Jackson Studio

Posted on Feb 13 • Edited on Feb 21

Stop Writing Scripts, Start Building Tools: The 5 Production CLI Patterns Every Python Dev Needs

#programming #tutorial #python #devops

You've built a Python script. It works on your machine. You share it with your team, and suddenly you're drowning in bug reports: "It crashed with no error message", "How do I enable debug mode?", "Is there a newer version?"

Sound familiar? 🎯

I've been there. After building dozens of CLI tools for automation and DevOps workflows, I learned the hard way: the difference between a script and a professional tool isn't the core logic—it's everything around it.

In this guide, I'll show you how to transform a basic Python script into a production-ready CLI tool with:

Structured logging (not just print() statements)
Graceful error handling (no more ugly stack traces for users)
Automatic version checking and updates (users always have the latest)
Progress indicators (because users hate silent commands)
Configuration management (sensible defaults + easy overrides)

By the end, you'll have a professional CLI tool that your users actually enjoy using. Let's build something maintainable.

1. Project Structure That Scales

First, let's set up a proper project structure. This isn't overkill—it's how you avoid technical debt.

my-cli-tool/
├── my_cli/
│   ├── __init__.py
│   ├── __version__.py       # Version info
│   ├── cli.py               # CLI entry point
│   ├── core.py              # Core logic
│   ├── logging_config.py    # Logging setup
│   ├── exceptions.py        # Custom exceptions
│   └── updater.py           # Auto-update logic
├── tests/
│   ├── __init__.py
│   └── test_core.py
├── setup.py                 # Installation config
├── requirements.txt
└── README.md

Why this structure?

✅ Separation of concerns: CLI interface (cli.py) is separate from business logic (core.py)
✅ Testability: Core logic can be tested without invoking the CLI
✅ Maintainability: Each module has a single responsibility
✅ Scalability: Easy to add new commands or features

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All patterns fully implemented
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2. Smart Logging (Not Print Debugging)

Stop using print() for debugging. Here's a production-grade logging setup:

my_cli/logging_config.py

import logging
import sys
from pathlib import Path

def setup_logging(verbose: bool = False, log_file: str = None):
    """
    Configure logging with console + optional file output.

    Args:
        verbose: Enable DEBUG level logging
        log_file: Optional file path for logs
    """
    # Create logger
    logger = logging.getLogger("my_cli")
    logger.setLevel(logging.DEBUG if verbose else logging.INFO)

    # Remove existing handlers (prevents duplicate logs)
    logger.handlers.clear()

    # Console handler with colored output
    console_handler = logging.StreamHandler(sys.stdout)
    console_handler.setLevel(logging.DEBUG if verbose else logging.INFO)

    # Different formats for verbose vs normal
    if verbose:
        console_format = logging.Formatter(
            '%(asctime)s [%(levelname)s] %(name)s.%(funcName)s:%(lineno)d - %(message)s',
            datefmt='%H:%M:%S'
        )
    else:
        console_format = logging.Formatter(
            '%(levelname)s: %(message)s'
        )

    console_handler.setFormatter(console_format)
    logger.addHandler(console_handler)

    # Optional file handler for persistent logs
    if log_file:
        file_handler = logging.FileHandler(log_file)
        file_handler.setLevel(logging.DEBUG)
        file_format = logging.Formatter(
            '%(asctime)s [%(levelname)s] %(name)s.%(funcName)s:%(lineno)d - %(message)s'
        )
        file_handler.setFormatter(file_format)
        logger.addHandler(file_handler)

    return logger

Usage in your code:

from my_cli.logging_config import setup_logging

logger = setup_logging(verbose=True)

logger.debug("This only shows in verbose mode")
logger.info("Processing file: example.txt")
logger.warning("Rate limit approaching")
logger.error("Failed to connect to API", exc_info=True)

Why this approach wins:

✅ Structured output (timestamp, level, module, line number)
✅ Verbosity control (--verbose flag)
✅ File logging for debugging production issues
✅ No more scattered print() statements
✅ Easy to add colors and custom formatting

3. Graceful Error Handling

Users should never see raw Python stack traces. Here's how:

my_cli/exceptions.py

class CLIError(Exception):
    """Base exception for all CLI errors."""
    def __init__(self, message: str, exit_code: int = 1):
        self.message = message
        self.exit_code = exit_code
        super().__init__(self.message)

class ConfigurationError(CLIError):
    """Raised when configuration is invalid."""
    pass

class NetworkError(CLIError):
    """Raised when network operations fail."""
    pass

class ValidationError(CLIError):
    """Raised when input validation fails."""
    pass

my_cli/cli.py (main entry point)

import sys
import click
from my_cli.logging_config import setup_logging
from my_cli.exceptions import CLIError
from my_cli.core import run_main_logic

@click.command()
@click.option('--verbose', '-v', is_flag=True, help='Enable verbose logging')
@click.option('--config', '-c', type=click.Path(), help='Path to config file')
def main(verbose: bool, config: str):
    """
    My CLI Tool - A production-ready Python CLI application.
    """
    logger = setup_logging(verbose=verbose)

    try:
        # Your main logic here
        result = run_main_logic(config=config)
        logger.info(f"✓ Operation completed successfully")
        sys.exit(0)

    except CLIError as e:
        # Expected errors: show user-friendly message
        logger.error(f"✗ {e.message}")
        sys.exit(e.exit_code)

    except KeyboardInterrupt:
        # User cancelled: clean exit
        logger.info("\nOperation cancelled by user")
        sys.exit(130)

    except Exception as e:
        # Unexpected errors: log full trace in verbose mode
        if verbose:
            logger.exception("Unexpected error occurred")
        else:
            logger.error(f"Unexpected error: {e}")
            logger.info("Run with --verbose for detailed error information")
        sys.exit(1)

if __name__ == '__main__':
    main()

This achieves:

✅ Custom exceptions for different error types
✅ User-friendly error messages (no scary stack traces)
✅ Proper exit codes (important for CI/CD pipelines)
✅ Verbose mode for power users and debugging

4. Automatic Version Updates

Nothing frustrates users more than outdated tools. Here's automatic update checking:

my_cli/__version__.py

__version__ = "1.0.3"

my_cli/updater.py

import requests
import logging
from packaging import version
from my_cli.__version__ import __version__

logger = logging.getLogger("my_cli.updater")

def check_for_updates(package_name: str = "my-cli-tool", timeout: int = 3) -> dict:
    """
    Check PyPI for newer versions.

    Returns:
        dict: {'update_available': bool, 'latest_version': str, 'current_version': str}
    """
    try:
        response = requests.get(
            f"https://pypi.org/pypi/{package_name}/json",
            timeout=timeout
        )
        response.raise_for_status()

        latest_version = response.json()["info"]["version"]
        current_version = __version__

        update_available = version.parse(latest_version) > version.parse(current_version)

        return {
            "update_available": update_available,
            "latest_version": latest_version,
            "current_version": current_version
        }

    except Exception as e:
        logger.debug(f"Could not check for updates: {e}")
        return {
            "update_available": False,
            "latest_version": None,
            "current_version": __version__
        }

def notify_if_outdated():
    """Show a non-intrusive update message if newer version exists."""
    result = check_for_updates()

    if result["update_available"]:
        print(f"\n💡 Update available: {result['current_version']} → {result['latest_version']}")
        print(f"   Run: pip install --upgrade my-cli-tool\n")

Add to your CLI entry point:

from my_cli.updater import notify_if_outdated

@click.command()
def main():
    # Check for updates (non-blocking)
    notify_if_outdated()

    # Rest of your CLI logic...

Why this works:

✅ Non-blocking (doesn't slow down the tool)
✅ Respects user's time (3-second timeout)
✅ Encourages users to stay updated
✅ Simple one-liner to integrate

5. Progress Indicators for Long Operations

Users hate silent commands. Show them what's happening:

from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn

def process_files(file_list: list):
    """Process multiple files with progress indication."""

    with Progress(
        SpinnerColumn(),
        TextColumn("[progress.description]{task.description}"),
        BarColumn(),
        TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
    ) as progress:

        task = progress.add_task("[cyan]Processing files...", total=len(file_list))

        for file_path in file_list:
            # Your processing logic
            process_single_file(file_path)

            progress.update(task, advance=1, description=f"[cyan]Processing {file_path.name}")

Install rich: pip install rich

Why it matters:

✅ Users know the tool is working (not frozen)
✅ Provides time estimates
✅ Professional appearance
✅ Increases confidence in your tool

Key Takeaways

Building production-ready CLI tools isn't about fancy features—it's about respecting your users' time and making debugging effortless.

The 5 critical patterns:

✅ Structured logging (not print debugging)

✅ Graceful error handling (no ugly stack traces)

✅ Automatic updates (users stay current)

✅ Progress indicators (show what's happening)

✅ Smart configuration (defaults + easy overrides)

Use these patterns together, and you'll build tools your team actually wants to use.

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Before you add another print statement — save yourself the pain.

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⏱ print() marathon: 47 minutes
⏱ pdb.set_trace(): 12 minutes
⏱ icecream (Technique #4): 3 minutes

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I've built a battle-tested Python CLI template with everything in this guide plus advanced patterns. Includes:

✅ Full working example with all patterns

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✅ Advanced error handling

✅ Performance profiling

✅ Deployment guides

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Questions or improvements? Drop a comment below. I read every single one.

Happy coding! 🚀

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