AnovaX: A Local, Multi-Agent Voice Assistant with LLM Planning, Typed Executors, and Adaptive Recovery
AnovaX is a local-first desktop voice assistant that runs entirely on the user's computer. It integrates a wake-word gate, speech pipeline, LLM planner (Gemini) emitting JSON plans, safety layer, multi-agent orchestrator with typed child agents on a bounded thread pool, and an adaptive recovery loop. Each tool is a specialized agent class with its own timeout and retry policy. A Flask server enables phone remote control over local WiFi, mirroring agent events and streaming the screen. The project demonstrates that a legible, few-thousand-line assistant can handle complex desktop tasks without cloud dependence.
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[Submitted on 16 Jul 2026]
Title:AnovaX: A Local, Multi-Agent Voice Assistant with LLM Planning, Typed Executors, and Adaptive Recovery
View a PDF of the paper titled AnovaX: A Local, Multi-Agent Voice Assistant with LLM Planning, Typed Executors, and Adaptive Recovery, by Raunak B Sinha
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Abstract:Desktop voice assistants are still dominated by cloud pipelines that ship raw audio off the machine and expose a fixed set of skills. We describe AnovaX, a small local-first assistant that runs entirely on the user's computer and treats the desktop itself as its action surface. A single Python process wires together a wake-word gate, a speech pipeline, an LLM planner (Gemini) that emits a JSON plan of tool calls, a whitelist-and-denylist safety layer, a multi-agent orchestrator that translates each plan into typed child agents on a bounded thread pool, and an adaptive recovery loop that takes over whenever a core step fails. Every tool corresponds to a specialized agent class (AppAgent, TypingAgent, BrowserAgent and six others) with its own timeout, retry policy, and shared-resource locks. A recursive MetaAgent lets the planner delegate a sub-goal back to itself, capped at two levels of nesting. The recovery loop uses a compact ReAct-style prompt and hides Gemini's latency behind speculative execution of read-only tools. A companion Flask server exposes a phone-friendly remote over the local WiFi, mirrors every agent lifecycle event to the phone in real time, and streams the laptop's screen back over MJPEG so the user can watch remote commands land as they run. The point of the project is less to compete with Siri or Alexa than to show that a legible, few-thousand-line assistant is enough to open apps, type into them, run searches, coordinate concurrent actions, recover from single-step failures, and be driven entirely from a phone in another room -- without the LLM ever touching the keyboard.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.15367 [cs.AI]
(or arXiv:2607.15367v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.15367
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Raunak B Sinha [view email] [v1] Thu, 16 Jul 2026 18:09:36 UTC (17 KB)
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