openclaw-memory-os

name	openclaw-memory-os
description	OpenClaw Memory-OS - Digital immortality service and cognitive continuity infrastructure for personal memory management
tags	["memory","knowledge-management","digital-immortality","cognitive-continuity","ai-memory","knowledge-graph","semantic-search","agent-memory","long-term-memory","openclaw"]

OpenClaw Memory-OS

Digital immortality service and cognitive continuity infrastructure

AI-powered personal memory management system for capturing, storing, and intelligently retrieving your digital memories.

数字永生服务 | 认知延续基础设施

Installation

Step 1: Install via ClawHub (Recommended)

# Install the skill
clawhub install openclaw-memory-os

Step 2: Install the npm package

# Global installation
npm install -g openclaw-memory-os

# Or from source
git clone https://github.com/ZhenRobotics/openclaw-memory-os.git
cd openclaw-memory-os
npm install
npm run build
npm link

Step 3: Initialize Memory-OS

# Initialize
openclaw-memory-os init

# Configure
openclaw-memory-os config set owner.name "Your Name"
openclaw-memory-os config set owner.email "[e-mail]"

Step 4: Verify Installation

openclaw-memory-os status

---

Usage

When to Use This Skill

AUTO-TRIGGER when user's message contains:

• Keywords: memory, remember, recall, 记忆, 回忆, 记住, 保存
• Wants to save information: "remember this", "save this note", "记住这个"
• Wants to retrieve information: "what did I say about", "find my notes on", "我之前关于...说了什么"
• Wants to manage knowledge: "organize my notes", "build knowledge graph"
• Agent needs long-term memory capabilities

TRIGGER EXAMPLES:

• "Remember: I prefer using TypeScript for all projects" ← Save as memory
• "What did I say about AI last week?" ← Query memory
• "Find all my notes about machine learning" ← Search memory
• "Show me what I did on March 1st" ← Timeline query
• "Build a knowledge graph of my ideas" ← Graph operation

DO NOT USE when:

• Simple reminders or todos (use task management)
• Real-time collaboration (use chat tools)

---

Core Features

Complete memory management system:

• 🎤 Multi-source Collection - Collect from chat, files, code, media
• 🧠 Semantic Search - AI-powered semantic understanding (planned)
• 📊 Knowledge Graph - Automatic relation discovery (planned)
• ⏰ Timeline - Complete temporal tracking
• 💬 Cognitive Chat - Talk with your digital self (planned)
• 🔒 Privacy-First - Local storage, full control
• 🔧 Extensible - Modular collectors and processors
• 🤖 Agent-Ready - Perfect for AI agent long-term memory

---

Agent Usage Guide

Important Notes

CRITICAL: This is a foundational system for digital memory. Use it for:

• Storing agent conversation history
• Building personal knowledge base
• Retrieving context from past interactions
• Maintaining long-term context

Package Name: When importing, use openclaw-memory-os:

import { MemoryOS, MemoryType } from 'openclaw-memory-os';

CLI Name: When using CLI, use openclaw-memory-os:

openclaw-memory-os init
openclaw-memory-os collect --source ~/Documents

Pattern 1: Save Memory

When user wants to remember something:

import { MemoryOS, MemoryType } from 'openclaw-memory-os';

const memory = new MemoryOS({});
await memory.init();

// Save text memory
await memory.collect({
  type: MemoryType.TEXT,
  content: 'User prefers using TypeScript for all projects',
  metadata: {
    tags: ['preference', 'development'],
    context: 'project-setup-discussion',
  },
});

// Response
console.log('✓ Saved to memory');

Pattern 2: Search Memory

When user wants to recall something:

// Semantic search
const results = await memory.search({
  query: 'TypeScript preferences',
  semantic: true,
  limit: 5,
});

// Display results
for (const result of results) {
  console.log(`[${result.memory.type}] ${result.memory.content}`);
  console.log(`  Relevance: ${(result.score * 100).toFixed(1)}%`);
  console.log(`  Date: ${result.memory.metadata.timestamp}`);
}

Pattern 3: Timeline Query

When user wants to see past activities:

// Query timeline
const timeline = await memory.timeline({
  date: new Date('2024-03-01'),
  range: 'day',
});

// Display
console.log(`\nActivities on ${timeline.date.toDateString()}:`);
console.log(`Total: ${timeline.stats.total} memories`);

timeline.memories.forEach(mem => {
  console.log(`- [${mem.type}] ${mem.content.substring(0, 60)}...`);
});

Pattern 4: Agent Long-term Memory

Integration with AI agents:

// In your agent conversation loop
async function handleConversation(userMessage: string) {
  // 1. Store current message
  await memory.collect({
    type: MemoryType.CHAT,
    content: {
      role: 'user',
      message: userMessage,
      timestamp: new Date(),
    },
    metadata: {
      source: 'agent-chat',
      tags: ['conversation'],
    },
  });

  // 2. Retrieve relevant context
  const relevant = await memory.search({
    query: userMessage,
    semantic: true,
    limit: 5,
  });

  // 3. Use context in response
  const context = relevant
    .map(r => `[${r.memory.metadata.timestamp}] ${r.memory.content}`)
    .join('\n');

  // 4. Generate response with context
  const response = await generateResponse({
    current: userMessage,
    context: context,
  });

  // 5. Store agent response
  await memory.collect({
    type: MemoryType.CHAT,
    content: {
      role: 'assistant',
      message: response,
      timestamp: new Date(),
    },
    metadata: {
      source: 'agent-chat',
      tags: ['conversation', 'response'],
    },
  });

  return response;
}

---

CLI Commands

Basic Operations

# Initialize
openclaw-memory-os init

# Collect memories
openclaw-memory-os collect --source ~/Documents
openclaw-memory-os collect --chat chat-export.json

# Search
openclaw-memory-os search "AI and machine learning"
openclaw-memory-os search --semantic "人工智能应用"

# Timeline
openclaw-memory-os timeline --date 2024-03-01
openclaw-memory-os timeline --range "last 7 days"

# Status
openclaw-memory-os status

Advanced Operations

# Graph operations (planned)
openclaw-memory-os graph explore --topic "AI"
openclaw-memory-os graph stats

# Maintenance
openclaw-memory-os rebuild
openclaw-memory-os optimize
openclaw-memory-os export ~/backup.json

---

Use Cases

1. Personal Knowledge Base

# Import all notes
openclaw-memory-os collect --source ~/Documents/Notes

# Search for specific topics
openclaw-memory-os search --semantic "machine learning algorithms"

# View knowledge graph (planned)
openclaw-memory-os graph explore

2. Agent Long-term Memory

// Agent with memory
import { MemoryOS, MemoryType } from 'openclaw-memory-os';

class MemoryAgent {
  private memory: MemoryOS;

  async initialize() {
    this.memory = new MemoryOS({});
    await this.memory.init();
  }

  async chat(userMessage: string) {
    // Store message
    await this.memory.collect({
      type: MemoryType.CHAT,
      content: userMessage,
    });

    // Retrieve context
    const context = await this.memory.search({
      query: userMessage,
      limit: 5,
    });

    // Use context in response...
  }
}

3. Developer Memory

# Collect code repos
openclaw-memory-os collect --code ~/projects

# Search code patterns
openclaw-memory-os search "authentication implementation"

# Timeline of changes
openclaw-memory-os timeline --type code --range "last month"

---

Configuration

Memory-OS stores config in ~/.memory-os/config.json:

{
  "storage": {
    "path": "~/.memory-os/data",
    "backend": "local"
  },
  "embedding": {
    "provider": "openai",
    "apiKey": "${OPENAI_API_KEY}",
    "model": "text-embedding-3-small"
  },
  "collectors": {
    "auto": true,
    "sources": ["~/Documents", "~/Downloads"]
  },
  "privacy": {
    "encryption": false,
    "shareStats": false
  }
}

---

Development

Project Structure

openclaw-memory-os/
├── src/
│   ├── core/         # Core engine
│   ├── collectors/   # Data collectors
│   ├── storage/      # Storage layer
│   ├── query/        # Query engine
│   ├── agents/       # Agent system
│   └── cli/          # CLI tool
├── docs/             # Documentation
└── tests/            # Tests

Adding Custom Collectors

import { BaseCollector, MemoryType } from 'openclaw-memory-os';

export class CustomCollector extends BaseCollector {
  constructor() {
    super('custom', MemoryType.TEXT);
  }

  async collect(source: string): Promise<CollectResult> {
    // Implement collection logic
    const memories = [];
    // ... collect data
    return {
      collected: memories.length,
      failed: 0,
      memories,
    };
  }

  async validate(source: string): Promise<boolean> {
    // Validate source
    return true;
  }
}

---

Architecture

┌─────────────────────────────────────────┐
│         Memory-OS Core                   │
├─────────────────────────────────────────┤
│                                          │
│  Collectors → Processors → Storage       │
│      ↓            ↓           ↓          │
│  Multi-source  AI Process  Multi-layer  │
│                                          │
│  Query & Retrieval Engine                │
│      ↓                                   │
│  Cognitive Interface                     │
│                                          │
└─────────────────────────────────────────┘

See ARCHITECTURE.md for details.

---

Skill Capabilities

This skill enables:

• ✅ Long-term memory for AI agents
• ✅ Personal knowledge management
• ✅ Semantic search across all memories (planned)
• ✅ Temporal memory tracking
• ✅ Knowledge graph construction (planned)
• ✅ Privacy-preserving local storage
• ✅ Extensible collector system
• ✅ Multi-modal memory support (planned)

---

Documentation

• README - Complete guide
• Architecture - System design
• Quickstart - 5-minute guide

---

Links

• ClawHub: https://clawhub.ai/ZhenStaff/openclaw-memory-os
• npm: https://www.npmjs.com/package/openclaw-memory-os
• GitHub: https://github.com/ZhenRobotics/openclaw-memory-os
• Issues: https://github.com/ZhenRobotics/openclaw-memory-os/issues

---

Contributing

OpenClaw Memory-OS is open source. Contributions welcome!

---

License

MIT License

---

Memory-OS - Digital Immortality Through Memory

Version: 0.1.0 Skill Name: openclaw-memory-os Package Name: openclaw-memory-os