Veda is a voice agent designed to remain present on the desktop as a small taskbar ring, waking when users say its name or press Win+Space. It can converse, answer follow-up questions, and operate applications, files, windows, media, settings, timers and reminders through keyboard- and mouse-equivalent actions. Speech recognition, conversation handling and voice are supplied by Deepgram services; the default configuration uses Nova-3 for recognition, Flux for turn detection, Aura-2 Helena for speech, and OpenAI’s gpt-4.1-mini as the language model, while settings can be changed through the Agent panel. The agent keeps a microphone listener open for wake-word detection, but local voice activity detection prevents audio from being sent while nobody is speaking. Recognition includes pre-roll audio, background-noise handling, wake-name matching and a ten-minute-per-hour cap. Once awake, a Voice Agent conversation streams 16 kHz microphone audio and 24 kHz voice output, ducks other audio, buffers network bursts and flushes queued speech when the user interrupts. Echo cancellation and a separate echo gate reduce the risk that the agent hears its own voice, while still allowing louder overlapping speech through. Veda exposes tools for desktop status, applications, windows, files, volume, Wi-Fi, wallpaper, notifications, timers, memory and system tasks. Routine actions run immediately; sensitive and destructive actions require approval, with destructive actions confirmed every time and requests expiring after three minutes. Process identity limits which components can approve requests or change settings, and application content is treated as data rather than instructions. Personal memory and recent conversation information remain in a private local JSON file, while the API key is kept in a private directory and excluded from the model prompt. Applications can become compatible by registering descriptions, state and explicit actions through the platform’s agent interface. The project includes host tests and deterministic agent simulators, but real Deepgram tests require an API key and incur charges. Current limitations include English-only operation, possible speech-recognition errors, dependence on Internet access and a Deepgram account, and reduced ability to interrupt the agent when speakers are very close to the microphone.
