feat(session): 会话存储体系实现 & Chat多Agent路由

- 新增诊断会话(diagnosis_session/agent_step/tool_invocation)三表 - AgentLoggingHook 持久化 agent_step，记录决策链和耗时 - LookupKnowledgeTool 写入 tool_invocation，记录L0/L1检索质量 - TokenTrackingChatModel 捕获真实token用量 - Chat接口支持意图路由：简单问题单Agent，复杂问题多Agent(Planner+Executor) - Prompt外置到 src/main/resources/prompts/ - 删除旧 diagnosis_record 表及相关文件 - 新增SessionContextHolder(ThreadLocal传递sessionId) - QuestionComplexity 复杂度判断工具 - 测试覆盖三张新表的Repository
2026-06-26 16:22:05 +08:00
parent a74ccea5be
commit 0d9cce75f9
33 changed files with 1941 additions and 470 deletions
@@ -83,16 +83,12 @@ public class ChatController {
            // 记录可用工具
            chatService.logAvailableTools();

+            ToolCallback[] toolCallbacks = tools != null ? tools.getToolCallbacks() : new ToolCallback[0];
+
+            // 根据问题复杂度自动选择单 Agent 或多 Agent
            logger.info("开始 ReactAgent 对话（支持自动工具调用）");
-            
-            // 构建系统提示词（包含历史消息）
-            String systemPrompt = chatService.buildSystemPrompt(history);
-            
-            // 创建 ReactAgent
-            ReactAgent agent = chatService.createReactAgent(chatModel, systemPrompt);
-            
-            // 执行对话
-            String fullAnswer = chatService.executeChat(agent, request.getQuestion());
+            String fullAnswer = chatService.executeChatWithStrategy(chatModel, toolCallbacks,
+                    request.getQuestion(), history);
            
            // 更新会话历史
            session.addMessage(request.getQuestion(), fullAnswer);
@@ -0,0 +1,64 @@
+package com.superbiz.agent.domain.entity;
+
+import jakarta.persistence.*;
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+import java.time.LocalDateTime;
+
+/**
+ * Agent 决策步骤实体
+ * 对应表: agent_step
+ */
+@Entity
+@Table(name = "agent_step", indexes = {
+    @Index(name = "idx_session_step", columnList = "session_id, step_index"),
+    @Index(name = "idx_agent_name", columnList = "agent_name")
+})
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class AgentStep {
+
+    @Id
+    @GeneratedValue(strategy = GenerationType.IDENTITY)
+    private Long id;
+
+    @Column(name = "session_id", nullable = false, length = 64)
+    private String sessionId;
+
+    @Column(name = "step_index", nullable = false)
+    private Integer stepIndex;
+
+    @Column(name = "agent_name", nullable = false, length = 32)
+    private String agentName;
+
+    @Column(name = "model_input", columnDefinition = "TEXT")
+    private String modelInput;
+
+    @Column(name = "model_output", columnDefinition = "TEXT")
+    private String modelOutput;
+
+    @Column(name = "thought", columnDefinition = "TEXT")
+    private String thought;
+
+    @Column(name = "has_tool_call")
+    private Boolean hasToolCall;
+
+    @Column(name = "duration_ms")
+    private Integer durationMs;
+
+    @Column(name = "token_count")
+    private Integer tokenCount;
+
+    @Column(name = "created_at", nullable = false, updatable = false)
+    private LocalDateTime createdAt;
+
+    @PrePersist
+    protected void onCreate() {
+        createdAt = LocalDateTime.now();
+    }
+}
@@ -1,128 +0,0 @@
-package com.superbiz.agent.domain.entity;
-
-import jakarta.persistence.*;
-import lombok.AllArgsConstructor;
-import lombok.Builder;
-import lombok.Data;
-import lombok.NoArgsConstructor;
-import com.superbiz.agent.domain.enums.DiagnosisStatus;
-import com.superbiz.agent.domain.enums.FaultCategory;
-import org.hibernate.annotations.JdbcTypeCode;
-import org.hibernate.type.SqlTypes;
-
-import java.time.LocalDateTime;
-import java.util.List;
-import java.util.Map;
-
-/**
- * 诊断记录实体
- * 对应表: diagnosis_record
- */
-@Entity
-@Table(name = "diagnosis_record", indexes = {
-    @Index(name = "idx_business_id", columnList = "business_id"),
-    @Index(name = "idx_trace_id", columnList = "trace_id"),
-    @Index(name = "idx_session_id", columnList = "session_id"),
-    @Index(name = "idx_fault_category", columnList = "fault_category"),
-    @Index(name = "idx_error_code", columnList = "error_code"),
-    @Index(name = "idx_created_at", columnList = "created_at"),
-    @Index(name = "idx_status", columnList = "status")
-})
-@Data
-@Builder
-@NoArgsConstructor
-@AllArgsConstructor
-public class DiagnosisRecord {
-
-    @Id
-    @GeneratedValue(strategy = GenerationType.IDENTITY)
-    private Long id;
-
-    @Column(name = "diagnosis_id", unique = true, nullable = false, length = 64)
-    private String diagnosisId;
-
-    // 关联信息
-    @Column(name = "session_id", length = 64)
-    private String sessionId;
-
-    @Column(name = "business_id", length = 128)
-    private String businessId;
-
-    @Column(name = "trace_id", length = 64)
-    private String traceId;
-
-    // 故障分类
-    @Enumerated(EnumType.STRING)
-    @Column(name = "fault_category", length = 32, columnDefinition = "VARCHAR(32)")
-    private FaultCategory faultCategory;
-
-    @Column(name = "fault_source", length = 128)
-    private String faultSource;
-
-    @Column(name = "fault_target", length = 256)
-    private String faultTarget;
-
-    // 错误信息
-    @Column(name = "error_code", length = 64)
-    private String errorCode;
-
-    @Column(name = "error_message", columnDefinition = "TEXT")
-    private String errorMessage;
-
-    @Column(name = "stack_trace", columnDefinition = "TEXT")
-    private String stackTrace;
-
-    // 诊断结果
-    @Column(name = "problem_type", length = 32)
-    private String problemType;
-
-    @Column(name = "root_cause", columnDefinition = "TEXT")
-    private String rootCause;
-
-    @Column(name = "solution", columnDefinition = "TEXT")
-    private String solution;
-
-    @Column(name = "report_markdown", columnDefinition = "TEXT")
-    private String reportMarkdown;
-
-    // 评估指标
-    @Enumerated(EnumType.STRING)
-    @Column(name = "status", length = 16, columnDefinition = "VARCHAR(16)")
-    private DiagnosisStatus status = DiagnosisStatus.PENDING;
-
-    @Column(name = "confidence")
-    private Integer confidence;
-
-    @Column(name = "duration")
-    private Integer duration;
-
-    // 用户反馈
-    @Column(name = "feedback", length = 16)
-    private String feedback;
-
-    // 调试字段 - JSON 类型
-    @JdbcTypeCode(SqlTypes.JSON)
-    @Column(name = "tool_calls", columnDefinition = "JSON")
-    private List<Map<String, Object>> toolCalls;
-
-    // 元数据
-    @Column(name = "created_by", length = 64)
-    private String createdBy;
-
-    @Column(name = "created_at", nullable = false, updatable = false)
-    private LocalDateTime createdAt;
-
-    @Column(name = "updated_at")
-    private LocalDateTime updatedAt;
-
-    @PrePersist
-    protected void onCreate() {
-        createdAt = LocalDateTime.now();
-        updatedAt = LocalDateTime.now();
-    }
-
-    @PreUpdate
-    protected void onUpdate() {
-        updatedAt = LocalDateTime.now();
-    }
-}
@@ -0,0 +1,80 @@
+package com.superbiz.agent.domain.entity;
+
+import jakarta.persistence.*;
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+import org.hibernate.annotations.JdbcTypeCode;
+import org.hibernate.type.SqlTypes;
+
+import java.time.LocalDateTime;
+
+/**
+ * 诊断会话实体
+ * 对应表: diagnosis_session
+ */
+@Entity
+@Table(name = "diagnosis_session", indexes = {
+    @Index(name = "idx_created_at", columnList = "created_at"),
+    @Index(name = "idx_status", columnList = "status"),
+    @Index(name = "idx_agent_flow", columnList = "agent_flow")
+})
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class DiagnosisSession {
+
+    @Id
+    @GeneratedValue(strategy = GenerationType.IDENTITY)
+    private Long id;
+
+    @Column(name = "session_id", unique = true, nullable = false, length = 64)
+    private String sessionId;
+
+    @Column(name = "query", nullable = false, columnDefinition = "TEXT")
+    private String query;
+
+    @Column(name = "status", length = 16)
+    private String status = "PENDING";
+
+    @Column(name = "agent_flow", length = 32)
+    private String agentFlow;
+
+    @Column(name = "total_duration_ms")
+    private Integer totalDurationMs;
+
+    @Column(name = "total_token_count")
+    private Integer totalTokenCount;
+
+    @Column(name = "step_count")
+    private Integer stepCount;
+
+    @Column(name = "tool_call_count")
+    private Integer toolCallCount;
+
+    @JdbcTypeCode(SqlTypes.JSON)
+    @Column(name = "self_evaluation", columnDefinition = "JSON")
+    private String selfEvaluation;
+
+    @Column(name = "feedback", length = 16)
+    private String feedback;
+
+    @Column(name = "created_at", nullable = false, updatable = false)
+    private LocalDateTime createdAt;
+
+    @Column(name = "updated_at")
+    private LocalDateTime updatedAt;
+
+    @PrePersist
+    protected void onCreate() {
+        createdAt = LocalDateTime.now();
+        updatedAt = LocalDateTime.now();
+    }
+
+    @PreUpdate
+    protected void onUpdate() {
+        updatedAt = LocalDateTime.now();
+    }
+}
@@ -0,0 +1,84 @@
+package com.superbiz.agent.domain.entity;
+
+import jakarta.persistence.*;
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+import org.hibernate.annotations.JdbcTypeCode;
+import org.hibernate.type.SqlTypes;
+
+import java.time.LocalDateTime;
+
+/**
+ * 工具调用明细实体
+ * 对应表: tool_invocation
+ */
+@Entity
+@Table(name = "tool_invocation", indexes = {
+    @Index(name = "idx_session_id", columnList = "session_id"),
+    @Index(name = "idx_tool_name", columnList = "tool_name"),
+    @Index(name = "idx_retrieval_layer", columnList = "retrieval_layer")
+})
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class ToolInvocation {
+
+    @Id
+    @GeneratedValue(strategy = GenerationType.IDENTITY)
+    private Long id;
+
+    @Column(name = "session_id", nullable = false, length = 64)
+    private String sessionId;
+
+    @Column(name = "step_id")
+    private Long stepId;
+
+    @Column(name = "tool_name", nullable = false, length = 64)
+    private String toolName;
+
+    @JdbcTypeCode(SqlTypes.JSON)
+    @Column(name = "input_params", nullable = false, columnDefinition = "JSON")
+    private String inputParams;
+
+    @Column(name = "output_preview", columnDefinition = "TEXT")
+    private String outputPreview;
+
+    @Column(name = "output_length")
+    private Integer outputLength;
+
+    @Column(name = "retrieval_layer", length = 8)
+    private String retrievalLayer;
+
+    @Column(name = "l0_match_count")
+    private Integer l0MatchCount;
+
+    @Column(name = "l1_match_count")
+    private Integer l1MatchCount;
+
+    @Column(name = "is_truncated")
+    private Boolean isTruncated;
+
+    @JdbcTypeCode(SqlTypes.JSON)
+    @Column(name = "retrieval_details", columnDefinition = "JSON")
+    private String retrievalDetails;
+
+    @Column(name = "duration_ms")
+    private Integer durationMs;
+
+    @Column(name = "success")
+    private Boolean success;
+
+    @Column(name = "error_message", columnDefinition = "TEXT")
+    private String errorMessage;
+
+    @Column(name = "created_at", nullable = false, updatable = false)
+    private LocalDateTime createdAt;
+
+    @PrePersist
+    protected void onCreate() {
+        createdAt = LocalDateTime.now();
+    }
+}
@@ -1,21 +0,0 @@
-package com.superbiz.agent.domain.enums;
-
-/**
- * 诊断状态枚举
- */
-public enum DiagnosisStatus {
-    PENDING("待处理"),
-    RUNNING("诊断中"),
-    SUCCESS("成功"),
-    FAILED("失败");
-
-    private final String description;
-
-    DiagnosisStatus(String description) {
-        this.description = description;
-    }
-
-    public String getDescription() {
-        return description;
-    }
-}
@@ -5,23 +5,39 @@ import com.alibaba.cloud.ai.graph.agent.hook.messages.AgentCommand;
 import com.alibaba.cloud.ai.graph.agent.hook.HookPosition;
 import com.alibaba.cloud.ai.graph.agent.hook.HookPositions;
 import com.alibaba.cloud.ai.graph.RunnableConfig;
+import com.superbiz.agent.domain.entity.AgentStep;
+import com.superbiz.agent.repository.AgentStepRepository;
+import com.superbiz.agent.util.SessionContextHolder;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.ai.chat.messages.Message;
 import org.springframework.ai.chat.messages.AssistantMessage;
 import org.springframework.ai.chat.messages.UserMessage;
 import org.springframework.ai.chat.messages.ToolResponseMessage;
-
 import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentHashMap;

 /**
 * Agent 日志 Hook
- * 用于记录 Agent 的思考过程、消息流转
+ * 记录 Agent 的思考过程、消息流转 + 持久化 agent_step 到 DB
 */
@Slf4j
@HookPositions({HookPosition.BEFORE_MODEL, HookPosition.AFTER_MODEL})
 public class AgentLoggingHook extends MessagesModelHook {

-    private int modelCallCount = 0;
+    private final AgentStepRepository agentStepRepository;
+    private final String agentName;
+
+    /** 每个 session 的步数计数器：sessionId → stepIndex */
+    private final ConcurrentHashMap<String, Integer> stepCounters = new ConcurrentHashMap<>();
+
+    /** beforeModel → afterModel 中间状态：sessionId_stepIndex → {stepId, startTime} */
+    private final ConcurrentHashMap<String, Map<String, Object>> pendingSteps = new ConcurrentHashMap<>();
+
+    public AgentLoggingHook(AgentStepRepository agentStepRepository, String agentName) {
+        this.agentStepRepository = agentStepRepository;
+        this.agentName = agentName;
+    }

    @Override
    public String getName() {
@@ -30,9 +46,12 @@ public class AgentLoggingHook extends MessagesModelHook {

    @Override
    public AgentCommand beforeModel(List<Message> previousMessages, RunnableConfig config) {
-        modelCallCount++;
+        String sessionId = SessionContextHolder.getSessionId();
+
+        int stepIndex = stepCounters.merge(sessionId, 0, (old, one) -> old + 1);
+
        log.info("========================================");
-        log.info("*** [Agent 思考] 第 {} 轮思考开始", modelCallCount);
+        log.info("*** [Agent 思考] 第 {} 轮思考开始", stepIndex + 1);
        log.info("*** [Agent 思考] 当前消息数量: {}", previousMessages.size());

        // 打印最后几条消息
@@ -40,28 +59,51 @@ public class AgentLoggingHook extends MessagesModelHook {
        if (lastN > 0) {
            log.info("*** [Agent 思考] 最近 {} 条消息:", lastN);
            List<Message> recentMessages = previousMessages.subList(previousMessages.size() - lastN, previousMessages.size());
-
            for (int i = 0; i < recentMessages.size(); i++) {
                Message msg = recentMessages.get(i);
                String role = getMessageRole(msg);
-
                log.info("  [{}] 角色: {}, 类型: {}", i + 1, role, msg.getClass().getSimpleName());
-                // Message 接口可能没有直接的 getContent() 方法，跳过内容打印
-                // 具体内容会在工具调用日志中体现
            }
        }

        log.info("*** [Agent 思考] 准备调用模型...");
        log.info("========================================");

-        // 不修改消息，直接返回
+        // 持久化 agent_step（beforeModel：先创建，先记 model_input 摘要）
+        if (sessionId != null) {
+            try {
+                String modelInputSummary = buildModelInputSummary(previousMessages);
+
+                AgentStep step = AgentStep.builder()
+                        .sessionId(sessionId)
+                        .stepIndex(stepIndex)
+                        .agentName(agentName)
+                        .modelInput(modelInputSummary)
+                        .build();
+                AgentStep saved = agentStepRepository.save(step);
+
+                // 记录中间状态供 afterModel 使用
+                pendingSteps.put(sessionId + "_" + stepIndex, Map.of(
+                        "stepId", saved.getId(),
+                        "startTime", System.currentTimeMillis()
+                ));
+
+                log.debug("agent_step 已创建: sessionId={}, stepIndex={}, id={}", sessionId, stepIndex, saved.getId());
+            } catch (Exception e) {
+                log.error("保存 agent_step 失败", e);
+                // 不中断 Agent 执行
+            }
+        }
+
        return new AgentCommand(previousMessages);
    }

    @Override
    public AgentCommand afterModel(List<Message> previousMessages, RunnableConfig config) {
+        String sessionId = SessionContextHolder.getSessionId();
+
        log.info("========================================");
-        log.info("*** [Agent 思考] 第 {} 轮思考完成", modelCallCount);
+        log.info("*** [Agent 思考] 第 {} 轮思考完成", stepCounters.getOrDefault(sessionId, 0));

        // 查找最后一条 AssistantMessage（模型的回复）
        AssistantMessage lastAssistant = null;
@@ -72,7 +114,15 @@ public class AgentLoggingHook extends MessagesModelHook {
            }
        }

+        boolean hasToolCall = false;
+
        if (lastAssistant != null) {
+            // 调试：打印 metadata
+            if (lastAssistant.getMetadata() != null && !lastAssistant.getMetadata().isEmpty()) {
+                log.info("*** [Agent 思考] 模型返回 metadata: {}", lastAssistant.getMetadata());
+            } else {
+                log.info("*** [Agent 思考] 模型返回 metadata: (空)");
+            }
            // 打印模型返回的文本内容
            String textContent = extractTextContent(lastAssistant);
            if (textContent != null && !textContent.isEmpty()) {
@@ -84,6 +134,7 @@ public class AgentLoggingHook extends MessagesModelHook {

            // 检查是否有工具调用
            if (lastAssistant.getToolCalls() != null && !lastAssistant.getToolCalls().isEmpty()) {
+                hasToolCall = true;
                log.info("*** [Agent 思考] 模型决定调用 {} 个工具:",
                    lastAssistant.getToolCalls().size());
                lastAssistant.getToolCalls().forEach(toolCall -> {
@@ -100,84 +151,178 @@ public class AgentLoggingHook extends MessagesModelHook {

        log.info("========================================");

-        // 不修改消息，直接返回
+        // 更新 agent_step（afterModel：补全 model_output、耗时等）
+        if (sessionId != null) {
+            int stepIndex = stepCounters.getOrDefault(sessionId, 0);
+            String stepKey = sessionId + "_" + stepIndex;
+            Map<String, Object> pending = pendingSteps.remove(stepKey);
+
+            if (pending != null) {
+                try {
+                    Long stepId = (Long) pending.get("stepId");
+                    long startTime = (long) pending.get("startTime");
+                    int durationMs = (int) (System.currentTimeMillis() - startTime);
+
+                    AgentStep step = agentStepRepository.findById(stepId).orElse(null);
+                    if (step != null) {
+                        String thought = extractTextContent(lastAssistant);
+                        if (thought != null && thought.length() > 2000) {
+                            thought = thought.substring(0, 2000);
+                        }
+
+                        step.setThought(thought);
+                        step.setHasToolCall(hasToolCall);
+                        step.setDurationMs(durationMs);
+
+                        if (lastAssistant != null) {
+                            String outputSummary = buildModelOutputSummary(lastAssistant);
+                            step.setModelOutput(outputSummary);
+
+                            // 读取实际 token 用量（由 TokenTrackingChatModel 写入）
+                            Integer tokenCount = TokenUsageHolder.get();
+                            if (tokenCount != null) {
+                                step.setTokenCount(tokenCount);
+                            }
+                        }
+
+                        agentStepRepository.save(step);
+                        log.debug("agent_step 已更新: sessionId={}, stepIndex={}, duration={}ms",
+                                sessionId, stepIndex, durationMs);
+                    }
+                } catch (Exception e) {
+                    log.error("更新 agent_step 失败", e);
+                }
+            }
+        }
+
+        // 清理 token 上下文
+        TokenUsageHolder.clear();
+
        return new AgentCommand(previousMessages);
    }

+    /**
+     * 构建模型输入摘要（前 N 条消息的 role + 截断内容）
+     */
+    private String buildModelInputSummary(List<Message> messages) {
+        StringBuilder sb = new StringBuilder();
+        int maxMessages = Math.min(messages.size(), 5);
+        for (int i = messages.size() - maxMessages; i < messages.size(); i++) {
+            Message msg = messages.get(i);
+            String role = getMessageRole(msg);
+            String content = msg.toString();
+            if (content.length() > 200) {
+                content = content.substring(0, 200) + "...";
+            }
+            sb.append("[").append(role).append("] ").append(content).append("\n");
+        }
+        String result = sb.toString();
+        if (result.length() > 500) {
+            result = result.substring(0, 500) + "...";
+        }
+        return result;
+    }
+
+    /**
+     * 构建模型输出摘要
+     */
+    private String buildModelOutputSummary(AssistantMessage message) {
+        String text = extractTextContent(message);
+        if (text == null) {
+            text = "";
+        }
+        if (text.length() > 500) {
+            text = text.substring(0, 500) + "...";
+        }
+        StringBuilder sb = new StringBuilder();
+        sb.append("{\"text\":\"").append(escapeJson(text)).append("\"");
+        if (message.getToolCalls() != null && !message.getToolCalls().isEmpty()) {
+            sb.append(",\"toolCalls\":[");
+            for (int i = 0; i < message.getToolCalls().size(); i++) {
+                if (i > 0) sb.append(",");
+                sb.append("{\"name\":\"").append(escapeJson(message.getToolCalls().get(i).name()))
+                  .append("\",\"arguments\":").append(message.getToolCalls().get(i).arguments()).append("}");
+            }
+            sb.append("]");
+        }
+        sb.append("}");
+        return sb.toString();
+    }
+
+    private String escapeJson(String s) {
+        if (s == null) return "";
+        return s.replace("\\", "\\\\")
+                .replace("\"", "\\\"")
+                .replace("\n", "\\n")
+                .replace("\r", "\\r")
+                .replace("\t", "\\t");
+    }
+
    /**
     * 提取 AssistantMessage 的文本内容
     */
    private String extractTextContent(AssistantMessage message) {
+        if (message == null) return null;
        try {
-            // 方法 1: 尝试通过反射获取 text 字段
+            // 方法 1: 反射获取 text 字段
            try {
                java.lang.reflect.Field textField = message.getClass().getDeclaredField("text");
                textField.setAccessible(true);
                Object value = textField.get(message);
                if (value != null) {
-                    String text = value.toString();
                    log.debug("通过 text 字段提取成功");
-                    return text;
+                    return value.toString();
                }
            } catch (NoSuchFieldException e) {
-                // text 字段不存在，尝试下一种方法
+                // 尝试下一种方法
            }

-            // 方法 2: 尝试 content 字段
+            // 方法 2: 反射获取 content 字段
            try {
                java.lang.reflect.Field contentField = message.getClass().getDeclaredField("content");
                contentField.setAccessible(true);
                Object value = contentField.get(message);
                if (value != null) {
-                    String text = value.toString();
                    log.debug("通过 content 字段提取成功");
-                    return text;
+                    return value.toString();
                }
            } catch (NoSuchFieldException e) {
-                // content 字段不存在，尝试下一种方法
+                // 尝试下一种方法
            }

-            // 方法 3: 尝试调用 getText() 方法
+            // 方法 3: 调用 getText() 方法
            try {
                java.lang.reflect.Method getTextMethod = message.getClass().getMethod("getText");
                Object value = getTextMethod.invoke(message);
                if (value != null) {
-                    String text = value.toString();
                    log.debug("通过 getText() 方法提取成功");
-                    return text;
+                    return value.toString();
                }
            } catch (NoSuchMethodException e) {
-                // getText() 方法不存在，尝试下一种方法
+                // 尝试下一种方法
            }

-            // 方法 4: 尝试调用 getContent() 方法
+            // 方法 4: 调用 getContent() 方法
            try {
                java.lang.reflect.Method getContentMethod = message.getClass().getMethod("getContent");
                Object value = getContentMethod.invoke(message);
                if (value != null) {
-                    String text = value.toString();
                    log.debug("通过 getContent() 方法提取成功");
-                    return text;
+                    return value.toString();
                }
            } catch (NoSuchMethodException e) {
-                // getContent() 方法不存在
+                // 方法不存在
            }

-            // 方法 5: 打印所有字段和方法，帮助调试
+            // 方法 5: 打印类结构信息
            log.warn("无法提取 AssistantMessage 文本内容，打印类信息:");
            log.warn("类名: {}", message.getClass().getName());
            log.warn("字段列表:");
            for (java.lang.reflect.Field field : message.getClass().getDeclaredFields()) {
                log.warn("  - {}: {}", field.getName(), field.getType().getSimpleName());
            }
-            log.warn("方法列表:");
-            for (java.lang.reflect.Method method : message.getClass().getMethods()) {
-                if (method.getName().startsWith("get") && method.getParameterCount() == 0) {
-                    log.warn("  - {}(): {}", method.getName(), method.getReturnType().getSimpleName());
-                }
-            }

-            // 方法 6: 最后尝试 toString()
+            // 方法 6: toString() 兜底
            String toString = message.toString();
            if (toString != null && !toString.startsWith("AssistantMessage@")) {
                log.debug("通过 toString() 提取");
@@ -0,0 +1,45 @@
+package com.superbiz.agent.hook;
+
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.chat.model.ChatResponse;
+import org.springframework.ai.chat.prompt.Prompt;
+import reactor.core.publisher.Flux;
+
+/**
+ * ChatModel 包装器 — 捕获每次模型调用的实际 token 用量
+ * 通过 TokenUsageHolder 传递给 AgentLoggingHook
+ */
+public class TokenTrackingChatModel implements ChatModel {
+
+    private final ChatModel delegate;
+
+    public TokenTrackingChatModel(ChatModel delegate) {
+        this.delegate = delegate;
+    }
+
+    @Override
+    public ChatResponse call(Prompt prompt) {
+        ChatResponse response = delegate.call(prompt);
+        captureTokenUsage(response);
+        return response;
+    }
+
+    @Override
+    public Flux<ChatResponse> stream(Prompt prompt) {
+        return delegate.stream(prompt);
+    }
+
+    private void captureTokenUsage(ChatResponse response) {
+        try {
+            if (response.getMetadata() != null && response.getMetadata().getUsage() != null) {
+                var usage = response.getMetadata().getUsage();
+                Integer total = usage.getTotalTokens();
+                if (total != null && total > 0) {
+                    TokenUsageHolder.set(total);
+                }
+            }
+        } catch (Exception e) {
+            // 不中断模型调用
+        }
+    }
+}
@@ -0,0 +1,22 @@
+package com.superbiz.agent.hook;
+
+/**
+ * Token 用量持有者（基于 ThreadLocal）
+ * ChatModel 调用后写入实际 token 数，AgentLoggingHook 读取
+ */
+public class TokenUsageHolder {
+
+    private static final ThreadLocal<Integer> TOKEN_COUNT = new ThreadLocal<>();
+
+    public static void set(Integer count) {
+        TOKEN_COUNT.set(count);
+    }
+
+    public static Integer get() {
+        return TOKEN_COUNT.get();
+    }
+
+    public static void clear() {
+        TOKEN_COUNT.remove();
+    }
+}
@@ -0,0 +1,24 @@
+package com.superbiz.agent.repository;
+
+import com.superbiz.agent.domain.entity.AgentStep;
+import org.springframework.data.jpa.repository.JpaRepository;
+import org.springframework.stereotype.Repository;
+
+import java.util.List;
+
+/**
+ * Agent 决策步骤 Repository
+ */
+@Repository
+public interface AgentStepRepository extends JpaRepository<AgentStep, Long> {
+
+    /**
+     * 根据会话ID查询所有步骤（按步骤号排序）
+     */
+    List<AgentStep> findBySessionIdOrderByStepIndex(String sessionId);
+
+    /**
+     * 统计某个会话的步骤数
+     */
+    int countBySessionId(String sessionId);
+}
@@ -1,73 +0,0 @@
-package com.superbiz.agent.repository;
-
-import com.superbiz.agent.domain.enums.DiagnosisStatus;
-import com.superbiz.agent.domain.enums.FaultCategory;
-import com.superbiz.agent.domain.entity.DiagnosisRecord;
-import org.springframework.data.domain.Page;
-import org.springframework.data.domain.Pageable;
-import org.springframework.data.jpa.repository.JpaRepository;
-import org.springframework.stereotype.Repository;
-
-import java.time.LocalDateTime;
-import java.util.List;
-import java.util.Optional;
-
-/**
- * 诊断记录 Repository
- */
-@Repository
-public interface DiagnosisRecordRepository extends JpaRepository<DiagnosisRecord, Long> {
-
-    /**
-     * 根据诊断ID查询
-     */
-    Optional<DiagnosisRecord> findByDiagnosisId(String diagnosisId);
-
-    /**
-     * 根据业务ID查询
-     */
-    Optional<DiagnosisRecord> findByBusinessId(String businessId);
-
-    /**
-     * 根据链路追踪ID查询
-     */
-    Optional<DiagnosisRecord> findByTraceId(String traceId);
-
-    /**
-     * 根据会话ID查询所有记录
-     */
-    List<DiagnosisRecord> findBySessionId(String sessionId);
-
-    /**
-     * 根据故障类别和错误码查询
-     */
-    List<DiagnosisRecord> findByFaultCategoryAndErrorCode(FaultCategory category, String errorCode);
-
-    /**
-     * 根据故障类别、故障源和错误码查询
-     */
-    List<DiagnosisRecord> findByFaultCategoryAndFaultSourceAndErrorCode(
-        FaultCategory category, String faultSource, String errorCode);
-
-    /**
-     * 根据状态查询
-     */
-    List<DiagnosisRecord> findByStatus(DiagnosisStatus status);
-
-    /**
-     * 根据时间范围查询（分页）
-     */
-    Page<DiagnosisRecord> findByCreatedAtBetween(
-        LocalDateTime start, LocalDateTime end, Pageable pageable);
-
-    /**
-     * 根据故障类别和时间范围查询（分页）
-     */
-    Page<DiagnosisRecord> findByFaultCategoryAndCreatedAtBetween(
-        FaultCategory category, LocalDateTime start, LocalDateTime end, Pageable pageable);
-
-    /**
-     * 查询有用反馈的高置信度记录（用于生成案例）
-     */
-    List<DiagnosisRecord> findByFeedbackAndConfidenceGreaterThanEqual(String feedback, Integer confidence);
-}
@@ -0,0 +1,12 @@
+package com.superbiz.agent.repository;
+
+import com.superbiz.agent.domain.entity.DiagnosisSession;
+import org.springframework.data.jpa.repository.JpaRepository;
+import org.springframework.stereotype.Repository;
+
+import java.util.Optional;
+
+@Repository
+public interface DiagnosisSessionRepository extends JpaRepository<DiagnosisSession, Long> {
+    Optional<DiagnosisSession> findBySessionId(String sessionId);
+}
@@ -0,0 +1,29 @@
+package com.superbiz.agent.repository;
+
+import com.superbiz.agent.domain.entity.ToolInvocation;
+import org.springframework.data.jpa.repository.JpaRepository;
+import org.springframework.stereotype.Repository;
+
+import java.util.List;
+
+/**
+ * 工具调用明细 Repository
+ */
+@Repository
+public interface ToolInvocationRepository extends JpaRepository<ToolInvocation, Long> {
+
+    /**
+     * 根据会话ID查询所有工具调用
+     */
+    List<ToolInvocation> findBySessionId(String sessionId);
+
+    /**
+     * 根据工具名查询所有调用
+     */
+    List<ToolInvocation> findByToolName(String toolName);
+
+    /**
+     * 根据会话ID和工具名查询
+     */
+    List<ToolInvocation> findBySessionIdAndToolName(String sessionId, String toolName);
+}
@@ -9,6 +9,13 @@ import com.superbiz.agent.agent.tool.DateTimeTools;
 import com.superbiz.agent.agent.tool.InternalDocsTools;
 import com.superbiz.agent.agent.tool.QueryLogsTools;
 import com.superbiz.agent.agent.tool.QueryMetricsTools;
+import com.superbiz.agent.domain.entity.AgentStep;
+import com.superbiz.agent.domain.entity.AgentStep;
+import com.superbiz.agent.domain.entity.DiagnosisSession;
+import com.superbiz.agent.hook.AgentLoggingHook;
+import com.superbiz.agent.repository.AgentStepRepository;
+import com.superbiz.agent.repository.DiagnosisSessionRepository;
+import com.superbiz.agent.util.SessionContextHolder;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import org.springframework.ai.chat.messages.AssistantMessage;
@@ -20,6 +27,7 @@ import com.superbiz.agent.tool.LookupKnowledgeTool;

 import java.util.List;
 import java.util.Optional;
+import java.util.UUID;

 /**
 * AI Ops 智能运维服务
@@ -48,6 +56,12 @@ public class AiOpsService {
    @Autowired
    private AiOpsPromptProperties promptProperties;

+    @Autowired
+    private DiagnosisSessionRepository diagnosisSessionRepository;
+
+    @Autowired
+    private AgentStepRepository agentStepRepository;
+
    /**
     * 执行 AI Ops 告警分析流程
     *
@@ -59,34 +73,65 @@ public class AiOpsService {
    public Optional<OverAllState> executeAiOpsAnalysis(ChatModel chatModel, ToolCallback[] toolCallbacks) throws GraphRunnerException {
        logger.info("开始执行 AI Ops 多 Agent 协作流程");

-        // 构建 Planner 和 Executor Agent
-        ReactAgent plannerAgent = buildPlannerAgent(chatModel, toolCallbacks);
-        ReactAgent executorAgent = buildExecutorAgent(chatModel, toolCallbacks);
+        String sessionId = UUID.randomUUID().toString().substring(0, 8);
+        long startTime = System.currentTimeMillis();

-        // 构建 Supervisor Agent
-        SupervisorAgent supervisorAgent = SupervisorAgent.builder()
-                .name("ai_ops_supervisor")
-                .description("负责调度 Planner 与 Executor 的多 Agent 控制器")
-                .model(chatModel)
-                .systemPrompt(promptProperties.getSupervisor())
-                .subAgents(List.of(plannerAgent, executorAgent))
+        // 创建诊断会话
+        DiagnosisSession session = DiagnosisSession.builder()
+                .sessionId(sessionId)
+                .query("AI Ops 告警分析")
+                .status("RUNNING")
+                .agentFlow("AI_OPS")
                .build();
+        diagnosisSessionRepository.save(session);

-        String taskPrompt = "你是企业级 SRE，接到了自动化告警排查任务。请结合工具调用，执行**规划→执行→再规划**的闭环，并最终按照固定模板输出《告警分析报告》。禁止编造虚假数据，如连续多次查询失败需诚实反馈无法完成的原因。";
+        // 设置 ThreadLocal 上下文（LookupKnowledgeTool 通过此获取 sessionId）
+        SessionContextHolder.setSessionId(sessionId);

-        logger.info("调用 Supervisor Agent 开始编排...");
+        try {
+            // 构建 Planner 和 Executor Agent（每个 Agent 各自带 Hook）
+            ReactAgent plannerAgent = buildPlannerAgent(chatModel, toolCallbacks);
+            ReactAgent executorAgent = buildExecutorAgent(chatModel, toolCallbacks);

-        Optional<OverAllState> stateOptional = supervisorAgent.invoke(taskPrompt);
+            // 构建 Supervisor Agent（不加 Hook）
+            SupervisorAgent supervisorAgent = SupervisorAgent.builder()
+                    .name("ai_ops_supervisor")
+                    .description("负责调度 Planner 与 Executor 的多 Agent 控制器")
+                    .model(chatModel)
+                    .systemPrompt(promptProperties.getSupervisor())
+                    .subAgents(List.of(plannerAgent, executorAgent))
+                    .build();

-        // 添加调试代码
-        if (stateOptional.isPresent()) {
-            OverAllState state = stateOptional.get();
-            logger.debug("Final State Keys: {}", state.data().keySet());  // 打印所有 key
-            logger.debug("Planner Plan: {}", state.value("planner_plan"));
-            logger.debug("Executor Feedback: {}", state.value("executor_feedback"));
+            String taskPrompt = "你是企业级 SRE，接到了自动化告警排查任务。请结合工具调用，执行**规划→执行→再规划**的闭环，并最终按照固定模板输出《告警分析报告》。禁止编造虚假数据，如连续多次查询失败需诚实反馈无法完成的原因。";
+
+            logger.info("调用 Supervisor Agent 开始编排...");
+
+            Optional<OverAllState> stateOptional = supervisorAgent.invoke(taskPrompt);
+
+            long duration = System.currentTimeMillis() - startTime;
+
+            // 更新诊断会话
+            session.setStatus(stateOptional.isPresent() ? "SUCCESS" : "FAILED");
+            session.setTotalDurationMs((int) duration);
+            backfillSessionMetrics(session);
+            diagnosisSessionRepository.save(session);
+
+            // 添加调试代码
+            if (stateOptional.isPresent()) {
+                OverAllState state = stateOptional.get();
+                logger.debug("Final State Keys: {}", state.data().keySet());
+                logger.debug("Planner Plan: {}", state.value("planner_plan"));
+                logger.debug("Executor Feedback: {}", state.value("executor_feedback"));
+            }
+
+            return stateOptional;
+        } catch (Exception e) {
+            session.setStatus("FAILED");
+            diagnosisSessionRepository.save(session);
+            throw e;
+        } finally {
+            SessionContextHolder.clear();
        }
-
-        return stateOptional;
    }

    /**
@@ -124,6 +169,7 @@ public class AiOpsService {
                .systemPrompt(promptProperties.getPlanner())
                .methodTools(buildMethodToolsArray())
                .tools(toolCallbacks)
+                .hooks(new AgentLoggingHook(agentStepRepository, "planner"))
                .outputKey("planner_plan")
                .build();
    }
@@ -139,6 +185,7 @@ public class AiOpsService {
                .systemPrompt(promptProperties.getExecutor())
                .methodTools(buildMethodToolsArray())
                .tools(toolCallbacks)
+                .hooks(new AgentLoggingHook(agentStepRepository, "executor"))
                .outputKey("executor_feedback")
                .build();
    }
@@ -157,4 +204,26 @@ public class AiOpsService {
            return new Object[]{dateTimeTools, lookupKnowledgeTool, queryMetricsTools};
        }
    }
+
+    /** 从 agent_step 汇总指标回填 diagnosis_session */
+    private void backfillSessionMetrics(DiagnosisSession session) {
+        try {
+            List<AgentStep> steps = agentStepRepository.findBySessionIdOrderByStepIndex(session.getSessionId());
+            if (steps.isEmpty()) return;
+
+            int totalTokens = 0;
+            int stepCount = 0;
+            int toolCallCount = 0;
+            for (AgentStep s : steps) {
+                stepCount++;
+                if (s.getTokenCount() != null) totalTokens += s.getTokenCount();
+                if (Boolean.TRUE.equals(s.getHasToolCall())) toolCallCount++;
+            }
+            session.setTotalTokenCount(totalTokens);
+            session.setStepCount(stepCount);
+            session.setToolCallCount(toolCallCount);
+        } catch (Exception e) {
+            logger.warn("回填会话指标失败: sessionId={}", session.getSessionId(), e);
+        }
+    }
 }
@@ -1,24 +1,40 @@
 package com.superbiz.agent.service;

+import com.alibaba.cloud.ai.graph.OverAllState;
 import com.alibaba.cloud.ai.graph.agent.ReactAgent;
+import com.alibaba.cloud.ai.graph.agent.flow.agent.SupervisorAgent;
 import com.alibaba.cloud.ai.graph.exception.GraphRunnerException;
 import com.superbiz.agent.agent.tool.DateTimeTools;
 import com.superbiz.agent.agent.tool.InternalDocsTools;
 import com.superbiz.agent.agent.tool.QueryLogsTools;
 import com.superbiz.agent.agent.tool.QueryMetricsTools;
-import com.superbiz.agent.tool.LookupKnowledgeTool;
+import com.superbiz.agent.domain.entity.DiagnosisSession;
 import com.superbiz.agent.hook.AgentLoggingHook;
+import com.superbiz.agent.hook.TokenTrackingChatModel;
+import com.superbiz.agent.hook.TokenUsageHolder;
+import com.superbiz.agent.repository.AgentStepRepository;
+import com.superbiz.agent.repository.DiagnosisSessionRepository;
+import com.superbiz.agent.tool.LookupKnowledgeTool;
+import com.superbiz.agent.util.QuestionComplexity;
+import com.superbiz.agent.util.SessionContextHolder;

+import jakarta.annotation.PostConstruct;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
+import org.springframework.ai.chat.messages.AssistantMessage;
 import org.springframework.ai.chat.model.ChatModel;
 import org.springframework.ai.tool.ToolCallback;
 import org.springframework.ai.tool.ToolCallbackProvider;
 import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.core.io.ClassPathResource;
 import org.springframework.stereotype.Service;

+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
 import java.util.List;
 import java.util.Map;
+import java.util.Optional;
+import java.util.UUID;

 /**
 * 聊天服务
@@ -50,6 +66,37 @@ public class ChatService {
    @Autowired
    private LookupKnowledgeTool lookupKnowledgeTool;

+    @Autowired
+    private DiagnosisSessionRepository diagnosisSessionRepository;
+
+    @Autowired
+    private AgentStepRepository agentStepRepository;
+
+    /** 多 Agent Chat 的 Prompt */
+    private String chatPlannerPrompt;
+    private String chatExecutorPrompt;
+
+    @PostConstruct
+    public void init() {
+        // 加载 Prompt
+        try {
+            chatPlannerPrompt = new String(
+                new ClassPathResource("prompts/chat-planner-prompt.md").getInputStream().readAllBytes(),
+                StandardCharsets.UTF_8);
+            chatExecutorPrompt = new String(
+                new ClassPathResource("prompts/chat-executor-prompt.md").getInputStream().readAllBytes(),
+                StandardCharsets.UTF_8);
+            logger.info("Chat 多 Agent Prompts 加载成功");
+        } catch (IOException e) {
+            logger.error("加载 Chat Prompt 文件失败", e);
+            throw new RuntimeException("Failed to load chat prompts", e);
+        }
+
+        // 包装 ChatModel 以捕获 token 用量
+        chatModel = new TokenTrackingChatModel(chatModel);
+        logger.info("ChatModel 已包装 TokenTrackingChatModel");
+    }
+
    /**
     * 获取注入的 ChatModel
     */
@@ -177,7 +224,7 @@ public class ChatService {
                .systemPrompt(systemPrompt)
                .methodTools(buildMethodToolsArray())
                .tools(getToolCallbacks())
-                .hooks(new AgentLoggingHook())  // 添加日志 Hook
+                .hooks(new AgentLoggingHook(agentStepRepository, "intelligent_assistant"))
                .build();
    }

@@ -191,16 +238,201 @@ public class ChatService {
        logger.info("========================================");
        logger.info("📝 用户问题: {}", question);

+        String sessionId = UUID.randomUUID().toString().substring(0, 8);
        long startTime = System.currentTimeMillis();
-        var response = agent.call(question);
-        long duration = System.currentTimeMillis() - startTime;

-        String answer = response.getText();
+        // 创建诊断会话
+        DiagnosisSession session = DiagnosisSession.builder()
+                .sessionId(sessionId)
+                .query(question)
+                .status("RUNNING")
+                .agentFlow("CHAT")
+                .build();
+        diagnosisSessionRepository.save(session);

-        logger.info("⏱️  总耗时: {} ms", duration);
-        logger.info("📏 输出长度: {} 字符", answer.length());
-        logger.info("========================================");
+        // 设置 ThreadLocal 上下文（LookupKnowledgeTool 通过此获取 sessionId）
+        SessionContextHolder.setSessionId(sessionId);

-        return answer;
+        try {
+            var response = agent.call(question);
+            long duration = System.currentTimeMillis() - startTime;
+
+            String answer = response.getText();
+
+            // 更新诊断会话
+            session.setStatus("SUCCESS");
+            session.setTotalDurationMs((int) duration);
+            backfillSessionMetrics(session);
+            diagnosisSessionRepository.save(session);
+
+            logger.info("⏱️  总耗时: {} ms", duration);
+            logger.info("📏 输出长度: {} 字符", answer.length());
+            logger.info("========================================");
+
+            return answer;
+        } catch (Exception e) {
+            session.setStatus("FAILED");
+            diagnosisSessionRepository.save(session);
+            throw e;
+        } finally {
+            SessionContextHolder.clear();
+        }
+    }
+
+    /**
+     * 根据问题复杂度自动选择执行策略
+     * @param chatModel 聊天模型
+     * @param toolCallbacks 工具回调
+     * @param question 用户问题
+     * @param history 历史消息
+     * @return AI 回复
+     */
+    public String executeChatWithStrategy(ChatModel chatModel, ToolCallback[] toolCallbacks,
+                                          String question, List<Map<String, String>> history) throws GraphRunnerException {
+        if (QuestionComplexity.isComplex(question)) {
+            logger.info("📊 问题判定为复杂，使用多 Agent（Planner + Executor）执行");
+            return executeChatComplex(chatModel, toolCallbacks, question, history);
+        } else {
+            logger.info("📊 问题判定为简单，使用单 Agent 执行");
+            String systemPrompt = buildSystemPrompt(history);
+            ReactAgent agent = createReactAgent(chatModel, systemPrompt);
+            return executeChat(agent, question);
+        }
+    }
+
+    /**
+     * 多 Agent 复杂对话执行（Planner + Executor + Supervisor）
+     */
+    public String executeChatComplex(ChatModel chatModel, ToolCallback[] toolCallbacks,
+                                     String question, List<Map<String, String>> history) throws GraphRunnerException {
+        String sessionId = UUID.randomUUID().toString().substring(0, 8);
+        long startTime = System.currentTimeMillis();
+
+        DiagnosisSession session = DiagnosisSession.builder()
+                .sessionId(sessionId)
+                .query(question)
+                .status("RUNNING")
+                .agentFlow("CHAT")
+                .build();
+        diagnosisSessionRepository.save(session);
+
+        SessionContextHolder.setSessionId(sessionId);
+
+        try {
+            ReactAgent planner = buildChatPlannerAgent(chatModel, toolCallbacks, history);
+            ReactAgent executor = buildChatExecutorAgent(chatModel, toolCallbacks, history);
+
+            SupervisorAgent supervisor = SupervisorAgent.builder()
+                    .name("chat_supervisor")
+                    .description("负责调度 Planner 与 Executor 的多 Agent 控制器")
+                    .model(chatModel)
+                    .systemPrompt("你是一个智能任务调度器。分析用户问题，调用 Planner 拆解步骤，调用 Executor 执行各步骤。")
+                    .subAgents(List.of(planner, executor))
+                    .build();
+
+            Optional<OverAllState> stateOptional = supervisor.invoke(question);
+            long duration = System.currentTimeMillis() - startTime;
+
+            String answer = null;
+            if (stateOptional.isPresent()) {
+                // 从 state 中提取 Executor 的最终输出
+                OverAllState state = stateOptional.get();
+                Optional<AssistantMessage> executorOutput = state.value("executor_feedback")
+                        .filter(AssistantMessage.class::isInstance)
+                        .map(AssistantMessage.class::cast);
+                if (executorOutput.isPresent()) {
+                    answer = executorOutput.get().getText();
+                }
+            }
+
+            if (answer == null || answer.isBlank()) {
+                answer = "抱歉，多 Agent 分析未能生成有效结论。";
+            }
+
+            session.setStatus("SUCCESS");
+            session.setTotalDurationMs((int) duration);
+            backfillSessionMetrics(session);
+            diagnosisSessionRepository.save(session);
+
+            logger.info("⏱️  多 Agent 总耗时: {} ms", duration);
+            logger.info("📏 输出长度: {} 字符", answer.length());
+
+            return answer;
+
+        } catch (Exception e) {
+            session.setStatus("FAILED");
+            diagnosisSessionRepository.save(session);
+            logger.error("多 Agent 执行失败", e);
+            return "执行失败: " + e.getMessage();
+        } finally {
+            SessionContextHolder.clear();
+        }
+    }
+
+    private ReactAgent buildChatPlannerAgent(ChatModel chatModel, ToolCallback[] toolCallbacks,
+                                             List<Map<String, String>> history) {
+        StringBuilder prompt = new StringBuilder(chatPlannerPrompt);
+        if (!history.isEmpty()) {
+            prompt.append("\n\n--- 对话历史 ---\n");
+            for (Map<String, String> msg : history) {
+                prompt.append(msg.get("role")).append(": ").append(msg.get("content")).append("\n");
+            }
+            prompt.append("--- 对话历史结束 ---\n");
+        }
+        return ReactAgent.builder()
+                .name("chat_planner")
+                .description("负责拆解问题、规划步骤")
+                .model(chatModel)
+                .systemPrompt(prompt.toString())
+                // Planner 不注入工具，只能规划不能执行
+                .hooks(new AgentLoggingHook(agentStepRepository, "planner"))
+                .outputKey("planner_plan")
+                .build();
+    }
+
+    private ReactAgent buildChatExecutorAgent(ChatModel chatModel, ToolCallback[] toolCallbacks,
+                                              List<Map<String, String>> history) {
+        StringBuilder prompt = new StringBuilder(chatExecutorPrompt);
+        if (!history.isEmpty()) {
+            prompt.append("\n\n--- 对话历史 ---\n");
+            for (Map<String, String> msg : history) {
+                prompt.append(msg.get("role")).append(": ").append(msg.get("content")).append("\n");
+            }
+            prompt.append("--- 对话历史结束 ---\n");
+        }
+        return ReactAgent.builder()
+                .name("chat_executor")
+                .description("负责执行具体步骤并及时反馈")
+                .model(chatModel)
+                .systemPrompt(prompt.toString())
+                .methodTools(buildMethodToolsArray())
+                .tools(toolCallbacks)
+                .hooks(new AgentLoggingHook(agentStepRepository, "executor"))
+                .outputKey("executor_feedback")
+                .build();
+    }
+
+    /** 从 agent_step 汇总 token、步数等指标回填 diagnosis_session */
+    private void backfillSessionMetrics(DiagnosisSession session) {
+        try {
+            List<com.superbiz.agent.domain.entity.AgentStep> steps =
+                agentStepRepository.findBySessionIdOrderByStepIndex(session.getSessionId());
+
+            if (steps.isEmpty()) return;
+
+            int totalTokens = 0;
+            int stepCount = 0;
+            int toolCallCount = 0;
+            for (var s : steps) {
+                stepCount++;
+                if (s.getTokenCount() != null) totalTokens += s.getTokenCount();
+                if (Boolean.TRUE.equals(s.getHasToolCall())) toolCallCount++;
+            }
+            session.setTotalTokenCount(totalTokens);
+            session.setStepCount(stepCount);
+            session.setToolCallCount(toolCallCount);
+        } catch (Exception e) {
+            logger.warn("回填会话指标失败: sessionId={}", session.getSessionId(), e);
+        }
    }
 }
@@ -1,8 +1,11 @@
 package com.superbiz.agent.tool;

+import com.superbiz.agent.domain.entity.ToolInvocation;
 import com.superbiz.agent.dto.*;
+import com.superbiz.agent.repository.ToolInvocationRepository;
 import com.superbiz.agent.service.KnowledgeIndexService;
 import com.superbiz.agent.service.VectorSearchService;
+import com.superbiz.agent.util.SessionContextHolder;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.ai.tool.annotation.Tool;
 import org.springframework.beans.factory.annotation.Autowired;
@@ -25,6 +28,9 @@ public class LookupKnowledgeTool {
    @Autowired
    private VectorSearchService vectorSearchService;

+    @Autowired
+    private ToolInvocationRepository toolInvocationRepository;
+
    /**
     * 查询知识库文档
     *
@@ -134,9 +140,120 @@ public class LookupKnowledgeTool {

        log.info("========================================");

+        // 记录 tool_invocation（持久化检索明细）
+        saveToolInvocation(query, l0Matches, l1Results, highConfidence, startTime, result);
+
        return result;
    }

+    /**
+     * 保存工具调用明细到 tool_invocation 表
+     */
+    private void saveToolInvocation(String query, List<KnowledgeEntry> l0Matches,
+                                    List<VectorSearchService.SearchResult> l1Results,
+                                    boolean highConfidence, long startTime, LookupResult result) {
+        try {
+            String sessionId = SessionContextHolder.getSessionId();
+            if (sessionId == null) return; // 非会话上下文不记录
+
+            boolean hasL0 = l0Matches != null && !l0Matches.isEmpty();
+            boolean hasL1 = l1Results != null && !l1Results.isEmpty();
+            long duration = System.currentTimeMillis() - startTime;
+
+            String layer;
+            String outputPreview = null;
+            int outputLength = 0;
+            int l0Count = 0;
+            int l1Count = 0;
+            boolean truncated = false;
+
+            if (hasL0 && !highConfidence) {
+                layer = "L0+L1";
+                l0Count = l0Matches.size();
+                l1Count = l1Results.size();
+            } else if (hasL0) {
+                layer = "L0";
+                l0Count = l0Matches.size();
+            } else if (hasL1) {
+                layer = "L1";
+                l1Count = l1Results.size();
+            } else {
+                layer = null;
+            }
+
+            // 拼接 output_preview（前500字符）
+            if (result != null && result.getPrimary() != null && result.getPrimary().getContent() != null) {
+                String content = result.getPrimary().getContent();
+                outputLength = content.length();
+                if (content.length() > 500) {
+                    outputPreview = content.substring(0, 500) + "...";
+                    truncated = true;
+                } else {
+                    outputPreview = content;
+                }
+            } else if (l1Results != null && !l1Results.isEmpty() && l1Results.get(0).getContent() != null) {
+                String content = l1Results.get(0).getContent();
+                outputLength = content.length();
+                if (content.length() > 500) {
+                    outputPreview = content.substring(0, 500) + "...";
+                    truncated = true;
+                } else {
+                    outputPreview = content;
+                }
+            }
+
+            // 构建检索明细 JSON
+            StringBuilder details = new StringBuilder("{");
+            if (hasL0) {
+                details.append("\"l0_titles\":[");
+                for (int i = 0; i < Math.min(3, l0Matches.size()); i++) {
+                    if (i > 0) details.append(",");
+                    details.append("\"").append(escapeJson(l0Matches.get(i).getTitle())).append("\"");
+                }
+                details.append("]");
+            }
+            if (hasL1) {
+                if (hasL0) details.append(",");
+                details.append("\"l1_scores\":[");
+                for (int i = 0; i < Math.min(3, l1Results.size()); i++) {
+                    if (i > 0) details.append(",");
+                    details.append(l1Results.get(i).getScore());
+                }
+                details.append("]");
+            }
+            details.append("}");
+
+            ToolInvocation inv = ToolInvocation.builder()
+                    .sessionId(sessionId)
+                    .toolName("lookup_knowledge")
+                    .inputParams("{\"query\":\"" + escapeJson(query) + "\"}")
+                    .outputPreview(outputPreview)
+                    .outputLength(outputLength)
+                    .retrievalLayer(layer)
+                    .l0MatchCount(hasL0 ? l0Count : null)
+                    .l1MatchCount(hasL1 ? l1Count : null)
+                    .isTruncated(truncated)
+                    .retrievalDetails(details.toString())
+                    .durationMs((int) duration)
+                    .success(true)
+                    .build();
+
+            toolInvocationRepository.save(inv);
+            log.debug("tool_invocation 已保存: sessionId={}, layer={}, duration={}ms", sessionId, layer, duration);
+        } catch (Exception e) {
+            log.error("保存 tool_invocation 失败", e);
+        }
+    }
+
+    private String escapeJson(String s) {
+        if (s == null) return "";
+        return s.replace("\\", "\\\\")
+                .replace("\"", "\\\"")
+                .replace("\n", "\\n")
+                .replace("\r", "\\r")
+                .replace("\t", "\\t");
+    }
+
    /**
     * 组装查询结果
     *
@@ -0,0 +1,44 @@
+package com.superbiz.agent.util;
+
+import java.util.List;
+
+/**
+ * 问题复杂度判断
+ * 用于决定使用单 Agent 还是多 Agent（Planner + Executor）处理
+ */
+public class QuestionComplexity {
+
+    /** 复杂问题关键词 — 需要多步分析、排查、根因定位 */
+    private static final List<String> COMPLEX_KEYWORDS = List.of(
+        "排查", "分析", "为什么", "根因", "调查", "对比", "影响范围",
+        "原因", "故障", "告警", "诊断", "链路", "流程", "步骤",
+        "root cause", "troubleshoot", "investigate"
+    );
+
+    /** 极简问题关键词 — 快速回答，无需多 Agent */
+    private static final List<String> SIMPLE_KEYWORDS = List.of(
+        "是什么", "查一下", "什么是", "时间", "天气", "定义",
+        "查", "找", "what is", "define", "time"
+    );
+
+    /**
+     * 判断是否为复杂问题
+     */
+    public static boolean isComplex(String question) {
+        if (question == null || question.isBlank()) return false;
+        String q = question.toLowerCase();
+
+        // 复杂关键词匹配 → 多 Agent
+        for (String kw : COMPLEX_KEYWORDS) {
+            if (q.contains(kw)) return true;
+        }
+
+        // 简单关键词匹配 → 单 Agent
+        for (String kw : SIMPLE_KEYWORDS) {
+            if (q.contains(kw)) return false;
+        }
+
+        // 默认：长问题（>30 字）视为复杂，短问题视为简单
+        return question.length() > 30;
+    }
+}
@@ -0,0 +1,39 @@
+package com.superbiz.agent.util;
+
+/**
+ * 会话上下文持有者（基于 ThreadLocal）
+ * <p>
+ * 用于在执行链路中传递 sessionId 和 agentName，覆盖 AgentLoggingHook 和
+ * LookupKnowledgeTool 等无法直接通过 RunnableConfig 获取上下文的组件。
+ * <p>
+ * 使用规范：
+ * 1. 调用方（ChatService/AiOpsService）在 Agent 执行前调用 setSessionId() 和 setAgentName()
+ * 2. AgentLoggingHook 和工具类通过 getSessionId() / getAgentName() 读取
+ * 3. 必须在 finally 块中调用 clear()，防止内存泄漏和线程污染
+ */
+public class SessionContextHolder {
+
+    private static final ThreadLocal<String> SESSION_ID = new ThreadLocal<>();
+    private static final ThreadLocal<String> AGENT_NAME = new ThreadLocal<>();
+
+    public static void setSessionId(String sessionId) {
+        SESSION_ID.set(sessionId);
+    }
+
+    public static String getSessionId() {
+        return SESSION_ID.get();
+    }
+
+    public static void setAgentName(String agentName) {
+        AGENT_NAME.set(agentName);
+    }
+
+    public static String getAgentName() {
+        return AGENT_NAME.get();
+    }
+
+    public static void clear() {
+        SESSION_ID.remove();
+        AGENT_NAME.remove();
+    }
+}
@@ -0,0 +1,75 @@
+-- V005: 创建会话存储体系（diagnosis_session + agent_step + tool_invocation）
+-- 设计文档：openspec/changes/session-storage/design.md
+
+CREATE TABLE diagnosis_session (
+    id BIGINT PRIMARY KEY AUTO_INCREMENT,
+    session_id VARCHAR(64) UNIQUE NOT NULL COMMENT '会话唯一 ID',
+
+    query TEXT NOT NULL COMMENT '用户原始问题',
+    status VARCHAR(16) DEFAULT 'PENDING' COMMENT 'PENDING/RUNNING/SUCCESS/FAILED',
+    agent_flow VARCHAR(32) COMMENT 'CHAT / AI_OPS',
+
+    total_duration_ms INT COMMENT '总耗时（毫秒）',
+    total_token_count INT COMMENT '总 Token 消耗',
+    step_count INT COMMENT 'Agent 步数',
+    tool_call_count INT COMMENT '工具调用次数',
+
+    self_evaluation JSON COMMENT '自评估信号：{"confidence":0-100,"reasoning":"..."}',
+    feedback VARCHAR(16) COMMENT '用户反馈：useful/not_useful/null',
+
+    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
+
+    INDEX idx_created_at (created_at),
+    INDEX idx_status (status),
+    INDEX idx_agent_flow (agent_flow)
+) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='诊断会话表';
+
+CREATE TABLE agent_step (
+    id BIGINT PRIMARY KEY AUTO_INCREMENT,
+    session_id VARCHAR(64) NOT NULL COMMENT '关联 diagnosis_session',
+
+    step_index INT NOT NULL COMMENT '当前 Agent 的第几步（从0开始）',
+    agent_name VARCHAR(32) NOT NULL COMMENT 'intelligent_assistant/planner/executor',
+
+    model_input JSON COMMENT '模型输入摘要',
+    model_output JSON COMMENT '模型输出摘要（含工具调用决策）',
+    thought TEXT COMMENT 'Agent 思考过程',
+    has_tool_call BOOLEAN DEFAULT FALSE COMMENT '本轮是否调用了工具',
+
+    duration_ms INT COMMENT '本轮耗时',
+    token_count INT COMMENT '本轮 Token 消耗',
+
+    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+
+    INDEX idx_session_step (session_id, step_index),
+    INDEX idx_agent_name (agent_name)
+) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='Agent 决策步骤表';
+
+CREATE TABLE tool_invocation (
+    id BIGINT PRIMARY KEY AUTO_INCREMENT,
+    session_id VARCHAR(64) NOT NULL COMMENT '关联 diagnosis_session',
+    step_id BIGINT COMMENT '关联 agent_step.id（可为空，不强制外键）',
+
+    tool_name VARCHAR(64) NOT NULL COMMENT 'lookup_knowledge/queryPrometheusAlerts/等',
+
+    input_params JSON NOT NULL COMMENT '工具入参',
+    output_preview TEXT COMMENT '输出前500字符',
+    output_length INT COMMENT '输出总字符数',
+
+    retrieval_layer VARCHAR(8) COMMENT 'L0/L1/L0+L1',
+    l0_match_count INT COMMENT 'L0 匹配数',
+    l1_match_count INT COMMENT 'L1 匹配数',
+    is_truncated BOOLEAN DEFAULT FALSE COMMENT '内容是否被截断',
+    retrieval_details JSON COMMENT '检索明细：{l0_titles:[], l1_scores:[]}',
+
+    duration_ms INT COMMENT '工具执行耗时',
+    success BOOLEAN DEFAULT TRUE COMMENT '是否成功',
+    error_message TEXT COMMENT '失败原因',
+
+    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+
+    INDEX idx_session_id (session_id),
+    INDEX idx_tool_name (tool_name),
+    INDEX idx_retrieval_layer (retrieval_layer)
+) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='工具调用明细表';
@@ -0,0 +1,6 @@
+-- V006: 将 agent_step 的 model_input / model_output 从 JSON 改为 TEXT
+-- 原因：buildModelInputSummary() 输出的是纯文本摘要，不是合法 JSON
+
+ALTER TABLE agent_step
+    MODIFY COLUMN model_input TEXT COMMENT '模型输入摘要',
+    MODIFY COLUMN model_output TEXT COMMENT '模型输出摘要（含工具调用决策）';
@@ -0,0 +1,2 @@
+-- V007: 删除旧的 diagnosis_record 表（已被 diagnosis_session + agent_step + tool_invocation 替代）
+DROP TABLE IF EXISTS diagnosis_record;
@@ -0,0 +1,12 @@
+你是任务执行器。执行 Planner 分配给你的具体步骤，并及时反馈结果。
+
+## 职责
+- 按步骤执行具体的查询任务
+- 使用知识库查询、日志查询等工具获取信息
+- 将执行结果汇总，给出完整的最终答案
+
+## 规则
+- 按顺序执行，不可跳过步骤
+- 所有需要外部信息的地方，都必须调用对应的工具
+- 不要凭记忆回答，必须基于工具返回的真实数据
+- 执行完成后，综合所有结果给出完整的答案
@@ -0,0 +1,20 @@
+你是智能任务规划器。分析用户的问题，拆解为具体的执行步骤。
+
+## 职责
+- 分析用户问题，拆解为可执行的步骤列表
+- **你不能调用任何工具**，你的职责是制定计划，不是执行
+- 输出 JSON 格式的计划，不输出其他内容
+
+## 输出格式
+
+```json
+{
+  "plan": ["步骤1描述", "步骤2描述", "步骤3描述"],
+  "reasoning": "规划思路说明"
+}
+```
+
+## 规则
+- 每个步骤应该是一个可以独立执行的任务
+- 步骤要具体可操作，不要模糊
+- 如果问题需要查知识库，明确在步骤中说明要查什么