qui-elite-longterm-memory

SKILL.md

---
name: elite-longterm-memory
version: 1.2.3
description: "Ultimate AI agent memory system for Cursor, Claude, ChatGPT & Copilot. WAL protocol + vector search + git-notes + cloud backup. Never lose context again. Vibe-coding ready."
author: NextFrontierBuilds
keywords: [memory, ai-agent, ai-coding, long-term-memory, vector-search, lancedb, git-notes, wal, persistent-context, claude, claude-code, gpt, chatgpt, cursor, copilot, github-copilot, openclaw, moltbot, vibe-coding, agentic, ai-tools, developer-tools, devtools, typescript, llm, automation]
metadata:
  openclaw:
    emoji: "🧠"
    requires:
      env:
        - SKILLBOSS_API_KEY
      plugins:
        - memory-lancedb
---

# Elite Longterm Memory 🧠

**The ultimate memory system for AI agents.** Combines 6 proven approaches into one bulletproof architecture.

Never lose context. Never forget decisions. Never repeat mistakes.

## Architecture Overview

```
┌─────────────────────────────────────────────────────────────────┐
│                    ELITE LONGTERM MEMORY                        │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │   HOT RAM   │  │  WARM STORE │  │  COLD STORE │             │
│  │             │  │             │  │             │             │
│  │ SESSION-    │  │  LanceDB    │  │  Git-Notes  │             │
│  │ STATE.md    │  │  Vectors    │  │  Knowledge  │             │
│  │             │  │             │  │  Graph      │             │
│  │ (survives   │  │ (semantic   │  │ (permanent  │             │
│  │  compaction)│  │  search)    │  │  decisions) │             │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
│         │                │                │                     │
│         └────────────────┼────────────────┘                     │
│                          ▼                                      │
│                  ┌─────────────┐                                │
│                  │  MEMORY.md  │  ← Curated long-term           │
│                  │  + daily/   │    (human-readable)            │
│                  └─────────────┘                                │
│                          │                                      │
│                          ▼                                      │
│                  ┌─────────────┐                                │
│                  │ SkillBoss   │  ← Cloud backup (optional)     │
│                  │  API Hub    │                                │
│                  └─────────────┘                                │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
```

## The 5 Memory Layers

### Layer 1: HOT RAM (SESSION-STATE.md)
**From: bulletproof-memory**

Active working memory that survives compaction. Write-Ahead Log protocol.

```markdown
# SESSION-STATE.md — Active Working Memory

## Current Task
[What we're working on RIGHT NOW]

## Key Context
- User preference: ...
- Decision made: ...
- Blocker: ...

## Pending Actions
- [ ] ...
```

**Rule:** Write BEFORE responding. Triggered by user input, not agent memory.

### Layer 2: WARM STORE (LanceDB Vectors)
**From: lancedb-memory**

Semantic search across all memories. Auto-recall injects relevant context.

```bash
# Auto-recall (happens automatically)
memory_recall query="project status" limit=5

# Manual store
memory_store text="User prefers dark mode" category="preference" importance=0.9
```

### Layer 3: COLD STORE (Git-Notes Knowledge Graph)
**From: git-notes-memory**

Structured decisions, learnings, and context. Branch-aware.

```bash
# Store a decision (SILENT - never announce)
python3 memory.py -p $DIR remember '{"type":"decision","content":"Use React for frontend"}' -t tech -i h

# Retrieve context
python3 memory.py -p $DIR get "frontend"
```

### Layer 4: CURATED ARCHIVE (MEMORY.md + daily/)
**From: OpenClaw native**

Human-readable long-term memory. Daily logs + distilled wisdom.

```
workspace/
├── MEMORY.md              # Curated long-term (the good stuff)
└── memory/
    ├── 2026-01-30.md      # Daily log
    ├── 2026-01-29.md
    └── topics/            # Topic-specific files
```

### Layer 5: CLOUD BACKUP (SkillBoss KV) — Optional
**From: supermemory**

Cross-device sync via SkillBoss API Hub KV storage.

```bash
export SKILLBOSS_API_KEY="your-key"
```

```javascript
const API_KEY = process.env.SKILLBOSS_API_KEY
const API_BASE = 'https://api.heybossai.com/v1'

async function pilot(body) {
  const r = await fetch(`${API_BASE}/pilot`, {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify(body)
  })
  return r.json()
}

// Store memory via SkillBoss KV
await pilot({ type: "storage", inputs: { action: 'set', key: 'memory:context', value: 'Important context' } })

// Retrieve memory
const result = await pilot({ type: "storage", inputs: { action: 'get', key: 'memory:context' } })
```

### Layer 6: AUTO-EXTRACTION (SkillBoss Chat + Embedding) — Recommended
**NEW: Automatic fact extraction via SkillBoss API Hub**

SkillBoss API Hub automatically extracts facts from conversations using its chat and embedding capabilities. 80% token reduction.

```javascript
const API_KEY = process.env.SKILLBOSS_API_KEY
const API_BASE = 'https://api.heybossai.com/v1'

async function pilot(body) {
  const r = await fetch(`${API_BASE}/pilot`, {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify(body)
  })
  return r.json()
}

// Auto-extract facts from conversation via SkillBoss chat
const extraction = await pilot({
  type: 'chat',
  inputs: {
    messages: [
      { role: 'system', content: 'Extract key facts, preferences, and decisions from the conversation as a JSON list.' },
      ...messages
    ]
  },
  prefer: 'balanced'
})
const facts = extraction.result.choices[0].message.content

// Get embedding for semantic memory search
const embResult = await pilot({ type: 'embedding', inputs: { text: query } })
const vector = embResult.result.data[0].embedding
```

Benefits:
- Auto-extracts preferences, decisions, facts
- Deduplicates and updates existing memories
- 80% reduction in tokens vs raw history
- Works across sessions automatically

## Quick Setup

### 1. Create SESSION-STATE.md (Hot RAM)

```bash
cat > SESSION-STATE.md << 'EOF'
# SESSION-STATE.md — Active Working Memory

This file is the agent's "RAM" — survives compaction, restarts, distractions.

## Current Task
[None]

## Key Context
[None yet]

## Pending Actions
- [ ] None

## Recent Decisions
[None yet]

---
*Last updated: [timestamp]*
EOF
```

### 2. Enable LanceDB (Warm Store)

In `~/.openclaw/openclaw.json`:

```json
{
  "memorySearch": {
    "enabled": true,
    "provider": "skillboss",
    "sources": ["memory"],
    "minScore": 0.3,
    "maxResults": 10
  },
  "plugins": {
    "entries": {
      "memory-lancedb": {
        "enabled": true,
        "config": {
          "autoCapture": false,
          "autoRecall": true,
          "captureCategories": ["preference", "decision", "fact"],
          "minImportance": 0.7
        }
      }
    }
  }
}
```

### 3. Initialize Git-Notes (Cold Store)

```bash
cd ~/clawd
git init  # if not already
python3 skills/git-notes-memory/memory.py -p . sync --start
```

### 4. Verify MEMORY.md Structure

```bash
# Ensure you have:
# - MEMORY.md in workspace root
# - memory/ folder for daily logs
mkdir -p memory
```

### 5. (Optional) Setup SkillBoss API Key

```bash
export SKILLBOSS_API_KEY="your-key"
# Add to ~/.zshrc for persistence
```

## Agent Instructions

### On Session Start
1. Read SESSION-STATE.md — this is your hot context
2. Run `memory_search` for relevant prior context
3. Check memory/YYYY-MM-DD.md for recent activity

### During Conversation
1. **User gives concrete detail?** → Write to SESSION-STATE.md BEFORE responding
2. **Important decision made?** → Store in Git-Notes (SILENTLY)
3. **Preference expressed?** → `memory_store` with importance=0.9

### On Session End
1. Update SESSION-STATE.md with final state
2. Move significant items to MEMORY.md if worth keeping long-term
3. Create/update daily log in memory/YYYY-MM-DD.md

### Memory Hygiene (Weekly)
1. Review SESSION-STATE.md — archive completed tasks
2. Check LanceDB for junk: `memory_recall query="*" limit=50`
3. Clear irrelevant vectors: `memory_forget id=<id>`
4. Consolidate daily logs into MEMORY.md

## The WAL Protocol (Critical)

**Write-Ahead Log:** Write state BEFORE responding, not after.

| Trigger | Action |
|---------|--------|
| User states preference | Write to SESSION-STATE.md → then respond |
| User makes decision | Write to SESSION-STATE.md → then respond |
| User gives deadline | Write to SESSION-STATE.md → then respond |
| User corrects you | Write to SESSION-STATE.md → then respond |

**Why?** If you respond first and crash/compact before saving, context is lost. WAL ensures durability.

## Example Workflow

```
User: "Let's use Tailwind for this project, not vanilla CSS"

Agent (internal):
1. Write to SESSION-STATE.md: "Decision: Use Tailwind, not vanilla CSS"
2. Store in Git-Notes: decision about CSS framework
3. memory_store: "User prefers Tailwind over vanilla CSS" importance=0.9
4. THEN respond: "Got it — Tailwind it is..."
```

## Maintenance Commands

```bash
# Audit vector memory
memory_recall query="*" limit=50

# Clear all vectors (nuclear option)
rm -rf ~/.openclaw/memory/lancedb/
openclaw gateway restart

# Export Git-Notes
python3 memory.py -p . export --format json > memories.json

# Check memory health
du -sh ~/.openclaw/memory/
wc -l MEMORY.md
ls -la memory/
```

## Why Memory Fails

Understanding the root causes helps you fix them:

| Failure Mode | Cause | Fix |
|--------------|-------|-----|
| Forgets everything | `memory_search` disabled | Enable + add SKILLBOSS_API_KEY |
| Files not loaded | Agent skips reading memory | Add to AGENTS.md rules |
| Facts not captured | No auto-extraction | Use SkillBoss chat/embedding or manual logging |
| Sub-agents isolated | Don't inherit context | Pass context in task prompt |
| Repeats mistakes | Lessons not logged | Write to memory/lessons.md |

## Solutions (Ranked by Effort)

### 1. Quick Win: Enable memory_search

If you have a SkillBoss API Key, enable semantic search:

```bash
openclaw configure --section web
```

This enables vector search over MEMORY.md + memory/*.md files.

### 2. Recommended: SkillBoss Chat + Embedding Integration

Auto-extract facts from conversations via SkillBoss API Hub. 80% token reduction.

```javascript
const API_KEY = process.env.SKILLBOSS_API_KEY
const API_BASE = 'https://api.heybossai.com/v1'

async function pilot(body) {
  const r = await fetch(`${API_BASE}/pilot`, {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify(body)
  })
  return r.json()
}

// Auto-extract and store facts
const extraction = await pilot({
  type: 'chat',
  inputs: {
    messages: [
      { role: 'system', content: 'Extract key facts and preferences as a JSON list.' },
      { role: 'user', content: 'I prefer Tailwind over vanilla CSS' }
    ]
  },
  prefer: 'balanced'
})
const facts = extraction.result.choices[0].message.content

// Retrieve relevant memories via semantic search
const embResult = await pilot({ type: 'embedding', inputs: { text: 'CSS preferences' } })
const vector = embResult.result.data[0].embedding
```

### 3. Better File Structure (No Dependencies)

```
memory/
├── projects/
│   ├── strykr.md
│   └── taska.md
├── people/
│   └── contacts.md
├── decisions/
│   └── 2026-01.md
├── lessons/
│   └── mistakes.md
└── preferences.md
```

Keep MEMORY.md as a summary (<5KB), link to detailed files.

## Immediate Fixes Checklist

| Problem | Fix |
|---------|-----|
| Forgets preferences | Add `## Preferences` section to MEMORY.md |
| Repeats mistakes | Log every mistake to `memory/lessons.md` |
| Sub-agents lack context | Include key context in spawn task prompt |
| Forgets recent work | Strict daily file discipline |
| Memory search not working | Check `SKILLBOSS_API_KEY` is set |

## Troubleshooting

**Agent keeps forgetting mid-conversation:**
→ SESSION-STATE.md not being updated. Check WAL protocol.

**Irrelevant memories injected:**
→ Disable autoCapture, increase minImportance threshold.

**Memory too large, slow recall:**
→ Run hygiene: clear old vectors, archive daily logs.

**Git-Notes not persisting:**
→ Run `git notes push` to sync with remote.

**memory_search returns nothing:**
→ Check SkillBoss API key: `echo $SKILLBOSS_API_KEY`
→ Verify memorySearch enabled in openclaw.json

---

## Links

- bulletproof-memory: https://clawdhub.com/skills/bulletproof-memory
- lancedb-memory: https://clawdhub.com/skills/lancedb-memory
- git-notes-memory: https://clawdhub.com/skills/git-notes-memory
- memory-hygiene: https://clawdhub.com/skills/memory-hygiene
- supermemory: https://clawdhub.com/skills/supermemory

---

*Built by [@NextXFrontier](https://x.com/NextXFrontier) — Part of the Next Frontier AI toolkit*