Introducing FastAPI

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


Key Features

  • 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% *.
  • Less 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. Less 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.


$ pip install fastapi
$ pip install uvicorn


Create it

  • Create a file with:
from fastapi import FastAPIapp = FastAPI()
def read_root():
return {"Hello": "World"}
def read_item(item_id: int, q: str = None):
return {"item_id": item_id, "q": q}

Check it

{"item_id": 5, "q": "somequery"}
  • 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

Alternative API docs

Example upgrade

from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
price: float
is_offer: bool = None
def read_root():
return {"Hello": "World"}
def read_item(item_id: int, q: str = None):
return {"item_id": item_id, "q": q}
def create_item(item_id: int, item: Item):
return {"item_name":, "item_id": item_id}

Interactive API docs upgrade

  • The interactive API documentation will be automatically updated, including the new body:
  • Click on the button “Try it out”, it allows you to fill the parameters and directly interact with the API:
  • 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:

Alternative API docs upgrade

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


item_id: int
item: Item
  • 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.
  • 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 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.
return {"item_name":, "item_id": item_id}
... "item_name": ...
... "item_price": item.price ...
  • 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.


Learn more

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Sebastián Ramírez

Sebastián Ramírez

Creator of FastAPI and Typer. Dev at Exposion AI. APIs, Deep Learning/Machine Learning, full-stack distributed systems, SQL/NoSQL, Python, Docker, JS, TS, etc.