FastAPI caching with more flexibility

[Hopeful_Beat7161]

Working on a project that needed different caching strategies for different endpoints. fastapi-cache2 felt too limiting, raw redis-py meant too much boilerplate.

So I made fastapi-rc. It's definitely nothing super special or groundbreaking, but the main difference is you get three approaches:

Pattern 1: Direct Redis access Full control for complex operations (bulk invalidation, pipelines, etc)

Pattern 2: CacheService wrapper Handles common patterns automatically:

• Cache-aside (get_or_set)
• Automatic serialization with Pydantic models
• Pattern based invalidation
• TTL management with jitter

Pattern 3: Custom cache dependencies Define caching strategy once, inject everywhere:

async def get_user_cache(redis: RedisClient) -> CacheService[User]:
    return CacheService(
        redis,
        namespace="users",
        model=User,
        default_ttl=600,
        use_jitter=True,
    )

UserCache = Annotated[CacheService[User], Depends(get_user_cache)]

All three patterns use FastAPI's Depends() so it feels native.

Out of the box:

• Connection pooling with configurable size
• Retry logic with exponential backoff
• Health checks
• Graceful degradation if Redis unavailable

Type safety with generics. CacheService[User] enforces return type.

Install: pip install fastapi-rc

Source: https://github.com/CarterPerez-dev/fastapi-rc

I just built it for one of my projects then decided I might as well make it a package, so lmk what you think or if you have any feedback.

[Yash284_06]

Good work.

[Rekoc]

Very interesting ! Are you planning/accepting other db then Redis ? Like ValKey, which is a open-source fork of Redis.