datafiles
File-based ORM for dataclasses.
Decision gist · record as of 2026-08-14
Yes. Datafiles is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers a clean, low-friction way to persist dataclasses to disk with bidirectional sync. Install it if you need automatic file-based persistence for configuration, state, or fixtures; skip it if you require a traditional database or need Python versions below 3.10.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (minimum 3.10, maximum <4.0).
- Low friction: pure Python wheel with seven runtime dependencies (cached_property, classproperties, json-five, minilog, parse, ruamel.yaml, tomlkit).
- Active maintenance with last commit 2026-08-05 and 213 repository stars; supports Python 3.10–3.14.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) imposes no restrictions on use, modification, or distribution in proprietary or open-source projects.
last release 2026-01-30 (196 days) · last repo commit 2026-08-05 · 213 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 133,532 downloads/mo, #11,508 on PyPI
Alternatives
Verify before relying
from dataclasses import dataclass
from datafiles import datafile
@datafile("config/{self.name}.yml")
@dataclass
class Config:
name: str
value: int = 0
config = Config("app")
config.value = 1 # automatically saved to config/app.yml- Whether round-trip formatting preservation works equally well across all supported formats (YAML, JSON, TOML, JSON5).
- Performance characteristics when synchronizing large dataclass instances or deeply nested structures.
- Behavior when multiple processes or threads modify the same dataclass instance concurrently.
What it is and what it does
Datafiles bridges Python dataclasses and the filesystem by automatically persisting object state to files and reloading changes from disk. You decorate a dataclass with a file path pattern, then read and write the object normally—changes sync bidirectionally without explicit save/load calls. It supports YAML, JSON, TOML, and JSON5 formats, preserving comments and formatting where possible.
The library is designed for configuration management, test fixtures, version-controlled state, and prototyping data models before committing to a database backend. It depends on ruamel.yaml, tomlkit, json-five, parse, minilog, classproperties, and cached_property to handle format-specific serialization and property introspection.
Use it for
- Store application configuration in YAML or TOML files that remain human-editable while staying type-safe in Python.
- Load test fixtures from files into dataclass instances, automatically coercing types and validating structure.
- Synchronize application state across machines using file-sharing services by treating the filesystem as a shared data layer.
- Prototype data models with file-based persistence before migrating to a database backend.
- Version-control program state and configuration by keeping dataclass instances synchronized to committed files.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Datafiles is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers a clean, low-friction way to persist dataclasses to disk with bidirectional sync. Install it if you need automatic file-based persistence for configuration, state, or fixtures; skip it if you require a traditional database or need Python versions below 3.10.
Install
datafiles on PyPI
Before you install
Low friction: pure Python wheel with seven runtime dependencies (cached_property, classproperties, json-five, minilog, parse, ruamel.yaml, tomlkit). Active maintenance with last commit 2026-08-05 and 213 repository stars; supports Python 3.10–3.14.
Requires Python 3.10 or later (minimum 3.10, maximum <4.0).
License in practice
MIT license (permissive) imposes no restrictions on use, modification, or distribution in proprietary or open-source projects.
Quickstart
from dataclasses import dataclass
from datafiles import datafile
@datafile("config/{self.name}.yml")
@dataclass
class Config:
name: str
value: int = 0
config = Config("app")
config.value = 1 # automatically saved to config/app.ymlVerify before relying
- Whether round-trip formatting preservation works equally well across all supported formats (YAML, JSON, TOML, JSON5).
- Performance characteristics when synchronizing large dataclass instances or deeply nested structures.
- Behavior when multiple processes or threads modify the same dataclass instance concurrently.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagescached_propertyclasspropertiesjson-fiveminilogparseruamel.yamltomlkit |
| Maintenance | Actively maintained 196 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 133,532 / month, #11,508 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 :: MIT LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Topic :: Software DevelopmentTopic :: Utilities |
Evidence: datafiles-2.5-py3-none-any.whl
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See also dataclass-wizard · YORM · dataclasses · typed-json-dataclass · databind · dataclasses-json · pyserde · linkml-runtime · draccus · shared