Cappa

• Full documentation here.
• Comparison vs existing libraries..
• Annotation inference details
• "invoke" (click-like) details

Cappa is a declarative command line parsing library, taking much of its inspiration from the "Derive" API from the Clap written in Rust.

from dataclasses import dataclass, field
import cappa
from typing import Literal
from typing_extensions import Annotated


@dataclass
class Example:
    positional_arg: str = "optional"
    boolean_flag: bool = False
    single_option: Annotated[int | None, cappa.Arg(short=True, help="A number")] = None
    multiple_option: Annotated[
        list[Literal["one", "two", "three"]],
        cappa.Arg(long=True, help="Pick one!"),
    ] = field(default_factory=list)


args: Example = cappa.parse(Example, backend=cappa.backend)
print(args)

Produces the following CLI:

In this way, you can turn any dataclass-like object (with some additional annotations, depending on what you're looking for) into a CLI.

You'll note that cappa.parse returns an instance of the class. This API should feel very familiar to argparse, except that you get the fully typed dataclass instance back instead of a raw Namespace.

Invoke

"invoke" documentation

The "invoke" API is meant to feel more like the experience you get when using click or typer. You can take the same dataclass, but register a function to be called on successful parsing of the command.

from dataclasses import dataclass
import cappa
from typing_extensions import Annotated

def function(example: Example):
    print(example)

@cappa.command(invoke=function)
class Example:  # identical to original class
    positional_arg: str
    boolean_flag: bool
    single_option: Annotated[int | None, cappa.Arg(long=True)]
    multiple_option: Annotated[list[str], cappa.Arg(short=True)]


cappa.invoke(Example)

(Note the lack of the dataclass decorator. You can optionally omit or include it, and it will be automatically inferred).

Alternatively you can make your dataclass callable, as a shorthand for an explcit invoke function:

@dataclass
class Example:
    ...   # identical to original class

    def __call__(self):
       print(self)

Note invoke=function can either be a reference to some callable, or a string module-reference to a function (which will get lazily imported and invoked).

With a single top-level command, the click-like API isn't particularly valuable by comparison. Click's command-centric API is primarily useful when composing a number of nested subcommands.

Subcommands

The useful aspect of click's functional composability is that you can define some number of subcommands functions under a parent command, whichever subcommand the function targets will be invoked.

import click

@click.group('example')
def example():
    ...

@example.command("print")
@click.option('--loudly', is_flag=True)
def print_cmd(loudly):
    if loudly:
      print("PRINTING!")
    else:
      print("printing!")

@example.command("fail")
@click.option('--code', type: int)
def fail_cmd(code):
    raise click.Exit(code=code)

# Called like:
# /example.py print
# /example.py fail

Whereas with argparse, you'd have had to manually match and call the funcitons yourself. This API does all of the hard parts of deciding which function to call.

Similarly, you can achieve the same thing with cappa.

from __future__ import annotations
from dataclasses import dataclass
import cappa

@dataclass
class Example:
    cmd: cappa.Subcommands[Print | Fail]


def print_cmd(print: Print):
    if print.loudly:
        print("PRINTING!")
    else:
        print("printing!")

@cappa.command(invoke=print_cmd)
class Print:
    loudly: bool

@dataclass
class Fail:
    code: int

    def __call__(self):  # again, __call__ is shorthand for the above explicit `invoke=` form.
        raise cappa.Exit(code=code)

cappa.invoke(Example)

Function-based Commands

Purely functions-based can only be used for certain kinds of CLI interfaces. However, they can reduce the ceremony required to define a given CLI command.

import cappa
from typing_extensions import Annotated

def function(foo: int, bar: bool, option: Annotated[str, cappa.Arg(long=True)] = "opt"):
    ...


cappa.invoke(function)

Such a CLI is exactly equivalent to a CLI defined as a dataclass with the function's arguments as the dataclass's fields.

There are various downsides to using functions. Given that there is no class to reference, any feature which relies on being able to name the type will be impossible to use. For example, subcommands cannot be naturally defined as functions (since there is no type with which to reference the subcommand).