Comparing
High-level concurrency and networking framework on top of asyncio or Trio
Python logging that doesn't make you want to cry — structured, colorful, zero-config, with automatic exception tracebacks.
| Metric | anyio | loguru |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Beginner | Beginner |
| Weekly downloads | 0 | 4.0M↑ |
| Latest version | v4.13.0 | v0.7.2 |
| Recipes | 1 | 2↑ |
anyio only
Shared
loguru 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.
Any Python application that needs logging — scripts, APIs, workers. Especially good for: colorized console output during development, structured JSON logs for production, automatic exception tracing.
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.
If your team's infrastructure is already built around stdlib logging and you can't change it easily — loguru can interop but it adds complexity.
anyio
loguru