APScheduler
In-process task scheduler with Cron-like capabilities
Decision gist · record as of 2026-08-14
Yes. APScheduler is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, minimal dependencies, and is widely used (top 1000 PyPI packages). Install it if you need in-process job scheduling with optional persistence; it's the standard choice for this use case in Python.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with only two runtime dependencies (tzlocal and backports.zoneinfo).
- Actively maintained with a recent release and 7605 repository stars.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) — you can use, modify, and distribute APScheduler freely in commercial and private projects with minimal restrictions.
last release 2026-06-28 (47 days) · last repo commit 2026-08-01 · 7,605 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 51,359,048 downloads/mo, #571 on PyPI
Alternatives
Verify before relying
pip install apscheduler
from apscheduler.schedulers.background import BackgroundScheduler
scheduler = BackgroundScheduler()
scheduler.add_job(lambda: print('Job'), 'interval', seconds=10)
scheduler.start()- Whether the package's asyncio, gevent, Tornado, Twisted, and Qt integrations require additional optional dependencies beyond the two listed runtime deps.
- Performance characteristics when managing large numbers of concurrent jobs or under high scheduling load.
What it is and what it does
APScheduler is an in-process task scheduler for Python that lets you define jobs to run at specific times, on intervals, or on cron schedules. It's designed to run inside your application rather than as a standalone daemon, and it can store job state in memory, SQL databases, MongoDB, Redis, RethinkDB, ZooKeeper, or Etcd so that jobs survive scheduler restarts and resume from where they left off.
The package supports three scheduling modes (cron-style, interval-based, and one-off delayed execution) and integrates with common Python frameworks like asyncio, gevent, Tornado, Twisted, and Qt. It's a cross-platform alternative to platform-specific schedulers like cron or Windows Task Scheduler, though it requires your application to be running to execute jobs.
Use it for
- Schedule periodic maintenance tasks (database cleanup, report generation) to run at specific times without external cron infrastructure.
- Run delayed notifications or emails after a user action, with job state persisted across application restarts.
- Implement background job queues for long-running operations triggered by web requests, integrated directly into your application.
- Coordinate timed events across multiple application instances using a shared job store (SQL, Redis, or MongoDB).
- Build a dedicated scheduler process that manages jobs for other services, using APScheduler's job store and state recovery.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
APScheduler is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, minimal dependencies, and is widely used (top 1000 PyPI packages). Install it if you need in-process job scheduling with optional persistence; it's the standard choice for this use case in Python.
Install
apscheduler on PyPI
Before you install
Low friction: pure Python wheel with only two runtime dependencies (tzlocal and backports.zoneinfo). Actively maintained with a recent release and 7605 repository stars.
License in practice
MIT license (permissive) — you can use, modify, and distribute APScheduler freely in commercial and private projects with minimal restrictions.
Quickstart
pip install apscheduler
from apscheduler.schedulers.background import BackgroundScheduler
scheduler = BackgroundScheduler()
scheduler.add_job(lambda: print('Job'), 'interval', seconds=10)
scheduler.start()Verify before relying
- Whether the package's asyncio, gevent, Tornado, Twisted, and Qt integrations require additional optional dependencies beyond the two listed runtime deps.
- Performance characteristics when managing large numbers of concurrent jobs or under high scheduling load.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagestzlocalbackports.zoneinfo |
| Maintenance | Actively maintained 47 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 51,359,048 / month, #571 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: PythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: apscheduler-3.11.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “delayed job runner”
- APSchedulerAPScheduler lets you schedule Python code to run later—once,…
- TGSchedulerTGScheduler is a pure Python task scheduler that runs one-time or…
- taskiq-aio-pikaProvides a RabbitMQ broker implementation for taskiq, enabling…
Give your agent the search over MCP, or paste the wish link into any chat.
Similar packages
A Python client library for Cronitor that monitors background jobs, scheduled tasks, and HTTP endpoints by sending telemetry pings and syncing monitor configurations via the Cronitor API.
Install it if you run background jobs or scheduled tasks and want centralized monitoring with minimal code changes—the Celery integration is particularly valuable for…
Adds persistent job scheduling to Django applications using APScheduler, storing scheduled tasks in the database and providing Django admin integration for job management and execution monitoring.
Flask-APScheduler integrates APScheduler with Flask, enabling you to define and manage scheduled jobs through Flask configuration and a REST API.
Schedule provides a human-friendly API for running Python functions periodically at specified intervals or times without requiring external processes or dependencies.
TGScheduler is a pure Python task scheduler that runs one-time or recurring tasks either in-process within threads, as forked processes, or synchronously.
An in-process job scheduler for Python that supports asyncio, threading, and timezone-aware execution with flexible scheduling rules (cyclic intervals, fixed times, weekdays, dates, weights, offsets, and execution counts).
See also timeloop · rq-scheduler · aiojobs · django-crontab