What Frameworks Shaped FastAPI? Lessons from Django, Flask, and More

The article explains what inspired the creation of FastAPI, compares it with alternative frameworks, and extracts the lessons learned. It emphasizes that without building on previous work, FastAPI would not exist.

Frameworks that Inspired FastAPI

Django

Django is the most popular Python framework, trusted for building systems like Instagram, and tightly coupled with relational databases such as MySQL or PostgreSQL, making NoSQL integration less straightforward.

Django REST Framework

Django REST Framework (DRF) is a flexible tool for building Web APIs and was the first framework to automatically generate API documentation, a key motivation for FastAPI. Its author, Tom Christie, also created Starlette and Uvicorn, which FastAPI builds upon.

DRF’s automatic API docs and UI inspired FastAPI’s own auto‑generated documentation and interactive web UI.

Flask

Flask is a lightweight micro‑framework without built‑in database integration, offering simplicity and flexibility that allow use of NoSQL databases and easy extension through plugins.

Its decoupled modules and simple routing system inspired FastAPI to become a micro‑framework that mixes and matches needed tools.

Requests

Requests is a popular HTTP client library. While FastAPI is not a replacement for Requests, it draws inspiration from Requests’ simple, intuitive design and reasonable defaults.

Requests is one of the most downloaded Python packages ever

Example of a GET request with Requests:

response = requests.get("http://example.com/some/url")

Equivalent FastAPI route:

@app.get("/some/url")
def read_url():
    return {"message": "Hello World"}

Both use similar syntax such as requests.get(...) and @app.get(...), reflecting FastAPI’s goal of a simple, intuitive API with powerful defaults.

Swagger / OpenAPI

The desire for automatic API documentation led to the adoption of the Swagger/OpenAPI standard, which describes APIs in JSON or YAML and provides a web UI (Swagger UI, ReDoc) for interactive exploration.

FastAPI adopts an open standard for API specifications and integrates standard UI tools like Swagger UI and ReDoc.

Flask REST Frameworks

Several Flask REST frameworks were evaluated, but many are abandoned or have long‑standing issues, making them unsuitable for FastAPI’s goals.

Marshmallow

Marshmallow provides data serialization (converting objects to JSON, etc.) and validation, automating checks that would otherwise require manual code.

FastAPI’s inspiration: use code to define data types and validation schemas, with automatic validation.

Frameworks Used by FastAPI

Pydantic

Pydantic uses Python type hints for data validation, serialization, and JSON‑Schema documentation, offering fast performance and excellent editor support.

FastAPI relies on Pydantic for all data validation, serialization, and automatic model documentation.

Starlette

Starlette is a lightweight ASGI framework/toolkit designed for high‑performance asyncio services. It offers impressive performance, WebSocket, GraphQL, background tasks, events, testing utilities, CORS, GZip, static files, sessions, and 100% type‑annotated code.

Starlette provides the core web functionality; FastAPI extends it with data validation, dependency injection, security utilities, and OpenAPI generation.

Uvicorn

Uvicorn is a lightning‑fast ASGI server built on uvloop and httptools. It is the recommended server for both Starlette and FastAPI, often combined with Gunicorn for multi‑process async serving.

FastAPI recommends Uvicorn as the primary web server for deployment.

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