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

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 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.

Typer, the FastAPI of CLIs

If you are building a CLI app to be used in the terminal instead of a web API, check out Typer.

Typer is FastAPI's little sibling. And it's intended to be the FastAPI of CLIs. ⌨️ 🚀

Requirements

FastAPI stands on the shoulders of giants:

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

Installation

Create and activate a virtual environment and then install FastAPI:

Note: Make sure you put "fastapi[standard]" in quotes to ensure it works in all terminals.

Example

Create it

Create a file main.py with:

```Python from typing import Union

from fastapi import FastAPI

app = FastAPI()

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

@app.get("/items/{itemid}") def readitem(itemid: int, q: Union[str, None] = None): return {"itemid": item_id, "q": q} ```

Run it

Run the server with:

Check it

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

You will see the JSON response as:

JSON {"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):

Alternative API docs

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

You will see the alternative automatic documentation (provided by 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.

```Python hl_lines="4 9-12 25-27" from typing import Union

from fastapi import FastAPI from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel): name: str price: float is_offer: Union[bool, None] = None

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

@app.get("/items/{itemid}") def readitem(itemid: int, q: Union[str, None] = None): return {"itemid": item_id, "q": q}

@app.put("/items/{itemid}") def updateitem(itemid: int, item: Item): return {"itemname": item.name, "itemid": itemid} ```

The fastapi dev server should reload automatically.

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:

• 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

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

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

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.

For example, for an int:

Python item_id: int

or for a more complex Item model:

Python 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 it 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:

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

...from:

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

...to:

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

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

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).
• GraphQL integration with Strawberry and other libraries.
• Many extra features (thanks to Starlette) as:
  • WebSockets
  • extremely easy tests based on HTTPX 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.

Dependencies

FastAPI depends on Pydantic and Starlette.

standard Dependencies

When you install FastAPI with pip install "fastapi[standard]" it comes with the standard group of optional dependencies:

Used by Pydantic:

• email-validator - for email validation.

Used by Starlette:

• httpx - Required if you want to use the TestClient.
• jinja2 - Required if you want to use the default template configuration.
• python-multipart - Required if you want to support form "parsing", with request.form().

Used by FastAPI:

• uvicorn - for the server that loads and serves your application. This includes uvicorn[standard], which includes some dependencies (e.g. uvloop) needed for high performance serving.
• fastapi-cli[standard] - to provide the fastapi command.
  • This includes fastapi-cloud-cli, which allows you to deploy your FastAPI application to FastAPI Cloud.

Without standard Dependencies

If you don't want to include the standard optional dependencies, you can install with pip install fastapi instead of pip install "fastapi[standard]".

Without fastapi-cloud-cli

If you want to install FastAPI with the standard dependencies but without the fastapi-cloud-cli, you can install with pip install "fastapi[standard-no-fastapi-cloud-cli]".

Additional Optional Dependencies

There are some additional dependencies you might want to install.

Additional optional Pydantic dependencies:

• pydantic-settings - for settings management.
• pydantic-extra-types - for extra types to be used with Pydantic.

Additional optional FastAPI dependencies:

• orjson - Required if you want to use ORJSONResponse.
• ujson - Required if you want to use UJSONResponse.

License

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