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

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

Build Status Coverage Package version Join the chat at https://gitter.im/tiangolo/fastapi


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.

Opinions

"[...] 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."

Kabir Khan - Microsoft (ref)

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

Brian Okken - Python Bytes podcast host (ref)

"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."

Timothy Crosley - Hug creator (ref)

Requirements

Python 3.6+

FastAPI stands on the shoulders of giants:

Installation

$ pip install fastapi

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

$ pip install uvicorn

Example

Create it

  • 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}
Or use async def...

If your code uses async / await, use async def:

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}

Note:

If you don't know, check the "In a hurry?" section about async and await in the docs.

Run it

Run the server with:

uvicorn main:app --reload
About the command uvicorn main:app --reload...

The command uvicorn main:app refers to:

  • main: the file main.py (the Python "module").
  • app: the object created inside of main.py with the line app = FastAPI().
  • --reload: make the server restart after code changes. Only do this for development.

Check it

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.

Interactive API docs

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

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

Swagger UI

Alternative API docs

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

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

ReDoc

Example upgrade

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).

Interactive API docs upgrade

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

  • The interactive API documentation will be automatically updated, including the new body:

Swagger UI

  • Click on the button "Try it out", it allows you to fill the parameters and directly interact with the API:

Swagger UI interaction

  • Then click on the "Execute" button, the user interface will communicate with your API, send the parameters, get the results and show them on the screen:

Swagger UI interaction

Alternative API docs upgrade

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

  • The alternative documentation will also reflect the new query parameter and body:

ReDoc

Recap

In summary, you declare once the types of parameters, body, etc. as function parameters.

You do that with standard modern Python types.

You don't have to learn a new syntax, the methods or classes of a specific library, etc.

Just standard Python 3.6+.

For example, for an int:

item_id: int

or for a more complex Item model:

item: Item

...and with that single declaration you get:

  • Editor support, including:
    • Completion.
    • Type checks.
  • Validation of data:
    • Automatic and clear errors when the data is invalid.
    • Validation even for deeply nested JSON objects.
  • Conversion of input data: coming from the network to Python data and types. Reading from:
    • JSON.
    • Path parameters.
    • Query parameters.
    • Cookies.
    • Headers.
    • Forms.
    • Files.
  • Conversion of output data: converting from Python data and types to network data (as JSON):
    • Convert Python types (str, int, float, bool, list, etc).
    • datetime objects.
    • UUID objects.
    • Database models.
    • ...and many more.
  • Automatic interactive API documentation, including 2 alternative user interfaces:
    • Swagger UI.
    • ReDoc.

Coming back to the previous code example, FastAPI will:

  • Validate that there is an item_id in the path for GET and PUT requests.
  • Validate that the item_id is of type int for GET and PUT requests.
    • If it is not, the client will see a useful, clear error.
  • Check if there is an optional query parameter named q (as in http://127.0.0.1:8000/items/foo?q=somequery) for GET requests.
    • As the q parameter is declared with = None, it is optional.
    • Without the None it would be required (as is the body in the case with PUT).
  • For PUT requests to /items/{item_id}, Read the body as JSON:
    • Check that it has a required attribute name that should be a str.
    • Check that is has a required attribute price that has to be a float.
    • Check that it has an optional attribute is_offer, that should be a bool, if present.
    • All this would also work for deeply nested JSON objects.
  • Convert from and to JSON automatically.
  • Document everything with OpenAPI, that can be used by:
    • Interactive documentation systems.
    • Automatic client code generation systems, for many languages.
  • Provide 2 interactive documentation web interfaces directly.

We just scratched the surface, but you already get the idea of how it all works.

Try changing the line with:

    return {"item_name": item.name, "item_id": item_id}

...from:

        ... "item_name": item.name ...

...to:

        ... "item_price": item.price ...

...and see how your editor will auto-complete the attributes and know their types:

editor support

For a more complete example including more features, see the Tutorial - User Guide.

Spoiler alert: the tutorial - user guide includes:

  • Declaration of parameters from other different places as: headers, cookies, form fields and files.
  • How to set validation constraints as maximum_length or regex.
  • A very powerful and easy to use Dependency Injection system.
  • Security and authentication, including support for OAuth2 with JWT tokens and HTTP Basic auth.
  • More advanced (but equally easy) techniques for declaring deeply nested JSON models (thanks to Pydantic).
  • Many extra features (thanks to Starlette) as:
    • WebSockets
    • GraphQL
    • extremely easy tests based on requests and pytest
    • CORS
    • Cookie Sessions
    • ...and more.

Performance

Independent TechEmpower benchmarks show FastAPI applications running under Uvicorn as one of the fastest Python frameworks available, only below Starlette and Uvicorn themselves (used internally by FastAPI). (*)

To understand more about it, see the section Benchmarks.

Optional Dependencies

Used by Pydantic:

Used by Starlette:

  • requests - Required if you want to use the TestClient.
  • aiofiles - Required if you want to use FileResponse or StaticFiles.
  • jinja2 - Required if you want to use the default template configuration.
  • python-multipart - Required if you want to support form "parsing", with request.form().
  • itsdangerous - Required for SessionMiddleware support.
  • pyyaml - Required for SchemaGenerator support.
  • graphene - Required for GraphQLApp support.
  • ujson - Required if you want to use UJSONResponse.

Used by FastAPI / Starlette:

  • uvicorn - for the server that loads and serves your application.

You can install all of these with pip3 install fastapi[all].

License

This project is licensed under the terms of the MIT license.

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