diff --git a/internal/agent/memory_compressor.go b/internal/agent/memory_compressor.go
deleted file mode 100644
index c830d1a9..00000000
--- a/internal/agent/memory_compressor.go
+++ /dev/null
@@ -1,491 +0,0 @@
-package agent
-
-import (
-	"context"
-	"errors"
-	"fmt"
-	"net/http"
-	"strings"
-	"sync"
-	"time"
-
-	"cyberstrike-ai/internal/config"
-	"cyberstrike-ai/internal/openai"
-
-	"github.com/pkoukk/tiktoken-go"
-	"go.uber.org/zap"
-)
-
-const (
-	// DefaultMinRecentMessage 压缩历史消息时保留的最近消息数量，确保最近的对话上下文不被压缩
-	DefaultMinRecentMessage = 5
-	// defaultChunkSize 压缩历史消息时每次处理的消息块大小，将旧消息分成多个块进行摘要
-	defaultChunkSize = 10
-	// defaultMaxImages 压缩时最多保留的图片数量，超过此数量的图片会被移除以节省上下文空间
-	defaultMaxImages = 3
-	// defaultSummaryTimeout 生成消息摘要时的超时时间
-	defaultSummaryTimeout = 10 * time.Minute
-
-	summaryPromptTemplate = `你是一名负责为安全代理执行上下文压缩的助手，任务是在保持所有关键渗透信息完整的前提下压缩扫描数据。
-
-必须保留的关键信息：
-- 已发现的漏洞与潜在攻击路径
-- 扫描结果与工具输出（可压缩，但需保留核心发现）
-- 获取到的访问凭证、令牌或认证细节
-- 系统架构洞察与潜在薄弱点
-- 当前评估进展
-- 失败尝试与死路（避免重复劳动）
-- 关于测试策略的所有决策记录
-
-压缩指南：
-- 保留精确技术细节（URL、路径、参数、Payload 等）
-- 将冗长的工具输出压缩成概述，但保留关键发现
-- 记录版本号与识别出的技术/组件信息
-- 保留可能暗示漏洞的原始报错
-- 将重复或相似发现整合成一条带有共性说明的结论
-
-请牢记：另一位安全代理会依赖这份摘要继续测试，他必须在不损失任何作战上下文的情况下无缝接手。
-
-需要压缩的对话片段：
-%s
-
-请给出技术精准且简明扼要的摘要，覆盖全部与安全评估相关的上下文。`
-)
-
-// MemoryCompressor 负责在调用LLM前压缩历史上下文，以避免Token爆炸。
-type MemoryCompressor struct {
-	maxTotalTokens   int
-	minRecentMessage int
-	maxImages        int
-	chunkSize        int
-	summaryModel     string
-	timeout          time.Duration
-
-	tokenCounter     TokenCounter
-	completionClient CompletionClient
-	logger           *zap.Logger
-}
-
-// MemoryCompressorConfig 用于初始化 MemoryCompressor。
-type MemoryCompressorConfig struct {
-	MaxTotalTokens   int
-	MinRecentMessage int
-	MaxImages        int
-	ChunkSize        int
-	SummaryModel     string
-	Timeout          time.Duration
-	TokenCounter     TokenCounter
-	CompletionClient CompletionClient
-	Logger           *zap.Logger
-
-	// 当 CompletionClient 为空时，可以通过 OpenAIConfig + HTTPClient 构造默认的客户端。
-	OpenAIConfig *config.OpenAIConfig
-	HTTPClient   *http.Client
-}
-
-// NewMemoryCompressor 创建新的 MemoryCompressor。
-func NewMemoryCompressor(cfg MemoryCompressorConfig) (*MemoryCompressor, error) {
-	if cfg.Logger == nil {
-		cfg.Logger = zap.NewNop()
-	}
-
-	// 如果没有显式配置 MaxTotalTokens，则后续逻辑会根据模型的最大上下文长度进行控制；
-	// 优先推荐在 config.yaml 的 openai.max_total_tokens 中统一配置。
-	if cfg.MinRecentMessage <= 0 {
-		cfg.MinRecentMessage = DefaultMinRecentMessage
-	}
-	if cfg.MaxImages <= 0 {
-		cfg.MaxImages = defaultMaxImages
-	}
-	if cfg.ChunkSize <= 0 {
-		cfg.ChunkSize = defaultChunkSize
-	}
-	if cfg.Timeout <= 0 {
-		cfg.Timeout = defaultSummaryTimeout
-	}
-	if cfg.SummaryModel == "" && cfg.OpenAIConfig != nil && cfg.OpenAIConfig.Model != "" {
-		cfg.SummaryModel = cfg.OpenAIConfig.Model
-	}
-	if cfg.SummaryModel == "" {
-		return nil, errors.New("summary model is required (either SummaryModel or OpenAIConfig.Model must be set)")
-	}
-	if cfg.TokenCounter == nil {
-		cfg.TokenCounter = NewTikTokenCounter()
-	}
-
-	if cfg.CompletionClient == nil {
-		if cfg.OpenAIConfig == nil {
-			return nil, errors.New("memory compressor requires either CompletionClient or OpenAIConfig")
-		}
-		if cfg.HTTPClient == nil {
-			cfg.HTTPClient = &http.Client{
-				Timeout: 5 * time.Minute,
-			}
-		}
-		cfg.CompletionClient = NewOpenAICompletionClient(cfg.OpenAIConfig, cfg.HTTPClient, cfg.Logger)
-	}
-
-	return &MemoryCompressor{
-		maxTotalTokens:   cfg.MaxTotalTokens,
-		minRecentMessage: cfg.MinRecentMessage,
-		maxImages:        cfg.MaxImages,
-		chunkSize:        cfg.ChunkSize,
-		summaryModel:     cfg.SummaryModel,
-		timeout:          cfg.Timeout,
-		tokenCounter:     cfg.TokenCounter,
-		completionClient: cfg.CompletionClient,
-		logger:           cfg.Logger,
-	}, nil
-}
-
-// UpdateConfig 更新OpenAI配置（用于动态更新模型配置）
-func (mc *MemoryCompressor) UpdateConfig(cfg *config.OpenAIConfig) {
-	if cfg == nil {
-		return
-	}
-
-	// 更新summaryModel字段
-	if cfg.Model != "" {
-		mc.summaryModel = cfg.Model
-	}
-
-	// 更新completionClient中的配置（如果是OpenAICompletionClient）
-	if openAIClient, ok := mc.completionClient.(*OpenAICompletionClient); ok {
-		openAIClient.UpdateConfig(cfg)
-		mc.logger.Info("MemoryCompressor配置已更新",
-			zap.String("model", cfg.Model),
-		)
-	}
-}
-
-// CompressHistory 根据 Token 限制压缩历史消息。reservedTokens 为预留给 tools 等非消息内容的 token 数，压缩时使用 (maxTotalTokens - reservedTokens) 作为消息上限。
-func (mc *MemoryCompressor) CompressHistory(ctx context.Context, messages []ChatMessage, reservedTokens int) ([]ChatMessage, bool, error) {
-	if len(messages) == 0 {
-		return messages, false, nil
-	}
-
-	mc.handleImages(messages)
-
-	systemMsgs, regularMsgs := mc.splitMessages(messages)
-	if len(regularMsgs) <= mc.minRecentMessage {
-		return messages, false, nil
-	}
-
-	effectiveMax := mc.maxTotalTokens
-	if reservedTokens > 0 && reservedTokens < mc.maxTotalTokens {
-		effectiveMax = mc.maxTotalTokens - reservedTokens
-	}
-
-	totalTokens := mc.countTotalTokens(systemMsgs, regularMsgs)
-	if totalTokens <= int(float64(effectiveMax)*0.9) {
-		return messages, false, nil
-	}
-
-	recentStart := len(regularMsgs) - mc.minRecentMessage
-	recentStart = mc.adjustRecentStartForToolCalls(regularMsgs, recentStart)
-	oldMsgs := regularMsgs[:recentStart]
-	recentMsgs := regularMsgs[recentStart:]
-
-	mc.logger.Info("memory compression triggered",
-		zap.Int("total_tokens", totalTokens),
-		zap.Int("max_total_tokens", mc.maxTotalTokens),
-		zap.Int("reserved_tokens", reservedTokens),
-		zap.Int("effective_max", effectiveMax),
-		zap.Int("system_messages", len(systemMsgs)),
-		zap.Int("regular_messages", len(regularMsgs)),
-		zap.Int("old_messages", len(oldMsgs)),
-		zap.Int("recent_messages", len(recentMsgs)))
-
-	var compressed []ChatMessage
-	for i := 0; i < len(oldMsgs); i += mc.chunkSize {
-		end := i + mc.chunkSize
-		if end > len(oldMsgs) {
-			end = len(oldMsgs)
-		}
-		chunk := oldMsgs[i:end]
-		if len(chunk) == 0 {
-			continue
-		}
-		summary, err := mc.summarizeChunk(ctx, chunk)
-		if err != nil {
-			mc.logger.Warn("chunk summary failed, fallback to raw chunk",
-				zap.Error(err),
-				zap.Int("start", i),
-				zap.Int("end", end))
-			compressed = append(compressed, chunk...)
-			continue
-		}
-		compressed = append(compressed, summary)
-	}
-
-	finalMessages := make([]ChatMessage, 0, len(systemMsgs)+len(compressed)+len(recentMsgs))
-	finalMessages = append(finalMessages, systemMsgs...)
-	finalMessages = append(finalMessages, compressed...)
-	finalMessages = append(finalMessages, recentMsgs...)
-
-	return finalMessages, true, nil
-}
-
-func (mc *MemoryCompressor) handleImages(messages []ChatMessage) {
-	if mc.maxImages <= 0 {
-		return
-	}
-	count := 0
-	for i := len(messages) - 1; i >= 0; i-- {
-		content := messages[i].Content
-		if !strings.Contains(content, "[IMAGE]") {
-			continue
-		}
-		count++
-		if count > mc.maxImages {
-			messages[i].Content = "[Previously attached image removed to preserve context]"
-		}
-	}
-}
-
-func (mc *MemoryCompressor) splitMessages(messages []ChatMessage) (systemMsgs, regularMsgs []ChatMessage) {
-	for _, msg := range messages {
-		if strings.EqualFold(msg.Role, "system") {
-			systemMsgs = append(systemMsgs, msg)
-		} else {
-			regularMsgs = append(regularMsgs, msg)
-		}
-	}
-	return
-}
-
-func (mc *MemoryCompressor) countTotalTokens(systemMsgs, regularMsgs []ChatMessage) int {
-	total := 0
-	for _, msg := range systemMsgs {
-		total += mc.countTokens(msg.Content)
-	}
-	for _, msg := range regularMsgs {
-		total += mc.countTokens(msg.Content)
-	}
-	return total
-}
-
-// getModelName 获取当前使用的模型名称（优先从completionClient获取最新配置）
-func (mc *MemoryCompressor) getModelName() string {
-	// 如果completionClient是OpenAICompletionClient，从它获取最新的模型名称
-	if openAIClient, ok := mc.completionClient.(*OpenAICompletionClient); ok {
-		if openAIClient.config != nil && openAIClient.config.Model != "" {
-			return openAIClient.config.Model
-		}
-	}
-	// 否则使用保存的summaryModel
-	return mc.summaryModel
-}
-
-func (mc *MemoryCompressor) countTokens(text string) int {
-	if mc.tokenCounter == nil {
-		return len(text) / 4
-	}
-	modelName := mc.getModelName()
-	count, err := mc.tokenCounter.Count(modelName, text)
-	if err != nil {
-		return len(text) / 4
-	}
-	return count
-}
-
-// CountTextTokens 对外暴露的文本 Token 计数，用于统计 tools 等非消息内容的 token（如 agent 侧序列化 tools 后计数）。
-func (mc *MemoryCompressor) CountTextTokens(text string) int {
-	return mc.countTokens(text)
-}
-
-// totalTokensFor provides token statistics without mutating the message list.
-func (mc *MemoryCompressor) totalTokensFor(messages []ChatMessage) (totalTokens int, systemCount int, regularCount int) {
-	if len(messages) == 0 {
-		return 0, 0, 0
-	}
-	systemMsgs, regularMsgs := mc.splitMessages(messages)
-	return mc.countTotalTokens(systemMsgs, regularMsgs), len(systemMsgs), len(regularMsgs)
-}
-
-func (mc *MemoryCompressor) summarizeChunk(ctx context.Context, chunk []ChatMessage) (ChatMessage, error) {
-	if len(chunk) == 0 {
-		return ChatMessage{}, errors.New("chunk is empty")
-	}
-	formatted := make([]string, 0, len(chunk))
-	for _, msg := range chunk {
-		formatted = append(formatted, fmt.Sprintf("%s: %s", msg.Role, mc.extractMessageText(msg)))
-	}
-	conversation := strings.Join(formatted, "\n")
-	prompt := fmt.Sprintf(summaryPromptTemplate, conversation)
-
-	// 使用动态获取的模型名称，而不是保存的summaryModel
-	modelName := mc.getModelName()
-	summary, err := mc.completionClient.Complete(ctx, modelName, prompt, mc.timeout)
-	if err != nil {
-		return ChatMessage{}, err
-	}
-	summary = strings.TrimSpace(summary)
-	if summary == "" {
-		return chunk[0], nil
-	}
-
-	return ChatMessage{
-		Role:    "assistant",
-		Content: fmt.Sprintf("<context_summary message_count='%d'>%s</context_summary>", len(chunk), summary),
-	}, nil
-}
-
-func (mc *MemoryCompressor) extractMessageText(msg ChatMessage) string {
-	return msg.Content
-}
-
-func (mc *MemoryCompressor) adjustRecentStartForToolCalls(msgs []ChatMessage, recentStart int) int {
-	if recentStart <= 0 || recentStart >= len(msgs) {
-		return recentStart
-	}
-
-	adjusted := recentStart
-	for adjusted > 0 && strings.EqualFold(msgs[adjusted].Role, "tool") {
-		adjusted--
-	}
-
-	if adjusted != recentStart {
-		mc.logger.Debug("adjusted recent window to keep tool call context",
-			zap.Int("original_recent_start", recentStart),
-			zap.Int("adjusted_recent_start", adjusted),
-		)
-	}
-
-	return adjusted
-}
-
-// TokenCounter 用于计算文本Token数量。
-type TokenCounter interface {
-	Count(model, text string) (int, error)
-}
-
-// TikTokenCounter 基于 tiktoken 的 Token 统计器。
-type TikTokenCounter struct {
-	mu               sync.RWMutex
-	cache            map[string]*tiktoken.Tiktoken
-	fallbackEncoding *tiktoken.Tiktoken
-}
-
-// NewTikTokenCounter 创建新的 TikTokenCounter。
-func NewTikTokenCounter() *TikTokenCounter {
-	return &TikTokenCounter{
-		cache: make(map[string]*tiktoken.Tiktoken),
-	}
-}
-
-// Count 实现 TokenCounter 接口。
-func (tc *TikTokenCounter) Count(model, text string) (int, error) {
-	enc, err := tc.encodingForModel(model)
-	if err != nil {
-		return len(text) / 4, err
-	}
-	tokens := enc.Encode(text, nil, nil)
-	return len(tokens), nil
-}
-
-func (tc *TikTokenCounter) encodingForModel(model string) (*tiktoken.Tiktoken, error) {
-	tc.mu.RLock()
-	if enc, ok := tc.cache[model]; ok {
-		tc.mu.RUnlock()
-		return enc, nil
-	}
-	tc.mu.RUnlock()
-
-	tc.mu.Lock()
-	defer tc.mu.Unlock()
-
-	if enc, ok := tc.cache[model]; ok {
-		return enc, nil
-	}
-
-	enc, err := tiktoken.EncodingForModel(model)
-	if err != nil {
-		if tc.fallbackEncoding == nil {
-			tc.fallbackEncoding, err = tiktoken.GetEncoding("cl100k_base")
-			if err != nil {
-				return nil, err
-			}
-		}
-		tc.cache[model] = tc.fallbackEncoding
-		return tc.fallbackEncoding, nil
-	}
-
-	tc.cache[model] = enc
-	return enc, nil
-}
-
-// CompletionClient 对话压缩时使用的补全接口。
-type CompletionClient interface {
-	Complete(ctx context.Context, model string, prompt string, timeout time.Duration) (string, error)
-}
-
-// OpenAICompletionClient 基于 OpenAI Chat Completion。
-type OpenAICompletionClient struct {
-	config *config.OpenAIConfig
-	client *openai.Client
-	logger *zap.Logger
-}
-
-// NewOpenAICompletionClient 创建 OpenAICompletionClient。
-func NewOpenAICompletionClient(cfg *config.OpenAIConfig, client *http.Client, logger *zap.Logger) *OpenAICompletionClient {
-	if logger == nil {
-		logger = zap.NewNop()
-	}
-	return &OpenAICompletionClient{
-		config: cfg,
-		client: openai.NewClient(cfg, client, logger),
-		logger: logger,
-	}
-}
-
-// UpdateConfig 更新底层配置。
-func (c *OpenAICompletionClient) UpdateConfig(cfg *config.OpenAIConfig) {
-	c.config = cfg
-	if c.client != nil {
-		c.client.UpdateConfig(cfg)
-	}
-}
-
-// Complete 调用OpenAI获取摘要。
-func (c *OpenAICompletionClient) Complete(ctx context.Context, model string, prompt string, timeout time.Duration) (string, error) {
-	if c.config == nil {
-		return "", errors.New("openai config is required")
-	}
-	if model == "" {
-		return "", errors.New("model name is required")
-	}
-
-	reqBody := OpenAIRequest{
-		Model: model,
-		Messages: []ChatMessage{
-			{Role: "user", Content: prompt},
-		},
-	}
-
-	requestCtx := ctx
-	var cancel context.CancelFunc
-	if timeout > 0 {
-		requestCtx, cancel = context.WithTimeout(ctx, timeout)
-		defer cancel()
-	}
-
-	var completion OpenAIResponse
-	if c.client == nil {
-		return "", errors.New("openai completion client not initialized")
-	}
-	if err := c.client.ChatCompletion(requestCtx, reqBody, &completion); err != nil {
-		if apiErr, ok := err.(*openai.APIError); ok {
-			return "", fmt.Errorf("openai completion failed, status: %d, body: %s", apiErr.StatusCode, apiErr.Body)
-		}
-		return "", err
-	}
-	if completion.Error != nil {
-		return "", errors.New(completion.Error.Message)
-	}
-
-	if len(completion.Choices) == 0 || completion.Choices[0].Message.Content == "" {
-		return "", errors.New("empty completion response")
-	}
-	return completion.Choices[0].Message.Content, nil
-}