fastapi-users VS fastapi

I'm working with it now as a backend to svelte! It's very ergonomic especially since I'm using sqlalchemy + postgres (asyncpg) so I could just copy paste the example code. Heads up though that it doesn't implement any form of token refresh pattern so you'll have to do that yourself.

It might be worth checking out fastapi-users. It has a lot of increased functionality like JWT with refresh, OAuth social login, cookie sessions, and more. It’s pretty cool: https://github.com/fastapi-users/fastapi-users

1) There is https://github.com/fastapi-users/fastapi-users library for FastAPI, but social auth is not very clear documented, DB not included.

found this: https://github.com/fastapi-users/fastapi-users

Well there is already an existing package that works pretty well : https://github.com/frankie567/fastapi-users

In the Python world, Flask and FastAPI are more performant, lighter frameworks. The FastAPI Users module looks like a good option for authentication.

I'd recommend you check out FastAPI-Users if you haven't already: https://github.com/frankie567/fastapi-users

It provides a users framework with OAuth2 integration, support for different ORM backends, and routes for authentication, verification, etc.

Full disclosure: I am a contributor to this project.

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.