openclaw-memory-enhancer（OpenClaw 记忆 enhancer）

🧠 OpenClaw Memory Enhancer

Give OpenClaw long-term memory - remember important information across sessions and automatically recall relevant context for conversations.

Core Capabilities

Quick Reference

When to Use

Use this skill when:

• You want OpenClaw to remember things across sessions
• You need to build a knowledge base from chat history
• You're working on long-term projects that need context
• You want automatic FAQ generation from conversations
• You're running on edge devices with limited memory

Don't use when:

• Simple note-taking apps are sufficient
• You don't need cross-session memory
• You have plenty of memory and want maximum accuracy (use standard version)

Versions

Edge Version ⭐ Recommended

Best for: Jetson, Raspberry Pi, embedded devices

python3 memory_enhancer_edge.py --load

Features:

• Zero dependencies (Python stdlib only)
• Memory usage < 10MB
• Lightweight keyword + vector matching
• Perfect for resource-constrained devices

Standard Version

Best for: Desktop/server, maximum accuracy

pip install sentence-transformers numpy
python3 memory_enhancer.py --load

Features:

• Uses sentence-transformers for high-quality embeddings
• Better semantic understanding
• Memory usage 50-100MB
• Requires model download (~50MB)

Installation

Via ClawHub (Recommended)

clawhub install openclaw-memory-enhancer

Via Git

git clone https://github.com/henryfcb/openclaw-memory-enhancer.git \
  ~/.openclaw/skills/openclaw-memory-enhancer

Usage Examples

Command Line

# Load existing OpenClaw memories
cd ~/.openclaw/skills/openclaw-memory-enhancer
python3 memory_enhancer_edge.py --load

# Search for memories
python3 memory_enhancer_edge.py --search "voice-call plugin setup"

# Add a new memory
python3 memory_enhancer_edge.py --add "User prefers dark mode"

# Show statistics
python3 memory_enhancer_edge.py --stats

# Export to Markdown
python3 memory_enhancer_edge.py --export

Python API

from memory_enhancer_edge import MemoryEnhancerEdge

# Initialize
memory = MemoryEnhancerEdge()

# Load existing memories
memory.load_openclaw_memory()

# Search for relevant memories
results = memory.search_memory("AI trends report", top_k=3)
for r in results:
    print(f"[{r['similarity']:.2f}] {r['content'][:100]}...")

# Recall context for a conversation
context = memory.recall_for_prompt("Help me check billing")
# Returns formatted memory context

# Add new memory
memory.add_memory(
    content="User prefers direct results",
    source="chat",
    memory_type="preference"
)

OpenClaw Integration

# In your OpenClaw agent
from skills.openclaw_memory_enhancer.memory_enhancer_edge import MemoryEnhancerEdge

class EnhancedAgent:
    def __init__(self):
        self.memory = MemoryEnhancerEdge()
        self.memory.load_openclaw_memory()
    
    def process(self, user_input: str) -> str:
        # 1. Recall relevant memories
        memory_context = self.memory.recall_for_prompt(user_input)
        
        # 2. Enhance prompt with context
        enhanced_prompt = f"""
{memory_context}

User: {user_input}
"""
        
        # 3. Call LLM with enhanced context
        response = call_llm(enhanced_prompt)
        
        return response

Memory Types

How It Works

Memory Encoding (Edge Version)

1. Keyword Extraction: Extract important words from text
2. Hash Vector: Map keywords to vector positions
3. Normalization: L2 normalize the vector
4. Storage: Save to local JSON file

Memory Retrieval

1. Query Encoding: Convert query to same vector format
2. Keyword Pre-filter: Fast filter by common keywords
3. Similarity Calculation: Cosine similarity between vectors
4. Ranking: Return top-k most similar memories

Privacy Protection

• All data stored locally in ~/.openclaw/workspace/knowledge-base/
• No network requests
• No external API calls
• No data leaves your device

Technical Specifications

Edge Version

Vector Dimensions: 128
Memory Usage: < 10MB
Dependencies: None (Python stdlib)
Storage Format: JSON
Max Memories: 1000 (configurable)
Query Latency: < 100ms

Standard Version

Vector Dimensions: 384
Memory Usage: 50-100MB
Dependencies: sentence-transformers, numpy
Storage Format: NumPy + JSON
Model Size: ~50MB download
Query Latency: < 50ms

Configuration

Edit these parameters in the code:

self.config = {
    "vector_dim": 128,        # Vector dimensions
    "max_memory_size": 1000,  # Max number of memories
    "chunk_size": 500,        # Content chunk size
    "min_keyword_len": 2,     # Minimum keyword length
}

Troubleshooting

No results found

# Lower the threshold
results = memory.search_memory(query, threshold=0.2)  # Default 0.3

# Increase top_k
results = memory.search_memory(query, top_k=10)  # Default 5

Memory limit reached

The system automatically removes oldest memories when limit is reached.

To increase limit:

self.config["max_memory_size"] = 5000  # Increase from 1000

Slow performance

• Use Edge version instead of Standard
• Reduce max_memory_size
• Use keyword pre-filtering (automatic)

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a Pull Request

License

MIT License - See LICENSE file for details.

Acknowledgments

• Built for the OpenClaw ecosystem
• Optimized for edge computing devices
• Inspired by long-term memory systems in AI

Not an official OpenClaw or Moonshot AI product.

Users must provide their own OpenClaw workspace and API keys.