---

See it work

Three real runs. The prompt is the whole input.

Desktop · Task: "Uninstall VLC media player."

No selectors and no script. The agent searches Finder, opens the right context menu and checks the screen after every action.

Web · Task: "Why is AMD stock price going up?"

The agent researches it live, records each finding as it confirms it, and answers with six sourced points.

Code · Task: "Fetch the latest Nvidia, Samsung and Apple quarterly results, chart them, and save a report for each."

The desktop agent hands all three jobs to its coding agent at once. Each writes Python, runs it, and saves its own report.

---

Pick a surface

One string decides what the agent drives. Everything else stays the same.

from Auto_Use.agent_launcher import run_agent

run_agent(mode="web use", provider="openrouter", model="gemini-3.8-flash",
          task="Open wikipedia.org and summarise today's featured article.")

mode	What it drives	Where it runs
"computer use"	The desktop. Clicks, typing, hotkeys, apps, AppleScript	macOS or Windows, auto detected
"shell use"	A coding agent, straight into a real terminal	macOS or Windows
"web use"	A real Chrome over the DevTools Protocol	Any host with Chrome
"mobile use, ios"	An iOS Simulator by default, or your paired iPhone or iPad	macOS host
"mobile use, android"	Not available yet

Linux has a working screen scanner but no agent yet. See Roadmap.

---

Why it holds up on real software

No pixel guessing. The model never sees a coordinate. It picks a numbered element from a view that fuses the operating system's accessibility tree with OCR, and a number it was never shown is rejected before anything moves.

No debugging port on your desktop. Input goes in at the operating system's own input layer. Mouse events posted at kCGHIDEventTap on macOS, pywinauto's real input path on Windows, and a kernel driver when Windows UAC blocks everything else. An ordinary application sees the same events it would get from your hand on the mouse.

No JavaScript in the pages it reads. The browser agent is a Rust crate speaking the Chrome DevTools Protocol over one WebSocket. The page scanner runs nothing in the page and never calls Runtime.enable, and the numbered boxes are painted into the screenshot, not into your DOM.

No context wall. A separate compression agent watches the transcript and splices a handoff summary into it mid-run, so a long task finishes instead of dying at the context limit. In the app you watch the memory bar fall.

No telemetry. Auto Use sends nothing anywhere except to the model provider you chose, and to Telegram if you connect it yourself.

---

Contents

• Quick start
• How it sees a screen
• How it acts on a screen
• The agent team
• The browser agent
• iPhone and iPad
• Windows, UAC and the kernel driver
• Automation testing and workflows
• Running tasks in parallel
• The desktop app
• Memory, vault, chats and skills
• Remote control from Telegram
• Providers and models
• Requirements and setup
• Limits and safety
• Project layout
• Roadmap
• Licence, credits and citation

Quick start

pip install auto-use

Put the four lines from Pick a surface in a main.py, then:

python main.py

That is the whole API.

What the wheel brings, and the one piece it cannot

Two of the three native pieces arrive on their own. The third is a Windows kernel driver, and pip is structurally unable to install one.

	How it reaches you
Browser agent — Rust	Ships prebuilt in the Apple Silicon macOS wheel. On every other platform pip builds it from the source distribution, which needs Rust and a C compiler.
WebDriverAgent — iOS	Cloned automatically the first time you run a "mobile use, ios" task. Needs full Xcode and git. Nothing to do by hand.
Interception — Windows UAC	Not installed by pip. One manual step, once — below.

Answering UAC prompts on Windows

Windows' user-mode SendInput cannot deliver input to the UAC secure desktop, so an elevation prompt can only be answered from kernel mode. That is the one thing the Interception driver does here.

pip install cannot set it up, and no packaging trick changes that: a wheel is unpacked and never executed, pip does not run elevated, and the driver binds its input slots only when it loads at boot. Without it every mode still works — you simply lose UAC handling, and a task that hits an elevation prompt stops there.

To enable it, pick one:

windows_setup.bat

from a checkout — it downloads the pinned release, verifies its SHA-256, installs it, binds it to your built-in keyboard only so external keyboards are never filtered, and reboots. Or install Interception yourself from its own release page and reboot.

It is never bundled because it is dual-licensed LGPL-3.0 non-commercial / paid commercial. Shipping it inside the wheel would make this project its distributor to every person who runs pip install, which that licence does not allow for free. You receive it from its author. See THIRD_PARTY_NOTICES.md.

A few things worth knowing on the first run

• Add ui=True to open the desktop app instead of running the task in the terminal.
• Put your provider key in a .env file, or paste it into Settings in the app.
• Model names come from model_list.txt and are case sensitive. A name that is not on the list is forwarded verbatim and comes back as a 404.
• Everything the agent keeps, chats, keys, your own skills and browser profiles, lands in an autouse_data/ folder created where you run Python from.

From a checkout

git clone https://gitlab.com/auto-use/auto-use.git
cd auto-use

bash MacOS_setup.sh          # macOS
windows_setup.bat            # Windows, self-elevates, installs the kernel driver, reboots

cp .env.example .env         # add your provider key
python main.py

main.py is the whole configuration surface. Edit it in place:

MODE     = "computer use"
PROVIDER = "anthropic"
MODEL    = "claude-sonnet-5"       # names come from model_list.txt
task     = """check the version of macOS."""

Every optional flag, ui, device, ios_version, sim_device, extra_tasks, speed, headless and save_conversation, is documented in agent_operation.md.

---

How it sees a screen

Every step the agent gets an element tree and a matching annotated image, then reasons over both.

macOS

1. Find the real front window. Window z-order comes from CGWindowListCopyWindowInfo, and the tree is walked per process from AXUIElementCreateApplication. The front app, any dialog owner, the Dock, Finder's desktop and menu-bar status-item owners are each walked separately, so a sheet on top of a window does not hide what owns it.
2. Wake the app up. AXEnhancedUserInterface is set when it is not already on, so apps publish their full tree, then the scanner waits 0.3 s for it to settle.
3. OCR whatever the tree missed. Apple Vision runs at the accurate level with a confidence floor of 0.4, on worker threads, while the accessibility walk is still running. Recognising at 1x logical resolution instead of 2x took a 2940 px capture from 1.17 s to 0.78 s with no loss in hit rate.
4. Drop what cannot be clicked. Every element is re-tested against the real window stack with a 20x20 grid of sample points. Anything under 1 percent visible is thrown away, so the model is never handed a number that sits behind another window.
5. Draw the numbers. The capture is downscaled first (long edge 2300 px, 3.3 megapixels), then 13 px magenta labels with a 2 px black rim are stamped on from cached tiles, and the frame is encoded as a 4:4:4 JPEG at quality 85. That chroma choice is deliberate. Default subsampling smears thin magenta digits into mush, and this is a tenth of PNG's bytes.

Windows

The same contract, built out of what Windows offers.

One UI Automation cached round trip pulls 18 properties for the whole subtree, then four threads run in parallel: WinRT OCR, a taskbar walk, a Win32 backend scan and a raw UIA scan of the Start menu. The results are merged and deduped at 5 px, with OCR lines nested into the deepest container that does not already hold a labelled element there. Chromium browsers are launched with --force-renderer-accessibility so they publish their full tree, and page loads are gated by polling the toolbar reload button until it stops saying "Stop".

Clicks aim at the pixel centroid of the element's own content rather than the middle of its box, so a wide button with a label on the left still gets hit where the label is.

Both platforms

A step that only digests a tool result, the turn after a web lookup or after the coder agent reports back, deliberately skips the scan entirely. No screenshot, no tree, just the payload and an instruction to pull the findings into the scratchpad. Scanning a screen that nobody looked at costs tokens and buys nothing.

---

How it acts on a screen

Nothing in the desktop input path opens a debugging port, sets an automation flag or injects JavaScript. Input goes in at the operating system's own input layer, so an ordinary application sees the same events it would get from your hand on the mouse.

macOS

Mouse events are posted at kCGHIDEventTap, the same tap a physical mouse feeds, from a private event source, after a genuine cursor warp with CGWarpMouseCursorPosition.

• A partially visible element tries the accessibility AXPress action first, then falls back to a synthetic click. A fully hidden element is refused with "scroll first" instead of being clicked blind.
• Multi-line text is put on the clipboard and pasted, then the previous clipboard is restored, because code editors auto-indent typed newlines and corrupt the text.
• Drags are 20 interpolated move events at 15 ms, not a teleport.
• applescript is a first-class tool, not a bash escape hatch. The model supplies a complete tell application block and the runtime handles launching, foregrounding and a 30 second cap.

Typing and hotkeys go through pyautogui and pynput rather than the private event source, so they are ordinary synthetic input.

Windows

Element clicks use pywinauto.click_input(), which is a real SendInput. When that fails because User Interface Privilege Isolation blocked it, the click is retried through the Interception kernel-mode driver. Typing switches to the driver when the front application is Windows Security. See Windows, UAC and the kernel driver.

A watcher for permission dialogs

macOS asks for consent the first time an app does something new, and a modal dialog will deadlock a run. Auto Use fingerprints those dialogs structurally, any window or sheet carrying a "Don't Allow" or "Deny" button, and clicks the affirmative. It runs on a one second loop around every AppleScript call and every shell command, and if a dialog is present but cannot be clicked, the command fails after 8 seconds with a permission-specific error instead of hanging forever.

Browser windows on the desktop

When a browser is in front, the agent gets browser rules injected into its prompt and the runtime changes behaviour:

• It reuses the browser you already have open, with your profile, cookies and sessions. The rules are emphatic that launching a second instance lands in a signed-out profile.
• macOS waits for a real AXWebArea before scanning, with a 1.5 second grace period and a 15 second load budget. No web area means it is not a web window, so scan it as it is. Firefox publishes no load property, so it falls through to a children-ready check.
• Known destinations are opened in one command with the query already in the URL, rather than opening a site to click its search box.

Tools the desktop agent has

	macOS	Windows
Click	left_click (1, 2 or 3 clicks), right_click	same
Type	input, typewrite	same
Navigate	scroll, hotkey, screenshot, drag_drop	scroll, hotkey, screenshot
System	open_app, shell (zsh), applescript	open_app, shell (PowerShell)
Delegate	cli_agent, cli_await, web	same
State	todo_list, update_todo, scratchpad, wait, done	same

19 tools on macOS, 17 on Windows. The names are a frozen allow-list. Anything else the model invents comes back as an error result and is never executed.

---

The agent team

Behind one task sits a hierarchy of agents, each in its own process, each with a hard step budget.

                    +---------------------+
                    |    Parent agent     |   GUI, apps, OS        100 steps
                    |   (computer use)    |
                    +----------+----------+
                               |  cli_agent spawns, cli_await joins
                    +----------v----------+
                    |     Coder agent     |   read, write, shell    50 steps
                    |     (shell use)     |
                    +----------+----------+
                               |  minion, N in parallel, implicitly awaited
        +----------------------+----------------------+
        v                      v                      v
   +---------+            +---------+            +---------+
   | Minion  |            | Minion  |     ...    | Minion  |   30 steps each
   +---------+            +---------+            +---------+
    read-only: shell, view, grep, glob, scratchpad, exit

The parent agent owns the screen and decides what a task needs. When it needs real code work it delegates and can carry on, or block until the result comes back.

The coder agent has 14 tools: shell, view, grep, glob, write, replace, web, plan, todo_list, update_todo, wait, scratchpad, minion and exit. Its operating procedure is EXPLORE, PLAN, EXECUTE, VERIFY, and it is written into the prompt with teeth:

• Exploration is delegated by rule. "Never read the codebase first-hand to build first-time understanding, send a minion."
• The plan is a structured Markdown document with real path:line anchors, not a restatement of the request.
• Every change with logic in it needs a throwaway test under ./.autouse_verify/ that is actually run, whose output is recorded, and which is deleted before exit.
• replace verifies the old block before writing and re-verifies after, so a stale line number fails with "mismatch at line X" instead of corrupting your file.

Minions are read-only scouts with six tools and no recursion. The restriction on tool names is structural: the minion's six-name set is the allow-list at the call router, so a hallucinated write comes back as an error result and never runs. Several minions fire from one coder step, run in parallel, and the loop blocks until all of them report back with findings anchored to exact path:line. Even a crashed minion returns its partial scratchpad, so the parent never hangs. To be precise about the boundary: the tool names are enforced, and the "read-only commands only" rule on its shell tool is held by the prompt.

Minions exist to keep the coder's context small. Every tool result is budgeted too: 200 lines or 15,000 characters, with shell keeping a 50-line tail, view capped at 2,000 lines and grep at 8 MB, so one wide search or noisy build log can never blow up the conversation.

Run either directly. They start in their own workspace, so point them at an absolute path when the work is in an existing project:

python -m Auto_Use.mac.agent.coder   --task "refactor the auth module in /Users/me/projects/api" --provider anthropic --model claude-sonnet-5
python -m Auto_Use.mac.agent.minions --task "where is _validate_token defined in /Users/me/projects/api and who calls it?" --provider anthropic --model claude-sonnet-5

---

The browser agent

"web use" is a different program from the rest of the framework. The whole browser agent, the agent loop, the page scanner, the controller and all seven provider adapters, is one Rust crate compiled into a single Python extension module and imported like any other.

A checkout builds it on first import with cargo build --release and rebuilds only when a .rs file actually changed. The pip wheel ships it already compiled. One binary covers every CPython from 3.10 up.

Raw CDP, hand written

No Playwright, no Selenium, no Puppeteer, no chromedriver. None of them appear anywhere in the repository. Instead there is one WebSocket to Chrome plus hand-written HTTP for tab management, one session per tab and a child session per cross-origin iframe, attached in flatten mode up to six levels deep.

The property that matters:

The page scanner runs no JavaScript, and Runtime.enable is never called.

The page model is two CDP calls per frame, DOMSnapshot.captureSnapshot plus Accessibility.getFullAXTree. Clicks and keystrokes go through CDP's trusted input path with a rising click count. Even the numbered boxes the model sees are painted into the JPEG in Rust with a hand-rolled 3x5 bitmap font, never into the DOM.

Two honest exceptions, both deliberate:

• run_script is the one door for JavaScript the model asks for. It is parse-probed with new Function, raced against a 10 second in-page timer, serialised in page and capped at 30,000 characters. Its own description tells the model to use it sparingly, because automation blockers can notice it.
• The "agent is driving" glow overlay and the box flash on click are injected into the page's main world, which makes them detectable by a page that looks for them. That is a product trade-off in favour of you being able to see what the agent is doing.

The page model

• Settling counts in-flight requests rather than sleeping. 150 ms of quiet, capped at 3 seconds.
• Occlusion by paint order demotes elements behind cookie banners and modals.
• Noise filtering collapses the wrappers and SVGs inside a button that would otherwise stack four boxes on the same pixels.
• [1] is always the page itself. Numbering follows document order and stops at 300 elements.
• Per-host overrides are shipped, for example YouTube turns off pointer-cursor detection and caps at 200 elements.
• Modal dialogs are answered the moment they open and their text is handed back to the model. Crashed renderers are recreated. Navigation failures, end of history and stale ids all come back as recoverable tool errors rather than silent successes.

Staying logged in

Chrome runs on a persistent profile keyed by name, so cookies, localStorage and logins survive between runs. As the source puts it, arriving already logged in matters far more than any fingerprint tuning, because a login wall is where a web agent usually stops.

16 tools: new_tab, switch_tab, close_tab, update_tab, navigate_tab, click, hold_click, input, keyboard, run_script, scroll, wait, scratchpad, todo_list, update_todo, done. A parallel agent pinned to one tab gets 13, with the three tab-lifecycle tools removed from its registry and refused at the router as a backstop.

---

iPhone and iPad

The same loop that drives your Mac drives a phone. Read the accessibility tree, annotate a screenshot, tap, swipe and type through WebDriverAgent. The model is never told which target it is on, because both paths end at the same endpoints.

	device="simulation" (default)	device="hardware"
Runs on	An iOS Simulator on your Mac	Your paired iPhone or iPad
Driven by	xcrun simctl plus one unsigned xcodebuild test	pymobiledevice3, USB forward plus XCUITest launch
Needs	Full Xcode. No Apple ID, no signing, no pairing	One-time pairing and Team ID signing
iOS version	ios_version="26.5", or the newest your Xcode can build for	Whatever the device runs
Best for	Everyday runs, testing, pinning a specific iOS version	Real apps with your logins, camera, cellular

run_agent(
    mode="mobile use, ios",
    provider="anthropic", model="claude-sonnet-5",
    device="simulation",        # or "hardware"
    ios_version=None,           # e.g. "26.5"; None picks the newest usable runtime
    sim_device="iphone",        # "iphone", "ipad", or an exact simulator name
    task="Open Settings, turn on Dark Mode, and confirm it on the home screen.",
)

Auto Use boots the simulator, starts WebDriverAgent, runs the task, then shuts everything down when the agent finishes, whether it succeeded, errored or you pressed Ctrl+C. The first run compiles WebDriverAgent once. After that it starts in well under a minute.

Three details worth knowing.

What simctl can boot is not the same as what xcodebuild can build for. Simulator runtimes are system wide and outlive Xcode upgrades, so Auto Use probes xcodebuild -showdestinations up front and filters to what is actually usable, instead of dying minutes into a run. If a parallel run needs one more device than you have, it creates one.

The element scan asks WebDriverAgent to skip its visible and accessible attributes, which it computes by hit-testing every element. On a 364-element home screen that takes the request from 3.60 s to 0.39 s, about 82 percent of the time. Visibility is recomputed from the element's centre, which is the exact point the tap will land.

There is a video_player tool because DRM players black out screenshots. When vision goes dark it drives play, pause, close and streaming checks from the accessibility tree, and proves "streaming" by watching the progress label advance across a four second window.

13 tools: open_app, click, input, scroll, wait, shell, web, vault, video_player, todo_list, update_todo, scratchpad, done.

---

Windows, UAC and the kernel driver

Windows UAC runs on a separate secure desktop. User-mode SendInput cannot reach it and screen capture fails there too. Most automation tools simply hang at an elevation prompt.

Auto Use turns that failure into the detector, then answers from kernel space.

1. Detect. The full-screen grab fails, so the scanner reports that UAC is up.
2. Ask the model. That step ships with no screenshot and no element tree. Just a one-shot prompt: "A Windows UAC prompt is blocking the screen. Based on your previous actions, do you want to allow this?" The agent answers alt+y or alt+n. Elevation is never automatic. It is a decision the model has to make in context.
3. Inject. Those two combinations are intercepted and sent as raw scancodes through the Interception kernel-mode input filter driver, which the secure desktop does accept.

The driver is also the fallback whenever User Interface Privilege Isolation blocks ordinary input, for example when driving Windows Security.

How it is installed. windows_setup.bat downloads the author's own signed v1.0.1 release, verifies it against a pinned SHA-256 and aborts rather than run an unverified kernel installer. It then binds the driver to the built-in keyboard and mouse only, at the device level. That detail matters. The driver has ten keyboard slots that are never freed, so the older class-wide binding burned one on every wireless-keyboard reconnect and eventually left keyboards dead until reboot. Device-level binding takes exactly one slot at boot and never filters another keyboard. If the bind fails, setup strips every filter rather than reboot into a configuration that could kill your keyboard.

Without the driver everything still works except three things: answering a UAC prompt, a click that User Interface Privilege Isolation blocked, and typing into Windows Security.

Licensing. Interception is dual licensed. LGPL v3.0 for non-commercial use, and commercial use requires a separate paid licence from its author. Auto Use's MIT licence does not and cannot grant it. See THIRD_PARTY_NOTICES.md.

macOS has no UAC and Auto Use does not elevate on it. The analogue is TCC, handled entirely in user space by the permission wizard and the consent-dialog watcher. There is no sudo anywhere in the agent paths.

---

Automation testing and workflows

A task here is a sentence, not a selector. Nothing in it names an XPath, a CSS class or a coordinate, so the redesign that breaks a selector-based suite does not break the task. The agent finds the button again because it reads the screen the way a person does.

What that buys you.

Need	How Auto Use covers it
End-to-end across surfaces	One harness covers a desktop app, a website and an iPhone. Change one string to move between them
A device or browser matrix	extra_tasks=[...] runs N tasks at once. One Chrome with a tab each, or one simulator each
Unattended runs	headless=True for Chrome. Desktop and iOS runs still need a logged-in graphical session
An audit trail	save_conversation=True writes exactly what the model saw and answered at every step
A result you can branch on	run_agent returns a status dict, and every parallel child writes its own result.json
Credentials in a test	On iOS the vault types the secret. The value never enters the model's context
Site or app specific rules	Skills are Markdown files loaded only when that site or app is in front
Long flows	Memory compression keeps a long checkout or onboarding flow inside the context window

A smoke test you can run today.

from Auto_Use.agent_launcher import run_agent

result = run_agent(
    mode="web use", provider="anthropic", model="claude-sonnet-5", headless=True,
    save_conversation=True,
    task="""
    Go to staging.example.com, sign in as demo@example.com, add any item to the basket,
    go to checkout and stop at the payment step. Report the basket total and whether the
    payment form rendered. Do not submit a payment.
    """,
)

assert result["status"] == "success", result["message"]

run_agent returns {"status", "message"}, where status is success when the agent called done, error when the loop died, and incomplete when it was stopped or ran out of steps. That is the hook you branch on.

What you get back to read afterwards. With save_conversation=True, conversation/ holds the exact payload sent to the model at every step and raw_reasoning/ holds its raw reply, so a failed run is readable rather than guessed at. A parallel run additionally writes result.json per child and exits 0 when it returned, 1 on a crash and 130 on Ctrl+C.

Those folders are cleared at the start of every run, whether the flag is on or not. Copy out anything you need to keep before running again.

One design decision matters more than any of this for reliability: the agent is told that a tool result reports that a tool ran, never that the screen changed. Every step judges the previous step's expected outcome against the new screenshot before it does anything else.

A cross-surface matrix in one call.

run_agent(
    mode="web use", provider="openrouter", model="gemini-3.8-flash", headless=True,
    task="Sign up for a new account on staging.example.com and confirm the welcome screen.",
    extra_tasks=[
        "Reset the password for demo@example.com and confirm the email prompt appears.",
        "Open the pricing page and check every plan card shows a price and a CTA button.",
    ],
)

Each task gets its own tab, its own agent and its own working directory under ./parallel/task_N/, with a result.json holding status and message. Live output is prefixed per task, and one Ctrl+C stops all of them.

Everyday workflow automation is the same machinery pointed at your own machine:

task = "Open Numbers, put last month's totals from ~/Downloads/report.csv into a new sheet, chart them, and save it to the Desktop as monthly.numbers"
task = "Go through my Gmail inbox, find every invoice from this month, and save the PDFs into ~/Documents/Invoices"
task = "Check the three competitor pricing pages in my bookmarks and tell me what changed since the notes in ~/Desktop/pricing.md"

Be honest about what this is

Auto Use is a language model driving real software. Two runs of the same task can take different routes. It is a strong fit for exploratory testing, smoke flows, repetitive back-office work and anything where writing selectors costs more than the test is worth. It is not a replacement for a deterministic unit or integration suite, and it has no assertion framework of its own. Use it where a human tester would otherwise be clicking.

---

Running tasks in parallel

Pass extra_tasks and every task, including the first, runs at the same time in its own child process.

run_agent(
    mode="web use", provider="anthropic", model="claude-sonnet-5", headless=True,
    task="find the cheapest flight to Tokyo next month",
    extra_tasks=[
        "summarise today's top Hacker News thread",
        "check my GitHub notifications",
    ],
)

	"web use"	"mobile use, ios" with device="simulation"
Each task gets	Its own agent pinned to its own tab	Its own simulator and its own WebDriverAgent port
Shared	One Chrome for everyone	Nothing. A phone screen cannot be split
Isolation	A 13-tool single-tab registry. It cannot touch another agent's tab	Own port (8100, 8101, ...), own scratchpad, own build directory
Ceiling	Tabs and RAM	Simulators, and it creates one when it needs another

Backgrounded tabs are told they are still focused, so pages that gate on focus keep behaving while another agent is in front. Output is prefixed per task, one Ctrl+C stops everything, and results land in ./parallel/task_N/. On iOS every simulator the run booted is shut down at the end, whether it succeeded, failed or was interrupted.

./parallel/ is deleted and recreated on every parallel run. Copy out anything you want to keep.

Parallel mode is for run_agent. The desktop app runs one agent at a time.

---

The desktop app

ui=True opens a native window: a Flask server rendered inside pywebview, 1140 by 700, with the OS title bar tinted to match the page so the whole surface reads as one. It even evicts a stale instance squatting on the port so a second launch never fails silently.

+--------------+----------------------------------------------+--+
|  Auto Use    |  live agent screenshot  |  tracking progress |  |
|              |  (what the agent sees)  |  (scratchpad notes)| m|
|  + New chat  +-------------------------+--------------------+ e|
|              |  tool-response chain    |  live TODO list    | m|
|  chat 1      |  "N tools used"         |  (agent's plan)    | o|
|  chat 2      +-------------------------+--------------------+ r|
|  chat 3      |   Agent Notes  or  Skills   (big centre)     | y|
|              +----------------------------------------------+  |
|  settings    |   composer  [fast] [mode] [model] [skills]   |  |
+--------------+----------------------------------------------+--+

Element	What it does
Live screenshot	The screen the agent just captured, updated every step
Tracking progress	Each scratchpad entry streams in as a bullet on a connecting line
Tool-response chain	Every tool call drawn as an animated canvas icon, so you can see why it did something
Live TODO	The agent's own plan, tailed from its file as it edits it, frozen with crosses if you stop it
Agent Notes	The final write-up, shown when a run ends, completed or stopped
Skills	Browse, preview, add, edit and delete domain knowledge files, live, for desktop and iOS
Memory bar	Context gauge down the right edge. It blinks red while compression runs and falls when the handoff lands
Fast / Quality	Leaner prompt and fewer tokens per step, or full reasoning, which is the default
Mode picker	Computer use, Mobile use, Shell use
Stop	Retires the run id instantly, so a still-running model call can never repaint your next chat

Chats are saved and resumable. Reopening one restores its history and puts the memory bar back where it was, and you can download the exact payload the model received. On macOS the first launch opens a setup wizard that walks the four permissions Auto Use needs, Accessibility, Full Disk Access, Screen Recording and Automation, one at a time, auto-advancing as each is granted. It reads the TCC database directly to notice a grant the in-process check cannot see, and repairs stale grants left by a previous build.

Two owners, one terminal prompt

In Shell use the terminal card has a single > prompt that either the agent or you can own.

	AI mode	Manual mode
Header	AutoUse Code	AutoUse Terminal
Prompt	Bare >, read only	Shows the real working directory
Who runs it	The coder agent, spawning minions and writing files	You. No agent, no model
Interrupt	The stop control	Ctrl+C, wired to a real signal on the process group

Click the card and you take the keyboard. Type ls, git status, npm test and it runs live, streaming as it goes, with cd tracked between commands.

The bridge is the interesting part. Every command you run by hand is captured, command, directory, exit code and output, and replayed into the agent's next run as a <manual_mode> block that tells it those effects are already applied. So you can drop in, check something yourself, fix a file, install a package, and the agent picks up already knowing. It is capped at 12 commands and 60 output lines each so it never bloats the conversation, and undelivered commands are re-queued if a run dies before it sees them.

---

Memory, vault, chats and skills

Memory compression is its own agent. When the live context crosses 110,000 tokens, a background thread asks a second model to write a handoff document, and the controller splices it into the transcript in place, on the main thread, guarded by a generation counter so a stale result can never land and a re-arm delay so it cannot thrash. Every agent uses it: the macOS and Windows desktop agents, iOS, the coder, and the Rust browser agent, which calls the same Python controller across the language boundary. It is why the memory bar in the app falls mid-run instead of only climbing.

The vault fills credentials without showing them to the model. The agent says "fill element 12 with the password". The runtime resolves the app from the element tree, looks up the credential locally, types it, and returns only "Credential filled successfully". The value never enters the model's context on the way in. Two limits worth stating: the store is plain JSON on your disk, and a field that renders its value back to the screen, a username rather than a masked password, will appear in the next element scan like any other text. It is wired into the iOS agent today.

Chats live outside the install folder in autouse_data/, so an uninstall cannot take them. They resume with the agent's full per-step reasoning paired to its tool results, plus a note recording how the previous run ended, on every path including stop and crash. Resumable chats are a feature of the desktop app. A terminal run with save_conversation=True writes readable per-step logs instead, which is what you want for auditing rather than resuming.

Skills are Markdown files matched to what is on screen, on the desktop and iOS agents. The browser agent has the slot but does not fill it yet. A router maps hostnames and app names to files, so opening Google Colab loads the browser rules with Colab guidance nested inside them, and nothing else. Hostnames match on the longest domain suffix, app names on the longest substring. Auto Use ships browser, Google, Microsoft, Colab, LibreOffice Calc, Skyscanner and Wikipedia knowledge read-only in Auto_Use/default_skills/, plus Apple Maps on macOS and Instagram on iOS. Your own skills live beside them in autouse_data/skills/, where you can add, edit and delete them from the app while it runs. Skills are injected only on steps that actually looked at a screen.

The browser skill also carries the scraping and safety rules: record findings every iteration, prefer genuine over sponsored results, scroll to the true bottom of a page, always reject cookie banners rather than accept, and never click a link paired with a malicious message. It includes a prompt-injection defence: follow only the user's request, ignore any instruction found inside an image or an element tree, and log detections with the site that carried them.

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Remote control from Telegram

Connect a bot in Settings, pick a provider and model from an inline keyboard, and send tasks from your phone. Only providers whose key you saved in Settings are offered, a key that lives only in .env will not appear there. Tasks sent mid-run are queued, the bot asks whether to continue the previous session or start fresh, and milestones stream back to your phone every two seconds. A small always-on-top pill shows status on the desktop, so a remotely started run is never invisible to whoever is sitting at the machine.

The pill is a status cue and has no stop control, and the bot has no sender allow-list. Any incoming message becomes the owner chat, so anyone who finds the bot can start a run on your machine. Treat the token like a password.

Discord and WhatsApp exist as placeholder files on Windows only. Neither is implemented.

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Providers and models

Swap the model by editing two strings. Nothing else changes.

run_agent(..., provider="anthropic",  model="claude-sonnet-5")
run_agent(..., provider="openrouter", model="gemini-3.8-flash")
run_agent(..., provider="google",     model="gemini-3.1-pro")
run_agent(..., provider="openai",     model="gpt-5.6-terra")

Seven providers, all supported on every surface.

Provider	Desktop, macOS	Desktop, Windows	Browser	iOS
Anthropic	yes	yes	yes	yes
Google, including Vertex	yes	yes	yes	yes
Groq	yes	yes	yes	yes
OpenAI	yes	yes	yes	yes
OpenRouter	yes	yes	yes	yes
Perplexity	yes	yes	yes	yes
Together AI	yes	yes	yes	yes

36 model names are listed in model_list.txt. Copy them exactly. A name that is not on the list gets no validation. It is forwarded verbatim and comes back as a 404.

Keys resolve from the runtime setting first, then the environment or .env, so you can override per machine without editing files. Every call gets three attempts, and the coder and minion agents additionally fall back to a second model.

The web tool uses each provider's own search: Claude web search, the OpenAI Responses web search tool, Gemini grounding, Groq's compound model, OpenRouter's Exa plugin running on your chosen model, and Perplexity Sonar.

Together AI has no native web search. Under Together the web tool hands the query to the browser agent running headless on its own dedicated port and profile, capped at 15 minutes, and returns its report. Expect that step to take minutes rather than seconds. It is available in source installs, not in the packaged binary build.

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Requirements and setup

• macOS on Apple Silicon or Intel, or Windows 10 and 11
• Python 3.10 or newer
• An API key from any supported provider
• For the browser agent from source: Rust and a C toolchain — both setup scripts install these for you
• For iOS: macOS with full Xcode, not just the Command Line Tools

Most users should install the binary build from the official site. No setup, full UI. The steps below are for running from source.

macOS

bash MacOS_setup.sh
cp .env.example .env
python main.py

The script installs uv, creates a virtualenv and installs the platform requirements. It then installs a Rust toolchain if you don't have one and precompiles the browser agent, so "web use" works on the first run instead of stopping with "cargo not found". It needs no sudo and does not reboot.

The Rust half needs Apple's Command Line Tools to link — if they're missing the script opens Apple's installer, tells you to re-run, and finishes anyway: every other mode works without them. To install the toolchain yourself instead:

xcode-select --install
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Grant Full Disk Access so the coder and minion agents can read and write Desktop, Documents and Downloads without permission popups. System Settings, Privacy and Security, Full Disk Access, then add AutoUse.app for the packaged build, or your Terminal, VS Code or python binary for development runs. The app opens this pane for you on first launch. A terminal run does not, so grant it yourself.

Windows

windows_setup.bat
copy .env.example .env
python main.py

Setup self-elevates, installs uv and a Rust toolchain with mingw-w64 so you do not need Visual Studio, precompiles the browser agent, pauses for consent before downloading the Interception driver, verifies its checksum, binds it to your built-in keyboard and mouse, then reboots. Python 3.11 to 3.13 is what it asks uv for.

iOS, optional

bash ios_setup.sh

Clones WebDriverAgent at pinned tag v15.1.1 into Auto_Use/ios_connector/. It is not bundled here, you get it from the Appium project directly. The script checks your Xcode toolchain and installs the device dependencies.

Simulator, the default, needs no Apple ID, no signing and no pairing. Xcode does need to be able to target a simulator, which on a fresh Mac is a separate one-time download the script offers to run:

xcodebuild -downloadPlatform iOS      # about 8.5 GB, once
sudo xcodebuild -runFirstLaunch       # run this yourself if Xcode still targets nothing

A booted simulator in xcrun simctl list is not proof this works. Runtimes are system wide and simctl can boot devices your selected Xcode cannot build for. The real test is xcodebuild -showdestinations listing platform:iOS Simulator entries.

A physical iPhone or iPad additionally needs an Apple ID in Xcode and a one-time signing and pairing pass, from Settings, Connect Device in the app, or standalone with python Auto_Use/ios_connector/setup.py. The app rewrites the Xcode targets to automatic signing for you, opens Xcode on the Accounts pane, and streams the build live.

A free Apple ID works, but its provisioning profiles expire after 7 days, so you re-sign weekly. A paid developer account lasts a year.

Where your data lives

Everything the agent keeps is in one autouse_data/ folder that no installer owns: chats, your API keys, the persistent Chrome profiles that keep the browser agent logged in, the credential vault and your own skills. It sits next to your checkout, in the directory you ran Python from for a pip install, or in your home folder for the packaged build. AUTOUSE_DATA_DIR overrides all three.

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Limits and safety

What protects you

• A Windows UAC prompt is handed to the model as an explicit allow-or-decline step, so elevation never happens on its own.
• The minion tool set has no write tool in it. Six names are the allow-list at the call router, so a hallucinated write comes back as an error result rather than an edit.
• An id the model was never shown is refused before anything moves, and so is a fully hidden element, which returns "scroll to make it visible first" instead of a blind click.
• Vault credentials are typed by the runtime, so the value is never shown to the model. Wired into the iOS agent today.
• The scraping ruleset carries a prompt-injection defence: follow only your request, and log anything that tries to give instructions from inside a page or an image.
• Stop is checked before every action, between characters while typing, and inside waits. The run id is retired the moment you click, and the coder's whole process tree is killed so its minions cannot survive as orphans burning API credit. A shell command already running is the exception. It finishes, or hits its cap.
• Shell commands are bounded: a 10 minute ceiling, a fixed working directory, and idle detection that turns "this program is waiting for input" into a structured result after 15 seconds instead of hanging the run.

What to know going in

• The shell tool is a shell, not a sandbox. It runs in a working directory, and its only path guard is a case-insensitive substring block on /system, /usr/sbin and /private/var on macOS, and c:\windows on Windows. Everything else in your home folder is reachable, and there is no human-in-the-loop approval prompt before a command runs. Run tasks you would be comfortable running yourself.
• Minions are read-only by tool set, but their shell tool is held to read-only commands by its description and prompt rather than by enforcement.
• The browser agent's glow overlay and click flash are injected into the page, so a site that looks for them can detect them. The page scanner injects nothing.
• On macOS the consent-dialog watcher clicks Allow on permission prompts it finds while a shell command or AppleScript runs. It keeps a run from deadlocking, and it does mean a first-time permission grant can happen without you being asked.
• The Telegram bot has no sender allow-list, and the owner chat is whoever messages it.
• There is no automated test suite in this repository.
• Auto Use drives your real machine, your real browser profile and your real logins. That is the point, and it is also the risk.

---

Project layout

main.py                    the only entry point, MODE / PROVIDER / MODEL / task
pyproject.toml             the auto-use package, dependencies and the maturin build
agent_operation.md         every optional flag, in detail
model_list.txt             provider and model names
autouse_data/              YOUR data: chats, keys, skills, browser profiles, vault

Auto_Use/
  agent_launcher.py        mode to AgentService dispatch, parallel fan-out, the ui flag
  frontend/                the desktop app: Flask, pywebview, chat, stages, skills, settings
  default_skills/          the shipped skills, read-only; yours go in autouse_data/skills/
  mac/  windows/           computer use: agent, controller, tree, sandbox, providers
    agent/main_driver/       the desktop loop
    agent/coder/             the coding agent
    agent/minions/           read-only scouts
    controller/tool/         shell, open_app, screenshot, applescript, kernel_input
    tree/                    accessibility scanner and OCR
  web/                     the Rust browser agent over raw CDP, one crate
  ios/                     the iPhone and iPad agent
  ios_connector/           WebDriverAgent transport: hardware and simulator sessions
  linux/tree/              the Linux accessibility scanner, see Roadmap
  memory_compression/      context gauge and rolling handoff compression
  agent_conversation/      resumable chat persistence
  vault/                   credential fill that never reaches the model

The macOS, Windows and iOS packages stay structurally identical and never import each other, because release binaries are platform specific. See Auto_Use/Structure.md.

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Roadmap

Linux is in progress. The scanner is real and you can run it today: it reads the desktop through AT-SPI2, captures the screen through the XDG portal, works on GNOME Wayland and X11, wakes up Electron and Chromium trees that publish nothing, and pulls whole application trees in one D-Bus call (1,952 GNOME Shell nodes in 29 ms). It emits the same numbered tree and annotated screenshot the other platforms consume.

What does not exist yet is the rest: no Linux agent, controller, input layer or launcher branch. "computer use" on a Linux host raises today. Try the scanner on its own:

sudo apt install python3-gi gir1.2-atspi-2.0 gir1.2-gtk-3.0
python3 -m Auto_Use.linux.tree.test 5 -v

Android is not available yet. "mobile use, android" raises a clear error.

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Licence, credits and citation

Built and maintained by Ashish Yadav. Issues and merge requests are welcome at gitlab.com/auto-use/auto-use.

Licensed under the MIT License, see LICENSE. You may use, copy, modify, merge, publish, distribute, sublicense and sell this software, including commercially. The only condition is to retain the copyright and permission notice. Not required but appreciated: credit the author and link back to the project.

Third-party components

Some components are not covered by the MIT licence above and are not redistributed in this repository. They are fetched at setup time from their authors. Full details in THIRD_PARTY_NOTICES.md.

Component	How it reaches you	Licence
WebDriverAgent, by Facebook, Inc. and the Appium project	ios_setup.sh clones it at pinned tag v15.1.1	BSD 3-Clause, some files Apache 2.0
Interception, a Windows kernel input driver by Francisco Lopes da Silva	windows_setup.bat downloads the author's signed v1.0.1 release and verifies its SHA-256	Dual: LGPL v3.0 non-commercial. Commercial use needs a paid licence from the author

If you ship or sell anything built on Auto Use that bundles or installs the Interception driver, you must obtain a commercial Interception licence yourself. Auto Use's MIT licence does not, and cannot, grant it.

How to cite

Ashish Yadav. Auto Use, a multi-agent framework for computer, web, mobile and shell automation. 2026. https://gitlab.com/auto-use/auto-use

@software{autouse2026,
  author = {Ashish Yadav},
  title  = {Auto Use: a multi-agent framework for computer, web, mobile and shell automation},
  year   = {2026},
  url    = {https://gitlab.com/auto-use/auto-use}
}