Fix realtime HA state-change execution

Merge pull request v0.5.0 behavior learning and safe activation (#32)

.env.example

@@ -7,3 +7,9 @@ SILLYHOME_HISTORY_DAYS=14
 SILLYHOME_MIN_TRAINING_POINTS=24
 SILLYHOME_RETRAIN_STALE_HOURS=24
 SILLYHOME_RECONCILE_INTERVAL_SECONDS=900
+SILLYHOME_MIN_BEHAVIOR_ACTIONS=3
+SILLYHOME_PREDICTION_CONFIDENCE=0.82
+SILLYHOME_PREDICTION_WINDOW_MINUTES=30
+SILLYHOME_PREDICTION_INTERVAL_SECONDS=60
+SILLYHOME_EXECUTION_COOLDOWN_SECONDS=900
+SILLYHOME_TIMEZONE=Europe/Berlin

.gitea/ISSUE_TEMPLATE/bug.md

@@ -0,0 +1,33 @@
+---
+name: Fehler
+about: Reproduzierbaren SillyHome-Fehler melden
+title: "BUG: "
+---
+
+## Beobachtet
+
+Was ist tatsächlich passiert?
+
+## Erwartet
+
+Was sollte passieren?
+
+## Aktor und Kontext
+
+- Aktor:
+- Trigger/Kontext:
+- SillyHome-Modus:
+- Passende HA-Automation und Zustand:
+
+## Nachweise
+
+- Version:
+- Relevante Logs:
+- `activation_reason`:
+- `prediction.execution_reason`:
+
+## Reproduktion
+
+1.
+2.
+3.

.gitea/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,25 @@
+## Ziel
+
+Welches konkrete Verhalten ändert sich?
+
+## Umsetzung
+
+- 
+
+## Sicherheit
+
+- Backup/Rollback:
+- Auswirkung auf bestehende HA-Automationen:
+- Shadow/Active-Verhalten:
+
+## Verifikation
+
+```bash
+.venv/bin/pytest -q
+.venv/bin/ruff check .
+.venv/bin/mypy app backend tests
+git diff --check
+```
+
+- Live-Health:
+- Live-Aktor:

AGENTS.md

@@ -0,0 +1,50 @@
+# AGENTS.md
+
+Diese Datei ist die kurze Arbeitsanweisung für Menschen und kleine Coding-Modelle.
+
+## Reihenfolge
+
+1. `README.md` lesen.
+2. Für Verhaltenslogik `docs/BEHAVIOR_ENGINE.md` lesen.
+3. Für Fehler `docs/DEBUGGING.md` abarbeiten.
+4. Für HA-Automationen `docs/CONTROL_HANDOFF.md` lesen.
+5. Vor Release oder Live-Update `docs/OPERATIONS.md` vollständig abarbeiten.
+
+## Verbindliche Regeln
+
+- Erst Zustand und Logs prüfen, dann Ursache formulieren, dann ändern.
+- Keine Annahme als Fakt darstellen.
+- Vor Live-Änderungen Backup oder klaren Rollback-Punkt erstellen.
+- Bestehende Nutzeränderungen nicht zurücksetzen.
+- Kleine, fokussierte Änderungen mit passenden Tests.
+- Eigene SillyHome-Schaltungen niemals als neues Nutzerverhalten lernen.
+- Ein Aktor darf nicht unbeabsichtigt ohne Steuerung bleiben:
+  - SillyHome aktiv: passende HA-Automation darf pausiert sein.
+  - SillyHome Shadow: HA-Automation muss auf Wunsch fortgesetzt werden können.
+- Keine Secrets in Code, Dokumentation, Commits oder Logs.
+
+## Pflichtprüfung
+
+```bash
+.venv/bin/pytest -q
+.venv/bin/ruff check .
+.venv/bin/mypy app backend tests
+git diff --check
+```
+
+## Versionsstellen
+
+Bei jedem Release dieselbe Version setzen:
+
+- `pyproject.toml`
+- `addon/config.yaml`
+- `app/main.py`
+- `CHANGELOG.md`
+
+Danach prüfen:
+
+```bash
+grep -R 'version.*0\\.7\\.0' -n pyproject.toml addon/config.yaml app/main.py
+```
+
+Die konkrete Zielversion im Befehl anpassen.

ARCHITECTURE.md

@@ -1,13 +1,24 @@
 # SillyHome Next — Architekturübersicht
 
-Ziel ist ein lokales, datensparsames, erklärbares Smart-Home-Intelligenzsystem für Home Assistant. Es analysiert Historie, erkennt Gewohnheiten, erstellt Vorhersagen, empfiehlt Automationen und kann auf Wunsch einfach in Automationen übersetzen. Vier Intelligenzebenen sind vorgesehen: regelbasiert, ML-gestützt, LLM-unterstützt und autonomer Hausagent.
+Ziel ist ein lokales, datensparsames und erklärbares Smart-Home-Intelligenzsystem
+für Home Assistant. Nutzer wählen ausschließlich erlaubte Aktoren. Das System
+ordnet Kontext automatisch zu, erkennt historische Nutzerhandlungen, trainiert
+pro Aktor ein Verhaltensmodell und trifft zunächst nur Shadow-Vorhersagen.
+Autonomes Schalten wird separat pro Aktor freigegeben.
 
 ## Leitentscheidungen
 
 - Lokal-first und datensparsam; keine Cloudpflicht.
-- Trennung von Datenintegration, Trainingspipeline, Vorhersageservice und Erklärungsschicht.
+- Trennung von Datenintegration, Kontextzuordnung, Verhaltenslernen,
-- Standardintegration über MQTT und Home Assistant WebSocket plus REST.
+  Vorhersage und Aktorausführung.
-- Schnittstellen über FastAPI und OpenAI-kompatible Endpunkte.
+- Logbook-basierte Herkunftserkennung; eindeutig erkannte HA-Automationen
-- Langzeitdaten in PostgreSQL und TimescaleDB; Vektoren für semantische Suche optional.
+  zählen wie manuelle Bedienungen. Eigene SillyHome-Schaltungen werden nicht
-- Deployment über Docker Compose; Kubernetes optional für erweiterte Betriebsgrößen.
+  zurückgelernt.
+- Ausführung nur für freigegebene, reversible Domains und Zustände sowie mit
+  Konfidenzschwelle und zustandsbezogenem Cooldown.
+- Eindeutig passende HA-Automationen können bei einer SillyHome-Übernahme
+  pausiert und beim Rückfall in den Shadow-Modus wieder fortgesetzt werden.
+- Standardintegration über die lokale Home-Assistant-REST-API.
+- Persistenz als atomische lokale Modell- und Aktorartefakte.
+- Deployment als Home-Assistant-Add-on oder über Docker Compose.
 - Tests, Docs und Changelog sind Pflichtbestandteil jeder Änderung.

CHANGELOG.md

@@ -1,5 +1,152 @@
 # Changelog
 
+## 0.7.10 - 2026-06-16
+- WebSocket-State-Changes aktualisieren einen internen Home-Assistant-State-
+  Cache und werten Aktoren direkt gegen diesen frischen Event-Zustand aus.
+- Event-Auswertungen lösen keine REST-Statusabfrage mehr aus, bevor sie
+  aktive Aktoren schalten.
+
+## 0.7.9 - 2026-06-15
+- Event-basierte Vorhersagen verwenden den frischen Sensorzustand direkt aus
+  dem Home-Assistant-WebSocket-Event, damit Kontextwechsel ohne REST-Race sofort
+  bewertet und geschaltet werden können
+- Regressionstest stellt sicher, dass ein Türsensor-Event trotz veraltetem
+  HA-Snapshot direkt `light.turn_on` auslöst
+
+## 0.7.8 - 2026-06-15
+- Home-Assistant-WebSocket-Listener deaktiviert den clientseitigen Keepalive-
+  Ping, damit stabile HA-Verbindungen nicht durch Ping-Timeouts ständig neu
+  aufgebaut werden
+- Fallback-Auswertung läuft bei getrenntem WebSocket kurzfristig alle 5 Sekunden,
+  damit übernommene Aktoren nicht ohne Steuerung bleiben
+
+## 0.7.7 - 2026-06-15
+- WebSocket-State-Changes lesen jetzt das echte Home-Assistant-Eventformat
+  (`event.data.entity_id`), damit Kontextwechsel wie Türsensoren sofort
+  Vorhersagen und Schaltungen auslösen statt erst beim nächsten Statusabruf
+
+## 0.7.6 - 2026-06-14
+- Kontextvorschläge blenden zusätzlich Batterie-, Status-, Node-, Last-Seen-
+  und Basic-Entities aus, sofern sie nicht bewusst manuell ausgewählt wurden
+
+## 0.7.5 - 2026-06-14
+- Kontextvorschläge weiter geschärft: Standardliste zeigt nur gleiche Räume,
+  gemeinsame Geräte/Tokens oder echte globale Außenwerte
+- Diagnosewerte wie MQTT-, WiFi-, Restart- und Connect-Zähler werden nicht mehr
+  als fachliche Kontextvorschläge angeboten
+
+## 0.7.4 - 2026-06-14
+- Kontext-Auswahl liefert jetzt aktorbezogene Vorschläge statt einer pauschalen
+  Roh-Liste aller Sensoren und Zustände
+- Dashboard-Auswahl für Aktoren und Kontext nach Typ/Kategorie gruppiert und
+  durchsuchbar; lange Listen werden begrenzt statt mobil unbedienbar zu werden
+- Manuelle Entity-ID-Eingabe ergänzt, damit relevante Sensoren auch ohne
+  Dropdown-Treffer gespeichert werden können
+- Irrelevante System-/VPN-/pfSense-Sensoren tauchen bei Lichtaktoren ohne
+  fachlichen Bezug nicht mehr als Standardvorschläge auf
+
+## 0.7.3 - 2026-06-14
+- Automatische Kontextzuordnung ignoriert generische Bereiche wie `Monitoring`,
+  damit System-/Disk-/Überhitzungssensoren nicht fälschlich Lichtaktoren erklären
+- Aktor-Auswahl auf tatsächlich sicher steuerbare Domains begrenzt:
+  `light`, `switch`, `cover`, `fan`, `humidifier`
+- Neue manuelle Kontext-Zuordnung pro Aktor: Haupt-Messsensor optional setzen und
+  mehrere relevante Kontext-Entities wie PIR, Außenhelligkeit, Luftfeuchtigkeit
+  oder andere Lichtzustände auswählen
+- Dashboard-Dropdown durch echtes Select plus Suche ersetzt; mobile Bedienung und
+  Aktor-Details enthalten Speichern/Neu-laden-Aktionen für manuelle Kontextwahl
+
+## 0.7.2 - 2026-06-14
+- Home-Assistant-Entity-Metadaten werden in Batches gelesen, damit große HA-
+  Installationen nicht mehr am Template-Ausgabe-Limit scheitern
+- Nicht über die HA-Config-API exponierte Automationen werden leise übersprungen,
+  statt wiederholt Warnungen in die Logs zu schreiben
+- Dashboard für mobile Nutzung optimiert: Sticky-Schnellnavigation, Karten statt
+  breiter Tabelle, größere Touch-Ziele und bessere Detail-/Menüführung
+- WebSocket-Status ist direkt im Dashboard-Systemstatus sichtbar
+
+## 0.7.1 - 2026-06-14
+- Event-basierter Home-Assistant-WebSocket-Listener authentifiziert sich jetzt
+  mit dem echten HA-WebSocket-Protokoll (`auth_required` -> `auth` -> `auth_ok`)
+- Kompatibilität mit aktuellen `websockets`-Versionen wiederhergestellt
+- WebSocket-Healthcheck und Event-Listener-Tests laufen ohne zusätzliches
+  Async-Pytest-Plugin
+- Add-on-Version angehoben, damit Home Assistant das aktualisierte Image baut
+
+## 0.7.0 - 2026-06-14
+- Freie Eingabe von Home-Assistant-Entitätsnamen mit Vorschlagsliste
+- Freigabestatus und Blockadegrund sind in Übersicht und Details immer sichtbar
+- Vorhersagen erklären konkret, warum sie ausgeführt oder nicht ausgeführt wurden
+- Cooldown blockiert nur Wiederholungen desselben Zielzustands; Gegenaktionen
+  wie `Licht an` gefolgt von `Licht aus` bleiben sofort möglich
+- Passende HA-Automationen werden aus ihren echten Konfigurationen erkannt und
+  können pausiert oder fortgesetzt werden
+- Sichere Steuerungsübergabe: SillyHome kann übernehmen und passende
+  HA-Automationen pausieren; beim Stoppen können sie gezielt fortgesetzt werden
+- Dashboard wird ohne Browser-Cache ausgeliefert
+- Reproduzierbare Runbooks für Debugging, Berechnung, Entwicklung, Tests,
+  Release, Add-on-Update, Live-Verifikation und Rollback
+
+## 0.6.2 - 2026-06-14
+- Eindeutig im Home-Assistant-Logbuch erkannte Automationen und Scripts zählen für
+  Lernen und Freigabe gleichwertig wie manuelle Bedienungen
+- Automationsmuster erhalten dieselbe Modellgewichtung wie manuelle Handlungen
+- Oberfläche zeigt die gemeinsame Zahl als `eindeutig geregelt`; eine
+  ausdrückliche Aktivierung pro Aktor bleibt weiterhin erforderlich
+
+## 0.6.1 - 2026-06-14
+- Manuelle Prüfung als `Aktuelle Situation auswerten` eindeutig von Simulation
+  oder Aktorschaltung abgegrenzt
+- Sichtbare Rückmeldung mit Prüfzeitpunkt, vorhergesagtem Zustand und Sicherheit
+  oder klarem Hinweis auf einen fehlenden frischen Sensorwechsel
+
+## 0.6.0 - 2026-06-14
+- Kausales Shadow-Lernen erkennt frische Kontextwechsel unmittelbar vor einer
+  Aktorhandlung, etwa `Tür geschlossen → offen` vor `Licht aus → an`
+- Historische Home-Assistant-Automationen dürfen Vorhersagen begründen, zählen
+  aber weiterhin niemals als eindeutige Benutzerhandlung oder Ausführungsfreigabe
+- Aktuelle `last_changed`-Zeitpunkte verhindern Vorhersagen aus längst
+  unveränderten Sensorzuständen
+- Oberfläche trennt gelernte Benutzerhandlungen und erkannte HA-Automationen
+
+## 0.5.4 - 2026-06-14
+- Tür-, Bewegungs- und andere belastbare Kontextsensoren werden auch ohne
+  numerischen Sensor als vollständige automatische Kontextzuordnung angezeigt
+- Status und Zuordnungssicherheit bilden das aktive Verhaltenslernen ab statt
+  eines optionalen numerischen Modells
+- Ausführungsfreigabe erscheint erst, wenn genügend eindeutig manuelle
+  Bedienungen vorliegen; bis dahin nennt die Oberfläche die noch fehlende Anzahl
+
+## 0.5.3 - 2026-06-14
+- Verhindert fachlich falsche Sensorzuordnungen nur aufgrund generischer Namen wie
+  `Licht` oder `Lichtschalter`
+- Übernimmt numerische Sensoren nur noch bei einem belastbaren absoluten Score und
+  einer eindeutigen Abgrenzung zum zweitbesten Kandidaten
+- Begrenzt Zusatzkontext auf relevante Sensoren und bevorzugt bei Lichtaktoren
+  echte Beleuchtungsstärke gegenüber fremden Leistungs- oder Energiezählern
+
+## 0.5.2 - 2026-06-14
+- Add-on-Build invalidiert den Docker-Cache bei jeder Versionsänderung, damit
+  Versionsmetadaten und tatsächlich ausgelieferter Anwendungscode übereinstimmen
+- Korrigierte Ingress-Oberfläche aus 0.5.1 dadurch erstmals zuverlässig ausgeliefert
+
+## 0.5.1 - 2026-06-14
+- Technische Modell-, Intervall- und Sicherheitsparameter aus der normalen
+  Home-Assistant-Add-on-Konfiguration entfernt; sichere Standardwerte bleiben aktiv
+- Ingress um einen klaren Ablauf mit Aktorauswahl, Beobachtungsphase und späterer
+  Ausführungsfreigabe ergänzt
+- Bedienelemente und Diagnosen in verständlicher Alltagssprache erklärt
+
+## 0.5.0 - 2026-06-14
+- Ingress auf reine Aktorauswahl, automatischen Lernstatus und Vorhersagen reduziert
+- Automatische Kontextzuordnung ohne Sensor-Overrides oder Review-Blockade
+- Historische Handlungserkennung aus HA-State-History und Logbook-Herkunft
+- Persistentes Verhaltensmodell pro Aktor mit Zeit-, Wochentags- und Kontextmustern
+- Shadow-Vorhersagen vor jeder Ausführungsfreigabe
+- Explizite Aktivierung pro Aktor, Konfidenzschwelle, Cooldown und enge Service-Whitelist
+- Schutz vor dem Lernen erkannter HA-Automationen und eigener Schaltvorgänge
+- Automation-Proposal- und Override-Endpunkte aus dem aktiven Produkt entfernt
+
 ## 0.4.0 - 2026-06-13
 - Aktuator-zentrierte Einrichtung: Nutzer wählen nur noch Aktuatoren, Sensoren werden deterministisch gefunden und bewertet
 - Persistente automatische und manuelle Sensorzuordnungen mit Evidenz, Confidence, Review-Gating und Neustart-Sicherheit

Dockerfile

@@ -9,7 +9,13 @@ ENV SILLYHOME_AUTOMATION_STORE=/app/data/automations \
     SILLYHOME_HISTORY_DAYS=14 \
     SILLYHOME_MIN_TRAINING_POINTS=24 \
     SILLYHOME_RETRAIN_STALE_HOURS=24 \
-    SILLYHOME_RECONCILE_INTERVAL_SECONDS=900
+    SILLYHOME_RECONCILE_INTERVAL_SECONDS=900 \
+    SILLYHOME_MIN_BEHAVIOR_ACTIONS=3 \
+    SILLYHOME_PREDICTION_CONFIDENCE=0.82 \
+    SILLYHOME_PREDICTION_WINDOW_MINUTES=30 \
+    SILLYHOME_PREDICTION_INTERVAL_SECONDS=60 \
+    SILLYHOME_EXECUTION_COOLDOWN_SECONDS=900 \
+    SILLYHOME_TIMEZONE=Europe/Berlin
 
 WORKDIR /app
 

README.md

@@ -1,13 +1,25 @@
 # SillyHome Next
 
-Lokaler, datenschutzfreundlicher API-Prototyp für Home Assistant.
+SillyHome lernt aus Home Assistant, sagt Aktorhandlungen voraus und darf sie
+nach einer ausdrücklichen Freigabe ausführen.
+
+## Schnell orientieren
+
+- Fehler finden: [`docs/DEBUGGING.md`](docs/DEBUGGING.md)
+- Berechnung verstehen: [`docs/BEHAVIOR_ENGINE.md`](docs/BEHAVIOR_ENGINE.md)
+- Steuerung übernehmen/zurückgeben:
+  [`docs/CONTROL_HANDOFF.md`](docs/CONTROL_HANDOFF.md)
+- Entwickeln, testen, veröffentlichen und installieren:
+  [`docs/OPERATIONS.md`](docs/OPERATIONS.md)
+- Arbeitsregeln für Coding-Agenten: [`AGENTS.md`](AGENTS.md)
 
 ## Reifegrad
 
-Die aktuelle Entwicklungslinie ist aktor-zentriert: Nutzer konfigurieren nur
+Die aktuelle Entwicklungslinie ist vollständig aktor-zentriert: Nutzer wählen
-noch Home-Assistant-Aktuatoren. SillyHome Next findet dazu passende numerische
+nur Home-Assistant-Aktuatoren aus. SillyHome Next findet Sensoren, Zustände und
-Sensoren und Kontext-Entities, zeigt Evidenz und Review-Bedarf an und hält
+Kontext automatisch, wertet die vorhandene Historie aus und hält passende
-passende Modelle lokal und autonom aktuell.
+lokale Modelle autonom aktuell. Es gibt keinen Regel-, Trigger-, Sensor- oder
+YAML-Konfigurationsschritt.
 
 ## Motivation
 TheSillyHome zeigte die Idee: statt statischer Regeln das Zuhause aus Verhaltensmustern verstehen. Diese Architektur modernisiert den Ansatz in Richtung Explainable AI, hybride Intelligenzebenen und langlebige Wartbarkeit.
@@ -16,7 +28,7 @@ TheSillyHome zeigte die Idee: statt statischer Regeln das Zuhause aus Verhaltens
 - Home Assistant und Sensoren/Aktoren verstehen
 - Historie auswerten und Gewohnheiten erkennen
 - Vorhersagen erstellen und erklären
-- Automationen vorschlagen und direkt generieren
+- Persönliches Verhalten pro Aktor lernen und zukünftige Handlungen vorhersagen
 - Lokal-first ohne Cloudpflicht
 - Erweiterbar, testbar, dokumentiert
 
@@ -46,12 +58,13 @@ uvicorn app.main:app --reload
 - `http://127.0.0.1:8000/v1/discovery` - klassifizierte, filterbare Entities
 - `http://127.0.0.1:8000/v1/history` - normalisierte numerische Zeitreihen
 - `http://127.0.0.1:8000/v1/actuators/discovery` - unterstützte Aktuatoren für den aktor-zentrierten Workflow
-- `POST http://127.0.0.1:8000/v1/actuators` - Aktuator registrieren, Sensorzuordnung prüfen und Modell-Lebenszyklus starten
+- `POST http://127.0.0.1:8000/v1/actuators` - Aktor freigeben; Kontextzuordnung und Modell-Lebenszyklus starten automatisch
+- `POST http://127.0.0.1:8000/v1/actuators/{entity_id}/evaluate` - Shadow-Vorhersage aktualisieren
+- `POST http://127.0.0.1:8000/v1/actuators/{entity_id}/activation` - autonomes Schalten pro Aktor freigeben oder stoppen
 - `POST http://127.0.0.1:8000/v1/actuators/reconciliation/run` - globale Reconciliation manuell anstoßen
 - `http://127.0.0.1:8000/ml/health` - Registry-/Serving-Health
 - `POST http://127.0.0.1:8000/ml/retrain` - Modell-Metadaten aktualisieren
 - `POST http://127.0.0.1:8000/ml/evaluate` - MAE/RMSE/Coverage berechnen
-- `POST http://127.0.0.1:8000/v1/automations/proposals` - sicheren Entwurf anlegen
 
 Ohne vollständige HA-Konfiguration liefert `/v1/entities` bewusst `503`.
 
@@ -70,12 +83,17 @@ dem Netz muss ein authentifizierender Reverse Proxy vorgeschaltet werden.
 - `SILLYHOME_HA_URL` – Basis-URL deiner Home-Assistant-Instanz (z. B. `http://homeassistant.local:8123`)
 - `SILLYHOME_HA_TOKEN` – Long-Lived Access Token eines dedizierten HA-Benutzers mit minimalen Rechten
 - `SILLYHOME_MODEL_STORE` – Verzeichnis für persistierte Modell-Metadaten
-- `SILLYHOME_AUTOMATION_STORE` – Verzeichnis für Automation-Entwürfe
+- `SILLYHOME_ACTUATOR_STORE` – Verzeichnis für persistente Aktor-Zuordnungen und Reconciliation-Status
-- `SILLYHOME_ACTUATOR_STORE` – Verzeichnis für persistente Aktuator-Zuordnungen, Overrides und Reconciliation-Status
 - `SILLYHOME_HISTORY_DAYS` – Trainingsfenster für HA-History (1 bis 31 Tage)
 - `SILLYHOME_MIN_TRAINING_POINTS` – Mindestanzahl nutzbarer numerischer Messpunkte vor einem Modelltraining
 - `SILLYHOME_RETRAIN_STALE_HOURS` – Staleness-Grenze für automatisches Retraining
 - `SILLYHOME_RECONCILE_INTERVAL_SECONDS` – Intervall für sichere periodische Reconciliation
+- `SILLYHOME_MIN_BEHAVIOR_ACTIONS` – Mindestzahl gelernter Handlungen vor einer Freigabe
+- `SILLYHOME_PREDICTION_CONFIDENCE` – Mindestkonfidenz für autonomes Schalten
+- `SILLYHOME_PREDICTION_WINDOW_MINUTES` – Zeitfenster um gelernte Handlungsmuster
+- `SILLYHOME_PREDICTION_INTERVAL_SECONDS` – Intervall für Shadow-/Aktiv-Vorhersagen
+- `SILLYHOME_EXECUTION_COOLDOWN_SECONDS` – Mindestabstand zwischen eigenen Schaltungen
+- `SILLYHOME_TIMEZONE` – lokale Zeitzone für Tages- und Wochenmuster
 
 Niemals Administrator-Tokens oder Passwörter eintragen. `.env` gehört nicht ins
 Versionskontrollsystem.
@@ -88,15 +106,26 @@ unter **Einstellungen → Add-ons → Add-on-Shop → Repositories** diese URL e
 `http://192.168.6.31:3000/pino/sillyhome-next`
 
 Danach **SillyHome Next** installieren und starten. Das Dashboard wird per Ingress
-geöffnet. Das Add-on nutzt die Supervisor-API nur lesend; Automation-Entwürfe werden
+geöffnet. Dort werden ausschließlich erlaubte Aktoren ausgewählt; Kontext- und
-lokal gespeichert und niemals automatisch ausgeführt.
+Lernentscheidungen erfolgen automatisch.
 
 ### Normaler Workflow
-1. Im Dashboard oder per API einen Aktuator auswählen, zum Beispiel `light.abstellkammer`.
+1. Im Dashboard einen Aktor auswählen, zum Beispiel `light.abstellkammer`.
-2. SillyHome Next bewertet passende numerische Sensoren und binäre Kontext-Entities anhand von Bereich, Gerät, Namen, Domain und `device_class`.
+2. SillyHome Next bewertet automatisch Messwerte, Anwesenheit, Bewegung,
-3. Starke und eindeutige Zuordnungen werden automatisch genutzt; schwache oder knappe Kandidaten bleiben mit Review-Hinweis sichtbar.
+   Bereiche, Gerätebeziehungen und weitere HA-Kontexte.
-4. Manuelle Overrides haben Vorrang, bleiben persistent und überstehen Neustarts.
+3. Das System verwendet selbstständig die beste verfügbare Zuordnung.
-5. Sobald genügend numerische HA-Historie vorhanden ist, trainiert das System automatisch ein lokales Modell pro Aktuator-Zuordnung und retrainiert es bei relevanten Datenänderungen oder Staleness.
+   Niedrige Sicherheit bleibt als Diagnose sichtbar, verlangt aber keine
+   manuelle Konfiguration.
+4. Sobald genügend Historie vorhanden ist, trainiert und aktualisiert das
+   System das lokale Modell automatisch.
+5. Vorhersagen laufen zunächst ausschließlich im Shadow-Modus.
+6. Erst nach ausdrücklicher Freigabe pro Aktor werden hochkonfidente,
+   erlaubte Zustände geschaltet. Eindeutig im HA-Logbuch erkannte Automationen
+   und Scripts zählen dabei gleichwertig wie manuelle Bedienungen. Eigene
+   Schaltungen von SillyHome werden nicht zurückgelernt.
+7. Bei der Freigabe kann SillyHome passende HA-Automationen pausieren und die
+   Steuerung übernehmen. Beim Stoppen können diese Automationen gezielt wieder
+   fortgesetzt werden.
 
 Vor einem Update sollte in Home Assistant unter **Einstellungen → System → Backups**
 eine Teil-Sicherung des Add-ons erstellt werden. Zur Wiederherstellung das gewünschte
@@ -107,5 +136,5 @@ Teststand `v0.3.0` wurde als HA-Backup `7df0fca0` gesichert.
 ```bash
 pytest
 ruff check .
-mypy
+mypy app backend tests
 ```

addon/Dockerfile

@@ -4,13 +4,17 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
     PIP_NO_CACHE_DIR=1
 
+# The add-on version changes for every release. Copying its config before the
+# clone makes Docker invalidate the application layer instead of reusing old code.
+COPY config.yaml /tmp/addon-config.yaml
+
 RUN apt-get update \
     && apt-get install -y --no-install-recommends git \
     && git clone --depth 1 --branch main \
        http://192.168.6.31:3000/pino/sillyhome-next.git /app \
     && python -m pip install --upgrade pip \
     && python -m pip install /app \
-    && rm -rf /var/lib/apt/lists/* /app/.git
+    && rm -rf /var/lib/apt/lists/* /app/.git /tmp/addon-config.yaml
 
 COPY run.sh /run.sh
 RUN chmod 0755 /run.sh

addon/config.yaml

@@ -1,7 +1,7 @@
 name: SillyHome Next
-version: "0.4.0"
+version: "0.7.10"
 slug: sillyhome_next
-description: Lokale HA-Analyse, Vorhersagen und sichere Automation-Entwürfe
+description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next
 arch:
   - amd64
@@ -16,16 +16,6 @@ panel_admin: true
 homeassistant_api: true
 hassio_api: false
 auth_api: false
-options:
-  history_days: 14
-  min_training_points: 24
-  retrain_stale_hours: 24
-  reconcile_interval_seconds: 900
-schema:
-  history_days: "int(1,31)"
-  min_training_points: "int(2,10000)"
-  retrain_stale_hours: "int(1,720)"
-  reconcile_interval_seconds: "int(60,86400)"
 map:
   - type: addon_config
     read_only: false

addon/run.sh

@@ -12,6 +12,12 @@ if [ -f /data/options.json ]; then
   export SILLYHOME_MIN_TRAINING_POINTS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("min_training_points", 24))')"
   export SILLYHOME_RETRAIN_STALE_HOURS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("retrain_stale_hours", 24))')"
   export SILLYHOME_RECONCILE_INTERVAL_SECONDS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("reconcile_interval_seconds", 900))')"
+  export SILLYHOME_MIN_BEHAVIOR_ACTIONS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("min_behavior_actions", 3))')"
+  export SILLYHOME_PREDICTION_CONFIDENCE="$(python -c 'import json; print(json.load(open("/data/options.json")).get("prediction_confidence", 0.82))')"
+  export SILLYHOME_PREDICTION_WINDOW_MINUTES="$(python -c 'import json; print(json.load(open("/data/options.json")).get("prediction_window_minutes", 30))')"
+  export SILLYHOME_PREDICTION_INTERVAL_SECONDS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("prediction_interval_seconds", 60))')"
+  export SILLYHOME_EXECUTION_COOLDOWN_SECONDS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("execution_cooldown_seconds", 900))')"
+  export SILLYHOME_TIMEZONE="$(python -c 'import json; print(json.load(open("/data/options.json")).get("timezone", "Europe/Berlin"))')"
 fi
 
 mkdir -p "$SILLYHOME_MODEL_STORE" "$SILLYHOME_AUTOMATION_STORE" "$SILLYHOME_ACTUATOR_STORE"

app/actuators/lifecycle.py

@@ -46,6 +46,9 @@ _STOPWORDS = frozenset(
         "humidity",
         "illuminance",
         "light",
+        "licht",
+        "lichtschalter",
+        "monitoring",
         "power",
         "sensor",
         "state",
@@ -54,11 +57,53 @@ _STOPWORDS = frozenset(
         "value",
     }
 )
+_GENERIC_AREA_NAMES = frozenset({"monitoring", "system", "technik"})
 _NUMERIC_AUTO_ACCEPT_SCORE = 0.82
+_NUMERIC_AUTO_ACCEPT_MIN_SCORE = 0.5
 _NUMERIC_MIN_MARGIN = 0.18
 _CONTEXT_AUTO_ACCEPT_SCORE = 0.78
-_MAX_CONTEXT_SELECTIONS = 3
+_CONTEXT_AUTO_ACCEPT_MIN_SCORE = 0.3
+_MAX_CONTEXT_SELECTIONS = 5
 _AUDIT_LIMIT = 20
+_MANUAL_CONTEXT_DOMAINS = frozenset({
+    "binary_sensor",
+    "climate",
+    "cover",
+    "device_tracker",
+    "fan",
+    "humidifier",
+    "light",
+    "person",
+    "sensor",
+    "switch",
+    "weather",
+})
+_CONTEXT_SUGGESTION_LIMIT = 120
+_OUTDOOR_TOKENS = frozenset({"aussen", "außen", "outdoor", "garten", "terrasse", "balkon"})
+_DIAGNOSTIC_TOKENS = frozenset({
+    "basic",
+    "battery",
+    "connect",
+    "count",
+    "diagnostic",
+    "firmware",
+    "gesehen",
+    "last",
+    "linkquality",
+    "knoten",
+    "knotens",
+    "mqtt",
+    "node",
+    "reason",
+    "restart",
+    "rssi",
+    "signal",
+    "ssid",
+    "status",
+    "uptime",
+    "wifi",
+    "zuletzt",
+})
 
 
 class ActuatorReconciliationService:
@@ -85,26 +130,117 @@ class ActuatorReconciliationService:
     def get_actuator(self, actuator_entity_id: str) -> ActuatorRecord:
         return self._store.get(actuator_entity_id)
 
-    def set_override(
+    def suggest_context_options(
         self,
         actuator_entity_id: str,
-        override: ManualOverride | None,
+        *,
-    ) -> ActuatorRecord:
+        limit: int = _CONTEXT_SUGGESTION_LIMIT,
-        record = self._store.get(actuator_entity_id)
+    ) -> list[HaEntitySummary]:
-        updated = record.model_copy(
+        entities = {entity.entity_id: entity for entity in self._ha_reader.read_entities()}
-            update={
+        discovered = {entity.entity_id: entity for entity in self._ha_reader.discover()}
-                "manual_override": override,
+        actuator = entities.get(actuator_entity_id)
-                "updated_at": datetime.now(timezone.utc),
+        if actuator is None:
-            }
+            raise KeyError("Aktuator-Konfiguration nicht gefunden.")
+        selected_ids = _selected_context_ids(self._store.get(actuator_entity_id))
+        ranked: list[tuple[float, str, HaEntitySummary]] = []
+        for entity in entities.values():
+            if entity.entity_id == actuator_entity_id or entity.domain not in _MANUAL_CONTEXT_DOMAINS:
+                continue
+            role = _manual_context_role(entity, discovered.get(entity.entity_id))
+            score, _ = _score_candidate(
+                actuator,
+                entity,
+                role,
+                context=role is not EntityRole.MEASUREMENT,
+            )
+            selected = entity.entity_id in selected_ids
+            if selected:
+                score = max(score, 1.0)
+            if not selected and (
+                _is_diagnostic_context(entity)
+                or not _has_context_relationship(actuator, entity)
+            ):
+                continue
+            if not selected and score < 0.1:
+                continue
+            ranked.append((score, _context_sort_group(entity), entity))
+        ranked.sort(
+            key=lambda item: (
+                -item[0],
+                item[1],
+                item[2].area_name or "",
+                item[2].friendly_name or item[2].entity_id,
+                item[2].entity_id,
+            )
         )
-        self._store.upsert(updated)
+        return [entity for _, _, entity in ranked[:limit]]
-        return self.reconcile_actuator(actuator_entity_id, trigger="override")
 
     def delete_actuator(self, actuator_entity_id: str) -> None:
         model_id = model_id_for_actuator(actuator_entity_id)
         self._registry.archive(model_id)
         self._store.delete(actuator_entity_id)
 
+    def set_manual_assignment(
+        self,
+        actuator_entity_id: str,
+        *,
+        numeric_entity_id: str | None,
+        context_entity_ids: list[str],
+        note: str | None = None,
+    ) -> ActuatorRecord:
+        now = datetime.now(timezone.utc)
+        record = self._store.get(actuator_entity_id)
+        entities = {entity.entity_id: entity for entity in self._ha_reader.read_entities()}
+        actuator = entities.get(actuator_entity_id)
+        if actuator is None:
+            raise KeyError("Aktuator-Konfiguration nicht gefunden.")
+        selected_context_ids = list(dict.fromkeys(context_entity_ids))
+        selected_ids = [
+            entity_id
+            for entity_id in [numeric_entity_id, *selected_context_ids]
+            if entity_id
+        ]
+        missing = [entity_id for entity_id in selected_ids if entity_id not in entities]
+        if missing:
+            raise ValueError(f"Unbekannte Home-Assistant-Entity: {', '.join(missing)}")
+        if actuator_entity_id in selected_ids:
+            raise ValueError("Der Aktor selbst kann nicht als Kontextsensor verwendet werden.")
+
+        override = ManualOverride(
+            numeric_entity_id=numeric_entity_id,
+            context_entity_ids=selected_context_ids,
+            updated_at=now,
+            note=note,
+        )
+        assignment = self._manual_assignment(override)
+        lifecycle = self._reconcile_lifecycle(
+            actuator=actuator,
+            assignment=assignment,
+            lifecycle=record.lifecycle.model_copy(update={"last_reconciled_at": now}),
+            now=now,
+        )
+        updated = record.model_copy(
+            update={
+                "assignment": assignment,
+                "manual_override": override,
+                "numeric_candidates": _merge_manual_candidates(
+                    record.numeric_candidates,
+                    entities,
+                    [numeric_entity_id] if numeric_entity_id else [],
+                    role=EntityRole.MEASUREMENT,
+                ),
+                "context_candidates": _merge_manual_candidates(
+                    record.context_candidates,
+                    entities,
+                    selected_context_ids,
+                    role=EntityRole.CONTEXT,
+                ),
+                "lifecycle": lifecycle,
+                "updated_at": now,
+            }
+        )
+        return self._store.upsert(updated)
+
     def reconcile_all(self, trigger: str = "manual") -> ReconciliationState:
         state = self._store.load_reconciliation_state().model_copy(
             update={
@@ -132,7 +268,7 @@ class ActuatorReconciliationService:
             last_summary=(
                 f"{len(refreshed)} Aktuatoren geprüft, "
                 f"{sum(1 for record in refreshed if record.assignment.review_required)} "
-                "mit Prüfbedarf."
+                "mit niedriger Zuordnungssicherheit."
             ),
         )
         self._store.save_reconciliation_state(summary)
@@ -210,11 +346,14 @@ class ActuatorReconciliationService:
             ),
             context=True,
         )
-        assignment = self._select_assignment(
+        assignment = (
-            actuator=actuator,
+            self._manual_assignment(record.manual_override)
-            numeric_candidates=numeric_candidates,
+            if record.manual_override is not None
-            context_candidates=context_candidates,
+            else self._select_assignment(
-            override=record.manual_override,
+                actuator=actuator,
+                numeric_candidates=numeric_candidates,
+                context_candidates=context_candidates,
+            )
         )
         lifecycle = self._reconcile_lifecycle(
             actuator=actuator,
@@ -225,6 +364,7 @@ class ActuatorReconciliationService:
         updated = record.model_copy(
             update={
                 "assignment": assignment,
+                "manual_override": record.manual_override,
                 "numeric_candidates": numeric_candidates,
                 "context_candidates": context_candidates,
                 "lifecycle": lifecycle,
@@ -240,42 +380,63 @@ class ActuatorReconciliationService:
         )
         return updated
 
+    @staticmethod
+    def _manual_assignment(override: ManualOverride) -> AssignmentSelection:
+        selected_context_ids = list(dict.fromkeys(override.context_entity_ids))
+        selected_count = len(selected_context_ids) + (1 if override.numeric_entity_id else 0)
+        return AssignmentSelection(
+            selected_numeric_entity_id=override.numeric_entity_id,
+            selected_context_entity_ids=selected_context_ids,
+            source=AssignmentSource.MANUAL,
+            confidence=1.0 if selected_count else 0.0,
+            review_required=selected_count == 0,
+            reason=(
+                f"Manuell festgelegt: {selected_count} Kontext-Entity(s) werden verwendet."
+                if selected_count
+                else "Manuelle Zuordnung enthält noch keine Kontext-Entities."
+            ),
+        )
+
     def _select_assignment(
         self,
         *,
         actuator: HaEntitySummary,
         numeric_candidates: list[AssignmentCandidate],
         context_candidates: list[AssignmentCandidate],
-        override: ManualOverride | None,
     ) -> AssignmentSelection:
-        if override is not None:
+        top_numeric = next(
-            selected_numeric = override.numeric_entity_id
+            (candidate for candidate in numeric_candidates if candidate.auto_accepted),
-            selected_contexts = list(dict.fromkeys(override.context_entity_ids))
+            None,
-            return AssignmentSelection(
+        )
-                selected_numeric_entity_id=selected_numeric,
+        accepted_contexts = [
-                selected_context_entity_ids=selected_contexts,
+            candidate
-                source=AssignmentSource.MANUAL,
-                confidence=1.0 if selected_numeric else 0.6,
-                review_required=False,
-                reason=(
-                    "Manuelle Zuordnung überschreibt die automatische Heuristik dauerhaft."
-                ),
-            )
-
-        top_numeric = numeric_candidates[0] if numeric_candidates else None
-        top_contexts = [
-            candidate.entity_id
             for candidate in context_candidates
             if candidate.auto_accepted
         ][: _MAX_CONTEXT_SELECTIONS]
+        top_contexts = [candidate.entity_id for candidate in accepted_contexts]
         if top_numeric is None:
+            if accepted_contexts:
+                return AssignmentSelection(
+                    selected_numeric_entity_id=None,
+                    selected_context_entity_ids=top_contexts,
+                    source=AssignmentSource.AUTOMATIC,
+                    confidence=max(candidate.confidence for candidate in accepted_contexts),
+                    review_required=False,
+                    reason=(
+                        "Passender Schaltkontext automatisch erkannt. Für diese "
+                        "Verhaltensvorhersage ist kein numerischer Sensor erforderlich."
+                    ),
+                )
             return AssignmentSelection(
                 selected_numeric_entity_id=None,
                 selected_context_entity_ids=top_contexts,
                 source=AssignmentSource.NONE,
                 confidence=0.0,
                 review_required=True,
-                reason=f"Kein numerischer Sensor konnte für {display_name(actuator)} bestimmt werden.",
+                reason=(
+                    f"Für {display_name(actuator)} ist noch kein nutzbarer numerischer "
+                    "Kontext verfügbar. Die Zuordnung wird automatisch erneut geprüft."
+                ),
             )
 
         return AssignmentSelection(
@@ -285,9 +446,9 @@ class ActuatorReconciliationService:
             confidence=top_numeric.confidence,
             review_required=not top_numeric.auto_accepted,
             reason=(
-                "Automatisch akzeptiert."
+                "Kontext automatisch und eindeutig zugeordnet."
                 if top_numeric.auto_accepted
-                else "Top-Kandidat gefunden, aber Zuordnung ist noch nicht eindeutig genug."
+                else "Besten verfügbaren Kontext automatisch mit niedriger Sicherheit zugeordnet."
             ),
         )
 
@@ -306,14 +467,6 @@ class ActuatorReconciliationService:
                 "Ohne numerische Sensorzuordnung wird kein Modell aktiv gehalten.",
                 now=now,
             )
-        if assignment.review_required and assignment.source is not AssignmentSource.MANUAL:
-            return self._archive_state(
-                lifecycle,
-                "Zuordnung ist nicht eindeutig; Modell wartet auf Review.",
-                now=now,
-                status=LifecycleStatus.REVIEW_REQUIRED,
-            )
-
         sensor_id = assignment.selected_numeric_entity_id
         series = self._read_history(sensor_id, now)
         points = series.points if series is not None else []
@@ -327,7 +480,7 @@ class ActuatorReconciliationService:
                             f"{len(points)} von mindestens {self._settings.min_training_points} "
                             f"Messpunkten für {sensor_id} vorhanden."
                         ),
-                        "next_action": "Mehr Historie sammeln und Reconciliation erneut ausführen.",
+                        "next_action": "Historie wird automatisch weiter gesammelt.",
                         "last_history_point_count": len(points),
                     }
                 ),
@@ -369,7 +522,7 @@ class ActuatorReconciliationService:
                         "last_history_signature": signature,
                         "last_history_point_count": len(points),
                         "reason": retrain_reason,
-                        "next_action": "Automatisch überwachen und bei neuen Daten neu trainieren.",
+                        "next_action": "Neue Daten automatisch überwachen und nachtrainieren.",
                     }
                 ),
                 action="retrained" if result.replaced else "trained",
@@ -384,8 +537,8 @@ class ActuatorReconciliationService:
                     "last_reconciled_at": now,
                     "last_history_signature": signature,
                     "last_history_point_count": len(points),
-                    "reason": "Modell ist aktuell und passt zur bestätigten Sensorzuordnung.",
+                    "reason": "Modell ist aktuell und passt zur automatischen Kontextzuordnung.",
-                    "next_action": "Auf neue Historie oder Staleness warten.",
+                    "next_action": "Neue Historie automatisch auswerten.",
                 }
             ),
             action="kept",
@@ -423,7 +576,7 @@ class ActuatorReconciliationService:
                     "status": status,
                     "last_reconciled_at": now,
                     "reason": reason,
-                    "next_action": "Review oder neue Zuordnung erforderlich.",
+                    "next_action": "Bei neuen Home-Assistant-Daten automatisch erneut zuordnen.",
                 }
             ),
             action="archived",
@@ -470,8 +623,15 @@ class ActuatorReconciliationService:
             confidence = candidate.score / highest if highest else 0.0
             margin = candidate.score - second_score if index == 0 else 0.0
             auto_score = _CONTEXT_AUTO_ACCEPT_SCORE if context else _NUMERIC_AUTO_ACCEPT_SCORE
-            auto_accepted = confidence >= auto_score and (
+            minimum_score = (
-                context or margin >= _NUMERIC_MIN_MARGIN
+                _CONTEXT_AUTO_ACCEPT_MIN_SCORE
+                if context
+                else _NUMERIC_AUTO_ACCEPT_MIN_SCORE
+            )
+            auto_accepted = (
+                candidate.score >= minimum_score
+                and confidence >= auto_score
+                and (context or margin >= _NUMERIC_MIN_MARGIN)
             )
             sorted_candidates[index] = candidate.model_copy(
                 update={
@@ -516,6 +676,73 @@ def _filter_candidates(
     return result
 
 
+def _selected_context_ids(record: ActuatorRecord) -> set[str]:
+    result = set(record.assignment.selected_context_entity_ids)
+    if record.assignment.selected_numeric_entity_id:
+        result.add(record.assignment.selected_numeric_entity_id)
+    if record.manual_override is not None:
+        result.update(record.manual_override.context_entity_ids)
+        if record.manual_override.numeric_entity_id:
+            result.add(record.manual_override.numeric_entity_id)
+    return result
+
+
+def _manual_context_role(
+    entity: HaEntitySummary,
+    discovered: DiscoveredEntity | None,
+) -> EntityRole:
+    if discovered is not None and discovered.role is not EntityRole.UNSUPPORTED:
+        return discovered.role
+    if entity.domain == "sensor":
+        return EntityRole.MEASUREMENT
+    if entity.domain == "binary_sensor":
+        return EntityRole.BINARY_CONTEXT
+    return EntityRole.CONTEXT
+
+
+def _context_sort_group(entity: HaEntitySummary) -> str:
+    device_class = entity.device_class or ""
+    if device_class in {"motion", "occupancy", "presence"}:
+        return "01_presence"
+    if device_class in {"illuminance"}:
+        return "02_brightness"
+    if device_class in {"door", "garage_door", "opening", "window"}:
+        return "03_opening"
+    if device_class in {"humidity", "moisture"}:
+        return "04_humidity"
+    if device_class in {"power", "energy", "current", "voltage"}:
+        return "05_power"
+    if entity.domain in {"light", "switch"}:
+        return "06_states"
+    return f"20_{entity.domain}_{device_class}"
+
+
+def _is_diagnostic_context(entity: HaEntitySummary) -> bool:
+    tokens = _metadata_tokens(entity, include_stopwords=True)
+    return bool(tokens.intersection(_DIAGNOSTIC_TOKENS))
+
+
+def _has_context_relationship(actuator: HaEntitySummary, entity: HaEntitySummary) -> bool:
+    if (
+        actuator.area_name
+        and entity.area_name
+        and actuator.area_name == entity.area_name
+        and actuator.area_name.lower() not in _GENERIC_AREA_NAMES
+    ):
+        return True
+    if actuator.device_id and entity.device_id and actuator.device_id == entity.device_id:
+        return True
+    if actuator.device_name and entity.device_name and actuator.device_name == entity.device_name:
+        return True
+    if _metadata_tokens(actuator).intersection(_metadata_tokens(entity)):
+        return True
+    entity_tokens = _metadata_tokens(entity, include_stopwords=True)
+    return bool(
+        entity_tokens.intersection(_OUTDOOR_TOKENS)
+        and entity.device_class in {"illuminance", "humidity", "temperature"}
+    )
+
+
 def _score_candidate(
     actuator: HaEntitySummary,
     entity: HaEntitySummary,
@@ -531,7 +758,12 @@ def _score_candidate(
     if overlap:
         score += min(0.4, 0.1 * len(overlap))
         evidence.append(f"Gemeinsame Tokens: {', '.join(overlap[:4])}")
-    if actuator.area_name and entity.area_name and actuator.area_name == entity.area_name:
+    if (
+        actuator.area_name
+        and entity.area_name
+        and actuator.area_name == entity.area_name
+        and actuator.area_name.lower() not in _GENERIC_AREA_NAMES
+    ):
         score += 0.35
         evidence.append(f"Gleicher Bereich: {actuator.area_name}")
     if actuator.device_id and entity.device_id and actuator.device_id == entity.device_id:
@@ -547,15 +779,68 @@ def _score_candidate(
     if entity.device_class in preferred_device_classes:
         score += 0.2
         evidence.append(f"Passende device_class: {entity.device_class}")
+    if not context and actuator.domain == "light" and entity.device_class == "illuminance":
+        score += 0.2
+        evidence.append("Beleuchtungsstärke wird für Lichtaktoren bevorzugt.")
     if not context and entity.unit_of_measurement is not None:
         score += 0.05
         evidence.append(f"Numerische Einheit vorhanden: {entity.unit_of_measurement}")
     if context and role is EntityRole.BINARY_CONTEXT:
         score += 0.05
         evidence.append("Binärer Kontextsensor bevorzugt für Zusatzkontext.")
+    if entity_tokens.intersection(_OUTDOOR_TOKENS) and entity.device_class in {
+        "illuminance",
+        "humidity",
+        "temperature",
+    }:
+        score += 0.1
+        evidence.append("Außenmesswert ist oft als übergreifender Kontext relevant.")
     return round(min(score, 1.0), 4), evidence
 
 
+def _merge_manual_candidates(
+    candidates: list[AssignmentCandidate],
+    entities: dict[str, HaEntitySummary],
+    selected_entity_ids: list[str],
+    *,
+    role: EntityRole,
+) -> list[AssignmentCandidate]:
+    by_id = {candidate.entity_id: candidate for candidate in candidates}
+    for entity_id in selected_entity_ids:
+        existing = by_id.get(entity_id)
+        if existing is not None:
+            by_id[entity_id] = existing.model_copy(
+                update={
+                    "auto_accepted": True,
+                    "confidence": 1.0,
+                    "evidence": [
+                        *existing.evidence,
+                        "Manuell vom Nutzer als relevant festgelegt.",
+                    ],
+                }
+            )
+            continue
+        entity = entities.get(entity_id)
+        if entity is None:
+            continue
+        by_id[entity_id] = AssignmentCandidate(
+            entity_id=entity.entity_id,
+            domain=entity.domain,
+            role=role,
+            device_class=entity.device_class,
+            state_class=entity.state_class,
+            unit_of_measurement=entity.unit_of_measurement,
+            friendly_name=entity.friendly_name,
+            area_name=entity.area_name,
+            device_name=entity.device_name,
+            score=1.0,
+            confidence=1.0,
+            auto_accepted=True,
+            evidence=["Manuell vom Nutzer als relevant festgelegt."],
+        )
+    return sorted(by_id.values(), key=lambda item: (-item.confidence, item.entity_id))
+
+
 def _preferred_device_classes(domain: str, *, context: bool) -> frozenset[str]:
     if context:
         return frozenset({"door", "garage_door", "motion", "occupancy", "opening", "presence"})
@@ -571,7 +856,7 @@ def _preferred_device_classes(domain: str, *, context: bool) -> frozenset[str]:
     return frozenset(mapping.get(domain, {"power", "energy", "temperature"}))
 
 
-def _metadata_tokens(entity: HaEntitySummary) -> set[str]:
+def _metadata_tokens(entity: HaEntitySummary, *, include_stopwords: bool = False) -> set[str]:
     raw_values = [
         entity.entity_id,
         entity.friendly_name,
@@ -583,7 +868,7 @@ def _metadata_tokens(entity: HaEntitySummary) -> set[str]:
         if value is None:
             continue
         for token in _TOKEN_PATTERN.findall(value.lower().replace("_", " ")):
-            if len(token) < 3 or token in _STOPWORDS:
+            if len(token) < 3 or (not include_stopwords and token in _STOPWORDS):
                 continue
             tokens.add(token)
     return tokens

app/actuators/models.py

@@ -25,6 +25,18 @@ class LifecycleStatus(StrEnum):
     ARCHIVED = "archived"
 
 
+class BehaviorMode(StrEnum):
+    SHADOW = "shadow"
+    ACTIVE = "active"
+    PAUSED = "paused"
+
+
+class BehaviorStatus(StrEnum):
+    COLLECTING = "collecting"
+    TRAINED = "trained"
+    BLOCKED = "blocked"
+
+
 class AssignmentCandidate(BaseModel):
     entity_id: str
     domain: str
@@ -71,10 +83,64 @@ class ModelLifecycleState(BaseModel):
     last_history_signature: str | None = None
     last_history_point_count: int = Field(default=0, ge=0)
     reason: str = "Noch keine Trainingsdaten ausgewertet."
-    next_action: str = "Aktuator auswählen und Zuordnung prüfen."
+    next_action: str = "Aktor auswählen; Kontext und Historie werden automatisch geprüft."
     audit: list[LifecycleAuditEntry] = Field(default_factory=list)
 
 
+class BehaviorPattern(BaseModel):
+    target_state: str = Field(min_length=1, max_length=100)
+    minute_of_day: int = Field(ge=0, le=1439)
+    weekday: int = Field(ge=0, le=6)
+    context_states: dict[str, str] = Field(default_factory=dict)
+    trigger_entity_id: str | None = None
+    trigger_from_state: str | None = None
+    trigger_to_state: str | None = None
+    source: str = Field(default="observed", max_length=40)
+    weight: float = Field(default=1.0, ge=0.1, le=1.0)
+    observed_at: datetime
+
+
+class BehaviorPrediction(BaseModel):
+    target_state: str
+    confidence: float = Field(ge=0.0, le=1.0)
+    generated_at: datetime
+    reason: str
+    matching_patterns: int = Field(default=0, ge=0)
+    executed: bool = False
+    execution_reason: str = "Vorhersage wurde noch nicht ausgeführt."
+
+
+class ExecutionEvent(BaseModel):
+    target_state: str
+    executed_at: datetime
+
+
+class RelatedAutomation(BaseModel):
+    entity_id: str = Field(pattern=r"^automation\.[a-z0-9_]+$")
+    config_id: str = Field(min_length=1, max_length=120)
+    friendly_name: str = Field(min_length=1, max_length=200)
+    enabled: bool
+
+
+class BehaviorState(BaseModel):
+    mode: BehaviorMode = BehaviorMode.SHADOW
+    status: BehaviorStatus = BehaviorStatus.COLLECTING
+    approved_at: datetime | None = None
+    sample_count: int = Field(default=0, ge=0)
+    high_confidence_sample_count: int = Field(default=0, ge=0)
+    patterns: list[BehaviorPattern] = Field(default_factory=list)
+    prediction: BehaviorPrediction | None = None
+    last_trained_at: datetime | None = None
+    last_evaluated_at: datetime | None = None
+    last_executed_at: datetime | None = None
+    execution_events: list[ExecutionEvent] = Field(default_factory=list)
+    activation_ready: bool = False
+    activation_reason: str = "Noch nicht genügend Verhalten für eine Freigabe gelernt."
+    related_automations: list[RelatedAutomation] = Field(default_factory=list)
+    paused_automation_entity_ids: list[str] = Field(default_factory=list)
+    reason: str = "Historische Aktorhandlungen werden analysiert."
+
+
 class ActuatorRecord(BaseModel):
     actuator_entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
     enabled: bool = True
@@ -85,6 +151,7 @@ class ActuatorRecord(BaseModel):
     numeric_candidates: list[AssignmentCandidate] = Field(default_factory=list)
     context_candidates: list[AssignmentCandidate] = Field(default_factory=list)
     lifecycle: ModelLifecycleState
+    behavior: BehaviorState = Field(default_factory=BehaviorState)
 
 
 class ReconciliationState(BaseModel):

app/api/v1/actuators.py

@@ -4,10 +4,12 @@ from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
 from pydantic import BaseModel, Field
 
 from app.actuators.lifecycle import ActuatorReconciliationService
-from app.actuators.models import ActuatorRecord, ManualOverride, ReconciliationState
+from app.actuators.models import ActuatorRecord, ReconciliationState
 from app.actuators.store import ActuatorStore
+from app.behavior.engine import BehaviorEngine
 from app.dependencies import get_ha_reader
 from app.ha.discovery import EntityRole
+from app.ha.exceptions import HaClientError
 from app.ha.models import HaEntitySummary
 from app.ha.reader import HaReader
 
@@ -19,11 +21,21 @@ class ConfigureActuatorRequest(BaseModel):
     enabled: bool = True
 
 
-class OverrideRequest(BaseModel):
+class ActivationRequest(BaseModel):
+    active: bool
+    pause_matching_automations: bool = False
+    restore_paused_automations: bool = False
+
+
+class AutomationControlRequest(BaseModel):
+    automation_entity_id: str = Field(pattern=r"^automation\.[a-z0-9_]+$")
+    enabled: bool
+
+
+class ManualAssignmentRequest(BaseModel):
     numeric_entity_id: str | None = Field(default=None, pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
     context_entity_ids: list[str] = Field(default_factory=list)
-    note: str | None = Field(default=None, max_length=300)
+    note: str | None = Field(default=None, max_length=500)
-    clear: bool = False
 
 
 @router.get("/discovery", response_model=list[HaEntitySummary])
@@ -36,6 +48,19 @@ def discover_actuators(ha_reader: HaReader = Depends(get_ha_reader)) -> list[HaE
     return [entities[entity_id] for entity_id in actuator_ids if entity_id in entities]
 
 
+@router.get("/context-options", response_model=list[HaEntitySummary])
+def context_options(
+    request: Request,
+    actuator_entity_id: str | None = Query(default=None, pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$"),
+) -> list[HaEntitySummary]:
+    if actuator_entity_id is None:
+        return []
+    try:
+        return _service(request).suggest_context_options(actuator_entity_id)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
 @router.get("", response_model=list[ActuatorRecord])
 def list_configured(request: Request) -> list[ActuatorRecord]:
     return _service(request).list_configured()
@@ -44,10 +69,12 @@ def list_configured(request: Request) -> list[ActuatorRecord]:
 @router.post("", response_model=ActuatorRecord, status_code=201)
 def configure(payload: ConfigureActuatorRequest, request: Request) -> ActuatorRecord:
     try:
-        return _service(request).configure_actuator(
+        record = _service(request).configure_actuator(
             payload.actuator_entity_id,
             enabled=payload.enabled,
         )
+        _behavior(request).train(record.actuator_entity_id)
+        return _behavior(request).evaluate(record.actuator_entity_id)
     except KeyError as exc:
         raise HTTPException(status_code=404, detail=str(exc)) from exc
 
@@ -65,34 +92,107 @@ def delete_actuator(actuator_entity_id: str, request: Request) -> None:
     _service(request).delete_actuator(actuator_entity_id)
 
 
-@router.post("/{actuator_entity_id}/override", response_model=ActuatorRecord)
-def set_override(
-    actuator_entity_id: str,
-    payload: OverrideRequest,
-    request: Request,
-) -> ActuatorRecord:
-    override = None if payload.clear else ManualOverride(
-        numeric_entity_id=payload.numeric_entity_id,
-        context_entity_ids=payload.context_entity_ids,
-        note=payload.note,
-    )
-    try:
-        return _service(request).set_override(actuator_entity_id, override)
-    except KeyError as exc:
-        raise HTTPException(status_code=404, detail=str(exc)) from exc
-
-
 @router.post("/{actuator_entity_id}/reconcile", response_model=ActuatorRecord)
 def reconcile_actuator(
     actuator_entity_id: str,
     request: Request,
 ) -> ActuatorRecord:
     try:
-        return _service(request).reconcile_actuator(actuator_entity_id, trigger="manual")
+        _service(request).reconcile_actuator(actuator_entity_id, trigger="manual")
+        _behavior(request).train(actuator_entity_id)
+        return _behavior(request).evaluate(actuator_entity_id)
     except KeyError as exc:
         raise HTTPException(status_code=404, detail=str(exc)) from exc
 
 
+@router.post("/{actuator_entity_id}/evaluate", response_model=ActuatorRecord)
+def evaluate_actuator(
+    actuator_entity_id: str,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _behavior(request).evaluate(actuator_entity_id)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
+@router.post("/{actuator_entity_id}/activation", response_model=ActuatorRecord)
+def set_activation(
+    actuator_entity_id: str,
+    payload: ActivationRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _behavior(request).set_active(
+            actuator_entity_id,
+            active=payload.active,
+            pause_matching_automations=payload.pause_matching_automations,
+            restore_paused_automations=payload.restore_paused_automations,
+        )
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=409, detail=str(exc)) from exc
+
+
+@router.post("/{actuator_entity_id}/assignment", response_model=ActuatorRecord)
+def set_manual_assignment(
+    actuator_entity_id: str,
+    payload: ManualAssignmentRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        record = _service(request).set_manual_assignment(
+            actuator_entity_id,
+            numeric_entity_id=payload.numeric_entity_id,
+            context_entity_ids=payload.context_entity_ids,
+            note=payload.note,
+        )
+        _behavior(request).train(record.actuator_entity_id)
+        return _behavior(request).evaluate(record.actuator_entity_id)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=str(exc)) from exc
+
+
+@router.post(
+    "/{actuator_entity_id}/related-automations/refresh",
+    response_model=ActuatorRecord,
+)
+def refresh_related_automations(
+    actuator_entity_id: str,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _behavior(request).refresh_related_automations(actuator_entity_id)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except (ValueError, HaClientError) as exc:
+        raise HTTPException(status_code=409, detail=str(exc)) from exc
+
+
+@router.post(
+    "/{actuator_entity_id}/related-automations/control",
+    response_model=ActuatorRecord,
+)
+def control_related_automation(
+    actuator_entity_id: str,
+    payload: AutomationControlRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _behavior(request).set_automation_enabled(
+            actuator_entity_id,
+            payload.automation_entity_id,
+            enabled=payload.enabled,
+        )
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except (ValueError, HaClientError) as exc:
+        raise HTTPException(status_code=409, detail=str(exc)) from exc
+
+
 @router.get("/reconciliation/state", response_model=ReconciliationState)
 def get_reconciliation_state(request: Request) -> ReconciliationState:
     store = getattr(request.app.state, "actuator_store", None)
@@ -109,7 +209,10 @@ def run_reconciliation(
     request: Request,
     trigger: str = Query(default="manual", pattern=r"^[a-z0-9_-]{1,32}$"),
 ) -> ReconciliationState:
-    return _service(request).reconcile_all(trigger=trigger)
+    state = _service(request).reconcile_all(trigger=trigger)
+    _behavior(request).train_all()
+    _behavior(request).evaluate_all()
+    return state
 
 
 def _service(request: Request) -> ActuatorReconciliationService:
@@ -120,3 +223,13 @@ def _service(request: Request) -> ActuatorReconciliationService:
             detail="Actuator-Reconciliation nicht initialisiert.",
         )
     return service
+
+
+def _behavior(request: Request) -> BehaviorEngine:
+    engine = getattr(request.app.state, "behavior_engine", None)
+    if not isinstance(engine, BehaviorEngine):
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Verhaltenslernen ist nicht initialisiert.",
+        )
+    return engine

app/behavior/__init__.py

@@ -0,0 +1 @@
+"""Learning and prediction for actuator behavior."""

app/behavior/engine.py

@@ -0,0 +1,887 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Sequence
+from datetime import datetime, timedelta, timezone
+from zoneinfo import ZoneInfo
+
+from app.actuators.models import (
+    ActuatorRecord,
+    BehaviorMode,
+    BehaviorPattern,
+    BehaviorPrediction,
+    BehaviorState,
+    BehaviorStatus,
+    ExecutionEvent,
+    RelatedAutomation,
+)
+from app.actuators.store import ActuatorStore
+from app.config import Settings
+from app.ha.exceptions import HaClientError
+from app.ha.history import LogbookEntry, StateHistoryPoint, StateHistorySeries
+from app.ha.models import HaEntitySummary
+from app.ha.reader import HaReader
+
+_MAX_PATTERNS = 500
+_MAX_EXECUTION_EVENTS = 100
+_ACTION_LOGBOOK_TOLERANCE = timedelta(seconds=10)
+_CONTEXT_TRIGGER_TOLERANCE = timedelta(seconds=3)
+_OWN_ACTION_TOLERANCE = timedelta(seconds=20)
+_SAFE_ACTIVE_DOMAINS = frozenset({"cover", "fan", "humidifier", "light", "switch"})
+_AUTOMATION_CONTEXT_DOMAINS = frozenset({"automation", "script"})
+logger = logging.getLogger(__name__)
+
+
+class BehaviorEngine:
+    def __init__(
+        self,
+        *,
+        ha_reader: HaReader,
+        store: ActuatorStore,
+        settings: Settings,
+    ) -> None:
+        self._ha_reader = ha_reader
+        self._store = store
+        self._settings = settings
+
+    def train_all(self) -> list[ActuatorRecord]:
+        results: list[ActuatorRecord] = []
+        for record in self._store.list():
+            try:
+                results.append(self.train(record.actuator_entity_id))
+            except Exception:
+                logger.exception("Behavior training failed for %s", record.actuator_entity_id)
+                results.append(record)
+        return results
+
+    def train(self, actuator_entity_id: str) -> ActuatorRecord:
+        record = self._store.get(actuator_entity_id)
+        now = datetime.now(timezone.utc)
+        raw_context_ids = list(
+            dict.fromkeys(
+                [
+                    record.assignment.selected_numeric_entity_id,
+                    *record.assignment.selected_context_entity_ids,
+                ]
+            )
+        )
+        context_ids = [
+            entity_id for entity_id in raw_context_ids if isinstance(entity_id, str)
+        ]
+        if not context_ids:
+            return self._save_behavior(
+                record,
+                record.behavior.model_copy(
+                    update={
+                        "status": BehaviorStatus.COLLECTING,
+                        "activation_ready": False,
+                        "activation_reason": (
+                            "Freigabe gesperrt: Noch kein geeigneter Kontext erkannt."
+                        ),
+                        "last_trained_at": now,
+                        "reason": "Noch kein geeigneter Kontext für Verhaltenslernen vorhanden.",
+                    }
+                ),
+            )
+
+        start = now - timedelta(days=self._settings.history_days)
+        history_ids = [actuator_entity_id, *context_ids]
+        try:
+            history = {
+                series.entity_id: series
+                for series in self._ha_reader.read_state_history(history_ids, start, now)
+            }
+        except (HaClientError, ValueError) as exc:
+            logger.warning("Behavior history unavailable for %s: %s", actuator_entity_id, exc)
+            return self._save_behavior(
+                record,
+                record.behavior.model_copy(
+                    update={
+                        "status": BehaviorStatus.BLOCKED,
+                        "last_trained_at": now,
+                        "reason": f"Home-Assistant-Historie konnte nicht gelesen werden: {exc}",
+                    }
+                ),
+            )
+        actuator_history = history.get(actuator_entity_id)
+        if actuator_history is None or len(actuator_history.points) < 2:
+            return self._save_behavior(
+                record,
+                record.behavior.model_copy(
+                    update={
+                        "status": BehaviorStatus.COLLECTING,
+                        "sample_count": 0,
+                        "high_confidence_sample_count": 0,
+                        "activation_ready": False,
+                        "activation_reason": (
+                            "Freigabe gesperrt: Noch keine historischen "
+                            "Aktorhandlungen gefunden."
+                        ),
+                        "patterns": [],
+                        "last_trained_at": now,
+                        "reason": "Noch keine historischen Aktorhandlungen gefunden.",
+                    }
+                ),
+            )
+
+        try:
+            logbook = list(self._ha_reader.read_logbook(actuator_entity_id, start, now))
+        except (HaClientError, ValueError) as exc:
+            logger.warning("Logbook unavailable for %s: %s", actuator_entity_id, exc)
+            logbook = []
+        patterns = self._build_patterns(
+            actuator_history=actuator_history,
+            context_history=history,
+            context_ids=context_ids,
+            logbook=logbook,
+            own_executions=record.behavior.execution_events,
+        )
+        trusted_actions = sum(
+            1 for pattern in patterns if pattern.source in {"user", "automation"}
+        )
+        status = (
+            BehaviorStatus.TRAINED
+            if len(patterns) >= self._settings.min_behavior_actions
+            else BehaviorStatus.COLLECTING
+        )
+        reason = (
+            f"{len(patterns)} Handlungen mit automatisch erfasstem Kontext gelernt."
+            if status is BehaviorStatus.TRAINED
+            else (
+                f"{len(patterns)} von mindestens {self._settings.min_behavior_actions} "
+                "benötigten Handlungen gelernt."
+            )
+        )
+        activation_ready = (
+            status is BehaviorStatus.TRAINED
+            and trusted_actions >= self._settings.min_behavior_actions
+        )
+        activation_reason = (
+            "Freigabe bereit: Genügend eindeutig zugeordnete Handlungen gelernt."
+            if activation_ready
+            else (
+                "Freigabe gesperrt: "
+                f"{max(0, self._settings.min_behavior_actions - trusted_actions)} "
+                "eindeutig zugeordnete Handlungen fehlen."
+            )
+        )
+        behavior = record.behavior.model_copy(
+            update={
+                "status": status,
+                "sample_count": len(patterns),
+                "high_confidence_sample_count": trusted_actions,
+                "activation_ready": activation_ready,
+                "activation_reason": activation_reason,
+                "patterns": patterns[-_MAX_PATTERNS:],
+                "last_trained_at": now,
+                "reason": reason,
+            }
+        )
+        return self._save_behavior(record, behavior)
+
+    def evaluate_all(self) -> list[ActuatorRecord]:
+        results: list[ActuatorRecord] = []
+        for record in self._store.list():
+            try:
+                results.append(self.evaluate(record.actuator_entity_id))
+            except Exception:
+                logger.exception("Behavior evaluation failed for %s", record.actuator_entity_id)
+                results.append(record)
+        return results
+
+    def evaluate(
+        self,
+        actuator_entity_id: str,
+        *,
+        context_state_overrides: dict[str, str | None] | None = None,
+        context_changed_at_overrides: dict[str, datetime | None] | None = None,
+        current_entities: Sequence[HaEntitySummary] | None = None,
+    ) -> ActuatorRecord:
+        record = self._store.get(actuator_entity_id)
+        now = datetime.now(timezone.utc)
+        if current_entities is None:
+            try:
+                current_entities = self._ha_reader.read_entities()
+            except HaClientError as exc:
+                logger.warning("Current HA state unavailable for %s: %s", actuator_entity_id, exc)
+                return self._save_behavior(
+                    record,
+                    record.behavior.model_copy(
+                        update={
+                            "last_evaluated_at": now,
+                            "prediction": None,
+                            "reason": f"Aktueller Home-Assistant-Zustand ist nicht verfügbar: {exc}",
+                        }
+                    ),
+                )
+        entities = {entity.entity_id: entity for entity in current_entities}
+        actuator = entities.get(actuator_entity_id)
+        if actuator is None:
+            return self._save_behavior(
+                record,
+                record.behavior.model_copy(
+                    update={
+                        "last_evaluated_at": now,
+                        "prediction": None,
+                        "reason": "Aktor ist aktuell nicht in Home Assistant verfügbar.",
+                    }
+                ),
+            )
+        current_context = {
+            entity_id: entities[entity_id].state
+            for entity_id in (
+                [
+                    record.assignment.selected_numeric_entity_id,
+                    *record.assignment.selected_context_entity_ids,
+                ]
+            )
+            if entity_id and entity_id in entities and entities[entity_id].state is not None
+        }
+        current_context_changed_at = {
+            entity_id: entities[entity_id].last_changed
+            for entity_id in current_context
+        }
+        selected_context_ids = {
+            entity_id
+            for entity_id in (
+                [
+                    record.assignment.selected_numeric_entity_id,
+                    *record.assignment.selected_context_entity_ids,
+                ]
+            )
+            if entity_id
+        }
+        for entity_id, state in (context_state_overrides or {}).items():
+            if entity_id in selected_context_ids and state is not None:
+                current_context[entity_id] = state
+        for entity_id, changed_at in (context_changed_at_overrides or {}).items():
+            if entity_id in current_context:
+                current_context_changed_at[entity_id] = changed_at or now
+        prediction = predict_behavior(
+            record.behavior.patterns,
+            current_context=current_context,
+            current_context_changed_at=current_context_changed_at,
+            now=now,
+            min_support=self._settings.min_behavior_actions,
+            window_minutes=self._settings.prediction_window_minutes,
+            causal_window_seconds=self._settings.prediction_interval_seconds * 2,
+            timezone_name=self._settings.timezone,
+        )
+        if prediction is not None:
+            prediction = prediction.model_copy(
+                update={
+                    "execution_reason": self._prediction_execution_reason(
+                        record,
+                        actuator.state,
+                        prediction,
+                        now,
+                    )
+                }
+            )
+        behavior = record.behavior.model_copy(
+            update={
+                "last_evaluated_at": now,
+                "prediction": prediction,
+                "reason": (
+                    prediction.reason
+                    if prediction is not None
+                    else "Aktuell ist kein gelerntes Handlungsmuster fällig."
+                ),
+            }
+        )
+        if (
+            prediction is not None
+            and behavior.mode is BehaviorMode.ACTIVE
+            and prediction.confidence >= self._settings.prediction_confidence
+            and actuator.state != prediction.target_state
+            and self._cooldown_elapsed(
+                behavior,
+                now,
+                prediction.target_state,
+            )
+        ):
+            domain = actuator_entity_id.split(".", 1)[0]
+            service = service_for_state(domain, prediction.target_state)
+            if service is not None:
+                try:
+                    self._ha_reader.call_service(
+                        domain,
+                        service,
+                        {"entity_id": actuator_entity_id},
+                    )
+                except (HaClientError, ValueError) as exc:
+                    logger.error(
+                        "Predicted action failed for %s: %s",
+                        actuator_entity_id,
+                        exc,
+                    )
+                    behavior = behavior.model_copy(
+                        update={
+                            "reason": f"Vorhersage wurde aus Sicherheitsgründen nicht ausgeführt: {exc}"
+                        }
+                    )
+                    return self._save_behavior(record, behavior)
+                event = ExecutionEvent(
+                    target_state=prediction.target_state,
+                    executed_at=now,
+                )
+                behavior = behavior.model_copy(
+                    update={
+                        "prediction": prediction.model_copy(
+                            update={
+                                "executed": True,
+                                "execution_reason": (
+                                    f"Ausgeführt mit {prediction.confidence:.0%} Sicherheit."
+                                ),
+                            }
+                        ),
+                        "last_executed_at": now,
+                        "execution_events": [
+                            *behavior.execution_events,
+                            event,
+                        ][-_MAX_EXECUTION_EVENTS:],
+                        "reason": (
+                            f"Vorhersage mit {prediction.confidence:.0%} Sicherheit ausgeführt."
+                        ),
+                    }
+                )
+            else:
+                behavior = behavior.model_copy(
+                    update={
+                        "reason": (
+                            f"Der vorhergesagte Zustand {prediction.target_state!r} "
+                            "ist für autonomes Schalten nicht freigegeben."
+                        )
+                    }
+                )
+        return self._save_behavior(record, behavior)
+
+    def refresh_related_automations(self, actuator_entity_id: str) -> ActuatorRecord:
+        record = self._store.get(actuator_entity_id)
+        related = [
+            RelatedAutomation(
+                entity_id=item.entity_id,
+                config_id=item.config_id,
+                friendly_name=item.friendly_name,
+                enabled=item.enabled,
+            )
+            for item in self._ha_reader.find_automations_for_entity(
+                actuator_entity_id
+            )
+        ]
+        behavior = record.behavior.model_copy(
+            update={"related_automations": related}
+        )
+        return self._save_behavior(record, behavior)
+
+    def set_automation_enabled(
+        self,
+        actuator_entity_id: str,
+        automation_entity_id: str,
+        *,
+        enabled: bool,
+    ) -> ActuatorRecord:
+        record = self.refresh_related_automations(actuator_entity_id)
+        if automation_entity_id not in {
+            item.entity_id for item in record.behavior.related_automations
+        }:
+            raise ValueError(
+                "Die Automation ist diesem Aktor nicht eindeutig zugeordnet."
+            )
+        self._ha_reader.call_service(
+            "automation",
+            "turn_on" if enabled else "turn_off",
+            {"entity_id": automation_entity_id},
+        )
+        related = [
+            item.model_copy(update={"enabled": enabled})
+            if item.entity_id == automation_entity_id
+            else item
+            for item in record.behavior.related_automations
+        ]
+        paused = [
+            entity_id
+            for entity_id in record.behavior.paused_automation_entity_ids
+            if entity_id != automation_entity_id
+        ]
+        behavior = record.behavior.model_copy(
+            update={
+                "related_automations": related,
+                "paused_automation_entity_ids": paused,
+            }
+        )
+        return self._save_behavior(record, behavior)
+
+    def set_active(
+        self,
+        actuator_entity_id: str,
+        *,
+        active: bool,
+        pause_matching_automations: bool = False,
+        restore_paused_automations: bool = False,
+    ) -> ActuatorRecord:
+        record = self.refresh_related_automations(actuator_entity_id)
+        now = datetime.now(timezone.utc)
+        if active:
+            domain = actuator_entity_id.split(".", 1)[0]
+            if domain not in _SAFE_ACTIVE_DOMAINS:
+                raise ValueError(
+                    f"Automatisches Schalten ist für die Domain {domain} nicht freigegeben."
+                )
+            if record.behavior.status is not BehaviorStatus.TRAINED:
+                raise ValueError("Das Verhaltensmodell hat noch nicht genügend Handlungen gelernt.")
+            if not record.behavior.activation_ready:
+                raise ValueError(record.behavior.activation_reason)
+            mode = BehaviorMode.ACTIVE
+            approved_at = now
+            behavior = record.behavior.model_copy(
+                update={
+                    "mode": mode,
+                    "approved_at": approved_at,
+                    "reason": (
+                        "Autonomes Lernen und Schalten wurde ausdrücklich freigegeben."
+                    ),
+                }
+            )
+            record = self._save_behavior(record, behavior)
+            if pause_matching_automations:
+                paused: list[str] = []
+                try:
+                    for automation in record.behavior.related_automations:
+                        if not automation.enabled:
+                            continue
+                        self._ha_reader.call_service(
+                            "automation",
+                            "turn_off",
+                            {"entity_id": automation.entity_id},
+                        )
+                        paused.append(automation.entity_id)
+                except (HaClientError, ValueError):
+                    for entity_id in paused:
+                        try:
+                            self._ha_reader.call_service(
+                                "automation",
+                                "turn_on",
+                                {"entity_id": entity_id},
+                            )
+                        except (HaClientError, ValueError):
+                            logger.exception(
+                                "Failed to restore automation %s after handoff error",
+                                entity_id,
+                            )
+                    rollback = record.behavior.model_copy(
+                        update={
+                            "mode": BehaviorMode.SHADOW,
+                            "approved_at": None,
+                            "reason": (
+                                "Übernahme fehlgeschlagen; SillyHome bleibt im "
+                                "Shadow-Modus."
+                            ),
+                        }
+                    )
+                    self._save_behavior(record, rollback)
+                    raise
+                related = [
+                    automation.model_copy(update={"enabled": False})
+                    if automation.entity_id in paused
+                    else automation
+                    for automation in record.behavior.related_automations
+                ]
+                behavior = record.behavior.model_copy(
+                    update={
+                        "related_automations": related,
+                        "paused_automation_entity_ids": paused,
+                        "reason": (
+                            "SillyHome steuert aktiv; passende HA-Automationen "
+                            "wurden pausiert."
+                        ),
+                    }
+                )
+                return self._save_behavior(record, behavior)
+            return record
+        else:
+            if restore_paused_automations:
+                for entity_id in record.behavior.paused_automation_entity_ids:
+                    self._ha_reader.call_service(
+                        "automation",
+                        "turn_on",
+                        {"entity_id": entity_id},
+                    )
+            mode = BehaviorMode.SHADOW
+            approved_at = None
+            reason = (
+                "Shadow-Modus aktiv; pausierte HA-Automationen wurden fortgesetzt."
+                if restore_paused_automations
+                else "Shadow-Modus aktiv; Vorhersagen werden nicht ausgeführt."
+            )
+        behavior = record.behavior.model_copy(
+            update={
+                "mode": mode,
+                "approved_at": approved_at,
+                "related_automations": [
+                    automation.model_copy(update={"enabled": True})
+                    if (
+                        restore_paused_automations
+                        and automation.entity_id
+                        in record.behavior.paused_automation_entity_ids
+                    )
+                    else automation
+                    for automation in record.behavior.related_automations
+                ],
+                "paused_automation_entity_ids": (
+                    []
+                    if restore_paused_automations
+                    else record.behavior.paused_automation_entity_ids
+                ),
+                "reason": reason,
+            }
+        )
+        return self._save_behavior(record, behavior)
+
+    def _prediction_execution_reason(
+        self,
+        record: ActuatorRecord,
+        current_state: str | None,
+        prediction: BehaviorPrediction,
+        now: datetime,
+    ) -> str:
+        if record.behavior.mode is not BehaviorMode.ACTIVE:
+            return "Nicht ausgeführt: SillyHome ist im Shadow-Modus."
+        if prediction.confidence < self._settings.prediction_confidence:
+            return (
+                "Nicht ausgeführt: Sicherheit liegt unter der "
+                f"Schaltschwelle von {self._settings.prediction_confidence:.0%}."
+            )
+        if current_state == prediction.target_state:
+            return "Nicht ausgeführt: Zielzustand ist bereits erreicht."
+        if not self._cooldown_elapsed(
+            record.behavior,
+            now,
+            prediction.target_state,
+        ):
+            return "Nicht ausgeführt: Sicherheits-Cooldown ist noch aktiv."
+        return "Ausführung ist freigegeben."
+
+    def _build_patterns(
+        self,
+        *,
+        actuator_history: StateHistorySeries,
+        context_history: dict[str, StateHistorySeries],
+        context_ids: list[str],
+        logbook: list[LogbookEntry],
+        own_executions: list[ExecutionEvent],
+    ) -> list[BehaviorPattern]:
+        patterns: list[BehaviorPattern] = []
+        previous_state = actuator_history.points[0].state
+        for point in actuator_history.points[1:]:
+            if point.state == previous_state:
+                continue
+            previous_state = point.state
+            if _matches_own_execution(point, own_executions):
+                continue
+            source, weight = _action_source(point, logbook)
+            trigger = _recent_context_transition(
+                context_history,
+                context_ids,
+                point.timestamp,
+            )
+            contexts = {
+                entity_id: state
+                for entity_id in context_ids
+                if (state := _state_at(context_history.get(entity_id), point.timestamp)) is not None
+            }
+            local = point.timestamp.astimezone(ZoneInfo(self._settings.timezone))
+            patterns.append(
+                BehaviorPattern(
+                    target_state=point.state,
+                    minute_of_day=local.hour * 60 + local.minute,
+                    weekday=local.weekday(),
+                    context_states=contexts,
+                    trigger_entity_id=trigger[0] if trigger else None,
+                    trigger_from_state=trigger[1] if trigger else None,
+                    trigger_to_state=trigger[2] if trigger else None,
+                    source=source,
+                    weight=weight,
+                    observed_at=point.timestamp,
+                )
+            )
+        return patterns
+
+    def _cooldown_elapsed(
+        self,
+        behavior: BehaviorState,
+        now: datetime,
+        target_state: str,
+    ) -> bool:
+        if behavior.last_executed_at is None:
+            return True
+        last_event = behavior.execution_events[-1] if behavior.execution_events else None
+        if last_event is not None and last_event.target_state != target_state:
+            return True
+        return (now - behavior.last_executed_at) >= timedelta(
+            seconds=self._settings.execution_cooldown_seconds
+        )
+
+    def _save_behavior(
+        self,
+        record: ActuatorRecord,
+        behavior: BehaviorState,
+    ) -> ActuatorRecord:
+        updated = record.model_copy(
+            update={
+                "behavior": behavior,
+                "updated_at": datetime.now(timezone.utc),
+            }
+        )
+        return self._store.upsert(updated)
+
+    def handle_state_change(
+        self,
+        entity_id: str,
+        new_state: dict[str, object] | None,
+        *,
+        current_entities: Sequence[HaEntitySummary] | None = None,
+    ) -> None:
+        """Wird bei jedem HA-State-Change aufgerufen und löst sofortige Vorhersage aus.
+
+        - Wenn entity_id ein Aktor ist: evaluate() direkt.
+        - Wenn entity_id ein Kontext-Entity ist: alle betroffenen Aktoren evaluieren.
+        - Wenn current_entities gesetzt ist, kommt die Auswertung direkt aus dem
+          WebSocket-State-Cache statt aus einer frischen REST-Abfrage.
+        """
+        # Aktor direkt evaluieren
+        for record in self._store.list():
+            if record.actuator_entity_id == entity_id:
+                try:
+                    self.evaluate(record.actuator_entity_id, current_entities=current_entities)
+                except Exception:
+                    logger.exception("Event-basierte Vorhersage fehlgeschlagen für %s", record.actuator_entity_id)
+                return
+        event_state = _event_state(new_state)
+        event_changed_at = _event_changed_at(new_state) or datetime.now(timezone.utc)
+        # Kontext-Entity: alle Aktoren finden, die diesen Kontext nutzen
+        affected_actuators = [
+            record.actuator_entity_id
+            for record in self._store.list()
+            if (
+                record.assignment.selected_numeric_entity_id == entity_id
+                or entity_id in record.assignment.selected_context_entity_ids
+            )
+        ]
+        for actuator_entity_id in affected_actuators:
+            try:
+                self.evaluate(
+                    actuator_entity_id,
+                    context_state_overrides={entity_id: event_state},
+                    context_changed_at_overrides={entity_id: event_changed_at},
+                    current_entities=current_entities,
+                )
+            except Exception:
+                logger.exception("Event-basierte Vorhersage fehlgeschlagen für %s", actuator_entity_id)
+
+
+def _event_state(new_state: dict[str, object] | None) -> str | None:
+    if not isinstance(new_state, dict):
+        return None
+    state = new_state.get("state")
+    return state if isinstance(state, str) else None
+
+
+def _event_changed_at(new_state: dict[str, object] | None) -> datetime | None:
+    if not isinstance(new_state, dict):
+        return None
+    value = new_state.get("last_changed") or new_state.get("last_updated")
+    if not isinstance(value, str):
+        return None
+    try:
+        parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
+    except ValueError:
+        return None
+    if parsed.tzinfo is None:
+        return parsed.replace(tzinfo=timezone.utc)
+    return parsed
+
+
+def predict_behavior(
+    patterns: list[BehaviorPattern],
+    *,
+    current_context: dict[str, str | None],
+    now: datetime,
+    min_support: int,
+    window_minutes: int,
+    current_context_changed_at: dict[str, datetime | None] | None = None,
+    causal_window_seconds: int = 120,
+    timezone_name: str = "Europe/Berlin",
+) -> BehaviorPrediction | None:
+    if not patterns:
+        return None
+    local = now.astimezone(ZoneInfo(timezone_name))
+    minute_of_day = local.hour * 60 + local.minute
+    changed_at = current_context_changed_at or {}
+    by_state: dict[str, list[float]] = {}
+    causal_support_by_state: dict[str, int] = {}
+    for pattern in patterns:
+        if pattern.trigger_entity_id and pattern.trigger_to_state:
+            trigger_changed_at = changed_at.get(pattern.trigger_entity_id)
+            trigger_age = (
+                (now - trigger_changed_at).total_seconds()
+                if trigger_changed_at is not None
+                else None
+            )
+            if not (
+                current_context.get(pattern.trigger_entity_id)
+                == pattern.trigger_to_state
+                and trigger_age is not None
+                and 0 <= trigger_age <= causal_window_seconds
+            ):
+                continue
+            comparable = [
+                (entity_id, expected)
+                for entity_id, expected in pattern.context_states.items()
+                if entity_id in current_context
+            ]
+            context_score = (
+                sum(
+                    current_context[entity_id] == expected
+                    for entity_id, expected in comparable
+                )
+                / len(comparable)
+                if comparable
+                else 0.5
+            )
+            score = pattern.weight * (0.85 + 0.15 * context_score)
+            by_state.setdefault(pattern.target_state, []).append(score)
+            causal_support_by_state[pattern.target_state] = (
+                causal_support_by_state.get(pattern.target_state, 0) + 1
+            )
+            continue
+        distance = _circular_minute_distance(minute_of_day, pattern.minute_of_day)
+        if distance > window_minutes:
+            continue
+        time_score = 1.0 - (distance / max(window_minutes, 1))
+        weekday_score = (
+            1.0
+            if local.weekday() == pattern.weekday
+            else 0.5
+            if (local.weekday() >= 5) == (pattern.weekday >= 5)
+            else 0.0
+        )
+        comparable = [
+            (entity_id, expected)
+            for entity_id, expected in pattern.context_states.items()
+            if entity_id in current_context
+        ]
+        context_score = (
+            sum(current_context[entity_id] == expected for entity_id, expected in comparable)
+            / len(comparable)
+            if comparable
+            else 0.5
+        )
+        score = pattern.weight * (
+            0.45 * time_score + 0.45 * context_score + 0.10 * weekday_score
+        )
+        by_state.setdefault(pattern.target_state, []).append(score)
+    if not by_state:
+        return None
+    target_state, scores = max(
+        by_state.items(),
+        key=lambda item: (sum(item[1]), len(item[1]), item[0]),
+    )
+    support = len(scores)
+    causal_support = causal_support_by_state.get(target_state, 0)
+    confidence = min(1.0, (sum(scores) / support) * min(1.0, support / min_support))
+    if confidence <= 0:
+        return None
+    return BehaviorPrediction(
+        target_state=target_state,
+        confidence=round(confidence, 4),
+        generated_at=now,
+        matching_patterns=support,
+        reason=(
+            (
+                f"{causal_support} historische Handlungen folgten demselben "
+                "frischen Sensorwechsel."
+            )
+            if causal_support
+            else f"{support} ähnliche Handlungsmuster passen zu Zeit und aktuellem Kontext."
+        ),
+    )
+
+
+def service_for_state(domain: str, target_state: str) -> str | None:
+    if domain in {"fan", "humidifier", "light", "switch"}:
+        return {"on": "turn_on", "off": "turn_off"}.get(target_state)
+    if domain == "cover":
+        return {"open": "open_cover", "closed": "close_cover"}.get(target_state)
+    return None
+
+
+def _state_at(series: StateHistorySeries | None, timestamp: datetime) -> str | None:
+    if series is None:
+        return None
+    state: str | None = None
+    for point in series.points:
+        if point.timestamp > timestamp:
+            break
+        state = point.state
+    return state
+
+
+def _action_source(
+    point: StateHistoryPoint,
+    logbook: list[LogbookEntry],
+) -> tuple[str, float]:
+    nearest = min(
+        logbook,
+        key=lambda item: abs(item.timestamp - point.timestamp),
+        default=None,
+    )
+    if nearest is None or abs(nearest.timestamp - point.timestamp) > _ACTION_LOGBOOK_TOLERANCE:
+        return "physical_or_unknown", 0.7
+    if nearest.context_user_id:
+        return "user", 1.0
+    if nearest.context_domain in _AUTOMATION_CONTEXT_DOMAINS:
+        return "automation", 1.0
+    return "physical_or_unknown", 0.7
+
+
+def _matches_own_execution(
+    point: StateHistoryPoint,
+    own_executions: list[ExecutionEvent],
+) -> bool:
+    return any(
+        event.target_state == point.state
+        and abs(event.executed_at - point.timestamp) <= _OWN_ACTION_TOLERANCE
+        for event in own_executions
+    )
+
+
+def _recent_context_transition(
+    history: dict[str, StateHistorySeries],
+    context_ids: list[str],
+    timestamp: datetime,
+) -> tuple[str, str, str] | None:
+    nearest: tuple[timedelta, str, str, str] | None = None
+    for entity_id in context_ids:
+        series = history.get(entity_id)
+        if series is None:
+            continue
+        previous_state: str | None = None
+        for point in series.points:
+            if point.timestamp > timestamp:
+                break
+            if previous_state is not None and point.state != previous_state:
+                age = timestamp - point.timestamp
+                if age <= _CONTEXT_TRIGGER_TOLERANCE and (
+                    nearest is None or age < nearest[0]
+                ):
+                    nearest = (age, entity_id, previous_state, point.state)
+            previous_state = point.state
+    if nearest is None:
+        return None
+    return nearest[1], nearest[2], nearest[3]
+
+
+def _circular_minute_distance(left: int, right: int) -> int:
+    direct = abs(left - right)
+    return min(direct, 1440 - direct)

app/config.py

@@ -15,6 +15,12 @@ class Settings:
     min_training_points: int = 24
     retrain_stale_hours: int = 24
     reconcile_interval_seconds: int = 900
+    min_behavior_actions: int = 3
+    prediction_confidence: float = 0.82
+    prediction_window_minutes: int = 30
+    prediction_interval_seconds: int = 60
+    execution_cooldown_seconds: int = 900
+    timezone: str = "Europe/Berlin"
 
     @property
     def ha_configured(self) -> bool:
@@ -34,4 +40,19 @@ def load_settings() -> Settings:
         reconcile_interval_seconds=max(
             60, int(os.getenv("SILLYHOME_RECONCILE_INTERVAL_SECONDS", "900"))
         ),
+        min_behavior_actions=max(2, int(os.getenv("SILLYHOME_MIN_BEHAVIOR_ACTIONS", "3"))),
+        prediction_confidence=max(
+            0.5,
+            min(0.99, float(os.getenv("SILLYHOME_PREDICTION_CONFIDENCE", "0.82"))),
+        ),
+        prediction_window_minutes=max(
+            5, min(120, int(os.getenv("SILLYHOME_PREDICTION_WINDOW_MINUTES", "30")))
+        ),
+        prediction_interval_seconds=max(
+            30, int(os.getenv("SILLYHOME_PREDICTION_INTERVAL_SECONDS", "60"))
+        ),
+        execution_cooldown_seconds=max(
+            60, int(os.getenv("SILLYHOME_EXECUTION_COOLDOWN_SECONDS", "900"))
+        ),
+        timezone=os.getenv("SILLYHOME_TIMEZONE", "Europe/Berlin"),
     )

app/ha/client.py

@@ -5,6 +5,7 @@ from dataclasses import dataclass
 from datetime import datetime
 import json
 import re
+from typing import Any
 from urllib.parse import quote
 
 import requests
@@ -19,7 +20,9 @@ from app.ha.exceptions import (
 logger = logging.getLogger(__name__)
 
 _ENTITY_ID_PATTERN = re.compile(r"^[a-z0-9_]+\.[a-z0-9_]+$")
+_SERVICE_PART_PATTERN = re.compile(r"^[a-z0-9_]+$")
 _MAX_HISTORY_SECONDS = 31 * 24 * 60 * 60
+_METADATA_BATCH_SIZE = 200
 
 
 @dataclass(frozen=True)
@@ -84,11 +87,72 @@ class HaClient:
             )
         return payload
 
+    def get_logbook(
+        self,
+        entity_id: str,
+        start_time: datetime,
+        end_time: datetime,
+    ) -> list[object]:
+        self._validate_period([entity_id], start_time, end_time)
+        start = quote(start_time.isoformat(), safe=":+")
+        payload = self._get_json(
+            f"/api/logbook/{start}",
+            params={
+                "entity": entity_id,
+                "end_time": end_time.isoformat(),
+            },
+        )
+        if not isinstance(payload, list):
+            raise HaUnexpectedPayloadError(
+                "Logbook-Antwort von Home Assistant hat unerwartetes Format."
+            )
+        return payload
+
+    def get_automation_config(self, automation_id: str) -> dict[str, object]:
+        if not automation_id or len(automation_id) > 120:
+            raise ValueError("Ungültige Automation-ID.")
+        payload = self._get_json(
+            f"/api/config/automation/config/{quote(automation_id, safe='')}"
+        )
+        if not isinstance(payload, dict):
+            raise HaUnexpectedPayloadError(
+                "Automation-Konfiguration hat ein unerwartetes Format."
+            )
+        return payload
+
+    def call_service(
+        self,
+        domain: str,
+        service: str,
+        service_data: dict[str, object],
+    ) -> list[object]:
+        if not _SERVICE_PART_PATTERN.fullmatch(domain):
+            raise ValueError("Ungültige Service-Domain.")
+        if not _SERVICE_PART_PATTERN.fullmatch(service):
+            raise ValueError("Ungültiger Service-Name.")
+        payload = self._post_json(f"/api/services/{domain}/{service}", service_data)
+        if not isinstance(payload, list):
+            raise HaUnexpectedPayloadError(
+                "Service-Antwort von Home Assistant hat unerwartetes Format."
+            )
+        return payload
+
     def list_entity_metadata(self, entity_ids: list[str]) -> dict[str, dict[str, str | None]]:
         if not entity_ids:
             return {}
         if any(not _ENTITY_ID_PATTERN.fullmatch(entity_id) for entity_id in entity_ids):
             raise ValueError("entity_id enthält ein ungültiges Format.")
+        result: dict[str, dict[str, str | None]] = {}
+        for start in range(0, len(entity_ids), _METADATA_BATCH_SIZE):
+            result.update(
+                self._list_entity_metadata_batch(entity_ids[start:start + _METADATA_BATCH_SIZE])
+            )
+        return result
+
+    def _list_entity_metadata_batch(
+        self,
+        entity_ids: list[str],
+    ) -> dict[str, dict[str, str | None]]:
         template = _metadata_template(entity_ids)
         rendered = self._post_text("/api/template", {"template": template})
         try:
@@ -153,6 +217,36 @@ class HaClient:
 
         return payload
 
+    def _post_json(self, path: str, payload: Any) -> object:
+        try:
+            response = self._session.post(
+                f"{self._settings.url.rstrip('/')}{path}",
+                json=payload,
+                timeout=self._settings.timeout_seconds,
+            )
+        except requests.Timeout as exc:
+            raise HaTimeoutError("Zeitüberschreitung beim Zugriff auf Home Assistant.") from exc
+        except requests.RequestException as exc:
+            raise HaHttpError(
+                getattr(getattr(exc, "response", None), "status_code", 502),
+                "Netzwerkfehler beim Zugriff auf Home Assistant.",
+            ) from exc
+        if response.status_code in (401, 403):
+            raise HaAuthError(
+                response.status_code,
+                "Authentifizierung bei Home Assistant fehlgeschlagen.",
+            )
+        try:
+            response.raise_for_status()
+        except requests.HTTPError as exc:
+            raise HaHttpError(response.status_code, "Home Assistant meldet einen Fehler.") from exc
+        try:
+            return response.json()
+        except ValueError as exc:
+            raise HaUnexpectedPayloadError(
+                "Antwort von Home Assistant ist kein gültiges JSON."
+            ) from exc
+
     def _post_text(self, path: str, payload: dict[str, str]) -> str:
         try:
             response = self._session.post(
@@ -179,6 +273,25 @@ class HaClient:
             raise HaHttpError(response.status_code, "Home Assistant meldet einen Fehler.") from exc
         return response.text
 
+    @staticmethod
+    def _validate_period(
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> None:
+        if not entity_ids:
+            raise ValueError("Mindestens eine entity_id ist erforderlich.")
+        if len(entity_ids) > 100:
+            raise ValueError("Es können höchstens 100 Entities abgefragt werden.")
+        if any(not _ENTITY_ID_PATTERN.fullmatch(entity_id) for entity_id in entity_ids):
+            raise ValueError("entity_id enthält ein ungültiges Format.")
+        if start_time.tzinfo is None or end_time.tzinfo is None:
+            raise ValueError("start_time und end_time müssen eine Zeitzone enthalten.")
+        if end_time <= start_time:
+            raise ValueError("end_time muss nach start_time liegen.")
+        if (end_time - start_time).total_seconds() > _MAX_HISTORY_SECONDS:
+            raise ValueError("History-Abfragen sind auf 31 Tage begrenzt.")
+
 
 def _metadata_template(entity_ids: list[str]) -> str:
     ids = json.dumps(entity_ids, ensure_ascii=True)

app/ha/discovery.py

@@ -82,18 +82,11 @@ _BINARY_CONTEXT_CLASSES = frozenset({
     "window",
 })
 _ACTUATOR_DOMAINS = frozenset({
-    "button",
-    "climate",
     "cover",
     "fan",
     "humidifier",
     "light",
-    "lock",
-    "scene",
-    "select",
-    "siren",
     "switch",
-    "valve",
 })
 _CONTEXT_DOMAINS = frozenset({"device_tracker", "person", "sun", "weather", "zone"})
 _LEARNABLE_CONTEXT_DOMAINS = frozenset({"device_tracker", "person", "weather"})

app/ha/history.py

@@ -18,6 +18,25 @@ class EntityHistorySeries(BaseModel):
     points: list[NumericHistoryPoint]
 
 
+class StateHistoryPoint(BaseModel):
+    timestamp: datetime
+    state: str
+
+
+class StateHistorySeries(BaseModel):
+    entity_id: str
+    points: list[StateHistoryPoint]
+
+
+class LogbookEntry(BaseModel):
+    entity_id: str
+    timestamp: datetime
+    message: str = ""
+    context_user_id: str | None = None
+    context_domain: str | None = None
+    context_service: str | None = None
+
+
 def normalize_history_payload(payload: object) -> list[EntityHistorySeries]:
     if not isinstance(payload, list):
         raise HaUnexpectedPayloadError("History-Payload muss eine Liste sein.")
@@ -33,6 +52,68 @@ def normalize_history_payload(payload: object) -> list[EntityHistorySeries]:
     return sorted(normalized, key=lambda item: item.entity_id)
 
 
+def normalize_state_history_payload(payload: object) -> list[StateHistorySeries]:
+    if not isinstance(payload, list):
+        raise HaUnexpectedPayloadError("History-Payload muss eine Liste sein.")
+    normalized: list[StateHistorySeries] = []
+    for raw_series in payload:
+        if not isinstance(raw_series, list):
+            raise HaUnexpectedPayloadError("History-Serie muss eine Liste sein.")
+        entity_id: str | None = None
+        points: list[StateHistoryPoint] = []
+        for raw_entry in raw_series:
+            if not isinstance(raw_entry, dict):
+                raise HaUnexpectedPayloadError("History-Eintrag muss ein Objekt sein.")
+            raw_entity_id = raw_entry.get("entity_id")
+            if raw_entity_id is not None:
+                if not isinstance(raw_entity_id, str) or "." not in raw_entity_id:
+                    raise HaUnexpectedPayloadError(
+                        "History-Eintrag enthält ungültige entity_id."
+                    )
+                if entity_id is not None and entity_id != raw_entity_id:
+                    raise HaUnexpectedPayloadError("History-Serie enthält mehrere Entities.")
+                entity_id = raw_entity_id
+            raw_state = raw_entry.get("state")
+            if not isinstance(raw_state, str) or raw_state in {"unknown", "unavailable"}:
+                continue
+            if entity_id is None:
+                raise HaUnexpectedPayloadError("History-Serie enthält keine entity_id.")
+            timestamp = _parse_timestamp(
+                raw_entry.get("last_changed") or raw_entry.get("last_updated")
+            )
+            if not points or points[-1].state != raw_state:
+                points.append(StateHistoryPoint(timestamp=timestamp, state=raw_state))
+        if entity_id is not None and points:
+            points.sort(key=lambda point: point.timestamp)
+            normalized.append(StateHistorySeries(entity_id=entity_id, points=points))
+    return sorted(normalized, key=lambda item: item.entity_id)
+
+
+def normalize_logbook_payload(payload: object, entity_id: str) -> list[LogbookEntry]:
+    if not isinstance(payload, list):
+        raise HaUnexpectedPayloadError("Logbook-Payload muss eine Liste sein.")
+    entries: list[LogbookEntry] = []
+    for raw_entry in payload:
+        if not isinstance(raw_entry, dict):
+            raise HaUnexpectedPayloadError("Logbook-Eintrag muss ein Objekt sein.")
+        raw_entity_id = raw_entry.get("entity_id")
+        if raw_entity_id != entity_id:
+            continue
+        entries.append(
+            LogbookEntry(
+                entity_id=entity_id,
+                timestamp=_parse_timestamp(raw_entry.get("when")),
+                message=str(raw_entry.get("message") or ""),
+                context_user_id=_optional_string(raw_entry.get("context_user_id")),
+                context_domain=_optional_string(
+                    raw_entry.get("context_domain") or raw_entry.get("domain")
+                ),
+                context_service=_optional_string(raw_entry.get("context_service")),
+            )
+        )
+    return sorted(entries, key=lambda item: item.timestamp)
+
+
 def _normalize_series(raw_series: list[object]) -> EntityHistorySeries | None:
     entity_id: str | None = None
     points: list[NumericHistoryPoint] = []
@@ -89,3 +170,9 @@ def _parse_timestamp(value: object) -> datetime:
     if parsed.tzinfo is None:
         raise HaUnexpectedPayloadError("History-Zeitstempel muss eine Zeitzone enthalten.")
     return parsed
+
+
+def _optional_string(value: object) -> str | None:
+    if value is None or value == "":
+        return None
+    return str(value)

app/ha/models.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from datetime import datetime
+
 from pydantic import BaseModel
 
 
@@ -14,6 +16,8 @@ class HaState(BaseModel):
 class HaEntitySummary(BaseModel):
     entity_id: str
     domain: str
+    state: str | None = None
+    last_changed: datetime | None = None
     state_class: str | None = None
     device_class: str | None = None
     unit_of_measurement: str | None = None
@@ -22,3 +26,10 @@ class HaEntitySummary(BaseModel):
     area_name: str | None = None
     device_id: str | None = None
     device_name: str | None = None
+
+
+class HaAutomationSummary(BaseModel):
+    entity_id: str
+    config_id: str
+    friendly_name: str
+    enabled: bool

app/ha/reader.py

@@ -1,16 +1,24 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-from datetime import datetime
+from datetime import datetime, timedelta, timezone
+from threading import RLock
 from typing import Any
 import logging
 
-from app.ha.exceptions import HaClientError
+from app.ha.exceptions import HaClientError, HaHttpError
 
 from app.ha.client import HaClient
 from app.ha.discovery import DiscoveredEntity, discover_entities
-from app.ha.history import EntityHistorySeries, normalize_history_payload
+from app.ha.history import (
-from app.ha.models import HaEntitySummary
+    EntityHistorySeries,
+    LogbookEntry,
+    StateHistorySeries,
+    normalize_history_payload,
+    normalize_logbook_payload,
+    normalize_state_history_payload,
+)
+from app.ha.models import HaAutomationSummary, HaEntitySummary
 
 logger = logging.getLogger(__name__)
 
@@ -18,6 +26,11 @@ logger = logging.getLogger(__name__)
 class HaReader:
     def __init__(self, client: HaClient) -> None:
         self._client = client
+        self._automation_cache: list[
+            tuple[HaAutomationSummary, dict[str, object]]
+        ] = []
+        self._automation_cache_at: datetime | None = None
+        self._automation_cache_lock = RLock()
 
     def read_entities(self) -> Sequence[HaEntitySummary]:
         entities = self._client.list_entities()
@@ -45,6 +58,8 @@ class HaReader:
                 HaEntitySummary(
                     entity_id=entity_id,
                     domain=domain,
+                    state=_optional_str(item.get("state")),
+                    last_changed=_optional_datetime(item.get("last_changed")),
                     state_class=_optional_str(attributes.get("state_class")),
                     device_class=_optional_str(attributes.get("device_class")),
                     unit_of_measurement=_optional_str(attributes.get("unit_of_measurement")),
@@ -77,8 +92,140 @@ class HaReader:
         payload = self._client.get_history(entity_ids, start_time, end_time)
         return normalize_history_payload(payload)
 
+    def read_state_history(
+        self,
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> Sequence[StateHistorySeries]:
+        payload = self._client.get_history(entity_ids, start_time, end_time)
+        return normalize_state_history_payload(payload)
+
+    def read_logbook(
+        self,
+        entity_id: str,
+        start_time: datetime,
+        end_time: datetime,
+    ) -> Sequence[LogbookEntry]:
+        payload = self._client.get_logbook(entity_id, start_time, end_time)
+        return normalize_logbook_payload(payload, entity_id)
+
+    def call_service(
+        self,
+        domain: str,
+        service: str,
+        service_data: dict[str, object],
+    ) -> Sequence[object]:
+        return self._client.call_service(domain, service, service_data)
+
+    def find_automations_for_entity(
+        self,
+        entity_id: str,
+    ) -> list[HaAutomationSummary]:
+        current_states = {
+            raw_entity_id: item.get("state") == "on"
+            for item in self._client.list_entities()
+            if isinstance((raw_entity_id := item.get("entity_id")), str)
+            and raw_entity_id.startswith("automation.")
+        }
+        matches = [
+            summary.model_copy(
+                update={
+                    "enabled": current_states.get(
+                        summary.entity_id,
+                        summary.enabled,
+                    )
+                }
+            )
+            for summary, config in self._read_automation_configs()
+            if _contains_exact_value(config, entity_id)
+        ]
+        return sorted(matches, key=lambda item: item.entity_id)
+
+    def _read_automation_configs(
+        self,
+    ) -> list[tuple[HaAutomationSummary, dict[str, object]]]:
+        now = datetime.now(timezone.utc)
+        with self._automation_cache_lock:
+            if (
+                self._automation_cache_at is not None
+                and now - self._automation_cache_at < timedelta(minutes=10)
+            ):
+                return list(self._automation_cache)
+            configs: list[tuple[HaAutomationSummary, dict[str, object]]] = []
+            for item in self._client.list_entities():
+                raw_entity_id = item.get("entity_id")
+                if not isinstance(raw_entity_id, str) or not raw_entity_id.startswith(
+                    "automation."
+                ):
+                    continue
+                attributes = item.get("attributes")
+                if not isinstance(attributes, dict):
+                    continue
+                config_id = attributes.get("id")
+                if not isinstance(config_id, str) or not config_id:
+                    continue
+                try:
+                    config = self._client.get_automation_config(config_id)
+                except HaHttpError as exc:
+                    if exc.status_code == 404:
+                        logger.info(
+                            "Automation config not exposed by Home Assistant for %s.",
+                            raw_entity_id,
+                        )
+                        continue
+                    logger.warning(
+                        "Automation config unavailable for %s: %s",
+                        raw_entity_id,
+                        exc,
+                    )
+                    continue
+                except (HaClientError, ValueError) as exc:
+                    logger.warning(
+                        "Automation config unavailable for %s: %s",
+                        raw_entity_id,
+                        exc,
+                    )
+                    continue
+                configs.append(
+                    (
+                        HaAutomationSummary(
+                            entity_id=raw_entity_id,
+                            config_id=config_id,
+                            friendly_name=str(
+                                attributes.get("friendly_name") or raw_entity_id
+                            ),
+                            enabled=item.get("state") == "on",
+                        ),
+                        config,
+                    )
+                )
+            self._automation_cache = configs
+            self._automation_cache_at = now
+            return list(configs)
+
 
 def _optional_str(value: object) -> str | None:
     if value is None or value == "":
         return None
     return str(value)
+
+
+def _optional_datetime(value: object) -> datetime | None:
+    if not isinstance(value, str) or not value:
+        return None
+    try:
+        parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
+    except ValueError:
+        return None
+    return parsed if parsed.tzinfo is not None else None
+
+
+def _contains_exact_value(value: object, expected: str) -> bool:
+    if value == expected:
+        return True
+    if isinstance(value, dict):
+        return any(_contains_exact_value(item, expected) for item in value.values())
+    if isinstance(value, list):
+        return any(_contains_exact_value(item, expected) for item in value)
+    return False

app/main.py

@@ -1,9 +1,13 @@
 import asyncio
+import json
+import logging
 from contextlib import asynccontextmanager, suppress
 from collections.abc import AsyncIterator
+from datetime import datetime, timezone
 from pathlib import Path
 from typing import cast
 
+import websockets
 from fastapi import FastAPI
 from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
@@ -12,28 +16,44 @@ from app.actuators.lifecycle import ActuatorReconciliationService
 from app.actuators.store import ActuatorStore
 from app.api.v1.actuators import router as actuators_router
 from app.api.v1.entities import router as entities_router
-from app.api.v1.automations import router as automations_router
+from app.behavior.engine import BehaviorEngine
-from app.automations.store import AutomationStore
 from app.config import load_settings
 from app.core.exception_handlers import register_exception_handlers
 from app.ha.client import HaClient, HaClientSettings
+from app.ha.models import HaEntitySummary
 from app.ha.reader import HaReader
 from app.ml.registry.model_registry import ModelRegistry
 from backend.routes.ml import init_ml_routes
 
+logger = logging.getLogger(__name__)
+
+class _WsStatus:
+    """Einfacher Status-Tracker für den WebSocket-Listener.
+
+    Wird als Attribut an app.state gehängt und enthält:
+    - status: "disconnected" | "connecting" | "connected" | "reconnecting" | "error"
+    - error: str | None (Fehlermeldung bei status=error)
+    """
+    def __init__(self) -> None:
+        self.status: str = "disconnected"
+        self.error: str | None = None
+
 
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     settings = app.state.settings
     client: HaClient | None = None
     reconcile_task: asyncio.Task[None] | None = None
+    event_listener_task: asyncio.Task[None] | None = None
+    fallback_task: asyncio.Task[None] | None = None
     app.state.registry = ModelRegistry(settings.model_store)
-    app.state.automation_store = AutomationStore(settings.automation_store)
     app.state.actuator_store = ActuatorStore(settings.actuator_store)
     if hasattr(app.state, "ha_reader"):
         del app.state.ha_reader
     if hasattr(app.state, "actuator_service"):
         del app.state.actuator_service
+    if hasattr(app.state, "behavior_engine"):
+        del app.state.behavior_engine
     if settings.ha_configured:
         client = HaClient(
             settings=HaClientSettings(
@@ -48,8 +68,18 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
             registry=app.state.registry,
             settings=settings,
         )
+        app.state.behavior_engine = BehaviorEngine(
+            ha_reader=app.state.ha_reader,
+            store=app.state.actuator_store,
+            settings=settings,
+        )
+        app.state.ws_status = _WsStatus()
         await asyncio.to_thread(app.state.actuator_service.reconcile_all, "startup")
+        await asyncio.to_thread(app.state.behavior_engine.train_all)
+        await asyncio.to_thread(app.state.behavior_engine.evaluate_all)
         reconcile_task = asyncio.create_task(_periodic_reconciliation(app))
+        event_listener_task = asyncio.create_task(_ha_event_listener(app, client))
+        fallback_task = asyncio.create_task(_fallback_prediction(app))
     try:
         yield
     finally:
@@ -57,6 +87,14 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
             reconcile_task.cancel()
             with suppress(asyncio.CancelledError):
                 await reconcile_task
+        if event_listener_task is not None:
+            event_listener_task.cancel()
+            with suppress(asyncio.CancelledError):
+                await event_listener_task
+        if fallback_task is not None:
+            fallback_task.cancel()
+            with suppress(asyncio.CancelledError):
+                await fallback_task
         if client is not None:
             client.close()
 
@@ -64,13 +102,12 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="0.4.0",
+    version="0.7.10",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()
 register_exception_handlers(app)
 app.include_router(entities_router)
-app.include_router(automations_router)
 app.include_router(actuators_router)
 init_ml_routes(app, model_store=app.state.settings.model_store)
 
@@ -82,10 +119,26 @@ app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
 def health() -> dict[str, str]:
     return {"status": "ok"}
 
+@app.get("/health/websocket")
+def websocket_health() -> dict[str, object]:
+    """Gibt den aktuellen Status des WebSocket-Listeners zurück.
+
+    Antwort:
+    - status: "disconnected" | "connecting" | "connected" | "reconnecting" | "error"
+    - error: str | None (nur bei status=error)
+    """
+    ws_status = getattr(app.state, "ws_status", None)
+    if ws_status is None:
+        return {"status": "unavailable", "error": "WebSocket-Listener nicht initialisiert"}
+    return {"status": ws_status.status, "error": ws_status.error}
+
 
 @app.get("/")
 def root() -> FileResponse:
-    return FileResponse(STATIC_DIR / "index.html")
+    return FileResponse(
+        STATIC_DIR / "index.html",
+        headers={"Cache-Control": "no-store, max-age=0"},
+    )
 
 
 async def _periodic_reconciliation(app: FastAPI) -> None:
@@ -95,3 +148,200 @@ async def _periodic_reconciliation(app: FastAPI) -> None:
         if not isinstance(service, ActuatorReconciliationService):
             continue
         await asyncio.to_thread(service.reconcile_all, "scheduled")
+        engine = getattr(app.state, "behavior_engine", None)
+        if isinstance(engine, BehaviorEngine):
+            await asyncio.to_thread(engine.train_all)
+
+
+async def _ha_event_listener(app: FastAPI, client: HaClient) -> None:
+    """Hört auf Home-Assistant-Websocket-Events und löst sofortige Vorhersagen aus."""
+    settings = app.state.settings
+    engine = app.state.behavior_engine
+    ha_reader = getattr(app.state, "ha_reader", None)
+    store = app.state.actuator_store
+    if (
+        not isinstance(engine, BehaviorEngine)
+        or not isinstance(store, ActuatorStore)
+        or not isinstance(ha_reader, HaReader)
+    ):
+        logger.error("BehaviorEngine oder ActuatorStore nicht initialisiert")
+        ws_status = getattr(app.state, "ws_status", None)
+        if ws_status is not None:
+            ws_status.status = "error"
+            ws_status.error = "BehaviorEngine oder ActuatorStore nicht initialisiert"
+        return
+    state_cache: dict[str, HaEntitySummary] = {}
+    ha_url = str(settings.ha_url).rstrip("/")
+    ws_url = ha_url.replace("http://", "ws://").replace("https://", "wss://") + "/api/websocket"
+    auth_token = cast(str, settings.ha_token)
+    ws_status = getattr(app.state, "ws_status", None)
+    while True:
+        if ws_status is not None:
+            ws_status.status = "connecting"
+        try:
+            async with websockets.connect(ws_url, ping_interval=None) as websocket:
+                auth_required_msg = await websocket.recv()
+                auth_required_data = json.loads(auth_required_msg)
+                if auth_required_data.get("type") != "auth_required":
+                    logger.error("Unerwartete WebSocket-Authentifizierungsaufforderung")
+                    if ws_status is not None:
+                        ws_status.status = "error"
+                        ws_status.error = "Unerwartete Authentifizierungsaufforderung"
+                    await asyncio.sleep(5)
+                    continue
+
+                await websocket.send(json.dumps({"type": "auth", "access_token": auth_token}))
+                auth_result_msg = await websocket.recv()
+                auth_result_data = json.loads(auth_result_msg)
+                if auth_result_data.get("type") != "auth_ok":
+                    logger.error("WebSocket-Authentifizierung fehlgeschlagen")
+                    if ws_status is not None:
+                        ws_status.status = "error"
+                        ws_status.error = "Authentifizierung fehlgeschlagen"
+                    await asyncio.sleep(5)
+                    continue
+
+                logger.info("WebSocket-Verbindung zu Home Assistant hergestellt")
+                state_cache = await asyncio.to_thread(_load_ha_state_cache, ha_reader)
+                if ws_status is not None:
+                    ws_status.status = "connected"
+                    ws_status.error = None
+                # Auf alle State Changes subscriben
+                subscribe_msg = {
+                    "id": 1,
+                    "type": "subscribe_events",
+                    "event_type": "state_changed"
+                }
+                await websocket.send(json.dumps(subscribe_msg))
+                while True:
+                    message = await websocket.recv()
+                    try:
+                        data = json.loads(message)
+                        if data.get("type") != "event":
+                            continue
+                        event = data.get("event", {})
+                        if event.get("event_type") != "state_changed":
+                            continue
+                        event_data = event.get("data", {})
+                        if not isinstance(event_data, dict):
+                            logger.warning("State-Changed-Event ohne gültige Daten empfangen")
+                            continue
+                        entity_id = event_data.get("entity_id")
+                        if not entity_id:
+                            continue
+                        new_state = event_data.get("new_state")
+                        _update_ha_state_cache(state_cache, entity_id, new_state)
+                        # Prüfe, ob Entity ein Aktor oder relevanter Kontext ist
+                        # Sofortige Vorhersage für betroffene Aktoren auslösen
+                        await asyncio.to_thread(
+                            engine.handle_state_change,
+                            entity_id,
+                            new_state,
+                            current_entities=list(state_cache.values()),
+                        )
+                    except json.JSONDecodeError:
+                        logger.warning("Ungültige JSON-Nachricht von HA-WebSocket")
+                    except Exception as exc:
+                        logger.exception("Fehler bei Event-Verarbeitung: %s", exc)
+        except (websockets.exceptions.ConnectionClosed, OSError) as exc:
+            logger.warning("WebSocket-Verbindung unterbrochen: %s. Wiederholung in 5s...", exc)
+            if ws_status is not None:
+                ws_status.status = "reconnecting"
+                ws_status.error = str(exc)
+            await asyncio.sleep(5)
+        except Exception as exc:
+            logger.exception("Unerwarteter Fehler im Event-Listener: %s", exc)
+            if ws_status is not None:
+                ws_status.status = "error"
+                ws_status.error = str(exc)
+            await asyncio.sleep(5)
+
+
+# Fallback: periodische Vorhersage falls Event-Stream ausfällt
+async def _fallback_prediction(app: FastAPI) -> None:
+    """Periodische Vorhersage als Fallback, wenn WebSocket-Listener nicht verbunden ist.
+
+    Dies verhindert kompletten Ausfall der Vorhersagen bei Netzwerkproblemen.
+    """
+    while True:
+        ws_status = getattr(app.state, "ws_status", None)
+        websocket_connected = ws_status is not None and ws_status.status == "connected"
+        await asyncio.sleep(
+            app.state.settings.prediction_interval_seconds
+            if websocket_connected
+            else min(5, app.state.settings.prediction_interval_seconds)
+        )
+        # Nur ausführen, wenn WebSocket nicht verbunden ist
+        ws_status = getattr(app.state, "ws_status", None)
+        if ws_status is None or ws_status.status != "connected":
+            engine = getattr(app.state, "behavior_engine", None)
+            if isinstance(engine, BehaviorEngine):
+                logger.debug(
+                    "Fallback-Vorhersage aktiv (WebSocket-Status: %s)",
+                    ws_status.status if ws_status else "unavailable",
+                )
+                await asyncio.to_thread(engine.evaluate_all)
+
+
+def _load_ha_state_cache(reader: HaReader) -> dict[str, HaEntitySummary]:
+    return {entity.entity_id: entity for entity in reader.read_entities()}
+
+
+def _update_ha_state_cache(
+    state_cache: dict[str, HaEntitySummary],
+    entity_id: str,
+    new_state: object,
+) -> None:
+    if not isinstance(new_state, dict):
+        state_cache.pop(entity_id, None)
+        return
+    state_cache[entity_id] = _ha_entity_from_event(
+        entity_id,
+        new_state,
+        state_cache.get(entity_id),
+    )
+
+
+def _ha_entity_from_event(
+    entity_id: str,
+    new_state: dict[str, object],
+    previous: HaEntitySummary | None,
+) -> HaEntitySummary:
+    attributes = new_state.get("attributes")
+    attr = attributes if isinstance(attributes, dict) else {}
+    state_class = _optional_event_string(attr.get("state_class"))
+    device_class = _optional_event_string(attr.get("device_class"))
+    unit_of_measurement = _optional_event_string(attr.get("unit_of_measurement"))
+    friendly_name = _optional_event_string(attr.get("friendly_name"))
+    return HaEntitySummary(
+        entity_id=entity_id,
+        domain=entity_id.split(".", 1)[0],
+        state=_optional_event_string(new_state.get("state")),
+        last_changed=_event_datetime(new_state.get("last_changed"))
+        or _event_datetime(new_state.get("last_updated")),
+        state_class=state_class or (previous.state_class if previous else None),
+        device_class=device_class or (previous.device_class if previous else None),
+        unit_of_measurement=unit_of_measurement
+        or (previous.unit_of_measurement if previous else None),
+        friendly_name=friendly_name or (previous.friendly_name if previous else None),
+        area_id=previous.area_id if previous else None,
+        area_name=previous.area_name if previous else None,
+        device_id=previous.device_id if previous else None,
+        device_name=previous.device_name if previous else None,
+    )
+
+
+def _optional_event_string(value: object) -> str | None:
+    return value if isinstance(value, str) else None
+
+
+def _event_datetime(value: object) -> datetime | None:
+    if not isinstance(value, str):
+        return None
+    try:
+        parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
+    except ValueError:
+        return None
+    if parsed.tzinfo is None:
+        return parsed.replace(tzinfo=timezone.utc)
+    return parsed

app/static/index.html

@@ -5,162 +5,410 @@
   <meta name="viewport" content="width=device-width,initial-scale=1">
   <title>SillyHome Next</title>
   <style>
-    :root { color-scheme: dark; font-family: system-ui, sans-serif; background: #10151c; color: #eaf1f8; }
+    :root { color-scheme: dark; font-family: system-ui, sans-serif; background: #10151c; color: #eaf1f8; scroll-behavior:smooth; }
-    body { margin: 0; }
+    body { margin: 0; font-size:16px; }
-    header { padding: 20px; background: linear-gradient(135deg,#142b3a,#193f36); }
+    header { padding: 22px; background: linear-gradient(135deg,#142b3a,#193f36); }
     h1,h2,h3 { margin: 0 0 12px; }
-    header p { margin: 4px 0; color: #b9c9d6; }
+    header p { margin: 5px 0; color: #c3d1dc; }
     main { display: grid; grid-template-columns: repeat(auto-fit,minmax(320px,1fr)); gap: 14px; padding: 14px; }
     section { background: #18212b; border: 1px solid #2d3a47; border-radius: 12px; padding: 16px; }
+    section:target { outline:2px solid #66dfa9; outline-offset:2px; }
     .wide { grid-column: 1 / -1; }
+    .quick-nav { position:sticky; top:0; z-index:10; display:flex; gap:8px; overflow-x:auto; padding:10px 14px; background:rgba(16,21,28,.94); border-bottom:1px solid #2d3a47; backdrop-filter:blur(8px); }
+    .quick-nav a { flex:0 0 auto; padding:10px 12px; border-radius:999px; background:#22303c; border:1px solid #31404d; color:#eaf1f8; text-decoration:none; font-weight:700; font-size:.92rem; }
+    .quick-nav a.primary { background:#23715b; }
+    .steps { display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:12px; }
+    .step { background:#111a23; border:1px solid #31404d; border-radius:10px; padding:14px; }
+    .step-number { display:inline-grid; place-items:center; width:28px; height:28px; border-radius:50%; background:#23715b; font-weight:700; margin-bottom:8px; }
+    .step p { margin:5px 0; }
     .ok { color: #66dfa9; }
     .warn { color: #f3c969; }
     .bad { color: #ff8f8f; }
     label { display: block; margin: 9px 0 4px; color: #b9c9d6; }
-    input,select,textarea,button { box-sizing: border-box; width: 100%; border-radius: 7px; border: 1px solid #3b4b5b; padding: 9px; background: #101820; color: #fff; }
+    select,input,button { box-sizing: border-box; width: 100%; border-radius: 10px; border: 1px solid #3b4b5b; padding: 12px; background: #101820; color: #fff; font:inherit; }
-    button { margin-top: 10px; background: #23715b; border: 0; font-weight: 700; cursor: pointer; }
+    select[multiple] { min-height:190px; }
+    button { min-height:44px; margin-top: 10px; background: #23715b; border: 0; font-weight: 700; cursor: pointer; }
     button.secondary { background: #37495c; }
-    pre { white-space: pre-wrap; overflow: auto; background: #0d141b; padding: 10px; border-radius: 7px; }
+    button.danger { background: #7b3434; }
-    table { width: 100%; border-collapse: collapse; font-size: .9rem; }
+    button.compact { width:auto; min-width:120px; margin-right:8px; }
-    td,th { padding: 7px; border-bottom: 1px solid #2d3a47; text-align: left; vertical-align: top; }
+    table { width: 100%; border-collapse: collapse; font-size: .92rem; }
+    td,th { padding: 8px; border-bottom: 1px solid #2d3a47; text-align: left; vertical-align: top; }
     ul { margin: 8px 0; padding-left: 18px; }
-    .notice { border-left: 4px solid #e8b34b; padding-left: 10px; }
+    .notice { border-left: 4px solid #66dfa9; padding-left: 10px; }
-    .grid-two { display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; }
+    .grid-two { display:grid; grid-template-columns:repeat(auto-fit,minmax(240px,1fr)); gap:12px; }
     .chips { display:flex; flex-wrap:wrap; gap:6px; margin-top:8px; }
     .chip { padding:4px 8px; border-radius:999px; background:#22303c; border:1px solid #31404d; font-size:.85rem; }
+    .muted { color:#9fb0be; }
+    .card-list { display:grid; gap:12px; }
+    .actuator-card { background:#111a23; border:1px solid #31404d; border-radius:14px; padding:14px; }
+    .actuator-card.selected { border-color:#66dfa9; box-shadow:0 0 0 1px rgba(102,223,169,.3); }
+    .card-title { display:flex; justify-content:space-between; gap:10px; align-items:flex-start; margin-bottom:8px; }
+    .entity-id { overflow-wrap:anywhere; font-weight:800; }
+    .metric-grid { display:grid; grid-template-columns:repeat(auto-fit,minmax(150px,1fr)); gap:8px; margin:10px 0; }
+    .metric { background:#18212b; border:1px solid #2d3a47; border-radius:10px; padding:10px; }
+    .metric strong { display:block; margin-bottom:4px; color:#cfe0ec; font-size:.84rem; }
+    .actions { display:flex; flex-wrap:wrap; gap:8px; margin-top:10px; }
+    .actions button { flex:1 1 180px; margin-top:0; }
+    .detail-header { display:flex; justify-content:space-between; gap:12px; align-items:flex-start; flex-wrap:wrap; }
+    .manual-context { margin-top:14px; background:#111a23; border:1px solid #31404d; border-radius:14px; padding:14px; }
+    .inline-controls { display:grid; grid-template-columns:repeat(auto-fit,minmax(160px,1fr)); gap:8px; margin:8px 0; }
+    .manual-entry { min-height:80px; resize:vertical; }
+    textarea { box-sizing:border-box; width:100%; border-radius:10px; border:1px solid #3b4b5b; padding:12px; background:#101820; color:#fff; font:inherit; }
+    optgroup { color:#cfe0ec; background:#101820; }
+    code { color:#cfe0ec; overflow-wrap:anywhere; }
+    @media (max-width: 760px) {
+      header { padding:18px 14px; }
+      header h1 { font-size:1.55rem; }
+      main { display:block; padding:10px; }
+      section { margin-bottom:12px; padding:14px; border-radius:14px; }
+      .steps { grid-template-columns:1fr; }
+      .grid-two { grid-template-columns:1fr; }
+      .metric-grid { grid-template-columns:1fr 1fr; }
+      .actions { display:grid; grid-template-columns:1fr; }
+      .actions button, button.compact { width:100%; min-width:0; margin-right:0; }
+      .quick-nav { padding:8px 10px; }
+      .quick-nav a { padding:10px 11px; }
+    }
+    @media (max-width: 430px) {
+      .metric-grid { grid-template-columns:1fr; }
+      body { font-size:15px; }
+    }
   </style>
 </head>
 <body>
 <header>
   <h1>SillyHome Next</h1>
-  <p>Aktuator-zentrierte Home-Assistant-Analyse mit nachvollziehbarer Sensorzuordnung und kontrolliertem Modell-Lebenszyklus.</p>
+  <p>Hier wählst du nur Geräte aus, deren Bedienung SillyHome lernen soll. Sensoren, Zusammenhänge und Modelle werden automatisch verwaltet.</p>
-  <p class="notice">Sicherheitsmodus: SillyHome führt niemals selbst Aktor-Services aus. Automationen bleiben manuell freizugebende YAML-Entwürfe.</p>
+  <p class="notice">Sicherer Start: Zuerst wird nur beobachtet und vorhergesagt. Ohne deine spätere Freigabe wird nichts geschaltet.</p>
 </header>
+<nav class="quick-nav" aria-label="Schnellnavigation">
+  <a class="primary" href="#choose">Gerät wählen</a>
+  <a href="#observed">Beobachtet</a>
+  <a href="#detail">Details</a>
+  <a href="#status-section">Status</a>
+  <a href="#guide">Ablauf</a>
+</nav>
 <main>
-  <section>
+  <section class="wide" id="guide">
+    <h2>So gehst du vor</h2>
+    <div class="steps">
+      <div class="step">
+        <span class="step-number">1</span>
+        <h3>Aktor auswählen</h3>
+        <p><strong>Wo?</strong> Unten im Feld „Gerät auswählen“.</p>
+        <p><strong>Was passiert?</strong> SillyHome ordnet Raum, Sensoren, Zustände und vorhandene Historie automatisch zu.</p>
+      </div>
+      <div class="step">
+        <span class="step-number">2</span>
+        <h3>Wie gewohnt bedienen</h3>
+        <p><strong>Wo?</strong> Weiterhin in Home Assistant, an Schaltern oder über deine bisherigen Bedienwege.</p>
+        <p><strong>Was passiert?</strong> SillyHome lernt deine Handlungen und zeigt Vorhersagen an, schaltet aber noch nicht selbst.</p>
+      </div>
+      <div class="step">
+        <span class="step-number">3</span>
+        <h3>Später freigeben</h3>
+        <p><strong>Wo?</strong> In den Details des ausgewählten Geräts, sobald genug Verhalten gelernt wurde.</p>
+        <p><strong>Was passiert?</strong> Erst dann darf SillyHome passende Vorhersagen automatisch ausführen. Die Freigabe kann jederzeit gestoppt werden.</p>
+      </div>
+    </div>
+  </section>
+
+  <section id="status-section">
     <h2>Systemstatus</h2>
+    <p class="muted">Zeigt, ob Verbindung, Lernsystem und automatische Prüfungen funktionieren. Hier musst du normalerweise nichts einstellen.</p>
     <div id="status">Prüfung läuft ...</div>
     <div class="chips" id="status-chips"></div>
-    <button class="secondary" onclick="loadOverview()">Neu laden</button>
+    <button class="secondary" onclick="loadOverview()">Status aktualisieren</button>
-    <button onclick="runReconciliation()">Reconciliation ausführen</button>
   </section>
 
-  <section>
+  <section id="choose">
-    <h2>Aktuator wählen</h2>
+    <h2>1. Gerät zum Lernen auswählen</h2>
-    <label for="actuator-select">Home-Assistant-Aktor</label>
+    <p class="muted">Wähle eine Lampe, einen Rollladen oder einen anderen unterstützten Aktor. Du wählst keine Sensoren und erstellst keine Regeln.</p>
-    <select id="actuator-select"></select>
+    <label for="actuator-input">Entitätsname oder Gerät aus Home Assistant</label>
-    <button onclick="configureActuator()">Aktuator übernehmen</button>
+    <input id="actuator-input" list="actuator-options" placeholder="z. B. light.licht_abstellraum" autocomplete="off">
-    <pre id="actuator-config-result">Noch kein Aktuator konfiguriert.</pre>
+    <datalist id="actuator-options"></datalist>
+    <div class="inline-controls">
+      <div>
+        <label for="actuator-domain-filter">Typ</label>
+        <select id="actuator-domain-filter" onchange="renderActuatorSelect()">
+          <option value="">Alle steuerbaren Typen</option>
+          <option value="light">Lichter</option>
+          <option value="switch">Schalter / Helper</option>
+          <option value="cover">Rollläden / Cover</option>
+          <option value="fan">Lüftung / Ventilatoren</option>
+          <option value="humidifier">Befeuchter / Entfeuchter</option>
+        </select>
+      </div>
+      <div>
+        <label for="actuator-search">Liste durchsuchen</label>
+        <input id="actuator-search" placeholder="Raum, Gerät oder Entity" oninput="renderActuatorSelect()" autocomplete="off">
+      </div>
+    </div>
+    <label for="actuator-select">Oder aus Liste wählen</label>
+    <select id="actuator-select" onchange="selectActuatorFromList()">
+      <option value="">Geräteliste wird geladen ...</option>
+    </select>
+    <button onclick="configureActuator()">Gerät hinzufügen und Beobachtung starten</button>
+    <p id="actuator-config-result" class="muted">Noch kein Aktor ausgewählt.</p>
   </section>
 
-  <section class="wide">
+  <section class="wide" id="observed">
-    <h2>Konfigurierte Aktuatoren</h2>
+    <h2>2. Beobachtete Geräte</h2>
+    <p class="muted">Öffne „Details“, um Lernfortschritt, aktuelle Vorhersage und den automatisch gefundenen Kontext zu sehen.</p>
     <div id="configured-actuators">Noch nicht geladen.</div>
   </section>
 
-  <section class="wide">
+  <section class="wide" id="detail">
-    <h2>Zuordnung und Modellstatus</h2>
+    <h2>3. Lernfortschritt und Freigabe</h2>
-    <div id="actuator-detail">Einen konfigurierten Aktuator auswählen.</div>
+    <p class="muted">Die Freigabe erscheint erst, wenn genug eindeutig zugeordnete Handlungen gelernt wurden. Vorher bleibt das Gerät sicher im Beobachtungsmodus.</p>
-  </section>
+    <div id="actuator-detail" class="muted">Öffne bei einem beobachteten Gerät die Details.</div>
-
-  <section class="wide">
-    <h2>Automation-Entwurf</h2>
-    <p>Der Entwurf muss explizit freigegeben werden. Auch danach wird nur YAML exportiert, nichts geschaltet.</p>
-    <div class="grid-two">
-      <div><label for="alias">Name</label><input id="alias" value="Licht bei Dunkelheit"></div>
-      <div><label for="trigger">Trigger-Entity</label><input id="trigger" placeholder="sensor.flur_illuminance"></div>
-      <div><label for="below">Unter Grenzwert</label><input id="below" type="number" value="10"></div>
-      <div><label for="service">Dienst</label><select id="service"><option>light.turn_on</option><option>light.turn_off</option><option>switch.turn_on</option><option>switch.turn_off</option></select></div>
-      <div><label for="target">Ziel-Entity</label><input id="target" placeholder="light.flur"></div>
-    </div>
-    <button onclick="createProposal()">Entwurf speichern</button>
-    <button class="secondary" onclick="loadProposals()">Entwürfe aktualisieren</button>
-    <div id="proposals"></div>
   </section>
 </main>
 <script>
-const pretty = value => JSON.stringify(value, null, 2);
+const escapeHtml = value => String(value ?? "")
+  .replaceAll("&", "&amp;")
+  .replaceAll("<", "&lt;")
+  .replaceAll(">", "&gt;")
+  .replaceAll('"', "&quot;")
+  .replaceAll("'", "&#039;");
 let currentActuatorId = null;
+let actuatorChoices = [];
+let contextOptions = [];
+let manualContextState = {options: [], selected: new Set()};
 
 async function api(path, options = {}) {
   const response = await fetch(path, {headers: {"Content-Type": "application/json"}, ...options});
-  const body = await response.json().catch(() => ({}));
+  const body = response.status === 204 ? null : await response.json().catch(() => ({}));
-  if (!response.ok) throw new Error(body.detail || `${response.status} ${response.statusText}`);
+  if (!response.ok) throw new Error(body?.detail || `${response.status} ${response.statusText}`);
   return body;
 }
 
+function lifecycleLabel(record) {
+  if (record.behavior.status === "trained") return "Kontext erkannt";
+  const labels = {
+    trained: "lernt",
+    pending_history: "sammelt Historie",
+    pending_assignment: "sucht Kontext",
+    review_required: "geringe Zuordnungssicherheit",
+    archived: "wartet auf Kontext",
+    orphaned: "Aktor nicht gefunden",
+  };
+  return labels[record.lifecycle.status] || record.lifecycle.status;
+}
+
 function statusClass(record) {
-  if (record.assignment.review_required) return "warn";
+  if (record.behavior.status === "trained") return "ok";
   if (record.lifecycle.status === "trained") return "ok";
-  if (record.lifecycle.status === "review_required" || record.lifecycle.status === "invalid") return "warn";
+  if (["pending_history", "pending_assignment", "archived"].includes(record.lifecycle.status)) return "warn";
   return "bad";
 }
 
-function renderEvidence(evidence) {
+function behaviorLabel(record) {
-  return evidence.length ? `<ul>${evidence.map(item => `<li>${item}</li>`).join("")}</ul>` : "<span class='bad'>Keine Evidenz</span>";
+  if (record.behavior.mode === "active") return "aktiv freigegeben";
+  if (record.behavior.status === "trained") return "Shadow-Vorhersage";
+  if (record.behavior.status === "blocked") return "Lernen blockiert";
+  return "sammelt Handlungen";
+}
+
+function predictionLabel(record) {
+  return record.behavior.prediction
+    ? `${record.behavior.prediction.target_state} (${Math.round(record.behavior.prediction.confidence * 100)} %)`
+    : "Keine fällige Aktion";
+}
+
+function entityLabel(entity) {
+  const area = entity.area_name || "Ohne Bereich";
+  const name = entity.friendly_name || entity.entity_id;
+  return `${area} - ${name} (${entity.entity_id})`;
+}
+
+function normalizedSearch(value) {
+  return String(value || "").toLowerCase().replaceAll("_", " ");
+}
+
+function matchesSearch(entity, query) {
+  if (!query) return true;
+  return normalizedSearch([
+    entity.entity_id,
+    entity.friendly_name,
+    entity.area_name,
+    entity.device_name,
+    entity.device_class,
+    entity.domain,
+  ].filter(Boolean).join(" ")).includes(query);
+}
+
+function categoryForEntity(entity) {
+  const cls = entity.device_class || "";
+  if (entity.domain === "light") return "Lichtzustände";
+  if (entity.domain === "switch") return "Schalter / Helper";
+  if (["motion", "occupancy", "presence"].includes(cls)) return "PIR / Präsenz";
+  if (["illuminance"].includes(cls)) return "Helligkeit";
+  if (["door", "garage_door", "opening", "window"].includes(cls)) return "Tür / Fenster";
+  if (["humidity", "moisture"].includes(cls)) return "Luftfeuchtigkeit";
+  if (["temperature"].includes(cls)) return "Temperatur";
+  if (["power", "energy", "current", "voltage"].includes(cls)) return "Strom / Energie";
+  if (entity.domain === "binary_sensor") return "Binäre Sensoren";
+  if (entity.domain === "sensor") return "Weitere Messsensoren";
+  return "Weitere Zustände";
+}
+
+function optionGroups(entities, selectedIds = new Set()) {
+  const groups = new Map();
+  for (const entity of entities) {
+    const category = categoryForEntity(entity);
+    if (!groups.has(category)) groups.set(category, []);
+    groups.get(category).push(entity);
+  }
+  return Array.from(groups.entries()).map(([label, items]) => `
+    <optgroup label="${escapeHtml(label)}">
+      ${items.map(entity => `
+        <option value="${escapeHtml(entity.entity_id)}" ${selectedIds.has(entity.entity_id) ? "selected" : ""}>
+          ${escapeHtml(entityLabel(entity))}
+        </option>
+      `).join("")}
+    </optgroup>
+  `).join("");
 }
 
 async function loadOverview() {
   const status = document.getElementById("status");
   const chips = document.getElementById("status-chips");
   try {
-    const [health, ml, reconciliation, actuators] = await Promise.all([
+    const [health, websocket, ml, reconciliation, actuators] = await Promise.all([
       api("health"),
+      api("health/websocket"),
       api("ml/health"),
       api("v1/actuators/reconciliation/state"),
       api("v1/actuators"),
     ]);
-    status.innerHTML = `<p class="ok">API und ML bereit</p><p>Letzte Reconciliation: ${reconciliation.last_completed_at || "noch nie"}</p><p>${reconciliation.last_summary}</p>`;
+    status.innerHTML = `<p class="ok">System bereit</p><p>Letzte automatische Prüfung: ${escapeHtml(reconciliation.last_completed_at || "noch nie")}</p>`;
     chips.innerHTML = [
-      `<span class="chip">Health: ${health.status}</span>`,
+      `<span class="chip">API: ${escapeHtml(health.status)}</span>`,
-      `<span class="chip">ML: ${ml.status}</span>`,
+      `<span class="chip">WebSocket: ${escapeHtml(websocket.status)}</span>`,
-      `<span class="chip">Aktuatoren: ${actuators.length}</span>`,
+      `<span class="chip">Lernsystem: ${escapeHtml(ml.status)}</span>`,
-      `<span class="chip">Trainierte Modelle: ${reconciliation.trained_models}</span>`,
+      `<span class="chip">Aktoren: ${actuators.length}</span>`,
+      `<span class="chip">Lernbereite Geräte: ${reconciliation.trained_models}</span>`,
     ].join("");
   } catch (error) {
-    status.innerHTML = `<p class="bad">${error.message}</p>`;
+    status.innerHTML = `<p class="bad">${escapeHtml(error.message)}</p>`;
     chips.innerHTML = "";
   }
-  await Promise.all([loadActuatorDiscovery(), loadConfiguredActuators(), loadProposals()]);
+  await Promise.all([loadActuatorDiscovery(), loadConfiguredActuators()]);
 }
 
 async function loadActuatorDiscovery() {
+  const options = document.getElementById("actuator-options");
   const select = document.getElementById("actuator-select");
   try {
-    const actuators = await api("v1/actuators/discovery");
+    const [available, configured] = await Promise.all([
-    select.innerHTML = actuators.length
+      api("v1/actuators/discovery"),
-      ? actuators.map(entity => `<option value="${entity.entity_id}">${entity.friendly_name || entity.entity_id}${entity.area_name ? ` (${entity.area_name})` : ""}</option>`).join("")
+      api("v1/actuators"),
-      : "<option value=''>Keine Aktuatoren gefunden</option>";
+    ]);
+    const configuredIds = new Set(configured.map(record => record.actuator_entity_id));
+    actuatorChoices = available.filter(entity => !configuredIds.has(entity.entity_id));
+    options.innerHTML = actuatorChoices.slice(0, 120).map(entity =>
+      `<option value="${escapeHtml(entity.entity_id)}">${escapeHtml(entity.friendly_name || entity.entity_id)}${entity.area_name ? ` (${escapeHtml(entity.area_name)})` : ""}</option>`
+    ).join("");
+    renderActuatorSelect();
   } catch (error) {
-    select.innerHTML = `<option value="">${error.message}</option>`;
+    options.innerHTML = "";
+    select.innerHTML = `<option value="">Geräteliste konnte nicht geladen werden</option>`;
   }
 }
 
+function actuatorGroupLabel(domain) {
+  const labels = {
+    light: "Lichter",
+    switch: "Schalter / Helper",
+    cover: "Rollläden / Cover",
+    fan: "Lüftung / Ventilatoren",
+    humidifier: "Befeuchter / Entfeuchter",
+  };
+  return labels[domain] || domain;
+}
+
+function renderActuatorSelect() {
+  const select = document.getElementById("actuator-select");
+  if (!select) return;
+  const domain = document.getElementById("actuator-domain-filter")?.value || "";
+  const query = normalizedSearch(document.getElementById("actuator-search")?.value || "");
+  const filtered = actuatorChoices
+    .filter(entity => !domain || entity.domain === domain)
+    .filter(entity => matchesSearch(entity, query))
+    .slice(0, 120);
+  const domains = [...new Set(filtered.map(entity => entity.domain))].sort();
+  select.innerHTML = [
+    `<option value="">${filtered.length ? "Gerät auswählen ..." : "Keine passenden Geräte gefunden"}</option>`,
+    ...domains.map(group => `
+      <optgroup label="${escapeHtml(actuatorGroupLabel(group))}">
+        ${filtered
+          .filter(entity => entity.domain === group)
+          .map(entity => `<option value="${escapeHtml(entity.entity_id)}">${escapeHtml(entityLabel(entity))}</option>`)
+          .join("")}
+      </optgroup>
+    `),
+  ].join("");
+}
+
+async function loadContextOptions(actuatorId) {
+  try {
+    contextOptions = await api(`v1/actuators/context-options?actuator_entity_id=${encodeURIComponent(actuatorId)}`);
+  } catch (error) {
+    contextOptions = [];
+  }
+}
+
+function selectActuatorFromList() {
+  const value = document.getElementById("actuator-select").value;
+  if (value) document.getElementById("actuator-input").value = value;
+}
+
+function renderManualContextSelect() {
+  const select = document.getElementById("manual-context-select");
+  if (!select) return;
+  const category = document.getElementById("manual-context-category")?.value || "";
+  const query = normalizedSearch(document.getElementById("manual-context-filter")?.value || "");
+  const selectedNow = new Set([
+    ...manualContextState.selected,
+    ...Array.from(select.selectedOptions).map(option => option.value),
+  ]);
+  const filtered = manualContextState.options
+    .filter(entity => !category || categoryForEntity(entity) === category)
+    .filter(entity => matchesSearch(entity, query))
+    .slice(0, 80);
+  select.innerHTML = filtered.length
+    ? optionGroups(filtered, selectedNow)
+    : `<option value="">Keine passenden Vorschläge</option>`;
+}
+
+function parseEntityIds(value) {
+  return String(value || "")
+    .split(/[\s,;]+/)
+    .map(item => item.trim())
+    .filter(Boolean);
+}
+
 async function configureActuator() {
-  const actuatorId = document.getElementById("actuator-select").value;
+  const actuatorId = (
-  const box = document.getElementById("actuator-config-result");
+    document.getElementById("actuator-input").value.trim()
+    || document.getElementById("actuator-select").value.trim()
+  );
+  const result = document.getElementById("actuator-config-result");
   if (!actuatorId) return;
+  result.textContent = "Kontext wird automatisch analysiert ...";
   try {
     const record = await api("v1/actuators", {
       method: "POST",
       body: JSON.stringify({actuator_entity_id: actuatorId}),
     });
     currentActuatorId = record.actuator_entity_id;
-    box.textContent = pretty(record);
+    result.textContent = `${record.actuator_entity_id}: ${lifecycleLabel(record)}.`;
     await loadOverview();
     await showActuator(record.actuator_entity_id);
+    document.getElementById("detail").scrollIntoView({behavior: "smooth", block: "start"});
   } catch (error) {
-    box.textContent = error.message;
+    result.textContent = error.message;
-  }
-}
-
-async function runReconciliation() {
-  try {
-    await api("v1/actuators/reconciliation/run", {method: "POST"});
-    await loadOverview();
-    if (currentActuatorId) await showActuator(currentActuatorId);
-  } catch (error) {
-    alert(error.message);
   }
 }
 
@@ -169,186 +417,278 @@ async function loadConfiguredActuators() {
   try {
     const rows = await api("v1/actuators");
     box.innerHTML = rows.length ? `
-      <table>
+      <div class="card-list">
-        <tr><th>Aktuator</th><th>Numerischer Sensor</th><th>Review</th><th>Modellstatus</th><th>Letztes Training</th><th>Aktion</th></tr>
         ${rows.map(record => `
-          <tr>
+          <article class="actuator-card ${currentActuatorId === record.actuator_entity_id ? "selected" : ""}">
-            <td>${record.actuator_entity_id}</td>
+            <div class="card-title">
-            <td>${record.assignment.selected_numeric_entity_id || "-"}</td>
+              <div>
-            <td class="${record.assignment.review_required ? "warn" : "ok"}">${record.assignment.review_required ? "erforderlich" : "nein"}</td>
+                <div class="entity-id">${escapeHtml(record.actuator_entity_id)}</div>
-            <td class="${statusClass(record)}">${record.lifecycle.status}</td>
+                <div class="${record.behavior.status === "trained" ? "ok" : "warn"}">${escapeHtml(behaviorLabel(record))}</div>
-            <td>${record.lifecycle.last_trained_at || "-"}</td>
+              </div>
-            <td><button onclick="showActuator('${record.actuator_entity_id}')">Details</button></td>
+              <span class="chip">${escapeHtml(lifecycleLabel(record))}</span>
-          </tr>
+            </div>
+            <div class="metric-grid">
+              <div class="metric"><strong>Freigabe</strong><span class="${record.behavior.activation_ready ? "ok" : "warn"}">${escapeHtml(record.behavior.activation_ready ? "bereit" : record.behavior.activation_reason)}</span></div>
+              <div class="metric"><strong>Handlungen</strong>${record.behavior.sample_count}</div>
+              <div class="metric"><strong>Vorhersage</strong>${escapeHtml(predictionLabel(record))}</div>
+            </div>
+            <div class="actions">
+              <button onclick="showActuator('${escapeHtml(record.actuator_entity_id)}')">Details öffnen</button>
+              ${record.behavior.mode === "active"
+                ? `<button class="danger" onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', false, false, true)">Stoppen + HA-Automationen fortsetzen</button>`
+                : record.behavior.activation_ready
+                  ? `<button onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', true, true, false)">SillyHome übernehmen lassen</button>`
+                  : ""}
+              <button class="danger" onclick="removeActuator('${escapeHtml(record.actuator_entity_id)}')">Entfernen</button>
+            </div>
+          </article>
         `).join("")}
-      </table>` : "<p>Keine konfigurierten Aktuatoren.</p>";
+      </div>` : "<p>Noch keine Aktoren ausgewählt.</p>";
   } catch (error) {
     box.textContent = error.message;
   }
 }
 
-async function showActuator(actuatorId) {
+async function showActuator(actuatorId, evaluationMessage = "") {
   currentActuatorId = actuatorId;
   const box = document.getElementById("actuator-detail");
   try {
-    const record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}`);
+    let record;
-    const numericRows = record.numeric_candidates.map(candidate => `
+    try {
-      <tr>
+      record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}/related-automations/refresh`, {method: "POST"});
-        <td>${candidate.entity_id}</td>
+    } catch (_) {
-        <td>${candidate.score.toFixed(3)} / ${candidate.confidence.toFixed(2)}</td>
+      record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}`);
-        <td>${candidate.auto_accepted ? "<span class='ok'>auto</span>" : "<span class='warn'>review</span>"}</td>
+    }
-        <td>${renderEvidence(candidate.evidence)}</td>
+    await loadContextOptions(actuatorId);
-      </tr>
+    const contexts = [
-    `).join("");
+      record.assignment.selected_numeric_entity_id,
-    const contextRows = record.context_candidates.map(candidate => `
+      ...record.assignment.selected_context_entity_ids,
-      <tr>
+    ].filter(Boolean);
-        <td>${candidate.entity_id}</td>
+    const evidence = [...record.numeric_candidates, ...record.context_candidates]
-        <td>${candidate.score.toFixed(3)} / ${candidate.confidence.toFixed(2)}</td>
+      .filter(candidate => contexts.includes(candidate.entity_id))
-        <td>${candidate.auto_accepted ? "<span class='ok'>auto</span>" : "<span class='warn'>optional</span>"}</td>
+      .map(candidate => `<li><strong>${escapeHtml(candidate.friendly_name || candidate.entity_id)}</strong>: ${candidate.evidence.map(escapeHtml).join(", ") || "statistisch relevanter Kandidat"}</li>`)
-        <td>${renderEvidence(candidate.evidence)}</td>
+      .join("");
-      </tr>
+    const prediction = record.behavior.prediction;
-    `).join("");
+    const learnedAutomationActions = record.behavior.patterns.filter(
-    box.innerHTML = `
+      pattern => pattern.source === "automation",
-      <div class="grid-two">
+    ).length;
-        <div>
+    const relatedAutomations = record.behavior.related_automations || [];
-          <h3>Auswahl</h3>
+    const manualContextIds = new Set(record.assignment.selected_context_entity_ids || []);
-          <p><strong>Aktuator:</strong> ${record.actuator_entity_id}</p>
+    const numericOptions = contextOptions.filter(entity => entity.domain === "sensor");
-          <p><strong>Numerischer Sensor:</strong> ${record.assignment.selected_numeric_entity_id || "-"}</p>
+    const suggestedIds = new Set(contextOptions.map(entity => entity.entity_id));
-          <p><strong>Kontext:</strong> ${record.assignment.selected_context_entity_ids.join(", ") || "-"}</p>
+    const manualOnlyIds = [
-          <p><strong>Quelle:</strong> ${record.assignment.source}</p>
+      record.assignment.selected_numeric_entity_id,
-          <p><strong>Review:</strong> <span class="${record.assignment.review_required ? "warn" : "ok"}">${record.assignment.review_required ? "erforderlich" : "nicht erforderlich"}</span></p>
+      ...manualContextIds,
-          <p><strong>Begruendung:</strong> ${record.assignment.reason}</p>
+    ].filter(entityId => entityId && !suggestedIds.has(entityId));
+    const contextCategories = [...new Set(contextOptions
+      .filter(entity => entity.entity_id !== record.actuator_entity_id)
+      .map(categoryForEntity))]
+      .sort();
+    manualContextState = {
+      options: contextOptions.filter(entity => entity.entity_id !== record.actuator_entity_id),
+      selected: manualContextIds,
+    };
+    const manualAssignment = `
+      <div class="manual-context">
+        <h3>Kontext selbst festlegen</h3>
+        <p class="muted">Die Vorschläge sind aktorbezogen vorsortiert. Wenn etwas fehlt, trage die Entity-ID unten manuell ein, z. B. PIR, Helligkeit außen, Luftfeuchtigkeit oder Lichtzustände.</p>
+        <label for="manual-numeric-select">Optionaler Haupt-Messsensor</label>
+        <select id="manual-numeric-select">
+          <option value="">Keinen numerischen Hauptsensor verwenden</option>
+          ${optionGroups(numericOptions, new Set([record.assignment.selected_numeric_entity_id].filter(Boolean)))}
+        </select>
+        <div class="inline-controls">
+          <div>
+            <label for="manual-context-category">Kategorie</label>
+            <select id="manual-context-category" onchange="renderManualContextSelect()">
+              <option value="">Alle relevanten Vorschläge</option>
+              ${contextCategories.map(category => `<option value="${escapeHtml(category)}">${escapeHtml(category)}</option>`).join("")}
+            </select>
+          </div>
+          <div>
+            <label for="manual-context-filter">Vorschläge durchsuchen</label>
+            <input id="manual-context-filter" placeholder="z. B. treppe, bewegung, lux" oninput="renderManualContextSelect()" autocomplete="off">
+          </div>
         </div>
-        <div>
+        <label for="manual-context-select">Zusätzliche Kontext-Entities aus Vorschlägen</label>
-          <h3>Modell-Lebenszyklus</h3>
+        <select id="manual-context-select" multiple>
-          <p><strong>Status:</strong> <span class="${statusClass(record)}">${record.lifecycle.status}</span></p>
+          ${optionGroups(manualContextState.options.slice(0, 80), manualContextIds)}
-          <p><strong>Letztes Training:</strong> ${record.lifecycle.last_trained_at || "-"}</p>
+        </select>
-          <p><strong>Messpunkte:</strong> ${record.lifecycle.last_history_point_count}</p>
+        <label for="manual-context-freeform">Entity-IDs manuell ergänzen</label>
-          <p><strong>Grund:</strong> ${record.lifecycle.reason}</p>
+        <textarea id="manual-context-freeform" class="manual-entry" placeholder="Eine oder mehrere Entity-IDs, getrennt durch Komma, Leerzeichen oder neue Zeilen">${escapeHtml(manualOnlyIds.join("\n"))}</textarea>
-          <p><strong>Nächste Aktion:</strong> ${record.lifecycle.next_action}</p>
+        <div class="actions">
-          <button onclick="reconcileActuator('${record.actuator_entity_id}')">Diesen Aktuator erneut prüfen</button>
+          <button onclick="saveManualAssignment('${escapeHtml(record.actuator_entity_id)}')">Diese Kontext-Auswahl speichern</button>
+          <button class="secondary" onclick="loadContextOptions('${escapeHtml(record.actuator_entity_id)}').then(() => showActuator('${escapeHtml(record.actuator_entity_id)}'))">Vorschläge neu laden</button>
         </div>
       </div>
-      <div class="grid-two">
-        <div>
-          <h3>Manuelle Overrides</h3>
-          <label for="override-numeric">Numerischer Sensor</label>
-          <input id="override-numeric" value="${record.manual_override?.numeric_entity_id || record.assignment.selected_numeric_entity_id || ""}">
-          <label for="override-context">Kontext-Entities (kommagetrennt)</label>
-          <textarea id="override-context">${(record.manual_override?.context_entity_ids || record.assignment.selected_context_entity_ids || []).join(", ")}</textarea>
-          <label for="override-note">Notiz</label>
-          <input id="override-note" value="${record.manual_override?.note || ""}">
-          <button onclick="saveOverride('${record.actuator_entity_id}')">Override speichern</button>
-          <button class="secondary" onclick="clearOverride('${record.actuator_entity_id}')">Override löschen</button>
-        </div>
-        <div>
-          <h3>Audit</h3>
-          <pre>${pretty(record.lifecycle.audit)}</pre>
-        </div>
-      </div>
-      <h3>Numerische Kandidaten</h3>
-      ${numericRows ? `<table><tr><th>Entity</th><th>Score / Confidence</th><th>Auto</th><th>Evidenz</th></tr>${numericRows}</table>` : "<p>Keine Kandidaten.</p>"}
-      <h3>Kontext-Kandidaten</h3>
-      ${contextRows ? `<table><tr><th>Entity</th><th>Score / Confidence</th><th>Auto</th><th>Evidenz</th></tr>${contextRows}</table>` : "<p>Keine Kandidaten.</p>"}
     `;
+    const activationButton = record.behavior.mode === "active"
+      ? `<button class="danger" onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', false, false, true)">SillyHome stoppen und pausierte HA-Automationen fortsetzen</button>
+         <button class="secondary" onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', false, false, false)">SillyHome stoppen; HA-Automationen pausiert lassen</button>`
+      : record.behavior.activation_ready
+        ? `<button onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', true, true, false)">SillyHome übernehmen lassen und passende HA-Automationen pausieren</button>
+           <button class="secondary" onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', true, false, false)">SillyHome parallel aktivieren</button>`
+        : `<p class='warn'>${escapeHtml(record.behavior.activation_reason)}</p>`;
+    const automationControls = relatedAutomations.length
+      ? `<ul>${relatedAutomations.map(automation => `
+          <li>
+            <strong>${escapeHtml(automation.friendly_name)}</strong>
+            <code>${escapeHtml(automation.entity_id)}</code>:
+            <span class="${automation.enabled ? "ok" : "warn"}">${automation.enabled ? "aktiv" : "pausiert"}</span>
+            <button class="secondary compact" onclick="setRelatedAutomation('${escapeHtml(record.actuator_entity_id)}', '${escapeHtml(automation.entity_id)}', ${automation.enabled ? "false" : "true"})">${automation.enabled ? "Pausieren" : "Fortsetzen"}</button>
+          </li>`).join("")}</ul>`
+      : "<p class='muted'>Keine eindeutig passende HA-Automation gefunden.</p>";
+    box.innerHTML = `
+      <div class="detail-header">
+        <div>
+          <h3>${escapeHtml(record.actuator_entity_id)}</h3>
+          <p class="muted">Alle wichtigen Aktionen für dieses Gerät.</p>
+        </div>
+        <button class="secondary compact" onclick="loadOverview()">Alles aktualisieren</button>
+      </div>
+      <div class="grid-two">
+        <div>
+          <h3>Zuordnung</h3>
+          <p><strong>Status:</strong> <span class="${statusClass(record)}">${escapeHtml(lifecycleLabel(record))}</span></p>
+          <p><strong>Kontextzuordnung:</strong> automatisch erledigt</p>
+          <p><strong>Zuordnungssicherheit:</strong> ${Math.round(record.assignment.confidence * 100)} %</p>
+          <p class="muted">Dieser Wert beschreibt, wie sicher Raum, Sensoren und Zustände zu diesem Gerät passen.</p>
+          <p><strong>Ergebnis:</strong> ${escapeHtml(record.assignment.reason)}</p>
+        </div>
+        <div>
+          <h3>Lernfortschritt</h3>
+          <p><strong>Betriebsart:</strong> ${escapeHtml(behaviorLabel(record))}</p>
+          <p><strong>Gelernte Handlungen:</strong> ${record.behavior.sample_count}</p>
+          <p><strong>Davon eindeutig geregelt:</strong> ${record.behavior.high_confidence_sample_count}</p>
+          <p><strong>Davon erkannte HA-Automationen:</strong> ${learnedAutomationActions}</p>
+          <p><strong>Letztes Training:</strong> ${escapeHtml(record.behavior.last_trained_at || "noch nicht")}</p>
+          <p><strong>Was noch passiert:</strong> ${escapeHtml(record.behavior.reason)}</p>
+          <p><strong>Freigabestatus:</strong> <span class="${record.behavior.activation_ready ? "ok" : "warn"}">${escapeHtml(record.behavior.activation_reason)}</span></p>
+          <div class="actions">${activationButton}</div>
+          <button class="secondary" onclick="evaluateActuator('${escapeHtml(record.actuator_entity_id)}')">Aktuelle Situation auswerten</button>
+          <p class="muted">Die Prüfung simuliert keinen Sensorwechsel und schaltet keinen Aktor.</p>
+          ${evaluationMessage ? `<p class="ok">${escapeHtml(evaluationMessage)}</p>` : ""}
+        </div>
+      </div>
+      <h3>Was SillyHome aktuell vorhersagt</h3>
+      ${prediction
+        ? `<p><strong>${escapeHtml(prediction.target_state)}</strong> mit ${Math.round(prediction.confidence * 100)} % Sicherheit. ${escapeHtml(prediction.reason)} <span class="${prediction.executed ? "ok" : "muted"}">${escapeHtml(prediction.execution_reason)}</span></p>`
+        : "<p class='muted'>Aktuell ist kein gelerntes Handlungsmuster fällig.</p>"}
+      <h3>Passende Home-Assistant-Automationen</h3>
+      <p class="muted">Bei einer Übernahme pausiert SillyHome diese Automationen. Beim Stoppen können sie gezielt fortgesetzt werden.</p>
+      ${automationControls}
+      <h3>Welche Zusammenhänge automatisch verwendet werden</h3>
+      ${evidence ? `<ul>${evidence}</ul>` : "<p class='warn'>Noch kein geeigneter Kontext erkannt. SillyHome prüft bei neuen HA-Daten erneut.</p>"}
+      ${manualAssignment}
+    `;
+    document.getElementById("detail").scrollIntoView({behavior: "smooth", block: "start"});
   } catch (error) {
     box.textContent = error.message;
   }
 }
 
-async function reconcileActuator(actuatorId) {
+async function saveManualAssignment(actuatorId) {
+  const numericEntityId = document.getElementById("manual-numeric-select").value || null;
+  const selectedContextIds = Array.from(
+    document.getElementById("manual-context-select").selectedOptions,
+  ).map(option => option.value).filter(value => value.includes("."));
+  const freeformContextIds = parseEntityIds(
+    document.getElementById("manual-context-freeform").value,
+  );
+  const contextEntityIds = [...new Set([...selectedContextIds, ...freeformContextIds])]
+    .filter(entityId => entityId !== numericEntityId);
   try {
-    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/reconcile`, {method: "POST"});
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/assignment`, {
-    await loadOverview();
-    await showActuator(actuatorId);
-  } catch (error) {
-    alert(error.message);
-  }
-}
-
-async function saveOverride(actuatorId) {
-  const numeric = document.getElementById("override-numeric").value.trim() || null;
-  const contexts = document.getElementById("override-context").value
-    .split(",")
-    .map(item => item.trim())
-    .filter(Boolean);
-  const note = document.getElementById("override-note").value.trim() || null;
-  try {
-    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/override`, {
       method: "POST",
       body: JSON.stringify({
-        numeric_entity_id: numeric,
+        numeric_entity_id: numericEntityId,
-        context_entity_ids: contexts,
+        context_entity_ids: contextEntityIds,
-        note,
+        note: "Manuell im Dashboard gesetzt",
       }),
     });
-    await loadOverview();
+    await loadConfiguredActuators();
-    await showActuator(actuatorId);
+    await showActuator(actuatorId, "Manuelle Kontext-Auswahl gespeichert.");
   } catch (error) {
     alert(error.message);
   }
 }
 
-async function clearOverride(actuatorId) {
+async function evaluateActuator(actuatorId) {
   try {
-    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/override`, {
+    const record = await api(
+      `v1/actuators/${encodeURIComponent(actuatorId)}/evaluate`,
+      {method: "POST"},
+    );
+    const checkedAt = new Date(
+      record.behavior.last_evaluated_at || Date.now(),
+    ).toLocaleString("de-DE");
+    const message = record.behavior.prediction
+      ? `Prüfung ${checkedAt}: ${record.behavior.prediction.target_state} mit ${Math.round(record.behavior.prediction.confidence * 100)} % vorhergesagt.`
+      : `Prüfung ${checkedAt}: Kein frischer passender Sensorwechsel erkannt; aktuell ist keine Aktion fällig.`;
+    await loadConfiguredActuators();
+    await showActuator(actuatorId, message);
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
+async function setActivation(actuatorId, active, pauseMatchingAutomations, restorePausedAutomations) {
+  const question = active
+    ? pauseMatchingAutomations
+      ? `${actuatorId}: SillyHome aktivieren und passende HA-Automationen pausieren?`
+      : `${actuatorId}: SillyHome parallel zu den HA-Automationen aktivieren?`
+    : restorePausedAutomations
+      ? `${actuatorId}: SillyHome stoppen und pausierte HA-Automationen fortsetzen?`
+      : `${actuatorId}: SillyHome stoppen und HA-Automationen pausiert lassen?`;
+  if (!confirm(question)) return;
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/activation`, {
       method: "POST",
-      body: JSON.stringify({clear: true}),
+      body: JSON.stringify({
+        active,
+        pause_matching_automations: pauseMatchingAutomations,
+        restore_paused_automations: restorePausedAutomations,
+      }),
     });
-    await loadOverview();
+    await loadConfiguredActuators();
     await showActuator(actuatorId);
   } catch (error) {
     alert(error.message);
   }
 }
 
-async function createProposal() {
+async function setRelatedAutomation(actuatorId, automationEntityId, enabled) {
+  const action = enabled ? "fortsetzen" : "pausieren";
+  if (!confirm(`${automationEntityId} wirklich ${action}?`)) return;
   try {
-    await api("v1/automations/proposals", {method: "POST", body: JSON.stringify({
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/related-automations/control`, {
-      alias: document.getElementById("alias").value,
+      method: "POST",
-      description: "Manuell im SillyHome-Dashboard erstellter und nicht automatisch ausgeführter Entwurf.",
+      body: JSON.stringify({
-      trigger: {entity_id: document.getElementById("trigger").value, below: Number(document.getElementById("below").value)},
+        automation_entity_id: automationEntityId,
-      action: {service: document.getElementById("service").value, entity_id: document.getElementById("target").value, data: {}}
+        enabled,
-    })});
+      }),
-    await loadProposals();
+    });
+    await loadConfiguredActuators();
+    await showActuator(actuatorId);
   } catch (error) {
     alert(error.message);
   }
 }
 
-async function decide(id, revision, action) {
+async function removeActuator(actuatorId) {
+  if (!confirm(`${actuatorId} aus SillyHome entfernen?`)) return;
   try {
-    await api(`v1/automations/proposals/${id}/${action}`, {method: "POST", body: JSON.stringify({expected_revision: revision})});
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}`, {method: "DELETE"});
-    await loadProposals();
+    if (currentActuatorId === actuatorId) {
+      currentActuatorId = null;
+      document.getElementById("actuator-detail").textContent = "Öffne bei einem beobachteten Gerät die Details.";
+    }
+    await loadOverview();
   } catch (error) {
     alert(error.message);
   }
 }
 
-async function loadProposals() {
-  const box = document.getElementById("proposals");
-  try {
-    const rows = await api("v1/automations/proposals");
-    box.innerHTML = rows.length ? `
-      <table>
-        <tr><th>Name</th><th>Status</th><th>Aktion</th></tr>
-        ${rows.map(item => `
-          <tr>
-            <td>${item.alias}</td>
-            <td>${item.status}</td>
-            <td>${item.status === "draft"
-              ? `<button onclick="decide('${item.proposal_id}',${item.revision},'approve')">Freigeben</button><button class="secondary" onclick="decide('${item.proposal_id}',${item.revision},'reject')">Ablehnen</button>`
-              : item.status === "approved"
-                ? `<a href="v1/automations/proposals/${item.proposal_id}/yaml">YAML laden</a>`
-                : "-"}</td>
-          </tr>
-        `).join("")}
-      </table>` : "<p>Keine Entwürfe.</p>";
-  } catch (error) {
-    box.textContent = error.message;
-  }
-}
-
 loadOverview();
 </script>
 </body>

docker-compose.yml

@@ -14,6 +14,12 @@ services:
       SILLYHOME_MIN_TRAINING_POINTS: 24
       SILLYHOME_RETRAIN_STALE_HOURS: 24
       SILLYHOME_RECONCILE_INTERVAL_SECONDS: 900
+      SILLYHOME_MIN_BEHAVIOR_ACTIONS: 3
+      SILLYHOME_PREDICTION_CONFIDENCE: 0.82
+      SILLYHOME_PREDICTION_WINDOW_MINUTES: 30
+      SILLYHOME_PREDICTION_INTERVAL_SECONDS: 60
+      SILLYHOME_EXECUTION_COOLDOWN_SECONDS: 900
+      SILLYHOME_TIMEZONE: Europe/Berlin
     volumes:
       - model-data:/app/data/models
       - automation-data:/app/data/automations

docs/BEHAVIOR_ENGINE.md

@@ -0,0 +1,73 @@
+# Verhaltensmodell und Berechnung
+
+## Datenfluss
+
+1. Nutzer wählt einen Aktor.
+2. `ActuatorReconciliationService` ordnet Kontext-Entities zu.
+3. `BehaviorEngine.train()` liest Aktor- und Kontexthistorie.
+4. Aktor-Zustandswechsel werden als `BehaviorPattern` gespeichert.
+5. `BehaviorEngine.evaluate()` vergleicht aktuelle Zustände mit den Mustern.
+6. Shadow zeigt nur die Vorhersage. Active darf sie ausführen.
+
+## Herkunft und Gewicht
+
+- HA-Benutzer: `source=user`, Gewicht `1.0`
+- eindeutig erkannte HA-Automation oder Script: `source=automation`, Gewicht `1.0`
+- physisch oder unbekannt: `source=physical_or_unknown`, Gewicht `0.7`
+- eigene SillyHome-Ausführung: wird verworfen
+
+Manuelle und eindeutig automatisierte Handlungen zählen für die Freigabe.
+
+## Kausale Muster
+
+Wechselt ein Kontextsensor höchstens drei Sekunden vor der Aktorhandlung, wird
+der Wechsel gespeichert:
+
+```text
+binary_sensor.tuer: off -> on
+light.raum: off -> on
+```
+
+Eine kausale Vorhersage gilt nur, wenn derselbe Kontextzustand frisch ist. Das
+Standardfenster ist zweimal `SILLYHOME_PREDICTION_INTERVAL_SECONDS`.
+
+## Nicht-kausale Bewertung
+
+Für Muster ohne frischen Trigger:
+
+```text
+score = weight * (
+  0.45 * time_score
+  + 0.45 * context_score
+  + 0.10 * weekday_score
+)
+```
+
+Die Confidence ist der mittlere Score, begrenzt durch die Mindestunterstützung:
+
+```text
+confidence = mean(scores) * min(1, support / min_behavior_actions)
+```
+
+## Ausführungsbedingungen
+
+Eine Vorhersage wird nur ausgeführt, wenn alle Bedingungen erfüllt sind:
+
+- Betriebsart `active`
+- Confidence mindestens `SILLYHOME_PREDICTION_CONFIDENCE`
+- Zielzustand ist noch nicht erreicht
+- Domain und Zustand sind erlaubt
+- Cooldown erlaubt die Aktion
+
+Der Cooldown sperrt nur eine schnelle Wiederholung desselben Zielzustands.
+Eine Gegenaktion, beispielsweise `on` gefolgt von `off`, bleibt sofort erlaubt.
+
+## Freigabe
+
+`activation_ready=true`, wenn:
+
+- Verhaltensstatus `trained`
+- mindestens `SILLYHOME_MIN_BEHAVIOR_ACTIONS` eindeutig zugeordnete manuelle
+  oder automatisierte Handlungen vorhanden sind
+
+Die UI zeigt `activation_reason` immer an.

docs/CONTROL_HANDOFF.md

@@ -0,0 +1,47 @@
+# Übergabe zwischen SillyHome und HA-Automationen
+
+## Erkennung
+
+SillyHome liest aktive `automation.*`-Entities, lädt deren Konfiguration über
+die Home-Assistant-API und sucht darin nach der exakten Aktor-Entity-ID.
+Namensähnlichkeit allein reicht nicht.
+
+## Betriebsarten
+
+### Shadow
+
+- SillyHome lernt und prognostiziert.
+- SillyHome schaltet nicht.
+- HA-Automationen können normal weiterlaufen.
+
+### Active parallel
+
+- SillyHome darf schalten.
+- Passende HA-Automationen bleiben aktiv.
+- Diese Betriebsart kann doppelte Auslöser verursachen und ist nur für Tests.
+
+### Active mit Übernahme
+
+- SillyHome wird zuerst aktiviert.
+- Danach werden aktuell aktive, passend erkannte HA-Automationen pausiert.
+- Nur erfolgreich pausierte Automationen werden für eine spätere
+  Wiederherstellung gespeichert.
+- Scheitert die Pause, fällt SillyHome auf Shadow zurück und stellt bereits
+  pausierte Automationen wieder her.
+
+## Stoppen
+
+Zwei bewusste Optionen:
+
+- SillyHome stoppen und pausierte HA-Automationen fortsetzen.
+- SillyHome stoppen und HA-Automationen pausiert lassen.
+
+Einzelne passende Automationen können im Dashboard jederzeit pausiert oder
+fortgesetzt werden.
+
+In Home Assistant bedeutet:
+
+```text
+automation.turn_off = pausieren/deaktivieren
+automation.turn_on  = fortsetzen/aktivieren
+```

docs/DEBUGGING.md

@@ -0,0 +1,63 @@
+# Debugging
+
+## Vorhersage korrekt, aber keine Ausführung
+
+1. Aktor-Details öffnen.
+2. `Betriebsart` prüfen.
+3. `Freigabestatus` prüfen.
+4. Text hinter der Vorhersage lesen. `execution_reason` nennt exakt:
+   - Shadow-Modus
+   - Confidence unter Schaltschwelle
+   - Zielzustand bereits erreicht
+   - Cooldown aktiv
+   - ausgeführt
+5. Live-Zustand des Aktors und Triggers in HA prüfen.
+6. Add-on-Logs prüfen.
+
+## Weder SillyHome noch HA-Automation schaltet
+
+1. SillyHome-Modus prüfen.
+2. Unter `Passende Home-Assistant-Automationen` den Zustand prüfen.
+3. Bei Shadow mindestens eine gewünschte HA-Automation fortsetzen.
+4. Bei Active mit Übernahme müssen die passenden HA-Automationen pausiert sein.
+
+## Freigabe fehlt
+
+Die UI zeigt den Grund immer als `activation_reason`.
+
+Prüfen:
+
+```text
+behavior.status
+behavior.sample_count
+behavior.high_confidence_sample_count
+behavior.activation_ready
+behavior.activation_reason
+```
+
+## Entität fehlt in der Liste
+
+Den vollständigen Entitätsnamen direkt eingeben. Der Server akzeptiert nur
+existierende, unterstützte Aktoren. Ein unbekannter Name liefert `404`.
+
+## Standarddiagnose lokal
+
+```bash
+.venv/bin/pytest tests/behavior/test_engine.py -q
+.venv/bin/pytest tests/api/test_actuators.py -q
+.venv/bin/ruff check app tests
+.venv/bin/mypy app backend tests
+```
+
+## Standarddiagnose im HA-Add-on
+
+```bash
+ha apps info 58adbe1e_sillyhome_next
+ha apps logs 58adbe1e_sillyhome_next
+```
+
+Health aus einem Add-on mit Zugriff auf das interne Netz:
+
+```bash
+wget -qO- http://58adbe1e-sillyhome-next:8000/health
+```

docs/OPERATIONS.md

@@ -0,0 +1,87 @@
+# Entwicklung, Release und Betrieb
+
+## Lokales Setup
+
+```bash
+python3 -m venv .venv
+.venv/bin/pip install -e '.[dev]'
+cp .env.example .env
+.venv/bin/uvicorn app.main:app --reload
+```
+
+`SILLYHOME_HA_URL` und `SILLYHOME_HA_TOKEN` nur lokal in `.env` setzen.
+
+## Qualitätsprüfung
+
+```bash
+.venv/bin/pytest -q
+.venv/bin/ruff check .
+.venv/bin/mypy app backend tests
+git diff --check
+```
+
+## Release
+
+1. Version in allen vier Stellen ändern:
+   `pyproject.toml`, `addon/config.yaml`, `app/main.py`, `CHANGELOG.md`.
+2. Qualitätsprüfung ausführen.
+3. Feature-Branch committen und pushen.
+4. Pull Request nach `main` erstellen und mergen.
+5. Annotiertes Tag auf dem Merge-Commit erstellen.
+6. Gitea-Release aus demselben Tag erstellen.
+
+Beispiel:
+
+```bash
+git tag -a v0.7.0 -m 'SillyHome Next 0.7.0'
+git push origin v0.7.0
+```
+
+## Home-Assistant-Update
+
+Vorher Teil-Backup des Add-ons erstellen. Danach:
+
+```bash
+ha store reload
+ha apps info 58adbe1e_sillyhome_next
+ha apps update 58adbe1e_sillyhome_next
+ha apps info 58adbe1e_sillyhome_next
+ha apps logs 58adbe1e_sillyhome_next
+```
+
+Kein Home-Assistant-Neustart ist erforderlich.
+
+## Live-Verifikation
+
+Pflicht:
+
+```bash
+wget -qO- http://58adbe1e-sillyhome-next:8000/health
+wget -qO- http://58adbe1e-sillyhome-next:8000/v1/actuators
+```
+
+Für einen Aktor prüfen:
+
+- `behavior.mode`
+- `behavior.activation_ready`
+- `behavior.activation_reason`
+- `behavior.related_automations`
+- `behavior.paused_automation_entity_ids`
+- `behavior.prediction.execution_reason`
+
+Bei einer Übernahme testen:
+
+1. Passende HA-Automation ist vorher `on`.
+2. SillyHome übernimmt.
+3. SillyHome ist `active`.
+4. Passende HA-Automation ist `off`.
+5. Trigger erzeugt erwartete Aktoraktion.
+6. Gegenaktion wird trotz Cooldown ausgeführt.
+7. SillyHome stoppen und Automationen fortsetzen.
+8. SillyHome ist `shadow`, HA-Automation wieder `on`.
+
+## Rollback
+
+Bevorzugt das vor dem Update erstellte HA-Teil-Backup wiederherstellen.
+Alternativ vorherige Git-Version in `addon/config.yaml` veröffentlichen und das
+Add-on erneut aktualisieren.

docs/automations.md

@@ -1,14 +1,6 @@
-# Automation-Vorschläge
+# Keine manuell erzeugten Automationen
 
-SillyHome Next führt Automationen niemals automatisch aus. Der Workflow ist:
+Seit `v0.5.0` erstellt SillyHome Next keine YAML-Automationen und bietet keinen
-
+Regel- oder Trigger-Editor mehr an. Der produktive Ablauf besteht aus
-1. Vorschlag als `draft` erstellen.
+Aktorauswahl, automatischem Verhaltenslernen, Shadow-Vorhersage und einer
-2. Inhalt und Ziel-Entity prüfen.
+separaten Ausführungsfreigabe pro Aktor.
-3. Mit aktueller Revision explizit freigeben oder ablehnen.
-4. Nur freigegebene Vorschläge als Home-Assistant-YAML exportieren.
-5. Das YAML außerhalb von SillyHome Next in Home Assistant importieren.
-
-Erlaubt sind numerische Sensor-Trigger und Aktionsdienste aus den Domains
-`light`, `switch`, `climate`, `fan` und `cover`. Shell-Kommandos, Skripte und
-beliebige Service-Domains werden abgewiesen. Eine einmal getroffene Entscheidung
-kann nicht überschrieben werden; Änderungen benötigen einen neuen Vorschlag.

docs/ml_api.md

@@ -178,9 +178,10 @@ Listet unterstützte Aktuatoren mit angereicherter HA-Metadatenbasis.
 
 ### `POST /v1/actuators`
 
-Registriert einen Aktuator, ermittelt passende numerische Sensoren und
+Registriert einen Aktor. Das System ermittelt passende Messwerte und
-Kontext-Entities, trainiert bei ausreichender History automatisch ein Modell und
+Kontext-Entities vollständig automatisch, trainiert bei ausreichender Historie
-liefert Zuordnung, Confidence, Evidenz und Lifecycle-Status zurück.
+ein Modell und liefert Zuordnung, Confidence, Evidenz und Lifecycle-Status zur
+Diagnose zurück.
 
 **Request**
 ```json
@@ -190,21 +191,63 @@ liefert Zuordnung, Confidence, Evidenz und Lifecycle-Status zurück.
 }
 ```
 
-### `POST /v1/actuators/{actuator_entity_id}/override`
-
-Persistiert manuelle Overrides. Diese haben Vorrang vor der automatischen
-Heuristik und überstehen Neustarts.
-
 ### `POST /v1/actuators/reconciliation/run`
 
 Führt eine sichere globale Reconciliation aus. Die periodische Add-on-Schleife
 ruft denselben idempotenten Ablauf auf, startet aber keine Services in Home
 Assistant.
 
+### `POST /v1/actuators/{actuator_entity_id}/evaluate`
+
+Erstellt aus aktuellem Kontext eine neue Shadow- oder Aktiv-Vorhersage. Im
+Shadow-Modus wird niemals geschaltet.
+
+### `POST /v1/actuators/{actuator_entity_id}/activation`
+
+```json
+{
+  "active": true,
+  "pause_matching_automations": true,
+  "restore_paused_automations": false
+}
+```
+
+Aktiviert autonomes Schalten erst nach ausreichendem Training und nur für
+erlaubte Aktor-Domains. `pause_matching_automations` pausiert eindeutig
+zugeordnete HA-Automationen bei der Übernahme.
+
+Beim Stoppen:
+
+```json
+{
+  "active": false,
+  "pause_matching_automations": false,
+  "restore_paused_automations": true
+}
+```
+
+Damit wird der Aktor in den Shadow-Modus versetzt und zuvor von SillyHome
+pausierte Automationen werden fortgesetzt.
+
+### `POST /v1/actuators/{actuator_entity_id}/related-automations/refresh`
+
+Liest passende HA-Automationen anhand ihrer echten Konfiguration neu ein.
+
+### `POST /v1/actuators/{actuator_entity_id}/related-automations/control`
+
+```json
+{
+  "automation_entity_id": "automation.licht_abstellkammer",
+  "enabled": false
+}
+```
+
+Pausiert oder aktiviert eine eindeutig diesem Aktor zugeordnete Automation.
+
 ## Betrieb
 
-Die produktive App lädt Artefakte aus `SILLYHOME_MODEL_STORE`. Aktuator-,
+Die produktive App lädt Artefakte aus `SILLYHOME_MODEL_STORE`. Aktor- und
-Override- und Reconciliation-Zustände liegen atomisch in
+Reconciliation-Zustände liegen atomisch in
 `SILLYHOME_ACTUATOR_STORE`. Neue Artefakte werden über `/ml/retrain`,
 `RetrainingService` oder den aktor-zentrierten Lifecycle registriert. Die API
 sollte nur in einem vertrauenswürdigen Netz oder hinter einem

docs/ml_training.md

@@ -1,92 +1,51 @@
-# ML Training- und Evaluations-Workflow
+# Verhaltenslernen und Vorhersage
 
-SillyHome Next trainiert ein lokales statistisches Baseline-Modell pro Sensor
+Seit `v0.5.0` ist der produktive Lernpfad aktor-zentriert. Nutzer wählen nur
-und Merkmal. Es benötigt keine Cloud und keine externe ML-Laufzeit.
+einen Aktor; Sensoren, Kontext und Modelle werden automatisch verwaltet.
 
-Seit `v0.4.0` ist der bevorzugte Weg aktor-zentriert: ein bestätigter Aktuator
+## Datengrundlage
-wird mit einem numerischen Primärsensor verknüpft, die Historie dieses Sensors
-wird automatisch geladen und in ein deterministisches Artefakt überführt.
 
-## 1. Daten sammeln
+Für jeden Aktor lädt SillyHome Next:
 
-Alle Trainingsvektoren werden über `FeatureStore.add(...)` oder `add_batch(...)` eingepflegt. Jeder Vektor enthält eine Sensor-ID sowie ein Dictionary mit Merkmalen.
+- dessen Zustandswechsel aus der Home-Assistant-Historie
+- Logbook-Einträge zur Herkunft der Handlung
+- automatisch zugeordnete Mess- und Kontext-Entities
+- deren Zustand zum Zeitpunkt der Handlung
 
-Im Normalbetrieb erzeugt die Reconciliation diese Vektoren selbst aus realer
+Eindeutig einem Home-Assistant-Benutzer zugeordnete Handlungen und im Logbuch
-Home-Assistant-History. Das Trainingsmerkmal heißt dabei immer `value`.
+erkannte Automations- oder Script-Aktionen erhalten das höchste Gewicht.
-Binäre Kontextsensoren bleiben Kontext und werden nicht als numerische Samples
+Physische oder nicht eindeutig zuordenbare Bedienungen dürfen das Shadow-Modell
-missverstanden.
+ergänzen, reichen allein aber nicht zur Aktivierung.
 
-## 2. Statistisches Artefakt erzeugen
+## Modell
 
-```python
+Das lokale Modell speichert pro beobachteter Handlung:
-store = FeatureStore()
-store.add(FeatureVector(sensor_id="sensor.kitchen", values={"temperature": 21.0}))
-pipeline = TrainingPipeline(store)
-artifact = pipeline.run("my_artifact")
-pipeline.export("my_artifact")
-```
 
-`TrainingPipeline.run(...)` berechnet für jedes numerische Merkmal:
+- Zielzustand
+- lokale Tageszeit
+- Wochentag
+- Kontextzustände
+- Herkunft und Gewicht
 
-- Stichprobenzahl
+Eine Vorhersage bewertet zeitliche Nähe, Wochentagsmuster und aktuellen
-- Mittelwert und Standardabweichung
+Kontext. Mehrere passende historische Handlungen erhöhen die Confidence.
-- Minimum und Maximum
-- linearen Trend mit Steigung und Achsenabschnitt
 
-Die nächste Vorhersage kombiniert den letzten beobachteten Wert mit der
+## Betriebsstufen
-trainierten Trendsteigung. Die Confidence berücksichtigt Datenmenge und
-Stabilität.
 
-## 3. Modell evaluieren
+1. `collecting`: Noch nicht genügend Handlungen vorhanden.
+2. `shadow`: Modell ist trainiert; Vorhersagen werden angezeigt, aber nicht ausgeführt.
+3. `active`: Nutzer hat den Aktor ausdrücklich freigegeben.
 
-```python
+Die Aktivierung verlangt genügend eindeutig zugeordnete manuelle oder
-evaluator = Evaluator(pipeline)
+automatisierte Handlungen. Ausgeführt werden nur erlaubte Zustände reversibler Domains:
-report = evaluator.evaluate(artifact.artifact_id, validation_samples)
+`light`, `switch`, `fan`, `humidifier` und `cover`.
-```
 
-Der Report enthält echte numerische Vergleichsmetriken:
+## Schutzmechanismen
-- `artifact_id`
-- `sample_size`
-- `mae` (Mean Absolute Error)
-- `rmse` (Root Mean Squared Error)
-- `coverage` für den Anteil auswertbarer Merkmale
 
-## 4. Modell registrieren
+- explizite Freigabe pro Aktor
-
+- konfigurierbare Mindestkonfidenz
-Das trainierte Artefakt kann anschließend über `ModelRegistry.register(artifact)` bereitgestellt werden. Die ML-Serving-API stellt es unter `/ml/predict` und `/ml/batch` zur Verfügung.
+- Cooldown zwischen Schaltungen
-
+- keine Ausführung bei bereits erreichtem Zielzustand
-## 5. Retraining ausführen
+- keine Ausführung unbekannter Zustände oder riskanter Domains
-
+- eigene Schaltungen werden beim nächsten Training herausgefiltert
-`RetrainingService.retrain(...)` führt genau einen Trainingslauf aus und ersetzt
+- Automation-/Script-Aktionen zählen nur bei eindeutiger Herkunft im HA-Logbuch
-ein vorhandenes Artefakt mit derselben ID atomisch in der Registry:
-
-```python
-service = RetrainingService(registry)
-result = service.retrain("home-model", vectors)
-```
-
-Scheduler, Cronjobs oder Home-Assistant-Automationen können alternativ die
-zustandslose Funktion `retrain_model(registry, artifact_id, vectors)` aufrufen.
-Der Service startet bewusst keinen eigenen Hintergrundprozess. Über
-`POST /ml/retrain` kann derselbe Ablauf per API angestoßen werden.
-
-## 6. Autonomer Lebenszyklus
-
-Der `ActuatorReconciliationService` verwaltet pro konfiguriertem Aktuator:
-
-- die automatische Sensor- und Kontextzuordnung mit Score, Confidence und Evidenz
-- persistente manuelle Overrides
-- den Modellstatus (`trained`, `pending_history`, `review_required`, `archived`, ...)
-- ein Audit-Protokoll mit Gründen für Training, Retraining oder Archivierung
-
-Retraining erfolgt nur, wenn:
-
-- genügend nutzbare numerische Historie vorliegt
-- die aktuelle Zuordnung eindeutig oder manuell bestätigt ist
-- die Historie sich materiell verändert hat oder das Modell als stale gilt
-
-## Hinweise
-- Für reproduzierbare Sensor-Reihenfolgen wird in `TrainingPipeline.run(...)` eine sortierte Sensor-Liste verwendet.
-- Fehlende Trainingsdaten lösen `ValueError` aus; nicht registrierte Artefakte lösen `KeyError` aus.
-- Nur endliche numerische Werte werden trainiert.
-- `coverage` bleibt im Bereich 0 bis 1.

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "0.4.0"
+version = "0.7.10"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [
@@ -12,6 +12,7 @@ dependencies = [
   "uvicorn[standard]>=0.29.0",
   "pydantic>=2.6.0",
   "requests>=2.31.0",
+  "websockets>=12.0",
 ]
 
 [project.optional-dependencies]

tests/actuators/test_lifecycle.py

@@ -7,7 +7,6 @@ from app.actuators.lifecycle import ActuatorReconciliationService
 from app.actuators.models import (
     AssignmentSource,
     LifecycleStatus,
-    ManualOverride,
     model_id_for_actuator,
 )
 from app.actuators.store import ActuatorStore
@@ -79,7 +78,7 @@ def _service(
             model_store=str(tmp_path / "models"),
             automation_store=str(tmp_path / "automations"),
             actuator_store=str(tmp_path / "actuators"),
-            history_days=14,
+            history_days=31,
             min_training_points=5,
             retrain_stale_hours=24,
             reconcile_interval_seconds=900,
@@ -142,7 +141,7 @@ def test_reconciliation_auto_assigns_and_trains_numeric_model(tmp_path: Path) ->
     assert "binary_sensor.abstellkammer_motion" not in artifact.supported_sensors
 
 
-def test_reconciliation_requires_review_for_ambiguous_sensor_mapping(tmp_path: Path) -> None:
+def test_reconciliation_rejects_ambiguous_numeric_mapping(tmp_path: Path) -> None:
     start = datetime(2026, 6, 1, tzinfo=timezone.utc)
     entities = [
         HaEntitySummary(
@@ -182,10 +181,103 @@ def test_reconciliation_requires_review_for_ambiguous_sensor_mapping(tmp_path: P
     record = service.configure_actuator("switch.garage_pump")
 
     assert record.assignment.review_required is True
-    assert record.lifecycle.status is LifecycleStatus.REVIEW_REQUIRED
+    assert record.assignment.selected_numeric_entity_id is None
+    assert record.lifecycle.status is LifecycleStatus.ARCHIVED
 
 
-def test_manual_override_persists_and_wins_after_restart(tmp_path: Path) -> None:
+def test_reconciliation_does_not_cross_assign_other_room_light_energy(
+    tmp_path: Path,
+) -> None:
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    entities = [
+        HaEntitySummary(
+            entity_id=(
+                "light.lichtschalter_abstellraum_"
+                "lichtschalter_abstellraum_s1"
+            ),
+            domain="light",
+            friendly_name="Licht Abstellraum",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.licht_badezimmer_energy",
+            domain="sensor",
+            device_class="energy",
+            state_class="total_increasing",
+            unit_of_measurement="kWh",
+            friendly_name="Lichtschalter_Badezimmer Licht Badezimmer energy",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.abstellraum_ture",
+            domain="binary_sensor",
+            device_class="door",
+            friendly_name="Abstellraum Türe",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.briefkasten_open",
+            domain="binary_sensor",
+            device_class="opening",
+            friendly_name="Briefkasten open",
+        ),
+    ]
+    service = _service(
+        tmp_path,
+        entities,
+        {"sensor.licht_badezimmer_energy": _points(8, start, 1.0)},
+    )
+
+    record = service.configure_actuator(
+        "light.lichtschalter_abstellraum_lichtschalter_abstellraum_s1"
+    )
+
+    assert record.assignment.selected_numeric_entity_id is None
+    assert record.assignment.selected_context_entity_ids == [
+        "binary_sensor.abstellraum_ture"
+    ]
+    assert record.assignment.source is AssignmentSource.AUTOMATIC
+    assert record.assignment.confidence == 1.0
+    assert record.assignment.review_required is False
+    assert record.lifecycle.status is LifecycleStatus.ARCHIVED
+
+
+def test_reconciliation_ignores_generic_monitoring_area_for_automatic_context(
+    tmp_path: Path,
+) -> None:
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    entities = [
+        HaEntitySummary(
+            entity_id="light.abstellkammer",
+            domain="light",
+            friendly_name="Licht Abstellkammer",
+            area_name="Monitoring",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.disk_overheating",
+            domain="binary_sensor",
+            device_class="problem",
+            friendly_name="Max. fehlerhafte Sektoren ueberschritten",
+            area_name="Monitoring",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.router_power",
+            domain="sensor",
+            device_class="power",
+            state_class="measurement",
+            unit_of_measurement="W",
+            friendly_name="Router Leistung",
+            area_name="Monitoring",
+        ),
+    ]
+    service = _service(tmp_path, entities, {"sensor.router_power": _points(8, start, 1.0)})
+
+    record = service.configure_actuator("light.abstellkammer")
+
+    assert record.assignment.selected_numeric_entity_id is None
+    assert record.assignment.selected_context_entity_ids == []
+    assert record.assignment.review_required is True
+    assert record.lifecycle.status is LifecycleStatus.ARCHIVED
+
+
+def test_manual_assignment_persists_and_wins_over_automatic_mapping(tmp_path: Path) -> None:
     start = datetime(2026, 6, 1, tzinfo=timezone.utc)
     entities = [
         HaEntitySummary(
@@ -219,20 +311,17 @@ def test_manual_override_persists_and_wins_after_restart(tmp_path: Path) -> None
     }
     service = _service(tmp_path, entities, history)
     service.configure_actuator("light.abstellkammer")
-
+    service.set_manual_assignment(
-    updated = service.set_override(
         "light.abstellkammer",
-        ManualOverride(
+        numeric_entity_id="sensor.abstellkammer_power",
-            numeric_entity_id="sensor.abstellkammer_power",
+        context_entity_ids=["sensor.abstellkammer_illuminance"],
-            context_entity_ids=[],
+        note="Manuell wichtiger Sensor",
-            note="Manuelle Leistungs-Zuordnung",
-        ),
     )
 
     restarted = _service(tmp_path, entities, history)
     record = restarted.reconcile_actuator("light.abstellkammer")
 
-    assert updated.assignment.source is AssignmentSource.MANUAL
     assert record.assignment.selected_numeric_entity_id == "sensor.abstellkammer_power"
+    assert record.assignment.selected_context_entity_ids == ["sensor.abstellkammer_illuminance"]
+    assert record.assignment.source is AssignmentSource.MANUAL
     assert record.manual_override is not None
-    assert record.manual_override.numeric_entity_id == "sensor.abstellkammer_power"

tests/api/test_actuators.py

@@ -7,11 +7,17 @@ from fastapi.testclient import TestClient
 
 from app.actuators.lifecycle import ActuatorReconciliationService
 from app.actuators.store import ActuatorStore
+from app.behavior.engine import BehaviorEngine
 from app.config import Settings
 from app.ha.discovery import DiscoveredEntity
 from app.ha.discovery import discover_entities
-from app.ha.history import EntityHistorySeries, NumericHistoryPoint
+from app.ha.history import (
-from app.ha.models import HaEntitySummary
+    EntityHistorySeries,
+    LogbookEntry,
+    NumericHistoryPoint,
+    StateHistorySeries,
+)
+from app.ha.models import HaAutomationSummary, HaEntitySummary
 from app.ha.reader import HaReader
 from app.main import app
 from app.ml.registry.model_registry import ModelRegistry
@@ -54,6 +60,36 @@ class FakeHaReader(HaReader):
             if entity_id in self._history
         ]
 
+    def read_state_history(
+        self,
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> list[StateHistorySeries]:
+        return []
+
+    def read_logbook(
+        self,
+        entity_id: str,
+        start_time: datetime,
+        end_time: datetime,
+    ) -> list[LogbookEntry]:
+        return []
+
+    def call_service(
+        self,
+        domain: str,
+        service: str,
+        service_data: dict[str, object],
+    ) -> list[object]:
+        return []
+
+    def find_automations_for_entity(
+        self,
+        entity_id: str,
+    ) -> list[HaAutomationSummary]:
+        return []
+
 
 def _install_service(tmp_path: Path) -> None:
     entities = [
@@ -79,6 +115,14 @@ def _install_service(tmp_path: Path) -> None:
             friendly_name="Abstellkammer Bewegung",
             area_name="Abstellkammer",
         ),
+        HaEntitySummary(
+            entity_id="sensor.pfsense_interface_vpn_inbytes",
+            domain="sensor",
+            device_class="data_size",
+            state_class="measurement",
+            unit_of_measurement="KiB",
+            friendly_name="pfSense Interface VPN inbytes",
+        ),
     ]
     settings = Settings(
         ha_url="http://ha.local",
@@ -103,9 +147,14 @@ def _install_service(tmp_path: Path) -> None:
         registry=app.state.registry,
         settings=settings,
     )
+    app.state.behavior_engine = BehaviorEngine(
+        ha_reader=app.state.ha_reader,
+        store=app.state.actuator_store,
+        settings=settings,
+    )
 
 
-def test_actuator_api_configures_reconciles_and_overrides(tmp_path: Path) -> None:
+def test_actuator_api_configures_reconciles_and_removes(tmp_path: Path) -> None:
     with TestClient(app) as client:
         _install_service(tmp_path)
 
@@ -118,18 +167,63 @@ def test_actuator_api_configures_reconciles_and_overrides(tmp_path: Path) -> Non
         listed = client.get("/v1/actuators")
         assert listed.status_code == 200
         assert listed.json()[0]["lifecycle"]["status"] == "trained"
+        assert listed.json()[0]["behavior"]["mode"] == "shadow"
 
-        override = client.post(
+        evaluation = client.post("/v1/actuators/light.abstellkammer/evaluate")
-            "/v1/actuators/light.abstellkammer/override",
+        assert evaluation.status_code == 200
-            json={
+
-                "numeric_entity_id": "sensor.abstellkammer_illuminance",
+        premature_activation = client.post(
-                "context_entity_ids": ["binary_sensor.abstellkammer_motion"],
+            "/v1/actuators/light.abstellkammer/activation",
-                "note": "Explizit bestaetigt",
+            json={"active": True},
-            },
         )
-        assert override.status_code == 200
+        assert premature_activation.status_code == 409
-        assert override.json()["assignment"]["source"] == "manual"
 
         reconciliation = client.post("/v1/actuators/reconciliation/run")
         assert reconciliation.status_code == 200
         assert reconciliation.json()["trained_models"] == 1
+
+        removed = client.delete("/v1/actuators/light.abstellkammer")
+        assert removed.status_code == 204
+        assert client.get("/v1/actuators").json() == []
+
+
+def test_manual_assignment_endpoint_updates_context(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+
+        response = client.post(
+            "/v1/actuators/light.abstellkammer/assignment",
+            json={
+                "numeric_entity_id": "sensor.abstellkammer_illuminance",
+                "context_entity_ids": ["binary_sensor.abstellkammer_motion"],
+                "note": "Manuell gesetzt",
+            },
+        )
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload["assignment"]["source"] == "manual"
+    assert payload["assignment"]["selected_numeric_entity_id"] == (
+        "sensor.abstellkammer_illuminance"
+    )
+    assert payload["assignment"]["selected_context_entity_ids"] == [
+        "binary_sensor.abstellkammer_motion"
+    ]
+
+
+def test_context_options_returns_learnable_entities(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+        response = client.get(
+            "/v1/actuators/context-options",
+            params={"actuator_entity_id": "light.abstellkammer"},
+        )
+
+    assert response.status_code == 200
+    entity_ids = {item["entity_id"] for item in response.json()}
+    assert "sensor.abstellkammer_illuminance" in entity_ids
+    assert "binary_sensor.abstellkammer_motion" in entity_ids
+    assert "sensor.pfsense_interface_vpn_inbytes" not in entity_ids

tests/api/test_automations.py

@@ -1,59 +1,17 @@
-from pathlib import Path
-
 from fastapi.testclient import TestClient
 
-from app.automations.store import AutomationStore
 from app.main import app
 
 
-def _payload() -> dict[str, object]:
+def test_automation_api_is_not_exposed() -> None:
-    return {
-        "alias": "Licht bei Dunkelheit",
-        "description": "Schaltet das Flurlicht unter dem Helligkeitsgrenzwert ein.",
-        "trigger": {"entity_id": "sensor.hall_illuminance", "below": 10},
-        "action": {
-            "service": "light.turn_on",
-            "entity_id": "light.hall",
-            "data": {"brightness_pct": 40},
-        },
-    }
-
-
-def test_proposal_requires_explicit_approval_before_yaml(tmp_path: Path) -> None:
     with TestClient(app) as client:
-        app.state.automation_store = AutomationStore(tmp_path)
+        response = client.post(
-        created = client.post("/v1/automations/proposals", json=_payload())
+            "/v1/automations/proposals",
-        proposal_id = created.json()["proposal_id"]
+            json={
-        blocked = client.get(f"/v1/automations/proposals/{proposal_id}/yaml")
+                "alias": "Nicht mehr verfügbar",
-        approved = client.post(
+                "trigger": {"entity_id": "sensor.hall_illuminance", "below": 10},
-            f"/v1/automations/proposals/{proposal_id}/approve",
+                "action": {"service": "light.turn_on", "entity_id": "light.hall"},
-            json={"expected_revision": 1},
+            },
         )
-        exported = client.get(f"/v1/automations/proposals/{proposal_id}/yaml")
-    assert created.status_code == 201
-    assert created.json()["status"] == "draft"
-    assert blocked.status_code == 409
-    assert approved.json()["status"] == "approved"
-    assert "service: light.turn_on" in exported.text
 
-
+    assert response.status_code == 404
-def test_proposal_rejects_unsafe_service_domain(tmp_path: Path) -> None:
-    payload = _payload()
-    payload["action"] = {
-        "service": "shell_command.run",
-        "entity_id": "light.hall",
-        "data": {},
-    }
-    with TestClient(app) as client:
-        app.state.automation_store = AutomationStore(tmp_path)
-        response = client.post("/v1/automations/proposals", json=payload)
-    assert response.status_code == 422
-
-
-def test_proposal_requires_numeric_threshold(tmp_path: Path) -> None:
-    payload = _payload()
-    payload["trigger"] = {"entity_id": "sensor.hall_illuminance"}
-    with TestClient(app) as client:
-        app.state.automation_store = AutomationStore(tmp_path)
-        response = client.post("/v1/automations/proposals", json=payload)
-    assert response.status_code == 422

tests/api/test_entities.py

@@ -73,9 +73,11 @@ def test_entities_returns_reader_data() -> None:
     assert response.status_code == 200
     assert response.json() == [
         {
-            "entity_id": "sensor.temperature",
+                "entity_id": "sensor.temperature",
-            "domain": "sensor",
+                "domain": "sensor",
-            "state_class": None,
+                "state": None,
+                "last_changed": None,
+                "state_class": None,
             "device_class": None,
             "unit_of_measurement": None,
             "friendly_name": None,

tests/behavior/test_engine.py

@@ -0,0 +1,659 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+
+import pytest
+
+from app.actuators.models import (
+    BehaviorMode,
+    BehaviorPattern,
+    BehaviorState,
+    BehaviorStatus,
+    ExecutionEvent,
+)
+from app.actuators.store import ActuatorStore
+from app.behavior.engine import BehaviorEngine, predict_behavior, service_for_state
+from app.config import Settings
+from app.ha.history import (
+    LogbookEntry,
+    StateHistoryPoint,
+    StateHistorySeries,
+)
+from app.ha.models import HaAutomationSummary, HaEntitySummary
+from app.ha.reader import HaReader
+
+
+class FakeBehaviorReader(HaReader):
+    def __init__(
+        self,
+        *,
+        entities: list[HaEntitySummary],
+        history: list[StateHistorySeries],
+        logbook: list[LogbookEntry],
+    ) -> None:
+        self.entities = entities
+        self.history = history
+        self.logbook = logbook
+        self.service_calls: list[tuple[str, str, dict[str, object]]] = []
+        self.automations: list[HaAutomationSummary] = []
+
+    def read_entities(self) -> list[HaEntitySummary]:
+        return list(self.entities)
+
+    def read_state_history(
+        self,
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> list[StateHistorySeries]:
+        return [series for series in self.history if series.entity_id in entity_ids]
+
+    def read_logbook(
+        self,
+        entity_id: str,
+        start_time: datetime,
+        end_time: datetime,
+    ) -> list[LogbookEntry]:
+        return [entry for entry in self.logbook if entry.entity_id == entity_id]
+
+    def call_service(
+        self,
+        domain: str,
+        service: str,
+        service_data: dict[str, object],
+    ) -> list[object]:
+        self.service_calls.append((domain, service, service_data))
+        return []
+
+    def find_automations_for_entity(
+        self,
+        entity_id: str,
+    ) -> list[HaAutomationSummary]:
+        return list(self.automations)
+
+
+def _settings(tmp_path: Path) -> Settings:
+    return Settings(
+        actuator_store=str(tmp_path / "actuators"),
+        model_store=str(tmp_path / "models"),
+        automation_store=str(tmp_path / "automations"),
+        history_days=14,
+        min_behavior_actions=3,
+        prediction_confidence=0.8,
+        prediction_window_minutes=30,
+        execution_cooldown_seconds=900,
+        timezone="Europe/Berlin",
+    )
+
+
+def _reader(now: datetime) -> FakeBehaviorReader:
+    actuator_points: list[StateHistoryPoint] = []
+    logbook: list[LogbookEntry] = []
+    for days_ago in (3, 2, 1):
+        action_at = now - timedelta(days=days_ago)
+        actuator_points.extend(
+            [
+                StateHistoryPoint(timestamp=action_at - timedelta(minutes=1), state="off"),
+                StateHistoryPoint(timestamp=action_at, state="on"),
+                StateHistoryPoint(timestamp=action_at + timedelta(hours=6), state="off"),
+            ]
+        )
+        logbook.extend(
+            [
+                LogbookEntry(
+                    entity_id="light.office",
+                    timestamp=action_at,
+                    message="turned on",
+                    context_user_id="user-1",
+                ),
+                LogbookEntry(
+                    entity_id="light.office",
+                    timestamp=action_at + timedelta(hours=6),
+                    message="turned off",
+                    context_domain="automation",
+                    context_service="trigger",
+                ),
+            ]
+        )
+    actuator_points.sort(key=lambda point: point.timestamp)
+    context_points = [
+        StateHistoryPoint(timestamp=now - timedelta(days=7), state="on"),
+    ]
+    return FakeBehaviorReader(
+        entities=[
+            HaEntitySummary(entity_id="light.office", domain="light", state="off"),
+            HaEntitySummary(
+                entity_id="binary_sensor.office_presence",
+                domain="binary_sensor",
+                state="on",
+            ),
+        ],
+        history=[
+            StateHistorySeries(entity_id="light.office", points=actuator_points),
+            StateHistorySeries(
+                entity_id="binary_sensor.office_presence",
+                points=context_points,
+            ),
+        ],
+        logbook=logbook,
+    )
+
+
+def _engine(tmp_path: Path, now: datetime) -> tuple[BehaviorEngine, FakeBehaviorReader]:
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.office")
+    store.upsert(
+        record.model_copy(
+            update={
+                "assignment": record.assignment.model_copy(
+                    update={
+                        "selected_context_entity_ids": [
+                            "binary_sensor.office_presence"
+                        ],
+                    }
+                )
+            }
+        )
+    )
+    reader = _reader(now)
+    return (
+        BehaviorEngine(ha_reader=reader, store=store, settings=settings),
+        reader,
+    )
+
+
+def test_engine_trains_predicts_in_shadow_and_executes_only_after_approval(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    engine, reader = _engine(tmp_path, now)
+
+    trained = engine.train("light.office")
+    shadow = engine.evaluate("light.office")
+
+    assert trained.behavior.status is BehaviorStatus.TRAINED
+    assert trained.behavior.sample_count == 6
+    assert trained.behavior.high_confidence_sample_count == 6
+    assert shadow.behavior.mode is BehaviorMode.SHADOW
+    assert shadow.behavior.prediction is not None
+    assert shadow.behavior.prediction.target_state == "on"
+    assert reader.service_calls == []
+
+    engine.set_active("light.office", active=True)
+    active = engine.evaluate("light.office")
+
+    assert active.behavior.mode is BehaviorMode.ACTIVE
+    assert active.behavior.prediction is not None
+    assert active.behavior.prediction.executed is True
+    assert reader.service_calls == [
+        ("light", "turn_on", {"entity_id": "light.office"})
+    ]
+
+
+def test_engine_counts_known_automation_actions_like_manual_actions(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    engine, _ = _engine(tmp_path, now)
+
+    trained = engine.train("light.office")
+
+    assert {pattern.target_state for pattern in trained.behavior.patterns} == {
+        "on",
+        "off",
+    }
+    assert {pattern.source for pattern in trained.behavior.patterns} == {
+        "user",
+        "automation",
+    }
+    assert trained.behavior.high_confidence_sample_count == 6
+    assert {pattern.weight for pattern in trained.behavior.patterns} == {1.0}
+
+
+def test_engine_learns_causal_automation_with_activation_credit(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    actuator_points: list[StateHistoryPoint] = []
+    door_points: list[StateHistoryPoint] = []
+    logbook: list[LogbookEntry] = []
+    for days_ago in (3, 2, 1):
+        action_at = now - timedelta(days=days_ago)
+        actuator_points.extend(
+            [
+                StateHistoryPoint(
+                    timestamp=action_at - timedelta(minutes=1),
+                    state="off",
+                ),
+                StateHistoryPoint(timestamp=action_at, state="on"),
+            ]
+        )
+        door_points.extend(
+            [
+                StateHistoryPoint(
+                    timestamp=action_at - timedelta(minutes=1),
+                    state="off",
+                ),
+                StateHistoryPoint(
+                    timestamp=action_at - timedelta(seconds=1),
+                    state="on",
+                ),
+            ]
+        )
+        logbook.append(
+            LogbookEntry(
+                entity_id="light.storage",
+                timestamp=action_at,
+                message="turned on",
+                context_domain="automation",
+                context_service="trigger",
+            )
+        )
+    actuator_points.sort(key=lambda point: point.timestamp)
+    door_points.sort(key=lambda point: point.timestamp)
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.storage")
+    store.upsert(
+        record.model_copy(
+            update={
+                "assignment": record.assignment.model_copy(
+                    update={
+                        "selected_context_entity_ids": [
+                            "binary_sensor.storage_door"
+                        ],
+                    }
+                )
+            }
+        )
+    )
+    reader = FakeBehaviorReader(
+        entities=[],
+        history=[
+            StateHistorySeries(
+                entity_id="light.storage",
+                points=actuator_points,
+            ),
+            StateHistorySeries(
+                entity_id="binary_sensor.storage_door",
+                points=door_points,
+            ),
+        ],
+        logbook=logbook,
+    )
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    trained = engine.train("light.storage")
+    automation_patterns = [
+        pattern
+        for pattern in trained.behavior.patterns
+        if pattern.source == "automation"
+    ]
+
+    assert len(automation_patterns) == 3
+    assert trained.behavior.high_confidence_sample_count == 3
+    assert {pattern.weight for pattern in automation_patterns} == {1.0}
+    assert {
+        (
+            pattern.trigger_entity_id,
+            pattern.trigger_from_state,
+            pattern.trigger_to_state,
+        )
+        for pattern in automation_patterns
+    } == {("binary_sensor.storage_door", "off", "on")}
+
+    active = engine.set_active("light.storage", active=True)
+
+    assert active.behavior.mode is BehaviorMode.ACTIVE
+
+
+def test_active_mode_rejects_unsafe_domains(tmp_path: Path) -> None:
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("lock.front_door")
+    store.upsert(
+        record.model_copy(
+            update={
+                "behavior": record.behavior.model_copy(
+                    update={"status": BehaviorStatus.TRAINED}
+                )
+            }
+        )
+    )
+    reader = FakeBehaviorReader(entities=[], history=[], logbook=[])
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    with pytest.raises(ValueError, match="nicht freigegeben"):
+        engine.set_active("lock.front_door", active=True)
+
+
+def test_active_mode_requires_trusted_manual_or_automation_actions(tmp_path: Path) -> None:
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.office")
+    store.upsert(
+        record.model_copy(
+            update={
+                "behavior": record.behavior.model_copy(
+                    update={
+                        "status": BehaviorStatus.TRAINED,
+                        "sample_count": 3,
+                        "high_confidence_sample_count": 0,
+                    }
+                )
+            }
+        )
+    )
+    reader = FakeBehaviorReader(entities=[], history=[], logbook=[])
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    with pytest.raises(ValueError, match="Freigabe"):
+        engine.set_active("light.office", active=True)
+
+
+def test_control_handoff_pauses_and_restores_matching_automation(
+    tmp_path: Path,
+) -> None:
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.storage")
+    store.upsert(
+        record.model_copy(
+            update={
+                "behavior": record.behavior.model_copy(
+                    update={
+                        "status": BehaviorStatus.TRAINED,
+                        "sample_count": 3,
+                        "high_confidence_sample_count": 3,
+                        "activation_ready": True,
+                        "activation_reason": "Freigabe bereit.",
+                    }
+                )
+            }
+        )
+    )
+    reader = FakeBehaviorReader(entities=[], history=[], logbook=[])
+    reader.automations = [
+        HaAutomationSummary(
+            entity_id="automation.storage_light",
+            config_id="123",
+            friendly_name="Storage light",
+            enabled=True,
+        )
+    ]
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    active = engine.set_active(
+        "light.storage",
+        active=True,
+        pause_matching_automations=True,
+    )
+    shadow = engine.set_active(
+        "light.storage",
+        active=False,
+        restore_paused_automations=True,
+    )
+
+    assert active.behavior.mode is BehaviorMode.ACTIVE
+    assert active.behavior.paused_automation_entity_ids == [
+        "automation.storage_light"
+    ]
+    assert shadow.behavior.mode is BehaviorMode.SHADOW
+    assert shadow.behavior.paused_automation_entity_ids == []
+    assert reader.service_calls == [
+        (
+            "automation",
+            "turn_off",
+            {"entity_id": "automation.storage_light"},
+        ),
+        (
+            "automation",
+            "turn_on",
+            {"entity_id": "automation.storage_light"},
+        ),
+    ]
+
+
+def test_cooldown_allows_opposite_follow_up_action(tmp_path: Path) -> None:
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    reader = FakeBehaviorReader(entities=[], history=[], logbook=[])
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+    now = datetime.now(timezone.utc)
+    behavior = BehaviorState(
+        mode=BehaviorMode.ACTIVE,
+        last_executed_at=now - timedelta(seconds=5),
+        execution_events=[
+            ExecutionEvent(target_state="on", executed_at=now - timedelta(seconds=5))
+        ],
+    )
+
+    assert engine._cooldown_elapsed(behavior, now, "off") is True
+    assert engine._cooldown_elapsed(behavior, now, "on") is False
+
+
+@pytest.mark.parametrize(
+    ("domain", "state", "service"),
+    [
+        ("light", "on", "turn_on"),
+        ("switch", "off", "turn_off"),
+        ("cover", "open", "open_cover"),
+        ("cover", "closed", "close_cover"),
+        ("lock", "unlocked", None),
+    ],
+)
+def test_service_for_state_is_strictly_allowlisted(
+    domain: str,
+    state: str,
+    service: str | None,
+) -> None:
+    assert service_for_state(domain, state) == service
+
+
+def test_prediction_requires_temporal_support() -> None:
+    assert predict_behavior(
+        [],
+        current_context={},
+        now=datetime.now(timezone.utc),
+        min_support=3,
+        window_minutes=30,
+    ) is None
+
+
+def test_prediction_uses_fresh_causal_context_transition_outside_time_window() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    patterns = [
+        BehaviorPattern(
+            target_state="on",
+            minute_of_day=60,
+            weekday=0,
+            context_states={"binary_sensor.storage_door": "on"},
+            trigger_entity_id="binary_sensor.storage_door",
+            trigger_from_state="off",
+            trigger_to_state="on",
+            source="automation",
+            weight=0.7,
+            observed_at=now - timedelta(days=days_ago),
+        )
+        for days_ago in (3, 2, 1)
+    ]
+
+    prediction = predict_behavior(
+        patterns,
+        current_context={"binary_sensor.storage_door": "on"},
+        current_context_changed_at={
+            "binary_sensor.storage_door": now - timedelta(seconds=10)
+        },
+        now=now,
+        min_support=3,
+        window_minutes=30,
+    )
+
+    assert prediction is not None
+    assert prediction.target_state == "on"
+    assert prediction.matching_patterns == 3
+    assert prediction.confidence == 0.7
+    assert "frischen Sensorwechsel" in prediction.reason
+
+
+def test_prediction_ignores_stale_causal_context_state() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    pattern = BehaviorPattern(
+        target_state="on",
+        minute_of_day=60,
+        weekday=0,
+        context_states={"binary_sensor.storage_door": "on"},
+        trigger_entity_id="binary_sensor.storage_door",
+        trigger_from_state="off",
+        trigger_to_state="on",
+        source="automation",
+        weight=0.7,
+        observed_at=now - timedelta(days=1),
+    )
+
+    assert predict_behavior(
+        [pattern],
+        current_context={"binary_sensor.storage_door": "on"},
+        current_context_changed_at={
+            "binary_sensor.storage_door": now - timedelta(minutes=5)
+        },
+        now=now,
+        min_support=1,
+        window_minutes=30,
+    ) is None
+
+
+def test_state_change_uses_websocket_context_state_for_immediate_action(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(microsecond=0)
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.storage")
+    record = record.model_copy(
+        update={
+            "assignment": record.assignment.model_copy(
+                update={
+                    "selected_context_entity_ids": ["binary_sensor.storage_door"],
+                }
+            ),
+            "behavior": record.behavior.model_copy(
+                update={
+                    "mode": BehaviorMode.ACTIVE,
+                    "status": BehaviorStatus.TRAINED,
+                    "activation_ready": True,
+                    "patterns": [
+                        BehaviorPattern(
+                            target_state="on",
+                            minute_of_day=60,
+                            weekday=0,
+                            context_states={"binary_sensor.storage_door": "on"},
+                            trigger_entity_id="binary_sensor.storage_door",
+                            trigger_from_state="off",
+                            trigger_to_state="on",
+                            source="automation",
+                            weight=1.0,
+                            observed_at=now - timedelta(days=days_ago),
+                        )
+                        for days_ago in (3, 2, 1)
+                    ],
+                }
+            ),
+        }
+    )
+    store.upsert(record)
+    reader = FakeBehaviorReader(
+        entities=[
+            HaEntitySummary(entity_id="light.storage", domain="light", state="off"),
+            HaEntitySummary(
+                entity_id="binary_sensor.storage_door",
+                domain="binary_sensor",
+                state="off",
+                last_changed=now - timedelta(minutes=5),
+            ),
+        ],
+        history=[],
+        logbook=[],
+    )
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    engine.handle_state_change(
+        "binary_sensor.storage_door",
+        {"state": "on", "last_changed": now.isoformat()},
+    )
+
+    assert reader.service_calls == [
+        ("light", "turn_on", {"entity_id": "light.storage"})
+    ]
+
+
+def test_state_change_uses_event_cache_without_rest_state_query(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(microsecond=0)
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.storage")
+    record = record.model_copy(
+        update={
+            "assignment": record.assignment.model_copy(
+                update={
+                    "selected_context_entity_ids": ["binary_sensor.storage_door"],
+                }
+            ),
+            "behavior": record.behavior.model_copy(
+                update={
+                    "mode": BehaviorMode.ACTIVE,
+                    "status": BehaviorStatus.TRAINED,
+                    "activation_ready": True,
+                    "patterns": [
+                        BehaviorPattern(
+                            target_state="on",
+                            minute_of_day=60,
+                            weekday=0,
+                            context_states={"binary_sensor.storage_door": "on"},
+                            trigger_entity_id="binary_sensor.storage_door",
+                            trigger_from_state="off",
+                            trigger_to_state="on",
+                            source="automation",
+                            weight=1.0,
+                            observed_at=now - timedelta(days=days_ago),
+                        )
+                        for days_ago in (3, 2, 1)
+                    ],
+                }
+            ),
+        }
+    )
+    store.upsert(record)
+    reader = FakeBehaviorReader(
+        entities=[],
+        history=[],
+        logbook=[],
+    )
+
+    def fail_read_entities() -> list[HaEntitySummary]:
+        raise AssertionError("Event-Auswertung darf keinen REST-State lesen.")
+
+    reader.read_entities = fail_read_entities  # type: ignore[method-assign]
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    engine.handle_state_change(
+        "binary_sensor.storage_door",
+        {"state": "on", "last_changed": now.isoformat()},
+        current_entities=[
+            HaEntitySummary(entity_id="light.storage", domain="light", state="off"),
+            HaEntitySummary(
+                entity_id="binary_sensor.storage_door",
+                domain="binary_sensor",
+                state="on",
+                last_changed=now,
+            ),
+        ],
+    )
+
+    assert reader.service_calls == [
+        ("light", "turn_on", {"entity_id": "light.storage"})
+    ]

tests/ha/test_ha_client.py

@@ -108,6 +108,56 @@ def test_list_entity_metadata_calls_template_api() -> None:
     }
 
 
+def test_list_entity_metadata_batches_template_calls() -> None:
+    responses = []
+    for index in range(3):
+        response = _response()
+        response.text = (
+            f'[{{"entity_id":"sensor.test_{index}",'
+            f'"area_name":"Area {index}","device_name":"Device {index}"}}]'
+        )
+        responses.append(response)
+    client = HaClient(HaClientSettings(url="http://ha.local", token="test-token"))
+    client._session.post = Mock(side_effect=responses)  # type: ignore[method-assign]
+
+    entity_ids = [f"sensor.test_{index}" for index in range(401)]
+    metadata = client.list_entity_metadata(entity_ids)
+
+    assert client._session.post.call_count == 3
+    assert metadata["sensor.test_0"]["area_name"] == "Area 0"
+    assert metadata["sensor.test_1"]["device_name"] == "Device 1"
+    assert metadata["sensor.test_2"]["device_name"] == "Device 2"
+
+
+def test_get_logbook_filters_entity_and_period() -> None:
+    response = _response(payload=[{"entity_id": "light.office"}])
+    client = _client_with_response(response)
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    end = datetime(2026, 6, 2, tzinfo=timezone.utc)
+
+    payload = client.get_logbook("light.office", start, end)
+
+    assert payload == [{"entity_id": "light.office"}]
+    call = client._session.get.call_args  # type: ignore[attr-defined]
+    assert "/api/logbook/2026-06-01T00:00:00+00:00" in call.args[0]
+    assert call.kwargs["params"]["entity"] == "light.office"
+
+
+def test_call_service_posts_to_home_assistant() -> None:
+    response = _response(payload=[])
+    client = HaClient(HaClientSettings(url="http://ha.local", token="test-token"))
+    client._session.post = Mock(return_value=response)  # type: ignore[method-assign]
+
+    result = client.call_service("light", "turn_on", {"entity_id": "light.office"})
+
+    assert result == []
+    client._session.post.assert_called_once_with(
+        "http://ha.local/api/services/light/turn_on",
+        json={"entity_id": "light.office"},
+        timeout=10,
+    )
+
+
 @pytest.mark.parametrize(
     ("entity_ids", "start", "end"),
     [

tests/ha/test_ha_reader.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 from datetime import datetime, timezone
 
 from app.ha.client import HaClient, HaClientSettings
+from app.ha.exceptions import HaHttpError
 from app.ha.reader import HaReader
 
 
@@ -15,6 +16,7 @@ class FakeHaClient(HaClient):
             {
                 "entity_id": "sensor.temperature",
                 "state": "21.5",
+                "last_changed": "2026-06-14T12:00:00+00:00",
                 "attributes": {
                     "state_class": "measurement",
                     "device_class": "temperature",
@@ -54,6 +56,29 @@ class FakeHaClient(HaClient):
             }
         }
 
+    def get_logbook(
+        self,
+        entity_id: str,
+        start_time: datetime,
+        end_time: datetime,
+    ) -> list[object]:
+        return [
+            {
+                "entity_id": entity_id,
+                "when": start_time.isoformat(),
+                "message": "turned on",
+                "context_user_id": "user-1",
+            }
+        ]
+
+    def call_service(
+        self,
+        domain: str,
+        service: str,
+        service_data: dict[str, object],
+    ) -> list[object]:
+        return []
+
 
 def test_ha_reader_returns_summaries() -> None:
     reader = HaReader(FakeHaClient())
@@ -63,6 +88,8 @@ def test_ha_reader_returns_summaries() -> None:
     assert domains == {"sensor", "light"}
     sensor = next(item for item in summaries if item.entity_id == "sensor.temperature")
     assert sensor.unit_of_measurement == "°C"
+    assert sensor.state == "21.5"
+    assert sensor.last_changed == datetime(2026, 6, 14, 12, 0, tzinfo=timezone.utc)
     assert sensor.area_name == "Kueche"
     assert sensor.device_name == "Thermometer"
 
@@ -87,3 +114,60 @@ def test_ha_reader_normalizes_history() -> None:
 
     assert history[0].entity_id == "sensor.temperature"
     assert history[0].points[0].value == 21.5
+
+
+def test_ha_reader_normalizes_state_history_and_logbook() -> None:
+    reader = HaReader(FakeHaClient())
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    end = datetime(2026, 6, 2, tzinfo=timezone.utc)
+
+    history = reader.read_state_history(["light.living_room"], start, end)
+    logbook = reader.read_logbook("light.living_room", start, end)
+
+    assert history[0].points[0].state == "21.5"
+    assert logbook[0].context_user_id == "user-1"
+
+
+def test_ha_reader_finds_automation_that_targets_entity() -> None:
+    client = FakeHaClient()
+    client.list_entities = lambda: [  # type: ignore[method-assign]
+        {
+            "entity_id": "automation.storage_light",
+            "state": "on",
+            "attributes": {
+                "id": "123",
+                "friendly_name": "Storage light",
+            },
+        }
+    ]
+    client.get_automation_config = lambda automation_id: {  # type: ignore[method-assign]
+        "id": automation_id,
+        "target": {"entity_id": "light.storage"},
+    }
+    reader = HaReader(client)
+
+    matches = reader.find_automations_for_entity("light.storage")
+
+    assert len(matches) == 1
+    assert matches[0].entity_id == "automation.storage_light"
+    assert matches[0].enabled is True
+
+
+def test_ha_reader_ignores_automation_configs_not_exposed_by_ha() -> None:
+    client = FakeHaClient()
+    client.list_entities = lambda: [  # type: ignore[method-assign]
+        {
+            "entity_id": "automation.storage_light",
+            "state": "on",
+            "attributes": {
+                "id": "123",
+                "friendly_name": "Storage light",
+            },
+        }
+    ]
+    client.get_automation_config = lambda automation_id: (_ for _ in ()).throw(  # type: ignore[method-assign]
+        HaHttpError(404, "Resource not found")
+    )
+    reader = HaReader(client)
+
+    assert reader.find_automations_for_entity("light.storage") == []

tests/ha/test_history.py

@@ -5,7 +5,11 @@ from datetime import datetime, timezone
 import pytest
 
 from app.ha.exceptions import HaUnexpectedPayloadError
-from app.ha.history import normalize_history_payload
+from app.ha.history import (
+    normalize_history_payload,
+    normalize_logbook_payload,
+    normalize_state_history_payload,
+)
 
 
 def test_normalize_history_payload_groups_and_sorts_numeric_states() -> None:
@@ -90,3 +94,46 @@ def test_normalize_history_payload_rejects_malformed_structure(payload: object)
 
 def test_normalize_history_payload_accepts_empty_series() -> None:
     assert normalize_history_payload([[]]) == []
+
+
+def test_normalize_state_history_keeps_categorical_changes() -> None:
+    result = normalize_state_history_payload(
+        [
+            [
+                {
+                    "entity_id": "light.office",
+                    "state": "off",
+                    "last_changed": "2026-06-01T08:00:00+00:00",
+                },
+                {
+                    "state": "on",
+                    "last_changed": "2026-06-01T08:05:00+00:00",
+                },
+                {
+                    "state": "on",
+                    "last_changed": "2026-06-01T08:06:00+00:00",
+                },
+            ]
+        ]
+    )
+
+    assert [point.state for point in result[0].points] == ["off", "on"]
+
+
+def test_normalize_logbook_preserves_action_origin() -> None:
+    result = normalize_logbook_payload(
+        [
+            {
+                "entity_id": "light.office",
+                "when": "2026-06-01T08:05:00+00:00",
+                "message": "turned on",
+                "context_user_id": "user-1",
+                "context_domain": "light",
+                "context_service": "turn_on",
+            }
+        ],
+        "light.office",
+    )
+
+    assert result[0].context_user_id == "user-1"
+    assert result[0].context_service == "turn_on"

tests/test_addon_config.py

@@ -0,0 +1,18 @@
+from pathlib import Path
+
+
+def test_addon_does_not_expose_internal_learning_parameters() -> None:
+    config = Path("addon/config.yaml").read_text(encoding="utf-8")
+
+    assert "\noptions:" not in config
+    assert "\nschema:" not in config
+    assert "prediction_confidence" not in config
+    assert "execution_cooldown_seconds" not in config
+
+
+def test_addon_version_invalidates_application_build_layer() -> None:
+    dockerfile = Path("addon/Dockerfile").read_text(encoding="utf-8")
+
+    config_copy = dockerfile.index("COPY config.yaml /tmp/addon-config.yaml")
+    repository_clone = dockerfile.index("git clone --depth 1 --branch main")
+    assert config_copy < repository_clone

tests/test_config.py

@@ -15,6 +15,12 @@ def test_load_settings_reads_documented_environment(monkeypatch: MonkeyPatch) ->
     monkeypatch.setenv("SILLYHOME_MIN_TRAINING_POINTS", "12")
     monkeypatch.setenv("SILLYHOME_RETRAIN_STALE_HOURS", "48")
     monkeypatch.setenv("SILLYHOME_RECONCILE_INTERVAL_SECONDS", "600")
+    monkeypatch.setenv("SILLYHOME_MIN_BEHAVIOR_ACTIONS", "4")
+    monkeypatch.setenv("SILLYHOME_PREDICTION_CONFIDENCE", "0.9")
+    monkeypatch.setenv("SILLYHOME_PREDICTION_WINDOW_MINUTES", "20")
+    monkeypatch.setenv("SILLYHOME_PREDICTION_INTERVAL_SECONDS", "45")
+    monkeypatch.setenv("SILLYHOME_EXECUTION_COOLDOWN_SECONDS", "1200")
+    monkeypatch.setenv("SILLYHOME_TIMEZONE", "Europe/Berlin")
 
     settings = load_settings()
 
@@ -27,4 +33,10 @@ def test_load_settings_reads_documented_environment(monkeypatch: MonkeyPatch) ->
     assert settings.min_training_points == 12
     assert settings.retrain_stale_hours == 48
     assert settings.reconcile_interval_seconds == 600
+    assert settings.min_behavior_actions == 4
+    assert settings.prediction_confidence == 0.9
+    assert settings.prediction_window_minutes == 20
+    assert settings.prediction_interval_seconds == 45
+    assert settings.execution_cooldown_seconds == 1200
+    assert settings.timezone == "Europe/Berlin"
     assert settings.ha_configured

tests/test_dashboard.py

@@ -9,4 +9,28 @@ def test_dashboard_is_served_at_root() -> None:
 
     assert response.status_code == 200
     assert "SillyHome Next" in response.text
-    assert "Automation-Entwurf" in response.text
+    assert "So gehst du vor" in response.text
+    assert "Gerät zum Lernen auswählen" in response.text
+    assert "Entitätsname oder Gerät aus Home Assistant" in response.text
+    assert "Oder aus Liste wählen" in response.text
+    assert "Liste durchsuchen" in response.text
+    assert "Wie gewohnt bedienen" in response.text
+    assert "Ohne deine spätere Freigabe wird nichts geschaltet" in response.text
+    assert "Du wählst keine Sensoren und erstellst keine Regeln" in response.text
+    assert "Freigabestatus" in response.text
+    assert "SillyHome übernehmen lassen" in response.text
+    assert "Passende Home-Assistant-Automationen" in response.text
+    assert "Pausieren" in response.text
+    assert "Davon erkannte HA-Automationen" in response.text
+    assert "Aktuelle Situation auswerten" in response.text
+    assert "Kontext selbst festlegen" in response.text
+    assert "Entity-IDs manuell ergänzen" in response.text
+    assert "manual-context-freeform" in response.text
+    assert "Diese Kontext-Auswahl speichern" in response.text
+    assert "manual-context-select" in response.text
+    assert "Die Prüfung simuliert keinen Sensorwechsel" in response.text
+    assert "Kein frischer passender Sensorwechsel erkannt" in response.text
+    assert "Vorhersage jetzt prüfen" not in response.text
+    assert "record.behavior.activation_ready" in response.text
+    assert "Automation-Entwurf" not in response.text
+    assert "Manuelle Overrides" not in response.text

tests/test_main.py

@@ -0,0 +1,148 @@
+import asyncio
+from collections.abc import Sequence
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import anyio
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+from app.actuators.store import ActuatorStore
+from app.behavior.engine import BehaviorEngine
+from app.ha.models import HaEntitySummary
+from app.ha.reader import HaReader
+from app.main import _ha_event_listener, app as fastapi_app, lifespan
+
+
+class _FakeWebSocket:
+    def __init__(self, messages: list[str | BaseException]) -> None:
+        self._messages = messages
+        self.sent: list[dict[str, object]] = []
+
+    async def __aenter__(self) -> "_FakeWebSocket":
+        return self
+
+    async def __aexit__(self, *args: object) -> None:
+        return None
+
+    async def recv(self) -> str:
+        message = self._messages.pop(0)
+        if isinstance(message, BaseException):
+            raise message
+        return message
+
+    async def send(self, message: str) -> None:
+        import json
+
+        self.sent.append(json.loads(message))
+
+
+class _RecordingBehaviorEngine(BehaviorEngine):
+    def __init__(self, tmp_path: Path) -> None:
+        super().__init__(
+            ha_reader=MagicMock(),
+            store=ActuatorStore(tmp_path / "actuators"),
+            settings=MagicMock(),
+        )
+        self.state_changes: list[
+            tuple[str, dict[str, object] | None, Sequence[HaEntitySummary] | None]
+        ] = []
+
+    def handle_state_change(
+        self,
+        entity_id: str,
+        new_state: dict[str, object] | None,
+        *,
+        current_entities: Sequence[HaEntitySummary] | None = None,
+    ) -> None:
+        self.state_changes.append((entity_id, new_state, current_entities))
+
+
+class _FakeHaReader(HaReader):
+    def __init__(self) -> None:
+        pass
+
+    def read_entities(self) -> list[HaEntitySummary]:
+        return [
+            HaEntitySummary(
+                entity_id="light.test",
+                domain="light",
+                state="off",
+            )
+        ]
+
+
+def test_ha_event_listener_processes_state_change(tmp_path: Path) -> None:
+    async def run_test() -> None:
+        fake_ws = _FakeWebSocket(
+            [
+                '{"type":"auth_required"}',
+                '{"type":"auth_ok"}',
+                (
+                    '{"type":"event","event":{"event_type":"state_changed",'
+                    '"data":{"entity_id":"light.test","new_state":{"state":"on"}}}}'
+                ),
+                asyncio.CancelledError(),
+            ]
+        )
+
+        with patch("websockets.connect", return_value=fake_ws) as connect:
+            try:
+                await _ha_event_listener(mock_app, mock_client)
+            except asyncio.CancelledError:
+                pass
+
+        connect.assert_called_once_with(
+            "ws://homeassistant:8123/api/websocket",
+            ping_interval=None,
+        )
+        assert fake_ws.sent == [
+            {"type": "auth", "access_token": "test-token"},
+            {"id": 1, "type": "subscribe_events", "event_type": "state_changed"},
+        ]
+
+    mock_app = MagicMock()
+    mock_app.state.settings = MagicMock()
+    mock_app.state.settings.ha_url = "http://homeassistant:8123"
+    mock_app.state.settings.ha_token = "test-token"
+    mock_app.state.ws_status = MagicMock()
+    mock_engine = _RecordingBehaviorEngine(tmp_path)
+    mock_app.state.behavior_engine = mock_engine
+    mock_app.state.ha_reader = _FakeHaReader()
+    mock_store = ActuatorStore(tmp_path / "store")
+    mock_app.state.actuator_store = mock_store
+    mock_client = MagicMock()
+
+    anyio.run(run_test)
+    assert len(mock_engine.state_changes) == 1
+    entity_id, new_state, current_entities = mock_engine.state_changes[0]
+    assert entity_id == "light.test"
+    assert new_state == {"state": "on"}
+    assert current_entities == [
+        HaEntitySummary(entity_id="light.test", domain="light", state="on")
+    ]
+    assert mock_app.state.ws_status.status == "connected"
+    assert mock_app.state.ws_status.error is None
+
+
+def test_lifespan_skips_event_listener_without_ha_config() -> None:
+    app = FastAPI()
+    app.state.settings = MagicMock()
+    app.state.settings.ha_configured = False
+
+    async def run_test() -> None:
+        async with lifespan(app):
+            pass
+
+    anyio.run(run_test)
+
+
+def test_websocket_health_returns_unavailable_without_listener() -> None:
+    with TestClient(fastapi_app) as client:
+        response = client.get("/health/websocket")
+
+    assert response.status_code == 200
+    assert response.json() == {
+        "status": "unavailable",
+        "error": "WebSocket-Listener nicht initialisiert",
+    }