Trio mode

Most users will want to enable “Trio mode”. Without Trio mode:

  • Pytest-trio only handles tests that have been decorated with @pytest.mark.trio

  • Pytest-trio only handles fixtures if they’re async and used by a test that’s decorated with @pytest.mark.trio, or if they’re decorated with @pytest_trio.trio_fixture (instead of @pytest.fixture).

When Trio mode is enabled, two extra things happen:

  • Async tests automatically have the trio mark added, so you don’t have to do it yourself.

  • Async fixtures using @pytest.fixture automatically get converted to Trio fixtures. (The main effect of this is that it helps you catch mistakes like using an async fixture with a non-async test.)

There are two ways to enable Trio mode.

The first option is to use a pytest configuration file. The exact rules for how pytest finds configuration files are a bit complicated, but you want to end up with something like:

# pytest.ini
trio_mode = true

The second option is use a file. Inside your tests directory, create a file called, with the following contents:

from pytest_trio.enable_trio_mode import *

This does exactly the same thing as setting trio_mode = true in pytest.ini, except for two things:

  • Some people like to ship their tests as part of their library, so they (or their users) can test the final installed software by running pytest --pyargs PACKAGENAME. In this mode, pytest.ini files don’t work, but files do.

  • Enabling Trio mode in pytest.ini always enables it globally for your entire testsuite. Enabling it in only enables it for test files that are in the same directory as the, or its subdirectories.

If you have software that uses multiple async libraries, then you can use to enable Trio mode for just the part of your testsuite that uses Trio; or, if you need even finer-grained control, you can leave Trio mode disabled and use @pytest.mark.trio explicitly on all your Trio tests.

Trio fixtures

Normally, pytest runs fixture code before starting the test, and teardown code afterwards. For technical reasons, we can’t wrap this whole process in – only the test itself. As a workaround, pytest-trio introduces the concept of a “Trio fixture”, which acts like a normal fixture for most purposes, but actually does the setup and teardown inside the test’s call to

The following fixtures are treated as Trio fixtures:

  • Any function decorated with @pytest_trio.trio_fixture.

  • Any async function decorated with @pytest.fixture, if Trio mode is enabled or this fixture is being requested by a Trio test.

  • Any fixture which depends on a Trio fixture.

The most notable difference between regular fixtures and Trio fixtures is that regular fixtures can’t use Trio APIs, but Trio fixtures can. Most of the time you don’t need to worry about this, because you normally only call Trio APIs from async functions, and when Trio mode is enabled, all async fixtures are automatically Trio fixtures. However, if for some reason you do want to use Trio APIs from a synchronous fixture, then you’ll have to use @pytest_trio.trio_fixture:

# This fixture is not very useful
# But it is an example where @pytest.fixture doesn't work
def trio_time():
    return trio.current_time()

Only Trio tests can use Trio fixtures. If you have a regular (synchronous) test that tries to use a Trio fixture, then that’s an error.

And finally, regular fixtures can be scoped to the test, class, module, or session, but Trio fixtures must be test scoped. Class, module, and session scope are not supported.

An important note about yield fixtures

Like any pytest fixture, Trio fixtures can contain both setup and teardown code separated by a yield:

async def my_fixture():
    ... setup code ...
    ... teardown code ...

When pytest-trio executes this fixture, it creates a new task, and runs the setup code until it reaches the yield. Then the fixture’s task goes to sleep. Once the test has finished, the fixture task wakes up again and resumes at the yield, so it can execute the teardown code.

So the yield in a fixture is sort of like calling await wait_for_test_to_finish(). And in Trio, any await-able operation can be cancelled. For example, we could put a timeout on the yield:

async def my_fixture():
    ... setup code ...
    with trio.move_on_after(5):
        yield  # this yield gets cancelled after 5 seconds
    ... teardown code ...

Now if the test takes more than 5 seconds to execute, this fixture will cancel the yield.

That’s kind of a strange thing to do, but there’s another version of this that’s extremely common. Suppose your fixture spawns a background task, and then the background task raises an exception. Whenever a background task raises an exception, it automatically cancels everything inside the nursery’s scope – which includes our yield:

async def my_fixture(nursery):
    yield   # this yield gets cancelled after the background task crashes
    ... teardown code ...

If you use fixtures with background tasks, you’ll probably end up cancelling one of these yields sooner or later. So what happens if the yield gets cancelled?

First, pytest-trio assumes that something has gone wrong and there’s no point in continuing the test. If the top-level test function is running, then it cancels it.

Then, pytest-trio waits for the test function to finish, and then begins tearing down fixtures as normal.

During this teardown process, it will eventually reach the fixture that cancelled its yield. This fixture gets resumed to execute its teardown logic, but with a special twist: since the yield was cancelled, the yield raises trio.Cancelled.

Now, here’s the punchline: this means that in our examples above, the teardown code might not be executed at all! This is different from how pytest fixtures normally work. Normally, the yield in a pytest fixture never raises an exception, so you can be certain that any code you put after it will execute as normal. But if you have a fixture with background tasks, and they crash, then your yield might raise an exception, and Python will skip executing the code after the yield.

In our experience, most fixtures are fine with this, and it prevents some weird problems that can happen otherwise. But it’s something to be aware of.

If you have a fixture where the yield might be cancelled but you still need to run teardown code, then you can use a finally block:

async def my_fixture(nursery):
        # This yield could be cancelled...
        # But this code will run anyway
        ... teardown code ...

(But, watch out: the teardown code is still running in a cancelled context, so if it has any awaits it could raise trio.Cancelled again.)

Or if you use with to handle teardown, then you don’t have to worry about this because with blocks always perform cleanup even if there’s an exception:

async def my_fixture(nursery):
    with get_obj_that_must_be_torn_down() as obj:
        nursery.start_soon(function_that_crashes, obj)
        # This could raise trio.Cancelled...
        # ...but that's OK, the 'with' block will still tear down 'obj'
        yield obj

Concurrent setup/teardown

If your test uses multiple fixtures, then for speed, pytest-trio will try to run their setup and teardown code concurrently whenever this is possible while respecting the fixture dependencies.

Here’s an example, where a test depends on fix_b and fix_c, and these both depend on fix_a:

def fix_a():

def fix_b(fix_a):

def fix_c(fix_a):

async def test_example(fix_b, fix_c):

When running test_example, pytest-trio will perform the following sequence of actions:

  1. Set up fix_a

  2. Set up fix_b and fix_c, concurrently.

  3. Run the test.

  4. Tear down fix_b and fix_c, concurrently.

  5. Tear down fix_a.

We’re seeking feedback on whether this feature’s benefits outweigh its negatives.

Handling of ContextVars

The contextvars module lets you create ContextVar objects to represent task-local variables. Normally, in Trio, each task gets its own Context, so that changes to ContextVar objects are only visible inside the task that performs them. But pytest-trio overrides this, and for each test it uses a single Context which is shared by all fixtures and the test function itself.

The benefit of this is that you can set ContextVar values inside a fixture, and your settings will be visible in dependent fixtures and the test itself. For example, trio-asyncio uses a ContextVar to hold the current asyncio loop object, so this lets you open a loop inside a fixture and then use it inside other fixtures or the test itself.

The downside is that if two fixtures are run concurrently (see previous section), and both mutate the same ContextVar, then there will be a race condition and the the final value will be unpredictable. If you make one fixture depend on the other, then this will force an ordering and make the final value predictable again.

Built-in fixtures

These fixtures are automatically available to any code using pytest-trio.


A trio.testing.MockClock, configured with rate=0, autojump_threshold=0.


A trio.testing.MockClock, with its default configuration (rate=0, autojump_threshold=inf).

What makes these particularly useful is that whenever pytest-trio runs a test, it checks the fixtures to see if one of them is a object. If so, it passes that object to So if your test requests one of these fixtures, it automatically uses that clock.

If you implement your own Clock, and implement a fixture that returns it, then it will work the same way.

Of course, like any pytest fixture, you also get the actual object available. For example, you can call jump():

async def test_time_travel(mock_clock):
    assert trio.current_time() == 0
    assert trio.current_time() == 10

A nursery created and managed by pytest-trio itself, which surrounds the test/fixture that requested it, and is automatically cancelled after the test/fixture completes. Basically, these are equivalent:

# Boring way
async def test_with_background_task():
    async with trio.open_nursery() as nursery:

# Fancy way
async def test_with_background_task(nursery):

For a fixture, the cancellation always happens after the fixture completes its teardown phase. (Or if it doesn’t have a teardown phase, then the cancellation happens after the teardown phase would have happened.)

This fixture is even more magical than most pytest fixtures, because if it gets requested several times within the same test, then it creates multiple nurseries, one for each fixture/test that requested it.

See Running a background server from a fixture for an example of how this can be used.

Integration with the Hypothesis library

There isn’t too much to say here, since the obvious thing just works:

from hypothesis import given
import hypothesis.strategies as st

async def test_trio_and_hypothesis(data):

Under the hood, this requires some coordination between Hypothesis and pytest-trio. Hypothesis runs your test multiple times with different examples of random data. For each example, pytest-trio calls again (so you get a fresh clean Trio environment), sets up any Trio fixtures, runs the actual test, and then tears down any Trio fixtures. Notice that this is a bit different than regular pytest fixtures, which are instantiated once and then re-used for all. Most of the time this shouldn’t matter (and is probably what you want anyway), but in some unusual cases it could surprise you. And this only applies to Trio fixtures – if a Trio test uses a mix of regular fixtures and Trio fixtures, then the regular fixtures will be reused, while the Trio fixtures will be repeatedly reinstantiated.

Also, pytest-trio only handles @given-based tests. If you want to write stateful tests for Trio-based libraries, then check out hypothesis-trio.

Using alternative Trio runners

If you are working with a library that provides integration with Trio, such as via guest mode, it can be used with pytest-trio as well. Setting trio_run in the pytest configuration makes your choice the global default for both tests explicitly marked with @pytest.mark.trio and those automatically marked by Trio mode. trio_run presently supports trio and qtrio.

# pytest.ini
trio_mode = true
trio_run = qtrio
import pytest

async def test():
    assert True

If you want more granular control or need to use a specific function, it can be passed directly to the marker.

import pytest

async def test():
    assert True