dargs
Process arguments for the deep modeling project.
Decision gist · record as of 2026-08-14
Yes, if you need declarative argument validation with built-in documentation. The package is actively maintained, has low install friction, and solves a real problem for projects with complex nested configuration. The LGPLv3 license requires careful review if you're building proprietary software, but the library itself is stable and dependency-light.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel and only two lightweight runtime dependencies (typeguard, typing_extensions).
- Repository is active with recent commits and no archived status.
License · maintenance · safety
copyleft license (copyleft) — LGPLv3 copyleft license requires that derivative works and modifications be distributed under the same license; proprietary applications using this as a library may need to comply with linking and disclosure obligations.
last release 2026-02-24 (171 days) · last repo commit 2026-08-10 · 7 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 134,661 downloads/mo, #11,467 on PyPI
Alternatives
Verify before relying
pip install dargs
from dargs import Argument
arg_def = Argument('config', dict, [Argument('key', str)])
arg_def.check({'key': 'value'})- Whether JSON schema generation integrates with external JSON editors as claimed in the description
- Scope and maturity of Sphinx and Jupyter Notebook integration features
- Performance characteristics when validating deeply nested or large argument structures
What it is and what it does
dargs is a Python argument validation and documentation library that examines input dictionaries against a schema you define using the Argument class. It checks types, validates nested keys and sub-argument types, and supports a special variant mode where dictionary contents can be determined by the value of a flag key. The library provides three main methods: check (validates structure), normalize (adds defaults and resolves aliases), and gendoc (outputs documentation with optional HTML anchors for cross-reference).
The package integrates with PEP 484 type annotations and offers native support for Sphinx, Jupyter Notebook, and DP-GUI. It can generate JSON schema from an Argument definition for use with JSON editors, and supports loading dictionary values from external JSON or YAML files via a $ref key. It's designed for projects that need declarative, reusable argument schemas with built-in documentation generation.
Use it for
- Define and validate configuration dictionaries for scientific computing or deep learning pipelines with nested parameters and type safety
- Generate documentation and JSON schemas from argument definitions for use in web UIs or IDE integrations
- Normalize user input by applying default values and resolving aliases before passing to downstream functions
- Handle variant argument structures where the allowed keys depend on the value of a discriminator flag
- Integrate argument validation into Sphinx-based documentation or Jupyter notebooks for interactive parameter exploration
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need declarative argument validation with built-in documentation.
The package is actively maintained, has low install friction, and solves a real problem for projects with complex nested configuration. The LGPLv3 license requires careful review if you're building proprietary software, but the library itself is stable and dependency-light.
Install
dargs on PyPI
Before you install
Low install friction with a pure-Python wheel and only two lightweight runtime dependencies (typeguard, typing_extensions). Repository is active with recent commits and no archived status.
License in practice
LGPLv3 copyleft license requires that derivative works and modifications be distributed under the same license; proprietary applications using this as a library may need to comply with linking and disclosure obligations.
Quickstart
pip install dargs
from dargs import Argument
arg_def = Argument('config', dict, [Argument('key', str)])
arg_def.check({'key': 'value'})Verify before relying
- Whether JSON schema generation integrates with external JSON editors as claimed in the description
- Scope and maturity of Sphinx and Jupyter Notebook integration features
- Performance characteristics when validating deeply nested or large argument structures
Package facts
| License | copyleft license copyleft |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagestypeguardtyping_extensions |
| Maintenance | Actively maintained 171 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 134,661 / month, #11,467 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)Programming Language :: Python :: 3.7 |
Evidence: dargs-0.5.0.post0-py3-none-any.whl
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See also dpgui · typeguard · sphinx-argparse · marshmallow-jsonschema · jschon · lxml-stubs · json-ref-dict · parse-type · flake8-annotations · darglint