# lidatong/dataclasses-json

Easily serialize Data Classes to and from JSON

Repository: https://github.com/lidatong/dataclasses-json
Canonical: https://ross.abutalabs.com/products/dataclasses-json
Language: Python
License: MIT
License Family: permissive
Topics: dataclasses, json, python
Last push: 2026-05-05T03:33:38+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 80, release rhythm 8, longevity 100
- inputs: {"age_days": 3056, "days_push": 120, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1486, forks 170 (observed 2026-08-28T04:04:51.894552+00:00)

## What it is
A Python library providing a simple decorator-based API for serializing dataclasses to and from JSON. It supports nested dataclasses, collections, datetime objects, letter-case configuration, and optional schema validation.

## Use cases
- serialize python dataclasses to
- decode  into typed dataclasses
- convert camelCase  to snake_case dataclass fields
- validate  payloads against dataclass schemas
- handle nested dataclass  serialization
- serialize datetime fields in dataclasses to

## When to choose
- you use Python dataclasses and need easy JSON round-tripping
- you want typed decoding of JSON with minimal boilerplate
- you need letter-case mapping between Python fields and JSON keys
- you want optional schema validation on deserialization

## When to avoid
- you need high-performance serialization for large datasets
- you prefer pydantic-style validation with rich constraints
- you need serialization formats other than JSON

## Facets
- artifact type: library
- maturity: stable
- function: serialization
- domain: developer-tools, apis
- platform: python
- tags: dataclasses, -serialization, schema-validation, letter-case, data-engineering

## Member repositories
- lidatong/dataclasses-json (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:51.894552+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:33:52.247088+00:00, confidence not recorded.
  - readme: https://github.com/lidatong/dataclasses-json (fetched 2026-08-28T04:04:51.894552+00:00, sha 9de75d64b70c)
  - registry_pypi: https://pypi.org/pypi/dataclasses-json/json (fetched 2026-08-29T11:40:02.266707+00:00, sha 6f8dc0e6aafd)
- Data as of 2026-08-30T08:39:29.467469+00:00.