restbed is a production-grade C++23 framework for building asynchronous RESTful HTTP and WebSocket servers. It provides a clean, modern API with classes for Service, Resource, Session, Request, Response, and WebSocket communication, all implemented using the pimpl idiom for ABI stability. The framework handles HTTP/1.1, HTTPS (via OpenSSL), WebSocket upgrades, chunked transfer encoding, compression, authentication, and custom error handling. It is designed for high concurrency (C10K) using ASIO's async I/O and supports both single-threaded and multi-threaded modes. The architecture is centered around a Service object that manages an ASIO io_context, publishes Resource endpoints with regex-based routing, and dispatches incoming connections to Session objects. Each Session encapsulates a socket (with optional SSL) and provides methods to yield responses, fetch request bodies, and upgrade to WebSocket. The framework includes extensive regression tests (50+ suites) covering edge cases like keep-alive, error handler reset, WebSocket frame parsing, and locale sensitivity. Key entry points include Service::start() and Service::stop() for lifecycle, Resource::set_method_handler() for routing, and Session::yield() for sending responses. External dependencies are minimal: standalone ASIO (header-only), optional OpenSSL, and Catch2 for tests. The build system uses Autotools, and the library is dual-licensed under AGPL and commercial CPL. Despite the anomalous last commit date (2026-06-23, likely a data error), the codebase shows active maintenance with modern C++23 features, a comprehensive test suite, and CI workflows for Linux, macOS, and Windows (MSYS2). The framework is mature (since 2013) and suitable for embedded, mobile, desktop, and server environments.
This repository implements a Linux-based voice satellite for Home Assistant, acting as a local voice assistant that connects to Home Assistant via the ESPHome protocol (aioesphomeapi). The application runs as a Python asyncio service, handling wake word detection using TensorFlow Lite models (MicroWakeWord and OpenWakeWord), audio input via the soundcard library, audio output via libmpv, and optional WebRTC noise suppression/gain. It also provides a WebSocket-based Peripheral API (port 6055) that allows external hardware controllers (LED rings, buttons, HAT boards) to register entities and receive real-time state events. The system is deployed via Docker with docker-compose and includes example peripheral implementations for devices like the ReSpeaker HATs, Jabra Speak 410, and Satellite1 HAT. The architecture is modular, with separate modules for the ESPHome API server (api_server.py), core voice pipeline protocol (satellite.py), entity management (entity.py), wake word loading (wake_word.py), audio playback (player/libmpv.py), and peripheral integration (peripheral_api.py). The application is configured via environment variables and command-line arguments, with preferences persisted to a JSON file. The codebase includes unit tests for most modules, CI/CD workflows, and a Docker build pipeline. External dependencies include aioesphomeapi, websockets, soundcard, python-mpv, webrtc-noise-gain, pymicro-wakeword, pyopen-wakeword, and zeroconf. Key data flows: Microphone audio is captured, optionally processed by WebRTC for noise suppression and gain, then fed into wake word detection. Upon wake word detection, the voice pipeline sends audio to Home Assistant for speech recognition and intent handling. Responses (TTS, media) are received and played via MPV. The Peripheral API allows external hardware to send button events and receive state changes (e.g., listening, thinking, muted). The system supports multiple wake words, stop word detection, timers, and media playback with playlist support.