CacheCore: Semantic agent caching with dependency invalidation

The increasing reliance on LLMs for core application functionality has introduced two significant operational challenges for enterprise developers: cost predictability and latency consistency. API billing structures, which often charge per token or per call, mean that repetitive prompts—even within the same application session—translate directly into escalating infrastructure costs. CacheCore enters the developer tool space to directly mitigate this financial and performance risk. Functionally, CacheCore introduces a robust caching proxy or layer situated between the application backend and the LLM provider API. The core value proposition is straightforward: intercepting identical requests and serving cached responses. The stated performance metric of achieving a 70ms cache hit time is highly relevant, positioning it as a serious contender for optimizing real-time user experiences that depend on timely AI responses. Furthermore, by intercepting the call, it effectively removes the associated variable costs, addressing the 'stop paying for the same call twice' mandate directly. While basic caching solutions often rely solely on hashing the input prompt and relying on time-to-live (TTL) expiration, CacheCore explicitly highlights 'dependency invalidation.' This feature is technically crucial because real-world LLM calls are rarely self-contained; they often depend on external state, databases, or complex internal logic. Simple TTLs are insufficient when a cached response needs to be invalidated because an external input (like a user profile status or inventory count) has changed, even if the prompt text remains identical. This capability elevates the product from a basic memoization utility to a sophisticated state-aware caching middleware. For developers building high-volume, cost-sensitive applications that utilize LLMs—such as personalized content generators, recommendation engines, or internal knowledge retrieval systems—CacheCore offers a focused optimization point. Its efficacy hinges on the implementation depth of its invalidation system. If the dependency tracking mechanism is mature, CacheCore represents a critical component for building durable, performant, and financially sustainable AI applications. It shifts the focus from simply making calls to managing the lifecycle and cost of the knowledge served by those calls.