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https://github.com/nexustech101/registers

A Python framework built with Developer Experience (DX) in mind. decorators uses a clean, ergonomic decorator registry design pattern to eliminate boilerplate when building CLI tools and database-backed applications.
https://github.com/nexustech101/registers

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A Python framework built with Developer Experience (DX) in mind. decorators uses a clean, ergonomic decorator registry design pattern to eliminate boilerplate when building CLI tools and database-backed applications.

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README 

          
# Registers



[![PyPI version](https://img.shields.io/pypi/v/registers)](https://pypi.org/project/registers/)

[![Python versions](https://img.shields.io/pypi/pyversions/registers)](https://pypi.org/project/registers/)

[![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)

[![Module](https://img.shields.io/badge/module-registers-green)](#registers)

[![CLI](https://img.shields.io/badge/module-registers.cli-blue)](#architecture)

[![DB](https://img.shields.io/badge/module-registers.db-darkorange)](#architecture)

[![Cron](https://img.shields.io/badge/module-registers.cron-purple)](#architecture)

[![FX Tool](https://img.shields.io/badge/tool-fx--tool-black)](https://github.com/nexustech101/fx-tool)

![Tests](https://img.shields.io/badge/tests-200%2B%20unit%20tests-brightgreen)



Registers 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 pairs with `fx-tool`, the project manager for scaffolding, running, validating, and operating Registers 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. Build, manage, and deploy at the speed of thought.



## Why Registers



- 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 support via `fx-tool`: run, install, update, pull plugins, and manage cron runtime.

- Plugin architecture: organize command suites into modules and load them cleanly.

- Production-minded behavior: structured state, health checks, operation history, and test coverage.

- Projects that use `registers.cli` module come with a built-in interactive shell.



## Install



```bash

pip install registers  # Package name is `registers`; module name is `registers`

```



Install the project manager (`fx-tool`) as a companion:



```bash

pip install fx-tool

# or from source

pip install git+https://github.com/nexustech101/fx.git

```



You can also clone directly from the repo `nexustech101/fx`:



```bash

git clone https://github.com/nexustech101/fx.git

```



## 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



```python

from __future__ import annotations



from enum import StrEnum

from time import strftime



import registers.cli as cli

import registers.db as db

from registers.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

    )

```



`registers.cli` also supports explicit instance-mode registries for isolated

command scopes:



```python

import registers.cli as cli



registry = cli.CommandRegistry()



@registry.register(description="Say hello")

@registry.argument("name", type=str)

@registry.option("--hello")

def hello(name: str) -> str:

    return f"Hello, {name}!"



if __name__ == "__main__":

    registry.run()

```



Run it as follows:



```bash

# 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:



```bash

# Run directly for interactive mode

python todo.py

```

Interactive mode:



![Screenshot](img1.png)



`fx-tool` is the recommended way to manage Registers projects end-to-end.

Think of it as the project operations companion for Registers, similar to how

`pip` supports Python package workflows or how `npm` supports Node package workflows.

For full `fx` usage, see the `fx-tool` docs in the separate repo.



### Database + FastAPI in 5 minutes



```python

from contextlib import asynccontextmanager

from fastapi import FastAPI, HTTPException

from pydantic import BaseModel

from registers.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)

```



```bash

# POST /customers

curl -X POST "http://localhost:8000/customers" \

  -H "Content-Type: application/json" \

  -d '{"name": "Alice Johnson", "email": "alice@example.com"}'



# Response

{"id": 1, "name": "Alice Johnson", "email": "alice@example.com"}



# GET /customers/1

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



# Response

{"id": 1, "name": "Alice Johnson", "email": "alice@example.com"}



# 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 `registers.cron` decorators to define interval/cron/event jobs.

For runtime lifecycle and workflow operations (`start`, `status`, `generate`,

`apply`, `register`, `run-workflow`), use `fx-tool`.



Both cron registration styles are supported:



```python

# Module-level style

import registers.cron as cron



@cron.job(

    name="nightly",

    trigger=cron.cron("0 2 * * *"),

    target="local_async",

    retry_policy="exponential",

    retry_max_attempts=5,

    retry_backoff_seconds=10,

    retry_max_backoff_seconds=180,

    retry_jitter_seconds=2,

)

def nightly() -> str:

    return "ok"

```



```python

# Explicit instance style

from registers.cron import CronRegistry



cron = CronRegistry()



@cron.job(

    name="nightly",

    trigger=cron.cron("0 2 * * *"),

    target="local_async",

    retry_policy="fixed",

    retry_max_attempts=3,

    retry_backoff_seconds=15,

)

def nightly() -> str:

    return "ok"

```



Retry-capable jobs are moved to `dead_letter` state when max attempts are exhausted.



## Architecture



- `registers.cli`

  Decorator-driven command registration (module facade + explicit registry instances), parser/dispatch, interactive shell, and plugin loading.



- `registers.db`

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



- `registers.cron`

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



- `fx-tool` (separate package)

  Project manager and operations CLI for Registers workflows (scaffolding, runtime ops, cron lifecycle, and workflow orchestration).



## 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.

- Any engineer who needs a fast and robust solution to data intensive applications.



## Documentation



- Project architecture spec: `PROJECT_SPEC.md`

- CLI manual: `src/registers/cli/USAGE.md`

- DB manual: `src/registers/db/USAGE.md`

- FX tool docs (separate package): `https://github.com/nexustech101/fx-tool`

- Cron manual: `src/registers/cron/USAGE.md` (if present in your version)



## Roadmap and Planned Extensions



Registers 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-tool + cli + db + cron` architecture rather than replace it.



## 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 (200+ tests) covering unit behavior, edge cases, and multi-dialect integration scenarios.



## License



MIT