Comparing
High-level concurrency and networking framework on top of asyncio or Trio
Python client for Redis — the in-memory data store for caching, pub/sub, and queues.
| Metric | anyio | redis |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Beginner ✓ | Intermediate |
| Weekly downloads | 0 | 8.5M↑ |
| Latest version | v4.13.0 | v5.0.8 |
| Recipes | 1 | 3↑ |
anyio only
Shared
redis 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.
Caching API responses or DB queries, session storage, rate limiting counters, pub/sub messaging, leaderboards (sorted sets), task queues (BLPOP), distributed locks.
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.
Primary persistent storage — Redis is RAM-first and can evict data. Complex relational queries. Large blobs (Redis excels at small, fast values).
anyio
redis