DEV Community

Turbo31150
Turbo31150

Posted on

I built JARVIS OS: 1000+ autonomous AI agents, on-prem, <300ms voice latency — here's the full architecture

#ai #python #opensource #machinelearning

I've spent the last 3 years building JARVIS OS — a fully autonomous, on-premise AI infrastructure that runs 1000+ autonomous agents simultaneously, processes voice in under 300ms, and costs a fraction of cloud alternatives.

Today I'm sharing the full architecture, the key decisions, and the lessons learned.

→ Live site & full details: jarvis-delmas.netlify.app

What is JARVIS OS?

JARVIS OS is a distributed AI operating system designed to run entirely on your own hardware — no OpenAI, no Azure, no data leaving your infrastructure.

Key production numbers:

  • 1000+ autonomous agents running simultaneously
  • <300ms voice latency (Whisper CUDA optimized)
  • 835 auto-healing pipelines with circuit-breakers
  • 280,741 lines of Python across 60 MIT-licensed repos
  • 12 GPUs in cluster
  • Benchmark: 81.6/100 (record session: 97/100)
  • -72% infrastructure cost vs equivalent cloud setup

The 9-Layer Architecture 

Layer 1: Hardware (GPU cluster, NVMe, InfiniBand)
Layer 2: OS + Virtualization (Linux, Docker, CUDA)
Layer 3: LLM Engine (LM Studio, Ollama, multi-model routing)
Layer 4: Memory System (working → episodic → semantic → procedural)
Layer 5: Agent Orchestration (OpenClaw Gateway, 1000+ agents)
Layer 6: MCP Toolkit (88 handlers, 20+ connectors)
Layer 7: Pipeline Engine (835 Domino auto-healing pipelines)
Layer 8: Voice Interface (Whisper → LLM → TTS <300ms)
Layer 9: External APIs (TradeOracle, Telegram, GitHub)
Enter fullscreen mode Exit fullscreen mode

5 Architectural Decisions That Made the Difference

1. On-Premise by Design

Most teams start with cloud and try to migrate later. We started on-prem from day one.

Result: zero cold start, zero API rate limits, GDPR-native.

Cost comparison:

  • Cloud equivalent: €50,000–500,000/year
  • JARVIS OS: one-shot deployment + maintenance

2. Protocol-First with MCP

Instead of direct integrations, everything goes through the Model Context Protocol (MCP).

Our MCP Toolkit has 88 handlers connecting: filesystem, GitHub, Notion, Slack, PostgreSQL, Redis, vector DBs, Telegram, browser automation, and custom CUDA endpoints.

Any new agent instantly has access to all 88 capabilities.

3. 4-Layer Memory Architecture 

# Memory hierarchy in JARVIS OS
working_memory    = RedisCache(ttl=3600)           # Current context
episodic_memory   = PostgreSQL(table="episodes")   # Recent events  
semantic_memory   = ChromaDB(collection="knowledge") # Facts & concepts
procedural_memory = FileSystem(path="./skills/")   # Learned skills
Enter fullscreen mode Exit fullscreen mode

The Π-vectorial compression achieves a 15:1 compression ratio — 15x more context in the same token budget.

4. Auto-Healing Pipelines

All 835 pipelines have built-in circuit-breakers and 13 auto-trigger mechanisms.

@circuit_breaker(failure_threshold=3, recovery_timeout=60)
@auto_retry(max_attempts=3, backoff_factor=2)
async def run_pipeline(pipeline_id: str, context: dict):
    # Pipeline execution with automatic recovery
    ...
Enter fullscreen mode Exit fullscreen mode

5. Voice Pipeline Under 300ms

Stack: Whisper (CUDA) → LLM routing → TTS → audio output

Optimizations:

  • CUDA-optimized Whisper with float16 precision
  • Streaming inference (token-by-token TTS)
  • Wake word detection on a separate thread
  • Audio buffer pre-warming

Average benchmark: 247ms end-to-end on P95 GPU.

The Open-Source Stack 

LLMs:          Ollama, LM Studio, GGUF models
Orchestration: OpenClaw Gateway (custom, MIT)
Memory:        PostgreSQL + pgvector, ChromaDB, Redis
Voice:         Whisper CUDA, custom TTS pipeline
MCP:           88 custom handlers
Containers:    Docker (10 services), NVIDIA GPU Operator
Monitoring:    Prometheus + Grafana
Languages:     Python (primary), Rust (performance-critical)
Enter fullscreen mode Exit fullscreen mode

All 60 repos available on GitHub under MIT license:
👉 github.com/Turbo31150

Real-World Modules Running on JARVIS OS

  • TradeOracle — 7 LLMs in consensus for crypto/equity signals
  • Healthcare Multi-Agent — FHIR-compatible medical transcription
  • Domino Engine — 835 self-healing data pipelines
  • OpenClaw Gateway — orchestrates 1000+ agents in production

Key Lessons After 3 Years

  1. Start on-prem — cloud migration is 10x harder than building on-prem from day 1
  2. Protocols over integrations — MCP saved us from integration hell
  3. Memory is the hardest problem — 80% of agent failures are memory coherence issues
  4. Voice latency is binary — users accept <300ms, reject >500ms
  5. Auto-healing or nothing — production pipelines need circuit-breakers from day 1

Learn to Build Your Own

If you want to build a similar system, I've documented everything:

🎓 Claude Code Mastery — 13 lessons, build your own agent system in 4 weeks

  • Module 1: FREE → your first agent in 30 minutes
  • Bundle M2+M3: €477 early-bird (vs €797)
  • 14-day "Agent or Refunded" guarantee

📚 62 PDF formations — from beginner to JARVIS expert
🚀 Clé-en-main deployment — I deploy on your hardware in 2–8 weeks

👉 jarvis-delmas.netlify.app

Questions? I answer everything in the comments.
GitHub: github.com/Turbo31150 — 60 repos, all MIT

Top comments (0)