"""FastAPI framework, high performance, easy to learn, fast to code, ready for production"""

__version__ = "0.128.0"

from starlette import status as status

from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
from .exceptions import HTTPException as HTTPException
from .exceptions import WebSocketException as WebSocketException

[build-system]
requires = ["pdm-backend"]
build-backend = "pdm.backend"

[project]
name = "fastapi"
dynamic = ["version"]
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
readme = "README.md"
license = "MIT"
license-files = ["LICENSE"]
requires-python = ">=3.9"
authors = [
    { name = "Sebastián Ramírez", email = "******@****.***" },
]
classifiers = [

Example:

    Source (English):

        «««
        [One of the fastest Python frameworks available](#performance)
        »»»

    Result (German):

        «««
        [Eines der schnellsten verfügbaren Python-Frameworks](#performance)
        »»»

* **Safer** types.
* Better **performance** and **less energy** consumption.
* Better **extensibility**.
* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀

In some cases, for pure data validation and processing, you can get performance improvements of **20x** or more. This means 2,000% or more. 🤯