Comparing
High-level concurrency and networking framework on top of asyncio or Trio
Distributed task queue for async and scheduled background jobs in Python.
| Metric | anyio | celery |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Beginner ✓ | Intermediate |
| Weekly downloads | 0 | 3.2M↑ |
| Latest version | v4.13.0 | v5.4.0 |
| Recipes | 1 | 3↑ |
anyio only
Shared
celery only
Use anyio when writing an async library or framework that you want to be runtime-agnostic. It's also useful in applications that want trio's structured concurrency model but need to interop with asyncio-based libraries like FastAPI or aiohttp.
Sending emails or notifications, processing uploads, running scheduled cron-style jobs, chaining multi-step workflows, fan-out parallel processing.
For application code that commits to asyncio (e.g. a FastAPI app), using asyncio directly is simpler — there's no need for the compatibility layer. anyio shines at the library level, not the application level.
Simple scripts that don't serve web traffic — just use asyncio or threading. If you need sub-millisecond latency, Celery's overhead is too high. For tiny projects, consider RQ (simpler) or FastAPI BackgroundTasks.
anyio
celery