fastmock 0.1.3

FastMock framework, mock your FastAPI APIs

Documentation: https://tpemeja.github.io/fastmock

Source Code: https://github.com/tpemeja/fastmock

FastMock is a Python framework designed to mock FastAPI APIs based on response models.

Key features of this project include:

• Ease of Use: Simply add the middleware to start mocking your APIs.
• Flexibility: Mocking can be customized through various parameters, such as activation, data generation, length, status codes, and more.
• Modularity: The package includes default functions to retrieve mocking parameters from API declarations to request headers. However, all these functions can be modified, allowing you to create your own custom functions.
• Fault Injection: Simulate latency and failures per-route or via request header, so you can test how your client handles a slow or unreliable API without writing any extra code.

Requirements

This package is build to be used with FastAPI, please learn on to use it on their well documented website - FastAPI Documentation

To mock an API, you need to describe the API using FastAPI's decorator key responses.

For example:

@app.get("/",
         status_code=status.HTTP_200_OK,
         responses={
             status.HTTP_200_OK: {"model": list[int]},
             status.HTTP_404_NOT_FOUND: {"model": str}
         })

Installation

$ pip install fastmock

---> 100%

Or, if you use uv:

$ uv add fastmock

---> 100%

Example

Create it

• Create a file main.py with:

from fastapi import FastAPI, status
from pydantic import BaseModel

from fastmock.decorator import FastMockDecorator
from fastmock.middleware import FastMockMiddleware

app = FastAPI()
app.add_middleware(FastMockMiddleware)


mock = FastMockDecorator()

class Item(BaseModel):
    name: str
    price: float
    is_offer: bool | None = None


@app.get("/",
         status_code=status.HTTP_200_OK,
         responses={
             status.HTTP_200_OK: {"model": dict[str, str]}
         })
def read_root():
    return {"Hello": "World"}


@mock(element_size=3)
@app.get("/items",
         status_code=status.HTTP_200_OK,
         responses={
             status.HTTP_200_OK: {"model": list[Item]}
         })
def read_items():
    return []

• Start the server with uvicorn main:app

Check it

Open your browser at http://127.0.0.1:8000/items.

You will see the JSON response as:

[
  {
    "name": "fuquvERvYTfWVEbYRKgi",
    "price": 18164954265977.8,
    "is_offer": null
  },
  {
    "name": "akMCejCxOhMjgGMPMrcb",
    "price": 40.3130726635657,
    "is_offer": null
  },
  {
    "name": "uEONHBXGCirPDrLJKgXu",
    "price": -9.23356705084994,
    "is_offer": null
  }
]

You just created an API using FastAPI that:

• Return value generated from defined response model
• Modify output list size using decorator

How does this compare to other FastAPI mocking libraries?

A couple of other projects cover similar ground. Here's how fastmock differs:

fastapi-mock is a great fit if you want the absolute lowest-friction way to stub an endpoint you haven't implemented yet — no decorator, no config, just raise NotImplementedError(). fastmock trades a little more setup for per-route and per-request control over data shape, status codes, and failure modes, which matters once you're mocking for QA, staging, or demo environments rather than just a placeholder.

Inspiration

• A project idea that came from my use of FastAPI by tiangolo, the GitHub project inspired me for the structure and documentation of this project.
• Ideas and code for data generation with Faker inspired by NiyazNz in the fastapi-mock-middleware project.

Contributing

This project uses uv for dependency management. To set up a development environment, run the tests, or cut a release, see the Contributing guide.

License

This project is licensed under the terms of the MIT license.