coding-python-clean-architecture

name	coding-python-clean-architecture
description	Use when structuring Python 3.10+ projects with layered ports-and-adapters architecture, reviewing layer dependency violations, scaffolding domain-driven designs, deciding where code belongs across domain, application, adapter, and composition layers, or setting up ports, UoW, outbox, and idempotency patterns

Layered Architecture for Python

Overview

Framework-agnostic, typed Python architecture optimized for change, testability, and clear boundaries. Inner layers never import from outer layers. Domain stays pure.

Primary goal: Keep the cost of change low over the lifetime of the system. Business logic should be easy to understand, test, and evolve independently of infrastructure choices.

Target: Python >=3.10 | Typed | Pure domain | Framework-agnostic core | No compatibility shims

When to Use

• Starting a new Python project that needs long-term maintainability
• Reviewing existing code for architecture violations (imports crossing layers)
• Deciding where a new class/function belongs (domain vs application vs adapter)
• Structuring data flow between external inputs, domain logic, and outputs
• Setting up testing strategy with proper isolation
• Implementing reliability patterns (UoW, outbox, idempotency)
• Refactoring framework-centric code to layered architecture

When NOT to use:

• Projects already using a different established architecture

Single-file scripts and throwaway prototypes stay in this skill - they route to SCRIPT mode (see script-mode.md and the mode-selection table below), one of this skill's four operating modes. Do not drop the skill for them.

Capability check (REVIEW / judgment). Spotting architecture/layer violations and the true-vs-accidental-duplication call is capability-sensitive - a weaker model misjudges it. Run REVIEW mode on a pinned sonnet subagent (opus for a large or high-stakes codebase); pin the tier per dispatch, never inherit the session model. If you keep it inline and the session is on a lesser tier, offer switch-model-or-continue. See bitranox:process-agents-subagent-driven-development ("The session model is fixed" / "Concrete tiers").

Layers & Dependency Rule

Inner layers never import from outer layers. Source code dependencies point inward only, toward more stable and abstract code.

Inner layers change less frequently and for more important reasons. Outer layers (I/O, formatting, frameworks) are plugins to the inner layers.

+------------------------------------------------------------+
|  Composition Root (outermost)                               |
|  compose.py, main entrypoints, framework wiring             |
|  +------------------------------------------------------+  |
|  |  Adapters                                             |  |
|  |  Route handlers, ORM repositories, Pydantic models,   |  |
|  |  message consumers, CLI parsers, HTTP clients          |  |
|  |  +------------------------------------------------+  |  |
|  |  |  Application                                    |  |  |
|  |  |  Use case classes, Protocol ports,               |  |  |
|  |  |  request/response dataclasses                    |  |  |
|  |  |  +------------------------------------------+   |  |  |
|  |  |  |  Domain                                   |   |  |  |
|  |  |  |  @dataclass(frozen=True) entities,         |   |  |  |
|  |  |  |  Value Objects, Domain Services,           |   |  |  |
|  |  |  |  Domain Events                             |   |  |  |
|  |  |  +------------------------------------------+   |  |  |
|  |  +------------------------------------------------+  |  |
|  +------------------------------------------------------+  |
+------------------------------------------------------------+

You may need more or fewer layers depending on complexity. The dependency rule always applies: dependencies point inward, abstraction increases inward.

Layer	Contains	Rules
Domain	Entities, Value Objects, Domain Services	Pure, sync, no I/O, no logs, no frameworks
Application	Use Cases, Ports (Protocols), DTOs	Orchestrates domain + ports; no framework types
Adapters	Transport, Persistence, Messaging	Implements ports; maps DTOs <-> external formats
Composition Root	Wiring	Binds adapters to ports per process; the only place that imports everything

Data Crossing Boundaries

Data passed across a boundary must always be in the form most convenient for the inner layer. Never pass ORM row structures or Pydantic request models inward -- adapters convert to domain types at the boundary.

Bounded and Sanitized Inputs at the Boundary

Adapters/edges are the only place external data enters, so two rules are enforced there:

Full per-sink escaping rules (SQL parametrization, HTML/XSS, shell, path traversal, deserialization) and the boundary-vs-internal-libs scope live in bitranox:coding-input-sanitization. Sanitize at the app/facing-API edge, NOT in the libraries between boundaries.

• Sanitize and bound every external input. Validate type and shape, enforce length limits (guard overflow/underflow), and handle arbitrary bytes/characters safely - non-ASCII, emoji, CJK, control characters, and binary data are rejected, normalized, or escaped, never trusted raw. The boundary parser (Pydantic at the edge) owns this, and it is covered by tests with adversarial and edge inputs.
• Keep memory bounded on large or unbounded data. When an adapter reads big files, huge database result sets, or huge log files, stream/iterate/chunk/paginate - never load the whole thing into memory or accumulate an unbounded collection. Ports expose iterators/pagination, not fully-materialized lists. Materialize only when the dataset is provably and safely bounded.
• Never trust structured input. Structured data crossing the boundary (a dict, JSON, an API/IPC payload, a deserialized object) is parsed into a typed model at the edge (Pydantic, then a domain type) - never consumed raw on the assumption its keys, types, or shape are correct. Exception: only where the user has deliberately chosen to skip the check.

Domain vs Use Cases

	Domain	Use Cases
Scope	Core business rules (exist without automation)	Application-specific workflows (exist because system is automated)
Change rate	Slowest	Faster (new features, workflow changes)
Dependencies	Know nothing about use cases	Depend on domain, never the reverse
Python	@dataclass(frozen=True, slots=True) with business methods	Classes in application/use_cases/ with execute()

Use case request/response models must be independent -- no framework types, no entity references, no knowledge of delivery mechanism.

Domain-Centric Project Structure

Your top-level directory structure should tell readers about the system, not the framework. When new developers look at the project, their first impression should be "This is a lending platform" -- not "This is a FastAPI app."

Bad -- framework-centric:

src/api/routes/ middleware/ dependencies/ models/ schemas/ crud/

Good -- domain-centric:

src/lending_platform/
  loans/
    domain/         # Loan entity, Money VO, interest rules
    application/
      use_cases/    # create_loan.py, approve_loan.py
      ports/        # loan_repository.py, credit_check.py
    adapters/       # sqlalchemy_loan_repo.py, experian_credit.py
  customers/
    domain/ application/ adapters/
  composition/      # compose.py -- FastAPI only appears here

Question	Good answer
Can you tell what the system does from top-level folders?	Yes -- bounded contexts visible
Can you tell what framework is used?	No -- hidden in adapters/composition
Can you unit-test all use cases without any framework?	Yes -- plain dataclasses + protocol ports
Could you swap the delivery mechanism (web -> CLI)?	Yes -- add adapter, zero core changes

Design Principles

Brief, practical guidelines for structuring modules in Python layered architecture.

One actor per module. Each use case serves one stakeholder group. If two different teams need changes to the same module, split it. Use a Facade if callers need a single entry point.

Extend by adding, not by modifying. New behavior should mean new adapters implementing existing ports, not editing use cases. New output format? New adapter -- zero changes to the use case.

Honor port contracts. All adapters implementing a port must satisfy the same behavioral contract. Verify via contract tests -- a single parameterized test suite run against every adapter implementation (in-memory, PostgreSQL, Redis). If any adapter needs special handling, the port contract is wrong.

Keep ports narrow. A UserRepository port with find, save, delete, search, bulk_import, export_csv serves too many consumers. Split into focused ports: UserReader(Protocol), UserWriter(Protocol). Each use case depends only on the port slice it needs.

Depend on abstractions for volatile code. Domain and application layers import only Protocols and dataclasses. Stable things (stdlib, well-established libraries) are fine to depend on directly. The composition root is the only place that imports concrete adapter classes.

No concrete paths, URLs, or hostnames in the domain. Filesystem paths, endpoint URLs, hostnames, and external-service strings live ONLY in config passed (as a Pydantic model) from the composition root into adapters. The domain/application layers never read an env var or hardcode a path. This is testable: a unit test that instantiates an adapter with explicit values and reads NO env var proves the layer is pure -- if a test needs an env var or a real path constant, a config value leaked into the wrong layer.

Keep the domain immutable. Use @dataclass(frozen=True, slots=True) for entities and value objects. Push mutable state to adapters where it belongs, protected by the Unit of Work pattern. This is not a preference: "no mutable state in the domain" is a non-negotiable below.

Use Protocol to invert dependencies. When the natural direction of a function call opposes the desired dependency direction, introduce a Protocol in the inner layer and implement it in the outer layer. This is what makes the dependency rule possible.

Data Modeling

External Input -> Pydantic (validate) -> Dataclass (domain) -> Pydantic (serialize) -> Output
     (raw)         (adapter boundary)     (pure business logic)   (adapter boundary)      (JSON/API)

Use Case	Type	Notes
Domain entities/VOs	@dataclass(frozen=True, slots=True)	Pure, no deps
Internal DTOs	@dataclass	When data already trusted
Boundary validation	Pydantic BaseModel	Parse untrusted input, coerce types
Serialization	Pydantic BaseModel	.model_dump(), .model_validate()
Configuration	Pydantic BaseSettings	Env var parsing

Never: Pass dict[str, Any] between modules or layers. Dicts acceptable only when: truly dynamic data, <3 keys, single function scope, no business logic.

Core Patterns

Ports (Application-owned Protocols)

class PaymentPort(Protocol):
    async def charge(self, customer_id: str, amount: Money) -> str: ...

Ports are Protocols in application/ports/. Adapters implement them. The business rules own the interface; adapters are plugins. See port-contracts.md for all standard port definitions (UoW, Outbox, IdempotencyStore, IdProvider, Clock).

Unit of Work

class UnitOfWork(Protocol[D]):
    async def run(self, fn: Callable[[D, RequestContext], Awaitable[T]], *, ctx: RequestContext | None = None) -> T: ...

• Runs callable inside transaction scope
• Supplies transaction-bound repos/adapters
• Never pass raw connections/locks into core

Reliability Patterns

Pattern	Rule
Lock ordering	Sort IDs before acquiring locks (prevents deadlocks)
Outbox	Persist events within same transaction; publish after commit
Idempotency	run_once(key, fn) with uniqueness guard
Timeouts	asyncio.wait_for(coro, timeout=); propagate CancelledError

For resilience/self-healing at the adapter boundary (retry+backoff, health-check/evict/replace, circuit breaker, graceful degradation), see bitranox:coding-resilience.

Boundaries

Plugin Architecture

The database and UI are plugins to the business rules. Core business rules are kept separate from, and independent of, components that are either optional or implemented in many different forms. All dependency arrows point inward.

# Business rules own the interface (port)
class WikiPageRepository(Protocol):
    def find(self, slug: str) -> WikiPage | None: ...
    def save(self, page: WikiPage) -> None: ...

# Adapters are plugins -- each implements the same port
class InMemoryWikiPageRepo: ...     # development/testing plugin
class FileSystemWikiPageRepo: ...   # flat-file plugin
class PostgresWikiPageRepo: ...     # production plugin

The boundary sits at the Protocol. Business rules know nothing about which adapter is plugged in.

Boundary Crossing Modes

Mode	Mechanism	Cost	Python example
Source-level (monolith)	Function calls, same process	Negligible	Standard import + Protocol
Deployment component	Separate pip packages, same process	Negligible	myapp-domain, myapp-adapters-postgres
Local process	Sockets / IPC	Moderate	FastAPI + Celery on same host
Service	Network (HTTP/gRPC/messaging)	High	Separate deployments

Recommended approach: Start at source-level. Push decoupling to where a service could be formed, but keep components in the same process. Escalate to deployment-level or service-level only when development, deployment, or operational issues demand it.

At runtime, boundary crossing is just a function call. The trick is managing source code dependencies so they always point toward the inner layer. Use Protocol to invert dependencies when control flow opposes the desired dependency direction.

Partial Boundaries

Full boundaries are expensive. When the cost is too high, use a partial strategy:

Strategy	Inverted?	When to use
Skip the Last Step	Yes	Full interface design, but keep both sides in same package. Expect to split later.
Strategy Pattern	One direction	Single Protocol with concrete implementation. Most common in Python.
Facade	No	Thin class grouping service calls. Unlikely to ever need full boundary.

When to draw boundaries: Watch for friction. Implement boundaries at the inflection point where the cost of implementing becomes less than the cost of ignoring. Review boundary decisions as the system evolves.

Signal	Action
Two modules change for different reasons/rates	Draw boundary
You want to swap an implementation	Draw boundary (introduce Protocol)
Cross-team ownership	Draw boundary (independent deployability)
Single developer, no swap foreseeable	Skip or use partial boundary

I/O Boundary Isolation

Split behaviors that are hard to test from behaviors that are easy to test into two modules. The I/O-facing module contains hard-to-test behavior stripped to its barest essence; the logic module contains all extracted testable logic.

Boundary	I/O Side (thin)	Logic Side (testable)
UI	View (template/renderer)	Presenter (formats View Model)
Database	Repository adapter (SQL/ORM)	Use case + repository port
External services	HTTP client adapter	Use case + service port
Message queues	Consumer adapter (deserialize + ack)	Use case + event handler

Presenter example:

@dataclass
class LoanViewModel:
    principal_display: str    # "$1,234.56"
    rate_display: str         # "4.50%"
    is_overdue: bool
    status_label: str         # "Active" | "Defaulted"

class LoanPresenter:
    def present(self, loan: Loan, today: date) -> LoanViewModel:
        return LoanViewModel(
            principal_display=f"${loan.principal.to_decimal():,.2f}",
            rate_display=f"{loan.rate * 100:.2f}%",
            is_overdue=loan.next_due_date < today,
            status_label="Defaulted" if loan.is_defaulted else "Active",
        )
# View just renders the ViewModel -- zero logic, strings/bools only

Principle: At every architectural boundary, look for the opportunity to isolate I/O. Extract testable behavior into a module that works with simple data structures and protocol ports.

Error Handling

Layer	Style
Domain/Application	Raise domain exceptions
Adapters/Boundaries	Catch -> map to result envelope {"ok": False, "error": {"code": "...", "message": "..."}}
Transport	Map -> HTTP status / exit codes

Never let raw exceptions leak to external consumers.

Folder Layout

src/<pkg>/
  <bounded_context>/
    domain/        # entities.py, values.py, events.py, services.py
    application/
      use_cases/
      ports/       # uow.py, outbox.py, idempotency.py, id_provider.py, clock.py, <entity>_repository.py
    adapters/
      memory/      # in-memory implementations
      uow/
    platform/      # config.py, observability.py
  composition/     # compose.py (wiring)
  testing/         # Testing API (NOT deployed to production)

Flatten if single small domain; re-introduce <bounded_context>/ on growth.

Guardrails: domain imports nothing outside domain. application imports domain and its own ports. adapters implement application.ports and map to/from domain DTOs. Composition wires everything.

Each package directory requires an __init__.py (can be empty). Omit only if using implicit namespace packages (PEP 420).

Package Organization

Keep packages cohesive and their dependency graph clean:

• Group by change reason. Modules that change together belong in the same package. A requirement change should ideally touch only one package.
• Split to reduce coupling. If consumers only use a fraction of your package, split it. Don't force dependents to pull in modules they never import. Break fat common/ packages into focused ones: common_types, common_testing, common_infra.
• No cycles in import graphs. Cycles make independent testing and reasoning impossible. Break cycles by introducing a Protocol in the inner layer or extracting shared types into a new package.
• Stable packages should be abstract. Packages with many dependents are hard to change -- protect them by making them mostly Protocols and base types. Packages with few dependents can be concrete and change freely.
• Enforce boundaries in Python. Python lacks package-private. Use import-linter contracts, _-prefixed modules, __all__ in __init__.py, and leading-underscore convention to enforce boundaries. Organization without encapsulation is just folder structure, not architecture.

Four packaging approaches from least to most robust:

Approach	Encapsulation	Recommended?
By Layer (web/, service/, data/)	Weak	No -- prototype only
By Feature (orders/, billing/)	Moderate	Small projects
Ports & Adapters (domain/, adapters/)	Strong	Default choice
By Component (single facade per feature)	Best	Monolith-to-microservice path

Cross-Bounded-Context Communication

Bounded contexts must not share domain internals. Choose one integration style per boundary:

Style	When	Mechanism
Domain Events	Eventual consistency acceptable	Outbox -> message broker -> consumer adapter
Application Service	Synchronous query needed	Context A's adapter calls Context B's use case via port
Shared Kernel	Tight coupling justified (e.g., Money VO)	Shared kernel/ package; both contexts depend inward on it
Anti-Corruption Layer	Integrating legacy/external systems	Adapter translates external model to local domain model

Rules:

• Never import directly between context_a.domain and context_b.domain
• Shared kernel must be minimal and change-controlled
• Event contracts are owned by the publisher; consumers maintain their own projections
• Use import-linter independence contracts to enforce (see review-checklists.md)

Independence & Decoupling

Decoupling Layers (horizontal)

Separate things that change for different reasons: UI concerns change independently from business rules, which change independently from persistence details.

Decoupling Use Cases (vertical)

Use cases are narrow vertical slices through all layers. Each use case changes at a different rate, for different reasons. Keep them separate: one module per use case in application/use_cases/.

Decoupling Modes

Mode	Communication	When to escalate
Source-level (monolith)	Direct calls	Default starting point
Deployment-level	Same process, separate packages	When teams need independent release cycles
Service-level	Network calls	When operational scaling demands it

Start source-level. A good architecture allows sliding up and back down this spectrum without rewriting.

True vs Accidental Duplication

Type	Definition	Action
True	Every change to one requires the same change to the other	Eliminate -- extract shared code
Accidental	Code looks similar now but evolves along different paths	Do not unify -- they will diverge

Common traps: Two use cases with similar DTOs (accidental -- each serves different actors). A DB record that looks like a view model (accidental -- keep layers decoupled). Two contexts with similar User types (accidental -- will evolve independently). Rule: When separating use cases or layers, similar code is usually accidental duplication. Verify before unifying.

Sync vs Async

The examples use async throughout. Adapt based on I/O profile:

Profile	Style	Notes
Network I/O dominant (APIs, DBs, messaging)	async	Use asyncio, async def, await
CPU-bound or simple CLI tools	Sync	Drop async/await; use regular functions
Mixed	async with run_in_executor	Keep domain pure either way

Domain stays the same regardless: pure, sync, no I/O. The choice affects application and adapter layers only. Ports can define sync or async methods -- pick one per project and stay consistent.

Testing Strategy

Type	Purpose
Unit	Domain + use cases with in-memory adapters
Contract	Same suite against all port implementations (parameterized)
Integration	Real infra via composition root
E2E	Public surface (HTTP/CLI)

The Test Boundary

Tests sit in the outermost layer -- nothing depends on them, but they depend inward. Tests that mirror production structure 1:1 (test class per production class) are structurally coupled and fragile. Refactoring production code breaks hundreds of tests.

Solution: Test through a Testing API -- a dedicated API that hides the structure of the application from tests.

Testing API

The Testing API has superpowers: seed data directly, freeze time, bypass auth, force testable states. It is a superset of the application's use cases.

# testing/api.py -- NOT deployed to production
class TestingAPI:
    def __init__(self, app: Application) -> None:
        self._app = app

    async def seed_user(self, user_id: str, name: str, role: str = "admin") -> User:
        """Bypass registration, create user directly."""
        ...

    def freeze_time(self, at: datetime) -> None:
        """Replace clock port with frozen clock."""
        self._app.clock = FrozenClock(at)

    async def execute_as(self, user_id: str, use_case, input):
        """Execute any use case as any user, bypassing auth."""
        ctx = RequestContext(user_id=user_id, roles=("admin",))
        return await use_case.execute(input, ctx=ctx)

# tests/conftest.py
@pytest.fixture
async def api() -> TestingAPI:
    app = create_test_app()  # In-memory adapters, no real infra
    return TestingAPI(app)

Test through behavior, not structure:

# GOOD: Tests business rules through the Testing API
class TestPlaceOrder:
    async def test_valid_order_succeeds(self, api: TestingAPI):
        await api.seed_user("u1", "Alice")
        order_id = await api.execute_as("u1", api.place_order, PlaceOrderCommand(...))
        assert order_id is not None

Keep the Testing API and its fakes/builders in a separate testing/ package that is never included in production builds.

Framework Isolation

Keep Frameworks at the Edge

Frameworks want deep integration. Protect your core by keeping them in outer layers only.

Risk	Description
Architecture pollution	Framework asks you to inherit base classes into your entities/use cases
Outgrowing the framework	As your product matures, the framework's design fights you
Evolution divergence	Framework deprecates features you depend on
Lock-in	A better alternative appears, but you're stuck

The Solution: Constrain Framework Imports

Principle	Python application
Keep at arm's length	Framework imports only in adapters/ and composition/, never in domain/ or application/
Don't derive business objects from framework bases	Entities are plain @dataclass, not Django Model or SQLAlchemy Base
Use proxies in outer layers	Adapter classes wrap framework behavior and implement your ports
Let only composition root know the framework	compose.py wires FastAPI/Django; it's the dirtiest module -- that's OK

# WRONG: Framework in domain
class Order(DeclarativeBase): ...  # Entity married to SQLAlchemy

# RIGHT: Framework stays in adapters
# domain/entities.py
@dataclass(frozen=True, slots=True)
class Order: ...

# adapters/persistence/models.py
class OrderModel(DeclarativeBase):
    def to_domain(self) -> Order: ...
    @classmethod
    def from_domain(cls, order: Order) -> "OrderModel": ...

Frameworks you must depend on (stdlib, typing, dataclasses): that's fine -- but it should be a decision, not an accident.

Typed Facade for Untyped Third-Party Libraries

An untyped or partially-typed dependency makes a strict type checker (pyright strict, mypy --strict) report errors at its call sites (reportUnknownMemberType, "type is partially unknown", reportUnknownArgumentType). Do NOT reach for the blunt fix of disabling the rule -- globally, via a per-directory executionEnvironments block, or scattered # type: ignore / # pyright: ignore at every call site. Disabling blinds the checker to real regressions in your OWN code too, and the suppression spreads.

Instead wrap the untyped surface behind a small typed facade / adapter module at the boundary: re-export the handful of affected callables through thin wrappers with explicit, fully-known signatures, and confine the single unavoidable # pyright: ignore (or # type: ignore) to that one file. The rest of the codebase imports the typed wrappers and stays strict-clean; the checker still guards everything else. Same edge-quarantine habit as the rest of Framework Isolation, applied to the type layer.

Keep the facade minimal: wrap only the members the checker flags (import cleanly-typed members directly), and give each wrapper a concrete, fully-known return type -- never an unbound generic that re-introduces "unknown" inference at call sites.

# adapters/cli/typed_click.py -- the ONE file carrying the suppression
import rich_click as click
from collections.abc import Callable
from typing import Any

# Non-generic alias: works both as @option(...) and as a value in a
# shared-options functools.reduce list.
_Decorator = Callable[[Callable[..., Any]], Callable[..., Any]]

def option(*param_decls: str, **attrs: Any) -> _Decorator:
    return click.option(*param_decls, **attrs)  # pyright: ignore[reportUnknownMemberType]

# Call sites import the typed wrapper and stay strict-clean:
#     from .typed_click import option
# Cleanly-typed members stay direct: click.command, click.echo, click.Context, ...

Disable the rule only as a genuine last resort -- when the untyped surface is too broad to wrap sensibly (an entire dynamic web framework emitting hundreds of unrelated findings, not a few decorators). Then scope the suppression as narrowly as possible (a single executionEnvironments root, never the whole project) and disable only that one rule, never the others.

Composition Root

The composition root is the outermost wiring point. Nothing depends on it -- it depends on everything.

Responsibilities: Create factories/strategies, wire concrete adapters to abstract ports, hand control to the application.

You can have multiple composition roots for different environments:

# composition/main_prod.py
def create_app() -> Application:
    config = ProdConfig.from_env()
    return Application(uow=PostgresUoW(config.db_url), cache=RedisCache(config.redis_url))

# composition/main_test.py
def create_app() -> Application:
    return Application(uow=InMemoryUoW(), cache=InMemoryCache())

# composition/main_dev.py
def create_app() -> Application:
    return Application(uow=SqliteUoW(":memory:"), cache=InMemoryCache())

The composition root is deliberately dirty. It imports every concrete class so the rest of the system stays abstract. Keep it thin: parse config, construct, wire, start. No business logic.

Deployment vs Architecture

Architecture is defined by boundaries and dependencies, not by process boundaries. Microservices are a deployment choice, not an architectural choice.

Fallacy	Reality
"Services are decoupled"	Still strongly coupled by shared data structures
"Services enable independent development"	Only if internal architecture is clean; monoliths can achieve the same
"Adding a new feature is easy"	Cross-cutting features require coordinated changes across all services

Rule: Start monolith with clean internal boundaries. Extract services only when you have a proven need for independent deployment. A well-structured monolith is architecturally superior to poorly structured microservices.

Details (What Is NOT Architecture)

Detail	Why it's not architecture	Python implication
Database	Just a mechanism to move data between disk and RAM	Repository ports return domain dataclasses, never ORM models
Web / UI	An I/O device -- one of many delivery mechanisms	Route handlers are thin adapters; no business logic in routes
Frameworks	Tools for someone else's problems, not your architecture	Use in adapters/composition only; domain/application import none

Key distinction: The data is architecturally significant. The database is not.

Observability & Security

• Structured logging at adapter boundaries; thread trace_id via contextvars
• Health (/health) and readiness (/ready) checks when transport exists
• RED metrics (Rate, Errors, Duration) per use case/adapter
• Centralize config in one module; never read os.environ outside it
• AuthN/Z in adapters; pass RequestContext (claims/roles) to use cases
• PII/PHI boundary mappers redact by default

Non-Negotiables Checklist

• Dependencies point inward only (enforced via import-linter)
• Domain pure (no I/O, logs, frameworks, mutable state)
• No concrete paths, URLs, hostnames, or env-var reads in domain/application (config passed in from the composition root)
• Use cases free of framework/driver types
• Request/response DTOs independent of entities and framework types
• UoW binds transaction-scoped repos
• Deterministic lock ordering for multi-aggregate ops
• Outbox + Idempotency implemented
• Boundary validation (Pydantic at edges)
• Boundary input sanitized and length-bounded (types, encoding; non-ASCII/emoji/CJK/binary handled safely) and tested
• Large/unbounded reads (big files, huge DB results, huge logs) stream/paginate; no full materialization unless provably bounded
• Money as integer minor units (cents)
• Observability hooks at boundaries (trace_id via contextvars)
• UoW generic over typed deps (UnitOfWork[D]); no Mapping[str, Any] for deps
• Domain events extend DomainEvent base TypedDict
• In-memory unit tests + contract test outline
• Top-level folders reveal the domain, not the framework
• Testing API with superpowers (seed, freeze time, bypass auth)
• No framework base classes in domain entities
• Package dependency graph is acyclic

Operating Modes

Mode	Output	Reference
GENERATE	Full domain-first project with tests	See canonical-example.md
REVIEW	Violations + PR-ready fix checklist	See review-checklists.md
LIBRARY	Small public API, py.typed, deprecation policy, plugins	See library-mode.md
SCRIPT	Single file with logical layer sections	See script-mode.md

Selecting a Mode

The agent selects the mode based on context:

Signal	Mode
"Create a new project/service/feature"	GENERATE
"Review this code for architecture violations"	REVIEW
"Build a reusable library/SDK/package"	LIBRARY
Deliverable is a single file; no pyproject.toml	SCRIPT

If ambiguous, ask the user. A project may use multiple modes over its lifecycle (GENERATE initially, REVIEW on changes, LIBRARY if extracted).

Import Enforcement (pyproject.toml)

[tool.importlinter]
root_package = "<pkg>"

[[tool.importlinter.contracts]]
name = "Clean layers"
type = "layers"
layers = ["<pkg>.domain", "<pkg>.application", "<pkg>.adapters"]

[[tool.importlinter.contracts]]
name = "No cross-context imports"
type = "independence"
modules = ["<pkg>.orders", "<pkg>.billing", "<pkg>.shipping"]

Multi-context: add per-context layering + independence contracts. See review-checklists.md.

Refactoring Path (framework-centric -> layered architecture)

1. Extract domain to .../domain/
2. Move orchestration to use cases in application/
3. Define ports (Protocols) for external needs
4. Wrap framework/infra as adapters
5. Add composition root and wire
6. Write contract tests for ports/adapters
7. Route handlers to use cases
8. Remove direct framework/ORM calls from core
9. Add the import-linter contracts and run them clean - this is what enforces the inward-only dependency rule the checklist calls non-negotiable

Common Mistakes

Mistake	Fix
Importing ORM models in domain	Domain uses plain dataclasses; adapters map to/from ORM
Pydantic models in domain layer	Pydantic at boundaries only; domain uses @dataclass(frozen=True, slots=True)
Use case returns framework response	Use case returns DTO; adapter maps to HTTP/CLI response
Passing dict[str, Any] between layers	Define typed dataclasses or Pydantic models at each boundary
Raw DB connections in use cases	Use UoW pattern; use case receives transaction-bound repos
Logging in domain entities	Domain stays pure; observability hooks at adapter boundaries
Reading os.environ in core	Centralize config parsing; inject via composition root
Framework types leaking into ports	No framework/driver types in ports. Stdlib (Protocol, dataclass, TypedDict) plus your own domain types and DTOs (Money, RequestContext) are fine
Unifying accidentally similar DTOs	Each use case gets its own request/response -- they serve different actors and will diverge
Passing entities across boundaries	Use separate DTOs; entities and view models change for different reasons
Folder structure reveals framework	Organize by bounded context, not by framework convention
Starting with microservices	Start monolith with clean boundaries; extract services when proven need exists
Tests structurally coupled to production	Test through a Testing API that hides internal structure
Premature boundary elimination	Verify duplication is real before unifying; similar code often serves different actors

Reference Files

File	Content
port-contracts.md	Standard Protocol definitions with full code: UnitOfWork, Outbox, IdempotencyStore, IdProvider, Clock, repository
canonical-example.md	Complete Money Transfer example (domain through composition)
library-mode.md	Library/SDK development: public API, versioning, plugins, packaging
script-mode.md	Single-file scripts: PEP 723, exit codes, logical layout
review-checklists.md	REVIEW mode checklists: architecture, reliability, boundaries, observability, tests, error handling, imports

Use the Read tool to load the relevant reference file for full detail.

Glossary

Term	Definition
Adapter	Concrete implementation of a port (transport/persistence/messaging); a plugin to the core
Application Layer	Use cases + port contracts (no framework types)
Boundary	A line separating software elements; restricts knowledge between sides
Composition Root	Process bootstrap wiring adapters to ports; the outermost wiring point
Domain	Pure business logic (entities, value objects, domain services); the most stable layer
DomainEvent	Base TypedDict all domain events extend (type, v, id, at)
Outbox	Transactional event persistence for reliable publish-after-commit
Partial Boundary	Lightweight boundary (strategy, facade) when full separation is too expensive
Plugin Architecture	External details (DB, UI) are plugins to business rules via ports
Port	Core-owned interface (Protocol) expressing an external dependency
Testing API	Test-only API with superpowers (seed, freeze time, bypass auth) hiding production structure
UoW	Port scoping a transaction, yielding tx-bound adapters to a work function