rpa-suite

RPA Suite

A comprehensive Python toolkit for Robotic Process Automation (RPA) development

Documentation • Installation • Quick Start • Features • Contributing

Overview

RPA Suite is a powerful and versatile Python library designed to streamline and optimize the development of Robotic Process Automation (RPA) projects. Built with simplicity and efficiency in mind, it provides a comprehensive set of tools that make automation development faster, more reliable, and more maintainable.

Whether you're working with Selenium, Botcity, or building custom automation solutions, RPA Suite offers the essential utilities you need to accelerate your development process.

Key Features

🕐 Time Management - Schedule executions, wait for specific times, and manage time-based automation flows
📧 Email Automation - Send emails via SMTP with HTML support and attachments
📝 Logging System - Comprehensive logging with file and stream support using Loguru
📁 File Operations - Screenshot capture, file counting, and flag file management
🗂️ Directory Management - Create and manage temporary directories with ease
🔍 Text Processing - Pattern matching, regex operations, and text validation
🌐 Browser Automation - Selenium-based browser control with Chrome support (optional)
⚡ Parallel & Async Execution - Run processes in parallel or asynchronously
🤖 Desktop Automation - PyAutoGUI-based desktop automation (Artemis module)
📄 OCR with AI - Document conversion with OCR capabilities (Iris module - optional)
💾 Database Tracking - Complete execution tracking and management system with multi-database support (SQLite, PostgreSQL, MySQL, SQL Server)
🎨 Colored Console Output - Beautiful terminal output with color-coded messages
✅ Data Validation - Email validation and pattern checking utilities

Installation

Basic Installation

Install RPA Suite using pip:

pip install rpa-suite

Or using conda:

conda install -c conda-forge rpa-suite

Optional Dependencies

Install only the extras you need:

# Browser automation (Selenium)
pip install rpa-suite[browser]

# Fuzzy image matching for Artemis (confidence)
pip install rpa-suite[opencv]

# OCR with AI (Iris module)
pip install rpa-suite[ocr]

# HTML dashboard (Flask)
pip install rpa-suite[dashboard]

# Database backends (SQLite is built-in)
pip install rpa-suite[postgres]
pip install rpa-suite[mysql]
pip install rpa-suite[sqlserver]

# All optional features
pip install rpa-suite[all]

Desktop automation (Artemis) is included in the base install (pyautogui). Fuzzy matching via confidence needs the optional extra rpa-suite[opencv].

Quick Start

After installation, import and use RPA Suite immediately:

from rpa_suite import rpa

# Send an email
rpa.email.send_smtp(
    email_user="your@email.com",
    email_password="your_password",
    email_to="recipient@email.com",
    subject_title="Hello from RPA Suite",
    body_message="<p>This is a test email</p>"
)

# Schedule a function to run at a specific time
rpa.clock.exec_at_hour('14:30', my_function, arg1, arg2)

# Wait before executing a function
rpa.clock.wait_for_exec(30, my_function)

# Take a screenshot
rpa.file.screen_shot(filename="screenshot.png")

# Print colored messages
rpa.success_print("Operation completed successfully!")
rpa.error_print("An error occurred!")

Database Module Example

Track your automation executions with the Database module. Prefer process_queue() (or claim_next_item_from_queue()) over a manual peek + start — claim is atomic.

from rpa_suite import rpa

# Initialize database (SQLite by default)
db = rpa.database()

# Start tracking an execution
exec_id = db.start_execution(automation_name="My Automation Bot")

# Add items to the processing queue
db.add_items(execution_id=exec_id, items=[
    {"item_identifier": "invoice_001", "item_data": {"value": 150.00}},
    {"item_identifier": "invoice_002", "item_data": {"value": 320.50}},
])

def handle_item(item: dict) -> str:
    # ... your processing logic here ...
    return f"Processed {item['item_identifier']}"

stats = db.process_queue(exec_id, handler=handle_item)
db.add_log_info(f"Queue done: {stats}", execution_id=exec_id)
db.finish_execution(exec_id, status="completed")

# Check the results
stats = db.get_statistics(execution_id=exec_id)

Requirements

Core Dependencies

Python 3.11+
colorama
email-validator
loguru
pillow
pyautogui
requests

Optional Dependencies

rpa-suite[browser] — selenium, webdriver-manager
rpa-suite[opencv] — opencv-python (Artemis confidence)
rpa-suite[ocr] — docling (Iris)
rpa-suite[dashboard] — flask
rpa-suite[postgres] — psycopg2-binary
rpa-suite[mysql] — mysql-connector-python
rpa-suite[sqlserver] — pyodbc (plus a system ODBC driver)

Features in Detail

Time Management (Clock Module)

Control execution timing and scheduling:

exec_at_hour() - Execute functions at specific times
wait_until_hour() - Block until a given HH:MM clock time
wait_for_exec() - Wait before executing functions
exec_and_wait() - Execute and wait pattern

Email (Email Module)

Send emails with full SMTP support:

HTML email support
File attachments
Custom SMTP configuration
Email validation

Logging (Log Module)

Comprehensive logging system based on Loguru:

File and console logging
Multiple log levels (debug, info, warning, error, critical)
Configurable log formats
Automatic log rotation

File Operations (File Module)

File and screenshot management:

Screenshot capture with custom naming
Flag file creation/deletion for process tracking
Wait for a download to finish (wait_for_file)
Read/write CSV without pandas
Copy, move, zip/unzip, and HTTP download
File counting with extension filtering

Date (Date Module)

get_dmy() / get_hms() - current day/month/year and hour/minute/second
stamp() - filename-friendly timestamp (dd_mm_YYYY-HH_MM_SS)
today_br() - current date as dd/mm/YYYY
shift_days() - day/month/year shifted by N days

Directory (Directory Module)

create_temp_dir() / delete_temp_dir() - create and remove a temp folder (reuses if it exists)
temp_dir() - context manager: create, use, then delete
ensure_dir() - create a path if missing
clear_dir() - empty a folder without removing the folder itself

Utils

set_importable_dir() - add the project path to sys.path
keep_awake() - Windows context manager to prevent sleep/lock (with rpa.utils.keep_awake():)

Database Tracking (Database Module)

Complete execution lifecycle management:

Multi-database support (SQLite, PostgreSQL, MySQL, SQL Server)
Execution tracking with status management
Atomic item queue (claim_next_item_from_queue, process_queue)
Automatic interruption detection
Reprocessing capabilities
Comprehensive statistics and reporting
Structured logging integration

Module Structure

Core Modules

clock - Time management and scheduling
date - Date and time formatting utilities
email - SMTP email sending
file - File operations and screenshots
directory - Directory management
log - Logging system
printer - Colored console output
regex - Pattern matching and regex operations
validate - Data validation utilities
notifier - Slack / Teams / Telegram webhooks
retry - Retry decorator with backoff

Advanced Modules

database - Execution tracking and database management (SQLite, PostgreSQL, MySQL, SQL Server)
browser - Selenium-based browser automation (pip install rpa-suite[browser])
parallel - Parallel process execution (rpa.parallel)
asyn - Asynchronous execution (rpa.asyn)
artemis - Desktop automation with PyAutoGUI (pip install rpa-suite[opencv] for confidence)
iris - OCR and document conversion (pip install rpa-suite[ocr])

Database Module Methods

Execution Management:

start_execution() - Start tracking a new execution
finish_execution() - Complete an execution
get_execution() - Retrieve execution details
get_executions() - List executions with filtering
detect_and_mark_interrupted_executions() - Auto-detect and mark interrupted executions

Item Processing:

add_item() - Add item to processing queue
add_items() - Batch add items
claim_next_item_from_queue() - Atomically claim the next item (preferred)
process_queue() - Claim → handler → finish loop
get_next_item_from_queue() - Read-only peek; does not change status
start_processing_item() - Mark item as processing (non-atomic alternative)
update_checkpoint() - Update processing checkpoint
finish_item() - Complete item processing
get_item() - Get item details
get_items() - List items with filtering
detect_and_mark_interrupted_items() - Auto-detect and mark interrupted items

Reprocessing:

is_reprocessable() - Check if an item is eligible for reprocessing
can_reprocess_execution() - Check if execution can be reprocessed
reprocess_interrupted_execution() - Restart interrupted execution
can_reprocess_item() - Check if item can be reprocessed
reprocess_interrupted_item() - Restart interrupted item
reprocess_items_from_execution() - Batch reprocess eligible items from an execution

Maintenance (Safe):

clear_pending_items() - Remove pending items
clear_interrupted_items() - Remove interrupted items
clear_interrupted_executions() - Remove interrupted executions

Maintenance (Protected - requires confirmation code):

clear_successful_items() - Remove successful items
clear_failed_items() - Remove failed items
clear_successful_executions() - Remove successful executions
clear_failed_executions() - Remove failed executions

Maintenance (Full - requires confirmation):

clear_executions_table() - Clear all executions
clear_items_table() - Clear all items
clear_logs_table() - Clear all logs
clear_database() - Clear entire database

Logging:

add_log() - Add log entry with custom level
add_log_debug() - Add DEBUG level log
add_log_info() - Add INFO level log
add_log_warn() / add_log_warning() - Add WARNING level log
add_log_error() - Add ERROR level log
add_log_critical() - Add CRITICAL level log
add_log_success() - Add SUCCESS level log
get_logs() - Retrieve execution logs
clear_logs() - Clear execution logs

Statistics:

get_statistics() - Get comprehensive statistics

Documentation

For detailed documentation, usage examples, and API reference, visit:

GitHub Wiki - Complete documentation and guides
PyPI Project Page - Package information and releases

Contributing

Contributions are welcome! If you'd like to contribute to RPA Suite:

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Camilo Costa de Carvalho

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Made with ❤️ for the RPA community

Release files for rpa-suite 1.9.2

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Source distribution for rpa-suite 1.9.2
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