Skip to main content

dataclass-click

Structure your click arguments.

dataclass-click lets you move your user arguments from kwargs to dataclasses, keeping things self-contained.

Click is pretty simple to start with, but when your program gets complex, you find yourself repeating a lot of work. This is particularly true if you have command groups with shared arguments.

The idea of dataclass-click is to move the @option and @argument decorators off into annotations on dataclasses and pass dataclass objects instead of kwargs.

Simple Example

from dataclasses import dataclass
from pathlib import Path
from typing import Annotated

import click
from dataclass_click import argument, dataclass_click, option


@dataclass
class Config:
    # Auto-inferred type for built-in click types
    target: Annotated[Path, argument()]  
    
    # Auto-inferred option names
    foo: Annotated[float, option(required=True)]
    
    # Automatically map mismatched names
    bar: Annotated[int | None, option("--other")]


@click.command()
@dataclass_click(Config)
def main(config: Config):
    # All args neatly packaged.
    print(config.target, config.foo, config.bar)
    print(list(config.target.iterdir())) 

Inference Features

Name inference

The pythonic name for options and attributes is copied from the dataclass attribute name. Options can then be named whatever you want. If you don't specify an option name, it will be inferred for you:

from dataclasses import dataclass
from dataclass_click import option, argument
from typing import Annotated

@dataclass
class Config:
    foo: Annotated[str, argument()]
    bar: Annotated[str, option()]
    baz: Annotated[str, option("--bonno")]
Usage: main.py [OPTIONS] FOO

Options:
  --bonno TEXT
  --bar TEXT
  --help        Show this message and exit.

Type Inference (Extensible)

Out of the box...

data-class click can infer click parameter type (eg: option(type=click.INT)) from the attribute type. These types are static so if, like click.Path, you need arguments, you can always specify the type yourself like normal.

from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from typing import Annotated
from uuid import UUID

from dataclass_click import option


@dataclass
class Config:
    a: Annotated[str, option()]       # click.STRING
    b: Annotated[bool, option()]      # click.BOOL
    c: Annotated[int, option()]       # click.INT
    d: Annotated[float, option()]     # click.FLOAT
    e: Annotated[UUID, option()]      # click.UUID
    f: Annotated[datetime, option()]  # click.DateTime()
    g: Annotated[Path, option()]      # click.Path(path_type=Path)

Extending it...

Type inference is extensible so a program can add and even change the default click ParameterTypes associated with a Python data type. Eg: if you want to add a Decimal:

from decimal import Decimal

import click
import dataclass_click


class DecimalParameterType(click.ParamType):
    name = "integer"

    def convert(self, value, param, ctx):
        if isinstance(value, Decimal):
            return value
        try:
            return Decimal(value)
        except ValueError:
            self.fail(f"{value!r} is not a valid decimal number", param, ctx)


DECIMAL = DecimalParameterType()

# Extend type inference to add Decimal
dataclass_click.register_type_inference(Decimal, DECIMAL)

Required Inference

To avoid tricky mismatches between required options and optional attributes, dataclass-click will infer a field is required if neither default= nor required= are explicitly set and the attribute type hint is not optional:

from dataclasses import dataclass
from typing import Annotated

from dataclass_click import option

@dataclass
class Config:
    # Infer --foo is required because str is not optional
    foo: Annotated[str, option()]
    
    # Infer --bar is not required because str | None is optional
    bar: Annotated[str | None, option()]
    
    # No inference performed because required= is set.
    baz: Annotated[str | None, option(required=True)]
    
    # Infer not required because default= set.
    bob: Annotated[str, option(default="no")]
    
    # If you want to shoot yourself in the foot, you can!
    # Not inferred because required=False.  
    # Click will pass None even though the attribute is not optional
    bonno: Annotated[str, option(required=False)]  
Usage: main.py [OPTIONS]

Options:
  --bob TEXT
  --baz TEXT  [required]
  --bar TEXT
  --foo TEXT  [required]
  --bonno
  --help      Show this message and exit.

Metadata

Release files for dataclass-click 1.0.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for dataclass-click 1.0.4
File Size Uploaded
dataclass_click-1.0.4.tar.gz 7.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for dataclass-click 1.0.4
File Interpreter ABI Platform
dataclass_click-1.0.4-py3-none-any.whl Python 3 none any Details

Total release size: 16.1 kB

Release files / dataclass_click-1.0.4.tar.gz

Download URL dataclass_click-1.0.4.tar.gz
Size 7.6 kB
Tags Source
SHA-256 checksum
How to use checksums
10e7de638dd9e68ae9abd5086f61d8ddee42b1873a70f5fd9fd2167856afac11
BLAKE2b-256 checksum
How to use checksums
89825b6035efd90621771fa039960eab3e1ec7ff2a8625033272856843e8bd27
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 10, 2025.

Transparency log

Release files / dataclass_click-1.0.4-py3-none-any.whl

Download URL dataclass_click-1.0.4-py3-none-any.whl
Size 8.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
a225d30c04e4abbdba411cc3d5ec0a2ea829e1dca6500afe5f87cc243e5ead72
BLAKE2b-256 checksum
How to use checksums
86dc38a94a2eb5f756724a6dc87a7aea38f7b747fe7b2e9daabc34a65e6cd9ac
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 10, 2025.

Transparency log

Release history Release notifications | RSS feed

This release

1.0.4 This release

2 release files

1.0.3

2 release files

1.0.2

2 release files

1.0.1

2 release files

1.0.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page