scheduler
A simple in-process python scheduler library with asyncio, threading and timezone support.
Decision gist · record as of 2026-08-14
Yes, if you need in-process job scheduling without external services and can accept the LGPL-3.0 copyleft license. Low install friction, active maintenance, no known vulnerabilities, and support for modern Python versions make it a solid choice for embedded scheduling. Not suitable if you require a proprietary license or need distributed scheduling across multiple processes.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- For asyncio scheduling, you must integrate exec_jobs() calls into your own event loop or threading model.
- Low friction install with a single runtime dependency (typeguard).
License · maintenance · safety
LGPL-3.0-only (copyleft) — Licensed under LGPL-3.0-only (copyleft). Derivative works and modifications must be distributed under the same license; proprietary use requires careful review of your distribution model.
last release 2026-02-15 (180 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 104,078 downloads/mo, #12,769 on PyPI
Alternatives
Verify before relying
pip install scheduler
from scheduler import Scheduler
from scheduler.trigger import Monday
import datetime as dt
schedule = Scheduler()
schedule.cyclic(dt.timedelta(minutes=10), lambda: print('task'))
schedule.weekly(Monday(), lambda: print('task'))
schedule.exec_jobs() # Call periodically in your event loop- Whether the scheduler handles missed job executions or drift over long-running processes.
- Performance characteristics under high job counts or rapid scheduling changes.
- How job prioritization interacts with asyncio vs. threading execution models in practice.
What it is and what it does
Scheduler is a lightweight, in-process job scheduling library that lets you define and execute tasks on recurring or one-time schedules without external dependencies like Celery or APScheduler. It supports both asyncio and threading models, making it suitable for embedding directly into Python applications. You define jobs using a fluent API (e.g., `schedule.daily()`, `schedule.weekly()`, `schedule.cyclic()`), then call `exec_jobs()` periodically to run pending tasks. The library handles timezone-aware scheduling, job prioritization, tagging, and batching.
The package is designed for scenarios where you want scheduling logic inside your own process rather than delegating to a separate service. It depends only on typeguard for runtime type checking and supports Python 3.10 through 3.14. The codebase is actively maintained (latest release in February 2026) and includes high test coverage. It's classified as Beta but has been in development since late 2021.
Use it for
- Schedule periodic background tasks (data cleanup, report generation) within a web application or service without external job queues.
- Implement recurring asyncio tasks with timezone support in async applications.
- Define complex scheduling rules (e.g., 'run every Monday at 4:30 PM in a specific timezone') with a readable API.
- Batch and prioritize multiple jobs, then execute them in a controlled order within your own event loop.
- Build lightweight automation scripts that need to run tasks on fixed intervals or specific dates without external dependencies.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need in-process job scheduling without external services and can accept the LGPL-3.0 copyleft license.
Low install friction, active maintenance, no known vulnerabilities, and support for modern Python versions make it a solid choice for embedded scheduling. Not suitable if you require a proprietary license or need distributed scheduling across multiple processes.
Install
scheduler on PyPI
Before you install
Low friction install with a single runtime dependency (typeguard). Actively maintained with a recent release (180 days ago) and supports current Python versions (3.10–3.14).
Requires Python 3.10 or later. For asyncio scheduling, you must integrate exec_jobs() calls into your own event loop or threading model.
License in practice
Licensed under LGPL-3.0-only (copyleft). Derivative works and modifications must be distributed under the same license; proprietary use requires careful review of your distribution model.
Quickstart
pip install scheduler
from scheduler import Scheduler
from scheduler.trigger import Monday
import datetime as dt
schedule = Scheduler()
schedule.cyclic(dt.timedelta(minutes=10), lambda: print('task'))
schedule.weekly(Monday(), lambda: print('task'))
schedule.exec_jobs() # Call periodically in your event loopVerify before relying
- Whether the scheduler handles missed job executions or drift over long-running processes.
- Performance characteristics under high job counts or rapid scheduling changes.
- How job prioritization interacts with asyncio vs. threading execution models in practice.
Package facts
| License | LGPL-3.0-only copyleft |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetypeguard |
| Maintenance | Actively maintained 180 days since the last release |
| First released | |
| Downloads | 104,078 / month, #12,769 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonTopic :: Scientific/EngineeringTopic :: Software Development :: LibrariesTyping :: Typed |
Evidence: scheduler-0.8.11-py3-none-any.whl
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See also schedule · tempora · TGScheduler · APScheduler · rq-scheduler · timeloop · Flask-APScheduler · django-apscheduler · aiojobs · fsrs