- name
- Heartbeat Memories
- slug
- heartbeat-memories
- version
- 1.0.0
- author
- JamieYang9996
- description
- >
- triggers
Heartbeat-Memories (HBM) - OpenClaw Skill
Solving the pain points of scattered memory files and emotionless AI responses Through five specialized memory banks + heartbeat recall simulation dialogue, making your OpenClaw truly understand you, remember you, and build exclusive emotional connections.
🚀 One-Click Installation (Manual)
Option 1: Install via Git
# Clone the repository
git clone https://github.com/JamieYang9996/Heartbeat-Memories.git
# Copy to OpenClaw skills directory (adjust path as needed)
cp -r Heartbeat-Memories ~/.openclaw/skills/heartbeat-memories
# Initialize the memory system
cd ~/.openclaw/skills/heartbeat-memories && python3 scripts/hbm_init.pyOption 2: Manual installation
- Download this skill folder
- Place it in your OpenClaw skills directory:
~/.openclaw/skills/ - Run initialization:
python3 scripts/hbm_init.py
Option 3: Via ClawHub (if published)
openclaw skill install heartbeat-memories🔄 Update
# Update from Git
cd ~/.openclaw/skills/heartbeat-memories && git pull origin main
# Re-initialize if needed
python3 scripts/hbm_init.py --upgrade🩺 System Diagnostics
# Run diagnostic check
cd ~/.openclaw/skills/heartbeat-memories && python3 scripts/hbm_doctor.py📁 System Architecture
heartbeat-memories/
├── SKILL.md # This file
├── README.md # Detailed documentation
├── memory/ # Memory bank templates
│ ├── 目标记忆库/GOALS_template.md
│ ├── 经验记忆库/TIPS_template.md
│ ├── 情感记忆库/DAILY_EMOTIONS_template.md
│ ├── 会话记忆库/YYYY-MM-DD_template.md
│ ├── 版本记忆库/CHANGELOG_template.md
│ └── 心跳回忆/心跳回忆机制.md
├── scripts/ # Core scripts
│ ├── hbm_init.py # Initialization script
│ ├── local_memory_system_v2.py
│ └── rag_system.py
├── config/ # Configuration files
│ └── hbm_config_template.json
└── requirements.txt # Python dependencies🎯 Core Features
1. Five Memory Banks (Automatic Recording)
- Goals Memory: Tracks user goals with P0/P1/P2 priorities
- Experience Memory: Records technical problems and solutions
- Emotion Memory: Analyzes user emotions and habit preferences
- Session Memory: Daily conversation summaries (10:1 compression ratio)
- Version Memory: System change history records
2. Semantic Search (Vector Retrieval)
- Based on ChromaDB vector database
- Natural language query of memory content
- Local model: all-MiniLM-L6-v2 (80MB, auto-download from ModelScope)
3. Heartbeat Recall Emotional Interaction (Core Innovation)
Solves AI's lengthy, emotionless responses by mimicking human conversation for long-term connections
🎭 Highly Realistic Human Interaction
- Smart Triggering: AI actively recalls like a friend (e.g., "By the way, remember last week's 'seaside café' 'sunset' 'photos', did you end up going?")
- Natural Conversation Flow: Randomly inserts memories during daily chats (30% probability), avoiding mechanical feel
- Emotional Intelligence: Analyzes user emotional state, adjusts interaction style
⚙️ Flexible Configurable System
- Configurable Probabilities: Each trigger scene has adjustable probability (30%/50%/100%)
- Frequency Control: Daily limits, minimum intervals, special holiday rules
- Scene Customization: Supports daily conversation, task completion, forgotten goals, holiday care, etc.
- Sensitive Day Avoidance: Automatically avoids sensitive holidays like Qingming Festival
🌱 Long-term Cultivation & Exclusivity
- Habit Learning: Records user work patterns, preferred topics, common keywords
- Exclusive Memories: Builds personalized memory bank based on historical conversations
- Progressive Optimization: Continuously optimizes trigger timing and wording through silent review
- Emotional Evolution: AI understands users better over time, building real "long-term relationships"
4. RAG Retrieval Augmentation (Optional)
- Improves answer accuracy and relevance
- Retrieves context from memory banks
- Configurable switch controls (default: OFF)
🔧 Usage
Basic Usage (Out-of-the-box)
After installation, Heartbeat-Memories automatically:
- Records important conversations to memory banks
- Responds to trigger words for retrieval
- Maintains memory bank integrity
Common Trigger Word Examples
User: "Save this, I want to learn Python"
AI: ✅ Recorded to Goals Memory
User: "How did we solve that server issue last time?"
AI: 🔍 Retrieved solution from Experience Memory...
User: "Check my goals for today"
AI: 📄 Reading from Goals Memory...
User: "Help me recall things we discussed last week"
AI: ❤️ Remember last week's "seaside café"...Advanced Configuration (Optional)
# 1. Modify configuration
vim ~/.openclaw/skills/heartbeat-memories/config/hbm_config.json
# 2. Custom memory location
export HBM_MEMORY_PATH="~/my-memories"⚙️ Technical Specifications
| Component | Specification | Description |
|---|---|---|
| Vector Database | ChromaDB + SQLite | Fully local storage |
| Text Vectorization | all-MiniLM-L6-v2 | 384 dimensions, 80MB |
| Model Download Source | ModelScope (China mirror) | Fast and stable |
| Storage Format | Markdown (.md) | Human readable |
| Cross-platform Support | Windows/Linux/macOS | Auto-adapts paths |
| Dependencies | Python 3.8+ | chromadb, sentence-transformers |
🐛 Troubleshooting
Common Issues
Q: No response after installation? A: Ensure correct directory: ~/.openclaw/skills/heartbeat-memories/, restart OpenClaw.
Q: Model download failed? A: Manual download: python3 scripts/download_model.py, or use mirror sources.
Q: Insufficient storage space? A: Memory bank files are small, vector model 80MB, RAG logs auto-compress monthly.
Q: Cross-platform compatibility? A: Adapted for Windows (WSL/Git Bash), Linux, macOS, auto-detects system.
Diagnostic Commands
# Check Heartbeat-Memories status
cd ~/.openclaw/skills/heartbeat-memories && python3 scripts/hbm_init.py --check
# View memory banks
ls -la ~/.openclaw/skills/heartbeat-memories/memory/
# Test semantic search
python3 scripts/local_memory_system_v2.py --test📈 Advanced Features
RAG System Optimization (Optional)
- Token Limit & Deduplication: Prevents overly long answers (default: OFF)
- Memory Cache: Improves retrieval speed (default: OFF)
- Log Compression: Auto-compresses log files monthly
Custom Extensions
# Extend new memory bank types
# Add new collections in scripts/local_memory_system_v2.py
# Custom trigger logic
# Modify trigger conditions in 心跳回忆/心跳回忆机制.md🛡️ Security & Privacy Statement (Required by ClawHub)
✅ 100% Local Operation
- All data stored locally on user's machine
- No data uploaded to any servers
- No API calls to external services
✅ No Automatic Background Processes
- No cron jobs - All operations are manually triggered by user or OpenClaw
- No system services - No daemons or background processes
- No auto-start - Does not run automatically on system boot
✅ No Privilege Escalation
- Operates only within skill directory and user workspace
- Does not access system files or other user directories
- All file operations are within permitted scope
✅ Transparent Installation
- All dependencies listed in requirements.txt
- No silent installation of packages
- Clear prompts for user confirmation
✅ Data Ownership
- Users own all their memory data
- Can export/backup memory banks at any time
- Can completely uninstall without data loss (manual backup recommended)
🤝 Contribution & Feedback
GitHub Repository
- Project URL: https://github.com/JamieYang9996/Heartbeat-Memories
- Issues: Report problems or suggest features
- Pull Requests: Code contributions welcome
Community Support
- OpenClaw Discord: https://discord.com/invite/clawd
- Chinese discussion: Telegram/WeChat groups (if available)
📝 License
MIT License - See LICENSE file
Heartbeat-Memories gives your OpenClaw true long-term memory, making it a smarter assistant that truly understands you!
*Last updated: 2026-03-25*