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)

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

# 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)

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

# 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)

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

# 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/)

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

Cross-device sync. Chat with your knowledge base.

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

Layer 6: AUTO-EXTRACTION (Mem0) — Recommended

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

npm install mem0ai
export MEM0_API_KEY="your-key"

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)

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:

{
  "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)

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

4. Verify MEMORY.md Structure

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

5. (Optional) Setup SuperMemory

export SUPERMEMORY_API_KEY="your-key"
# Add to ~/.zshrc for persistence

Agent Instructions

On Session Start

• Read SESSION-STATE.md — this is your hot context
• Run memory_search for relevant prior context
• Check memory/YYYY-MM-DD.md for recent activity

During Conversation

• User gives concrete detail? → Write to SESSION-STATE.md BEFORE responding
• Important decision made? → Store in Git-Notes (SILENTLY)
• Preference expressed? → memory_store with importance=0.9

On Session End

• Update SESSION-STATE.md with final state
• Move significant items to MEMORY.md if worth keeping long-term
• Create/update daily log in memory/YYYY-MM-DD.md

Memory Hygiene (Weekly)

• Review SESSION-STATE.md — archive completed tasks
• Check LanceDB for junk: memory_recall query="" limit=50
• Clear irrelevant vectors: memory_forget id=
• Consolidate daily logs into MEMORY.md

The WAL Protocol (Critical)

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

User: "Let's use Tailwind for this project, not vanilla CSS"
Agent (internal):
Write to SESSION-STATE.md: "Decision: Use Tailwind, not vanilla CSS"
Store in Git-Notes: decision about CSS framework
memory_store: "User prefers Tailwind over vanilla CSS" importance=0.9
THEN respond: "Got it — Tailwind it is..."

# 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:

Solutions (Ranked by Effort)

1. Quick Win: Enable memory_search

If you have an OpenAI key, enable semantic search:

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.

npm install mem0ai

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

Built by @NextXFrontier — Part of the Next Frontier AI toolkit*