{"categories":[{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks/2"}],"enrichment":{"capability":"Draccus parses command-line arguments and configuration files into Python dataclasses, with support for nested configs, config file inclusion, and runtime type selection via choice registries.","skillfed_tags":["configuration-management","dataclass-based","cli-framework"],"use_cases":["Machine learning experiment configs: define model, optimizer, and training parameters as nested dataclasses and switch between them via config files.","Multi-environment deployment: split base config, environment-specific overrides, and secrets into separate files and include them in a main config.","CLI tools with complex options: replace argparse boilerplate with a clean dataclass definition and automatic --help generation.","Configuration inheritance: use choice registries to let users select between different implementations (e.g., database backends) at startup.","Config validation and IDE support: leverage dataclass type hints for autocomplete and static type checking of your configuration structure."],"what_it_does":"Draccus is a configuration framework that binds command-line arguments and YAML/TOML/JSON config files directly to Python dataclasses. It extends Pyrallis with support for config file inclusion (allowing you to split configs across files), choice registries (runtime selection between different config subtypes), and better handling of nested config containers. Your dataclass fields become both type-checked configuration parameters and IDE-autocompleted attributes; the framework parses arguments from the command line, a config file, or both, and initializes your dataclass with the result.\n\nThe library depends on pyyaml, toml, mergedeep, and typing-inspect to handle parsing, merging, and type resolution. It's designed for projects that need structured, nested configurations\u2014particularly machine learning workflows where you might choose between different model architectures or optimizer types at runtime. The decorator-based API (@draccus.wrap) keeps the boilerplate minimal.","worth_installing":"Yes. Draccus is actively maintained, has no known vulnerabilities, and solves a real problem\u2014structured config management with IDE support\u2014more cleanly than raw argparse or manual YAML loading. The low install friction and permissive license make it a low-risk addition. The Alpha status reflects its origin as a fork, not instability; it's already in production use. Install it if you need nested configs, config file inclusion, or runtime type selection; skip it if your config needs are simple enough for a basic argparse setup."},"id":"draccus","links":{"html":"https://skillfed.io/packages/draccus","md":"https://skillfed.io/packages/draccus.md","pypi":"https://pypi.org/project/draccus/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-12","license_spdx":null,"license_treatment":"permissive","name":"draccus","python_support":"supports_current","summary":"A slightly opinionated framework for simple dataclass-based configurations based on Pyrallis."},"popularity":{"monthly_downloads":838513,"position":4928,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.11.6"}