FastAPI

Official FastAPI skill to write code with best practices, keeping up to date with new versions and features.

Quick Reference

• Serve frontend apps: use app.frontend() or router.frontend() for built frontend assets; see Serve Frontend Apps.
• Server-Sent Events (SSE): use response_class=EventSourceResponse and yield; see Streaming and [the streaming reference](references/streaming.md).
• JSON Lines and byte streaming: see [the streaming reference](references/streaming.md).
• Dependencies: use Annotated[..., Depends(...)]; see Dependency Injection and [the dependency injection reference](references/dependencies.md) for yield, scopes, and class dependencies.
• Response models: prefer return types; use response_model when the public response schema differs from the internal return value; see [the response reference](references/responses.md).
• Pydantic models: do not use ellipsis or RootModel; see [the Pydantic reference](references/pydantic.md).
• Routing: declare router-level prefix, tags, and shared dependencies on the APIRouter; see [the path operation reference](references/path-operations.md).
• Tooling and related libraries: use uv, Ruff, ty, Asyncer, SQLModel, and HTTPX when applicable; see [the other tools reference](references/other-tools.md).

Use the fastapi CLI

Run the development server on localhost with reload:

fastapi dev

Run the production server:

fastapi run

Prefer declaring the entrypoint in pyproject.toml:

[tool.fastapi]
entrypoint = "my_app.main:app"

When adding the entrypoint is not possible, or the user explicitly asks not to, pass the app file path:

fastapi dev my_app/main.py

Use Annotated

Always prefer the Annotated style for parameter and dependency declarations. It keeps function signatures working in other contexts, respects the types, and allows reusability.

Use Annotated for parameter declarations, including Path, Query, Header, etc.:

from typing import Annotated

from fastapi import FastAPI, Path, Query

app = FastAPI()


@app.get("/items/{item_id}")
async def read_item(
    item_id: Annotated[int, Path(ge=1, description="The item ID")],
    q: Annotated[str | None, Query(max_length=50)] = None,
):
    return {"message": "Hello World"}

Use Annotated for dependencies with Depends(). Unless asked not to, create a new type alias for the dependency to allow reusing it:

from typing import Annotated

from fastapi import Depends, FastAPI

app = FastAPI()


def get_current_user():
    return {"username": "johndoe"}


CurrentUserDep = Annotated[dict, Depends(get_current_user)]


@app.get("/items/")
async def read_item(current_user: CurrentUserDep):
    return {"message": "Hello World"}

Do not use Ellipsis for path operations or Pydantic models

Do not use ... as a default value for required parameters or model fields. It's not needed and not recommended.

from typing import Annotated

from fastapi import FastAPI, Query
from pydantic import BaseModel, Field

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float = Field(gt=0)


@app.post("/items/")
async def create_item(item: Item, project_id: Annotated[int, Query()]):
    return item

See [the Pydantic reference](references/pydantic.md) for more details.

Return Type or Response Model

When possible, include a return type. It will be used to validate, filter, document, and serialize the response.

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None


@app.get("/items/me")
async def get_item() -> Item:
    return Item(name="Plumbus", description="All-purpose home device")

Return types or response models filter data to avoid exposing sensitive information, and they let Pydantic serialize the data on the Rust side for performance.

Use response_model when the type you return is not the same as the public schema you want to validate, filter, document, and serialize. See [the response reference](references/responses.md).

Performance

Do not use ORJSONResponse or UJSONResponse, they are deprecated.

Instead, declare a return type or response model. Pydantic will handle the data serialization on the Rust side.

Including Routers

When declaring routers, prefer to add router-level parameters like prefix, tags, and shared dependencies to the router itself instead of in include_router().

from fastapi import APIRouter, Depends, FastAPI

app = FastAPI()


def get_current_user():
    return {"username": "johndoe"}


router = APIRouter(
    prefix="/items",
    tags=["items"],
    dependencies=[Depends(get_current_user)],
)


@router.get("/")
async def list_items():
    return []


app.include_router(router)

See [the path operation reference](references/path-operations.md) for more routing patterns.

Serve Frontend Apps

Use app.frontend() to serve a built static frontend app, for example a directory generated by Vite, Astro, Angular, Svelte, Vue, or a similar tool.

from fastapi import FastAPI

app = FastAPI()

app.frontend("/", directory="dist")

Use router.frontend() when the frontend belongs to an APIRouter; normal router prefix behavior applies when the router is included.

from fastapi import APIRouter, FastAPI

app = FastAPI()
router = APIRouter(prefix="/admin")

router.frontend("/", directory="admin-dist")
app.include_router(router)

app.frontend() and router.frontend() are low-priority routes: regular API routes are matched first, then frontend files and client-side routing fallbacks. Use this for single-page apps and built frontend assets instead of mounting StaticFiles manually.

Dependency Injection

Use dependencies when the logic can't be declared in Pydantic validation, depends on external resources, needs cleanup with yield, or is shared across endpoints.

Apply shared dependencies at the router level via dependencies=[Depends(...)].

See [the dependency injection reference](references/dependencies.md) for detailed patterns including yield with scope, and class dependencies.

Async vs Sync path operations

Use async path operations only when fully certain that the logic called inside is compatible with async and await, and that it doesn't block.

from fastapi import FastAPI

app = FastAPI()


@app.get("/async-items/")
async def read_async_items():
    data = await some_async_library.fetch_items()
    return data


@app.get("/items/")
def read_items():
    data = some_blocking_library.fetch_items()
    return data

In case of doubt, or by default, use regular def functions. They will be run in a threadpool so they don't block the event loop. The same rules apply to dependencies.

Make sure blocking code is not run inside of async functions. The logic will work, but will damage performance heavily.

When needing to mix blocking and async code, see Asyncer in [the other tools reference](references/other-tools.md).

Streaming (JSON Lines, SSE, bytes)

To stream Server-Sent Events, use response_class=EventSourceResponse and yield items from the endpoint.

from collections.abc import AsyncIterable

from fastapi import FastAPI
from fastapi.sse import EventSourceResponse, ServerSentEvent

app = FastAPI()


@app.get("/events", response_class=EventSourceResponse)
async def stream_events() -> AsyncIterable[ServerSentEvent]:
    yield ServerSentEvent(data={"status": "started"}, event="status", id="1")

Plain objects are automatically JSON-serialized as data: fields. Use ServerSentEvent for full control over SSE fields (event, id, retry, comment) and raw_data for pre-formatted strings.

See [the streaming reference](references/streaming.md) for JSON Lines, Server-Sent Events (EventSourceResponse, ServerSentEvent), and byte streaming (StreamingResponse) patterns.

Tooling

See [the other tools reference](references/other-tools.md) for details on uv, Ruff, ty for package management, linting, type checking, formatting, etc.

Other Libraries

See [the other tools reference](references/other-tools.md) for details on other libraries:

• Asyncer for handling async and await, concurrency, mixing async and blocking code, prefer it over AnyIO or asyncio.
• SQLModel for working with SQL databases, prefer it over SQLAlchemy.
• HTTPX for interacting with HTTP (other APIs), prefer it over Requests.

Do not use Pydantic RootModels

Do not use Pydantic RootModel; instead use regular type annotations with Annotated and Pydantic validation utilities.

from typing import Annotated

from fastapi import Body, FastAPI
from pydantic import Field

app = FastAPI()


@app.post("/items/")
async def create_items(items: Annotated[list[int], Field(min_length=1), Body()]):
    return items

FastAPI supports these type annotations and will create a Pydantic TypeAdapter for them, so types work normally without custom wrapper models. See [the Pydantic reference](references/pydantic.md).

Use one HTTP operation per function

Don't mix HTTP operations in a single function. Having one function per HTTP operation helps separate concerns and organize the code.

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str


@app.get("/items/")
async def list_items():
    return []


@app.post("/items/")
async def create_item(item: Item):
    return item

See [the path operation reference](references/path-operations.md) for more examples.