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#

bash

# 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.py

Option 2: Manual installation#

1. Download this skill folder
2. Place it in your OpenClaw skills directory: ~/.openclaw/skills/
3. Run initialization: python3 scripts/hbm_init.py

Option 3: Via ClawHub (if published)#

bash

openclaw skill install heartbeat-memories

🔄 Update#

bash

# Update from Git
cd ~/.openclaw/skills/heartbeat-memories && git pull origin main

# Re-initialize if needed
python3 scripts/hbm_init.py --upgrade

🩺 System Diagnostics#

bash

# Run diagnostic check
cd ~/.openclaw/skills/heartbeat-memories && python3 scripts/hbm_doctor.py

📁 System Architecture#

text

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:

1. Records important conversations to memory banks
2. Responds to trigger words for retrieval
3. Maintains memory bank integrity

Common Trigger Word Examples#

text

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)#

bash

# 1. Modify configuration
vim ~/.openclaw/skills/heartbeat-memories/config/hbm_config.json

# 2. Custom memory location
export HBM_MEMORY_PATH="~/my-memories"

⚙️ Technical Specifications#

ComponentSpecificationDescriptionVector DatabaseChromaDB + SQLiteFully local storageText Vectorizationall-MiniLM-L6-v2384 dimensions, 80MBModel Download SourceModelScope (China mirror)Fast and stableStorage FormatMarkdown (.md)Human readableCross-platform SupportWindows/Linux/macOSAuto-adapts pathsDependenciesPython 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#

bash

# 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#

python

# 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

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Heartbeat-Memories gives your OpenClaw true long-term memory, making it a smarter assistant that truly understands you!

Last updated: 2026-03-25