daiquiri
Library to configure Python logging easily
Decision gist · record as of 2026-08-14
Yes. Daiquiri is actively maintained, has no known vulnerabilities, carries a permissive Apache 2.0 license, and installs with minimal friction. It's a good fit if you want to reduce logging boilerplate in Python applications targeting Python 3.10 or later. Install it if you prefer declarative logging setup over manual configuration; skip it if your project already has a mature logging strategy.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction install with a single runtime dependency.
- The package is actively maintained with a recent commit on 2026-08-13 and supports Python 3.10, 3.11, 3.12, and 3.13.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), which allows free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2025-09-04 (344 days) · last repo commit 2026-08-13 · 342 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 124,202 downloads/mo, #11,878 on PyPI
Alternatives
Verify before relying
pip install daiquiri
import daiquiri
daiquiri.setup()- What specific custom formatters and handlers does daiquiri provide beyond python-json-logger's capabilities?
- Does daiquiri support async logging or only synchronous handlers?
- What are the typical performance characteristics when using daiquiri for high-volume logging?
What it is and what it does
Daiquiri is a Python logging configuration helper that reduces boilerplate when setting up logging in applications. Instead of manually configuring loggers, handlers, and formatters, you call daiquiri's setup function to get a working logging pipeline. It wraps python-json-logger to enable structured JSON logging and provides additional custom formatters and handlers for common use cases.
The package targets system administrators and IT professionals who need reliable, easy-to-configure logging for production applications. It's designed for POSIX/Linux environments and supports Python 3.10 and later. With low install friction and no security vulnerabilities, it's a straightforward addition to projects that want structured logging without extensive configuration code.
Use it for
- Set up JSON-structured logging in a new application with a single function call instead of manual handler and formatter configuration.
- Configure logging for microservices or containerized applications where structured logs are parsed by log aggregation systems.
- Add consistent logging to system administration scripts and tools running on Linux servers.
- Replace ad-hoc print statements in CLI tools with proper logging that can be easily toggled between console and file output.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Daiquiri is actively maintained, has no known vulnerabilities, carries a permissive Apache 2.0 license, and installs with minimal friction. It's a good fit if you want to reduce logging boilerplate in Python applications targeting Python 3.10 or later. Install it if you prefer declarative logging setup over manual configuration; skip it if your project already has a mature logging strategy.
Install
daiquiri on PyPI
Before you install
Low friction install with a single runtime dependency. The package is actively maintained with a recent commit on 2026-08-13 and supports Python 3.10, 3.11, 3.12, and 3.13.
Requires Python 3.10 or later.
License in practice
Licensed under Apache 2.0 (permissive), which allows free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
pip install daiquiri
import daiquiri
daiquiri.setup()Verify before relying
- What specific custom formatters and handlers does daiquiri provide beyond python-json-logger's capabilities?
- Does daiquiri support async logging or only synchronous handlers?
- What are the typical performance characteristics when using daiquiri for high-volume logging?
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepython-json-logger |
| Maintenance | Actively maintained 344 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 124,202 / month, #11,878 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Information TechnologyIntended Audience :: System AdministratorsLicense :: OSI Approved :: Apache Software LicenseOperating System :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: daiquiri-3.4.0-py3-none-any.whl
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See also logfmter · mdc · loguru · JSON-log-formatter · logging-json · json-logging · django-structlog · logzero · google-cloud-logging · django-datadog-logger