01
Define the job
Keep the task close to the Python code that does the work.
Python-first. Reliable by design.
Background jobs,
without the heavy lift.
A Python-first job queue for production workloads. A simpler path from defining a task to operating it when the work starts to grow.
Pre-launch direction. The workflow is still being validated with Python teams.
Proposed runPre-launch
@job(queue="emails", retry=3)
01def send_digest(user_id: int) -> None:
02 emails = build_digest(user_id)
03 return deliver(emails)
Definedtask
Queuedwaiting
Runningworker
Handledoutcome
A small, visible model for the work: define a Python task, put it in motion, and keep the outcome in view.
The shape
Focused, not noisy.
The point is not to replace the decisions your team has already made. It is to explore a more focused experience for the background work that keeps a production system moving.
Python teams often reach for Celery or RQ as their workloads grow. Python Job Queue starts with the friction around setup and operation, then works backward toward a smaller surface area.
02
Run the worker
Give production workloads a clear path from task to process.
03
Observe the outcome
Make queue state and failure handling part of the operating picture.
A pre-launch question
What gets simpler when the queue is Python-first?
The promise is provisional: less setup and less operational burden, while preserving the capabilities production teams need. The concrete switching threshold still needs validation.
Job volume
When background work moves beyond ad hoc handling.
Failure handling
When retries and recovery need a clearer home.
Operational load
When the current Celery or RQ setup feels heavier to run.
Start with the workflow
Bring the job you already run.
Python Job Queue is pre-launch. The useful next conversation is concrete: the task, the worker, and the part of your current setup that feels heavier than it should.