Tell an AI agent what you want. It configures your Loxone Miniserver.
"Set up the kitchen so the light comes on when dark, blinds go up in strong wind, and they close after five minutes of sunshine if there's no wind." → Agent searches block types, adds 5 logic blocks, wires 3 circuits, fixes a pulse duration error after sim feedback, and verifies all 7 test scenarios — in under 5 minutes.
▶ Click to play — Copilot CLI builds 3 kitchen circuits from a natural language prompt
Why
Loxone Config is a 400MB Windows-only desktop app. You drag blocks onto a canvas, wire them by hand, and hope you didn't miss a connection. For a single room with lighting, blinds, and climate you're easily placing 15+ blocks and 30+ wires.
This CLI replaces that with shell commands an AI agent can call. The agent reads a skill reference (block types, connectors, common mistakes), builds the circuit, then self-tests with an offline SPS simulator — catching wiring errors before anything touches your live system.
Works with GitHub Copilot CLI, Claude Code, OpenCode, or any agent that can run shell commands.
Install
Homebrew (macOS/Linux):
brew install eisber/tap/lox-cli
Prebuilt binaries (Linux, macOS, Windows) — see Releases:
curl -LO https://github.com/eisber/lox-cli/releases/latest/download/lox-cli-linux-x86_64
chmod +x lox-cli-linux-x86_64 && sudo mv lox-cli-linux-x86_64 /usr/local/bin/loxFrom crates.io:
cargo install lox-cli
From source:
git clone https://github.com/eisber/lox-cli && cd lox-cli cargo build --release # Binary at target/release/lox (~14MB)
Requirements: Loxone Miniserver Gen 1 or 2 (firmware 12.0+), local network access.
Quick Start
# Connect to your Miniserver lox setup set --host https://192.168.1.100 --user admin --pass secret # Download & inspect config lox config download --extract lox config describe config.Loxone ## How It Works
User: "Close the living room blinds when it's sunny and above 25°C" ↓ Agent reads skill reference (.github/skills/loxone-config/) ↓ Agent runs CLI commands: lox blocks search "threshold" → GreaterEqual lox config add --type GreaterEqual ... → adds block, returns connector UUIDs lox config wire-connector ... → wires sensor → logic → actuator lox sim run config.Loxone --sim '...' → ✅ signal propagates correctly ↓ Config ready to deploy: lox config push config.Loxone --reboot
The CLI handles LoxCC compression, CRC32 checksums, UUID generation, connector maps for 190+ block types, and wiring validation. All commands support `-o json` for agent consumption.
---
## Config-as-Code
```bash
# Add logic blocks & wire them
lox config add --type GreaterEqual --title "Temp über 25" config.Loxone -o json
# {"ok":true,"uuid":"...","type":"GreaterEqual","connectors":[{"key":"Input1","uuid":"...","direction":"I"},...]
lox config set-param config.Loxone "Temp über 25" Input2 25
lox config wire-connector config.Loxone "Temp über 25.Input1" "Außentemperatur.AQ"
lox config wire-connector config.Loxone "Jalousie 1 [Wohnzimmer].InputTriggerDown" "Temp über 25.Q"
# Validate & test
lox config check config.Loxone -o json # Structured warnings/errors
lox sim run config.Loxone --sim '{"inputs":{"Außentemperatur":30},...}'
# Deploy
lox config push config.Loxone --reboot --force # Upload + SPS reload (~4s)
Room templates for common setups:
lox config template config.Loxone bedroom --room "DG Schlafzimmer" # Creates LightController2, JalousieUpDown2, Thermostat with sensible defaults
GitOps versioning:
lox config pull # Download → diff → semantic commit message lox config log # Change history
SPS Simulator
Offline Miniserver SPS simulator — test config changes without hardware:
- 195 block types — logic, math, lighting, HVAC, timers, I/O
- Topological engine — automatic evaluation order, cycle detection
- Temporal testing — multi-step specs for heating cycles, timer delays, schedules
- 6k lines of Rust in
lox-sim/with 367 unit tests
lox sim run config.Loxone --sim '{ "inputs": {"Außentemperatur": 30, "Sonnenschein": 1}, "ticks": 10, "dt": 0.1, "expected_outputs": {"Jalousie 1 [Wohnzimmer].InputTriggerDown": {">": 0.5}} }' # {"pass":true,"passed":1,"scenarios":[{"pass":true,...}]}
Eval Results
The eval harness tests whether an AI agent can correctly configure a Loxone Miniserver from natural language instructions. Each case: utterance → agent builds circuit via CLI → Rust simulator verifies signals propagate correctly.
285 test cases across 10 categories.
Raw LLM pass rate (agent builds circuit from scratch each run):
| Section | Score | Examples |
|---|---|---|
| Synthetic | 20/20 (100%) | "Close blinds when sunny and above 25°C" |
| Patterns | 25/25 (100%) | Memory blocks, delayed triggers, schedules |
| Reference | 24/24 (100%) | Threshold, negation, fan-out, stairway light |
| Generated | 50/50 (100%) | Natural language variants, German/English |
| Rooms | 15/15 (100%) | Full room configurations (bedroom, bathroom, garden) |
| Extended | 14/15 (93%) | Pushbutton, AutoJalousie, sunrise triggers |
| Advanced | 28/29 (97%) | Multi-room goodnight, frost protection, solar pool |
| Use-Cases | 35/40 (88%) | Wallbox charging, NFC lockers, alarm systems |
| HVAC | 15/18 (83%) | Weather-compensated heating, dewpoint cooling |
| HVAC-Extracted | 26/32 (81%) | IRC zones, fancoil, HRV ventilation, heat pump |
| Total | 252/268 (94%) | Core automation: 5 sections at 100% |
Non-deterministic: scores vary ±5% across runs. The remaining failures are complex HVAC chains and specialized hardware (wallbox billing, NFC lockers) where the agent times out or misses wiring.
How Evals Work
Each eval case is a JSON spec with an utterance and simulation tests. The fixture config (fixture.Loxone) provides pre-configured rooms with sensors (temperature, motion, brightness) and actuators (blinds, lights, fans) — the agent adds logic blocks and wires them:
{
"id": "s01-piano-protection",
"utterance": "Lower blinds in the living room when it's sunny and the temperature is over 25 degrees",
"expected": {
"simulation": [
{
"name": "blinds close when hot+sunny",
"inputs": {"Außentemperatur": 30, "Sonnenschein": 1},
"ticks": 10, "dt": 0.1,
"expected_outputs": {"Jalousie 1 [Wohnzimmer].InputTriggerDown": {">": 0.5}}
},
{
"name": "blinds stay when cold",
"inputs": {"Außentemperatur": 15, "Sonnenschein": 0},
"ticks": 10, "dt": 0.1,
"expected_outputs": {"Jalousie 1 [Wohnzimmer].InputTriggerDown": {"<": 0.5}}
}
]
}
}The pipeline:
- Agent receives the utterance + fixture config (with sensors & actuators) + skill reference
- Agent uses
lox config add,wire-connector,set-paramto add logic blocks and wire them - Simulator injects test values into sensors, ticks the engine, checks actuator outputs
- Pass/fail based on whether the right actuator fires with the right inputs
Sample Transcript (actual LLM run)
Utterance: "When humidity in the bathroom goes above 70%, turn on the fan. Keep it running for 5 minutes after humidity drops back down."
The agent reads the skill reference, searches for block types, then builds:
# 1. Search for the right blocks lox blocks search "greater equal threshold" # → GreaterEqual (Threshold comparator) lox blocks search "off delay timer" # → OffDelay (keeps output on after input drops) # 2. Add blocks lox config add --type GreaterEqual --title "Feuchte hoch" --room Bad config.Loxone # ✓ Added GreaterEqual 'Feuchte hoch' on page 'Bad' lox config add --type OffDelay --title "Lüfter Nachlauf" --room Bad config.Loxone # ✓ Added OffDelay 'Lüfter Nachlauf' on page 'Bad' # 3. Set parameters lox config set-param config.Loxone "Feuchte hoch" Input2 70 # ✓ Set 'Input2' to 70 on 'Feuchte hoch' lox config set-param config.Loxone "Lüfter Nachlauf" Time 300 # ✓ Set 'Time' to 300 on 'Lüfter Nachlauf' # 4. Wire the signal path lox config wire-connector config.Loxone "Feuchte hoch.Input1" "Raumfeuchtigkeit Bad.AQ" # ✓ Wired Raumfeuchtigkeit Bad.AQ → Feuchte hoch.Input1 lox config wire-connector config.Loxone "Lüfter Nachlauf.InputTrigger" "Feuchte hoch.Q" # ✓ Wired Feuchte hoch.Q → Lüfter Nachlauf.InputTrigger lox config wire-connector config.Loxone "Lüfter Bad.I1" "Lüfter Nachlauf.Q" # ✓ Wired Lüfter Nachlauf.Q → Lüfter Bad.I1 # 5. Validate lox config check config.Loxone # 1 ok, 0 errors # 6. Test with simulator lox sim run config.Loxone --sim '{"inputs":{"Raumfeuchtigkeit Bad":80}, "ticks":10,"dt":0.1,"expected_outputs":{"Lüfter Bad.I1":{">":0.5}}}' # {"pass":true,"passed":1,"scenarios":[{"pass":true, ...}]} lox sim run config.Loxone --sim '{"inputs":{"Raumfeuchtigkeit Bad":50}, "ticks":10,"dt":0.1,"expected_outputs":{"Lüfter Bad.I1":{"==":0}}}' # {"pass":true,"passed":1,"scenarios":[{"pass":true, ...}]}
Result: Humidity 80% → fan ON ✅ · Humidity 50% → fan OFF ✅ · Total: 94 seconds, 6 premium requests.
Running Evals
# Single case python3 tests/eval/scripts/eval-agent.py --case s01-piano-protection --agent copilot # Full suite (parallel) python3 tests/eval/scripts/eval-agent.py --all --parallel 2 --agent copilot # Re-eval saved configs (no LLM calls) lox sim run saved-config.Loxone --sim '[...]'
For AI Agents
Skill references in .github/skills/ give agents everything they need:
- loxone-config — CLI commands, 190+ block types, worked examples, common mistakes
- loxone-sim — simulator testing commands and patterns
- loxone-patterns — 13 automation recipes (threshold, timer, schedule, HVAC)
All errors include fuzzy matching suggestions and available options:
Error: Block 'Threshold' not found.
Did you mean: GreaterEqual, LessEqual, AnalogThresholdTrigger?
Multiple Miniservers
lox ctx add home --host https://192.168.1.100 --user admin --pass secret lox ctx add office --host https://10.0.0.50 --user admin --pass secret lox ctx use home
Architecture
Single static Rust binary (~14MB). No runtime dependencies. Works on Linux, macOS, and Windows.
See COMMANDS.md for the full command reference, DESIGN.md for architecture details, and AGENTS.md for AI agent integration guidance.
License
Dual-licensed: AGPL-3.0 for open use, Commercial License for proprietary redistribution.
Based on lox by Christoph Neumüller (GPL-3.0). See NOTICE for attribution details.
Copyright © 2025-2026 Markus Cozowicz