القائمة
Ultimate AI agent memory system. Combines bulletproof WAL protocol, vector search, git-based knowledge graphs, cloud backup, and maintenance hygiene. Never lose context again. For Clawdbot, Moltbot, Claude, GPT agents.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | elite-longterm-memory |
| version | 1.2.0 |
| description | Ultimate AI agent memory system. Combines bulletproof WAL protocol, vector search, git-based knowledge graphs, cloud backup, and maintenance hygiene. Never lose context again. For Clawdbot, Moltbot, Claude, GPT agents. |
| author | NextFrontierBuilds |
| keywords | ["memory","ai-agent","long-term-memory","vector-search","lancedb","git-notes","wal","persistent-context","claude","gpt","clawdbot","moltbot"] |
| metadata | {"clawdbot":{"emoji":"🧠","requires":{"env":["OPENAI_API_KEY"],"plugins":["memory-lancedb"]}}} |
The ultimate memory system for AI agents. Combines 6 proven approaches into one bulletproof architecture.
Never lose context. Never forget decisions. Never repeat mistakes.
┌─────────────────────────────────────────────────────────────────┐
│ 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 │ │
│ └─────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
From: bulletproof-memory
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.
From: lancedb-memory
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
From: git-notes-memory
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"
From: Clawdbot 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
From: supermemory
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..."
NEW: Automatic fact extraction
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:
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
In ~/.clawdbot/clawdbot.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
}
}
}
}
}
cd ~/clawd
git init # if not already
python3 skills/git-notes-memory/memory.py -p . sync --start
# Ensure you have:
# - MEMORY.md in workspace root
# - memory/ folder for daily logs
mkdir -p memory
export SUPERMEMORY_API_KEY="your-key"
# Add to ~/.zshrc for persistence
memory_search for relevant prior contextmemory_store with importance=0.9memory_recall query="*" limit=50memory_forget id=<id>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.
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..."
# Audit vector memory
memory_recall query="*" limit=50
# Clear all vectors (nuclear option)
rm -rf ~/.clawdbot/memory/lancedb/
clawdbot gateway restart
# Export Git-Notes
python3 memory.py -p . export --format json > memories.json
# Check memory health
du -sh ~/.clawdbot/memory/
wc -l MEMORY.md
ls -la memory/
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 |
If you have an OpenAI key, enable semantic search:
clawdbot configure --section web
This enables vector search over MEMORY.md + memory/*.md files.
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" });
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.
| 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 |
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 clawdbot.json
Built by @NextXFrontier — Part of the Next Frontier AI toolkit