Agent stacks leave semantic caching to the data layer

Across the catalog's 100-product priority cohort of agent frameworks, memory layers and model platforms, semantic caching shows up as a shipping feature in exactly one place: LangChain's SemanticCache. Every other entry that claims caching is token-level prompt caching (Anthropic, OpenAI), CDN-style response caching, or vector-store dedup.

That is a gap in the agent stack, not in the market. Semantic caching is being productised one layer down: Redis ships LangCache as a managed semantic-caching service, and open-source libraries and AI gateways offer it as an add-on. An earlier version of this finding called semantic caching an empty market; that over-read the cohort and has been corrected.

The practical takeaway for builders: if repeated or paraphrased prompts drive your costs, look at your database or gateway vendor rather than your agent framework. Prompt caching (now broadly adopted) only deduplicates exact-prefix repeats, while semantic caching catches reformulated repeats, which is where much of the long-tail LLM cost lives.