value-object-pattern 1.30.0

The Value Object Pattern is a Python package that streamlines the creation and management of value objects in your projects.

📦 Value Object Pattern

The Value Object Pattern is a Python 🐍 package for building immutable, self-validating value objects 📦. It helps move validation, normalization, primitive conversion, and domain-specific constraints out of scattered application code and into small typed objects that can be reused across models, services, APIs, and tests.

Table of Contents

• 📥 Installation
• 📚 Documentation
• ⚡ Quick Start
• 🧩 Core Ideas
• 🤔 Why Value Objects?
• 📦 Core Models
• ✅ Reusable Value Objects
• 🔁 Primitive Conversion
• 🤝 Contributing
• 🔑 License

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📥 Installation

You can install Value Object Pattern using pip:

pip install value-object-pattern

You can install the companion AI-agent skill from skills.sh with Vercel's skills CLI:

npx skills add adriamontoto/value-object-pattern

Review the skill source in skills/value-object-pattern before installing it in sensitive environments.

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📚 Documentation

The root README is the entry point. Deeper guides live in this repository and are linked here:

• docs/README.md: Documentation hub.
• docs/usage/README.md: Custom value objects, validators, processors, and model composition.
• docs/catalog/README.md: Feature map by reusable value-object category.
• Catalog details: primitives, dates, identifiers, internet, and money.
• docs/conversion/README.md: Primitive conversion and nested model behavior.
• AI Skill: Installable skill package that teaches AI agents how to use Value Object Pattern.

This project's DeepWiki documentation is also available for generated repository navigation.

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⚡ Quick Start

Create a value object by subclassing ValueObject[T] and adding validation hooks:

from value_object_pattern import ValueObject, validation


class Age(ValueObject[int]):
    @validation(order=0)
    def _ensure_value_is_integer(self, value: int) -> None:
        if type(value) is not int:
            raise TypeError('Age value must be an integer.')

    @validation(order=1)
    def _ensure_value_is_positive(self, value: int) -> None:
        if value <= 0:
            raise ValueError('Age value must be positive.')


age = Age(value=42)

print(age.value)
# >>> 42
print(repr(age))
# >>> Age(value=42)

Use reusable value objects when the package already provides the constraint:

from value_object_pattern.usables import NotEmptyStringValueObject, PositiveIntegerValueObject

name = NotEmptyStringValueObject(value='Ada')
quantity = PositiveIntegerValueObject(value=3)

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🧩 Core Ideas

Value objects in this package are designed around a few consistent rules:

• They wrap exactly one value and expose it through .value.
• They validate input during construction.
• They can normalize input with @process hooks.
• They reject attribute mutation after construction.
• They compare by concrete class and wrapped value.
• They can customize validation error context with title and parameter.

from value_object_pattern import process
from value_object_pattern.usables import StringValueObject


class LowerTrimmedName(StringValueObject):
    @process(order=0)
    def _trim(self, value: str) -> str:
        return value.strip()

    @process(order=1)
    def _lower(self, value: str) -> str:
        return value.lower()


name = LowerTrimmedName(value='  ADA  ')

assert name.value == 'ada'

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🤔 Why Value Objects?

Value objects make domain rules explicit. A plain str, int, or dict can carry almost anything, so every function that receives it has to remember what "valid" means. A value object gives that rule a name and enforces it at the point where the value enters the model.

Without a value object, the same rule tends to be repeated across services, controllers, tests, and serializers:

def register_user(email: str, age: int) -> None:
    if '@' not in email:
        raise ValueError('Invalid email.')

    if age <= 0:
        raise ValueError('Invalid age.')

With value objects, the signature communicates the expected shape and invalid values are rejected before the rest of the code depends on them:

from value_object_pattern.usables import PositiveIntegerValueObject
from value_object_pattern.usables.internet import EmailAddressValueObject


def register_user(email: EmailAddressValueObject, age: PositiveIntegerValueObject) -> None:
    assert '@' in email.value
    assert age.value > 0

This is useful when a value has a reusable meaning: email address, positive quantity, trimmed name, country code, URL, UUID, money identifier, or any project-specific concept such as TenantSlug, OrderLimit, or CustomerName.

Raw literals and primitives are still the right choice when the value is simple or intentionally exact:

• Use raw primitives inside low-level calculations where no domain rule is being expressed.
• Use hardcoded values for exact examples, snapshots, JSON, SQL, URLs, error messages, and public documentation output.
• Use explicit boundary values for deliberate limits such as zero, one, minimum length, maximum length, empty strings, first date, last date, or a known invalid format.
• Use .value when crossing into libraries, APIs, or storage layers that expect primitives.

from value_object_pattern.usables import PositiveIntegerValueObject

quantity = PositiveIntegerValueObject(value=10)

assert quantity.value == 10
assert quantity.value + 5 == 15

The practical rule is simple: use value objects for named domain constraints, and use primitives for exact literals, low-level operations, and boundary examples.

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📦 Core Models

Model	Purpose
ValueObject[T]	Base class for immutable validated single-value wrappers.
EnumerationValueObject[E]	Stores enum members while accepting enum members or raw enum values.
UnionValueObject[T]	Accepts and converts values that match a union annotation.
BaseModel	Adds representation, equality, copying, and primitive conversion for aggregate-like models.
ListValueObject[T]	Immutable typed list wrapper with helper methods that return new instances.
DictValueObject[K, V]	Immutable typed dictionary wrapper with helper methods that return new instances.

See docs/usage/README.md for examples of each model.

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✅ Reusable Value Objects

The package includes reusable validators for common shapes:

Category	Examples
Primitives	strings, bytes, booleans, integers, floats, None / not-None
String formats	non-empty, trimmed, alpha, alphanumeric, lower/upper case, snake case, kebab case, secret strings
Dates	date, datetime, date strings, datetime strings, timezone objects, timezone names
Identifiers	UUIDs and UUID strings, world codes, Spanish identifiers and vehicle plates
Internet	URLs, hosts, domains, ports, emails, IP addresses, networks, MAC addresses, slugs, keys
Money	IBANs and credit card values

See docs/catalog/README.md for import paths and category guidance.

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🔁 Primitive Conversion

BaseModel, ListValueObject, DictValueObject, and UnionValueObject can convert between primitive data and richer types.

from value_object_pattern import BaseModel
from value_object_pattern.usables import NotEmptyStringValueObject, PositiveIntegerValueObject


class User(BaseModel):
    def __init__(self, name: NotEmptyStringValueObject, age: PositiveIntegerValueObject) -> None:
        self.name = name
        self.age = age


user = User.from_primitives(primitives={'name': 'Ada', 'age': 42})

assert isinstance(user.name, NotEmptyStringValueObject)
assert user.to_primitives() == {'age': 42, 'name': 'Ada'}

More details are available in docs/conversion/README.md.

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🤝 Contributing

We love community help! Before you open an issue or pull request, please read:

• 🤝 How to Contribute
• 🧭 Code of Conduct
• 🔐 Security Policy

Thank you for helping make 📦 Value Object Pattern package awesome! 🌟

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🔑 License

This project is licensed under the terms of the MIT license.

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