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AutoControl

PyPI Python License: MIT Documentation

AutoControl is a cross-platform Python GUI automation framework providing mouse control, keyboard input, image recognition, screen capture, action scripting, and report generation — all through a unified API that works on Windows, macOS, and Linux (X11).

繁體中文 | 简体中文

Table of Contents

Features

  • Mouse Automation — move, click, press, release, drag, and scroll with precise coordinate control
  • Keyboard Automation — press/release individual keys, type strings, hotkey combinations, key state detection
  • Image Recognition — locate UI elements on screen using OpenCV template matching with configurable threshold
  • Accessibility Element Finder — query the OS accessibility tree (Windows UIA / macOS AX) to locate buttons, menus, and controls by name/role
  • AI Element Locator (VLM) — describe a UI element in plain language and let a vision-language model (Anthropic / OpenAI) find its screen coordinates
  • OCR — extract text from screen regions using Tesseract; wait for, click, or locate rendered text; regex search and full-region dump
  • LLM Action Planner — translate a plain-language description into a validated AC_* action list using Claude
  • Runtime Variables & Control Flow — ${var} substitution at execution time, plus AC_set_var / AC_inc_var / AC_if_var / AC_for_each / AC_loop / AC_retry for data-driven scripts
  • Remote Desktop — stream this machine's screen and accept remote input over a token-authenticated TCP protocol, or connect to another machine and view + control it (host + viewer GUIs included). Optional TLS (HTTPS-grade encryption), WebSocket transport (ws:// + wss:// for browser / firewall-friendly clients), persistent 9-digit Host ID, host→viewer audio streaming, bidirectional clipboard sync (text + image), and chunked file transfer (drag-drop + progress bar; arbitrary destination path; no size cap). Plus folder sync (additive mirror — local deletions never propagate) and a self-hosted coturn TURN config bundle generator (turnserver.conf + systemd unit + docker-compose + README). AnyDesk-style popout: when the viewer authenticates, the live remote desktop opens in its own resizable top-level window so the control panel stays uncluttered. The Remote Desktop tabs are wrapped in QScrollArea so the panel stays usable on small windows and stretches edge-to-edge on 4K displays. Driveable headlessly via je_auto_control and over MCP through the new ac_remote_* tools
  • Driver-level input backends (opt-in) — for games / apps that ignore SendInput (Win) or XTest (Linux): Interception driver backend for Windows (HID-layer keyboard / mouse injection via Oblita's WHQL-signed driver, opt-in via JE_AUTOCONTROL_WIN32_BACKEND=interception), uinput backend for Linux (kernel /dev/uinput synthetic HID device, opt-in via JE_AUTOCONTROL_LINUX_BACKEND=uinput), and ViGEm virtual gamepad for Windows games that read controllers (virtual Xbox 360 pad with friendly button / dpad / stick / trigger API, exposed as AC_gamepad_* executor commands and ac_gamepad_* MCP tools). All three fall back gracefully when the driver isn't installed, so existing deployments keep working unchanged
  • Clipboard — read/write system clipboard text on Windows, macOS, and Linux
  • Screenshot & Screen Recording — capture full screen or regions as images, record screen to video (AVI/MP4)
  • Action Recording & Playback — record mouse/keyboard events and replay them
  • JSON-Based Action Scripting — define and execute automation flows using JSON action files (dry-run + step debug)
  • Scheduler — run scripts on an interval or cron expression; jobs persist across restarts
  • Global Hotkey Daemon — bind OS-level hotkeys to action scripts (Windows today; macOS/Linux stubs in place)
  • Event Triggers — fire scripts when an image appears, a window opens, a pixel changes, or a file is modified
  • Run History — SQLite-backed run log across scheduler / triggers / hotkeys / REST with auto error-screenshot artifacts
  • Report Generation — export test records as HTML, JSON, or XML reports with success/failure status
  • MCP Server — JSON-RPC 2.0 Model Context Protocol server (stdio + HTTP/SSE) so Claude Desktop / Claude Code / custom tool-use loops can drive AutoControl. ~100 tools, full protocol coverage (resources, prompts, sampling, roots, logging, progress, cancellation, elicitation), bearer-token auth + TLS, audit log, rate limit, plugin hot-reload, CI fake backend. New in this release: ac_remote_host_start / ac_remote_host_stop / ac_remote_host_status / ac_remote_viewer_connect / ac_remote_viewer_disconnect / ac_remote_viewer_status / ac_remote_viewer_send_input wrap the same singleton remote-desktop registry the GUI uses, so a model can spin up a host, open a viewer to another machine, and forward mouse / keyboard / type / hotkey actions through the active session
  • Remote Automation — TCP socket server and hardened REST API: bearer-token auth, per-IP rate limit + lockout, SQLite audit hook, Prometheus /metrics, OpenAPI-style endpoint table (/health, /screen_size, /sessions, /screenshot, /execute, /audit/list, /audit/verify, /inspector/recent, /usb/devices, /diagnose, ...), and a vanilla-JS browser dashboard at /dashboard (any phone with HTTP reach can monitor the host)
  • Plugin Loader — drop .py files exposing AC_* callables into a directory and register them as executor commands at runtime
  • Shell Integration — execute shell commands within automation workflows with async output capture
  • Callback Executor — trigger automation functions with callback hooks for chaining operations
  • Dynamic Package Loading — extend the executor at runtime by importing external Python packages
  • Project & Template Management — scaffold automation projects with keyword/executor directory structure
  • Window Management — send keyboard/mouse events directly to specific windows (Windows/Linux)
  • GUI Application — built-in PySide6 graphical interface with live language switching (English / 繁體中文 / 简体中文 / 日本語)
  • CLI Runner — python -m je_auto_control.cli run|list-jobs|start-server|start-rest
  • Cross-Platform — unified API across Windows, macOS, and Linux (X11)
  • Multi-Host Admin Console — register N AutoControl REST endpoints in one address book, poll them in parallel for health/sessions/jobs, broadcast actions to all of them. Persisted to ~/.je_auto_control/admin_hosts.json (mode 0600 on POSIX). Bad-token hosts surface as unhealthy with the actual HTTP error
  • Tamper-Evident Audit Log — SQLite events table with SHA-256 hash chain (prev_hash + row_hash per row); editing any past row breaks the chain. verify_chain() walks rows top-down and reports the first broken link. Legacy tables get backfilled at startup ("trust on first use")
  • WebRTC Packet Inspector — process-global rolling window of StatsSnapshot samples (default 600 / ~10 min @ 1Hz) fed by the existing WebRTC stats pollers. Per-metric last/min/max/avg/p95 for RTT, FPS, bitrate, packet loss, jitter
  • USB Device Enumeration — read-only cross-platform device listing. Tries pyusb (libusb) first; falls back to platform-specific (Windows Get-PnpDevice, macOS system_profiler, Linux /sys/bus/usb/devices). Phase 2 (passthrough) intentionally deferred pending design review
  • System Diagnostics — single-command "is everything OK?" probe across platform, optional deps, executor command count, audit chain, screenshot, mouse, disk space, REST registry. CLI exits 0 if all green / 1 otherwise; REST /diagnose; severity-tagged GUI tab
  • USB Hotplug Events — polling-based hotplug watcher (UsbHotplugWatcher) with bounded ring buffer + sequence-numbered events; GET /usb/events?since=N lets late subscribers catch up. GUI auto-refresh toggle on the USB tab.
  • OpenAPI 3.1 + Swagger UI — GET /openapi.json (auth-gated, generated from the live route table) + GET /docs (browser Swagger UI with bearer token bar). Drift test in CI catches new routes added without metadata.
  • Configuration Bundle — single-file JSON export/import of user config (admin hosts, address book, trusted viewers, known hosts, host service, IDs). Atomic write with <name>.bak.<timestamp> backups; CLI python -m je_auto_control.utils.config_bundle export|import; POST /config/{export,import}; GUI buttons on the REST API tab.
  • USB Passthrough (experimental, opt-in) — wire-level protocol over a WebRTC usb DataChannel (10 opcodes, CREDIT-based flow control, 16 KiB payload cap). Host-side UsbPassthroughSession end-to-end on the Linux libusb backend; Windows WinUSB backend with full ctypes wiring (hardware-unverified); macOS IOKit skeleton. Viewer-side blocking client (UsbPassthroughClient → ClientHandle.control_transfer / bulk_transfer / interrupt_transfer). Persistent ACL (~/.je_auto_control/usb_acl.json, default deny, mode 0600) with host-side prompt QDialog and tamper-evident audit-log integration. Default off — opt-in via enable_usb_passthrough(True) or JE_AUTOCONTROL_USB_PASSTHROUGH=1. Phase 2e external security review checklist included; default-on requires sign-off.

Architecture

The runtime is layered: client surfaces (CLI, GUI, MCP/REST/socket servers) sit on top of the headless API (wrapper/ + utils/), which resolves to a per-OS backend chosen at import time by wrapper/platform_wrapper.py. The package façade (je_auto_control/__init__.py) re-exports every public name so users need only import je_auto_control regardless of which surface or backend they hit.

flowchart LR
    subgraph Clients["Client Surfaces"]
        direction TB
        Claude[["Claude Desktop /<br/>Claude Code"]]
        APIUser[["Custom Anthropic /<br/>OpenAI tool loops"]]
        HTTPClient[["HTTP / SSE clients"]]
        TCPClient[["Socket / REST clients"]]
        Browser[["Browser<br/>(/dashboard · /docs)"]]
        GUIUser[["PySide6 GUI"]]
        CLIUser[["python -m<br/>je_auto_control[.cli]"]]
        Library[["Library users<br/>(import je_auto_control)"]]
    end

    subgraph Transports["Transports & Servers"]
        direction TB
        Stdio["MCP stdio<br/>JSON-RPC 2.0"]
        HTTPMCP["MCP HTTP /<br/>SSE + auth + TLS"]
        REST["REST server :9939<br/>bearer auth · rate-limit ·<br/>OpenAPI · /metrics · /dashboard"]
        Socket["Socket server<br/>:9938"]
        WebRTC["WebRTC sessions<br/>(remote desktop ·<br/>files · audio · USB)"]
    end

    subgraph MCP["mcp_server/"]
        direction TB
        Dispatcher["MCPServer<br/>(JSON-RPC dispatcher)"]
        Tools["tools/<br/>~90 ac_* + aliases"]
        Resources["resources/<br/>files · history ·<br/>commands · screen-live"]
        Prompts["prompts/<br/>built-in templates"]
        Context["context · audit ·<br/>rate-limit · log-bridge"]
        FakeBE["fake_backend<br/>(CI smoke)"]
    end

    subgraph Core["Headless Core (wrapper/ + utils/)"]
        direction TB
        Wrapper["wrapper/<br/>mouse · keyboard · screen ·<br/>image · record · window"]
        Executor["executor/<br/>AC_* JSON action engine"]
        Vision["vision/ · ocr/ ·<br/>accessibility/"]
        Recorder["scheduler/ · triggers/ ·<br/>hotkey/ · plugin_loader/<br/>run_history/"]
        IOUtils["clipboard/ · cv2_utils/ ·<br/>shell_process/ · json/"]
    end

    subgraph Ops["Operations Layer (utils/)"]
        direction TB
        Admin["admin/<br/>multi-host poll +<br/>broadcast"]
        Audit["remote_desktop/<br/>audit_log<br/>(SHA-256 chain)"]
        Inspector["remote_desktop/<br/>webrtc_inspector"]
        Diag["diagnostics/<br/>self-test"]
        ConfigB["config_bundle/<br/>export/import"]
    end

    subgraph USB["USB"]
        direction TB
        UsbEnum["usb/<br/>list + hotplug events"]
        UsbPass["usb/passthrough/<br/>session · client · ACL ·<br/>libusb · WinUSB · IOKit"]
    end

    subgraph Remote["Remote Desktop (utils/remote_desktop/)"]
        direction TB
        RDHost["host · webrtc_host ·<br/>signaling · multi_viewer"]
        RDFiles["webrtc_files · file_sync ·<br/>clipboard_sync · audio"]
        RDTrust["trust_list · fingerprint ·<br/>turn_config · lan_discovery"]
    end

    subgraph Backends["Per-OS Backends"]
        direction TB
        Win["windows/<br/>Win32 ctypes"]
        Mac["osx/<br/>pyobjc · Quartz"]
        X11["linux_with_x11/<br/>python-Xlib"]
    end

    Claude --> Stdio
    APIUser --> Stdio
    HTTPClient --> HTTPMCP
    TCPClient --> Socket
    TCPClient --> REST
    Browser --> REST

    Stdio --> Dispatcher
    HTTPMCP --> Dispatcher
    Dispatcher --> Tools
    Dispatcher --> Resources
    Dispatcher --> Prompts
    Dispatcher -.- Context
    Tools -.optional.-> FakeBE

    Tools --> Wrapper
    Tools --> Executor
    Tools --> Vision
    Tools --> Recorder
    Tools --> IOUtils
    Resources --> Recorder
    Resources --> Wrapper

    REST --> Executor
    REST --> Ops
    REST --> USB
    Socket --> Executor
    WebRTC --> Remote
    WebRTC --> UsbPass

    GUIUser --> Wrapper
    GUIUser --> Recorder
    GUIUser --> Ops
    GUIUser --> USB
    GUIUser --> Remote
    CLIUser --> Executor
    Library --> Wrapper
    Library --> Executor
    Library --> Ops

    Admin --> REST
    Inspector -.- WebRTC
    Audit -.- REST
    Audit -.- USB
    UsbPass --> Backends

    Wrapper --> Backends
    Vision -.- Wrapper
    Recorder -.- Executor
Loading 
je_auto_control/
├── wrapper/                    # Platform-agnostic API layer
│   ├── platform_wrapper.py     # Auto-detects OS and loads the correct backend
│   ├── auto_control_mouse.py   # Mouse operations
│   ├── auto_control_keyboard.py# Keyboard operations
│   ├── auto_control_image.py   # Image recognition (OpenCV template matching)
│   ├── auto_control_screen.py  # Screenshot, screen size, pixel color
│   ├── auto_control_window.py  # Cross-platform window manager facade
│   └── auto_control_record.py  # Action recording/playback
├── windows/                    # Windows-specific backend (Win32 API / ctypes)
├── osx/                        # macOS-specific backend (pyobjc / Quartz)
├── linux_with_x11/             # Linux-specific backend (python-Xlib)
├── gui/                        # PySide6 GUI application
└── utils/
    ├── mcp_server/             # MCP server (stdio + HTTP/SSE) — server, tools/, resources, prompts, audit, rate_limit, fake_backend, plugin_watcher
    ├── executor/               # JSON action executor engine
    ├── callback/               # Callback function executor
    ├── cv2_utils/              # OpenCV screenshot, template matching, video recording
    ├── accessibility/          # UIA (Windows) / AX (macOS) element finder
    ├── vision/                 # VLM-based locator (Anthropic / OpenAI backends)
    ├── ocr/                    # Tesseract-backed text locator
    ├── clipboard/              # Cross-platform clipboard (text + image)
    ├── llm/                    # Plain-language → AC_* action planner
    ├── scheduler/              # Interval + cron scheduler
    ├── hotkey/                 # Global hotkey daemon
    ├── triggers/               # Image/window/pixel/file triggers
    ├── run_history/            # SQLite run log + error-screenshot artifacts
    ├── rest_api/               # Stdlib HTTP/REST server — auth · audit · rate-limit · OpenAPI · /metrics · dashboard · Swagger UI
    ├── admin/                  # Multi-host AdminConsoleClient (poll + broadcast)
    ├── diagnostics/            # System self-test runner + CLI
    ├── config_bundle/          # Single-file user-config export / import
    ├── usb/                    # Cross-platform enumeration, hotplug events, passthrough/{protocol, session, viewer client, ACL, libusb / WinUSB / IOKit}
    ├── remote_desktop/         # WebRTC host + viewer, signalling, multi-viewer, file/clipboard/audio sync, audit log (hash chain), trust list, TURN config, mDNS discovery, WebRTC stats inspector
    ├── plugin_loader/          # Dynamic AC_* plugin discovery
    ├── socket_server/          # TCP socket server for remote automation
    ├── shell_process/          # Shell command manager
    ├── generate_report/        # HTML / JSON / XML report generators
    ├── test_record/            # Test action recording
    ├── script_vars/            # Script variable interpolation
    ├── watcher/                # Mouse / pixel / log watchers (Live HUD)
    ├── recording_edit/         # Trim, filter, re-scale recorded actions
    ├── json/                   # JSON action file read/write
    ├── project/                # Project scaffolding & templates
    ├── package_manager/        # Dynamic package loading
    ├── logging/                # Logging
    └── exception/              # Custom exception classes

The platform_wrapper.py module automatically detects the current operating system and imports the corresponding backend, so all wrapper functions work identically regardless of platform.

Installation

Basic Installation

pip install je_auto_control

With GUI Support (PySide6)

pip install je_auto_control[gui]

Linux Prerequisites

On Linux, install the following system packages before installing:

sudo apt-get install cmake libssl-dev

Requirements

  • Python >= 3.10
  • pip >= 19.3

Dependencies

Package Purpose
je_open_cv Image recognition (OpenCV template matching)
pillow Screenshot capture
mss Fast multi-monitor screenshot
pyobjc macOS backend (auto-installed on macOS)
python-Xlib Linux X11 backend (auto-installed on Linux)
PySide6 GUI application (optional, install with [gui])
qt-material GUI theme (optional, install with [gui])
uiautomation Windows accessibility backend (optional, loaded on demand)
pytesseract + Tesseract OCR engine (optional, loaded on demand)
anthropic VLM locator — Anthropic backend (optional, loaded on demand)
openai VLM locator — OpenAI backend (optional, loaded on demand)

See Third_Party_License.md for a full list of third-party components and their licenses.

Quick Start

Mouse Control

import je_auto_control

# Get current mouse position
x, y = je_auto_control.get_mouse_position()
print(f"Mouse at: ({x}, {y})")

# Move mouse to coordinates
je_auto_control.set_mouse_position(500, 300)

# Left click at current position (use key name)
je_auto_control.click_mouse("mouse_left")

# Right click at specific coordinates
je_auto_control.click_mouse("mouse_right", x=800, y=400)

# Scroll down
je_auto_control.mouse_scroll(scroll_value=5)

Keyboard Control

import je_auto_control

# Press and release a single key
je_auto_control.type_keyboard("a")

# Type a whole string character by character
je_auto_control.write("Hello World")

# Hotkey combination (e.g., Ctrl+C)
je_auto_control.hotkey(["ctrl_l", "c"])

# Check if a key is currently pressed
is_pressed = je_auto_control.check_key_is_press("shift_l")

Image Recognition

import je_auto_control

# Find all occurrences of an image on screen
positions = je_auto_control.locate_all_image("button.png", detect_threshold=0.9)
# Returns: [[x1, y1, x2, y2], ...]

# Find a single image and get its center coordinates
cx, cy = je_auto_control.locate_image_center("icon.png", detect_threshold=0.85)
print(f"Found at: ({cx}, {cy})")

# Find an image and automatically click it
je_auto_control.locate_and_click("submit_button.png", mouse_keycode="mouse_left")

Accessibility Element Finder

Query the OS accessibility tree to locate controls by name, role, or app. Works on Windows (UIA, via uiautomation) and macOS (AX).

import je_auto_control

# List all visible buttons in the Calculator app
elements = je_auto_control.list_accessibility_elements(app_name="Calculator")

# Find a specific element
ok = je_auto_control.find_accessibility_element(name="OK", role="Button")
if ok is not None:
    print(ok.bounds, ok.center)

# Click it directly
je_auto_control.click_accessibility_element(name="OK", app_name="Calculator")

Raises AccessibilityNotAvailableError if no accessibility backend is installed for the current platform.

AI Element Locator (VLM)

When template matching and accessibility both fail, describe the element in plain language and let a vision-language model find its coordinates.

import je_auto_control

# Uses Anthropic by default if ANTHROPIC_API_KEY is set, else OpenAI.
x, y = je_auto_control.locate_by_description("the green Submit button")

# Or click it in one shot
je_auto_control.click_by_description(
    "the cookie-banner 'Accept all' button",
    screen_region=[0, 800, 1920, 1080],   # optional crop
)

Configuration (environment variables only — keys are never persisted or logged):

Variable Effect
ANTHROPIC_API_KEY Enables the Anthropic backend
OPENAI_API_KEY Enables the OpenAI backend
AUTOCONTROL_VLM_BACKEND anthropic or openai to force a backend
AUTOCONTROL_VLM_MODEL Override the default model (e.g. claude-opus-4-7, gpt-4o-mini)

Raises VLMNotAvailableError if neither SDK is installed or no API key is set.

OCR (Text on Screen)

import je_auto_control as ac

# Locate all matches of a piece of text
matches = ac.find_text_matches("Submit")

# Center of the first match, or None
cx, cy = ac.locate_text_center("Submit")

# Click text in one call
ac.click_text("Submit")

# Block until text appears (or timeout)
ac.wait_for_text("Loading complete", timeout=15.0)

If Tesseract is not on PATH, point at it explicitly:

ac.set_tesseract_cmd(r"C:\Program Files\Tesseract-OCR\tesseract.exe")

Dump every recognised text record in a region (or full screen), or search by regex when the text varies:

import je_auto_control as ac

# Every hit in a region as TextMatch records (text, bounding box, confidence)
for match in ac.read_text_in_region(region=[0, 0, 800, 600]):
    print(match.text, match.center, match.confidence)

# Regex — accepts a pattern string or a compiled re.Pattern
for match in ac.find_text_regex(r"Order#\d+"):
    print(match.text, match.center)

GUI: OCR Reader tab.

LLM Action Planner

Translate plain-language descriptions into validated AC_* action lists using an LLM (Anthropic Claude by default). Output is leniently parsed (strips code fences, extracts the first JSON array from prose) and then validated by the same schema the executor uses, so the result can be piped straight into execute_action:

import je_auto_control as ac
from je_auto_control.utils.executor.action_executor import executor

actions = ac.plan_actions(
    "click the Submit button, then type 'done' and save",
    known_commands=executor.known_commands(),
)
executor.execute_action(actions)

# Or in a single call:
ac.run_from_description("open Notepad and type hello", executor=executor)
Variable Effect
ANTHROPIC_API_KEY Enables the Anthropic backend
AUTOCONTROL_LLM_BACKEND anthropic to force a backend
AUTOCONTROL_LLM_MODEL Override the default model (e.g. claude-opus-4-7)

GUI: LLM Planner tab — description box, QThread-backed Plan button, action-list preview, and a Run plan button.

Runtime Variables & Control Flow

The executor resolves ${var} placeholders per command call rather than pre-flattening, so nested body / then / else lists keep their placeholders and re-bind on every iteration. Combined with new mutation commands, scripts can drive themselves from data without Python glue:

[
    ["AC_set_var", {"name": "items", "value": ["alpha", "beta"]}],
    ["AC_set_var", {"name": "i", "value": 0}],
    ["AC_for_each", {
        "items": "${items}", "as": "name",
        "body": [
            ["AC_inc_var", {"name": "i"}],
            ["AC_if_var", {
                "name": "i", "op": "ge", "value": 2,
                "then": [["AC_break"]], "else": []
            }]
        ]
    }]
]

AC_if_var operators: eq, ne, lt, le, gt, ge, contains, startswith, endswith. GUI: Variables tab — live view of executor.variables with single-set, JSON seed, and clear-all controls.

Remote Desktop

Stream this machine's screen and accept remote input, or view and control another machine. The wire format is a length-prefixed framing on raw TCP (no extra deps), starting with an HMAC-SHA256 challenge / response handshake; viewers that fail auth are dropped before they can see a frame. JPEG frames are produced at the configured FPS / quality and broadcast to authenticated viewers via a shared latest-frame slot, so a slow viewer drops frames instead of blocking the rest. Viewer input is JSON, validated against an allowlist, and applied through the existing wrappers.

# Be remoted — start a host and hand the token + port to whoever views you
from je_auto_control import RemoteDesktopHost
host = RemoteDesktopHost(token="hunter2", bind="127.0.0.1",
                          port=0, fps=10, quality=70)
host.start()
print("listening on", host.port, "viewers:", host.connected_clients)
# Control another machine — connect a viewer and send input
from je_auto_control import RemoteDesktopViewer
viewer = RemoteDesktopViewer(host="10.0.0.5", port=51234, token="hunter2",
                              on_frame=lambda jpeg: ...)
viewer.connect()
viewer.send_input({"action": "mouse_move", "x": 100, "y": 200})
viewer.send_input({"action": "type", "text": "hello"})
viewer.disconnect()

GUI: Remote Desktop tab with two sub-tabs.

  • Host — token field with a Generate button, security warning about the bind address, start / stop controls, refreshing port + viewer-count status, and a 4 fps preview pane below the controls so the user being remoted sees what viewers see.
  • Viewer — address / port / token form, Connect / Disconnect, and a custom frame-display widget that paints incoming JPEG frames scaled with KeepAspectRatio. Mouse / wheel / key events on the display are remapped from widget coordinates back to the remote screen's pixel space using the latest frame's dimensions, then forwarded as INPUT messages.

⚠️ Anyone with the host:port and token gets full mouse / keyboard control of the host machine. Default bind is 127.0.0.1; expose externally only via SSH tunnel or TLS front-end. The token is the only line of defence — treat it like a password.

Encrypted transports + alternate protocols. Pass an ssl_context to either RemoteDesktopHost or RemoteDesktopViewer to wrap every connection in TLS. For firewall-friendly access, use the in-tree WebSocket variants (no extra deps) — same protocol, RFC 6455 framing, and wss:// if you also pass ssl_context:

from je_auto_control import (
    WebSocketDesktopHost, WebSocketDesktopViewer,
)
host = WebSocketDesktopHost(token="hunter2", ssl_context=server_ctx)
viewer = WebSocketDesktopViewer(
    host="example.com", port=443, token="hunter2",
    ssl_context=client_ctx, expected_host_id="123456789",
)

Persistent Host ID. Every host owns a stable 9-digit numeric ID (persisted at ~/.je_auto_control/remote_host_id), announced in AUTH_OK and verifiable via the viewer's expected_host_id:

print(host.host_id)            # e.g. "123456789"
viewer = RemoteDesktopViewer(
    host=..., port=..., token=...,
    expected_host_id="123456789",   # AuthenticationError on mismatch
)

Audio streaming (host → viewer). Optional sounddevice dep; opt in with an AudioCaptureConfig on the host, attach an AudioPlayer (or your own callback) on the viewer:

from je_auto_control.utils.remote_desktop import AudioCaptureConfig
host = RemoteDesktopHost(
    token="tok",
    audio_config=AudioCaptureConfig(enabled=True),    # default mic
)
# Or pick a loopback / monitor device:
# audio_config=AudioCaptureConfig(enabled=True, device=12)

from je_auto_control.utils.remote_desktop import AudioPlayer
player = AudioPlayer(); player.start()
viewer = RemoteDesktopViewer(host=..., on_audio=player.play)

Clipboard sync (text + image, bidirectional). Explicit per-call — no auto-poll loops. Image clipboard works on Windows (CF_DIB via ctypes) and Linux (xclip -t image/png); macOS get is supported via Pillow ImageGrab, set requires PyObjC.

viewer.send_clipboard_text("hello")
viewer.send_clipboard_image(open("logo.png", "rb").read())
host.broadcast_clipboard_text("greetings")

File transfer with progress. Bidirectional, chunked, arbitrary destination path, no size cap; the GUI viewer also accepts drag-drop:

viewer.send_file(
    "local.bin", "/tmp/uploaded.bin",
    on_progress=lambda tid, done, total: print(done, total),
)
host.send_file_to_viewers("local.bin", "/tmp/from_host.bin")

⚠️ Path is unrestricted and there is no aggregate size limit. Anyone with the token can write any file to any location and can fill the disk — keep "trusted token holders == trusted users" in mind, or wrap with your own FileReceiver subclass that vets destination paths.

Clipboard

import je_auto_control as ac
ac.set_clipboard("hello")
text = ac.get_clipboard()

Backends: Windows (Win32 via ctypes), macOS (pbcopy/pbpaste), Linux (xclip or xsel).

Screenshot

import je_auto_control

# Take a full-screen screenshot and save to file
je_auto_control.pil_screenshot("screenshot.png")

# Take a screenshot of a specific region [x1, y1, x2, y2]
je_auto_control.pil_screenshot("region.png", screen_region=[100, 100, 500, 400])

# Get screen resolution
width, height = je_auto_control.screen_size()

# Get pixel color at coordinates
color = je_auto_control.get_pixel(500, 300)

Action Recording & Playback

import je_auto_control
import time

# Start recording mouse and keyboard events
je_auto_control.record()

time.sleep(10)  # Record for 10 seconds

# Stop recording and get the action list
actions = je_auto_control.stop_record()

# Replay the recorded actions
je_auto_control.execute_action(actions)

JSON Action Scripting

Create a JSON action file (actions.json):

[
    ["AC_set_mouse_position", {"x": 500, "y": 300}],
    ["AC_click_mouse", {"mouse_keycode": "mouse_left"}],
    ["AC_write", {"write_string": "Hello from AutoControl"}],
    ["AC_screenshot", {"file_path": "result.png"}],
    ["AC_hotkey", {"key_code_list": ["ctrl_l", "s"]}]
]

Execute it:

import je_auto_control

# Execute from file
je_auto_control.execute_action(je_auto_control.read_action_json("actions.json"))

# Or execute from a list directly
je_auto_control.execute_action([
    ["AC_set_mouse_position", {"x": 100, "y": 200}],
    ["AC_click_mouse", {"mouse_keycode": "mouse_left"}]
])

Available action commands:

Category Commands
Mouse AC_click_mouse, AC_set_mouse_position, AC_get_mouse_position, AC_get_mouse_table, AC_press_mouse, AC_release_mouse, AC_mouse_scroll, AC_mouse_left, AC_mouse_right, AC_mouse_middle
Keyboard AC_type_keyboard, AC_press_keyboard_key, AC_release_keyboard_key, AC_write, AC_hotkey, AC_check_key_is_press, AC_get_keyboard_keys_table
Image AC_locate_all_image, AC_locate_image_center, AC_locate_and_click
Screen AC_screen_size, AC_screenshot
Accessibility AC_a11y_list, AC_a11y_find, AC_a11y_click
VLM (AI Locator) AC_vlm_locate, AC_vlm_click
OCR AC_locate_text, AC_click_text, AC_wait_text, AC_read_text_in_region, AC_find_text_regex
LLM planner AC_llm_plan, AC_llm_run
Clipboard AC_clipboard_get, AC_clipboard_set
Window AC_list_windows, AC_focus_window, AC_wait_window, AC_close_window
Flow control AC_loop, AC_break, AC_continue, AC_if_image_found, AC_if_pixel, AC_if_var, AC_while_image, AC_for_each, AC_wait_image, AC_wait_pixel, AC_sleep, AC_retry
Variables AC_set_var, AC_get_var, AC_inc_var
Remote desktop AC_start_remote_host, AC_stop_remote_host, AC_remote_host_status, AC_remote_connect, AC_remote_disconnect, AC_remote_viewer_status, AC_remote_send_input
Record AC_record, AC_stop_record, AC_set_record_enable
Report AC_generate_html, AC_generate_json, AC_generate_xml, AC_generate_html_report, AC_generate_json_report, AC_generate_xml_report
Run history AC_history_list, AC_history_clear
Project AC_create_project
Shell AC_shell_command
Process AC_execute_process
Executor AC_execute_action, AC_execute_files, AC_add_package_to_executor, AC_add_package_to_callback_executor
MCP server AC_start_mcp_server, AC_start_mcp_http_server

MCP Server (Use AutoControl from Claude)

Expose AutoControl as a Model Context Protocol server so any MCP-compatible client (Claude Desktop, Claude Code, custom Anthropic / OpenAI tool-use loops) can drive the host machine. Stdlib-only — JSON-RPC 2.0 over stdio or HTTP+SSE.

Register with Claude Code:

claude mcp add autocontrol -- python -m je_auto_control.utils.mcp_server

Register with Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "autocontrol": {
      "command": "python",
      "args": ["-m", "je_auto_control.utils.mcp_server"]
    }
  }
}

Start programmatically:

import je_auto_control as ac

# Stdio (blocks until stdin closes)
ac.start_mcp_stdio_server()

# Or HTTP / SSE with bearer-token auth + optional TLS
ac.start_mcp_http_server(host="127.0.0.1", port=9940,
                         auth_token="hunter2")

Inspect the catalogue without starting the server:

je_auto_control_mcp --list-tools
je_auto_control_mcp --list-tools --read-only
je_auto_control_mcp --list-resources
je_auto_control_mcp --list-prompts

What ships:

Surface Coverage
Tools (~90) mouse · keyboard · drag · screen / multi-monitor · screenshot-as-image · diff · OCR · image · windows (move/min/max/restore/...) · clipboard text+image · process / shell · recording · screen recording · scheduler / triggers / hotkeys · accessibility tree · VLM locator · executor · history
Aliases click, type, screenshot, find_image, drag, shell, wait_image, ... — toggle with JE_AUTOCONTROL_MCP_ALIASES=0
Resources autocontrol://files/<name>, autocontrol://history, autocontrol://commands, autocontrol://screen/live (with resources/subscribe)
Prompts automate_ui_task, record_and_generalize, compare_screenshots, find_widget, explain_action_file
Protocol tools / resources / prompts / sampling / roots / logging / progress / cancellation / list_changed / elicitation
Transports stdio, HTTP POST /mcp, SSE streaming when Accept: text/event-stream
Safety tool annotations · JE_AUTOCONTROL_MCP_READONLY · JE_AUTOCONTROL_MCP_CONFIRM_DESTRUCTIVE · audit log · token-bucket rate limiter · auto-screenshot on error
Ops bearer-token auth · TLS via ssl_context · PluginWatcher hot-reload · JE_AUTOCONTROL_FAKE_BACKEND=1 for CI

See docs/source/Eng/doc/mcp_server/mcp_server_doc.rst for the full reference (or the 繁體中文 version).

⚠️ The MCP server can move the mouse, send keystrokes, capture the screen, and execute arbitrary AC_* actions. Only register it with MCP clients you trust. HTTP defaults to 127.0.0.1; binding to 0.0.0.0 requires explicit reason and must be paired with auth_token plus ssl_context.

Scheduler (Interval & Cron)

import je_auto_control as ac

# Interval job — run every 30 seconds
job = ac.default_scheduler.add_job(
    script_path="scripts/poll.json", interval_seconds=30, repeat=True,
)

# Cron job — 09:00 on weekdays (minute hour dom month dow)
cron_job = ac.default_scheduler.add_cron_job(
    script_path="scripts/daily.json", cron_expression="0 9 * * 1-5",
)

ac.default_scheduler.start()

Both flavours coexist; job.is_cron tells them apart.

Global Hotkey Daemon

Bind OS-level hotkeys to action JSON scripts (Windows backend today; macOS / Linux raise NotImplementedError on start() with Strategy- pattern seams in place).

from je_auto_control import default_hotkey_daemon

default_hotkey_daemon.bind("ctrl+alt+1", "scripts/greet.json")
default_hotkey_daemon.start()

Event Triggers

Poll-based triggers that fire a script when a condition becomes true:

from je_auto_control import (
    default_trigger_engine, ImageAppearsTrigger,
    WindowAppearsTrigger, PixelColorTrigger, FilePathTrigger,
)

default_trigger_engine.add(ImageAppearsTrigger(
    trigger_id="", script_path="scripts/click_ok.json",
    image_path="templates/ok_button.png", threshold=0.85, repeat=True,
))
default_trigger_engine.start()

Run History

Every run from the scheduler, trigger engine, hotkey daemon, REST API, and manual GUI replay is recorded to ~/.je_auto_control/history.db. Errors automatically attach a screenshot under ~/.je_auto_control/artifacts/run_{id}_{ms}.png for post-mortem.

from je_auto_control import default_history_store

for run in default_history_store.list_runs(limit=20):
    print(run.id, run.source, run.status, run.artifact_path)

The GUI Run History tab exposes filter/refresh/clear and double-click-to-open on the artifact column.

Report Generation

import je_auto_control

# Enable test recording first
je_auto_control.test_record_instance.set_record_enable(True)

# ... perform automation actions ...
je_auto_control.set_mouse_position(100, 200)
je_auto_control.click_mouse("mouse_left")

# Generate reports
je_auto_control.generate_html_report("test_report")   # -> test_report.html
je_auto_control.generate_json_report("test_report")   # -> test_report.json
je_auto_control.generate_xml_report("test_report")    # -> test_report.xml

# Or get report content as string
html_string = je_auto_control.generate_html()
json_string = je_auto_control.generate_json()
xml_string = je_auto_control.generate_xml()

Reports include: function name, parameters, timestamp, and exception info (if any) for each recorded action. HTML reports display successful actions in cyan and failed actions in red.

Remote Automation (Socket / REST)

Two servers are available — a raw TCP socket and a stdlib HTTP/REST server. Both default to 127.0.0.1; binding to 0.0.0.0 is an explicit, documented opt-in.

import je_auto_control as ac

# TCP socket server (default: 127.0.0.1:9938)
ac.start_autocontrol_socket_server(host="127.0.0.1", port=9938)

# REST API server (default: 127.0.0.1:9939)
ac.start_rest_api_server(host="127.0.0.1", port=9939)
# Endpoints:
#   GET  /health           liveness probe
#   GET  /jobs             scheduler job list
#   POST /execute          body: {"actions": [...]}

Client example:

import socket
import json

sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect(("localhost", 9938))

# Send an automation command
command = json.dumps([
    ["AC_set_mouse_position", {"x": 500, "y": 300}],
    ["AC_click_mouse", {"mouse_keycode": "mouse_left"}]
])
sock.sendall(command.encode("utf-8"))

# Receive response
response = sock.recv(8192).decode("utf-8")
print(response)
sock.close()

Plugin Loader

Drop .py files defining top-level AC_* callables into a directory, then register them as executor commands at runtime:

from je_auto_control import (
    load_plugin_directory, register_plugin_commands,
)

commands = load_plugin_directory("./my_plugins")
register_plugin_commands(commands)

# Now usable from any JSON action script:
# [["AC_greet", {"name": "world"}]]

Warning: Plugin files execute arbitrary Python on load. Only load from directories you control.

Shell Command Execution

import je_auto_control

# Using the default shell manager
je_auto_control.default_shell_manager.exec_shell("echo Hello")
je_auto_control.default_shell_manager.pull_text()  # Print captured output

# Or create a custom ShellManager
shell = je_auto_control.ShellManager(shell_encoding="utf-8")
shell.exec_shell("ls -la")
shell.pull_text()
shell.exit_program()

Screen Recording

import je_auto_control
import time

# Method 1: ScreenRecorder (manages multiple recordings)
recorder = je_auto_control.ScreenRecorder()
recorder.start_new_record(
    recorder_name="my_recording",
    path_and_filename="output.avi",
    codec="XVID",
    frame_per_sec=30,
    resolution=(1920, 1080)
)
time.sleep(10)
recorder.stop_record("my_recording")

# Method 2: RecordingThread (simple single recording, outputs MP4)
recording = je_auto_control.RecordingThread(video_name="my_video", fps=20)
recording.start()
time.sleep(10)
recording.stop()

Callback Executor

Execute an automation function and trigger a callback upon completion:

import je_auto_control

def my_callback():
    print("Action completed!")

# Execute set_mouse_position then call my_callback
je_auto_control.callback_executor.callback_function(
    trigger_function_name="AC_set_mouse_position",
    callback_function=my_callback,
    x=500, y=300
)

# With callback parameters
def on_done(message):
    print(f"Done: {message}")

je_auto_control.callback_executor.callback_function(
    trigger_function_name="AC_click_mouse",
    callback_function=on_done,
    callback_function_param={"message": "Click finished"},
    callback_param_method="kwargs",
    mouse_keycode="mouse_left"
)

Package Manager

Dynamically load external Python packages into the executor at runtime:

import je_auto_control

# Add all functions/classes from a package to the executor
je_auto_control.package_manager.add_package_to_executor("os")

# Now you can use os functions in JSON action scripts:
# ["os_getcwd", {}]
# ["os_listdir", {"path": "."}]

Project Management

Scaffold a project directory structure with template files:

import je_auto_control

# Create a project structure
je_auto_control.create_project_dir(project_path="./my_project", parent_name="AutoControl")

# This creates:
# my_project/
# └── AutoControl/
#     ├── keyword/
#     │   ├── keyword1.json        # Template action file
#     │   ├── keyword2.json        # Template action file
#     │   └── bad_keyword_1.json   # Error handling template
#     └── executor/
#         ├── executor_one_file.py  # Execute single file example
#         ├── executor_folder.py    # Execute folder example
#         └── executor_bad_file.py  # Error handling example

Window Management

Send events directly to specific windows (Windows and Linux only):

import je_auto_control

# Send keyboard event to a window by title
je_auto_control.send_key_event_to_window("Notepad", keycode="a")

# Send mouse event to a window handle
je_auto_control.send_mouse_event_to_window(window_handle, mouse_keycode="mouse_left", x=100, y=50)

GUI Application

Launch the built-in graphical interface (requires [gui] extra):

import je_auto_control
je_auto_control.start_autocontrol_gui()

Or from the command line:

python -m je_auto_control

Command-Line Interface

AutoControl can be used directly from the command line:

# Execute a single action file
python -m je_auto_control -e actions.json

# Execute all action files in a directory
python -m je_auto_control -d ./action_files/

# Execute a JSON string directly
python -m je_auto_control --execute_str '[["AC_screenshot", {"file_path": "test.png"}]]'

# Create a project template
python -m je_auto_control -c ./my_project

A richer subcommand CLI built on the headless APIs:

# Run a script, optionally with variables, and/or a dry-run
python -m je_auto_control.cli run script.json
python -m je_auto_control.cli run script.json --var name=alice --dry-run

# List scheduler jobs
python -m je_auto_control.cli list-jobs

# Start the socket or REST server
python -m je_auto_control.cli start-server --port 9938
python -m je_auto_control.cli start-rest   --port 9939

--var name=value is parsed as JSON when possible (so count=10 becomes an int), otherwise treated as a string.

Platform Support

Platform Status Backend Notes
Windows 10 / 11 Supported Win32 API (ctypes) Full feature support
macOS 10.15+ Supported pyobjc / Quartz Action recording not available; send_key_event_to_window / send_mouse_event_to_window not supported
Linux (X11) Supported python-Xlib Full feature support
Linux (Wayland) Not supported — May be added in a future release
Raspberry Pi 3B / 4B Supported python-Xlib Runs on X11

Development

Setting Up

git clone https://github.com/Intergration-Automation-Testing/AutoControl.git
cd AutoControl
pip install -r dev_requirements.txt

Running Tests

# Unit tests
python -m pytest test/unit_test/

# Integration tests
python -m pytest test/integrated_test/

Project Links

License

MIT License © JE-Chen. See Third_Party_License.md for the licenses of bundled and optional third-party dependencies.