| name | fast-unified-memory |
| description | Fast Unified Memory |
Skill: Fast Unified Memory
A high-performance unified memory system that integrates OpenClaw memory with semantic memory storage using Ollama's nomic-embed-text model for ultra-fast embeddings.
Overview
This skill provides a unified memory layer that combines:
- OpenClaw Memory: Standard file-based memory storage
- Semantic Memory: Vector-based memory using Ollama embeddings
Features
- ⚡ Ultra-fast: ~130ms for combined search (embedding ~40ms + search ~90ms)
- 🔒 Private: All processing done locally via Ollama
- 💰 Free: No API costs - uses local Ollama instance
- 🧠 Semantic: Uses nomic-embed-text for intelligent similarity matching
Requirements
- Ollama installed and running
nomic-embed-text model pulled: ollama pull nomic-embed-text
Installation
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull nomic-embed-text
ollama serve
Usage
Commands
node fast-unified-memory.js search "your query"
node fast-unified-memory.js add "User prefers concise responses"
node fast-unified-memory.js list
node fast-unified-memory.js stats
Architecture
┌─────────────────────────────────────────────┐
│ FAST UNIFIED MEMORY │
│ │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ OpenClaw │ │ Semantic │ │
│ │ Memory │ │ Memory │ │
│ │ (files) │ │ (vectors) │ │
│ └─────────────┘ └─────────────┘ │
│ ↓ ↓ │
│ [Keyword Match] [Cosine Similarity] │
│ │
│ Unified Results (ranked) │
└─────────────────────────────────────────────┘
Performance
| Metric | Value |
|---|
| Embedding generation | ~40ms |
| Vector search | ~50ms |
| File search | ~40ms |
| Total search | ~130ms |
Configuration
The skill uses these defaults:
- Ollama URL:
http://localhost:11434
- Embedding model:
nomic-embed-text
- Memory storage:
~/.mem0/fast-store.json
- OpenClaw memory:
~/.openclaw/workspace/memory/
Files
fast-unified-memory.js - Main CLI tool
SKILL.md - This documentation
Troubleshooting
Ollama not running:
ollama serve
Model not found:
ollama pull nomic-embed-text
Port conflict:
The skill assumes Ollama is on port 11434. Update the OLLAMA_URL constant if using a different port.
License
MIT