decorates 6.0.0

Decorator-driven CLI and database toolkit for Pydantic and SQLAlchemy.

Functionals

Functionals is a DX-first Python framework for building:

• CLI tooling systems
• Data and API services
• Scheduled/event automation workflows

It uses decorators for command, model, and job definitions, and ships with fx, a built-in project manager for scaffolding, running, validating, and operating projects.

This framework is for teams and developers who want one coherent toolkit for backend development and DevOps workflows instead of stitching together many unrelated layers.

Why Functionals

• Fast setup: generate ready-to-run CLI or DB/API projects with fx init.
• Unified patterns: decorators for commands (cli), models (db), and jobs (cron).
• Operational workflow built in: run, install, update, pull plugins, and manage cron from fx.
• Plugin architecture: organize command suites into modules and load them cleanly.
• Production-minded behavior: structured state, health checks, operation history, and test coverage.

Install

pip install decorates  # Package name is `decorates`; module name is `functionals`

Quick Start Guide

1. Build one CLI command with a decorator.
2. Build one DB model with a decorator.
3. Use Model.objects for CRUD.

CLI in minutes

from __future__ import annotations

from enum import StrEnum
from time import strftime

import functionals.cli as cli
import functionals.db as db
from functionals.db import db_field
from pydantic import BaseModel

DB_PATH = "todos.db"
TABLE = "todos"
NOW = lambda: strftime("%Y-%m-%d %H:%M:%S")


class TodoStatus(StrEnum):
    PENDING = "pending"
    COMPLETED = "completed"


@db.database_registry(DB_PATH, table_name=TABLE, key_field="id")
class TodoItem(BaseModel):
    id: int | None = None
    title: str = db_field(index=True)
    description: str = db_field(default="")
    status: TodoStatus = db_field(default=TodoStatus.PENDING.value)
    created_at: str = db_field(default_factory=NOW)
    updated_at: str = db_field(default_factory=NOW)


@cli.register(name="add", description="Create a todo item")
@cli.argument("title", type=str, help="Todo title")
@cli.argument("description", type=str, default="", help="Todo description")
@cli.option("--add")
@cli.option("-a")
def add_todo(title: str, description: str = "") -> str:
    todo = TodoItem(title=title, description=description)
    todo.save()
    return f"Added: {todo.title} (ID: {todo.id})"


@cli.register(name="list", description="List todo items")
@cli.option("--list")
@cli.option("-l")
def list_todos() -> str:
    todos = TodoItem.objects.all()
    if not todos:
        return "No todo items found."
    return "\n".join(f"{t.id}: {t.title} [{t.status}]" for t in todos)


@cli.register(name="complete", description="Mark a todo item as completed")
@cli.argument("todo_id", type=int, help="Todo ID")
@cli.option("--complete")
@cli.option("-c")
def complete_todo(todo_id: int) -> str:
    todo = TodoItem.objects.get(id=todo_id)
    if not todo:
        return f"Todo item with ID {todo_id} not found."

    todo.status = TodoStatus.COMPLETED.value
    todo.updated_at = NOW()
    todo.save()
    return f"Completed todo ID {todo_id}."


@cli.register(name="update", description="Update a todo item")
@cli.argument("todo_id", type=int, help="Todo ID")
@cli.argument("title", type=str, default=None, help="New title")
@cli.argument("description", type=str, default=None, help="New description")
@cli.option("--update")
@cli.option("-u")
def update_todo(todo_id: int, title: str | None = None, description: str | None = None) -> str:
    todo = TodoItem.objects.get(id=todo_id)
    if not todo:
        return f"Todo item with ID {todo_id} not found."

    todo.title = title or ""
    todo.description = description or ""
    todo.updated_at = NOW()
    todo.save()
    return f"Updated todo ID {todo_id}."


if __name__ == "__main__":
    cli.run(
        shell_title="Todo Console",
        shell_description="Manage tasks.",
        shell_colors=None,
        shell_banner=True,
        shell_usage=True,  # Prints usage menu on startup
    )

Run it as follows:

# Add
python todo.py add "Buy groceries" "Milk, eggs, bread"
python todo.py --add "Buy groceries" "Milk, eggs, bread"
python todo.py -a "Buy groceries" "Milk, eggs, bread"
python todo.py add --title "Buy groceries" --description "Milk, eggs, bread"

# List
python todo.py list
python todo.py --list
python todo.py -l

# Complete
python todo.py complete 1
python todo.py --complete 1
python todo.py -c 1

# Update
python todo.py update 1 "Read two books" "Finish both novels this week"
python todo.py update 1 --title "Read two books" --description "Finish both novels this week"
python todo.py --update 1 --title "Read two books"

Or:

# Run directly for interactive mode
python todo.py

Interactive mode:

functionals.fx in minutes (project-type init + health)

functionals.fx is the project tooling layer built on top of the CLI + DB modules. After local install (pip install -e .), you can run:

fx --help

Create a CLI-first project structure:

fx init cli TodoService
fx health TodoService

Expected structure:

pyproject.toml
README.md
src/app/__main__.py
src/app/todo.py
src/app/plugins/__init__.py
tests/test_todo_cli.py
.fx/fx.db

Create a DB-first project structure:

fx init db DataService
fx health DataService

Expected structure:

pyproject.toml
README.md
src/app/__main__.py
src/app/api.py
src/app/models.py
src/app/plugins/__init__.py
tests/test_user_api.py
.fx/fx.db

Notes:

• fx init <project_name> still works and defaults to cli.
• If root is omitted, fx init uses <project_name> as the project directory.
• fx health is the canonical check command (--doctor is kept as a compatibility alias).

Additional FX commands:

# Show installed fx version
fx --version

# Run project entrypoint (auto-detected)
fx run TodoService

# Editable install (active env or project venv)
fx install TodoService
fx install TodoService --venv .venv --extras dev

# Update decorates package source
fx update TodoService                          # source=pypi
fx update TodoService --source git --repo https://github.com/nexustech101/functionals.git --ref main
fx update TodoService --source path --path ../framework

# Pull plugins safely from a git repository
fx pull https://github.com/example/plugins-repo.git TodoService --ref main --subdir plugins

# Manage cron runtime and jobs
fx cron start TodoService
fx cron jobs TodoService
fx cron trigger nightly-build TodoService
fx cron generate TodoService
fx cron apply TodoService
fx cron stop TodoService

fx worktree is currently spec-defined only and planned for a later release after the graph/tree data-structure layer is implemented.

Database + FastAPI in 5 minutes

from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from functionals.db import (
    RecordNotFoundError,
    UniqueConstraintError,
    database_registry,
)

DB_URL = "sqlite:///shop.db"

# --- Models ---

@database_registry(DB_URL, table_name="customers", unique_fields=["email"])
class Customer(BaseModel):
    id: int | None = None
    name: str
    email: str

@database_registry(DB_URL, table_name="products")
class Product(BaseModel):
    id: int | None = None
    name: str
    price: float

@database_registry(DB_URL, table_name="orders")
class Order(BaseModel):
    id: int | None = None
    customer_id: int
    product_id: int
    quantity: int
    total: float

# --- App ---

@asynccontextmanager
async def lifespan(app: FastAPI):
    for model in (Customer, Product, Order):
        model.create_schema()
    yield
    for model in (Customer, Product, Order):
        model.objects.dispose()

app = FastAPI(lifespan=lifespan)

# --- Routes ---

@app.post("/customers", response_model=Customer, status_code=201)
def create_customer(name: str, email: str):
    return Customer.objects.create(name=name, email=email)

@app.get("/customers/{customer_id}", response_model=Customer)
def get_customer(customer_id: int):
    return Customer.objects.require(customer_id)

@app.post("/products", response_model=Product, status_code=201)
def create_product(name: str, price: float):
    return Product.objects.create(name=name, price=price)

@app.post("/orders", response_model=Order, status_code=201)
def create_order(customer_id: int, product_id: int, quantity: int):
    product = Product.objects.require(product_id)
    return Order.objects.create(
        customer_id=customer_id,
        product_id=product_id,
        quantity=quantity,
        total=product.price * quantity,
    )

@app.get("/orders/desc", response_model=list[Order])
def list_orders_desc(limit: int = 20, offset: int = 0):  # Filter by oldest   (1, 2, 3,..., n)
    return Order.objects.filter(order_by="id", limit=limit, offset=offset)

@app.get("/orders/asc", response_model=list[Order])
def list_orders_asc(limit: int = 20, offset: int = 0):  # Filter by newest  (n,..., 3, 2, 1)
    return Order.objects.filter(order_by="-id", limit=limit, offset=offset)

# POST /customers
curl -X POST "http://localhost:8000/customers" \
  -H "Content-Type: application/json" \
  -d '{"name": "Alice Johnson", "email": "[e-mail]"}'

# Response
{"id": 1, "name": "Alice Johnson", "email": "[e-mail]"}


# GET /customers/1
curl "http://localhost:8000/customers/1"

# Response
{"id": 1, "name": "Alice Johnson", "email": "[e-mail]"}


# POST /products
curl -X POST "http://localhost:8000/products" \
  -H "Content-Type: application/json" \
  -d '{"name": "Wireless Keyboard", "price": 49.99}'

# Response
{"id": 1, "name": "Wireless Keyboard", "price": 49.99}


# POST /orders
curl -X POST "http://localhost:8000/orders" \
  -H "Content-Type: application/json" \
  -d '{"customer_id": 1, "product_id": 1, "quantity": 2}'

# Response
{"id": 1, "customer_id": 1, "product_id": 1, "quantity": 2, "total": 99.98}


# GET /orders/asc  (oldest first)
curl "http://localhost:8000/orders/asc?limit=20&offset=0"

# Response
[
  {"id": 1, "customer_id": 1, "product_id": 1, "quantity": 2, "total": 99.98}
]


# GET /orders/desc  (newest first)
curl "http://localhost:8000/orders/desc?limit=20&offset=0"

# Response
[
  {"id": 1, "customer_id": 1, "product_id": 1, "quantity": 2, "total": 99.98}
]

Cron + Workflow Operations

Use functionals.cron decorators to define interval/cron/event jobs and manage runtime through fx.

fx cron start .
fx cron status .
fx cron jobs .
fx cron trigger <job_name> .
fx cron generate .
fx cron apply .
fx cron stop .

For centralized DevOps workflow organization:

fx cron workspace .
fx cron register deploy-workflow . --workflow-file ops/workflows/ci/deploy-workflow.yml --job nightly-build --target github_actions
fx cron workflows .
fx cron run-workflow deploy-workflow . --payload '{"env":"prod"}'

Architecture

• functionals.cli Decorator-driven command registration, parser/dispatch, interactive shell, and plugin loading.

• functionals.db Decorator-driven persistence for Pydantic models with SQLAlchemy-backed storage and model manager patterns.

• functionals.cron Decorator-driven interval/cron/event jobs with async runtime and deployment artifact generation.

• functionals.fx Built-in operations layer for project structuring, environment lifecycle, plugin workflows, cron operations, health checks, and history.

Who This Is For

• Backend engineers building internal tools and service utilities.
• Platform and DevOps engineers standardizing automation workflows.
• Teams building plugin-based command ecosystems for shared operations.
• AI tooling teams that need a clear path from local workflows to managed automation.

Documentation

• CLI manual: src/functionals/cli/USAGE.md
• DB manual: src/functionals/db/USAGE.md
• FX manual: src/functionals/fx/USAGE.md
• Cron manual: src/functionals/cron/USAGE.md (if present in your version)

Roadmap and Planned Extensions

Functionals is production-ready today and actively expanding into agentic tooling workflows. Planned additions include:

• MCP support: A decorator-based framework for defining and operating MCP servers.

• Worktree data capabilities: Structured storage/retrieval of project workspace state for tooling and automation contexts.

• Data-structure library for AI tooling: Graph and tree primitives (including knowledge graph patterns) for efficient lookup, relationship modeling, hierarchy traversal, and large-project representation.

• LLM tooling decorators: Decorator-driven tool definitions and memory/knowledge wiring for agent workflows.

These additions are designed to work with the current fx + cli + db + cron architecture rather than replace it.

Documentation

• DB guide: src/functionals/db/USAGE.md
• FX guide: src/functionals/fx/USAGE.md
• CLI source API: src/functionals/cli
• DB source API: src/functionals/db

Requirements

• Python 3.10+
• pydantic>=2.0
• sqlalchemy>=2.0

Testing

• The default pytest suite includes SQLite coverage along with PostgreSQL/MySQL integration tests for rename-state behavior.
• Run Docker Desktop, or another compatible Docker engine, before executing the backend integration suite so the services in docker-compose.test-db.yml can boot successfully.
• The package is backed by a rigorous, production-focused test suite (170+ tests) covering unit behavior, edge cases, and multi-dialect integration scenarios.

License

MIT ite includes SQLite coverage along with PostgreSQL/MySQL integration tests for rename-state behavior.

• Run Docker Desktop, or another compatible Docker engine, before executing the backend integration suite so the services in docker-compose.test-db.yml can boot successfully.
• The package is backed by a rigorous, production-focused test suite (170+ tests) covering unit behavior, edge cases, and multi-dialect integration scenarios.

License

MIT uite includes SQLite coverage along with PostgreSQL/MySQL integration tests for rename-state behavior.

• Run Docker Desktop, or another compatible Docker engine, before executing the backend integration suite so the services in docker-compose.test-db.yml can boot successfully.
• The package is backed by a rigorous, production-focused test suite (170+ tests) covering unit behavior, edge cases, and multi-dialect integration scenarios.

License

MIT