aiops/backend/pkg/agentcli/core/agent.go

// Package core 实现 Agent CLI 的 ReAct 核心引擎。 // Logan.Li
// 设计参考 opsxcli internal/agent/core，剥离 TUI（Bubble Tea），改用 EventCallback 推送事件流
// （由 controller 序列化为 WebSocket 消息推给前端 + 持久化到 DB）。
// 核心算法保留：16 轮 ReAct 主循环、自适应检查点、循环检测（同参>3 次拒绝）、连续失败追踪、Evolver 集成。
package core

import (
	"context"
	"encoding/json"
	"fmt"
	"strings"
	"sync"
	"time"

	"ezops/pkg/agentcli/evolver"
	"ezops/pkg/agentcli/llm"
	"ezops/pkg/agentcli/prompt"
	"ezops/pkg/agentcli/safety"
	"ezops/pkg/agentcli/tools"
)

// EventType 事件类型（驱动前端渲染与 DB 持久化） // Logan.Li
type EventType string

const (
	EventThought    EventType = "thought"     // AI 思考/推理文本
	EventToolCall   EventType = "tool_call"   // 即将执行的工具（名/参数/风险）
	EventToolResult EventType = "tool_result" // 工具执行结果（输出/成败/耗时）
	EventProgress   EventType = "progress"    // 轮次进度 + token
	EventDone       EventType = "done"        // 任务完成（最终答案）
	EventError      EventType = "error"       // 错误（含达到最大轮次）
)

// Event 引擎产生的事件（controller 序列化后通过 WebSocket 推送） // Logan.Li
type Event struct {
	Type     EventType              `json:"type"`
	Round    int                    `json:"round,omitempty"`
	Thought  string                 `json:"thought,omitempty"`
	ToolName string                 `json:"tool_name,omitempty"`
	ToolArgs map[string]interface{} `json:"tool_args,omitempty"`
	Risk     string                 `json:"risk,omitempty"`
	Output   string                 `json:"output,omitempty"`
	Success  bool                   `json:"success,omitempty"`
	Duration string                 `json:"duration,omitempty"`
	Tokens   int                    `json:"tokens,omitempty"`
	Answer   string                 `json:"answer,omitempty"`
	Message  string                 `json:"message,omitempty"`
}

// EventCallback 事件回调（controller 注入） // Logan.Li
type EventCallback func(Event)

// Config Agent 运行配置 // Logan.Li
type Config struct {
	MaxIterations    int
	Temperature      float64
	ToolTimeout      time.Duration
	MaxTokens        int
	SafetyMode       safety.Mode
	OutputMaxLength  int
	MaxContextTokens int
	MemoryDir        string // Evolver 记忆目录
}

// DefaultConfig 默认配置 // Logan.Li
func DefaultConfig() *Config {
	return &Config{
		MaxIterations:    16,
		Temperature:      0.3,
		ToolTimeout:      120 * time.Second,
		MaxTokens:        4096,
		SafetyMode:       safety.ModeBalanced,
		OutputMaxLength:  10000,
		MaxContextTokens: 8000,
		MemoryDir:        "data/agentcli",
	}
}

// Agent ReAct 核心引擎 // Logan.Li
type Agent struct {
	llm         llm.Client
	registry    *tools.Registry
	config      *Config
	safetyCtl   *safety.Controller
	evolver     *evolver.EvolverEngine
	callback    EventCallback
	totalTokens int
	mu          sync.Mutex
}

// NewAgent 创建引擎。evolver 为 nil 时降级（无自学习）。 // Logan.Li
func NewAgent(client llm.Client, registry *tools.Registry, cfg *Config, sc *safety.Controller, ev *evolver.EvolverEngine) *Agent {
	if cfg == nil {
		cfg = DefaultConfig()
	}
	if sc == nil {
		sc = safety.NewController(cfg.SafetyMode)
	}
	if cfg.MaxContextTokens <= 0 {
		cfg.MaxContextTokens = 8000
	}
	return &Agent{llm: client, registry: registry, config: cfg, safetyCtl: sc, evolver: ev}
}

// SetCallback 注入事件回调 // Logan.Li
func (a *Agent) SetCallback(cb EventCallback) { a.callback = cb }

// emit 安全触发回调 // Logan.Li
func (a *Agent) emit(e Event) {
	if a.callback != nil {
		a.callback(e)
	}
}

// Run 执行单次任务（ReAct 循环 + Evolver 集成） // Logan.Li
func (a *Agent) Run(ctx context.Context, query, serverInfo string) (*tools.Result, error) {
	a.totalTokens = 0
	start := time.Now()

	// Evolver PREDICT：获取历史经验注入 system prompt
	evolveCtx := ""
	if a.evolver != nil {
		evolveCtx = a.evolver.GetContextForPrompt(query)
	}

	// 工具描述
	toolDescs := a.buildToolDescriptions()
	systemPrompt := prompt.BuildSystemPrompt(serverInfo, toolDescs, evolveCtx)

	messages := []llm.Message{
		{Role: "system", Content: systemPrompt},
		{Role: "user", Content: query},
	}
	llmTools := a.registry.ToLLMTools()

	toolCallHistory := make(map[string]int) // 循环检测
	var toolRecords []evolver.ToolCallRecord
	var consecutiveFailures int
	var totalToolCalls int

	for iter := 0; iter < a.config.MaxIterations; iter++ {
		// 检查中断
		if err := ctx.Err(); err != nil {
			return nil, err
		}

		a.emit(Event{Type: EventProgress, Round: iter + 1})

		// 自适应检查点
		if hint := a.checkAdaptiveCheckpoint(iter, toolCallHistory, consecutiveFailures, totalToolCalls); hint != "" {
			messages = append(messages, llm.Message{Role: "user", Content: hint})
		}

		// 裁剪历史
		trimmed := a.trimMessages(messages)

		// 调用 LLM
		resp, err := a.llm.Complete(ctx, &llm.CompletionRequest{
			Messages:    trimmed,
			Tools:       llmTools,
			Temperature: a.config.Temperature,
			MaxTokens:   a.config.MaxTokens,
		})
		if err != nil {
			a.emit(Event{Type: EventError, Message: "LLM 调用失败: " + err.Error()})
			return nil, fmt.Errorf("LLM调用失败: %w", err)
		}

		a.totalTokens += resp.Usage.TotalTokens
		a.emit(Event{Type: EventProgress, Tokens: a.totalTokens})

		// 推送思考文本
		if strings.TrimSpace(resp.Message.Content) != "" {
			a.emit(Event{Type: EventThought, Round: iter + 1, Thought: resp.Message.Content})
		}
		messages = append(messages, resp.Message)

		// 无工具调用 → 最终答案
		if len(resp.Message.ToolCalls) == 0 {
			answer := resp.Message.Content
			a.emit(Event{Type: EventDone, Answer: answer, Tokens: a.totalTokens})
			a.triggerEvolve(ctx, query, serverInfo, answer, toolRecords, true, start)
			return &tools.Result{Success: true, Output: answer}, nil
		}

		// 处理工具调用
		observations, records, failInc := a.processToolCalls(ctx, resp.Message.ToolCalls, toolCallHistory, iter+1)
		toolRecords = append(toolRecords, records...)
		messages = append(messages, observations...)
		totalToolCalls += len(resp.Message.ToolCalls)
		consecutiveFailures = a.updateConsecutiveFailures(observations, consecutiveFailures) + failInc
	}

	// 达到最大轮次
	msg := fmt.Sprintf("达到最大迭代次数(%d)，任务未能完成。可能任务过于复杂或需要更多步骤。", a.config.MaxIterations)
	a.emit(Event{Type: EventError, Message: msg, Tokens: a.totalTokens})
	a.triggerEvolve(ctx, query, serverInfo, "", toolRecords, false, start)
	return nil, fmt.Errorf("%s", msg)
}

// processToolCalls 处理工具调用，返回 observation 消息 + 记录 // Logan.Li
func (a *Agent) processToolCalls(ctx context.Context, calls []llm.ToolCall, history map[string]int, round int) ([]llm.Message, []evolver.ToolCallRecord, int) {
	obs := make([]llm.Message, 0, len(calls))
	records := make([]evolver.ToolCallRecord, 0, len(calls))
	var failInc int

	for _, tc := range calls {
		key := tc.Function.Name + ":" + tc.Function.Arguments
		history[key]++

		// 循环检测
		if history[key] > 3 {
			obs = append(obs, llm.Message{Role: "tool", ToolCallID: tc.ID, Name: tc.Function.Name,
				Content: "错误: 检测到循环调用，相同参数已调用超过3次，请换一种方式"})
			continue
		}

		tool, err := a.registry.Get(tc.Function.Name)
		if err != nil {
			obs = append(obs, llm.Message{Role: "tool", ToolCallID: tc.ID, Name: tc.Function.Name,
				Content: fmt.Sprintf("错误: 工具 '%s' 未找到", tc.Function.Name)})
			continue
		}

		var args map[string]interface{}
		if err := json.Unmarshal([]byte(tc.Function.Arguments), &args); err != nil {
			obs = append(obs, llm.Message{Role: "tool", ToolCallID: tc.ID, Name: tc.Function.Name,
				Content: "错误: 参数解析失败: " + err.Error()})
			continue
		}

		risk := a.toolRisk(tool, args)
		a.emit(Event{Type: EventToolCall, Round: round, ToolName: tc.Function.Name, ToolArgs: args, Risk: risk.Display()})

		// 安全检查（高危触发 confirmFn，由 controller 异步确认）
		approved, err := a.safetyCtl.Check(tool, args)
		if err != nil {
			obs = append(obs, llm.Message{Role: "tool", ToolCallID: tc.ID, Name: tc.Function.Name,
				Content: "安全检查失败: " + err.Error()})
			continue
		}
		if !approved {
			obs = append(obs, llm.Message{Role: "tool", ToolCallID: tc.ID, Name: tc.Function.Name,
				Content: "操作已被用户拒绝执行"})
			a.emit(Event{Type: EventToolResult, Round: round, ToolName: tc.Function.Name,
				Output: "（用户拒绝执行）", Success: false})
			continue
		}

		// 执行
		toolStart := time.Now()
		toolCtx, cancel := context.WithTimeout(ctx, a.config.ToolTimeout)
		result, execErr := tool.Execute(toolCtx, args)
		cancel()
		dur := time.Since(toolStart)

		var output, errMsg string
		success := false
		if result != nil {
			output, errMsg, success = result.Output, result.Error, result.Success
		}
		if execErr != nil {
			errMsg = execErr.Error()
			success = false
		}

		// 输出截断
		if len(output) > a.config.OutputMaxLength {
			output = output[:a.config.OutputMaxLength] +
				fmt.Sprintf("\n[输出已截断，原始 %d 字符]", len(output))
		}

		a.emit(Event{Type: EventToolResult, Round: round, ToolName: tc.Function.Name,
			Output: output, Success: success, Duration: dur.Truncate(time.Millisecond).String()})

		records = append(records, evolver.ToolCallRecord{
			ToolName: tc.Function.Name, Args: args, Output: output,
			Duration: dur, Success: success, RiskLevel: int(risk),
		})

		// 构建 observation
		var content string
		switch {
		case execErr != nil:
			content = "执行错误: " + execErr.Error()
			failInc++
		case !success:
			content = fmt.Sprintf("执行失败: %s\n输出: %s", errMsg, output)
			failInc++
		default:
			content = output
		}
		obs = append(obs, llm.Message{Role: "tool", ToolCallID: tc.ID, Name: tc.Function.Name, Content: content})
	}
	return obs, records, failInc
}

// triggerEvolve 异步触发进化（带超时 + panic 恢复，绝不阻塞主循环） // Logan.Li
func (a *Agent) triggerEvolve(ctx context.Context, query, serverHost, answer string, records []evolver.ToolCallRecord, success bool, start time.Time) {
	if a.evolver == nil {
		return
	}
	exec := &evolver.TaskExecution{
		Query: query, ToolCalls: records, TotalSteps: len(records),
		TotalTokens: a.totalTokens, Duration: time.Since(start),
		Success: success, FinalAnswer: answer, Timestamp: time.Now(), ServerHost: serverHost,
	}
	go func() {
		defer func() { _ = recover() }()
		eCtx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
		defer cancel()
		_ = a.evolver.Evolve(eCtx, exec)
	}()
}

// buildToolDescriptions 工具描述文本 // Logan.Li
func (a *Agent) buildToolDescriptions() string {
	var sb strings.Builder
	for _, t := range a.registry.List() {
		sb.WriteString(fmt.Sprintf("- %s: %s\n", t.Name(), t.Description()))
	}
	return sb.String()
}

// toolRisk 取工具风险（动态优先） // Logan.Li
func (a *Agent) toolRisk(tool tools.Tool, args map[string]interface{}) tools.RiskLevel {
	if dt, ok := tool.(tools.DynamicRiskTool); ok {
		return dt.RiskLevelForArgs(args)
	}
	return tool.RiskLevel()
}

// checkAdaptiveCheckpoint 自适应检查点（iter 3/7/11 注入引导） // Logan.Li
func (a *Agent) checkAdaptiveCheckpoint(iter int, history map[string]int, consecFail, totalCalls int) string {
	switch iter {
	case 3:
		repeat := 0
		for _, c := range history {
			if c > 1 {
				repeat += c - 1
			}
		}
		if totalCalls > 0 && (float64(repeat)/float64(totalCalls) > 0.5 || consecFail >= 3) {
			return "【系统提示】检测到重复使用相同工具或连续失败，请尝试换一种思路或不同命令。"
		}
	case 7:
		return "【系统提示】已执行到中段，请总结进展。信息足够就直接给出最终答案，否则说明剩余步骤。"
	case 11:
		return fmt.Sprintf("【系统提示】仅剩 %d 步，请立即整理已有信息给出最终答案，不要再调用新工具。", a.config.MaxIterations-iter-1)
	}
	return ""
}

// updateConsecutiveFailures 连续失败计数 // Logan.Li
func (a *Agent) updateConsecutiveFailures(obs []llm.Message, current int) int {
	errs := 0
	for _, o := range obs {
		c := o.Content
		if strings.HasPrefix(c, "错误") || strings.HasPrefix(c, "失败") || strings.HasPrefix(c, "执行错误") || strings.HasPrefix(c, "执行失败") {
			errs++
		}
	}
	if errs == 0 {
		return 0
	}
	return current + errs
}

// trimMessages 裁剪历史（保留 system + 最近消息，token 不超 90%） // Logan.Li
func (a *Agent) trimMessages(msgs []llm.Message) []llm.Message {
	if len(msgs) <= 1 {
		return msgs
	}
	maxT := a.config.MaxContextTokens * 9 / 10
	sysT := estimateTokens(msgs[0].Content)
	kept := make([]llm.Message, 0, len(msgs))
	keptT := 0
	for i := len(msgs) - 1; i >= 1; i-- {
		t := estimateTokens(msgs[i].Content)
		if sysT+keptT+t > maxT {
			break
		}
		keptT += t
		kept = append([]llm.Message{msgs[i]}, kept...)
	}
	// 最少保留最近 4 条，避免上下文过短
	if len(kept) < 4 && len(msgs) > 4 {
		kept = msgs[len(msgs)-4:]
	}
	result := append([]llm.Message{msgs[0]}, kept...)
	return ensureToolMessageIntegrity(result)
}

// ensureToolMessageIntegrity 确保 tool 消息前面有对应 assistant(tool_calls) // Logan.Li
func ensureToolMessageIntegrity(msgs []llm.Message) []llm.Message {
	validIDs := make(map[string]bool)
	for _, m := range msgs {
		if m.Role == "assistant" {
			for _, tc := range m.ToolCalls {
				validIDs[tc.ID] = true
			}
		}
	}
	out := make([]llm.Message, 0, len(msgs))
	for i, m := range msgs {
		if m.Role == "tool" {
			if !validIDs[m.ToolCallID] || i == 0 || msgs[i-1].Role != "assistant" {
				continue
			}
			matched := false
			for _, tc := range msgs[i-1].ToolCalls {
				if tc.ID == m.ToolCallID {
					matched = true
					break
				}
			}
			if !matched {
				continue
			}
		}
		out = append(out, m)
	}
	return out
}

// estimateTokens 粗略 token 估算（中文约 1 字/token，英文约 1/4，折中 rune/3） // Logan.Li
func estimateTokens(s string) int {
	return len([]rune(s))/3 + 1
}

// Close 释放资源 // Logan.Li
func (a *Agent) Close() {
	if a.registry != nil {
		// Registry 无 Closer 实现（Web 工具无连接池），保留接口位
	}
}