Deep Memory

One-click clone of a production-grade semantic memory system: HOT/WARM/COLD tiered storage + Qdrant vector DB + Neo4j graph DB + qwen3-embedding. Enables cro...

Deep Memory Skill 🧠

A production-grade semantic memory system for AI agents. Combines tiered file storage with vector search and graph relationships.

Architecture

┌─────────────────────────────────────┐
│        File Layer (always-on)        │
│  HOT / WARM / COLD Markdown files   │
│  semantic_memory.json               │
└──────────────┬──────────────────────┘
               ↓
┌─────────────────────────────────────┐
│        Vector Layer (Docker)         │
│  Qdrant: semantic similarity search │
│  Collection: semantic_memories       │
│  Dimensions: 4096 (qwen3-embedding)  │
└──────────────┬──────────────────────┘
               ↓
┌─────────────────────────────────────┐
│        Graph Layer (Docker)          │
│  Neo4j: entity relationship memory  │
│  Constraints: Memory.key + Entity.id │
└─────────────────────────────────────┘
               ↓
┌─────────────────────────────────────┐
│     Embedding Model (Ollama)         │
│  qwen3-embedding:8b (4096 dims)      │
│  Local, free, no API calls          │
└─────────────────────────────────────┘

Prerequisites

• Docker Desktop (running)
• Ollama installed (brew install ollama on macOS)

Usage

Setup (first time)

python3 ~/.openclaw/workspace/skills/deep-memory/scripts/setup.py

Write a memory

from deep_memory import MemorySystem
mem = MemorySystem()
mem.store("user_sir", "Sir prefers direct communication, no pleasantries", tags=["preference", "communication"])

Search memories

results = mem.search("how does Sir like to communicate?", top_k=5)
for r in results:
    print(r['content'], r['score'])

Joint query (vector + graph)

results = mem.joint_query("investment strategy", entity="Sir", top_k=3)

Setup Flow

When triggered, the setup script will:

1. Check Docker is running
2. Check Ollama is installed and pull qwen3-embedding:8b if needed
3. Start Qdrant container (port 6333/6334)
4. Start Neo4j container (port 7474/7687)
5. Create Qdrant collection (semantic_memories, 4096 dims, Cosine)
6. Create Neo4j constraints (Memory.key, Entity.id)
7. Create HOT/WARM/COLD directory structure
8. Copy Python toolkit to workspace
9. Run end-to-end verification test

Agent Integration

In your SOUL.md or AGENTS.md, add:

## Memory Retrieval
Before answering questions about prior work, decisions, or preferences:
1. Run: python3 ~/.openclaw/workspace/.lib/qdrant_memory.py search "<query>"
2. Combine with memory_search tool results
3. Use top results as context