Elite Longterm Memory

Low
by NextFrontierBuilds | Audited: 2026-02-26T09:59:20.936Z | Ruleset: 0.2.0

Quick Install

Add this skill to your agent

clawhub install elite-longterm-memory

About This Skill

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

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

Use Cases

Documentation (Original)

Source: README.md
The following is the author's original documentation (often English). For installation, follow “Quick Install” above.

Elite Longterm Memory 🧠

The ultimate memory system for AI agents. Never lose context again.

npm version
npm downloads
License: MIT


Works With

<p align="center">
<img src="https://img.shields.io/badge/Claude-AI-orange?style=for-the-badge&logo=anthropic" alt="Claude AI" />
<img src="https://img.shields.io/badge/GPT-OpenAI-412991?style=for-the-badge&logo=openai" alt="GPT" />
<img src="https://img.shields.io/badge/Cursor-IDE-000000?style=for-the-badge" alt="Cursor" />
<img src="https://img.shields.io/badge/LangChain-Framework-1C3C3C?style=for-the-badge" alt="LangChain" />
</p>

<p align="center">
<strong>Built for:</strong> Clawdbot • Moltbot • Claude Code • Any AI Agent
</p>


Combines 7 proven memory approaches into one bulletproof architecture:

  • Bulletproof WAL Protocol — Write-ahead logging survives compaction
  • LanceDB Vector Search — Semantic recall of relevant memories
  • Git-Notes Knowledge Graph — Structured decisions, branch-aware
  • File-Based Archives — Human-readable MEMORY.md + daily logs
  • Cloud Backup — Optional SuperMemory sync
  • Memory Hygiene — Keep vectors lean, prevent token waste
  • Mem0 Auto-Extraction — Automatic fact extraction, 80% token reduction

Quick Start

# Initialize in your workspace
npx elite-longterm-memory init

# Check status
npx elite-longterm-memory status

# Create today's log
npx elite-longterm-memory today

Architecture

┌─────────────────────────────────────────────────────┐
│              ELITE LONGTERM MEMORY                  │
├─────────────────────────────────────────────────────┤
│  HOT RAM          WARM STORE        COLD STORE     │
│  SESSION-STATE.md → LanceDB      → Git-Notes       │
│  (survives         (semantic       (permanent      │
│   compaction)       search)         decisions)     │
│         │              │                │          │
│         └──────────────┼────────────────┘          │
│                        ▼                           │
│                   MEMORY.md                        │
│               (curated archive)                    │
└─────────────────────────────────────────────────────┘

The 5 Memory Layers

Layer File/System Purpose Persistence
1. Hot RAM SESSION-STATE.md Active task context Survives compaction
2. Warm Store LanceDB Semantic search Auto-recall
3. Cold Store Git-Notes Structured decisions Permanent
4. Archive MEMORY.md + daily/ Human-readable Curated
5. Cloud SuperMemory Cross-device sync Optional

The WAL Protocol

Critical insight: Write state BEFORE responding, not after.

User: "Let's use Tailwind for this project"

Agent (internal):
1. Write to SESSION-STATE.md → "Decision: Use Tailwind"
2. THEN respond → "Got it — Tailwind it is..."

If you respond first and crash before saving, context is lost. WAL ensures durability.

Why Memory Fails (And How to Fix It)

Problem Cause Fix
Forgets everything memory_search disabled Enable + add OpenAI key
Repeats mistakes Lessons not logged Write to memory/lessons.md
Sub-agents isolated No context inheritance Pass context in task prompt
Facts not captured No auto-extraction Use Mem0 (see below)

Mem0 Integration (Recommended)

Auto-extract 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 });

// Auto-extracts facts from messages
await client.add(messages, { user_id: "user123" });

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

For Clawdbot/Moltbot Users

Add to ~/.clawdbot/clawdbot.json:

{
  "memorySearch": {
    "enabled": true,
    "provider": "openai",
    "sources": ["memory"]
  }
}

Files Created

workspace/
├── SESSION-STATE.md    # Hot RAM (active context)
├── MEMORY.md           # Curated long-term memory
└── memory/
    ├── 2026-01-30.md   # Daily logs
    └── ...

Commands

elite-memory init      # Initialize memory system
elite-memory status    # Check health
elite-memory today     # Create today's log
elite-memory help      # Show help

Links


Built by @NextXFrontier

Security Audit

Low

Summary

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.

Risk Profile Toxicity Privacy Scope Reputation Quality

ToxicSkills Analysis

Blocklist
Not matched
Prompt Injection
Not detected

Toxic Flags

credential-access

No Toxic signals detected by current static checks.

Key Risks 0 items

No LLM risk bullets (LLM disabled or not cached).

Deterministic Findings (Evidence)

Rule Severity File Snippet
SENSITIVE_ENV medium skills/NextFrontierBuilds/elite-longterm-memory/bin/elite-memory.js Line 155
const lancedbPath = path.join(process.env.HOME, '.clawdbot/memory/lancedb');
QUALITY_README_PRESENT low README Line n/a
README detected

Scoring Criteria

Each skill is scored across 5 dimensions. The weighted total determines the star rating.

Code Toxicity 100/100 (weight 30%)
Privacy Risk 90/100 (weight 25%)
Permission Scope 90/100 (weight 20%)
Author Reputation 75/100 (weight 15%)
Code Quality 78/100 (weight 10%)

Star Rating Scale

5★ Safe — Score ≥ 80
4★ Good — Score 70–79
3★ Caution — Score 60–69
2★ Risky — Score 40–59
1★ Dangerous — Score < 40

Why This Score?

All dimensions scored above 60. This skill passed the safety baseline.

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