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---
name: fastapi
description: FastAPI best practices and conventions. Use when working with FastAPI APIs, Pydantic models, dependencies, streaming responses including Server-Sent Events (SSE), and serving frontend apps. Keeps FastAPI code clean and up to date with the latest features and patterns.
---
# 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](#serve-frontend-apps).
* Server-Sent Events (SSE): use `response_class=EventSourceResponse` and `yield`; see [Streaming](#streaming-json-lines-sse-bytes) 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](#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:
```bash
fastapi dev
```
Run the production server:
```bash
fastapi run
```
Prefer declaring the entrypoint in `pyproject.toml`:
```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:
```bash
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.:
```python
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:
```python
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.
```python
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.
```python
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()`.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.