python3 memory_enhancer_edge.py --load

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

clawhub install openclaw-memory-enhancer

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

# 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

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"
)

# 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

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

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

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
}

# 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

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