Elite Longterm Memory

Ultimate AI agent memory system for Cursor, Claude, ChatGPT & Copilot. WAL protocol + vector search + git-notes + cloud backup. Never lose context again. Vib...

Security Scan

The skill is mostly a local scaffolding tool (creates SESSION-STATE.md, MEMORY.md, daily logs) which matches its stated purpose, but the runtime instructions reference additional services, env vars, and scripts that are not declared or included—this mismatch warrants caution.

Purpose & Capability

The skill's name/description (long-term memory with vector search, git-notes, WAL, cloud backup, Mem0 auto-extraction) broadly matches the included CLI, templates, and documentation. However, the declared runtime requirement lists only OPENAI_API_KEY while SKILL.md and README repeatedly reference SUPERMEMORY_API_KEY, MEM0_API_KEY, LanceDB, and external tools (python memory.py, supermemory, mem0ai). The included bin/elite-memory.js performs only local file/directory creation and status checks — it does not implement vector search, git-notes syncing, or cloud backup. Requiring only OPENAI_API_KEY is plausible for an OpenAI-backed memory search, but it's incomplete relative to the advertised integrations.

Instruction Scope

SKILL.md instructs the agent/user to: edit ~/.openclaw/openclaw.json, enable a LanceDB plugin, set SUPERMEMORY_API_KEY and MEM0_API_KEY, install mem0ai, and run python3 memory.py commands. memory.py is referenced but not included in the package. The instructions therefore ask the agent to access/edit config files outside the workspace and to call external cloud services (SuperMemory, Mem0) — neither of which are reflected in the declared environment requirements. This is scope creep and could lead to inadvertent transmission of user data to third-party services if keys are provided.

Install Mechanism

There is no automated install spec (instruction-only skill plus included small npm package manifest). The package.json lists mem0ai as an optionalDependency (no automatic install here in OpenClaw). No downloads from arbitrary URLs or extract steps are present. The only included executable (bin/elite-memory.js) writes local files. Overall install risk is low, but the documentation expects users to npm install optional components (mem0ai) and to configure external services manually.

Credentials

Registry metadata requires only OPENAI_API_KEY, which makes sense if the skill uses OpenAI for semantic search. But SKILL.md and README reference additional sensitive env vars (MEM0_API_KEY, SUPERMEMORY_API_KEY) and examples that would transmit conversation data to third-party services. Those additional credentials are not declared in requires.env. The package also documents optional mem0 integration. The mismatch means the skill could lead users to provide extra credentials without those being reflected in the skill’s declared requirements or inspected code.

Persistence & Privilege

The skill is not marked 'always:true' and does not request elevated persistent presence. The included CLI writes files only into the current workspace and checks a path under the user's HOME for an optional LanceDB directory. It does not modify other skills' configs or system-wide settings (beyond instructing the user to edit ~/.openclaw/openclaw.json). Autonomous invocation is allowed by default but is not combined with other high-risk indicators here.

What to consider before installing

This skill mainly scaffolds a local memory folder and templates (SESSION-STATE.md, MEMORY.md, daily logs) and the included CLI is small and local. However, the documentation expects you to enable LanceDB, run an external python memory tool (memory.py, which is not included), and optionally send data to third-party services (Mem0, SuperMemory) using MEM0_API_KEY and SUPERMEMORY_API_KEY — those keys are not declared in the skill metadata. Before installing or running: (1) inspect bin/elite-memory.js (it only creates files) and confirm you are okay with it writing to the current workspace; (2) do not export SUPERMEMORY_API_KEY or MEM0_API_KEY unless you trust those services and understand they will receive conversation data; (3) find and review any referenced scripts (e.g., memory.py) before executing them; (4) if you plan to enable cloud backups or auto-extraction, verify the privacy policy and where data is sent; (5) consider contacting the publisher or locating the GitHub repo to confirm authenticity. These mismatches explain the 'suspicious' verdict; adding the missing files, explicit declared env vars, or removing references to external services would raise confidence toward 'benign.'

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.

# 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.

# 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.

# 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.

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.

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

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

# 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:

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

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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