tiered-memory

EvoClaw 分层内存架构 v2.2.0 - 由 LLM 驱动的三层内存系统，具有自动每日笔记摄取、结构化元数据提取、URL 保存、验证和云优先同步。

## 概述（中文）

EvoClaw 分层内存架构 v2.2.0 - 由 LLM 驱动的三层内存系统，具有自动每日笔记摄取、结构化元数据提取、URL 保存、验证和云优先同步。

## 原文

# Tiered Memory System v2.2.0

> *A mind that remembers everything is as useless as one that remembers nothing. The art is knowing what to keep.* 🧠

EvoClaw-compatible three-tier memory system inspired by human cognition and PageIndex tree retrieval.

## What's New in v2.2.0

🔄 **Automatic Daily Note Ingestion**
- Consolidation (`daily`/`monthly`/`full` modes) now auto-runs `ingest-daily`
- Bridges `memory/YYYY-MM-DD.md` files → tiered memory system
- No more manual ingestion required — facts flow automatically
- Fixes the "two disconnected data paths" problem

## What's New in v2.1.0

🎯 **Structured Metadata Extraction**
- Automatic extraction of URLs, shell commands, and file paths from facts
- Preserved during distillation and consolidation
- Searchable by URL fragment

✅ **Memory Completeness Validation**
- Check daily notes for missing URLs, commands, and next steps
- Proactive warnings for incomplete information
- Actionable suggestions for improvement

🔍 **Enhanced Search**
- Search facts by URL fragment
- Get all stored URLs from warm memory
- Metadata-aware fact storage

🛡️ **URL Preservation**
- URLs explicitly preserved during LLM distillation
- Fallback metadata extraction if LLM misses them
- Command-line support for adding metadata manually

## Architecture

```
┌─────────────────────────────────────────────────────┐
│              AGENT CONTEXT (~8-15KB)                │
│                                                     │
│  ┌──────────┐  ┌────────────────────────────────┐  │
│  │  Tree    │  │  Retrieved Memory Nodes         │  │
│  │  Index   │  │  (on-demand, 1-3KB)            │  │
│  │  (~2KB)  │  │                                │  │
│  │          │  │  Fetched per conversation      │  │
│  │  Always  │  │  based on tree reasoning       │  │
│  │  loaded  │  │                                │  │
│  └────┬─────┘  └────────────────────────────────┘  │
│       │                                             │
└───────┼─────────────────────────────────────────────┘
        │
        │ LLM-powered tree search
        │
┌───────▼─────────────────────────────────────────────┐
│              MEMORY TIERS                           │
│                                                     │
│  🔴 HOT (5KB)      🟡 WARM (50KB)     🟢 COLD (∞)  │
│                                                     │
│  Core memory       Scored facts      Full archive  │
│  - Identity        - 30-day         - Turso DB     │
│  - Owner profile   - Decaying       - Queryable    │
│  - Active context  - On-device      - 10-year      │
│  - Lessons (20 max)                                │
│                                                     │
│  Always in         Retrieved via     Retrieved via │
│  context           tree search       tree search   │
└─────────────────────────────────────────────────────┘
```

## Design Principles

### From Human Memory
- **Consolidation** — Short-term → long-term happens during consolidation cycles
- **Relevance Decay** — Unused memories fade; accessed memories strengthen
- **Strategic Forgetting** — Not remembering everything is a feature
- **Hierarchical Organization** — Navigate categories, not scan linearly

### From PageIndex
- **Vectorless Retrieval** — LLM reasoning instead of embedding similarity
- **Tree-Structured Index** — O(log n) navigation, not O(n) scan
- **Explainable Results** — Every retrieval traces a path through categories
- **Reasoning-Based Search** — "Why relevant?" not "how similar?"

### Cloud-First (EvoClaw)
- **Device is replaceable** — Soul lives in cloud (Turso)
- **Critical sync** — Hot + tree sync after every conversation
- **Disaster recovery** — Full restore in <2 minutes
- **Multi-device** — Same agent across phone/desktop/embedded

## Memory Tiers

### 🔴 Hot Memory (5KB max)

**Purpose:** Core identity and active context, always in agent's context window.

**Structure:**
```json
{
  "identity": {
    "agent_name": "Agent",
    "owner_name": "User",
    "owner_preferred_name": "User",
    "relationship_start": "2026-01-15",
    "trust_level": 0.95
  },
  "owner_profile": {
    "personality": "technical, direct communication",
    "family": ["Sarah (wife)", "Luna (daughter, 3yo)"],
    "topics_loved": ["AI architecture", "blockchain", "system design"],
    "topics_avoid": ["small talk about weather"],
    "timezone": "Australia/Sydney",
    "work_hours": "9am-6pm"
  },
  "active_context": {
    "projects": [
      {
        "name": "EvoClaw",
        "description": "Self-evolving agent framework",
        "status": "Active - BSC integration for hackathon"
      }
    ],
    "events": [
      {"text": "Hackathon deadline Feb 15", "timestamp": 1707350400}
    ],
    "tasks": [
      {"text": "Deploy to BSC testnet", "status": "pending", "timestamp": 1707350400}
    ]
  },
  "critical_lessons": [
    {
      "text": "Always test on testnet before mainnet",
      "category": "blockchain",
      "importance": 0.9,
      "timestamp": 1707350400
    }
  ]
}
```

**Auto-pruning:**
- Lessons: Max 20, removes lowest-importance when full
- Events: Keeps last 10 only
- Tasks: Max 10 pending
- Total size: Hard limit at 5KB, progressively prunes if exceeded

**Generates:** `MEMORY.md` — auto-rebuilt from structured hot state

### 🟡 Warm Memory (50KB max, 30-day retention)

**Purpose:** Recent distilled facts with decay scoring.

**Entry format:**
```json
{
  "id": "abc123def456",
  "text": "Decided to use zero go-ethereum deps for EvoClaw to keep binary small",
  "category": "projects/evoclaw/architecture",
  "importance": 0.8,
  "created_at": 1707350400,
  "access_count": 3,
  "score": 0.742,
  "tier": "warm"
}
```

**Scoring:**
```
score = importance × recency_decay(age) × reinforcement(access_count)

recency_decay(age) = exp(-age_days / 30)
reinforcement(access) = 1 + 0.1 × access_count
```

**Tier classification:**
- `score >= 0.7` → Hot (promote to hot state)
- `score >= 0.3` → Warm (keep)
- `score >= 0.05` → Cold (archive)
- `score < 0.05` → Frozen (delete after retention period)

**Eviction triggers:**
1. Age > 30 days AND score < 0.3
2. Total warm size > 50KB (evicts lowest-scored)
3. Manual consolidation

### 🟢 Cold Memory (Unlimited, Turso)

**Purpose:** Long-term archive, queryable but never bulk-loaded.

**Schema:**
```sql
CREATE TABLE cold_memories (
  id TEXT PRIMARY KEY,
  agent_id TEXT NOT NULL,
  text TEXT NOT NULL,
  category TEXT NOT NULL,
  importance REAL DEFAULT 0.5,
  created_at INTEGER NOT NULL,
  access_count INTEGER DEFAULT 0
);

CREATE TABLE critical_state (
  agent_id TEXT PRIMARY KEY,
  data TEXT NOT NULL,  -- {hot_state, tree_nodes, timestamp}
  updated_at INTEGER NOT NULL
);
```

**Retention:** 10 years (configurable)
**Cleanup:** Monthly consolidation removes frozen entries older than retention period

## Tree Index

**Purpose:** Hierarchical category map for O(log n) retrieval.

**Constraints:**
- Max 50 nodes
- Max depth 4 levels
- Max 2KB serialized
- Max 10 children per node

**Example:**
```
Memory Tree Index
==================================================
📂 Root (warm:15, cold:234)
  📁 owner — Owner profile and preferences
     Memories: warm=5, cold=89
  📁 projects — Active projects
     Memories: warm=8, cold=67
    📁 projects/evoclaw — EvoClaw framework
       Memories: warm=6, cold=45
      📁 projects/evoclaw/bsc — BSC integration
         Memories: warm=3, cold=12
  📁 technical — Technical setup and config
     Memories: warm=2, cold=34
  📁 lessons — Learned lessons and rules
     Memories: warm=0, cold=44

Nodes: 7/50
Size: 1842 / 2048 bytes
```

**Operations:**
- `--add PATH DESC` — Add category node
- `--remove PATH` — Remove node (only if no data)
- `--prune` — Remove dead nodes (no activity in 60+ days)
- `--show` — Pretty-print tree

## Distillation Engine

**Purpose:** Three-stage compression of conversations.

**Pipeline:**
```
Raw conversat