Python Logging Libraries
Python logging libraries build upon the standard logging module to provide more advanced implementations. Loguru, Structlog, and others offer features needed for modern Python applications including simple APIs, structured logging, and async support.
Key Features
- Multiple Output Levels
- Structured Logging
- High Performance
- Multiple Output Destinations
- Rich Ecosystem
Representative Libraries
- Pino: Ultra-fast JSON logging library
- Winston: Most versatile logging library
- Bunyan: Structured JSON-focused logging library
- Morgan: Express.js HTTP logging middleware
GitHub Star Comparison
| No | Name | GitHub Stars | Description | Trend | License | Type | Official Site |
|---|---|---|---|---|---|---|---|
| 1 | Rich (Logging) | ⭐ 52.9k | Logging functionality provided as part of rich text display library. Offers abundant display options including beautiful formatting, syntax highlighting, table format display, and progress bars. Enables high-quality output experience in console applications. | Growing alongside demand for console application quality improvement in 2025. Increasing adoption in CLI tools and debugging tools. Contributing significantly to developer experience improvement through expressive output beyond simple logging. | MIT | Rich Text Display | Official |
| 2 | python-json-logger | ⭐ 1.8k | JSON output formatter for Python's standard library logging. Outputs log entries in structured JSON format, facilitating integration with log aggregation systems. Supports modern log management infrastructure while maintaining full compatibility with standard library. | Demand rapidly increasing due to cloud-native environments and microservices proliferation in 2025. Growing demand for integration with log analysis platforms like ELK stack, Fluentd, and Datadog, expanding adoption in DevOps teams. | BSD | JSON Output | Official |
| 3 | coloredlogs | ⭐ 553 | Color output extension library for Python's standard library logging. Provides customizable color formatter using ANSI escape sequences, enabling color-coded display based on log levels. Maintains full compatibility with standard library. | Maintains steady demand as development experience enhancement tool in 2025. Contributes to improved visibility in IDE integrated environments and terminal work. Valued for low introduction cost to existing codebases as standard library extension. | MIT | Color Enhancement | Official |
| 4 | logging (Standard Library) | - | Powerful logging framework included in Python's standard library. Excellent configurability and extensibility, widely adopted in complex enterprise applications. Enables multi-level configuration and unified management with third-party libraries without external dependencies. | Maintains stable position as industry standard in 2025. Demand remains consistently high in complex systems, essential in environments where compatibility with third-party libraries is prioritized. High learning cost but very powerful once mastered. | Python Software Foundation License | Built-in Framework | Official |
| 5 | Loguru | - | Most popular third-party logging library for Python (15,000+ GitHub stars). Simple and intuitive API with rich pre-configured features enables complex logging setup with minimal code. Provides colorful console output and detailed stack trace display. | Maintains high growth rate as primary recommended library in 2025. Adoption rate in new projects rapidly increasing. Balanced simplicity and powerful features are highly valued, established as first choice for small to medium-scale projects. | MIT | Simple Framework | Official |
| 6 | structlog | - | Python library specialized in structured log output (2,500+ GitHub stars). Supports structured output in JSON and Logfmt formats, generates beautiful console output in development environments and machine-readable logs in production. Supports asyncio, context variables, and type hints. | Gaining attention as importance of structured logging increases in 2025. Expanding demand for integration with log aggregation systems and usage in microservices environments. High future potential evaluated due to support for modern Python technologies (asyncio, type hints). | MIT | Structured Framework | Official |