tealetio 0.1.0rc1

Scheduler, synchronisation, selector, and asyncio helpers for tealet

tealetio

tealetio: async without the async.

Where tealet gives you stack-slicing primitives, tealetio turns them into a runtime framework with a familiar shape. It adds scheduler, task, synchronisation, selector, runner, and asyncio coexistence APIs for ordinary tealet code.

In effect, async operations can work without the async keyword: tealet-powered stack slicing lets ordinary-looking functions block, resume, and compose through the scheduler.

The top-level package is meant to feel direct: import the common classes and helpers from tealetio, just as you would from asyncio. Submodules remain available when you want to name the implementation home explicitly.

from tealetio import Event, Scheduler, gather, run, wait_for

Installation

For the usual scheduler and synchronisation APIs, install the base package:

python -m pip install tealetio

Need to run async methods efficiently from tealetio? Install the optional asyncio extra. It pulls in asynkit, which tealetio uses to optimise the asyncio bridge where available:

python -m pip install 'tealetio[asyncio]'

Quick Start

Need a scheduler for synchronous tealet code? Use run() and ask for the running scheduler inside your entry point:

from tealetio import Event, get_running_scheduler, run


def main() -> str:
    scheduler = get_running_scheduler()
    event = Event()
    seen: list[str] = []

    def worker() -> None:
        seen.append("waiting")
        event.swait()
        seen.append("done")

    task = scheduler.spawn(worker)
    scheduler.call_soon(event.set)
    task.wait()
    return ", ".join(seen)


assert run(main) == "waiting, done"

Already inside an asyncio program? Use AsyncRunner to host tealet work without leaving the asyncio world:

import asyncio

from tealetio import AsyncRunner, Event, get_running_scheduler


async def main() -> list[str]:
    async with AsyncRunner() as runner:
        def entry() -> list[str]:
            scheduler = get_running_scheduler()
            event = Event()
            seen: list[str] = []

            def worker() -> None:
                seen.append("waiting")
                event.swait()
                seen.append("done")

            task = scheduler.spawn(worker)
            scheduler.call_soon(event.set)
            task.wait()
            return seen

        return await runner.run(entry)


assert asyncio.run(main()) == ["waiting", "done"]

Public API

The common API is available directly from tealetio:

• schedulers and runners: Scheduler, SelectorScheduler, AsyncScheduler, Runner, AsyncRunner, run, run_async
• tasks and futures: Future, Task, spawn, create_task, get_current, CancelledError, shield
• wait helpers: gather, wait, wait_for, as_completed, ensure_future, to_thread
• synchronisation primitives: Event, Lock, Semaphore, Condition, Barrier, Queue
• rendezvous communication: Channel
• asyncio coexistence helpers: asyncio_get_current, run_in_asyncio, run_asyncio_in_tealet, TealetSelectorEventLoop

If you prefer explicit homes, submodules such as tealetio.scheduler, tealetio.tasks, tealetio.locks, tealetio.runner, tealetio.selector, and tealetio.asyncio define the same objects.

Asyncio Model

The design intentionally follows asyncio where the mapping is useful. In effect, you already know much of the shape: the learning curve stays small, and interop with asyncio-hosted programs stays straightforward. Some names differ to match tealet execution: Scheduler fills the role normally held by an event loop, and spawn(...) is the native tealet-facing equivalent of create_task(...). The package root also exports create_task(...) as a familiar alias.

Synchronisation primitives are asyncio-compatible where practical and add s-prefixed methods for tealet-blocking operations, such as Event.swait(), Lock.sacquire(), and Queue.sget(). tealetio also reuses asyncio exceptions where that preserves familiar behaviour and compatibility.

Channel is inspired by Stackless Python channels. It provides rendezvous-style communication between tasks, with selectable sender/receiver preference models, and can also be used for inter-thread communication through scheduler-safe wakeup paths.

Notice one important runtime difference: exception delivery. Exceptions such as CancelledError are delivered immediately by switching to the target tealet, instead of being kept as pending exceptions. This avoids races where multiple pending exceptions can accumulate, and removes the need to track pending cancellations separately. These deviations are intentional, but the exact API shape remains subject to change before a stable release.

Status

tealetio is pre-1.0 software. APIs are usable for experimentation and in-repo testing, but may change before a stable release.

Documentation

• Python API reference
• Asyncio coexistence
• Scheduler runtime API spec