Home Assistant

RuView is an open-source Wi-Fi sensing platform from ruvnet that uses Channel State Information (CSI) captured by low-cost ESP32 sensors to turn disturbances in radio waves into spatial data. It detects presence and occupancy through walls, estimates breathing and heart rate without contact, recognizes activities such as walking, sitting, gestures, and falls, and uses RF fingerprinting to identify rooms and environmental changes. The platform also supports camera-free pose estimation, CSI recording and model training, and loading RVF and LoRA profiles. RuView can publish sensing data to Home Assistant through MQTT and integrate with Apple Home, Google Home, Amazon Alexa, and SmartThings through supported bridges and Matter. Its repository also documents local automation, room-level semantic states, and the RuView MetaHarness, an AI-assisted workflow for onboarding, calibration, training, verification, and checking sensing claims.

Renfield is a self-hosted, voice-first AI household assistant built with FastAPI, React, and Ollama. It uses Raspberry Pi Zero 2 W voice satellites with ReSpeaker HATs for local wake-word detection, Whisper speech recognition, Piper text-to-speech, and SpeechBrain speaker recognition. The system can track room presence through BLE scanning, voice recognition, and web authentication, and integrates with Home Assistant and DLNA for multi-room control. Its ReAct agent chains tools through 10 MCP servers covering weather, web search, news, calendars, Jellyfin, DLNA, n8n workflows, Home Assistant, Paperless, email, and related services. It also provides conversational long-term memory with contradiction detection, a knowledge graph with validated entity-relation triples, and a RAG knowledge base that combines dense pgvector embeddings with BM25 full-text search using reciprocal-rank fusion. Documents can be uploaded from formats including PDF, DOCX, PPTX, XLSX, HTML, Markdown, and TXT, with Tesseract or EasyOCR available for scanned or poor-quality PDFs. The project is designed to run entirely on local hardware without cloud dependencies and includes a web interface, satellite monitoring, proactive notifications, plugin hooks, and Paperless document-audit workflows. The repository describes more than 100 available tools and supports Docker-based self-hosting.

Home Assistant ESP Screens is an MIT-licensed firmware and Home Assistant add-on for turning supported ESP32 touchscreen panels into configurable control screens. It supports the 2.8-inch CYD and 4-inch Guition panels, with tiles for lights, climate, blinds and curtains, vacuums, media, weather, history graphs, cameras, clocks, and timers. The ESP Screen Manager app runs inside Home Assistant. Users select entities and actions, arrange tiles by dragging them across pages, and save the layout to the screen without writing YAML, using MQTT, or manually reflashing for ordinary changes. The app builds and installs firmware over USB, sends updates over Wi-Fi, and can update screens through OTA; the firmware uses LVGL and communicates with Home Assistant through ESPHome. Screens can also display alerts, camera images on supported Guition hardware, dark mode, brightness, standby, night-hour settings, and automation-controlled actions.

Homelable is a self-hosted homelab infrastructure visualizer that builds interactive network diagrams and monitors device status. Its network scanner runs Nmap service scans against configured CIDR ranges, placing discovered devices in a queue for approval, hiding, or ignoring; it also imports infrastructure from Proxmox, Zigbee, and Z-Wave. Health checks can use ping or TCP probes and health APIs, while diagrams support floor plans, multiple canvases, customizable device styles, and rack layouts with mounted equipment and port-to-port patch cables. It can be installed from pre-built Docker images, as a Proxmox LXC, or from source, and includes an MCP server for AI assistants.

CarWatch is an open-source, offline in-car AI agent from ThinkOffApp that runs on a Raspberry Pi 5 with a locally hosted language model. It joins chat rooms as a vehicle agent, answers questions from the car's owner's manual using lexical retrieval-augmented generation and page citations, and reads live vehicle and system data while stating when information cannot be sensed. A continuous energy-based voice listener and whisper.cpp provide local speech input without a wake word, with responses played through the car's speakers. The system can monitor OBD and engine data, send departure and arrival messages, trip summaries, and dashcam clips, and expose a phone dashboard for approvals, replies, model selection, and maintenance. The repository describes systemd-managed services for the model server, room agent, voice listener, dashboard, and engine watcher, with Python standard-library code and no cloud subscription required.