voluptuous
Python data validation library
Decision gist · record as of 2026-08-14
Yes, if you need straightforward schema validation for structured data. Voluptuous is stable, has no dependencies, and is widely used. The contributions-only maintenance model is a trade-off: expect community-driven fixes and no proactive feature work, but the core library is mature and unlikely to need frequent changes. Choose it if you prefer declarative schemas over class-based validators.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- Low friction—pure Python wheel with no runtime dependencies.
- Actively maintained with recent releases; however, the maintainer has stated the project is in contributions-only mode and no longer uses it personally, so expect community-driven fixes rather than proactive feature development.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive; you may use, modify, and distribute voluptuous freely in commercial and open-source projects with minimal restrictions.
last release 2025-12-18 (239 days) · last repo commit 2026-07-25 · 1,850 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 6,890,471 downloads/mo, #1,820 on PyPI
Alternatives
Verify before relying
pip install voluptuous
from voluptuous import Schema, Required, All, Range
schema = Schema({
Required('q'): str,
'per_page': All(int, Range(min=1, max=20)),
})
result = schema({'q': 'search term', 'per_page': 10})- Performance characteristics on very large or deeply nested data structures.
- Extent of community support and PR review turnaround given contributions-only status.
What it is and what it does
Voluptuous is a Python data validation library designed to check incoming data (from JSON, YAML, APIs, etc.) against a schema you define. Rather than writing custom validation code, you describe your data structure using simple Python types and callables—dictionaries, lists, type checks, and custom validators—and Voluptuous matches input against that schema, raising detailed exceptions when validation fails.
The library prioritizes simplicity and useful error messages. Schemas are just Python data structures: `{int: str}` means a dict with integer keys and string values, `[int, float, str]` means a list containing only those types. You can nest schemas arbitrarily and compose validators using functions like `Required`, `All`, `Range`, and `Length`. When validation fails, you get a clear path to the problem in your data.
Use it for
- Validate API request parameters before processing them in a web service.
- Check configuration files (JSON/YAML) match expected structure at application startup.
- Enforce data contracts in data pipelines before passing records downstream.
- Validate form submissions or user input in web applications.
- Ensure nested data structures from external sources conform to expected types and constraints.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need straightforward schema validation for structured data.
Voluptuous is stable, has no dependencies, and is widely used. The contributions-only maintenance model is a trade-off: expect community-driven fixes and no proactive feature work, but the core library is mature and unlikely to need frequent changes. Choose it if you prefer declarative schemas over class-based validators.
Install
voluptuous on PyPI
Before you install
Low friction—pure Python wheel with no runtime dependencies. Actively maintained with recent releases; however, the maintainer has stated the project is in contributions-only mode and no longer uses it personally, so expect community-driven fixes rather than proactive feature development.
Requires Python 3.9 or later.
License in practice
BSD-3-Clause is permissive; you may use, modify, and distribute voluptuous freely in commercial and open-source projects with minimal restrictions.
Quickstart
pip install voluptuous
from voluptuous import Schema, Required, All, Range
schema = Schema({
Required('q'): str,
'per_page': All(int, Range(min=1, max=20)),
})
result = schema({'q': 'search term', 'per_page': 10})Verify before relying
- Performance characteristics on very large or deeply nested data structures.
- Extent of community support and PR review turnaround given contributions-only status.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 239 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 6,890,471 / month, #1,820 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9 |
Evidence: voluptuous-0.16.0-py3-none-any.whl
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See also voluptuous-serialize · voluptuous-openapi · schema · jsonschema · xmlschema · avro-validator · yamale · jsonschema-rs · zope.schema · fastjsonschema