Fast Unified Memory

Provides a high-performance unified memory combining file-based OpenClaw storage with semantic vector search using local Ollama embeddings for fast, private...

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

# Install Ollama first
curl -fsSL https://ollama.ai/install.sh | sh

# Pull the embedding model
ollama pull nomic-embed-text

# Start Ollama
ollama serve

Usage

Commands

# Search both memory systems
node fast-unified-memory.js search "your query"

# Add a memory
node fast-unified-memory.js add "User prefers concise responses"

# List all memories
node fast-unified-memory.js list

# Show system stats
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