pytest-fixedpoint 0.1.9

Pytest plugin for recording and replaying deterministic function calls

fixed-point

• Fixed-Point is a pytest plugin designed to record and replay deterministic function calls, making your tests stable and reproducible.
• Flaky tests are the bane of every developer's existence. I prefer not to rely on retries, mocks that drift from reality, or fragile test setups to keep things green.
• To tackle this problem, Fixed-Point captures real function outputs and replays them faithfully, so your tests always land on the same answer — a fixed point.
• It boasts simplicity, making it exceptionally easy to adopt in any pytest project.
• Fixed-Point serves as a specialized testing tool, particularly tailored for pinning down the behavior of functions that talk to external services. If you encounter any cases not covered by the plugin, please don't hesitate to create an issue for further assistance.

Installation

You can install pytest-fixedpoint using pip, the Python package manager:

pip install pytest-fixedpoint

Getting Started

To start using fixed-point in your project, simply decorate the functions you want to pin down with @recordable:

from fixedpoint import recordable

@recordable
def call_external_api(query):
    return external_service.search(query)

def test_search(fixedpoint):
    result = call_external_api("hello")
    assert result == expected

The first time you run with --fixedpoint=record_once, it captures real outputs. Every subsequent run replays them — no network calls, no flakiness.

Async Support

@recordable works with async functions and methods out of the box — no extra setup needed:

from fixedpoint import recordable

@recordable
async def fetch_user(user_id):
    return await external_service.get_user(user_id)

@recordable
async def search(query):
    return await external_service.search(query)

async def test_fetch_user(fixedpoint):
    user = await fetch_user(42)
    assert user["name"] == "Alice"

It also works with async methods on classes:

from fixedpoint import recordable

class UserClient:
    @recordable
    async def get_user(self, user_id):
        return await self._session.get(f"/users/{user_id}")

    @recordable
    async def list_users(self):
        return await self._session.get("/users")

async def test_user_client(fixedpoint):
    client = UserClient()
    user = await client.get_user(42)
    assert user["name"] == "Alice"

The decorator detects whether the function is a coroutine and wraps it accordingly. Recording and replay work identically — the cassette format is the same for sync and async functions.

Modes

Fixed-Point supports 4 operational modes via the --fixedpoint CLI option:

Mode	Behavior
off	Default. Plugin is disabled, functions execute normally.
record_once	Record new calls, replay existing ones. Ideal for initial setup.
replay	Replay only. Fails with CassetteNotFoundError if no recording exists. Perfect for CI.
rewrite	Always re-execute and overwrite recordings. Use when the real behavior has changed.

# Record cassettes for the first time
pytest tests/ --fixedpoint=record_once

# Replay from cassettes (safe for CI)
pytest tests/ --fixedpoint=replay

# Force re-record everything
pytest tests/ --fixedpoint=rewrite

# Disable (default)
pytest tests/

Cassettes

Recordings are stored as human-readable YAML files in tests/cassettes/:

tests/cassettes/
  test_module_name/
    test_function_name.yaml

A cassette file looks like:

version: 1
calls:
  myapp.api.fetch_user:
    - args: [42]
      kwargs: {}
      return: {"name": "Alice", "age": 30}

Cassettes are committed to your repo so the whole team replays the same results.

Supported Types

The serializer handles these types out of the box:

Type	Serialized As
None, bool, int, float, str	Direct values
bytes	{"__bytes__": "<base64>"}
enum.Enum	{"__enum__": "module.EnumClass", "value": ...}
tuple	{"__tuple__": [...]}
set	{"__set__": [...]}
list, dict	Direct JSON arrays/objects
dataclass	{"__dataclass__": "module.Class", ...fields}
pydantic.BaseModel	{"__pydantic__": "module.Model", "data": {...}}

Enum Support

Python enums (including IntEnum, StrEnum, etc.) are automatically serialized:

from enum import Enum
from fixedpoint import recordable

class Status(Enum):
    PENDING = "pending"
    ACTIVE = "active"
    DONE = "done"

@recordable
def get_status(user_id):
    return external_api.fetch_status(user_id)

def test_status(fixedpoint):
    status = get_status(42)
    assert status == Status.ACTIVE  # Works perfectly

The cassette stores both the enum class path and the value:

return:
  __enum__: myapp.models.Status
  value: active

Pydantic Support

Pydantic models are automatically serialized using model_dump(mode='json'):

from pydantic import BaseModel
from fixedpoint import recordable

class User(BaseModel):
    name: str
    age: int
    email: str | None = None

@recordable
def fetch_user(user_id):
    return external_api.get_user(user_id)

def test_fetch_user(fixedpoint):
    user = fetch_user(42)
    assert isinstance(user, User)
    assert user.name == "Alice"

The cassette stores the model's data as a plain dict:

return:
  __pydantic__: myapp.models.User
  data:
    name: Alice
    age: 30
    email: [e-mail]

Note: Pydantic is an optional dependency. If it's not installed, fixed-point will still work with all other types.

Error Handling

Fixed-Point raises clear errors when things don't match:

Exception	When
CassetteNotFoundError	No cassette file found in replay mode
CassetteMismatchError	Recorded args don't match actual call, or too many calls
SerializationError	Unsupported type passed to a @recordable function

When you see a CassetteMismatchError, the error message includes a hint:

Run with --fixedpoint=rewrite to re-record

Comparison

vs VCR.py / responses / requests-mock

VCR-style tools record at the HTTP layer — every header, cookie, content-type, and redirect ends up in your cassette. This means:

• Cassettes are bloated with details you don't care about (auth tokens, timestamps, trace IDs).
• An unrelated header change breaks your tests even though the actual data hasn't changed.
• You're testing HTTP plumbing, not your application logic.

Fixed-Point records at the function layer. You choose exactly which functions to pin down with @recordable, and the cassette only contains the args and return values — nothing more.

vs unittest.mock / monkeypatch

Mocking is powerful, but it comes at a cost:

• You have to manually write the return values. Guess wrong and your mock drifts from reality.
• As your code evolves, you spend more time maintaining mocks than writing actual tests.
• Mocks tell you nothing about what the real function actually returned — they only tell you what you assumed it would return.

Fixed-Point records real outputs on the first run. No guessing, no hand-crafting fixtures. When reality changes, just --fixedpoint=rewrite and you're back in sync.

Summary

	VCR.py	mock	fixed-point
Records at	HTTP layer	N/A (manual)	Function layer
Setup effort	Low	High	Low
Cassette noise	High (headers, cookies, etc.)	N/A	Low (args + return only)
Stays in sync with reality	Fragile	Drifts over time	rewrite to refresh

Why fixed-point?

• In math, a fixed point is a value that stays the same no matter what function you throw at it. This project does the same for your tests — run them once, pin the result, replay forever.
• But honestly, I named it "fixed-point" because of my wife. She's my fixed point — always cute, always kind, and somehow always right. No matter how chaotic things get, she's the constant I can count on.
• So this one's for her. And if it makes your tests less flaky along the way, that's a nice bonus too.