A curated list of awesome Python logging libraries, tools, and resources for production-ready structured logging that's simple, powerful, and fast.
- Built-in Libraries
- Third-Party Libraries
- Structured Logging
- Log Aggregation & Management
- Formatters & Handlers
- Testing & Debugging
- Performance & Monitoring
- Resources
The Python standard library's built-in logging module provides a flexible framework for emitting log messages from Python programs.
Features:
- Hierarchical logger naming
- Multiple severity levels (DEBUG, INFO, WARNING, ERROR, CRITICAL)
- Configurable handlers and formatters
- Thread-safe logging
- Configuration via dictConfig or fileConfig
When to use: Built into Python, no dependencies required. Perfect for simple applications and when you need maximum compatibility.
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
logger.info("Application started")loguru - Ready to use out of the box without boilerplate.
Why it's the best choice for most projects:
- Zero configuration - works immediately
- Automatic string formatting
- Exceptions catching
- Structured logging support
- File rotation and retention
- Asynchronous, thread-safe, and multiprocess-safe
- Beautiful colored console output
from loguru import logger
logger.info("That's it, beautiful and simple logging!")
logger.add("file_{time}.log", rotation="500 MB")Use Loguru when: You want the simplest, most Pythonic logging experience with powerful features out of the box.
structlog - Structured logging for Python.
Features:
- True structured logging (not just JSON)
- Context binding
- Processor pipelines
- Integration with standard library logging
- Type hints throughout
- Excellent performance
import structlog
log = structlog.get_logger()
log.info("user.logged_in", user_id=42, username="alice")Use structlog when: You need production-grade structured logging with context preservation and integration with existing logging infrastructure.
python-json-logger - JSON formatter for the standard library.
Features:
- Drops into existing logging setup
- Configurable field names
- Custom fields support
- Exception formatting
- Compatible with log aggregators
Use when: You want JSON logging but need to stick with the standard library's logging module.
eliot - Logging that tells you why it happened.
Features:
- Action-based logging
- Causal chains
- Message correlation
- Structured by design
- Cross-process tracing
Use when: You need to understand complex system behaviors and trace causality across operations.
logbook - A cool logging replacement for Python.
Features:
- More Pythonic than standard logging
- Contextual logging
- Flexible handler system
- Better performance
- Thread and multiprocess support
pygogo - Structured logging with superpowers.
Features:
- Reduces logging boilerplate
- Automatic log formatting
- Hierarchical logging
- Multiple output formats
- Email notifications
sentry-sdk - Official Python SDK for Sentry.io
Features:
- Error tracking and monitoring
- Performance monitoring
- Release tracking
- Breadcrumbs
- Context enrichment
elasticsearch-py - Official Python client for Elasticsearch
Use with: ELK stack (Elasticsearch, Logstash, Kibana) for centralized logging.
fluent-logger-python - Python client for Fluentd
Features:
- Structured event collector
- Unifies data collection
- JSON-based logging
colorlog - Colored terminal output for Python's logging module
Features:
- ANSI color support
- Custom color schemes
- Compatible with standard logging
- Windows support
rich - Beautiful terminal formatting
Features:
- Rich text and formatting
- Syntax highlighting
- Progress bars
- Tables and panels
- Logging handler included
from rich.logging import RichHandler
import logging
logging.basicConfig(
level="INFO",
format="%(message)s",
handlers=[RichHandler(rich_tracebacks=True)]
)notifiers - Send notifications from your logs
Features:
- 40+ notification services
- Slack, Discord, Email, Telegram, etc.
- Easy integration with logging
pytest-logging - Capture log messages during pytest runs
testfixtures - Collection of helpers for testing, including log capture
logassert - Assert on log messages in tests
logging-tree - Introspect and display the logging tree
logging-color - High-performance colored logging
aiologger - Asynchronous logging for asyncio applications
- Python Logging HOWTO - Official Python documentation
- Logging Best Practices - The Hitchhiker's Guide to Python
- 12-Factor App Logs - Logging as event streams
- Good logging practice in Python
- The Ultimate Guide to Logging in Python
- Structured Logging: The Best Friend You'll Want When Things Go Wrong
- Logging in Python - Real Python
- Advanced Python: Logging - Socratica
For new projects: Use Loguru - it's the most developer-friendly option with excellent defaults.
For enterprise applications: Use structlog - best for production environments requiring structured logging with context preservation.
For standard library compatibility: Use python-json-logger - adds JSON formatting to existing logging setup.
For microservices: Use structlog or eliot - both excel at distributed tracing and correlation.
For debugging complex flows: Use eliot - designed specifically for understanding causality.
For simple scripts: Use Python's built-in logging - no dependencies needed.
| Feature | logging | loguru | structlog | eliot |
|---|---|---|---|---|
| Zero config | ❌ | ✅ | ❌ | ✅ |
| Structured logging | ❌ | ✅ | ✅ | |
| Context binding | ❌ | ✅ | ✅ | |
| Performance | Good | Excellent | Excellent | Good |
| Learning curve | Medium | Low | Medium | High |
| Async support | ✅ | ✅ | ✅ | |
| Type hints | ❌ | ✅ | ❌ | |
| Dependencies | 0 | 0 | 0 | Few |
Contributions are welcome! Please read the contribution guidelines first.
To the extent possible under law, Carrington Muleya has waived all copyright and related or neighboring rights to this work.