Understudy – Teach a desktop agent by demonstrating a task once
I built Understudy because a lot of real work still spans native desktop apps, browser tabs, terminals, and chat tools. Most current agents live in only one of those surfaces. Understudy is a local-first desktop agent runtime that can operate GUI apps, browsers, shell tools, files, and messaging in one session. The part I'm most interested in feedback on is teach-by-demonstration: you do a task once, the agent records screen video + semantic events, extracts the intent rather than coordinates, and turns it into a reusable skill. Demo video:…
In plain words
Understudy is a local-first desktop agent that operates across native apps, browsers, terminals, and messaging tools in a single session. Users can teach it tasks by demonstrating them once—the agent records the action, extracts intent rather than specific coordinates, and converts it into a reusable skill. It's designed for people whose work spans multiple applications and need automation that understands intent across these different surfaces, not brittle coordinate-based macros.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I built Understudy because a lot of real work still spans native desktop apps, browser tabs, terminals, and chat tools. Most current agents live in only one of those surfaces. Understudy is a local-first desktop agent runtime that can operate GUI apps, browsers, shell tools, files, and messaging in one session. The part I'm most interested in feedback on is teach-by-demonstration: you do a task once, the agent records screen video + semantic events, extracts the intent rather than coordinates, and turns it into a reusable skill. Demo video: https://www.youtube.com/watch?v=3d5cRGnlb_0 In the demo I teach it: Google Image search -> download a photo -> remove background in Pixelmator Pro -> export -> send via Telegram. Then I ask it to do the same for Elon Musk. The replay isn't a brittle macro: the published skill stores intent steps, route options, and GUI hints only as a fallback. In this example it can also prefer faster routes when they are available instead of repeating every GUI step. Current state: macOS only. Layers 1-2 are working today; Layers 3-4 are partial and still early. npm install -g @understudy-ai/understudy understudy wizard GitHub: https://github.com/understudy-ai/understudy Happy to answer questions about the architecture, teach-by-demonstration, or the limits of the current implementation.
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