moai-lang-python

name	moai-lang-python
version	4.0.0
description	Enterprise-grade Python expertise with production patterns for Python 3.13.9, FastAPI 0.115.x, Django 5.2 LTS, Pydantic v2, SQLAlchemy 2.0; activates for API development, ORM usage, async patterns, testing frameworks, and production deployment strategies.
allowed-tools	["Read","Bash","WebSearch","WebFetch"]
status	stable

Modern Python Development — Enterprise v4.0

Quick Summary

Primary Focus: Python 3.13 with FastAPI, Django, async patterns, and production deployment Best For: REST APIs, microservices, async programming, ORM usage, testing Key Libraries: FastAPI 0.115, Django 5.2 LTS, Pydantic v2, SQLAlchemy 2.0, pytest 8.3 Auto-triggers: FastAPI, Django, async, SQLAlchemy, Pydantic, pytest, asyncio

Version	Release	Support
Python 3.13.9	2025-10	Oct 2029
FastAPI 0.115	2025-11	Active
Django 5.2 LTS	2025-08	Apr 2027
SQLAlchemy 2.0	2024-01	Active

---

Three-Level Learning Path

Level 1: Fundamentals (Read examples.md)

Core Python 3.13 concepts with practical examples:

• Python 3.13 Core: JIT compiler, free-threaded mode, REPL improvements
• FastAPI Basics: API structure, Pydantic validation, dependency injection
• Django Setup: Project structure, async views, model inheritance
• Async/Await: Concurrent HTTP requests, context managers, timeouts
• Examples: See examples.md for full code samples

Level 2: Advanced Patterns (See reference.md)

Production-ready enterprise patterns:

• SQLAlchemy 2.0 ORM: Async sessions, relationships, CRUD operations
• Pydantic v2 Validation: Field validation, custom validators, JSON schemas
• pytest Testing: Fixtures, parametrization, async test functions
• Web Frameworks: FastAPI middleware, Django authentication, error handling
• Pattern Reference: See reference.md for API details and best practices

Level 3: Production Deployment (Consult security/performance skills)

Enterprise deployment and optimization:

• Docker & Compose: Container packaging with multi-stage builds
• Performance: Query optimization, Redis caching, N+1 prevention
• Monitoring: Prometheus metrics, structured logging, observability
• Scaling: Worker management, connection pooling, graceful shutdown
• Details: Skill("moai-essentials-perf"), Skill("moai-security-backend")

---

Technology Stack (November 2025 Stable)

Runtime & Core

• Python 3.13.9 (Latest, Oct 2025)
  • JIT compiler (PEP 744) for hot paths
  • Free-threaded mode (PEP 703) for parallelism
  • Enhanced REPL and error messages
• asyncio (stdlib) - Native async/await support
• typing (stdlib) - Type hints and runtime validation

Web & API

• FastAPI 0.115 (production-ready async framework)
  • OpenAPI/Swagger documentation
  • Dependency injection system
  • Async-first request handling
• Django 5.2 LTS (2025-08, support until Apr 2027)
  • Streaming responses and async views
  • MariaDB 10.9+ support
  • Improved model forms
• Flask 3.1 (lightweight alternative)

Data & Validation

• Pydantic v2.9 - Type validation with JSON schema generation
• SQLAlchemy 2.0 - Modern ORM with async support
• Tortoise ORM 0.21 - Async-first alternative

Testing & Quality

• pytest 8.3 - Fixtures, parametrization, plugins
• pytest-asyncio 0.24 - Async test support
• mypy 1.8 - Static type checking
• ruff 0.13 - Fast Python linter

Deployment

• Uvicorn 0.30 - ASGI server for async apps
• Gunicorn 22 - WSGI server with workers
• asyncpg 0.30 - Async PostgreSQL client
• aiohttp 3.10 - Async HTTP client

---

FastAPI Essential Patterns

Project Structure

myapp/
├── main.py           # FastAPI app instance
├── models.py         # Pydantic models
├── database.py       # SQLAlchemy setup
├── schemas.py        # Request/response DTOs
├── api/
│   ├── __init__.py
│   └── routes.py     # Route handlers
├── services/
│   └── user_service.py   # Business logic
└── tests/
    ├── test_api.py
    └── test_services.py

Minimal API

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI(title="API", version="1.0.0")

class Item(BaseModel):
    name: str
    price: float

@app.get("/items/{item_id}")
async def get_item(item_id: int) -> Item:
    return Item(name="Sample", price=9.99)

@app.post("/items/", status_code=201)
async def create_item(item: Item) -> Item:
    return item

Dependency Injection

from fastapi import Depends
from sqlalchemy.ext.asyncio import AsyncSession

async def get_db() -> AsyncSession:
    async with async_session() as session:
        yield session

async def get_current_user(token: str = Depends(oauth2_scheme)):
    return decode_token(token)

@app.get("/profile")
async def profile(
    user = Depends(get_current_user),
    db: AsyncSession = Depends(get_db)
):
    return user

---

SQLAlchemy 2.0 Async ORM

Setup

from sqlalchemy.ext.asyncio import (
    create_async_engine,
    AsyncSession,
    async_sessionmaker
)
from sqlalchemy.orm import declarative_base

DATABASE_URL = "postgresql+asyncpg://user:pass@localhost/db"
engine = create_async_engine(DATABASE_URL, echo=False)
async_session = async_sessionmaker(engine, class_=AsyncSession)
Base = declarative_base()

Models

from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.orm import relationship

class User(Base):
    __tablename__ = "users"
    id = Column(Integer, primary_key=True)
    username = Column(String(50), unique=True, nullable=False)
    posts = relationship("Post", back_populates="author")

class Post(Base):
    __tablename__ = "posts"
    id = Column(Integer, primary_key=True)
    title = Column(String(200), nullable=False)
    author_id = Column(Integer, ForeignKey("users.id"))
    author = relationship("User", back_populates="posts")

Async CRUD

from sqlalchemy import select

async def create_user(username: str) -> User:
    async with async_session() as session:
        user = User(username=username)
        session.add(user)
        await session.commit()
        return user

async def get_user(user_id: int) -> User:
    async with async_session() as session:
        result = await session.execute(select(User).where(User.id == user_id))
        return result.scalars().first()

---

Async/Await Fundamentals

Concurrent Operations

import asyncio
import aiohttp

async def fetch_url(url: str) -> str:
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()

async def fetch_multiple(urls):
    tasks = [fetch_url(url) for url in urls]
    return await asyncio.gather(*tasks)

# Run
results = asyncio.run(fetch_multiple(urls))

Timeouts & Cancellation

async def with_timeout():
    try:
        result = await asyncio.wait_for(long_task(), timeout=10.0)
    except asyncio.TimeoutError:
        print("Timed out")

async def cancellable_task():
    try:
        await asyncio.sleep(100)
    except asyncio.CancelledError:
        print("Cancelled")
        raise

---

Pydantic v2 Validation

Models

from pydantic import BaseModel, Field, field_validator, EmailStr

class UserSchema(BaseModel):
    id: int = Field(..., gt=0)
    username: str = Field(..., min_length=3, max_length=50)
    email: EmailStr
    bio: str = Field(None, max_length=500)

    @field_validator('username')
    @classmethod
    def validate_username(cls, v):
        if not v.isalnum():
            raise ValueError('Alphanumeric only')
        return v.lower()

# Usage
user = UserSchema(id=1, username="john", email="[e-mail]")
schema = UserSchema.model_json_schema()  # JSON Schema

---

Testing with pytest

Basic Tests

import pytest
from fastapi.testclient import TestClient

@pytest.fixture
def client():
    return TestClient(app)

def test_create_item(client):
    response = client.post("/items/", json={"name": "Item", "price": 10})
    assert response.status_code == 201

Async Tests

@pytest.mark.asyncio
async def test_async_fetch():
    result = await fetch_url("http://example.com")
    assert result is not None

---

Production Best Practices

1. Always use type hints for IDE support and validation
2. Prefer async/await for I/O-bound operations (not threads)
3. Use Pydantic v2 for data validation (not manual checks)
4. Test async code with pytest-asyncio markers
5. Implement error handling with custom exceptions
6. Use SQLAlchemy 2.0 for ORM (not raw SQL)
7. Cache frequently accessed data with Redis
8. Monitor metrics in production (Prometheus, Sentry)
9. Use Uvicorn for FastAPI (not development server)
10. Enable JIT compiler for performance-critical code in Python 3.13

---

Learn More

• Examples: See examples.md for FastAPI, Django, pytest, and async patterns
• Reference: See reference.md for API details, configuration, and troubleshooting
• FastAPI Docs: https://fastapi.tiangolo.com/
• SQLAlchemy 2.0: https://docs.sqlalchemy.org/en/20/orm/
• Python 3.13: https://docs.python.org/3/whatsnew/3.13.html
• pytest Guide: https://docs.pytest.org/

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Skills: Skill("moai-essentials-debug"), Skill("moai-essentials-perf"), Skill("moai-security-backend") Auto-loads: Python projects mentioning FastAPI, Django, async, SQLAlchemy, Pydantic, pytest