Elite Longterm Memory

低风险
作者:NextFrontierBuilds | 审计时间:2026-02-26T09:59:20.936Z | 规则集:0.2.0

快速安装

将技能安装到你的 Agent

clawhub install elite-longterm-memory

技能介绍

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

使用场景

文档(原文)

来源:README.md
以下为作者原文(通常为英文)。安装请以页面顶部“快速安装”为准。

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

安全审计

低风险

摘要

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.

风险画像 危险 隐私 范围 声誉 质量

ToxicSkills 分析

黑名单
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提示词注入
未检测到

Toxic 标签

credential-access

当前静态检测未发现 Toxic 信号。

关键风险 0 项

暂无 LLM 风险要点(LLM 未启用或无缓存)。

确定性发现(证据)

规则 严重性 文件 片段
SENSITIVE_ENV skills/NextFrontierBuilds/elite-longterm-memory/bin/elite-memory.js 行 155
const lancedbPath = path.join(process.env.HOME, '.clawdbot/memory/lancedb');
QUALITY_README_PRESENT README 行 无
README detected

评分标准

每个技能从 5 个维度评分,加权总分决定星级。

代码毒性 100/100 (权重 30%)
隐私风险 90/100 (权重 25%)
权限范围 90/100 (权重 20%)
作者声誉 75/100 (权重 15%)
代码质量 78/100 (权重 10%)

星级说明

5★ 安全 — 总分 ≥ 80
4★ 良好 — 总分 70–79
3★ 注意 — 总分 60–69
2★ 有风险 — 总分 40–59
1★ 危险 — 总分 < 40

为何是这个评分?

所有维度均高于 60 分,该技能通过安全基线。

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