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AI tools are changing how we use software — but they still cover only a fraction of our work. Our daily tasks are scattered across browsers, desktop apps, terminals, and messaging tools — each with its own interface and habits, disconnected from each other.

Understudy is a teachable desktop agent. It operates your computer like a human colleague — GUI, browser, shell, file system, all in one local runtime. You show it a task once, it extracts the intent (not just the coordinates), remembers the successful path, discovers faster execution routes over time, and eventually handles routine work on its own. No API integrations required. No workflow builders. Just demonstrate once.

The Five Layers

Understudy is designed as a layered progression — the same journey a new hire takes when they grow into a reliable colleague.

Day 1:    Watches how things are done
Week 1:   Imitates the process, asks questions
Month 1:  Remembers the routine, does it independently
Month 3:  Finds shortcuts and better ways
Month 6:  Anticipates needs, acts proactively

That's why it's called Understudy — in theater, an understudy watches the lead, learns the role, and steps in when needed.

Each of the five layers maps to a stage of this journey:

Layer 1 ┃ Operate Software Natively     Operate any app a human can — see, click, type, verify
────────╋──────────────────────────────────────────────────────────────────────────────────
Layer 2 ┃ Learn from Demonstrations     User shows a task once — agent extracts intent, validates, learns
────────╋──────────────────────────────────────────────────────────────────────────────────
Layer 3 ┃ Crystallized Memory           Agent accumulates experience from daily use, hardens successful paths
────────╋──────────────────────────────────────────────────────────────────────────────────
Layer 4 ┃ Route Optimization            Automatically discover and upgrade to faster execution routes
────────╋──────────────────────────────────────────────────────────────────────────────────
Layer 5 ┃ Proactive Autonomy            Notice and act in its own workspace, without disrupting the user

Current status: Layers 1-2 are implemented and usable today. Layers 3-4 are partially implemented. Layer 5 is still the long-term direction.

Every layer depends on the one below it. No shortcuts — the system earns its way up. Read the full story: Overview → | Chinese Overview → | Product Design →

Showcase

Demo environment: macOS + GPT-5.4 via Codex. See Supported Models for all providers.

The video has been sped up. Full-length original: Google Drive. For inline playback, visit the Overview page.

The published skill artifact generated from this showcase demo is available at examples/published-skills/taught-person-photo-cutout-bc88ec/SKILL.md, so you can inspect the exact output directly.

What It Can Do Today

Layer 1 — Operate Your Computer Natively

Status: implemented today on macOS.

Understudy is not just a GUI clicker. It's a unified desktop runtime that mixes every execution route your computer offers — in one agent loop, one session, one policy pipeline:

Route	Implementation	What it covers
GUI	13 tools + screenshot grounding + native input	Any macOS desktop app
Browser	Playwright managed + Chrome extension relay	Any website, with login sessions
Shell	bash tool with full local access	CLI tools, scripts, file system
Web	web_search + web_fetch	Real-time information retrieval
Memory	Semantic memory across sessions	Persistent context and preferences
Messaging	8 channel adapters	Telegram, Slack, Discord, WhatsApp, Signal, LINE, iMessage, Web
Scheduling	Cron + one-shot timers	Automated recurring tasks
Subagents	Child sessions for parallel work	Complex multi-step delegation

The planner decides which route to use for each step. A single task might browse a website, run a shell command, click through a native app, and send a message — all in one session.

GUI grounding — dual-model architecture: the main model decides what to do, a separate grounding model decides where on screen. HiDPI-aware across Retina displays, with automatic high-res refinement for small targets and two grounding modes (simple prediction or multi-round validation with simulation overlays). Grounding benchmark: 30/30 targets resolved — explicit labels, ambiguous targets, icon-only elements, fuzzy prompts.

For implementation details (coordinate spaces, stabilization, cross-attempt feedback, capture modes), see Product Design.

Layer 2 — Learn from Demonstrations

Status: implemented today. Teach can record a demo, analyze it, clarify the task, optionally validate the replay, and publish a reusable skill.

Explicit teaching: you deliberately show the agent how to do a task. Not a macro recorder — Understudy learns intent, not coordinates.

/teach start                              Start dual-track recording (screen video + semantic events)
                                          → Perform the task once with mouse and keyboard

/teach stop "file the weekly expense report"   AI analyzes the recording → extracts intent, parameters,
                                               steps, success criteria → creates a teach draft
                                               → Multi-turn dialogue to refine the task

/teach confirm [--validate]               Lock the task card; optionally replay to validate

/teach publish <draftId> [skill-name]     Generate SKILL.md → hot-load into active sessions

The published SKILL.md is a three-layer abstraction: intent procedure (natural language steps), route options (preferred / fallback paths), and GUI replay hints (last resort only, re-grounded from the current screenshot each time). UI redesigns, window resizing, even switching to a similar app — as long as the semantic target still exists, the skill works.

A real published output example is available at examples/published-skills/taught-person-photo-cutout-bc88ec/SKILL.md. It is kept under examples/ on purpose, so the repo documents the artifact format without auto-loading it as a real workspace skill.

For the full teach pipeline, evidence pack construction, and validation details, see Product Design.

Layer 3 — Remember What Worked

Status: partially implemented. Understudy now has a working workflow crystallization loop for repeated day-to-day use, but promotion policy and automatic route upgrading are still early.

Implicit learning: as you use Understudy day-to-day, it automatically identifies recurring patterns and crystallizes successful paths — no explicit teaching needed:

Stage	What Happens	User Experience
Stage 0	Full exploration	"The AI is figuring it out"
Stage 1	Remembered path	"It's faster than last time"
Stage 2	Deterministic substeps	"It knows the routine"
Stage 3	Crystallized execution	"One-click"
Stage 4	Proactive triggering	"It did it before I asked"

What the user experiences today

• You use Understudy normally; no /teach command is required.
• Repeated multi-turn work in the same workspace is compacted in the background.
• Once a pattern repeats often enough, Understudy publishes a workspace skill automatically, hot-loads it into active sessions, and sends a notification that a crystallized workflow is ready.
• From that point on, the agent can pick the new skill through the normal ## Skills (mandatory) flow, just like a taught skill.

Current boundaries

• The learning loop is intentionally conservative: if the pattern is not clear enough, Understudy keeps exploring instead of pretending it has learned it.
• Segmentation, clustering, and skill synthesis are currently LLM-first.
• Promotion thresholds are still heuristic rather than fully policy-driven.
• Layer 4 style route upgrading is still limited; crystallization mainly produces reusable skills today rather than fully automatic route replacement.

For the implementation details (crystallization pipeline, segmentation, clustering, skill synthesis), see Product Design.

Layer 4 — Get Faster Over Time

Status: partially implemented. Route preferences, route guard policies, and teach route annotations are working today. Fully automatic route discovery and promotion are still in progress.

The same feature can be accomplished in multiple ways. Take "send a Slack message" as an example:

Fastest ──→  1. API call         Hit the Slack API directly (milliseconds)
             2. CLI tool         slack-cli send (seconds)
             3. Browser          Type and send in Slack's web app (seconds)
Slowest ──→  4. GUI              Screenshot-ground, click, type in the Slack desktop client (seconds~10s)

Starts at GUI on Day 1 — because GUI is the universal fallback that works with any app. As usage accumulates, Understudy progressively discovers faster ways to accomplish the same feature, upgrading after safe verification.

What the user experiences today

• The system prompt explicitly steers the agent toward faster routes: direct tool/API > Shell/CLI > browser > GUI.
• A route guard tracks consecutive failures per route. If one route keeps failing (e.g., GUI grounding is unreliable for a particular app), the agent is prompted to switch to an alternative.
• Teach-produced skills annotate each step with preferred / fallback / observed routes, so the agent can skip GUI when a faster path is available.
• Browser mode auto-falls back: tries Chrome extension relay first, switches to managed Playwright if that fails.
• The GUI capability matrix dynamically enables/disables tool subsets based on available permissions, so the agent never attempts a route it can't execute.

What this looks like in practice

• A newly learned task may start with GUI as the observed fallback path.
• Over time, the agent starts preferring higher-level routes — an existing skill, browser flow, or shell command — when those preserve the same result.
• In practice this usually feels like fewer brittle GUI steps, faster execution, and fewer repeated detours.
• When a better route is uncertain, the agent falls back to the slower but safer path rather than risk a failure.

Current boundaries

• Route optimization today is guidance and safe preference ordering, not a fully autonomous optimizer.
• The agent can prefer a known faster route, but does not yet run a broad automatic search over all possible routes for every recurring task.
• Automatic route promotion (discover → verify → promote to default) is designed but not yet fully implemented.

For the full route selection mechanisms, upgrade policy, and future direction, see Product Design.

Layer 5 — Proactive Autonomy

Status: mostly vision today. Scheduling and runtime surfaces exist, but passive observation, isolated workspace, and proactive autonomy are still ahead.

Earned, not assumed. Understudy's ultimate goal isn't "wait for instructions" — it's becoming a colleague that observes long-term, acts proactively.

Long-term observation and learning — beyond explicit teach, Understudy passively observes your daily operations, analyzes recurring patterns, and understands your work habits and preferences. It's not recording every click — it's understanding what you do each day, when you do it, and which tools you use.

Proactive suggestions — based on accumulated observations, proactively suggests what to do next: emails to process, tasks to follow up on, reports to run. Suggestions are presented non-intrusively; only executed after you confirm.

Isolated workspace — executes tasks in its own desktop, without occupying your screen:

Phase	Implementation	User Experience
Current	Controlled foreground window + app focus	AI completes tasks reliably
Near-term	macOS second desktop / headless window	User can switch to view AI work
Long-term	Docker + VNC / cloud VM	AI works 24/7

Cross-app orchestration — the isolated workspace unlocks true multi-app parallel operation. In its own desktop, Understudy can control multiple apps simultaneously, coordinating data flow between them — extract info from email into a spreadsheet, update the calendar, and send results via Slack. No longer a single-threaded, one-window-at-a-time model.

Progressive trust model — each skill starts at manual, promoted only through sustained success:

Level	Behavior
manual	User triggers every run (default)
suggest	AI suggests, user confirms before execution
auto_with_confirm	AI executes, user reviews result
full_auto	AI executes + verifies, notifies only on exceptions

Architecture

 ┌──────────┐ ┌───────────┐ ┌─────────┐ ┌──────────┐ ┌─────────┐
 │ Terminal  │ │ Dashboard │ │ WebChat │ │ Telegram │ │  Slack  │ ...
 └────┬─────┘ └─────┬─────┘ └────┬────┘ └────┬─────┘ └────┬────┘
      └──────────────┴────────────┴────────────┴────────────┘
                                  │
                    Gateway (HTTP + WebSocket + JSON-RPC)
                                  │
                     ┌────────────┴────────────┐
                     │                         │
              Session Runtime            Built-in Tools
              + Policy Pipeline    ┌──────────────────────┐
                                   │ gui_*  │ browser     │
                                   │ bash   │ memory      │
                                   │ web    │ schedule    │
                                   │ message│ subagents   │
                                   └──────────────────────┘

• Local-first — screenshots, recordings, and traces are stored locally by default; GUI grounding and teach analysis may send selected images or keyframes to your configured model provider
• One gateway — terminal, web, mobile, messaging apps all connect through one endpoint
• One session runtime — chat, teach, scheduled tasks, subagents share the same loop
• Policy pipeline — every tool call passes through safety, trust, and logging hooks

Quick Start

Install from npm

npm install -g @understudy-ai/understudy understudy wizard # walks you through setup

Install from source

git clone https://github.com/understudy-ai/understudy.git cd understudy pnpm install && pnpm build pnpm start -- wizard

Start using it

Recommended: start the gateway daemon, then use the terminal

understudy daemon --start # Start the gateway background process (or: understudy gateway --port 23333) understudy chat # Terminal interactive mode (auto-connects to running gateway)

Management UI

understudy dashboard # Open the control panel in your browser

Other entry points

understudy webchat # Browser chat interface understudy agent --message "..." # Script/CI single-turn call (requires gateway)

Requirements

Required for all installs:

Dependency	Install	Purpose
Node.js >= 20.6	brew install node or nvm	Core runtime

Required for source installs / development:

Dependency	Install	Purpose
pnpm >= 10	corepack enable && corepack prepare pnpm@latest --activate	Workspace package manager

macOS GUI automation (required for GUI tools and teach-by-demonstration on macOS):

Dependency	Install	Purpose
Xcode Command Line Tools	xcode-select --install	Compiles the Swift native helper (swiftc) for screen capture and input events
Accessibility permission	See macOS Permissions	Mouse/keyboard input injection, window queries, demonstration event capture
Screen Recording permission	See macOS Permissions	Screenshots, GUI grounding, demonstration video recording

Optional:

Dependency	Install	Purpose
Chrome	chrome.google.com	Extension-relay browser mode — access logged-in tabs. Without it, falls back to Playwright managed browser
Playwright	pnpm exec playwright install chromium	Managed browser for the browser tool. Installed as optional dependency, needs browser binary download
ffmpeg + ffprobe	brew install ffmpeg	Video analysis for teach-by-demonstration evidence packs
tesseract	brew install tesseract	On-device OCR for vision_read. Without it, OCR is skipped gracefully
signal-cli	brew install signal-cli	Signal messaging channel

Platform Support

Understudy is currently developed and tested on macOS. Core features (CLI, gateway, browser, channels) are cross-platform by design, but native GUI automation and teach-by-demonstration require macOS today. Linux and Windows GUI backends are planned — contributions welcome.

Supported Models

Understudy is model-agnostic. Configure models with provider/model (for example anthropic/claude-sonnet-4-6).

The source of truth is the model catalog bundled with your current Understudy runtime plus any custom entries in your local model registry. That catalog evolves over time, so this README intentionally avoids freezing an exhaustive provider/model matrix.

Use one of these to see what your install can actually use:

understudy models --list understudy wizard

Auth and provider notes:

Provider family	Auth	Notes
Anthropic	ANTHROPIC_API_KEY or OAuth	Claude models available in your current Understudy runtime
OpenAI	OPENAI_API_KEY	GPT-4.x, GPT-5.x, o*, and related OpenAI models available in your current Understudy runtime
OpenAI Codex	OPENAI_API_KEY or OAuth	Codex/Responses models such as gpt-5.4; shares auth with OpenAI
Google / Gemini	GOOGLE_API_KEY or GEMINI_API_KEY	gemini is accepted as a provider alias for google
Additional compatible providers	Provider-specific auth	Depending on your current Understudy runtime, examples may include GitHub Copilot, OpenRouter, xAI, Mistral, Groq, Bedrock, Vertex, and more
Custom providers / models	Provider-specific auth	You can add custom model entries through the underlying model registry

Default: openai-codex/gpt-5.4. The wizard chooses from models detected in your local runtime auth/model registry.

macOS Permissions

Full screenshot-grounded GUI automation on macOS depends on the native helper plus two system permissions. Missing permissions degrade or hide parts of the GUI toolset rather than failing every GUI capability equally.

Accessibility

What it enables: Mouse clicks, typing, drag, scroll, hotkeys against app targets, and demonstration event capture.

If missing: Input-driving tools are blocked. Observation-oriented tools such as screenshots and GUI reads may still work when Screen Recording is available.

How to grant:

1. Open System Settings → Privacy & Security → Accessibility
2. Click the + button, add your terminal app (Terminal.app, iTerm2, VS Code, etc.)
3. Toggle it on

Screen Recording

What it enables: Screenshots for GUI grounding and verification, screenshot-based reads, and demonstration video recording.

If missing: Screenshot-grounded tools are blocked. Keyboard-only tools such as gui_keypress and gui_hotkey can still remain available, and some tools can still operate in targetless mode.

How to grant:

1. Open System Settings → Privacy & Security → Screen Recording
2. Click the + button, add your terminal app
3. Toggle it on
4. Restart your terminal app for the permission to take effect

Both permissions must be granted to the terminal application that runs understudy, not to Understudy itself.

If GUI behavior still looks partial after granting permissions, run understudy doctor --deep to check the native helper, grounding availability, OCR, browser runtime, and teach-analysis dependencies.

Skills

47 built-in skills + workspace skills you create or install.

understudy skills --list # browse available skills understudy skills install <name-or-url> # install from registry or URL

Or teach by demonstration

/teach start → demonstrate → /teach stop → /teach confirm → /teach publish <draftId>

Optional: /teach validate before or after publish

Channels

8 built-in channel adapters: Web, Telegram, Discord, Slack, WhatsApp, Signal, LINE, iMessage.

understudy channels --list understudy channels --add telegram

Repository Layout

apps/cli           CLI entrypoints, 30+ operator commands
packages/core      Agent session runtime, config, auth, skills, policies
packages/gateway   HTTP + WebSocket gateway, session runtime, web surfaces
packages/gui       Native GUI runtime, screenshot grounding, demo recorder
packages/tools     Built-in tools: browser, web, memory, schedule, GUI, message
packages/channels  Channel adapters (8 platforms)
packages/types     Shared TypeScript type definitions
skills/            47 built-in skill modules
examples/          Sample published artifacts and reference outputs
docs/              Vision, product design documentation

Development

pnpm install # install dependencies pnpm build # build all packages pnpm test # run tests pnpm lint # lint with oxlint pnpm typecheck # type-check all packages pnpm check # full validation: build + lint + typecheck + test

Star History

Acknowledgments

Understudy builds on ideas and code from several outstanding open-source projects:

Project	What we learned
OpenClaw	Gateway architecture, multi-channel design, skill ecosystem patterns
pi-mono	Agent core runtime, unified LLM API, TUI infrastructure
NanoClaw	Minimal agent design, extension patterns
OSWorld	GUI agent benchmarking methodology, computer use evaluation
openai-cua-sample-app	Computer use agent reference patterns

Special thanks to Mario Zechner for the pi-agent-core foundation that powers Understudy's agent loop.

Contributing

See CONTRIBUTING.md for guidelines.

We're looking for contributors in these areas:

• GUI backends — Linux (AT-SPI) and Windows (UIA) native GUI support
• Skills — new skill modules for popular apps and workflows
• Route discovery — automatic API detection and upgrade logic (Layer 4)
• Teach improvements — better evidence pack analysis and validation
• Documentation & translations

License

MIT

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Watch → Learn → Remember → Optimize → Anticipate

That's the whole product.