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APIs & servers (backend)280k · #54

fastapi-patterns

Has your agent follow best practices when building applications with FastAPI. It helps with project structure, schemas, dependency injection, and testing.

What it does

This guide teaches how to organize and build modern web applications using FastAPI. It covers everything from project structure to security and testing.

When to use it

  • ✓When starting a new web project with FastAPI.
  • ✓When reviewing or improving an existing FastAPI project.
  • ✓When learning about best practices for web development with Python.

Example of what to ask your AI

“How do I structure a FastAPI project for production?”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/affaan-m/ECC/blob/main/pi/core/skills/fastapi-patterns/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/affaan-m/ECC/blob/main/pi/core/skills/fastapi-patterns/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

It shows an example of how to divide code into folders (project structure), how to configure the application, define data with Pydantic, manage database connections, and secure routes.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add affaan-m/ECC --skill fastapi-patterns

Or download just the file:

curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/pi/core/skills/fastapi-patterns/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

FastAPI Patterns

Modern, production-grade FastAPI development: project layout, Pydantic v2 schemas, dependency injection, async patterns, auth, transactional service methods, and testing.

Project Structure

my_app/
|-- app/
|   |-- main.py               # App factory, lifespan, middleware
|   |-- config.py             # Settings via pydantic-settings
|   |-- dependencies.py       # Shared FastAPI dependencies
|   |-- database.py           # SQLAlchemy engine + session
|   |-- routers/
|   |   `-- users.py
|   |-- models/               # SQLAlchemy ORM models
|   |   `-- user.py
|   |-- schemas/              # Pydantic request/response schemas
|   |   `-- user.py
|   `-- services/             # Business logic layer
|       `-- user_service.py
|-- tests/
|   |-- conftest.py
|   `-- test_users.py
|-- pyproject.toml
`-- .env

App Factory and Lifespan

# app/main.py
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware

from app.config import settings
from app.database import engine, Base
from app.routers import users


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Automatically create tables on startup for ease of use in dev/demo environments.
    # For strict production applications, manage schemas via Alembic migrations instead.
    async with engine.begin() as conn:
        await conn.run_sync(Base.metadata.create_all)
    yield
    # Shutdown: close pooled resources.
    await engine.dispose()


def create_app() -> FastAPI:
    app = FastAPI(
        title=settings.app_name,
        version=settings.app_version,
        lifespan=lifespan,
    )

    app.add_middleware(
        CORSMiddleware,
        allow_origins=settings.allowed_origins,
        allow_credentials=settings.allow_credentials,
        allow_methods=settings.allowed_methods,
        allow_headers=settings.allowed_headers,
    )

    app.include_router(users.router, prefix="/users", tags=["users"])

    return app


app = create_app()

Configuration with pydantic-settings

# app/config.py
from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")

    app_name: str = "My App"
    app_version: str = "0.1.0"
    debug: bool = False

    database_url: str
    secret_key: str
    algorithm: str = "HS256"
    access_token_expire_minutes: int = 30

    # Pydantic-settings v2 safely evaluates mutable list literals directly
    allowed_origins: list[str] = ["http://localhost:3000"]
    allowed_methods: list[str] = ["GET", "POST", "PATCH", "DELETE", "OPTIONS"]
    allowed_headers: list[str] = ["Authorization", "Content-Type"]
    allow_credentials: bool = True


settings = Settings()

Pydantic Schemas (v2)

# app/schemas/user.py
from datetime import datetime
from pydantic import BaseModel, EmailStr, Field, model_validator


class UserBase(BaseModel):
    email: EmailStr
    username: str = Field(min_length=3, max_length=50)


class UserCreate(UserBase):
    password: str = Field(min_length=8)
    password_confirm: str

    @model_validator(mode="after")
    def passwords_match(self) -> "UserCreate":
        if self.password != self.password_confirm:
            raise ValueError("Passwords do not match")
        return self


class UserUpdate(BaseModel):
    username: str | None = Field(default=None, min_length=3, max_length=50)
    email: EmailStr | None = None


class UserResponse(UserBase):
    id: int
    is_active: bool
    created_at: datetime

    model_config = {"from_attributes": True}


class UserListResponse(BaseModel):
    total: int
    items: list[UserResponse]

Dependency Injection

# app/dependencies.py
from typing import Annotated, AsyncGenerator
from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from jose import JWTError, jwt
from sqlalchemy.ext.asyncio import AsyncSession

from app.config import settings
from app.database import AsyncSessionLocal
from app.models.user import User

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/users/token")


async def get_db() -> AsyncGenerator[AsyncSession, None]:
    async with AsyncSessionLocal() as session:
        try:
            yield session
        except Exception:
            await session.rollback()
            raise


async def get_current_user(

…
FAQ

What is FastAPI?

FastAPI is a modern, fast framework for building APIs with Python, based on standard Python type hints.

What are Pydantic schemas?

Pydantic schemas are used to define the structure of the data your API sends and receives, ensuring data correctness.

What is dependency injection?

It's a way to organize code where functions receive what they need (like database connections) as arguments, making the code easier to test and manage.

Details

Original description by the author

FastAPI best practices covering project structure, Pydantic v2 schemas, dependency injection, async handlers, authentication, authorization, transactional service layers, and testing with httpx and pytest. Use when building or reviewing FastAPI apps — Pydantic schemas, dependencies, async handlers, auth, or tests.

repo: affaan-m/ECC

Popularity

GitHub stars
280k
Ranking position
#54
License
MIT
Updated
10/15/2026

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