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
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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
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.
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."
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.
Never: Pass dict[str, Any] between modules or layers. Dicts acceptable only when: truly dynamic data, <3 keys, single function scope, no business logic.
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).
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)classWikiPageRepository(Protocol):
deffind(self, slug: str) -> WikiPage | None: ...
defsave(self, page: WikiPage) -> None: ...
# Adapters are plugins -- each implements the same portclassInMemoryWikiPageRepo: ... # development/testing pluginclassFileSystemWikiPageRepo: ... # flat-file pluginclassPostgresWikiPageRepo: ... # 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.
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.
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 productionclassTestingAPI:
def__init__(self, app: Application) -> None:
self._app = app
asyncdefseed_user(self, user_id: str, name: str, role: str = "admin") -> User:
"""Bypass registration, create user directly."""
...
deffreeze_time(self, at: datetime) -> None:
"""Replace clock port with frozen clock."""self._app.clock = FrozenClock(at)
asyncdefexecute_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",))
returnawait use_case.execute(input, ctx=ctx)
# tests/conftest.py@pytest.fixtureasyncdefapi() -> TestingAPI:
app = create_test_app() # In-memory adapters, no real infrareturn TestingAPI(app)
Test through behavior, not structure:
# GOOD: Tests business rules through the Testing APIclassTestPlaceOrder:
asyncdeftest_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 isnotNone
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 domainclassOrder(DeclarativeBase): ... # Entity married to SQLAlchemy# RIGHT: Framework stays in adapters# domain/entities.py@dataclass(frozen=True, slots=True)classOrder: ...
# adapters/persistence/models.pyclassOrderModel(DeclarativeBase):
defto_domain(self) -> Order: ...
@classmethoddeffrom_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 suppressionimport rich_click as click
from collections.abc importCallablefrom typing importAny# Non-generic alias: works both as @option(...) and as a value in a# shared-options functools.reduce list.
_Decorator = Callable[[Callable[..., Any]], Callable[..., Any]]
defoption(*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:
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).
