Openclaw Memory Enhancer

🧠 OpenClaw Memory Enhancer

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

Core Capabilities

| Capability | Description | |------------|-------------| | 🔍 Semantic Search | Vector similarity search, understanding intent not just keywords | | 📂 Auto Load | Automatically reads all files from memory/ directory | | 💡 Smart Recall | Finds relevant historical memory during conversations | | 🔗 Memory Graph | Builds connections between related memories | | 💾 Local Storage | 100% local, no cloud, complete privacy | | 🚀 Edge Optimized | <10MB memory, runs on Jetson/Raspberry Pi |

Quick Reference

| Task | Command (Edge Version) | Command (Standard Version) | |------|------------------------|---------------------------| | Load memories | python3 memory_enhancer_edge.py --load | python3 memory_enhancer.py --load | | Search | --search "query" | --search "query" | | Add memory | --add "content" | --add "content" | | Export | --export | --export | | Stats | --stats | --stats |

When to Use

Use this skill when:

Don't use when:

Versions

Edge Version ⭐ Recommended

Best for: Jetson, Raspberry Pi, embedded devices

python3 memory_enhancer_edge.py --load

Features:

Standard Version

Best for: Desktop/server, maximum accuracy

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

Features:

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

| Type | Description | Example | |------|-------------|---------| | daily_log | Daily memory files | memory/2026-02-22.md | | capability | Capability records | Skills, tools | | core_memory | Core conventions | Important rules | | qa | Question & Answer | Q: How to... A: You should... | | instruction | Direct instructions | "Remember: always do X" | | solution | Technical solutions | Step-by-step guides | | preference | User preferences | "User likes dark mode" |

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

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

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

Not an official OpenClaw or Moonshot AI product.

Users must provide their own OpenClaw workspace and API keys.

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
}

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