hypothesis-jsonschema
Generate test data from JSON schemata with Hypothesis
Decision gist · record as of 2026-08-14
Yes, if you use Hypothesis for property-based testing and need to generate JSON data. The package is stable, has no known vulnerabilities, and low install friction. The aging maintenance status (no release in 898 days) is a minor concern for new features but not a blocker for current use—the API is small and the underlying dependencies are mature. Suitable for production test suites.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later.
- Hypothesis must be installed and configured for your test runner.
- Low friction: pure Python wheel with only two runtime dependencies (hypothesis and jsonschema).
License · maintenance · safety
MPL 2.0 (copyleft) — Licensed under MPL 2.0 (copyleft). You must disclose source modifications and can use it in proprietary code, but derivative works of the library itself must remain open-source under the same license.
last release 2024-02-28 (898 days) · last repo commit 2025-12-05 · 1 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,380,433 downloads/mo, #2,645 on PyPI
Alternatives
Verify before relying
from hypothesis import given
from hypothesis_jsonschema import from_schema
@given(from_schema({"type": "integer", "minimum": 1, "exclusiveMaximum": 10}))
def test_integers(value):
assert isinstance(value, int)
assert 1 <= value < 10- Whether the package still actively maintains support for JSON Schema drafts beyond 07, or if newer drafts require manual updates.
- Current compatibility with the latest versions of hypothesis and jsonschema, given the 898-day gap since last release.
What it is and what it does
hypothesis-jsonschema bridges Hypothesis (a property-based testing framework) and JSON Schema by converting a schema into a Hypothesis strategy that generates valid JSON data matching that schema. Rather than hand-writing test inputs, you define a schema and the package generates diverse, valid examples automatically—useful for testing APIs, data pipelines, or any code that consumes JSON.
The package exposes a single public function, `from_schema()`, which takes a JSON schema and optional configuration (custom format handlers, encoding constraints) and returns a strategy for Hypothesis's `@given` decorator. It supports JSON Schema drafts 04, 05, and 07, handles non-recursive schema references, and allows you to customize how custom formats are generated. Dependencies are minimal: only hypothesis and jsonschema.
Use it for
- Test REST API endpoints by generating valid JSON payloads that match your OpenAPI or JSON Schema specification.
- Fuzz data validation logic by creating diverse inputs that conform to a schema but exercise edge cases.
- Verify data transformation pipelines by generating schema-compliant input and checking output correctness.
- Build schema-driven test suites where test data is derived automatically from your schema rather than hardcoded.
- Validate custom format handlers by generating data with specific string formats (e.g., email, UUID, card numbers).
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use Hypothesis for property-based testing and need to generate JSON data.
The package is stable, has no known vulnerabilities, and low install friction. The aging maintenance status (no release in 898 days) is a minor concern for new features but not a blocker for current use—the API is small and the underlying dependencies are mature. Suitable for production test suites.
Install
hypothesis-jsonschema on PyPI
Before you install
Low friction: pure Python wheel with only two runtime dependencies (hypothesis and jsonschema). Maintenance status is aging—last release was 898 days ago, though the repository remains active with a recent commit on 2025-12-05.
Requires Python 3.8 or later. Hypothesis must be installed and configured for your test runner.
License in practice
Licensed under MPL 2.0 (copyleft). You must disclose source modifications and can use it in proprietary code, but derivative works of the library itself must remain open-source under the same license.
Quickstart
from hypothesis import given
from hypothesis_jsonschema import from_schema
@given(from_schema({"type": "integer", "minimum": 1, "exclusiveMaximum": 10}))
def test_integers(value):
assert isinstance(value, int)
assert 1 <= value < 10Verify before relying
- Whether the package still actively maintains support for JSON Schema drafts beyond 07, or if newer drafts require manual updates.
- Current compatibility with the latest versions of hypothesis and jsonschema, given the 898-day gap since last release.
Package facts
| License | MPL 2.0 copyleft |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packageshypothesisjsonschema |
| Maintenance | Aging 898 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,380,433 / month, #2,645 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaFramework :: HypothesisIntended Audience :: DevelopersLicense :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)Programming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Education :: TestingTopic :: Software Development :: TestingTyping :: Typed |
Evidence: hypothesis_jsonschema-0.23.1-py3-none-any.whl
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See also hypothesis-graphql · hypothesis · schemathesis · hegel-core · jsonschema-typed-v2 · marshmallow-jsonschema · dydantic · jsonschema-specifications · jschon · json-ref-dict