django-ninja VS flama

Django Ninja [1], it forever changed how I write Django project, in a way so elegant and productive.

[1]: https://django-ninja.dev/

Django Ninja is a RESTful wrapper for Django, while UtilMeta Python Framework uses a more concise declarative ORM Schema for Django and other future-supporting ORMs like sqlachemy and Peewee to build RESTful APIs more efficiently, and supports not only Django but all Python mainstream frameworks like Django, Flask, Starlette, FastAPI, Sanic, Tornado, etc.

I can't recommend [django-ninja](https://github.com/vitalik/django-ninja) enough. It's an easy to use, extremely fast, typed API for django. I've found it to be better in almost all aspects when compared to djangorestframework.

It's gaining popularity but is still widely unknown.

> The only place I really see Django at large companies is as an api using DRF or something.

This is not a bad thing. Using Django as an API backend is amazingly fast in terms of development time, especially with modern frameworks such as django-ninja [1].

Just use the built-in ORM to create models, write your endpoints, and use the built-in admin interface to play with the database if you don't have endpoints for everything.

There is also a less known feature of Django called admindocs [2], which automatically generates a human readable, hyperlinked documentation for your models and relations between them.

[1] https://django-ninja.rest-framework.com/

[2] https://docs.djangoproject.com/en/4.2/ref/contrib/admin/admi...

Personally, I also prefer django-ninja to DRF.

Actually that's not fully true. If you mix async and sync codes in django-ninja there will be some errors. Where's the proof ? django-ninja doesn't support async auth

Subscribe to this issue :D

Also recommend Django-Ninja. It basically reimplements fastapi's type and decorator-based API construction, but embedded directly in django so you have access to django's ORM and middleware library.

There has been a considerable effort in the last few years to try and standardise the way in which these type of APIs are implemented via different frameworks. However, over the last few years, a new type of functionality has become more and more popular: machine learning (ML) models; and the existing frameworks for building APIs are not well suited for this type of functionality. In this series of posts, we will learn how to build APIs using a Framework for the development of Lightweight Applications and Machine-learning Automation, also known as Flama.

*some options that were mentioned in the thread I linked to include - Starlite (made to bypass perceived shortcomings of FastAPI) - Flama (Starlette w/ Marshmellow instead of Pydantic) - APIFlask (Flask w/ Pydantic) - Django Ninja - Quart-Schema

Flama (Starlette, Marshmallow instead of Pydantic)