Python FastAPI API

A FastAPI todo service gives you generated API docs, request validation, a health check, and routes for listing or adding todos. Tokay runs it at a live URL as one Web Service.

Last updated August 10, 2026

AI agent? Start with llms.txt.

This example matches a normal FastAPI project. Tokay analyzes the source and requirements.txt to detect the FastAPI start command and dependencies.

Runtime: Python
Framework: FastAPI
Service types: Web Service
Resources: None
Required secrets: None

What it does

Open /docs to explore every route and send requests from FastAPI's interactive page.

The todo list begins with three samples. Add a nonempty title and the API returns a new item. Empty or oversized titles are rejected before they reach the list.

The root response shows the Python version. /health returns a compact liveness result.

Deploy it

Bring this folder to Tokay any way you like. Paste or upload it in the dashboard, push it with git, or hand it to your AI agent along with our agent instructions. Tokay figures out the rest.

Working in Claude, ChatGPT, VS Code, or another MCP client? Connect the Tokay MCP server and ask your agent to deploy this example. It signs in through your browser, so there is no token to paste.

New to Tokay? The getting started guide walks you through your first deploy.

Try it

Set your live URL, create one todo, then confirm FastAPI saved it in the running process.

export APP_URL="https://your-app.tokay.app"
curl -X POST "$APP_URL/todos" \
  -H 'Content-Type: application/json' \
  -d '{"title":"ship the next thing"}'
curl "$APP_URL/todos"

The response list now includes your todo. Restart or deploy again and the samples return because this small starter has no database.

How it works

FastAPI and Pydantic generate the API docs and validation from the route and model definitions in main.py. Tokay installs the requirements and starts the app without changing its structure.

Secrets

This starter has no secrets. Every endpoint is ready to try after deployment.

Grow from here

Try the AI research queue when your FastAPI app needs saved jobs and a Background Worker.

Source code

Download ZIP GitHub

main.py Raw

import platform
from typing import Annotated

from fastapi import FastAPI
from pydantic import BaseModel, StringConstraints


app = FastAPI(title="Tiny Todo API")
todos = [
    {"id": 1, "title": "deploy this ✓"},
    {"id": 2, "title": "add a database"},
    {"id": 3, "title": "tell a friend"},
]


class TodoInput(BaseModel):
    title: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1, max_length=200)]


@app.get("/")
def hello():
    return {
        "message": "Hello from FastAPI",
        "runtime": f"Python {platform.python_version()}",
    }


@app.get("/health")
def health():
    return {"ok": True}


@app.get("/todos")
def list_todos():
    return todos


@app.post("/todos", status_code=201)
def create_todo(todo_input: TodoInput):
    todo = {"id": todos[-1]["id"] + 1 if todos else 1, "title": todo_input.title}
    todos.append(todo)
    return todo

requirements.txt Raw

fastapi==0.116.1
uvicorn[standard]==0.35.0

Built from revision 2b22698

Guide: Deploy a FastAPI app