Docker: Runs all persistent memory infrastructure (databases & API)
NPX: Runs Desktop Commander for direct system access
This hybrid approach maximizes both isolation (Docker) and system control (NPX)
# Location: C:\Users\ourpr\OneDrive\Desktop\SLOS-ROM\Docker-Memory-Test\ services: redis: image: redis:alpine container_name: slos-redis ports: ["6379:6379"] volumes: ["./data/redis:/data"] chromadb: image: chromadb/chroma container_name: slos-chroma ports: ["8000:8000"] volumes: ["./data/chroma:/chroma/chroma"] api: build: ./api container_name: slos-api ports: ["8080:8080"] depends_on: [redis, chromadb]
# Desktop Commander runs directly on Windows via NPX
{
"mcpServers": {
"desktop-commander": {
"command": "npx",
"args": ["-y", "@wonderwhy-er/desktop-commander@latest"]
}
}
}
| Component | Deployment | Reason |
|---|---|---|
| Redis | Docker Container | Isolated, persistent, networked database |
| ChromaDB | Docker Container | Complex dependencies, easier via container |
| FastAPI | Docker Container | Clean Python environment, auto-restart |
| Desktop Commander | NPX (Native) | Needs direct OS access for file/process control |
# Store session state desktop-commander:start_process "python3 -i" # Interact with Redis directly import redis r = redis.Redis(host='localhost', port=6379) r.set('session:49:energy', 8) r.hset('tasks', 'dan_hollis', '7-star-complete') # Retrieve state energy = r.get('session:49:energy') task_status = r.hget('tasks', 'dan_hollis')
# Store semantic patterns import chromadb client = chromadb.HttpClient(host='localhost', port=8000) collection = client.get_or_create_collection("patterns") # Add pattern with metadata collection.add( documents=["Dan Hollis proposal achieved 7-star value"], metadatas=[{"type": "success", "score": 10}], ids=["pattern_001"] ) # Query similar patterns results = collection.query( query_texts=["client success patterns"], n_results=5 )
| Workflow | Commands | Purpose |
|---|---|---|
| Boot SLOS System | cd C:\...\Docker-Memory-Test; docker-compose up -d |
Start all memory containers |
| Store Session State | curl -X POST http://localhost:8080/state -d "{...}" |
Persist current session data |
| Analyze CSV Data | start_process("python3 -i") → pandas operations |
Process local data files |
| Pattern Recognition | ChromaDB similarity search |
Find semantic patterns |
| Multi-Agent Handoff | Redis state update → Agent switch |
Coordinate between agents |
| System Monitoring | list_processes() → analyze resources |
Track system performance |
All agents access the same Redis + ChromaDB instances, enabling seamless handoffs and collective intelligence building.
# Agent 1: SLOS Operational redis.hset('agent:status', 'slos', 'active') redis.lpush('task:queue', 'analyze_proposal') # Agent 2: Strategic Advisor task = redis.rpop('task:queue') redis.hset('agent:status', 'strategic', 'processing') # Pattern sharing via ChromaDB collection.add( documents=["Strategic insight from analysis"], metadatas=[{"agent": "strategic", "timestamp": now}] )
Combine file reading, data analysis, pattern matching, and document generation in a single workflow.
Query ChromaDB for similar past situations, retrieve context, apply learned patterns.
Clone repos, install dependencies, configure tools, start services - all automated.
Container CPU Memory Port ───────────────────────────────────── slos-redis 0.1% 12MB 6379 slos-chroma 0.3% 156MB 8000 slos-api 0.2% 45MB 8080 desktop-cmd 0.5% 89MB -
System-critical commands are blocked: format, diskpart, shutdown, reboot, iptables, etc.
Full C:\ access granted. Use responsibly. All operations logged.