llm-flaky 0.1.0

Pytest plugin for running non-deterministic LLM tests with automatic retry and beautiful reports

llm-flaky

Pytest plugin for running non-deterministic LLM tests.

LLM tests are inherently non-deterministic due to the probabilistic nature of language models. This plugin handles flakiness by automatically retrying tests and requiring an 80% pass rate (4/5 by default).

Features

• Auto-marking: Automatically applies @pytest.mark.flaky to tests with @pytest.mark.llm
• 80% accuracy default: Tests pass if 4 out of 5 runs succeed (configurable)
• Beautiful reports: Replaces standard flaky output with a formatted table
• Environment variable support: Use FLAKY_MAX_RUNS to control retries
• pytest-xdist compatible: Works correctly with parallel test execution

Installation

pip install llm-flaky

Usage

Mark your LLM tests with @pytest.mark.llm:

import pytest

@pytest.mark.llm
async def test_llm_response():
    response = await call_llm("What is 2+2?")
    assert "4" in response

The plugin automatically applies flaky retry logic. No additional code needed!

Example output

══════════════════════════════════════════════════════════════════════════════
 LLM TESTS SUMMARY
══════════════════════════════════════════════════════════════════════════════

 Test                                                     Passed       Result
 ────────────────────────────────────────────────────────────────────────────
 test_llm_response_quality                                 4 / 4     ✓ PASSED
 test_llm_context_handling[short]                          4 / 4     ✓ PASSED
 test_llm_context_handling[long]                           3 / 4     ✓ PASSED

 ✗ FAILED TESTS:
 ────────────────────────────────────────────────────────────────────────────
 test_llm_edge_case                                        2 / 4     ✗ FAILED
 ────────────────────────────────────────────────────────────────────────────
 ⚠ Total                                                   3 / 4       75.0%
══════════════════════════════════════════════════════════════════════════════

Configuration

Environment variables

FLAKY_MAX_RUNS=3 pytest  # Run each test up to 3 times (min_passes=2)

Command line options

pytest --llm-flaky-max-runs=5           # Max runs for LLM tests (default: 5)
pytest --llm-flaky-min-passes=4         # Min passes required (default: max_runs - 1)
pytest --llm-flaky-exclude-marker=skip  # Marker to exclude from flaky
pytest --llm-flaky-title="My Title"     # Custom report title
pytest --no-llm-flaky-report            # Disable beautiful report

pytest.ini options

[pytest]
llm_flaky_max_runs = 5
llm_flaky_min_passes = 4
llm_flaky_exclude_marker = langsmith_dataset
llm_flaky_title = LLM TESTS SUMMARY

Priority

Configuration is read in this order (highest priority first):

1. FLAKY_MAX_RUNS environment variable
2. Command line options (--llm-flaky-*)
3. pytest.ini options (llm_flaky_*)
4. Defaults (max_runs=5, min_passes=4)

How it works

1. Collection phase: Plugin finds all tests with @pytest.mark.llm
2. Auto-marking: Applies @pytest.mark.flaky(max_runs=5, min_passes=4)
3. Execution: pytest-flaky handles retry logic
4. Reporting: Beautiful summary table replaces standard output

Excluding tests

Tests with @langsmith_dataset marker are excluded by default (they use LangSmith's built-in evaluation):

@pytest.mark.llm
@langsmith_dataset("my_dataset.yaml")
async def test_with_langsmith():
    # This test won't get flaky retry - LangSmith handles evaluation
    pass

Requirements

• Python >= 3.9
• pytest >= 7.0.0
• flaky >= 3.7.0

License

MIT