---
id: jsf
version: "0.11.2"
license: unclear
license_treatment: permissive
maintenance: aging
---

# jsf — Creates fake JSON files from a JSON schema

License: permissive · Maintenance: aging · Downloads: 748.8K/mo

## What it is and what it does

jsf is a Python port of json-schema-faker that turns JSON Schema definitions into streams of realistic fake data. You define a schema with optional $provider directives (pointing to faker methods or custom functions), and jsf generates valid JSON objects matching that schema. It validates output against the schema, supports multi-level state for dependent fields (e.g., siblings sharing a surname), and can convert schemas to Pydantic models with generated examples.

The package is designed for test-data generation, rapid prototyping, and API mocking. It integrates with FastAPI to auto-generate example responses and schemas in OpenAPI docs. You can use it as a library in Python code, load schemas from JSON files, or run it from the command line or Docker. Six runtime dependencies (faker, jsonschema, pydantic, rstr, smart-open, typing-extensions) handle schema validation, data generation, and file I/O.

Use it for:

- Generate realistic test fixtures for unit and integration tests without hand-crafting mock data
- Populate FastAPI endpoints with auto-generated example responses and interactive schema documentation
- Create synthetic datasets for development and staging environments that match your data contracts
- Prototype APIs and data pipelines before real data is available or during schema design
- Validate JSON schemas by generating and verifying conformant fake data

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Generates realistic fake JSON data from JSON Schema definitions, using pluggable data providers like faker to populate fields with consistent, meaningful test data.

Yes, if you need test-data generation from JSON schemas. The package is stable, permissively licensed, and has low install friction. Maintenance is aging (last release March 2024, no commits for 871 days), so it's best suited for established schemas that don't require frequent updates. No known vulnerabilities. Good fit for testing, mocking, and prototyping workflows.

## Install

pip install jsf
uv add jsf
poetry add jsf

## Installing jsf

Before you install: Low friction install with six runtime dependencies (faker, jsonschema, pydantic, rstr, smart-open, typing-extensions). Maintenance is aging—last release was in March 2024 and no commits for 871 days—but the repository remains active and the package is stable enough for test-data use.

License in practice: MIT License (permissive). You can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.

Quickstart:

from jsf import JSF

faker = JSF({
    "type": "object",
    "properties": {
        "name": {"type": "string", "$provider": "faker.name"},
        "email": {"type": "string", "$provider": "faker.email"},
    },
    "required": ["name", "email"],
})

fake_json = faker.generate()

Verify before relying:

- Whether custom $provider lambdas can reference all faker methods or only a documented subset
- Performance characteristics when generating large volumes of data or deeply nested schemas
- Extent of contentMediaType support beyond the noted 'subset' of string types

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 748.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

json schema fake data generator, test data generation from schema, faker json schema, mock json data, synthetic data from schema, json schema to fake objects, test fixture generation, test-data, schema-driven, fastapi-integration

[View on SkillFed](https://skillfed.io/packages/jsf) · [View on PyPI](https://pypi.org/project/jsf/)