apte 0.3.0

Async-first Python testing framework

Write your tests and your LLM evals in one async-first framework.

An eval is just a test that returns a value - scored, not asserted. Your evals get real fixtures, dependency injection and parallelism, and live right next to the tests they ship with. Python 3.10+, installs lean (Rich UI optional).

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Why Apte?

Explicit Injection (IDE-Ready)

Ctrl+Click works. Your IDE knows every type. No guessing where fixtures come from.

def test_user(db: Annotated[Database, Use(database)]): ...

Native Async & Parallelism

Tests run as coroutines on a single event loop. No plugin needed.

apte run tests:session -n 10

Smart Tagging (Tag Propagation)

Tag a fixture once, every test using it inherits the tag automatically.

@fixture(tags=["database"])
def db(): ...

session.bind(db)

@session.test()
def test_users(repo: Annotated[Repo, Use(user_repo)]): ...  # Also tagged "database"

apte run tests:session --no-tag database  # Skips ALL tests touching DB

Infra vs Code Errors (Error ≠ Fail)

✗ test_create_user: AssertionError       # Your bug - TEST FAILED
⚠ test_with_db: [FIXTURE] ConnectionError  # Infra issue - SETUP ERROR

Typed Parameterization

CODES = ForEach([200, 201])

@session.test()
def test_status(code: Annotated[int, From(CODES)]): ...

Native LLM Evals

Score model outputs alongside your tests - same fixtures, same parallelism, same apte CLI. Cases get pass/fail + numeric metrics, persisted to JSONL for run-over-run comparison.

from typing import Annotated
from apte import ForEach, From, ApteSession
from apte.evals import EvalCase, EvalSuite
from apte.evals.evaluators import contains_keywords

session = ApteSession()
chatbot_suite = EvalSuite("chatbot")
session.add_suite(chatbot_suite)

cases = ForEach([
    EvalCase(name="capital_fr", inputs="Capital of France?", expected="Paris"),
])

@chatbot_suite.eval(evaluators=[contains_keywords(keywords=["paris"])])
async def chatbot(case: Annotated[EvalCase, From(cases)]) -> str:
    return await my_agent(case.inputs)  # your LLM call

apte eval evals.session:session   # runs are recorded to .apte/history.jsonl

See Evals docs for evaluators, judges, and scoring.

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Quick Start

from apte import ApteSession

session = ApteSession()


def inc(x):
    return x + 1


@session.test()
def test_answer():
    assert inc(3) == 4

apte run test_sample:session

Installation

Apte is not yet on PyPI. Install directly from GitHub:

# With uv (recommended)
uv add git+https://github.com/renaudcepre/apte.git

# With pip
pip install git+https://github.com/renaudcepre/apte.git

CLI

apte run module:session                    # Run tests
apte run module:session -n 4               # Parallel (4 workers)
apte run module:session --lf               # Re-run failed tests only
apte run module:session --collect-only     # List tests without running
apte run module:session --cache-clear      # Clear cache before run
apte run module:session --app-dir src      # Look for module in src/
apte run module:session --ctrf-output r.json  # CTRF report for CI/CD

apte eval module:session                   # Run LLM evals
apte eval module:session --tag safety      # Filter by case tag
apte eval module:session --last-failed     # Re-run failed cases only

Features

• Explicit DI - No guessing which fixture you're using
• Async native - No plugin needed, just async def
• Parallel execution - Built-in with -n 4
• Scoped fixtures - SESSION, SUITE, TEST
• Mix sync/async - They just work together
• Factory fixtures - Callables to create instances on-demand
• Plugin system - Custom reporters, filters
• Last-failed mode - Re-run only failed tests with --lf
• CTRF reports - Standardized JSON for CI/CD integration
• Native LLM evals - Scored cases, JSONL history, apte eval (see evals docs)

Why Not pytest?

	pytest	Apte
Fixtures	Implicit (by name)	Explicit (Use(fixture))
Params	Hidden in fixture	Visible in test (From() + factory)
Async	Plugin required	Native
Parallel	Plugin required	Built-in
Cycles	Runtime error	Prevented at registration
Evals	External (deepeval, pydantic-…)	Native (apte eval, JSONL history)

pytest has a large ecosystem and extensive community. Apte is an alternative if you prefer FastAPI-style explicit dependencies and native async in your tests.

Documentation

Full API reference, guides, and examples: renaudcepre.github.io/apte

License

MIT