Python Logging

Overview

Choose the logging system based on project boundaries. Core principle: use stdlib logging for reusable libraries and ecosystem integration; use loguru only when an app or CLI owns the whole logging surface.

Use When

• Choosing between stdlib logging and loguru.
• Configuring log levels, handlers, formatters, or structured context.
• Adding logging to libraries, apps, CLIs, or services.
• Deciding how logging should interact with ops tooling.

Quick Reference

Need	Use
Library or long-lived service	stdlib logging
Simple app or CLI	loguru
Integrations (Sentry/OTel)	stdlib logging
Mixed library + app	stdlib in library; app config at boundary

Decision Rules

Use stdlib logging when:

• Building a reusable library
• You need handler hierarchies or integration with ops tooling
• You expect callers to configure logging

Use loguru when:

• You want minimal setup and readable output
• You are building a small app or CLI
• You control process startup and logging configuration

Workflow

1. Decide whether the code is a library, service, CLI, or one-off app.
2. Keep libraries passive: create logging.getLogger(__name__) and do not call basicConfig().
3. Configure handlers and levels once at the application entry point.
4. Avoid mixing stdlib logging and loguru unless the app owns the bridge and boundary.

Example

Stdlib logger setup:

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
logger.info("App started")

Common Mistakes

• Forcing loguru in a reusable library.
• Mixing two logging systems without a clear boundary.
• Calling basicConfig() inside imported library modules.
• Creating handlers repeatedly in functions that run more than once.

Red Flags

• Logging recommendations with no rationale for library vs app use.
• Global logging setup hidden in modules that are imported by tests or other applications.

References

• references/logging.md - stdlib logging patterns
• references/loguru.md - loguru patterns