When should I use stdlib logging?

Use stdlib logging for libraries, long-lived services, and when you need handler hierarchies or integrations like Sentry/OTel.

When should I use loguru?

Use loguru for small apps or CLIs where you want minimal setup and readable, pleasant output.

Can I mix stdlib logging and loguru?

Best practice is to avoid mixing within the same component; expose stdlib logging from libraries and let apps opt into loguru separately with clear boundaries.

python-logging

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npx machina-cli add skill narumiruna/agent-skills/python-logging --openclaw

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SKILL.md

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Python Logging

Overview

Choose the logging system based on project needs. Core principle: stdlib logging for libraries and ecosystem integration, loguru for fast, simple app/CLI logging.

Quick Reference

Need	Use
Library or long-lived service	stdlib `logging`
Simple app or CLI	`loguru`
Integrations (Sentry/OTel)	stdlib `logging`

Decision Rules

Use stdlib logging when:

Building a reusable library
You need handler hierarchies or integration with ops tooling

Use loguru when:

You want minimal setup and readable output
You are building a small app or CLI

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.

Red Flags

Logging recommendations with no rationale for library vs app use.

References

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

Source

git clone https://github.com/narumiruna/agent-skills/blob/main/skills/python-logging/SKILL.mdView on GitHub

Overview

Python Logging helps you decide between stdlib logging and loguru. Use stdlib logging for libraries and ecosystem integration, and loguru for fast, simple app or CLI logging. This skill guides you to pick the right tool based on project type and goals.

How This Skill Works

Evaluate the project type first: libraries and long-lived services benefit from stdlib logging with hierarchical handlers and ecosystem compatibility, while small apps or CLIs benefit from loguru's minimal setup and readable output. For integrations like Sentry or OTel, stdlib logging is preferred. Avoid mixing systems in the same component and document the boundary.

When to Use It

If you're building a reusable library or long-lived service
If you're creating a simple app or CLI and want minimal setup
If you need integrations (Sentry/OTel) with your logging
If you require handler hierarchies or ops tooling integration (stdlib logging)
If you want to maintain a clear boundary between library code and application logging

Quick Start

Step 1: Assess project type (library vs app) and integration needs
Step 2: Choose stdlib logging for libraries/integrations or loguru for simple apps, and avoid mixing
Step 3: Implement the chosen system with clear boundaries and document the decision

Best Practices

Use stdlib logging for libraries and ecosystem integrations
Use loguru for small apps/CLIs with minimal setup and readable output
Do not mix two logging systems inside the same component; keep boundaries clear
Prefer stdlib logging when integrating with Sentry/OTel for compatibility
Document the chosen approach and rationale in project docs

Example Use Cases

A reusable library that exposes a module logger via stdlib logging for consumer apps to configure
A long-running service using stdlib logging with console and file handlers and central log aggregation
A CLI tool using loguru to provide quick, readable logs with minimal boilerplate
An application that integrates Sentry/OTel using stdlib logging for unified observability
A library refactor that avoids forcing loguru on library users and maintains a clear stdlib boundary

Frequently Asked Questions

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