Hierarchical Memory System

TAISUN's hierarchical memory architecture based on Mem0 research, providing 26% accuracy improvement through structured memory layers.

Architecture Overview

┌─────────────────────────────────────────────────────────────────┐
│                    HIERARCHICAL MEMORY                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐ │
│  │   SHORT-TERM    │  │    LONG-TERM    │  │    EPISODIC     │ │
│  │   (Session)     │  │   (Persistent)  │  │   (Events)      │ │
│  ├─────────────────┤  ├─────────────────┤  ├─────────────────┤ │
│  │ taisun-proxy    │  │ Qdrant Vector   │  │ claude-mem      │ │
│  │ InMemoryStore   │  │ Database        │  │ Observations    │ │
│  ├─────────────────┤  ├─────────────────┤  ├─────────────────┤ │
│  │ TTL: Session    │  │ TTL: Permanent  │  │ TTL: 30 days    │ │
│  │ Size: 100 items │  │ Size: Unlimited │  │ Size: 50/day    │ │
│  │ Search: Token   │  │ Search: Vector  │  │ Search: ID/Time │ │
│  └─────────────────┘  └─────────────────┘  └─────────────────┘ │
│           │                   │                    │            │
│           └───────────────────┼────────────────────┘            │
│                               ▼                                 │
│                    ┌─────────────────┐                         │
│                    │  MEMORY ROUTER  │                         │
│                    │  (Consolidation)│                         │
│                    └─────────────────┘                         │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Memory Layers

1. Short-Term Memory (Working Memory)

System: taisun-proxy InMemoryStore Purpose: Current session context

Property	Value
Storage	In-memory
TTL	Session duration
Max Items	100
Search	Token-based
Use Cases	Current task context, recent commands, temp data

# Store in short-term
memory_add type="short-term" content="現在のタスク: API実装"

# Retrieve
memory_search query="タスク"

2. Long-Term Memory (Semantic Memory)

System: Qdrant Vector Database Purpose: Persistent knowledge and patterns

Property	Value
Storage	Qdrant (localhost:6333)
TTL	Permanent
Max Items	Unlimited
Search	Vector similarity
Use Cases	Code patterns, learned solutions, domain knowledge

# Store important pattern
qdrant-store text="認証にはJWTを使用し..." metadata={topic: "auth"}

# Semantic search
qdrant-find query="認証の実装方法"

3. Episodic Memory (Event Memory)

System: claude-mem Observations Purpose: Decision history and context trails

Property	Value
Storage	JSONL files
TTL	30 days
Max Items	~50/day
Search	ID, timestamp, type
Use Cases	Past decisions, debugging context, learning history

# Auto-captured by hooks
# Access via MCP
mcp__claude-mem-search__search query="bugfix"
mcp__claude-mem-search__timeline date="2026-01-19"

Memory Flow

Information Lifecycle

1. CAPTURE (Short-Term)
   User input → Session context → Working memory

2. CONSOLIDATE (Short → Long)
   Important patterns → Vector embedding → Qdrant storage

3. OBSERVE (Episodic)
   Decisions, discoveries → claude-mem → Timestamped records

4. RETRIEVE (All Layers)
   Query → Router → Best matching layer → Response

Consolidation Rules

Trigger	Action
Session end	Important short-term → Long-term
Pattern detected	Auto-store in Qdrant
Decision made	Log to episodic
Error resolved	Store solution in long-term

Usage Patterns

1. Remember Important Information

User: このAPIパターンを覚えておいて
      [code snippet]

AI: 1. Short-term に即座に保存
    2. 重要度判定（コードパターン = HIGH）
    3. Qdrant に永続化
    4. claude-mem に観察記録

2. Retrieve Past Knowledge

User: 以前話した認証の実装方法は？

AI: 1. Qdrant でセマンティック検索
    2. claude-mem でエピソード検索
    3. 関連情報を統合
    4. コンテキスト付きで回答

3. Learn From Session

# Session end hook automatically:
1. Extracts key decisions
2. Stores successful patterns
3. Records errors and solutions
4. Updates long-term memory

Performance Benefits (Mem0 Research)

Metric	Improvement
Accuracy	+26%
P95 Latency	-91%
Token Usage	-90%

Source: Mem0 Research Paper

Integration Points

With Existing TAISUN Systems

System	Integration
taisun-proxy	memory_add, memory_search tools
Qdrant MCP	qdrant-store, qdrant-find tools
claude-mem	Auto-observation hooks
SessionStart	State injection
SessionEnd	Memory consolidation

With Other MCPs

# Context7 + Long-Term Memory
「use context7 でReact 19の新機能を学習して、覚えておいて」

# GPT Researcher + Memory
「市場調査して、重要なポイントを長期記憶に保存」

Best Practices

1. Explicit Memory Commands

   ✅ 「これを長期記憶に保存して」
   ✅ 「前回のセッションで話した〇〇について」
   ❌ 「覚えておいて」（曖昧）
   

2. Tag Important Information

   metadata: { topic: "auth", type: "pattern", priority: "high" }
   

3. Regular Memory Cleanup

   Outdated patterns should be removed from long-term memory
   

4. Trust the Consolidation

   Let auto-hooks handle session → long-term migration
   

Troubleshooting

Memory Not Found

1. Check if Qdrant is running (

   curl localhost:6333/health

   )
2. Verify collection exists
3. Check search query specificity

Slow Retrieval

1. Limit search scope with filters
2. Use appropriate memory layer
3. Check Qdrant index status

Sources

• Mem0 GitHub
• Mem0 Research (26% Accuracy Boost)
• Mem0 arXiv Paper
• MCP-Mem0 Template