polyfactory

Polyfactory is a typed mock-data factory library: declare ModelFactory[T] (or DataclassFactory, MsgspecFactory, AttrsFactory, TypedDictFactory) and .build() returns a fully-populated, validation-passing instance of T. Because the factory inspects the model's annotations and constraints, generated data already respects msgspec.Meta ranges, Pydantic Field constraints, and attrs validators — no additional fixtures needed for happy-path tests.

In Litestar projects, polyfactory's pytest plugin is the canonical way to feed TestClient.post(...) / AsyncTestClient.put(...) payloads. The companion skill litestar:litestar-testing covers the request side.

Code Style Rules

• PEP 604 unions: T | None, never Optional[T].
• from __future__ import annotations rule — Modules that define factory subclasses with introspected Meta config (__model__, __random_seed__, __set_as_default_factory_for_type__) are library-like and SHOULD avoid future annotations on the factory module itself if the model class is also defined there. Test modules that use factories (call .build(), register fixtures) MAY and typically SHOULD use future annotations — they are pure consumer code. The same rule applies as msgspec/dishka/SAQ.
• One factory per model. Don't reuse a factory across unrelated models — clarity beats DRY when the test fails at 3am.
• Pick the right factory base by backend: ModelFactory (Pydantic), DataclassFactory, MsgspecFactory, AttrsFactory, TypedDictFactory. The wrong base silently degrades to attribute-by-attribute introspection and produces lower-quality data.
• Prefer register_fixture over hand-rolled @pytest.fixture wrappers — it gives you both the fixture and the factory class with one decorator.

Quick Reference

Picking the right factory base

Model kind	Factory base	Import
pydantic.BaseModel	ModelFactory	from polyfactory.factories.pydantic_factory import ModelFactory
@dataclass	DataclassFactory	from polyfactory.factories import DataclassFactory
msgspec.Struct	MsgspecFactory	from polyfactory.factories.msgspec_factory import MsgspecFactory
@attrs.define / attr.s	AttrsFactory	from polyfactory.factories.attrs_factory import AttrsFactory
TypedDict	TypedDictFactory	from polyfactory.factories.typed_dict_factory import TypedDictFactory
Beanie / Odmantic / SQLA	dedicated bases	see factories.md

Defining a factory

from dataclasses import dataclass
from polyfactory.factories import DataclassFactory


@dataclass
class Order:
    id: int
    customer_email: str
    total_cents: int
    status: str


class OrderFactory(DataclassFactory[Order]):
    __model__ = Order


# Use it
one = OrderFactory.build()
many = OrderFactory.batch(10)

build() returns a single populated instance. batch(n) returns list[T] of size n. coverage() yields one instance per Union/Optional branch — useful for parametrized tests across discriminated unions.

Customizing fields

from polyfactory import Use
from polyfactory.factories import DataclassFactory


class OrderFactory(DataclassFactory[Order]):
    __model__ = Order

    # Plain literal — every build returns this exact value
    status = "pending"

    # Callable — re-evaluated per build
    customer_email = Use(lambda: "test@example.com")

    # Random choice — re-evaluated per build
    total_cents = Use(DataclassFactory.__random__.randint, 100, 10_000)

Use(callable, *args, **kwargs) is re-invoked on every build(), so each generated instance gets a fresh value.

Determinism

class OrderFactory(DataclassFactory[Order]):
    __model__ = Order
    __random_seed__ = 42  # same seed → same output across runs

Set __random_seed__ (or __faker__ = Faker(seed=...) for finer Faker control) when test assertions depend on the exact generated values.

Default factory registration

class CustomerFactory(DataclassFactory[Customer]):
    __model__ = Customer
    __set_as_default_factory_for_type__ = True


@dataclass
class Order:
    id: int
    customer: Customer  # automatically populated by CustomerFactory.build()


class OrderFactory(DataclassFactory[Order]):
    __model__ = Order

When __set_as_default_factory_for_type__ = True, polyfactory uses that factory whenever the type appears as a field on another model — no manual nesting required.

Pytest fixture from a factory

import pytest
from polyfactory.pytest_plugin import register_fixture
from polyfactory.factories import DataclassFactory


@register_fixture
class OrderFactory(DataclassFactory[Order]):
    __model__ = Order


def test_order_total(order_factory: OrderFactory) -> None:
    order = order_factory.build()
    assert order.total_cents >= 0

@register_fixture turns the class into a pytest fixture (snake-cased class name). The factory itself is still importable as OrderFactory for use outside fixtures.

Cross-referencing fixtures

import pytest
from polyfactory import Fixture
from polyfactory.pytest_plugin import register_fixture


@register_fixture
class CustomerFactory(DataclassFactory[Customer]):
    __model__ = Customer


@register_fixture
class OrderFactory(DataclassFactory[Order]):
    __model__ = Order
    customer = Fixture(CustomerFactory)  # pull from the customer fixture

Fixture(OtherFactory) forwards to another registered factory, making cross-model wiring explicit.

<workflow>

Workflow

Step 1: Pick the factory base

Match the base to your model backend (table above). Wrong base = silent degradation to generic attribute introspection. If your project uses multiple backends (e.g., Pydantic for HTTP DTOs + msgspec for internal events), import each base separately and don't try to share a factory across backends.

Step 2: Define one factory per model

Subclass the appropriate base, set __model__. Keep the factory adjacent to the test files that consume it — typically tests/factories.py or tests/<feature>/factories.py. Don't put factories in production code paths.

Step 3: Customize only what the test cares about

If a field can take any valid value, leave it for the factory to randomize. Override (literal value or Use(...)) only fields the assertion depends on. Tests that pin every field defeat the purpose of using a factory.

Step 4: Register as a pytest fixture (if used widely)

For factories used in many tests, decorate with @register_fixture and consume via the snake-cased fixture name. For one-off use, call Factory.build() directly inline.

Step 5: Wire cross-model relationships

Set __set_as_default_factory_for_type__ = True on a base factory and let nested fields be populated automatically, or use Fixture(OtherFactory) to forward to a registered fixture explicitly.

Step 6: Pin determinism only when needed

Tests that assert on specific generated values need __random_seed__. Tests that assert on shape or invariants (e.g., total >= 0) should not — leaving randomization on widens coverage across runs.

</workflow> <guardrails>

Guardrails

• Always set __model__. The factory base reads __model__ to introspect annotations; without it .build() errors at runtime, not at class definition.
• Don't override fields you're about to assert on with random values. Either pin the value (status = "pending") or assert on shape, not both.
• Don't reuse __random_seed__ across factories that share a Faker instance. They will collide and produce unexpected duplicates. Use a different seed per factory or a single shared seeded __faker__.
• Use the right base for the backend. ModelFactory on a msgspec.Struct falls back to generic introspection and can produce values that violate Meta constraints. Always use MsgspecFactory for msgspec.
• Factories belong under tests/. Importing them from production modules ties test data to runtime code and is a refactor hazard.
• coverage() is a parametrize tool, not a build tool. It returns one instance per Union/Optional branch, not per field — use it via pytest.mark.parametrize to fan out test cases over polymorphic shapes.
• from __future__ import annotations — same rule as msgspec / dishka. The module that defines the factory + model SHOULD avoid future annotations if the model is runtime-introspected. Test modules that use factories MAY freely use future annotations.

</guardrails> <validation>

Validation Checkpoint

Before delivering polyfactory code, verify:

• Factory base matches the model backend (Pydantic → ModelFactory, dataclass → DataclassFactory, msgspec → MsgspecFactory, attrs → AttrsFactory).
• __model__ is set on every factory subclass.
• Factories live under tests/ (or a sibling test-only module), never in production code.
• Fields overridden in the factory match what the test asserts on; fields the test does not care about are left for randomization.
• If the test asserts on exact values, __random_seed__ is set; otherwise it is not.
• @register_fixture is used for factories shared across more than ~2 test files; one-offs call .build() inline.
• Cross-model relationships use __set_as_default_factory_for_type__ or Fixture(OtherFactory) (not manual nesting in Use(...)).
• Factory module avoids from __future__ import annotations if and only if it co-defines runtime-introspected model classes.

</validation> <example>

Example: Litestar handler test with msgspec DTOs and polyfactory

# tests/factories.py
from polyfactory.factories.msgspec_factory import MsgspecFactory
from polyfactory.pytest_plugin import register_fixture

from myapp.events import OrderCreatedEvent  # msgspec.Struct


@register_fixture
class OrderCreatedEventFactory(MsgspecFactory[OrderCreatedEvent]):
    __model__ = OrderCreatedEvent

# tests/test_orders.py
from __future__ import annotations  # consumer module — fine to use future annotations

from litestar.testing import AsyncTestClient

import pytest


@pytest.mark.anyio
async def test_create_order_emits_event(
    client: AsyncTestClient,
    order_created_event_factory: OrderCreatedEventFactory,
) -> None:
    payload = order_created_event_factory.build()
    response = await client.post("/orders", json=payload)

    assert response.status_code == 201
    assert response.json()["id"] == payload.id

The factory provides a fully-populated, validation-passing OrderCreatedEvent; the test focuses on the request/response contract instead of constructing fake data inline.

</example>

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References Index

For detailed guides, refer to the following documents in references/:

• Factories — Per-backend factory bases (Pydantic / dataclass / msgspec / attrs / TypedDict / ODM), randomization control (__random_seed__, __faker__, __allow_none_optionals__), __set_as_default_factory_for_type__ defaults, dynamic factories via Factory.create_factory, coverage() for discriminated unions.
• Pytest integration — @register_fixture, Fixture(...) cross-references, fixture scoping, the polyfactory.pytest_plugin module, async fixtures, and the difference between class-decorator and function-decorator forms.
• Litestar patterns — Using factories with TestClient / AsyncTestClient, parametrizing handler tests via coverage(), integrating with litestar-testing fixtures, msgspec DTOs, advanced-alchemy model factories, and SAQ task payload generation.

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Official References

• https://polyfactory.litestar.dev/
• https://polyfactory.litestar.dev/usage/index.html
• https://polyfactory.litestar.dev/usage/library_factories/index.html
• https://polyfactory.litestar.dev/usage/decorators.html
• https://polyfactory.litestar.dev/usage/fixtures.html
• https://polyfactory.litestar.dev/usage/configuration.html
• https://github.com/litestar-org/polyfactory

Shared Styleguide Baseline

• General Principles
• Python