Litestar
@litestar-org
Exciting news out in the Litestar ecosystem!
Evidently, the open-source machine learning observability platform, has migrated from FastAPI to Litestar!
What is Evidently AI?
From the horses mouth:
Evidently AI allows you to evaluate, test, and monitor machine learning (ML) models from validation to production. From tabular data to natural language processing (NLP) and large language models (LLM). Built for data scientists and ML engineers.
The site you're on now - Polar.sh - enhances what we can do as open source maintainers and with the advent of the Polar GitHub integration, we can expect Polar to skyrocket in use!
From a maintainer standpoint, Polar gives us some great, well polished features including:
Hey Litestar Community!
Exciting news! We've just launched a new apparel line. Now you can show off your Litestar spirit with branded t-shirts, hoodies, and sweatshirts.
We've teamed up with Bonfire to bring you top-notch apparel that's perfect for coding sessions, tech meetups, or just chilling out. Each piece showcases a sleek Litestar design that keeps it professional yet casual.
On the 2023-08-21, after about 7 months of intense development, Litestar 2.0 was released! Let's talk about it.
What's new?
Let's start this off with a non-exhaustive list of a few particularly noteworthy features:
This post is meant for the r/Python community, and has been cross-posted here.
Greetings my friendly neighborhood Pythonistas, Pythonians, Pythoons, future Rustaceans! I am excited to bring you another update on our project after quite a long r/Python break due to Reddit meta-issues and internal hunkering down to churn out new features, bug fixes, and more.
With the beta release of Litestar 2.0 just around the corner, here’s a little sneak peek of some of its exciting new features, in particular its extended WebSocket support and the newly introduced channels module.
The combination of these two makes for some nice and easy patterns, providing powerful features, for example, WebSocket broadcasting, supporting amongst other things inter-process communication and a configurable history.
Let’s leave the background information aside for now and dive straight into the code, so you can see what this is capable of.
The app
Recap
What’s Starlite?
Starlite is a flexible and highly performant ASGI framework, focused on building APIs while delivering great developer experience by offering ready-built solutions for common tasks such as ORM integration, caching, session management, key/value stores, OpenAPI-schema generation and interactive API docs, type safety and much more.
You can read more about Starlite’s features in our documentation!
So what’s new?
This post has endured many platform transfers, and some links may be broken. To view the Starlite v1 documentation, visit https://docs.litestar.dev/1/!
Hi Pythonistas!
This post is an update on the development status of Starlite.
Short Intro to Starlite
Let me start with a short intro about what is Starlite - for those of you unfamiliar with it.
I’m excited to announce the release of v1.0.0 of Starlite. This release includes a thorough rewrite of the core logic, boosting performance and reducing resource usage quite drastically.
Starlite is a Python ASGI API framework built on top of the Starlette ASGI toolkit and pydantic. It has some similarities to FastAPI — in both frameworks type annotations are used to automatically generate OpenAPI documentation as well as parse, validate and inject request data.
This makes Starlite compatible with most of the FastAPI ecosystem, which is very nice. It also makes migration quite straightforward. Yet, Starlite is not FastAPI — it’s quite different both in its conception and in its implementation. It’s defined by the following traits:
It’s Opinionated
I’m very excited to announce the release of Starlite — a new python asynchronous API framework (ASGI).
Starlite is built on top of the Starlette ASGI toolkit, which offers high-performance async message handling, and pydantic, which offers fantastic data validation and parsing using type hints.
Relation to FastAPI