This release is a pre-release and may not be stable for production use.
rtest
A Python test runner built with Rust, featuring high-performance test collection and parallel test execution.
⚠️ Development Status: This project is in early development (v0.0.x). Expect bugs, breaking changes, and evolving features as we work toward stability.
Performance
rtest discovers tests through static AST analysis without importing test modules. CI measures collection on fixed
Click, Flask, and Pydantic source revisions using a release build, 3 warmups, and 20 measured runs.
The benchmark job publishes timings, collected test IDs, and raw Hyperfine reports. See the benchmark guide to reproduce a run.
Static discovery and pytest's import-based collection perform different work and can discover different tests. The benchmark reports collection time only; it does not claim equivalent test coverage, execution speedups, or performance regressions across different CI machines.
Quick Start
Installation
pip install rtest
Requires Python 3.10+
Building from source requires Rust 1.97+ and the initialized Ruff submodule (git submodule update --init).
Basic Usage
# Collect tests (fast AST-based collection)
rtest --collect-only
# Run tests with native runner
rtest --runner native
# Run tests in parallel (4 workers)
rtest --runner native -n 4
Native Runner
The native runner (--runner native) executes tests using rtest's own decorators:
import rtest
@rtest.mark.cases("value", [1, 2, 3])
def test_example(value):
assert value > 0
@rtest.mark.skip(reason="Not implemented yet")
def test_skipped():
pass
The native runner respects python_files, python_classes, and python_functions patterns from your
pyproject.toml under [tool.pytest.ini_options].
For compatibility, @pytest.mark.parametrize and @pytest.mark.skip decorators are also supported.
Roadmap
- Fixtures -
@rtest.fixturewith function/class/module scopes and dependency resolution - conftest.py support - Fixture discovery across directory hierarchy
- Distribution modes - Group tests by module/class, optimized scheduling algorithms
- Built-in assertions -
rtest.raises()and other assertion helpers - Additional markers -
@rtest.mark.xfail,@rtest.mark.skipif - Test selection -
-kexpression filtering,--last-failed,--failed-first - Better error formatting - Rich diffs, colorized output, traceback filtering
- Async test support - Native
async def test_*()handling - Watch mode - Re-run tests automatically on file changes
Known Limitations
Parametrized Test Discovery
rtest supports limited static expansion of parametrized tests, including simple uses of:
- Literal values: numbers, strings, booleans, None, lists/tuples of literals
- Module-level constants:
DATA = [1, 2, 3]then@cases("x", DATA) - Class constants:
Config.MAX_SIZE - Enum members:
Color.RED - Nested class constants:
Outer.Inner.VALUE
from enum import Enum
import rtest
class Color(Enum):
RED = 1
GREEN = 2
DATA = [1, 2, 3]
@rtest.mark.cases("value", DATA) # Resolves to [1, 2, 3]
def test_module_constant(value):
assert value > 0
@rtest.mark.cases("color", [Color.RED, Color.GREEN]) # Resolves enum members
def test_enum_values(color):
assert color.value in [1, 2]
When static expansion fails, rtest emits a warning and falls back to the base test name:
@pytest.mark.parametrize("value", get_data()) # Function call
def test_dynamic(value):
assert value > 0
@pytest.mark.parametrize("value", [x for x in range(3)]) # Comprehension
def test_comprehension(value):
assert value > 0
warning: Cannot statically expand test cases for 'test.py::test_dynamic': argvalues contains function call 'get_data'
warning: Cannot statically expand test cases for 'test.py::test_comprehension': argvalues contains a comprehension
In these cases, test execution is still functionally equivalent - pytest automatically runs all parametrized variants when given the base function name.
Project-specific parametrize decorators
Projects that wrap pytest.mark.parametrize in their own decorator can describe the wrapper in pyproject.toml so
rtest expands it statically:
[[tool.rtest.parametrize-wrappers]]
decorator = "myproject.testing.parametrize" # fully qualified decorator name
argname = "p" # the single argname the wrapper passes to pytest
record = "myproject.testing.Params" # optional: row type accepted in place of tuples
id-field = "id" # optional: field whose value becomes the test ID
The wrapper is called as @parametrize(argnames, argvalues, ids=None) and passes every case to the test as one
argname argument: argvalues that are already record calls are used as-is, and other rows are zipped with
argnames. IDs come from a static ids= list, then from id-field when the argnames include it, and otherwise
default to {argname}{index}. Anything that cannot be resolved statically falls back to pytest, as above.
Path Separator Handling
rtest uses platform-specific path separators in test nodeids, while pytest normalizes all paths to use forward
slashes (/) regardless of platform. For example:
On Windows:
- pytest shows:
tests/unit/test_example.py::test_function - rtest shows:
tests\unit\test_example.py::test_function
On Unix/macOS:
- Both show:
tests/unit/test_example.py::test_function
This difference is intentional as rtest preserves the native path format of the operating system.
Native Runner Scope
The native runner (--runner native) does not currently support pytest fixtures or conftest.py discovery. Projects
that rely on fixtures should use --runner pytest instead. Fixture support is tracked in
#105.
pytest-xdist Serialization with Decimal Objects
When using --runner pytest -n N (parallel pytest execution), projects that use Decimal objects in parametrized test
data may fail with a serialization error:
execnet.gateway_base.DumpError: can't serialize <class 'decimal.Decimal'>
This is a limitation of pytest-xdist's inter-process communication, which cannot serialize certain Python types. Tests
pass when run without parallelization (--runner pytest without -n). See
#116 for details.
Collection Failures on Complex Codebases
Some projects with complex runtime behavior (e.g., extensive use of plugins, dynamic imports, or metaclass-heavy
patterns) may fail during rtest collection despite working correctly with pytest. For example, the
pydantic repository encounters errors when collected by rtest. See
#55 for details.
Contributing
We welcome contributions! See Contributing Guide.
License
MIT - see LICENSE file for details.
Acknowledgments
This project takes inspiration from Astral and leverages crates from [ruff].
1 maintainer
Metadata
1 maintainer
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