AI Belongs on Your Hardware

While enterprises were negotiating API contracts and arguing about data residency, the homelab community solved local inference. DeepSeek R1 trained for $6 million and matched models that cost 20 times more. The price per token dropped 100x in a single year.

The canonical homelab AI stack in 2026 is Ollama plus Open WebUI: two containers, five minutes, a private ChatGPT that runs on a $200 mini PC. No API key. No cloud account. No phone-home telemetry. Your hardware. Your models. Your data.

Inference is solved. The community solved it. What comes next is harder.

The gap between chat and control

Right now, local AI in a homelab means chat. You ask a model a question. It answers. Maybe you have RAG running against some documents. Maybe you connected it to a workflow tool for basic automation. But the AI does not know your infrastructure. It does not manage your hosts.

It does not SSH into your Proxmox nodes, check your MikroTik firewall rules, or restart a container that crashed at 3am.

The canonical 2026 homelab AI stack chains Ollama, Open WebUI, and n8n into working automation, but stops right before infrastructure management. Inference: solved. Chat UI: solved. Workflows: solved. AI managing your actual infrastructure: missing.

The frustration is real. You have a 3090 running a 70B model, 40 VMs across three Proxmox nodes, a MikroTik router, a full monitoring stack, and the AI cannot do anything with any of it. The tools that exist are all cloud-native. They assume Kubernetes. They assume managed infrastructure. They assume you want to pay monthly for something you could run yourself. That is not how homelabs work.

What self-hosted AI is actually missing

• Multi-agent orchestration. Ollama runs one model. Open WebUI gives it a chat interface. Nothing in the self-hosted ecosystem coordinates multiple specialist agents, delegates between them, and returns a combined result.
• Infrastructure awareness. Your AI can discuss Kubernetes concepts. It cannot SSH into nw-backup-01 and check why the Plex container is using 98% CPU. No fleet awareness. No host inventory. No real-time state.
• Audit trails. When an AI agent modifies a firewall rule or restarts a service, there is no record of what it did, why, or what the output was. You are debugging from memory and docker logs.
• Tool use beyond chat. MCP (Model Context Protocol) is gaining traction in enterprise tooling. In the homelab space, it barely exists. Local models cannot execute SSH commands, read the output, and act on the result.
• Memory across sessions. Every conversation starts from zero. The AI does not remember that nw-web-01 had disk issues last week, or that the last time someone restarted the DNS container it took down half the lab.

What we built

Quox is an AI agent platform that runs on your hardware. Docker Compose. No cloud. No Kubernetes. No phone-home.

• QuoxAgent (Go binary). Single binary per host. Installs with one command. Runs as a systemd service, sends heartbeats to the central collector, exposes a local API for command execution. No Java. No dependencies.
• QuoxBastion (SSH gateway, Go). Every connection to every host goes through a single auditable gateway. Every session logged. Every command traceable. If you have 40 machines and a drawer full of SSH keys, this is the fix.
• QuoxTerminal (Desktop app, Tauri + Rust + React). Not Electron. Not a browser tab. A proper terminal with multi-pane layouts, AI chat, SSH fleet management, and vim keybindings. macOS and Linux.
• 49 specialist agents (Orchestrated delegation). Security scanning, network management, Proxmox operations, monitoring queries, file analysis, Docker fleet management. Flat hierarchy. Full delegation chain recorded.
• Proxmox + MikroTik (API integration). Your VMs and network gear in one dashboard, manageable through AI or directly. Not a web scraper. Actual API integration with your infrastructure.
• Prometheus, Grafana, Uptime Kuma (Monitoring integration). Your monitoring data is available to the AI. When you ask what is using the most memory across your Docker hosts, it queries Prometheus. It does not guess.

The whole stack deploys with Docker Compose. No cloud account. No licence key to start. Source-available, public GitHub repos, real commit history.

Governance is not just for enterprises

"I do not need audit trails. I am not a bank."

Fair. But consider this. You set up an AI agent that monitors your infrastructure and takes automated actions. It restarts containers that crash. It updates firewall rules based on threat feeds. It scales Proxmox VMs based on load. You go to bed.

At 3am, something breaks. The AI made a decision. You wake up to a broken network. What did it change? When? Why? What was the state before the change?

Without an audit trail, you are grepping through logs across 12 machines trying to reconstruct what happened. With one, you trace the decision chain: the trigger, the agent that handled it, the command that was executed, and the output it produced.

How Quox records agent actions:

• VOLT: Every agent action recorded in a hash-chained evidence trail. Not a log file. A cryptographic chain where any modification is detectable.
• AEE: Every agent communication wrapped in a standardised envelope with sender, recipient, timestamp, and correlation fields.
• WARD: A separate witnessing layer monitors the evidence chain without storing content.

If you would not give a junior sysadmin root access without logging, you should not give an AI agent the same access without logging either.

Try it

If you run a homelab and you have been waiting for AI tooling that does not require a cloud subscription, a Kubernetes cluster, or a leap of faith, this is what we built.

Quox is source-available under BSL 1.1. 12 repositories. Go, Rust, TypeScript, React, Node.js. Four open protocol specifications. Built by a solo founder who runs his own homelab and got tired of managing 50 hosts from six different browser tabs.