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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
  • 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
  • Remote Automation — TCP socket server and REST API server to receive automation commands
  • 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 Runnerpython -m je_auto_control.cli run|list-jobs|start-server|start-rest
  • Cross-Platform — unified API across Windows, macOS, and Linux (X11)

Architecture

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_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/
    ├── 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
    ├── 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
    ├── 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")

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_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
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
Clipboard AC_clipboard_get, AC_clipboard_set
Record AC_record, AC_stop_record
Report AC_generate_html, AC_generate_json, AC_generate_xml, AC_generate_html_report, AC_generate_json_report, AC_generate_xml_report
Project AC_create_project
Shell AC_shell_command
Process AC_execute_process
Executor AC_execute_action, AC_execute_files

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.