Building the Home AI Lab
What is REX?
REX is my home AI lab — a Windows 11 Pro machine running Hyper-V with a collection of VMs, a Juniper core switch, pfSense firewall, and a growing stack of self-hosted AI services. The goal is to run capable AI agents against real infrastructure, locally.
The VM Fleet
| VM | Purpose |
|---|---|
| KaliLinux | Security scanning — Greenbone (GVM) and Nmap |
| UbuntuClaw | Local inference — vLLM serving Gemma4-26B |
| DebIdentity | Identity & secrets — Authentik, VaultWarden, Step-CA |
| DebApps | Self-hosted apps — Home Assistant, SearXNG, Traefik |
AI Routing
All AI agents (Claude Code, Gemini CLI, LangGraph agents) route through a LiteLLM proxy running on GCP. The proxy sits behind an nginx reverse proxy with internal TLS (Step-CA) and caches calls to Vertex AI — Claude and Gemini models are both available behind a single OpenAI-compatible endpoint at https://litellm.sdx.local.
For Gemini CLI specifically, a /v1beta/ bypass routes directly to the Generative Language API — Vertex AI rejects the function_response.id field that Gemini CLI includes in tool call history.
MCP Servers
Four custom MCP servers run in WSL2 and give agents direct access to network infrastructure:
- Juniper — NETCONF management for the EX3300 core switch
- pfSense — Firewall REST API
- Aruba — WiFi controller monitoring
- Synology — NAS management (FileStation, DownloadStation, health)
LangGraph agents consume these via langchain-mcp-adapters, spawning them as stdio subprocesses.
What’s Next
The next step is building the first real LangGraph agent — likely a network topology agent that can query the Juniper MCP server, cross-reference with pfSense firewall rules, and surface anomalies. Stay tuned.