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

Documentation: https://fastapi.tiangolo.com

Source Code: https://github.com/tiangolo/fastapi

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.

The key features are:

• Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available.

• Fast to code: Increase the speed to develop features by about 200% to 300% *.

• Fewer bugs: Reduce about 40% of human (developer) induced errors. *

• Intuitive: Great editor support. Completion everywhere. Less time debugging.

• Easy: Designed to be easy to use and learn. Less time reading docs.

• Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.

• Robust: Get production-ready code. With automatic interactive documentation.

• Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.

* estimation based on tests on an internal development team, building production applications.

"[...] I'm using FastAPI a ton these days. [...] I'm actually planning to use it for all of my team's ML services at Microsoft. Some of them are getting integrated into the core Windows product and some Office products."

"I’m over the moon excited about FastAPI. It’s so fun!"

"Honestly, what you've built looks super solid and polished. In many ways, it's what I wanted Hug to be - it's really inspiring to see someone build that."

"If you're looking to learn one modern framework for building REST APIs, check out FastAPI [...] It's fast, easy to use and easy to learn [...]"

"We've switched over to FastAPI for our APIs [...] I think you'll like it [...]"

"We adopted the FastAPI library to spawn a REST server that can be queried to obtain predictions. [for Ludwig]"

Python 3.6+

FastAPI stands on the shoulders of giants:

• Starlette for the web parts.
• Pydantic for the data parts.

pip install fastapi

You will also need an ASGI server, for production such as Uvicorn or Hypercorn.

pip install uvicorn

• Create a file main.py with:

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
async def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
async def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}

Run the server with:

uvicorn main:app --reload

Open your browser at http://127.0.0.1:8000/items/5?q=somequery.

You will see the JSON response as:

{"item_id": 5, "q": "somequery"}

You already created an API that:

• Receives HTTP requests in the paths / and /items/{item_id}.
• Both paths take GET operations (also known as HTTP methods).
• The path /items/{item_id} has a path parameter item_id that should be an int.
• The path /items/{item_id} has an optional str query parameter q.

Now go to http://127.0.0.1:8000/docs.

You will see the automatic interactive API documentation (provided by Swagger UI):

And now, go to http://127.0.0.1:8000/redoc.

You will see the alternative automatic documentation (provided by ReDoc):

Now modify the file main.py to receive a body from a PUT request.

Declare the body using standard Python types, thanks to Pydantic.

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: bool = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}


@app.put("/items/{item_id}")
def update_item(item_id: int, item: Item):
    return {"item_name": item.name, "item_id": item_id}

The server should reload automatically (because you added --reload to the uvicorn command above).