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Peekaboo 🫣 — Mac automation that sees the screen and does the clicks.

CI npm GitHub release macOS 15+ Swift 6.2 Node License Homebrew Ask DeepWiki

Peekaboo is a macOS CLI and menu-bar app for screen capture, accessibility inspection, and native UI automation. Use it directly, let its agent plan multi-step work, or expose the same toolset to MCP clients.

Peekaboo banner

Install

The released CLI and app require macOS 15 or later.

CLI with Homebrew

brew install steipete/tap/peekaboo

MCP package with npm

The npm package requires Node.js 22 or later and includes the CLI plus its MCP launcher.

npx -y @steipete/peekaboo --version

See MCP setup to connect it to Codex, Claude Code, Cursor, or another MCP client.

Mac app

Download the signed DMG from the latest GitHub release. The menu-bar app provides permission onboarding, visual feedback, and agent sessions; install the CLI separately when you also need peekaboo on PATH.

For source builds and alternative install details, see the installation guide.

Quick start

Check the permissions available to Peekaboo, then take a screenshot:

peekaboo permissions status
peekaboo image --mode screen --path /tmp/peekaboo-screen.png

Screen capture requires Screen Recording permission. Accessibility permission enables UI inspection and control; the permissions guide covers setup and the additional permission used for synthetic input.

Inspect a running app to get a structured UI map with opaque element IDs:

peekaboo see --app Finder --json

That is the core loop: observe the current screen, choose an element from the result, and act on it.

Automate an app

Target an element by its accessible label, then send text to the same app:

peekaboo click "Address and search bar" --app Safari
peekaboo type "github.com/openclaw/Peekaboo" --app Safari --return

Targeted input uses background delivery when Peekaboo can resolve the process, so the app does not have to become frontmost. Add --foreground when an app only accepts input in its focused key window. See the automation guide for element IDs, coordinates, snapshots, waits, and input behavior.

Agent and MCP

The agent combines the same observation and action tools into a natural-language run:

peekaboo agent "Open Safari, go to github.com, and search for Peekaboo"

Agent runs need a configured model provider. See agent setup for providers and sessions, or MCP setup to expose Peekaboo's tools to another client.

Command map

Goal Commands Guide
Observe the desktop image, see, list, screen Capture and inspection
Interact with UI click, type, press, scroll, drag, set-value, perform-action Automation
Control macOS app, window, menu, menubar, dock, dialog, space Command reference
Run workflows agent, run Agent · Scripts
Integrate with clients mcp, browser, tools MCP

Run peekaboo help <command> for live CLI help. The complete command index links to flags, examples, and troubleshooting for every command.

Configuration

Peekaboo stores provider credentials and settings under ~/.peekaboo. Use peekaboo config to inspect or change them, and consult the configuration guide for profiles, environment variables, and custom providers. The provider reference covers hosted, compatible, and local model backends.

Shell completions for zsh, bash, and fish come from peekaboo completions; see the completion guide for persistent setup.

Learn more

Community

  • PeekabooWin — Windows-first rewrite of the Peekaboo automation loop (JavaScript + PowerShell) by @FelixKruger
  • PeekabooX — Linux-first rewrite of the Peekaboo automation loop (Rust + Python) by @nordbyte

Development

Source builds require macOS 15 or later, Swift 6.2 or later, Node.js 22 or later, and the repository's submodules.

pnpm install --frozen-lockfile
pnpm run build:cli
pnpm run lint:docs
pnpm run test:safe

More build, signing, and test details live in docs/building.md.

License

MIT. See LICENSE.

About

Peekaboo is a macOS CLI & optional MCP server that enables AI agents to capture screenshots of applications, or the entire system, with optional visual question answering through local or remote AI models.

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