fastapi is a free, open source api development & testing project written in Python and released under MIT. It has 104,542 GitHub stars, 10,173 forks and 78 open issues, and was last pushed 31 hours ago. On this registry it ranks #1 of 186 tracked projects in API Development & Testing, with 5 head-to-head comparisons available. It gained 1,811 stars over the last 30 tracked days.

What is fastapi?

FastAPI is an MIT-licensed, high-performance Python web framework for building APIs with standard Python type hints, aimed at Python developers and teams that need production-ready HTTP services with automatic OpenAPI documentation.

What it is

FastAPI is a modern Python framework for building APIs. It is built on two foundations: Starlette handles the web parts, and Pydantic handles the data parts. Its design is based on, and fully compatible with, the open standards for APIs — OpenAPI (formerly known as Swagger) and JSON Schema. The project lives on GitHub at fastapi/fastapi, is published on PyPI as fastapi, and carries a documentation site at https://fastapi.tiangolo.com. The repository reports 102,395 stars, 9,902 forks, 78 open issues, and a last push of 2026-09-14.

The concrete problem it solves is the boilerplate that sits between a Python function and a working HTTP API: request parsing, parameter validation, response serialization, and API documentation. In FastAPI, a parameter declaration drives multiple features at once, so a signature such as item_id: int, q: str | None = None supplies validation and typing without separate wiring. Handlers may be written as plain def or as async def when the code uses async and await. The framework then produces automatic interactive documentation from the same declarations, which is the specific work it replaces.

Key capabilities

  • Route declaration through decorators on an FastAPI() app instance, for example @app.get("/items/{item_id}") with typed path and query parameters.
  • Native async / await support by declaring handlers with async def.
  • Standards-based schemas: OpenAPI and JSON Schema compatibility, which yields automatic interactive documentation.
  • Pydantic-backed data validation and serialization as the data layer.
  • Starlette as the underlying web layer for the ASGI parts.
  • Installation as an extra-bundled package, "fastapi[standard]", quoted for terminal compatibility.
  • A companion project, Typer, described as "the FastAPI of CLIs" for terminal applications rather than web APIs.

Who uses it and how

  • Microsoft: an engineer reported using FastAPI for all of a team's ML services, with some integrated into the core Windows product and some Office products.
  • Uber: adopted the library to spawn a REST server queried for predictions, in the context of Ludwig.
  • Netflix: released Dispatch, a crisis management orchestration framework, built with FastAPI.
  • Cisco: described as a key component in an API-first development strategy, driving automations and services including a Virtual TAC Engineer.
  • Teams building production Python APIs at large scale, per the practitioner recommendations carried in the README.

Getting started

Install uv, then add FastAPI to a project with uv add "fastapi[standard]"; users who prefer pip install fastapi[standard] inside a virtual environment. The minimal entry point is a main.py file that imports FastAPI, creates app = FastAPI(), and decorates a handler.

How it compares

The supplied facts name no competing framework or paid product, so no like-for-like contrast is available here. Within the project's own materials, Starlette supplies the web layer and Pydantic the data layer, while Typer is described as the sibling tool for command-line interfaces; the README positions performance as on par with NodeJS and Go, and as one of the fastest Python frameworks available.

When to use it — and when not to

Adopters build on Starlette and Pydantic, and the README does not describe the operational surface around deployment, databases, or mail delivery, so those remain the self-hoster's responsibility. The productivity claims — about 200% to 300% faster feature development and roughly 40% fewer developer-induced errors — are footnoted in the README as estimates from an internal development team's tests, not independent benchmarks, and should be read that way. Teams outside Python, or those wanting a CLI rather than a web API, are not the intended audience; the README points the latter group to Typer.

project readme (upstream, from github) — read inline

FastAPI

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

Test Coverage Package version Supported Python versions


Documentation: https://fastapi.tiangolo.com

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


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 conducted by an internal development team, building production applications.

Sponsors

Keystone Sponsor

Gold Sponsors

Silver Sponsors

Other sponsors

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)


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

Piero Molino, Yaroslav Dudin, and Sai Sumanth Miryala - Uber (ref)


"Netflix is pleased to announce the open-source release of our crisis management orchestration framework: Dispatch! [built with FastAPI]"

Kevin Glisson, Marc Vilanova, Forest Monsen - Netflix (ref)


"If anyone is looking to build a production Python API, I would highly recommend FastAPI. It is beautifully designed, simple to use and highly scalable, it has become a key component in our API first development strategy and is driving many automations and services such as our Virtual TAC Engineer."

Deon Pillsbury - Cisco (ref)


FastAPI mini documentary

There's a FastAPI mini documentary released at the end of 2025, you can watch it online:

FastAPI Mini Documentary

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:

Installation

First, install uv, and then add FastAPI to your project:

$ uv add "fastapi[standard]"

---> 100%

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

If you prefer to use pip, install fastapi[standard] inside a virtual environment. See the installation guide for the alternative steps.

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 = 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 = 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:

$ uv run fastapi dev

 ╭────────── FastAPI CLI - Development mode ───────────╮
 │                                                     │
 │  Serving at: http://127.0.0.1:8000                  │
 │                                                     │
 │  API docs: http://127.0.0.1:8000/docs               │
 │                                                     │
 │  Running in development mode, for production use:   │
 │                                                     │
 │  fastapi run                                        │
 │                                                     │
 ╰─────────────────────────────────────────────────────╯

INFO:     Will watch for changes in these directories: ['/home/user/code/awesomeapp']
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Started reloader process [2248755] using WatchFiles
INFO:     Started server process [2248757]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
About the command fastapi dev...

The command fastapi dev reads your main.py file automatically, detects the FastAPI app in it, and starts a server using Uvicorn.

By default, fastapi dev will start with auto-reload enabled for local development.

You can read more about it in the FastAPI CLI docs.

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 = None


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


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

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.

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 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:

    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 such as: headers, cookies, form fields and files.
  • How to set validation constraints such 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) such as:
    • WebSockets
    • extremely easy tests based on HTTPX and pytest
    • CORS
    • Cookie Sessions
    • ...and more.

Deploy your app (optional)

You can optionally deploy your FastAPI app to FastAPI Cloud with a single command. 🚀

$ uv run fastapi deploy

Deploying to FastAPI Cloud...

✅ Deployment successful!

🐔 Ready the chicken! Your app is ready at https://myapp.fastapicloud.dev

The CLI will automatically detect your FastAPI application and deploy it to the cloud. If you are not logged in, your browser will open to complete the authentication process.

That's it! Now you can access your app at that URL. ✨

About FastAPI Cloud

FastAPI Cloud is built by the same author and team behind FastAPI.

It streamlines the process of building, deploying, and accessing an API with minimal effort.

It brings the same developer experience of building apps with FastAPI to deploying them to the cloud. 🎉

FastAPI Cloud is the primary sponsor and funding provider for the FastAPI and friends open source projects. ✨

Deploy to other cloud providers

FastAPI is open source and based on standards. You can deploy FastAPI apps to any cloud provider you choose.

Follow your cloud provider's guides to deploy FastAPI apps with them. 🤓

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 uv add "fastapi[standard]" it comes with the standard group of optional dependencies:

Used by Pydantic:

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 uv add fastapi instead of uv add "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 uv add "fastapi[standard-no-fastapi-cloud-cli]".

Additional Optional Dependencies

There are some additional dependencies you might want to install.

Additional optional Pydantic dependencies:

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.

Frequently asked questions

Is fastapi free to use?

fastapi is open source under the MIT licence. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does fastapi do?

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

What is fastapi written in?

fastapi is primarily written in Python. Its source is publicly available at https://github.com/fastapi/fastapi, and it has 104,542 GitHub stars.