feat(memory): add LLM-driven memory extraction from conversations

- memory/extractor: async LLM-based memory extraction from chat history
- Parses JSON lines from LLM response as memory entries
- Wire into orchestrator pipeline via PostProcess hook
- Non-blocking: runs in goroutine with 30s timeout
- Automatic dedup via existing keyword overlap logic

backend/cmd/cyrene/main.go

@@ -182,6 +182,11 @@ func main() {
 	// === 11. AI-Core: Orchestrator ===
 	semanticCache := llm.NewSemanticCache()
 	orch := orchestrator.NewPipeline(llmSelector, semanticCache, ctxStore, subMgr, personaInjector)
+	if memSvc != nil {
+		memAdapter := llmSelector.DefaultAdapter()
+		memExtractor := memory.NewExtractor(memSvc, &memory.LLMAdapterBridge{Adapter: memAdapter})
+		orch.SetMemoryExtractor(memExtractor)
+	}
 	log.Printf("[orchestrator] pipeline ready")
 
 	// === 11.5 Platform Bridge ===

backend/services/ai-core/orchestrator/orchestrator.go

@@ -15,14 +15,20 @@ import (
 	"git.yeij.top/AskaEth/Cyrene_v2/services/ai-core/subsession"
 )
 
+// MemoryExtractor is the interface for async memory extraction.
+type MemoryExtractor interface {
+	ExtractFromConversation(ctx context.Context, userID string, conversation []model.Message)
+}
+
 // Pipeline orchestrates the full dialogue flow.
 type Pipeline struct {
-	llmSelector   *llm.ModelSelector
-	cache         *llm.SemanticCache
-	ctxStore      *ctxpkg.Store
-	ctxBuilder    *ctxpkg.Builder
-	subMgr        *subsession.Manager
-	personaInjector *persona.Injector
+	llmSelector      *llm.ModelSelector
+	cache            *llm.SemanticCache
+	ctxStore         *ctxpkg.Store
+	ctxBuilder       *ctxpkg.Builder
+	subMgr           *subsession.Manager
+	personaInjector  *persona.Injector
+	memoryExtractor  MemoryExtractor
 }
 
 // NewPipeline creates a new dialogue pipeline.
@@ -43,6 +49,11 @@ func NewPipeline(
 	}
 }
 
+// SetMemoryExtractor injects the memory extraction capability.
+func (p *Pipeline) SetMemoryExtractor(e MemoryExtractor) {
+	p.memoryExtractor = e
+}
+
 // ProcessInput processes a user message through the full pipeline.
 // Returns a channel of StreamToken for the LLM response.
 func (p *Pipeline) ProcessInput(ctx context.Context, sessionID, userMessage string) (<-chan model.StreamToken, *model.Intent, error) {
@@ -87,6 +98,14 @@ func (p *Pipeline) ProcessInput(ctx context.Context, sessionID, userMessage stri
 	return tokenCh, intent, nil
 }
 
+// PostProcess triggers async memory extraction after a conversation round completes.
+func (p *Pipeline) PostProcess(sessionID, userID string) {
+	if p.memoryExtractor == nil { return }
+	history := p.ctxStore.Get(sessionID)
+	if len(history) == 0 { return }
+	p.memoryExtractor.ExtractFromConversation(nil, userID, history)
+}
+
 // --- Intent Analysis ---
 
 // controlWords are common IoT control verbs.

backend/services/memory/bridge_llm.go

@@ -0,0 +1,17 @@
+package memory
+
+import (
+	"context"
+
+	"git.yeij.top/AskaEth/Cyrene_v2/services/ai-core/llm"
+	"git.yeij.top/AskaEth/Cyrene_v2/services/ai-core/model"
+)
+
+// LLMAdapterBridge adapts llm.Adapter to LLMClient for memory extraction.
+type LLMAdapterBridge struct {
+	Adapter *llm.Adapter
+}
+
+func (b *LLMAdapterBridge) Chat(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error) {
+	return b.Adapter.Chat(ctx, messages)
+}

backend/services/memory/extractor.go

@@ -0,0 +1,117 @@
+package memory
+
+import (
+	"context"
+	"encoding/json"
+	"fmt"
+	"strings"
+	"time"
+
+	"git.yeij.top/AskaEth/Cyrene_v2/services/ai-core/model"
+)
+
+// LLMClient is the interface for calling an LLM.
+type LLMClient interface {
+	Chat(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error)
+}
+
+// Extractor uses LLM to extract memories from conversations.
+type Extractor struct {
+	svc     *Service
+	llm     LLMClient
+	enabled bool
+}
+
+// NewExtractor creates a memory extractor.
+func NewExtractor(svc *Service, llm LLMClient) *Extractor {
+	return &Extractor{svc: svc, llm: llm, enabled: llm != nil}
+}
+
+// ExtractedMemory represents a single memory extracted by the LLM.
+type ExtractedMemory struct {
+	Content  string  `json:"content"`
+	Category string  `json:"category"`
+	Priority float64 `json:"priority"`
+}
+
+// ExtractFromConversation analyzes a conversation and extracts memories asynchronously.
+// Does not block — runs in a goroutine.
+func (e *Extractor) ExtractFromConversation(ctx context.Context, userID string, conversation []model.Message) {
+	if !e.enabled || e.llm == nil || len(conversation) == 0 {
+		return
+	}
+
+	go func() {
+		bgCtx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
+		defer cancel()
+
+		memories, err := e.extract(bgCtx, conversation)
+		if err != nil {
+			return // Silent fail — don't block main flow
+		}
+
+		for _, m := range memories {
+			if m.Content == "" { continue }
+			if m.Priority <= 0 { m.Priority = 0.7 }
+			if m.Category == "" { m.Category = CatFact }
+			e.svc.Add(bgCtx, userID, "", m.Content, m.Category, m.Priority)
+		}
+	}()
+}
+
+func (e *Extractor) extract(ctx context.Context, messages []model.Message) ([]ExtractedMemory, error) {
+	// Build conversation text
+	var convText strings.Builder
+	for _, msg := range messages {
+		role := "用户"
+		if msg.Role == "assistant" || msg.Role == "action" {
+			role = "AI"
+		}
+		convText.WriteString(fmt.Sprintf("%s: %s\n", role, msg.Content))
+	}
+
+	prompt := []model.LLMMessage{
+		{Role: "system", Content: `你是一个记忆提取助手。从对话中提取关于用户的重要信息。
+每条记忆用一行 JSON 表示，格式：{"content":"记忆内容","category":"category","priority":0.8}
+
+类别可选: fact(事实), preference(偏好), event(事件), plan(计划)
+优先级 0-1，重要信息用高优先级。
+只提取新信息，不重复已有内容。
+只输出 JSON 行，不要其他文字。`},
+		{Role: "user", Content: fmt.Sprintf("从以下对话中提取记忆：\n%s", convText.String())},
+	}
+
+	resp, err := e.llm.Chat(ctx, prompt)
+	if err != nil {
+		return nil, fmt.Errorf("llm extract: %w", err)
+	}
+
+	return parseExtractedMemories(resp.Content), nil
+}
+
+func parseExtractedMemories(text string) []ExtractedMemory {
+	var memories []ExtractedMemory
+	lines := strings.Split(text, "\n")
+	for _, line := range lines {
+		line = strings.TrimSpace(line)
+		if line == "" || !strings.HasPrefix(line, "{") {
+			continue
+		}
+		var m ExtractedMemory
+		if err := json.Unmarshal([]byte(line), &m); err != nil {
+			// Try to extract just the JSON part
+			start := strings.Index(line, "{")
+			end := strings.LastIndex(line, "}")
+			if start >= 0 && end > start {
+				if err := json.Unmarshal([]byte(line[start:end+1]), &m); err == nil {
+					if m.Content != "" { memories = append(memories, m) }
+				}
+			}
+			continue
+		}
+		if m.Content != "" {
+			memories = append(memories, m)
+		}
+	}
+	return memories
+}

backend/services/memory/extractor_test.go

@@ -0,0 +1,99 @@
+package memory
+
+import (
+	"context"
+	"testing"
+
+	"git.yeij.top/AskaEth/Cyrene_v2/services/ai-core/model"
+)
+
+func TestParseExtractedMemories(t *testing.T) {
+	tests := []struct {
+		input    string
+		minCount int
+	}{
+		{`{"content":"用户喜欢咖啡","category":"preference","priority":0.8}
+{"content":"用户每天跑步","category":"fact","priority":0.7}`, 2},
+		{`这是提取的记忆：
+{"content":"用户名叫张三","category":"fact","priority":0.9}
+{"content":"用户在北京工作","category":"fact","priority":0.85}`, 2},
+		{`no json here`, 0},
+		{``, 0},
+	}
+
+	for _, tt := range tests {
+		t.Run("", func(t *testing.T) {
+			result := parseExtractedMemories(tt.input)
+			if len(result) < tt.minCount {
+				t.Errorf("expected >= %d memories, got %d: %+v", tt.minCount, len(result), result)
+			}
+			for _, m := range result {
+				if m.Content == "" {
+					t.Error("memory content should not be empty")
+				}
+				if m.Category == "" {
+					t.Error("memory category should not be empty")
+				}
+			}
+		})
+	}
+}
+
+func TestExtractorDisabled(t *testing.T) {
+	e := NewExtractor(nil, nil)
+	if e.enabled {
+		t.Error("extractor should be disabled when llm is nil")
+	}
+
+	// Should not panic
+	e.ExtractFromConversation(nil, "user-1", nil)
+}
+
+func TestExtractorParseEdgeCases(t *testing.T) {
+	// Test JSON with whitespace
+	result := parseExtractedMemories(`  {"content":"test","category":"fact","priority":0.5}  `)
+	if len(result) != 1 {
+		t.Errorf("expected 1, got %d", len(result))
+	}
+
+	// Test empty JSON object
+	result = parseExtractedMemories(`{}`)
+	if len(result) != 0 {
+		t.Errorf("expected 0 for empty object, got %d", len(result))
+	}
+
+	// Test invalid JSON line mixed with valid
+	result = parseExtractedMemories(`invalid
+{"content":"ok","category":"fact","priority":0.5}
+not json either`)
+	if len(result) != 1 {
+		t.Errorf("expected 1 valid, got %d", len(result))
+	}
+}
+
+// mockLLM implements LLMClient for testing.
+type mockLLM struct {
+	response string
+	err      error
+}
+
+func (m *mockLLM) Chat(_ context.Context, _ []model.LLMMessage) (*model.LLMResponse, error) {
+	if m.err != nil { return nil, m.err }
+	return &model.LLMResponse{Content: m.response, Model: "mock"}, nil
+}
+
+func TestExtractorWithMockLLM(t *testing.T) {
+	mock := &mockLLM{
+		response: `{"content":"用户喜欢喝咖啡","category":"preference","priority":0.8}
+{"content":"用户在2024年去过日本","category":"event","priority":0.9}`,
+	}
+
+	// We can't easily test the full extraction without a real DB
+	// But verify mock LLM works
+	resp, err := mock.Chat(nil, nil)
+	if err != nil { t.Fatalf("mock LLM: %v", err) }
+	memories := parseExtractedMemories(resp.Content)
+	if len(memories) != 2 {
+		t.Errorf("expected 2 memories, got %d", len(memories))
+	}
+}