Docket is a distributed background task system for Python functions with a focus on the scheduling of future work as seamlessly and efficiently as immediate work.

At a glance

from datetime import datetime, timedelta, timezone

from docket import Docket


async def greet(name: str, greeting="Hello") -> None:
    print(f"{greeting}, {name} at {datetime.now()}!")


async with Docket() as docket:
    await docket.add(greet)("Jane")

    now = datetime.now(timezone.utc)
    soon = now + timedelta(seconds=3)
    await docket.add(greet, when=soon)("John", greeting="Howdy")

from docket import Docket, Worker

async with Docket() as docket:
    docket.register(greet)
    async with Worker(docket) as worker:
        await worker.run_until_finished()

Hello, Jane at 2025-03-05 13:58:21.552644!
Howdy, John at 2025-03-05 13:58:24.550773!

Check out our docs for more details, examples, and the API reference.

Why docket?

⚡️ Snappy one-way background task processing without any bloat

📅 Schedule immediate or future work seamlessly with the same interface

⏭️ Skip problematic tasks or parameters without redeploying

🌊 Purpose-built for Redis streams

🧩 Fully type-complete and type-aware for your background task functions

💉 Dependency injection like FastAPI, Typer, and FastMCP for reusable resources

Installing docket

Docket is available on PyPI under the package name pydocket. It targets Python 3.10 or above.

With uv:

uv pip install pydocket

or

uv add pydocket

With pip:

pip install pydocket

Docket requires Redis 6.2 or later, or Valkey 8.0 or later. Docket is tested with:

• Redis 6.2 and 8.10, and Redis 8.10 in cluster mode
• Valkey 8.0 and 9.1
• In-memory backend via burner-redis for testing

For testing without Redis, use the in-memory backend:

from docket import Docket

async with Docket(name="my-docket", url="memory://my-docket") as docket:
    # Use docket normally - all operations are in-memory
    ...

See Testing with Docket for more details.

Hacking on docket

We use uv for project management. The repository is a uv workspace, and the Python package lives in its python/ directory. From a clone of the repository:

uv sync
cd python
uv run pytest

We aim to maintain 100% test coverage, which is required for all PRs to docket. We believe that docket should stay small, simple, understandable, and reliable, and that begins with testing all the dusty branches and corners. This will give us the confidence to upgrade dependencies quickly and to adapt to new versions of Redis over time.

To work on the documentation locally, from the repository root:

uv sync
uv run zensical serve

This will start a local preview server. The docs are built with Zensical and configured in mkdocs.yml.