add-endpoint-python

To scaffold a FastAPI endpoint with SQLAlchemy and Alembic, you provide a feature name and data model to generate the full stack. This creates typed router handlers, async database integration, and migration files, eliminating manual boilerplate for complete persistence.

The best way to structure a production-ready FastAPI backend is using router, service, and repository layers. This scaffold generates that architecture automatically, adding Pydantic v2 schemas, async SQLAlchemy 2.x models, audit logging, and idempotency support for safe retries.

Yes, you can use Pydantic v2 and async SQLAlchemy 2.x together for FastAPI endpoint creation. The scaffold enforces strong typing and validation with Pydantic v2 models and handles async database operations through SQLAlchemy 2.x, including Decimal money types and EncryptedStr for PII protection.

To add idempotency and audit logging to a FastAPI service layer, you implement mutation tracking and request state validation. This scaffold includes an audit-logging workflow for mutations and built-in idempotency support for safe retries directly within the generated service layer.

Yes, FastAPI scaffolding works with pytest and testcontainers for integration tests. The generated endpoint scaffold includes unit tests for service logic alongside integration tests using testcontainers, ensuring both business logic and database interactions are fully testable.

You need Alembic migrations when building a new FastAPI API feature to manage database schema changes alongside code updates. The scaffold generates Alembic migration files automatically, ensuring your async SQLAlchemy 2.x models are synchronized with the database schema for complete feature persistence.