 1785755719.6479437, "n": 7131, "h": "96da363605b19633", "c": "package org.cland.alice.core.agent;\n\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\nimport io.vertx.core.Vertx;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\n\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n * <p>\n * 基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。\n * 通过 ModelProvider 与底层模型交互，通过 AgentExecutor 驱动响应式执行循环。\n * <p>\n * 使用示例：\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n    private static final Logger logger = LoggerFactory.getLogger(Agent.class);\n\n    private final String agentId;\n    private final AgentCore agentCore;\n    private final AgentConfig config;\n    private final Vertx vertx;\n    private final AgentExecutor executor;\n\n    // ========== 构造 ==========\n\n    public Agent() {\n        this(null, AgentConfig.defaults());\n    }\n\n    public Agent(String agentId) {\n        this(agentId, AgentConfig.defaults());\n    }\n\n    public Agent(AgentConfig config) {\n        this(null, config);\n    }\n\n    private Agent(String agentId, AgentConfig config) {\n        this.agentId = agentId != null\n            ? agentId\n            : java.util.UUID.randomUUID().toString().substring(0, 8);\n        this.config = config;\n        this.agentCore = new AgentCore(this.agentId, config);\n        this.vertx = Vertx.vertx();\n        this.executor = new AgentExecutor(vertx, agentCore);\n    }\n\n    // ========== 属性 ==========\n\n    public String agentId()                  { return agentId; }\n    public AgentCore agentCore()             { return agentCore; }\n    public AgentConfig config()              { return config; }\n\n    // ========== 同步 API ==========\n\n    /**\n     * 使用默认上下文运行 Agent（同步阻塞）。\n     */\n    public void run() {\n        run(new AgentContext());\n    }\n\n    /**\n     * 使用指定上下文运行 Agent（同步阻塞）。\n     * <p>\n     * 内部执行 PPAO 循环，等待结果返回。\n     */\n    public void run(AgentContext context) {\n        String prompt = context.containsKey(\"prompt\")\n            ? context.get(\"prompt\").toString()\n            : \"Hello!\";\n\n        String modelId = context.containsKey(\"model\")\n            ? context.get(\"model\").toString()\n            : config.defaultModelId();\n\n                logger.info(\"Agent {} running with model {}\", agentId, modelId);\n\n        // 同步执行 PPAO 循环\n        CountDownLatch latch = new CountDownLatch(1);\n        AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n        executor.execute(prompt, context)\n            .onSuccess(ctx -> {\n                resultRef.set(ctx);\n                latch.countDown();\n            })\n            .onFailure(err -> {\n            logger.error(\"Agent {} PPAO loop failed\", agentId, err);\n                context.put(\"error\", err.getMessage());\n                context.put(\"status\", \"FATAL_ERROR\");\n                latch.countDown();\n            });\n\n        try {\n            latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n        } catch (InterruptedException e) {\n            Thread.currentThread().interrupt();\n            context.put(\"error\", \"Interrupted: \" + e.getMessage());\n        }\n\n        AgentContext resultCtx = resultRef.get();\n        if (resultCtx != null) {\n            context.putAll(resultCtx.asMap());\n        }\n\n        logger.info(\"Agent {} completed\");\n    }\n\n    /**\n     * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n     * <p>\n     * 对应设计文档中 Agent 的 ask() 方法。\n     *\n     * @param prompt 用户输入的提示词\n     * @return 模型返回的响应文本\n     */\n    public String ask(String prompt) {\n        return ask(prompt, config.defaultModelId());\n    }\n\n    /**\n     * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n     * <p>\n     * 使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer，\n     * 否则回退到直接调用 ModelProvider。\n     *\n     * @param prompt  用户输入的提示词\n     * @param modelId 目标模型 ID\n     * @return 模型返回的响应文本\n     */\n    public String ask(String prompt, String modelId) {\n                logger.info(\"Agent {} ask model={}\", agentId, modelId);\n\n        AgentContext context = new AgentContext(config.maxIterations());\n        context.put(\"prompt\", prompt);\n        context.put(\"model\", modelId);\n\n        // 同步执行 PPAO 循环\n        CountDownLatch latch = new CountDownLatch(1);\n        AtomicReference<String> resultRef = new AtomicReference<>();\n        AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n        executor.execute(prompt, context)\n            .onSuccess(ctx -> {\n                String result = ctx.containsKey(\"result\")\n                    ? ctx.get(\"result\").toString()\n                    : null;\n                if (result == null) {\n                    // 回退：直接调用 LLM\n                    result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                latch.countDown();\n            })\n            .onFailure(err -> {\n                errorRef.set(err);\n                latch.countDown();\n            });\n\n        try {\n            latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n        } catch (InterruptedException e) {\n            Thread.currentThread().interrupt();\n            throw new RuntimeException(\"Agent ask interrupted\", e);\n        }\n\n        if (errorRef.get() != null) {\n            throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n        }\n\n        String result = resultRef.get();\n        if (result == null) {\n            throw new RuntimeException(\"Agent ask returned null result\");\n        }\n\n            logger.info(\"Agent {} response length={}\", agentId, result.length());\n        return result;\n    }\n\n    // ========== 异步 API ==========\n\n    /**\n     * 异步执行 PPAO 循环。\n     *\n     * @param prompt 用户输入\n     * @return 异步结果（io.vertx.core.Future）\n     */\n    public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n        AgentContext context = new AgentContext(config.maxIterations());\n        context.put(\"prompt\", prompt);\n        return executor.execute(prompt, context);\n    }\n\n    /**\n     * 获取底层的 Vertx 实例，便于调用方集成。\n     */\n    public Vertx vertx() {\n        return vertx;\n    }\n\n    /**\n     * 关闭 Agent 释放资源。\n     */\n    public void close() {\n        vertx.close();\n    }\n\n    // ========== 辅助 ==========\n\n    /** 直接调用 LLM（回退逻辑） */\n    private String callLlmDirect(String prompt, String modelId) {\n                logger.debug(\"Falling back to direct LLM call: model={}\", modelId);\n        ModelProvider provider = ModelProvider.getInstance();\n        Call result = provider.dispatch(modelId, prompt);\n\n        if (result.result() == null) {\n            throw new RuntimeException(\"Agent call failed: \" + result.status());\n        }\n\n            logger.info(\"Agent {} direct LLM response status={}, tokens={}\", agentId, result.status(),\n            result.metrics().tokenUsage() != null ? result.metrics().tokenUsage().totalTokens() : \"N/A\");\n\n        return result.result().content();\n    }\n}\n"}, "c7878a1738a7ea5258cb17b6c4efb771b5d877b3": {"t": 1785755721.0662656, "n": 7255, "h": "6cdbbbc6bb3ee909", "c": "package org.cland.alice.core.agent;\n\nimport io.vertx.core.Vertx;\nimport org.cland.alice.core.agent.executor.AgentExecutor;\nimport org.cland.alice.core.agent.result.StepResult;\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\n\nimport java.util.concurrent.CountDownLatch;\nimport java.util.concurrent.TimeUnit;\nimport java.util.concurrent.atomic.AtomicReference;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n * <p>\n * 基于 PPAO (Perceive-Plan-Act-Observe-Verify) 核心循环。\n * 通过 ModelProvider 与底层模型交互，通过 AgentExecutor 驱动响应式执行循环。\n * <p>\n * 使用示例：\n * <pre>\n *   Agent agent = new Agent();\n *   String result = agent.ask(\"What is the capital of France?\");\n *   System.out.println(result); // \"Paris\"\n * </pre>\n */\npublic class Agent {\n\n    private static final System.Logger logger = System.getLogger(Agent.class.getName());\n\n    private final String agentId;\n    private final AgentCore agentCore;\n    private final AgentConfig config;\n    private final Vertx vertx;\n    private final AgentExecutor executor;\n\n    // ========== 构造 ==========\n\n    public Agent() {\n        this(null, AgentConfig.defaults());\n    }\n\n    public Agent(String agentId) {\n        this(agentId, AgentConfig.defaults());\n    }\n\n    public Agent(AgentConfig config) {\n        this(null, config);\n    }\n\n    private Agent(String agentId, AgentConfig config) {\n        this.agentId = agentId != null\n            ? agentId\n            : java.util.UUID.randomUUID().toString().substring(0, 8);\n        this.config = config;\n        this.agentCore = new AgentCore(this.agentId, config);\n        this.vertx = Vertx.vertx();\n        this.executor = new AgentExecutor(vertx, agentCore);\n    }\n\n    // ========== 属性 ==========\n\n    public String agentId()                  { return agentId; }\n    public AgentCore agentCore()             { return agentCore; }\n    public AgentConfig config()              { return config; }\n\n    // ========== 同步 API ==========\n\n    /**\n     * 使用默认上下文运行 Agent（同步阻塞）。\n     */\n    public void run() {\n        run(new AgentContext());\n    }\n\n    /**\n     * 使用指定上下文运行 Agent（同步阻塞）。\n     * <p>\n     * 内部执行 PPAO 循环，等待结果返回。\n     */\n    public void run(AgentContext context) {\n        String prompt = context.containsKey(\"prompt\")\n            ? context.get(\"prompt\").toString()\n            : \"Hello!\";\n\n        String modelId = context.containsKey(\"model\")\n            ? context.get(\"model\").toString()\n            : config.defaultModelId();\n\n        logger.log(System.Logger.Level.INFO, \"Agent {0} running with model {1}\", agentId, modelId);\n\n        // 同步执行 PPAO 循环\n        CountDownLatch latch = new CountDownLatch(1);\n        AtomicReference<AgentContext> resultRef = new AtomicReference<>();\n\n        executor.execute(prompt, context)\n            .onSuccess(ctx -> {\n                resultRef.set(ctx);\n                latch.countDown();\n            })\n            .onFailure(err -> {\n                logger.log(System.Logger.Level.ERROR, \"Agent {0} PPAO loop failed\", agentId, err);\n                context.put(\"error\", err.getMessage());\n                context.put(\"status\", \"FATAL_ERROR\");\n                latch.countDown();\n            });\n\n        try {\n            latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n        } catch (InterruptedException e) {\n            Thread.currentThread().interrupt();\n            context.put(\"error\", \"Interrupted: \" + e.getMessage());\n        }\n\n        AgentContext resultCtx = resultRef.get();\n        if (resultCtx != null) {\n            context.putAll(resultCtx.asMap());\n        }\n\n        logger.log(System.Logger.Level.INFO, \"Agent {0} completed\");\n    }\n\n    /**\n     * 向 Agent 发送 prompt，返回响应内容（同步阻塞）。\n     * <p>\n     * 对应设计文档中 Agent 的 ask() 方法。\n     *\n     * @param prompt 用户输入的提示词\n     * @return 模型返回的响应文本\n     */\n    public String ask(String prompt) {\n        return ask(prompt, config.defaultModelId());\n    }\n\n    /**\n     * 向 Agent 发送 prompt，指定模型（同步阻塞）。\n     * <p>\n     * 使用 PPAO 循环执行，如果循环产生 Finish 结果则返回 answer，\n     * 否则回退到直接调用 ModelProvider。\n     *\n     * @param prompt  用户输入的提示词\n     * @param modelId 目标模型 ID\n     * @return 模型返回的响应文本\n     */\n    public String ask(String prompt, String modelId) {\n        logger.log(System.Logger.Level.INFO, \"Agent {0} ask model={1}\", agentId, modelId);\n\n        AgentContext context = new AgentContext(config.maxIterations());\n        context.put(\"prompt\", prompt);\n        context.put(\"model\", modelId);\n\n        // 同步执行 PPAO 循环\n        CountDownLatch latch = new CountDownLatch(1);\n        AtomicReference<String> resultRef = new AtomicReference<>();\n        AtomicReference<Throwable> errorRef = new AtomicReference<>();\n\n        executor.execute(prompt, context)\n            .onSuccess(ctx -> {\n                String result = ctx.containsKey(\"result\")\n                    ? ctx.get(\"result\").toString()\n                    : null;\n                if (result == null) {\n                    // 回退：直接调用 LLM\n                    result = callLlmDirect(prompt, modelId);\n                }\n                resultRef.set(result);\n                latch.countDown();\n            })\n            .onFailure(err -> {\n                errorRef.set(err);\n                latch.countDown();\n            });\n\n        try {\n            latch.await(config.actionTimeoutMs() * 2, TimeUnit.MILLISECONDS);\n        } catch (InterruptedException e) {\n            Thread.currentThread().interrupt();\n            throw new RuntimeException(\"Agent ask interrupted\", e);\n        }\n\n        if (errorRef.get() != null) {\n            throw new RuntimeException(\"Agent ask failed\", errorRef.get());\n        }\n\n        String result = resultRef.get();\n        if (result == null) {\n            throw new RuntimeException(\"Agent ask returned null result\");\n        }\n\n        logger.log(System.Logger.Level.INFO, \"Agent {0} response length={1}\", agentId, result.length());\n        return result;\n    }\n\n    // ========== 异步 API ==========\n\n    /**\n     * 异步执行 PPAO 循环。\n     *\n     * @param prompt 用户输入\n     * @return 异步结果（io.vertx.core.Future）\n     */\n    public io.vertx.core.Future<AgentContext> askAsync(String prompt) {\n        AgentContext context = new AgentContext(config.maxIterations());\n        context.put(\"prompt\", prompt);\n        return executor.execute(prompt, context);\n    }\n\n    /**\n     * 获取底层的 Vertx 实例，便于调用方集成。\n     */\n    public Vertx vertx() {\n        return vertx;\n    }\n\n    /**\n     * 关闭 Agent 释放资源。\n     */\n    public void close() {\n        vertx.close();\n    }\n\n    // ========== 辅助 ==========\n\n    /** 直接调用 LLM（回退逻辑） */\n    private String callLlmDirect(String prompt, String modelId) {\n        logger.log(System.Logger.Level.DEBUG, \"Falling back to direct LLM call: model={0}\", modelId);\n        ModelProvider provider = ModelProvider.getInstance();\n        Call result = provider.dispatch(modelId, prompt);\n\n        if (result.result() == null) {\n            throw new RuntimeException(\"Agent call failed: \" + result.status());\n        }\n\n        logger.log(System.Logger.Level.INFO, \"Agent {0} direct LLM response status={1}, tokens={2}\",\n            agentId, result.status(),\n            result.metrics().tokenUsage() != null ? result.metrics().tokenUsage().totalTokens() : \"N/A\");\n\n        return result.result().content();\n    }\n}\n"}, "5cd4ded9241b820badb62cc22bbcb97b77d2bb24": {"t": 1785755722.4880087, "n": 2668, "h": "3684491b72ec0e8a", "c": "package org.cland.alice.core.agent;\n\nimport org.cland.alice.model.Call;\nimport org.cland.alice.model.ModelProvider;\n\n/**\n * Agent 核心类，代表一个 AI Agent 实例。\n * 通过 ModelProvider 与底层模型交互。\n */\npublic class Agent {\n\n    private static final System.Logger logger = System.getLogger(Agent.class.getName());\n\n    private final String agentId;\n\n    public Agent() {\n        this.agentId = java.util.UUID.randomUUID().toString().substring(0, 8);\n    }\n\n    public Agent(String agentId) {\n        this.agentId = agentId;\n    }\n\n    public String agentId() {\n        return agentId;\n    }\n\n    /**\n     * 使用默认上下文运行 Agent。\n     */\n    public void run() {\n        run(new AgentContext());\n    }\n\n    /**\n     * 使用指定上下文运行 Agent。\n     */\n    public void run(AgentContext context) {\n        String prompt = context.containsKey(\"prompt\")\n            ? context.get(\"prompt\").toString()\n            : \"Hello!\";\n\n        String modelId = context.containsKey(\"model\")\n            ? context.get(\"model\").toString()\n            : \"gpt-4o-mini\";\n\n        logger.log(System.Logger.Level.INFO, \"Agent {0} running with model {1}\", agentId, modelId);\n\n        ModelProvider provider = ModelProvider.getInstance();\n        Call result = provider.dispatch(modelId, prompt);\n\n        logger.log(System.Logger.Level.INFO, \"Agent {0} completed: status={1}, latency={2}ms\",\n            agentId, result.status(), result.metrics().latencyMs());\n\n        context.put(\"result\", result.result() != null ? result.result().content() : null);\n        context.put(\"status\", result.status());\n        context.put(\"traceId\", result.traceId());\n    }\n\n    /**\n     * 向 Agent 发送 prompt，返回响应内容。\n     * <p>\n     * 对应设计文档中 Agent 的 ask() 方法。\n     *\n     * @param prompt 用户输入的提示词\n     * @return 模型返回的响应文本\n     */\n    public String ask(String prompt) {\n        return ask(prompt, \"gpt-4o-mini\");\n    }\n\n    /**\n     * 向 Agent 发送 prompt，指定模型。\n     *\n     * @param prompt  用户输入的提示词\n     * @param modelId 目标模型 ID\n     * @return 模型返回的响应文本\n     */\n    public String ask(String prompt, String modelId) {\n        logger.log(System.Logger.Level.INFO, \"Agent {0} ask model={1}\", agentId, modelId);\n\n        ModelProvider provider = ModelProvider.getInstance();\n        Call result = provider.dispatch(modelId, prompt);\n\n        logger.log(System.Logger.Level.INFO, \"Agent {0} response status={1}, tokens={2}\",\n            agentId, result.status(),\n            result.metrics().tokenUsage() != null ? result.metrics().tokenUsage().totalTokens() : \"N/A\");\n\n        if (result.result() == null) {\n            throw new RuntimeException(\"Agent call failed: \" + result.status());\n        }\n        return result.result().content();\n    }\n}\n"}}, "docs/alice-model/CONFIG.md": {"8ebb2c40d3906a86e5a1eaa8ea381b21d0df9bd9": {"t": 1785746607.6394594, "n": 5696, "h": "578da7753bf2f31a", "c": "---\ntitle: \"alice-model 配置文件说明\"\nsummary: \"~/.alice/model.json 配置文件完整说明 — providers.<name>.available_models[] 结构，Jackson 解析\"\nread_when:\n  - \"配置模型提供商或修改默认模型\"\n  - \"理解 enable_thinking / reasoning_effort 参数\"\n  - \"排查模型加载问题\"\n---\n\n## model.json 配置说明文档\n\n### 1. 文件位置\n\n```\n~/.alice/model.json\n```\n\n| 操作系统 | 完整路径 |\n|----------|----------|\n| Linux / macOS | `/home/[username]/.alice/model.json` |\n| Windows | `C:\\Users\\[username]\\.alice\\model.json` |\n\n### 2. 创建配置文件\n\n```bash\nmkdir -p ~/.alice\ntouch ~/.alice/model.json\n```\n\n### 3. 配置结构\n\n```\n~/.alice/model.json\n├── default_model     { provider, model, enable_thinking, reasoning_effort }\n├── planner           { instruction_model_id, reasoning_model_id,\n│                       instruction{ enable_thinking, reasoning_effort },\n│                       reasoning{ enable_thinking, reasoning_effort } }\n└── providers         { <name>:\n    ├── base_url\n    ├── api_key        (支持 ${ENV_VAR})\n    └── available_models[]\n        └── { name, model, max_tokens, max_output_tokens, capabilities }\n```\n\n---\n\n### 4. 完整配置模板\n\n```json\n{\n  \"default_model\": {\n    \"provider\": \"deepseek\",\n    \"model\": \"deepseek-v4-flash\",\n    \"enable_thinking\": true,\n    \"reasoning_effort\": \"high\"\n  },\n  \"planner\": {\n    \"instruction_model_id\": \"deepseek-v4-flash\",\n    \"reasoning_model_id\": \"deepseek-v4-flash\",\n    \"instruction\": {\n      \"enable_thinking\": false,\n      \"reasoning_effort\": \"low\"\n    },\n    \"reasoning\": {\n      \"enable_thinking\": true,\n      \"reasoning_effort\": \"high\"\n    }\n  },\n  \"providers\": {\n    \"deepseek\": {\n      \"base_url\": \"https://api.deepseek.com/v1\",\n      \"api_key\": \"${DEEPSEEK_API_KEY}\",\n      \"available_models\": [\n        {\n          \"name\": \"deepseek-v4-flash\",\n          \"model\": \"deepseek-v4-flash\",\n          \"max_tokens\": 131072,\n          \"max_output_tokens\": 32000,\n          \"capabilities\": {\n            \"tools\": true,\n            \"images\": false,\n            \"parallel_tool_calls\": true,\n            \"prompt_cache_key\": true,\n            \"chat_completions\": true\n          }\n        }\n      ]\n    },\n    \"openai\": {\n      \"base_url\": \"https://api.openai.com/v1\",\n      \"api_key\": \"${OPENAI_API_KEY}\",\n      \"available_models\": [\n        {\n          \"name\": \"o3-mini\",\n          \"model\": \"o3-mini\",\n          \"max_tokens\": 128000,\n          \"max_output_tokens\": 16000,\n          \"capabilities\": {\n            \"tools\": true,\n            \"images\": false,\n            \"parallel_tool_calls\": false,\n            \"prompt_cache_key\": false,\n            \"chat_completions\": true\n          }\n        }\n      ]\n    }\n  }\n}\n```\n\n### 5. 字段说明\n\n#### 5.1 default_model\n\n| 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|\n| `provider` | string | ✅ | 提供商名称，对应 `providers` 中的键 |\n| `model` | string | ✅ | 模型标识符，对应 `available_models[].name` |\n| `enable_thinking` | boolean | ❌ | 全局思考开关。默认 `true` |\n| `reasoning_effort` | string | ❌ | `low` / `medium` / `high` / `xhigh`。默认 `high`（thinking=true）或 `low`（thinking=false） |\n\n#### 5.2 planner\n\n| 字段 | 类型 | 说明 |\n|------|------|------|\n| `instruction_model_id` | string | 快路径模型 ID，未设置回退到 `default_model.model` |\n| `reasoning_model_id` | string | 慢路径模型 ID，未设置回退到 `default_model.model` |\n| `instruction` | object | 快路径 thinking 参数 `{ enable_thinking, reasoning_effort }` |\n| `reasoning` | object | 慢路径 thinking 参数 `{ enable_thinking, reasoning_effort }` |\n\n#### 5.3 providers.<name>\n\n| 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|\n| `base_url` | string | ✅ | API 基础 URL，须以 `http://` 或 `https://` 开头 |\n| `api_key` | string | ❌ | API 密钥，支持 `${ENV_VAR}` 环境变量引用 |\n| `available_models` | array | ✅ | 可用模型列表 |\n\n#### 5.4 available_models[]\n\n| 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|\n| `name` | string | ✅ | 模型名称（调用标识符） |\n| `model` | string | ❌ | 发送给 API 的模型名，默认同 `name` |\n| `max_tokens` | integer | ✅ | 最大上下文总长度 |\n| `max_output_tokens` | integer | ❌ | 单次最大输出 token 数，默认同 `max_tokens` |\n| `capabilities` | object | ✅ | 模型能力标识集 |\n\n#### 5.5 capabilities\n\n| 字段 | 类型 | 说明 |\n|------|------|------|\n| `tools` | boolean | 是否支持工具/函数调用 |\n| `images` | boolean | 是否支持图像输入（多模态） |\n| `parallel_tool_calls` | boolean | 是否支持并行工具调用 |\n| `prompt_cache_key` | boolean | 是否支持提示缓存键 |\n| `chat_completions` | boolean | 是否支持 `/chat/completions` 端点 |\n\n---\n\n### 6. 映射规则\n\n`enable_thinking` / `reasoning_effort` 在 `OpenAiSupplier` 中按供应商映射：\n\n| 供应商 | enable_thinking | API 行为 |\n|--------|----------------|----------|\n| DeepSeek | `false` | `thinking:{\"type\":\"disabled\"}` |\n| DeepSeek | `true` + `effort` | `thinking:{\"type\":\"enabled\", \"effort\":\"...\"}` |\n| OpenAI o 系列 | `false` | 强制 `reasoning_effort=\"low\"` |\n| OpenAI o 系列 | `true` + `effort` | 透传 `reasoning_effort` |\n\n### 7. 双路径模型\n\n`DefaultPlannerModelSupplier` 根据路径自动注入 thinking 参数：\n\n| 路径 | enable_thinking | reasoning_effort | 用途 |\n|------|----------------|------------------|------|\n| **FastPath** (指令模型) | `false` | `low` | 快速响应，关闭 LLM 思考 |\n| **SlowPath** (推理模型) | `true` | `high` | 深度推理、MCTS 规划 |\n\n### 8. 环境变量配置\n\n```bash\nexport DEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nexport OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nexport ANTHROPIC_API_KEY=sk-ant-xxxxxxxxxxxxxxxxxxxxx\n```\n\n### 9. 校验规则\n\n| 校验项 | 规则 | 错误示例 |\n|--------|------|----------|\n| `max_tokens` | ≥ `max_output_tokens` | `max_tokens: 1000, max_output_tokens: 2000` ❌ |\n| `base_url` | 必须以 `http://` 或 `https://` 开头 | `api.example.com/v1` ❌ |\n| `name` | 非空字符串 | `\"\"` ❌ |\n| `available_models` | 至少 1 个模型 | `[]` ❌ |\n\n### 10. 权限建议\n\n```bash\nchmod 700 ~/.alice\nchmod 600 ~/.alice/model.json\n```\n\n### 11. 常见问题\n\n| 问题 | 解决方案 |\n|------|----------|\n| 配置文件找不到 | 确认 `~/.alice/model.json` 是否存在 |\n| API Key 未生效 | 检查环境变量名称是否与 `${VAR_NAME}` 一致 |\n| 权限错误 | 执行 `chmod 600 ~/.alice/model.json` |\n| JSON 格式错误 | 使用 `jq . ~/.alice/model.json` 验证 |\n"}, "542dfef86a8f56af43d54230584fe2edf9c80f6a": {"t": 1785755688.5572386, "n": 5696, "h": "578da7753bf2f31a", "c": "---\ntitle: \"alice-model 配置文件说明\"\nsummary: \"~/.alice/model.json 配置文件完整说明 — providers.<name>.available_models[] 结构，Jackson 解析\"\nread_when:\n  - \"配置模型提供商或修改默认模型\"\n  - \"理解 enable_thinking / reasoning_effort 参数\"\n  - \"排查模型加载问题\"\n---\n\n## model.json 配置说明文档\n\n### 1. 文件位置\n\n```\n~/.alice/model.json\n```\n\n| 操作系统 | 完整路径 |\n|----------|----------|\n| Linux / macOS | `/home/[username]/.alice/model.json` |\n| Windows | `C:\\Users\\[username]\\.alice\\model.json` |\n\n### 2. 创建配置文件\n\n```bash\nmkdir -p ~/.alice\ntouch ~/.alice/model.json\n```\n\n### 3. 配置结构\n\n```\n~/.alice/model.json\n├── default_model     { provider, model, enable_thinking, reasoning_effort }\n├── planner           { instruction_model_id, reasoning_model_id,\n│                       instruction{ enable_thinking, reasoning_effort },\n│                       reasoning{ enable_thinking, reasoning_effort } }\n└── providers         { <name>:\n    ├── base_url\n    ├── api_key        (支持 ${ENV_VAR})\n    └── available_models[]\n        └── { name, model, max_tokens, max_output_tokens, capabilities }\n```\n\n---\n\n### 4. 完整配置模板\n\n```json\n{\n  \"default_model\": {\n    \"provider\": \"deepseek\",\n    \"model\": \"deepseek-v4-flash\",\n    \"enable_thinking\": true,\n    \"reasoning_effort\": \"high\"\n  },\n  \"planner\": {\n    \"instruction_model_id\": \"deepseek-v4-flash\",\n    \"reasoning_model_id\": \"deepseek-v4-flash\",\n    \"instruction\": {\n      \"enable_thinking\": false,\n      \"reasoning_effort\": \"low\"\n    },\n    \"reasoning\": {\n      \"enable_thinking\": true,\n      \"reasoning_effort\": \"high\"\n    }\n  },\n  \"providers\": {\n    \"deepseek\": {\n      \"base_url\": \"https://api.deepseek.com/v1\",\n      \"api_key\": \"${DEEPSEEK_API_KEY}\",\n      \"available_models\": [\n        {\n          \"name\": \"deepseek-v4-flash\",\n          \"model\": \"deepseek-v4-flash\",\n          \"max_tokens\": 131072,\n          \"max_output_tokens\": 32000,\n          \"capabilities\": {\n            \"tools\": true,\n            \"images\": false,\n            \"parallel_tool_calls\": true,\n            \"prompt_cache_key\": true,\n            \"chat_completions\": true\n          }\n        }\n      ]\n    },\n    \"openai\": {\n      \"base_url\": \"https://api.openai.com/v1\",\n      \"api_key\": \"${OPENAI_API_KEY}\",\n      \"available_models\": [\n        {\n          \"name\": \"o3-mini\",\n          \"model\": \"o3-mini\",\n          \"max_tokens\": 128000,\n          \"max_output_tokens\": 16000,\n          \"capabilities\": {\n            \"tools\": true,\n            \"images\": false,\n            \"parallel_tool_calls\": false,\n            \"prompt_cache_key\": false,\n            \"chat_completions\": true\n          }\n        }\n      ]\n    }\n  }\n}\n```\n\n### 5. 字段说明\n\n#### 5.1 default_model\n\n| 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|\n| `provider` | string | ✅ | 提供商名称，对应 `providers` 中的键 |\n| `model` | string | ✅ | 模型标识符，对应 `available_models[].name` |\n| `enable_thinking` | boolean | ❌ | 全局思考开关。默认 `true` |\n| `reasoning_effort` | string | ❌ | `low` / `medium` / `high` / `xhigh`。默认 `high`（thinking=true）或 `low`（thinking=false） |\n\n#### 5.2 planner\n\n| 字段 | 类型 | 说明 |\n|------|------|------|\n| `instruction_model_id` | string | 快路径模型 ID，未设置回退到 `default_model.model` |\n| `reasoning_model_id` | string | 慢路径模型 ID，未设置回退到 `default_model.model` |\n| `instruction` | object | 快路径 thinking 参数 `{ enable_thinking, reasoning_effort }` |\n| `reasoning` | object | 慢路径 thinking 参数 `{ enable_thinking, reasoning_effort }` |\n\n#### 5.3 providers.<name>\n\n| 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|\n| `base_url` | string | ✅ | API 基础 URL，须以 `http://` 或 `https://` 开头 |\n| `api_key` | string | ❌ | API 密钥，支持 `${ENV_VAR}` 环境变量引用 |\n| `available_models` | array | ✅ | 可用模型列表 |\n\n#### 5.4 available_models[]\n\n| 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|\n| `name` | string | ✅ | 模型名称（调用标识符） |\n| `model` | string | ❌ | 发送给 API 的模型名，默认同 `name` |\n| `max_tokens` | integer | ✅ | 最大上下文总长度 |\n| `max_output_tokens` | integer | ❌ | 单次最大输出 token 数，默认同 `max_tokens` |\n| `capabilities` | object | ✅ | 模型能力标识集 |\n\n#### 5.5 capabilities\n\n| 字段 | 类型 | 说明 |\n|------|------|------|\n| `tools` | boolean | 是否支持工具/函数调用 |\n| `images` | boolean | 是否支持图像输入（多模态） |\n| `parallel_tool_calls` | boolean | 是否支持并行工具调用 |\n| `prompt_cache_key` | boolean | 是否支持提示缓存键 |\n| `chat_completions` | boolean | 是否支持 `/chat/completions` 端点 |\n\n---\n\n### 6. 映射规则\n\n`enable_thinking` / `reasoning_effort` 在 `OpenAiSupplier` 中按供应商映射：\n\n| 供应商 | enable_thinking | API 行为 |\n|--------|----------------|----------|\n| DeepSeek | `false` | `thinking:{\"type\":\"disabled\"}` |\n| DeepSeek | `true` + `effort` | `thinking:{\"type\":\"enabled\", \"effort\":\"...\"}` |\n| OpenAI o 系列 | `false` | 强制 `reasoning_effort=\"low\"` |\n| OpenAI o 系列 | `true` + `effort` | 透传 `reasoning_effort` |\n\n### 7. 双路径模型\n\n`DefaultPlannerModelSupplier` 根据路径自动注入 thinking 参数：\n\n| 路径 | enable_thinking | reasoning_effort | 用途 |\n|------|----------------|------------------|------|\n| **FastPath** (指令模型) | `false` | `low` | 快速响应，关闭 LLM 思考 |\n| **SlowPath** (推理模型) | `true` | `high` | 深度推理、MCTS 规划 |\n\n### 8. 环境变量配置\n\n```bash\nexport DEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nexport OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nexport ANTHROPIC_API_KEY=sk-ant-xxxxxxxxxxxxxxxxxxxxx\n```\n\n### 9. 校验规则\n\n| 校验项 | 规则 | 错误示例 |\n|--------|------|----------|\n| `max_tokens` | ≥ `max_output_tokens` | `max_tokens: 1000, max_output_tokens: 2000` ❌ |\n| `base_url` | 必须以 `http://` 或 `https://` 开头 | `api.example.com/v1` ❌ |\n| `name` | 非空字符串 | `\"\"` ❌ |\n| `available_models` | 至少 1 个模型 | `[]` ❌ |\n\n### 10. 权限建议\n\n```bash\nchmod 700 ~/.alice\nchmod 600 ~/.alice/model.json\n```\n\n### 11. 常见问题\n\n| 问题 | 解决方案 |\n|------|----------|\n| 配置文件找不到 | 确认 `~/.alice/model.json` 是否存在 |\n| API Key 未生效 | 检查环境变量名称是否与 `${VAR_NAME}` 一致 |\n| 权限错误 | 执行 `chmod 600 ~/.alice/model.json` |\n| JSON 格式错误 | 使用 `jq . ~/.alice/model.json` 验证 |\n"}, "c2c7eac01472b99309c1f867fa8a080907dba673": {"t": 1785755689.9465733, "n": 7541, "h": "7bf59a926be0f3f6", "c": "明白了，配置文件路径为 `~/.alice/model.json`。以下是针对该路径的完整配置说明文档。\n\n---\n\n## model.json 配置说明文档\n\n### 1. 文件位置\n\n```\n~/.alice/model.json\n```\n\n即用户主目录下的 `.alice` 目录中：\n\n| 操作系统 | 完整路径 |\n|----------|----------|\n| Linux / macOS | `/home/[username]/.alice/model.json` |\n| macOS | `/Users/[username]/.alice/model.json` |\n| Windows | `C:\\Users\\[username]\\.alice\\model.json` |\n\n---\n\n### 2. 创建配置文件\n\n```bash\n# 创建配置目录\nmkdir -p ~/.alice\n\n# 创建配置文件\ntouch ~/.alice/model.json\n```\n\n---\n\n### 3. 完整配置模板\n\n将以下内容写入 `~/.alice/model.json`：\n\n```json\n{\n  \"language_models\": {\n    \"openai_compatible\": {\n      \"my-provider\": {\n        \"api_url\": \"https://api.myprovider.com/v1\",\n        \"api_key\": \"${MY_PROVIDER_API_KEY}\",\n        \"available_models\": [\n          {\n            \"name\": \"deepseek\",\n            \"max_tokens\": 200000,\n            \"max_output_tokens\": 32000,\n            \"max_completion_tokens\": 200000,\n            \"capabilities\": {\n              \"tools\": true,\n              \"images\": true,\n              \"parallel_tool_calls\": true,\n              \"prompt_cache_key\": true,\n              \"chat_completions\": true\n            }\n          }\n        ]\n      }\n    }\n  }\n}\n```\n\n---\n\n### 4. 字段说明表\n\n| 层级 | 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|------|\n| 根 | `language_models` | object | ✅ | 语言模型配置根节点 |\n| 根 | `openai_compatible` | object | ✅ | OpenAI 兼容接口标识 |\n| 2 | `[provider_name]` | object | ✅ | 提供商自定义名称（如 `my-provider`） |\n| 3 | `api_url` | string | ✅ | API 基础 URL，如 `https://api.xxx.com/v1` |\n| 3 | `api_key` | string | ❌ | API 密钥，支持 `${ENV_VAR}` 环境变量引用 |\n| 3 | `available_models` | array | ✅ | 支持的模型列表 |\n| 4 | `name` | string | ✅ | 模型名称，调用时使用的标识符 |\n| 4 | `max_tokens` | integer | ✅ | 最大上下文总长度（输入 + 输出 token 数） |\n| 4 | `max_output_tokens` | integer | ✅ | 单次最大输出 token 数 |\n| 4 | `max_completion_tokens` | integer | ✅ | 补全接口最大 token 数 |\n| 5 | `capabilities.tools` | boolean | ✅ | 是否支持工具/函数调用 |\n| 5 | `capabilities.images` | boolean | ✅ | 是否支持图像输入（多模态） |\n| 5 | `capabilities.parallel_tool_calls` | boolean | ✅ | 是否支持并行工具调用 |\n| 5 | `capabilities.prompt_cache_key` | boolean | ✅ | 是否支持提示缓存键 |\n| 5 | `capabilities.chat_completions` | boolean | ✅ | 是否支持 `/chat/completions` 端点 |\n\n---\n\n### 5. 多提供商配置示例\n\n```json\n{\n  \"language_models\": {\n    \"openai_compatible\": {\n      \"openai\": {\n        \"api_url\": \"https://api.openai.com/v1\",\n        \"api_key\": \"${OPENAI_API_KEY}\",\n        \"available_models\": [\n          {\n            \"name\": \"gpt-4-turbo\",\n            \"max_tokens\": 128000,\n            \"max_output_tokens\": 4096,\n            \"max_completion_tokens\": 128000,\n            \"capabilities\": {\n              \"tools\": true,\n              \"images\": true,\n              \"parallel_tool_calls\": true,\n              \"prompt_cache_key\": false,\n              \"chat_completions\": true\n            }\n          },\n          {\n            \"name\": \"gpt-4o\",\n            \"max_tokens\": 128000,\n            \"max_output_tokens\": 16384,\n            \"max_completion_tokens\": 128000,\n            \"capabilities\": {\n              \"tools\": true,\n              \"images\": true,\n              \"parallel_tool_calls\": true,\n              \"prompt_cache_key\": false,\n              \"chat_completions\": true\n            }\n          }\n        ]\n      },\n      \"deepseek\": {\n        \"api_url\": \"https://api.deepseek.com/v1\",\n        \"api_key\": \"${DEEPSEEK_API_KEY}\",\n        \"available_models\": [\n          {\n            \"name\": \"deepseek-v4-flash\",\n            \"max_tokens\": 200000,\n            \"max_output_tokens\": 32000,\n            \"max_completion_tokens\": 200000,\n            \"capabilities\": {\n              \"tools\": true,\n              \"images\": false,\n              \"parallel_tool_calls\": true,\n              \"prompt_cache_key\": true,\n              \"chat_completions\": true\n            }\n          }\n        ]\n      },\n      \"local\": {\n        \"api_url\": \"http://localhost:8080/v1\",\n        \"available_models\": [\n          {\n            \"name\": \"llama3\",\n            \"max_tokens\": 8192,\n            \"max_output_tokens\": 2048,\n            \"max_completion_tokens\": 8192,\n            \"capabilities\": {\n              \"tools\": false,\n              \"images\": false,\n              \"parallel_tool_calls\": false,\n              \"prompt_cache_key\": false,\n              \"chat_completions\": true\n            }\n          }\n        ]\n      }\n    }\n  }\n}\n```\n\n---\n\n### 6. 环境变量配置\n\n在 `~/.bashrc`、`~/.zshrc` 或 `.env` 文件中设置：\n\n```bash\n# ~/.bashrc 或 ~/.zshrc\nexport MY_PROVIDER_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nexport OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nexport DEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\n```\n\n或创建 `~/.alice/.env`：\n\n```bash\n# ~/.alice/.env\nMY_PROVIDER_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nOPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nDEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\n```\n\n---\n\n### 7. 代码加载示例\n\n```python\nimport json\nimport os\nfrom pathlib import Path\n\ndef load_model_config():\n    config_path = Path.home() / \".alice\" / \"model.json\"\n    \n    if not config_path.exists():\n        raise FileNotFoundError(f\"Config file not found: {config_path}\")\n    \n    with open(config_path, 'r') as f:\n        config = json.load(f)\n    \n    # 展开环境变量\n    def expand_env(value):\n        if isinstance(value, str) and value.startswith(\"${\") and value.endswith(\"}\"):\n            env_var = value[2:-1]\n            return os.environ.get(env_var, value)\n        return value\n    \n    # 递归展开所有 api_key\n    providers = config.get(\"language_models\", {}).get(\"openai_compatible\", {})\n    for provider in providers.values():\n        if \"api_key\" in provider:\n            provider[\"api_key\"] = expand_env(provider[\"api_key\"])\n    \n    return config\n\n# 使用\nconfig = load_model_config()\nprint(config[\"language_models\"][\"openai_compatible\"][\"my-provider\"][\"api_url\"])\n```\n\n```javascript\n// Node.js\nconst fs = require('fs');\nconst path = require('path');\nconst os = require('os');\n\nfunction loadModelConfig() {\n  const configPath = path.join(os.homedir(), '.alice', 'model.json');\n  \n  if (!fs.existsSync(configPath)) {\n    throw new Error(`Config file not found: ${configPath}`);\n  }\n  \n  const config = JSON.parse(fs.readFileSync(configPath, 'utf8'));\n  \n  // 展开环境变量\n  const expandEnv = (value) => {\n    if (typeof value === 'string' && value.startsWith('${') && value.endsWith('}')) {\n      const envVar = value.slice(2, -1);\n      return process.env[envVar] || value;\n    }\n    return value;\n  };\n  \n  const providers = config.language_models?.openai_compatible || {};\n  for (const key of Object.keys(providers)) {\n    if (providers[key].api_key) {\n      providers[key].api_key = expandEnv(providers[key].api_key);\n    }\n  }\n  \n  return config;\n}\n\n// 使用\nconst config = loadModelConfig();\nconsole.log(config.language_models.openai_compatible['my-provider'].api_url);\n```\n\n---\n\n### 8. 配置校验规则\n\n| 校验项 | 规则 | 错误示例 |\n|--------|------|----------|\n| `max_tokens` | ≥ `max_output_tokens` | `max_tokens: 1000, max_output_tokens: 2000` ❌ |\n| `max_completion_tokens` | 建议等于 `max_tokens` | 相差较大时可能产生歧义 |\n| `api_url` | 必须以 `http://` 或 `https://` 开头 | `api.example.com/v1` ❌ |\n| `name` | 非空字符串 | `\"\"` ❌ |\n| `capabilities` | 所有布尔字段必须显式设置 | 缺失字段可能导致运行时错误 |\n\n---\n\n### 9. 配置文件权限建议\n\n```bash\n# 设置目录权限（仅用户可读写）\nchmod 700 ~/.alice\n\n# 设置文件权限（仅用户可读写）\nchmod 600 ~/.alice/model.json\n```\n\n---\n\n### 10. 常见问题\n\n| 问题 | 解决方案 |\n|------|----------|\n| 配置文件找不到 | 确认 `~/.alice/model.json` 是否存在，目录名是否正确（注意点号） |\n| API Key 未生效 | 检查环境变量名称是否与配置中 `${VAR_NAME}` 一致，确认已 `source ~/.bashrc` |\n| 权限错误 | 执行 `chmod 600 ~/.alice/model.json` 修复权限 |\n| JSON 格式错误 | 使用 `jq . ~/.alice/model.json` 验证 JSON 格式 |\n\n---\n\n如需进一步调整（如添加自定义 headers、代理配置、超时设置等），请告知具体需求，我可以帮您扩展配置结构。\n"}, "f067dcf4ddbb3b1be45693b752657ddd5f93eaf9": {"t": 1785755691.3153095, "n": 7537, "h": "94b701ba77c07ad7", "c": "明白了，配置文件路径为 `~/.alice/model.json`。以下是针对该路径的完整配置说明文档。\n\n---\n\n## model.json 配置说明文档\n\n### 1. 文件位置\n\n```\n~/.alice/model.json\n```\n\n即用户主目录下的 `.alice` 目录中：\n\n| 操作系统 | 完整路径 |\n|----------|----------|\n| Linux / macOS | `/home/[username]/.alice/model.json` |\n| macOS | `/Users/[username]/.alice/model.json` |\n| Windows | `C:\\Users\\[username]\\.alice\\model.json` |\n\n---\n\n### 2. 创建配置文件\n\n```bash\n# 创建配置目录\nmkdir -p ~/.alice\n\n# 创建配置文件\ntouch ~/.alice/model.json\n```\n\n---\n\n### 3. 完整配置模板\n\n将以下内容写入 `~/.alice/model.json`：\n\n```json\n{\n  \"language_models\": {\n    \"openai_compatible\": {\n      \"my-provider\": {\n        \"api_url\": \"https://api.myprovider.com/v1\",\n        \"api_key\": \"${MY_PROVIDER_API_KEY}\",\n        \"available_models\": [\n          {\n            \"name\": \"deepseek\",\n            \"max_tokens\": 200000,\n            \"max_output_tokens\": 32000,\n            \"max_completion_tokens\": 200000,\n            \"capabilities\": {\n              \"tools\": true,\n              \"images\": true,\n              \"parallel_tool_calls\": true,\n              \"prompt_cache_key\": true,\n              \"chat_completions\": true\n            }\n          }\n        ]\n      }\n    }\n  }\n}\n```\n\n---\n\n### 4. 字段说明表\n\n| 层级 | 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|------|\n| 根 | `language_models` | object | ✅ | 语言模型配置根节点 |\n| 根 | `openai_compatible` | object | ✅ | OpenAI 兼容接口标识 |\n| 2 | `[provider_name]` | object | ✅ | 提供商自定义名称（如 `my-provider`） |\n| 3 | `api_url` | string | ✅ | API 基础 URL，如 `https://api.xxx.com/v1` |\n| 3 | `api_key` | string | ❌ | API 密钥，支持 `${ENV_VAR}` 环境变量引用 |\n| 3 | `available_models` | array | ✅ | 支持的模型列表 |\n| 4 | `name` | string | ✅ | 模型名称，调用时使用的标识符 |\n| 4 | `max_tokens` | integer | ✅ | 最大上下文总长度（输入 + 输出 token 数） |\n| 4 | `max_output_tokens` | integer | ✅ | 单次最大输出 token 数 |\n| 4 | `max_completion_tokens` | integer | ✅ | 补全接口最大 token 数 |\n| 5 | `capabilities.tools` | boolean | ✅ | 是否支持工具/函数调用 |\n| 5 | `capabilities.images` | boolean | ✅ | 是否支持图像输入（多模态） |\n| 5 | `capabilities.parallel_tool_calls` | boolean | ✅ | 是否支持并行工具调用 |\n| 5 | `capabilities.prompt_cache_key` | boolean | ✅ | 是否支持提示缓存键 |\n| 5 | `capabilities.chat_completions` | boolean | ✅ | 是否支持 `/chat/completions` 端点 |\n\n---\n\n### 5. 多提供商配置示例\n\n```json\n{\n  \"language_models\": {\n    \"openai_compatible\": {\n      \"openai\": {\n        \"api_url\": \"https://api.openai.com/v1\",\n        \"api_key\": \"${OPENAI_API_KEY}\",\n        \"available_models\": [\n          {\n            \"name\": \"gpt-4-turbo\",\n            \"max_tokens\": 128000,\n            \"max_output_tokens\": 4096,\n            \"max_completion_tokens\": 128000,\n            \"capabilities\": {\n              \"tools\": true,\n              \"images\": true,\n              \"parallel_tool_calls\": true,\n              \"prompt_cache_key\": false,\n              \"chat_completions\": true\n            }\n          },\n          {\n            \"name\": \"gpt-4o\",\n            \"max_tokens\": 128000,\n            \"max_output_tokens\": 16384,\n            \"max_completion_tokens\": 128000,\n            \"capabilities\": {\n              \"tools\": true,\n              \"images\": true,\n              \"parallel_tool_calls\": true,\n              \"prompt_cache_key\": false,\n              \"chat_completions\": true\n            }\n          }\n        ]\n      },\n      \"deepseek\": {\n        \"api_url\": \"https://api.deepseek.com/v1\",\n        \"api_key\": \"${DEEPSEEK_API_KEY}\",\n        \"available_models\": [\n          {\n            \"name\": \"deepseek-chat\",\n            \"max_tokens\": 200000,\n            \"max_output_tokens\": 32000,\n            \"max_completion_tokens\": 200000,\n            \"capabilities\": {\n              \"tools\": true,\n              \"images\": false,\n              \"parallel_tool_calls\": true,\n              \"prompt_cache_key\": true,\n              \"chat_completions\": true\n            }\n          }\n        ]\n      },\n      \"local\": {\n        \"api_url\": \"http://localhost:8080/v1\",\n        \"available_models\": [\n          {\n            \"name\": \"llama3\",\n            \"max_tokens\": 8192,\n            \"max_output_tokens\": 2048,\n            \"max_completion_tokens\": 8192,\n            \"capabilities\": {\n              \"tools\": false,\n              \"images\": false,\n              \"parallel_tool_calls\": false,\n              \"prompt_cache_key\": false,\n              \"chat_completions\": true\n            }\n          }\n        ]\n      }\n    }\n  }\n}\n```\n\n---\n\n### 6. 环境变量配置\n\n在 `~/.bashrc`、`~/.zshrc` 或 `.env` 文件中设置：\n\n```bash\n# ~/.bashrc 或 ~/.zshrc\nexport MY_PROVIDER_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nexport OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nexport DEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\n```\n\n或创建 `~/.alice/.env`：\n\n```bash\n# ~/.alice/.env\nMY_PROVIDER_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nOPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\nDEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx\n```\n\n---\n\n### 7. 代码加载示例\n\n```python\nimport json\nimport os\nfrom pathlib import Path\n\ndef load_model_config():\n    config_path = Path.home() / \".alice\" / \"model.json\"\n    \n    if not config_path.exists():\n        raise FileNotFoundError(f\"Config file not found: {config_path}\")\n    \n    with open(config_path, 'r') as f:\n        config = json.load(f)\n    \n    # 展开环境变量\n    def expand_env(value):\n        if isinstance(value, str) and value.startswith(\"${\") and value.endswith(\"}\"):\n            env_var = value[2:-1]\n            return os.environ.get(env_var, value)\n        return value\n    \n    # 递归展开所有 api_key\n    providers = config.get(\"language_models\", {}).get(\"openai_compatible\", {})\n    for provider in providers.values():\n        if \"api_key\" in provider:\n            provider[\"api_key\"] = expand_env(provider[\"api_key\"])\n    \n    return config\n\n# 使用\nconfig = load_model_config()\nprint(config[\"language_models\"][\"openai_compatible\"][\"my-provider\"][\"api_url\"])\n```\n\n```javascript\n// Node.js\nconst fs = require('fs');\nconst path = require('path');\nconst os = require('os');\n\nfunction loadModelConfig() {\n  const configPath = path.join(os.homedir(), '.alice', 'model.json');\n  \n  if (!fs.existsSync(configPath)) {\n    throw new Error(`Config file not found: ${configPath}`);\n  }\n  \n  const config = JSON.parse(fs.readFileSync(configPath, 'utf8'));\n  \n  // 展开环境变量\n  const expandEnv = (value) => {\n    if (typeof value === 'string' && value.startsWith('${') && value.endsWith('}')) {\n      const envVar = value.slice(2, -1);\n      return process.env[envVar] || value;\n    }\n    return value;\n  };\n  \n  const providers = config.language_models?.openai_compatible || {};\n  for (const key of Object.keys(providers)) {\n    if (providers[key].api_key) {\n      providers[key].api_key = expandEnv(providers[key].api_key);\n    }\n  }\n  \n  return config;\n}\n\n// 使用\nconst config = loadModelConfig();\nconsole.log(config.language_models.openai_compatible['my-provider'].api_url);\n```\n\n---\n\n### 8. 配置校验规则\n\n| 校验项 | 规则 | 错误示例 |\n|--------|------|----------|\n| `max_tokens` | ≥ `max_output_tokens` | `max_tokens: 1000, max_output_tokens: 2000` ❌ |\n| `max_completion_tokens` | 建议等于 `max_tokens` | 相差较大时可能产生歧义 |\n| `api_url` | 必须以 `http://` 或 `https://` 开头 | `api.example.com/v1` ❌ |\n| `name` | 非空字符串 | `\"\"` ❌ |\n| `capabilities` | 所有布尔字段必须显式设置 | 缺失字段可能导致运行时错误 |\n\n---\n\n### 9. 配置文件权限建议\n\n```bash\n# 设置目录权限（仅用户可读写）\nchmod 700 ~/.alice\n\n# 设置文件权限（仅用户可读写）\nchmod 600 ~/.alice/model.json\n```\n\n---\n\n### 10. 常见问题\n\n| 问题 | 解决方案 |\n|------|----------|\n| 配置文件找不到 | 确认 `~/.alice/model.json` 是否存在，目录名是否正确（注意点号） |\n| API Key 未生效 | 检查环境变量名称是否与配置中 `${VAR_NAME}` 一致，确认已 `source ~/.bashrc` |\n| 权限错误 | 执行 `chmod 600 ~/.alice/model.json` 修复权限 |\n| JSON 格式错误 | 使用 `jq . ~/.alice/model.json` 验证 JSON 格式 |\n\n---\n\n如需进一步调整（如添加自定义 headers、代理配置、超时设置等），请告知具体需求，我可以帮您扩展配置结构。\n"}}, "docs/config/example.yaml": {"8ebb2c40d3906a86e5a1eaa8ea381b21d0df9bd9": {"t": 1786068212.9364786, "n": 4236, "h": "f9e2ca001a0ee1a7", "c": "# Alice Agent 配置键参考\n#\n# Alice Agent 使用两个配置文件：\n#   ~/.alice/config.json  —  系统配置（由 AliceConfigStore 管理）\n#   ~/.alice/model.json   —  模型提供商配置（由 ModelConfigLoader 管理）\n#\n# 本 YAML 仅为 ~/.alice/config.json 的配置键参考文档。\n# 模型/提供商相关配置（openai、anthropic、deepseek 等）请参见 model.json：\n#   https://github.com/cland/alice-agent/docs/config/README.md#2-model-config-alicemodeljson\n#\n# 配置读取优先级：环境变量 > ~/.alice/config.json > 内建默认值\n#\n# CLI 使用点分隔键名（如 default.timeout），\n# JSON 存储使用下划线键名（如 default_timeout），\n# 由 AliceConfigStore 自动转换。\n\n# ==================== System Config ====================\n\ndefault.timeout:\n  json_key: default_timeout\n  env: DEFAULT_TIMEOUT\n  default: 180\n  description: \"默认任务超时时长（秒）\"\n\ndefault.verbose:\n  json_key: default_verbose\n  env: DEFAULT_VERBOSE\n  default: false\n  description: \"默认是否启用详细输出模式\"\n\nmax_iterations:\n  json_key: max_iterations\n  env: MAX_ITERATIONS\n  default: 10\n  description: \"最大 PPAO 迭代次数\"\n\naction_timeout_ms:\n  json_key: action_timeout_ms\n  env: ACTION_TIMEOUT_MS\n  default: 30000\n  description: \"Action 执行超时（毫秒）\"\n\nmax_micro_depth:\n  json_key: max_micro_depth\n  env: MAX_MICRO_DEPTH\n  default: 30\n  description: \"Micro-ReAct 最大递归深度（熔断阈值），高于 Macro 迭代以支持多步骤工具链\"\n\n# ==================== Model Override Config ====================\n# 以下键为 config.json 中可选的模型覆盖配置。\n# 主要模型配置位于 ~/.alice/model.json 的 language_models.openai_compatible 结构下，\n# config.json 中的值优先级低于 model.json 的 default_model 字段。\n\ndefault.model:\n  json_key: default_model\n  env: DEFAULT_MODEL\n  default: \"gpt-4o-mini\"\n  description: \"默认 LLM 模型 ID（可选覆盖，主配置见 model.json 中的 default_model）\"\n\nagent.max_iterations:\n  json_key: max_iterations\n  env: MAX_ITERATIONS\n  default: 10\n  description: \"最大迭代次数（同 max_iterations，可选覆盖）\"\n\nagent.max_micro_depth:\n  json_key: max_micro_depth\n  env: MAX_MICRO_DEPTH\n  default: 30\n  description: \"Micro-ReAct 最大递归深度（同 max_micro_depth，可选覆盖）\"\n\n# ==================== Provider Config (已迁移至 model.json) ====================\n#\n# 以下 openai.* / anthropic.* 键为旧版扁平配置路径，\n# 在 AliceConfigStore 中仍然可以 get/set，但不再被模型初始化流程使用。\n#\n# 所有提供商配置（API Key、Base URL、可用模型列表）已迁移至：\n#   ~/.alice/model.json → language_models.openai_compatible.{provider_name}\n#\n# 环境变量仍作为最终的 fallback 用于模型初始化：\n#   OPENAI_API_KEY    — OpenAI API 密钥\n#   ANTHROPIC_API_KEY — Anthropic API 密钥\n#   DEEPSEEK_API_KEY  — DeepSeek API 密钥\n#\n# 示例 model.json 结构（新格式）：\n# {\n#   \"default_model\": { \"provider\":\"deepseek\", \"model\":\"deepseek-v4-flash\",\n#       \"enable_thinking\":true, \"reasoning_effort\":\"high\" },\n#   \"planner\": {\n#     \"instruction_model_id\": \"deepseek-v4-flash\",\n#     \"reasoning_model_id\": \"deepseek-v4-flash\",\n#     \"instruction\": { \"enable_thinking\":false, \"reasoning_effort\":\"low\" },\n#     \"reasoning\":   { \"enable_thinking\":true,  \"reasoning_effort\":\"high\" }\n#   },\n#   \"providers\": {\n#     \"deepseek\": {\n#       \"base_url\": \"https://api.deepseek.com/v1\",\n#       \"api_key\": \"${DEEPSEEK_API_KEY}\",\n#       \"available_models\": [\n#         { \"name\":\"deepseek-v4-flash\", \"model\":\"deepseek-v4-flash\",\n#           \"max_tokens\":131072, \"max_output_tokens\":32000,\n#           \"capabilities\": { \"tools\":true, \"images\":false, ... } }\n#       ]\n#     }\n#   }\n# }\n#\n# 以下条目保留仅用于向后兼容的 get/set 支持：\n# ===========================================================================\n\n# openai.api_key:\n#   json_key: openai_api_key\n#   env: OPENAI_API_KEY\n#   default: ~\n#   description: \"[已迁移] OpenAI API 密钥。请在 model.json 中配置。\"\n\n# openai.model:\n#   json_key: openai_model\n#   env: OPENAI_MODEL\n#   default: ~\n#   description: \"[已迁移] OpenAI provider 使用的模型。请在 model.json 的 available_models 中配置。\"\n\n# openai.base_url:\n#   json_key: openai_base_url\n#   env: OPENAI_BASE_URL\n#   default: \"https://api.openai.com/v1\"\n#   description: \"[已迁移] OpenAI API 端点 URL。请在 model.json 中配置。\"\n\n# anthropic.api_key:\n#   json_key: anthropic_api_key\n#   env: ANTHROPIC_API_KEY\n#   default: ~\n#   description: \"[已迁移] Anthropic API 密钥。请在 model.json 中配置。\"\n\n# anthropic.model:\n#   json_key: anthropic_model\n#   env: ANTHROPIC_MODEL\n#   default: ~\n#   description: \"[已迁移] Anthropic provider 使用的模型。请在 model.json 的 available_models 中配置。\"\n\n# anthropic.base_url:\n#   json_key: anthropic_base_url\n#   env: ANTHROPIC_BASE_URL\n#   default: \"https://api.anthropic.com/v1\"\n#   description: \"[已迁移] Anthropic API 端点 URL。请在 model.json 中配置。\"\n"}}, "e2e/smoke/test_smoke_case_2.py": {"8ebb2c40d3906a86e5a1eaa8ea381b21d0df9bd9": {"t": 1786068241.441999, "n": 4255, "h": "badc81b3b11b129c", "c": "#!/usr/bin/env python3\n\"\"\"\nPMTEV Smoke Test — Case 2: 多工具协同与跨文件依赖修复测试\n\n校验 Agent 任务规划（Plan）能力，可跨文件全局检索变量、同步修改多处关联代码。\n\n真实验证：Agent 执行后，检查 config.py 是否将 TIMEOUT_MS 替换为 TIMEOUT_SEC，\n且 client.py 中的所有引用也同步更新，单位换算正确。\n\nPMTEV: Plan (cross-file refactoring) + Tool (edit multiple)\n\nSpec reference: docs/Agent 冒烟测试用例规范文档.md §Case 2\n\nUsage:\n    python -m e2e.smoke.test_smoke_case_2\n\"\"\"\n\nimport os\nimport subprocess\nimport sys\nimport unittest\nfrom pathlib import Path\n\nsys.path.insert(0, str(Path(__file__).resolve().parents[2]))\nfrom e2e.smoke.cases import CASE_2\nfrom e2e.smoke.config import PROJECT_MODEL, DEEPSEEK_API_KEY, WORKSPACE_DIR\nfrom e2e.helpers import GRADLEW, PROJECT_ROOT\n\n\nclass TestSmokeCase2(unittest.TestCase):\n    \"\"\"Case 2: TIMEOUT_MS → TIMEOUT_SEC 跨文件重命名 + 单位换算\"\"\"\n\n    maxDiff = None\n\n    WORKSPACE = WORKSPACE_DIR / \"smoke__case-2\"\n    FIXTURE_DIR = Path(__file__).resolve().parent / \"fixtures\" / \"cross_file_config\"\n\n    @classmethod\n    def setUpClass(cls):\n        if cls.WORKSPACE.exists():\n            import shutil\n            shutil.rmtree(cls.WORKSPACE)\n        dst = cls.WORKSPACE\n        dst.mkdir(parents=True, exist_ok=True)\n        import shutil\n        for f in cls.FIXTURE_DIR.iterdir():\n            if f.is_dir():\n                continue\n            shutil.copy2(f, dst / f.name)\n\n    def _run_agent(self):\n        \"\"\"Run Alice Agent with the case prompt.\"\"\"\n        prompt_flat = self.case.problem_description.replace(\"\\n\", \" \").strip()\n        cmd = [\n            str(GRADLEW),\n            \":alice-bootstrap:run\",\n            \"--rerun-tasks\",\n            \"--args\",\n            f'run \"{prompt_flat}\" --model {PROJECT_MODEL} --verbose',\n        ]\n        env = os.environ.copy()\n        if DEEPSEEK_API_KEY:\n            env[\"DEEPSEEK_API_KEY\"] = DEEPSEEK_API_KEY\n        result = subprocess.run(\n            cmd, cwd=PROJECT_ROOT, capture_output=True, text=True,\n            timeout=self.case.timeout_seconds, env=env,\n        )\n        return result.returncode, result.stdout + result.stderr\n\n    def setUp(self):\n        self.case = CASE_2\n        # Agent 的工作目录是项目根目录，path 解析相对于项目根\n        # 所以 Agent 会修改原始 fixture 文件（而非 temp 副本）\n        self.config_file = Path(__file__).resolve().parent / \"fixtures\" / \"cross_file_config\" / \"config.py\"\n        self.client_file = Path(__file__).resolve().parent / \"fixtures\" / \"cross_file_config\" / \"client.py\"\n\n    @classmethod\n    def tearDownClass(cls):\n        # 恢复原始 fixture\n        import subprocess\n        for f in [\"config.py\", \"client.py\"]:\n            fixture = cls.FIXTURE_DIR / f\n            subprocess.run(\n                [\"git\", \"checkout\", \"--\", str(fixture)],\n                cwd=PROJECT_ROOT, capture_output=True)\n\n    def test_timeout_renamed_across_files(self):\n        \"\"\"config.py 和 client.py 都必须将 TIMEOUT_MS 替换为 TIMEOUT_SEC\"\"\"\n        code, output = self._run_agent()\n        self.assertEqual(code, 0, f\"Agent 退出码非0\\n---\\n{output[-500:]}\")\n\n        # 验证 config.py\n        config_content = self.config_file.read_text(encoding=\"utf-8\")\n        self.assertIn(\n            \"TIMEOUT_SEC\",\n            config_content,\n            f\"config.py 未包含 TIMEOUT_SEC\\n当前内容:\\n{config_content}\",\n        )\n        self.assertNotIn(\n            \"TIMEOUT_MS\",\n            config_content,\n            f\"config.py 仍包含旧字段 TIMEOUT_MS\\n当前内容:\\n{config_content}\",\n        )\n\n        # 验证 client.py\n        client_content = self.client_file.read_text(encoding=\"utf-8\")\n        self.assertIn(\n            \"TIMEOUT_SEC\",\n            client_content,\n            f\"client.py 未引用 TIMEOUT_SEC\\n当前内容:\\n{client_content}\",\n        )\n        self.assertNotIn(\n            \"TIMEOUT_MS\",\n            client_content,\n            f\"client.py 仍引用旧字段 TIMEOUT_MS\\n当前内容:\\n{client_content}\",\n        )\n\n    def test_unit_conversion_correct(self):\n        \"\"\"单位换算正确：5秒 = 5000毫秒\"\"\"\n        code, output = self._run_agent()\n        self.assertEqual(code, 0, f\"Agent 退出码非0\\n---\\n{output[-500:]}\")\n\n        config_content = self.config_file.read_text(encoding=\"utf-8\")\n\n        # 原 TIMEOUT_MS = 5000 → 换算后 TIMEOUT_SEC = 5\n        self.assertIn(\n            \"5\", config_content,\n            f\"未找到正确的单位换算: TIMEOUT_SEC 应=5\\n当前内容:\\n{config_content}\",\n        )\n\n\nif __name__ == \"__main__\":\n    unittest.main(verbosity=2)\n"}}}}