SkillHub

fast-unified-memory

v1.0.1

提供高性能统一内存，结合基于文件的OpenClaw存储与本地Ollama嵌入的语义向量搜索，实现快速且私密的...

Sourced from ClawHub, Authored by Broedkrummen

Installation

Please help me install the skill `fast-unified-memory` from SkillHub official store. npx skills add Broedkrummen/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

# 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