Elite Longterm Memory

# 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) │ │ └─────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ SuperMemory │ ← Cloud backup (optional) │ │ │ API │ │ │ └─────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────┘ ```

## 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 (SuperMemory) — Optional **From: supermemory**

Cross-device sync. Chat with your knowledge base.

```bash export SUPERMEMORY_API_KEY="your-key" supermemory add "Important context" supermemory search "what did we decide about..." ```

### Layer 6: AUTO-EXTRACTION (Mem0) — Recommended **NEW: Automatic fact extraction**

Mem0 automatically extracts facts from conversations. 80% token reduction.

```bash npm install mem0ai export MEM0_API_KEY="your-key" ```

```javascript const { MemoryClient } = require('mem0ai'); const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });

// Conversations auto-extract facts await client.add(messages, { user_id: "user123" });

// Retrieve relevant memories const memories = await client.search(query, { user_id: "user123" }); ```

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": "openai", "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 SuperMemory

```bash export SUPERMEMORY_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 OpenAI key | | Files not loaded | Agent skips reading memory | Add to AGENTS.md rules | | Facts not captured | No auto-extraction | Use Mem0 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 an OpenAI key, enable semantic search:

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

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

### 2. Recommended: Mem0 Integration

Auto-extract facts from conversations. 80% token reduction.

```bash npm install mem0ai ```

```javascript const { MemoryClient } = require('mem0ai');

const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });

// Auto-extract and store await client.add([ { role: "user", content: "I prefer Tailwind over vanilla CSS" } ], { user_id: "ty" });

// Retrieve relevant memories const memories = await client.search("CSS preferences", { user_id: "ty" }); ```

### 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 `OPENAI_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 OpenAI API key: `echo $OPENAI_API_KEY` → Verify memorySearch enabled in openclaw.json

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## 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

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