Source: MarkTechPost
Vercel Labs has released agent-desktop, an open-source CLI for AI agent computer use on macOS, Windows and Linux. agent-desktop reads each app through the operating system’s accessibility tree, not through screenshots. An agent gets a JSON snapshot with a ref for every element it can act on. Version 1.0.2 shipped on October 30, 2026, under Apache-2.0. For developers, any shell-capable coding agent can now drive native apps without a vision model.
TL;DR
- Library: Rust CLI and a C library. 60 command names, 56 operational.
- Runs on: macOS 13+, Windows 10 1809+ or Server 2019+, Ubuntu 24.04+, on x64 and ARM64. Plain CPU, no GPU.
- Performance: Deterministic, retry-safe ref actions. No independent computer-use benchmark yet.
- Best: 78% to 96% fewer tokens on dense Electron apps via skeleton traversal (self-reported).
What is agent-desktop?
agent-desktop is a Rust CLI and C library from Vercel Labs for reading and operating desktop apps. It is a tool that agents call, not an agent. It embeds no LLM and has no GUI. Every command prints one JSON envelope and exits. The observe, act and verify loop stays in your agent.
How does agent-desktop work?
Each command flows through a fixed chain. The CLI or C library calls agent-desktop-core, which reaches the OS only through a PlatformAdapter trait. One platform crate then talks to the native accessibility API:
- macOS: AX API
- Windows: UI Automation
- Linux: AT-SPI
Core never imports a platform crate. Each binary is compiled to drive exactly one OS.
Snapshots and refs
A snapshot assigns qualified refs such as @s8f3k2p9:e8. The snapshot ID inside the ref makes it self-describing. A bare @e8 is rejected with INVALID_ARGS. When the UI changes, an action returns STALE_REF or AMBIGUOUS_TARGET. The agent then takes a fresh snapshot.
Every error carries a disposition. Agents should retry only when it says safe. Ref resolution is strict and fails closed rather than guessing.
Skeleton and drill-down
Dense apps can expose thousands of accessibility nodes. A --skeleton snapshot reads only 3 levels deep. Cut containers report a children_count and get a drill ref. snapshot --root then reads one region into the same snapshot.
The builder reported a 78% to 96% token cut versus full-tree dumps in Slack, VS Code and Notion. In Apple Maps, he reported 3,811 tokens per look falling to 273. A simulated 52-step task dropped from 198k to 31k tokens. These are self-reported figures, not an independent benchmark.
Headless by default
Ref actions go through the accessibility API without moving the pointer or taking focus. That is what lets agents work while you keep using the machine. The global --headed flag allows physical input when needed. Every ref action also auto-waits up to 5,000 ms for a visible, enabled, stable target.
How does it compare with pixel-based computer use?
Model-side computer use tools work from screenshots and pixel coordinates. Anthropic’s docs say each screenshot costs roughly 1,000 to 1,800 input tokens. Its toolset definition adds about 4,500 input tokens per request. OpenAI’s tool similarly returns click, type and scroll actions your code executes.
agent-desktop is model-agnostic and returns structure, not images. Microsoft’s UFO² also uses UI Automation, but ships as a full Windows agent.
| Feature | agent-desktop (Vercel Labs) | Claude computer use | OpenAI computer use | Microsoft UFO² |
|---|---|---|---|---|
| Type | CLI + C library tool | API client toolset | API tool | Full agent framework |
| How it sees the UI | OS accessibility tree | Screenshots | Screenshots or code (PyAutoGUI, Playwright) | UIA + visual hybrid |
| Model bundled | None, any agent | Claude models | OpenAI models | Configurable LLM |
| Platforms | macOS, Windows, Linux | Any env you host (reference: Linux Docker) | Browser or desktop you host | Windows (UFO²) |
| Moves your cursor | No, headless by default | Yes, pixel clicks | Yes, pixel clicks | GUI + API actions |
| Token cost per look | 273 (Apple Maps skeleton, self-reported) | ~1,000 to 1,800 per screenshot | Not disclosed | Not disclosed |
| License | Apache-2.0 | Proprietary API | Proprietary API | MIT |
| Price | Free | Standard token pricing | Standard token pricing | Free (plus LLM costs) |
How do you install and use agent-desktop?
Install with npm install -g agent-desktop. Bun, Yarn and pnpm work too. The package bundles native binaries for x64 and ARM64.
- macOS: grant Accessibility permission with
agent-desktop permissions --guide. - Windows: no grant needed for apps at your terminal’s integrity level.
- Linux: needs a graphical session with
at-spi2-core.
Agents learn the tool from bundled docs via agent-desktop skills get desktop. A skill is also listed on skills.sh. The C library lets Python, Swift, Go, Ruby and Node hosts call it in process. For Electron web contents, launch --cdp opens a DevTools port for agent-browser.
Key Takeaways
- agent-desktop gives any agent computer use through OS accessibility trees.
- It runs on macOS, Windows and Linux, x64 and ARM64, under Apache-2.0.
- Headless ref actions never move your pointer or steal focus.
- Skeleton snapshots cut tokens 78% to 96% (self-reported).
- Apps without an accessibility tree remain out of reach.
Check out the GitHub repo, docs and npm package. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Michal Sutter
Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.