Make AI do the work on your computer—not just talk about it
Open source, single Go binary that runs on your computer. From live research and file work to automation,
Agenvoy takes action and delivers results; through MCP, it shares sandboxed tools with Claude Code, Codex, and other agents.
Agenvoy is a Taiwan-developed AI Agent Harness built to turn conversation into completed work on your computer. It coordinates models, context, tools, real-time data, task routing, memory, schedules, and execution in one workflow, while keeping control of your files and environment in your hands. Through the Web interface, it also supports hands-free voice interaction with natural speech, wake-word detection, and interruptible spoken replies; the full result remains available in the chat.
A chat can give you an answer; work needs a result. Agenvoy breaks requests into steps, calls tools, and delivers outcomes on your computer—while you retain control of files, tools, schedules, and working context.
- Turns conversation into deliverable work — Research live data, organize files, and complete multi-step tasks with an agent that acts and reports the result.
- Fills capability gaps itself — Creates, tests, and keeps a new tool when no suitable one exists, ready to reuse next time.
- Shares one tool library across agents — Agenvoy, Claude Code, Codex, and other agents use the same sandboxed tools instead of rebuilding them.
- Keeps automation running — Create schedules in one sentence; recurring work runs in your environment and pushes the result.
- Makes every step visible and controllable — Command output streams to the TUI and Web dashboard, while sensitive paths and restricted actions still require confirmation.
- Connects models and external services freely — Route models by task and configure image generation, STT, TTS, stdio/HTTP MCP servers, and OAuth.
- Provides private access from anywhere — The local daemon connects outward to Telegram and Discord, without making your host public or opening inbound ports.
Agenvoy has already implemented the design directions below, and other teams have since adopted similar approaches, confirming that these directions solve real problems agents face in practice. Every milestone links to a public commit or release, so you can check it yourself.
