Observed Signal · Jul 25, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Kmemo: Semantic LLM Cache That Avoids Wrong Hits
Kmemo is an open-source semantic cache for LLM calls that supplements embedding similarity with a chain of lexical guards and optional verification to avoid serving incorrect cached answers. Published as kmemo-core 1.0.0 (Apache-2.0), it embeds prompts once, reuses vectors for lookup and writes, coalesces concurrent requests, and exposes explainability and metrics hooks. Kmemo ships integrations and store adapters (in-memory, Redis/RediSearch KNN, Postgres/pgvector, optional in-process HNSW) and provides configurable guard strictness, threshold calibration, and an optional Verifier model for world-knowledge near-misses. On a blind validation split, its guards reject 67% of near misses and retain 88% of paraphrases. The project is available on GitHub (NaCode-Studios/Kmemo) and Maven Central (io.github.nacode-studios:kmemo-core:1.0.0).
Provides a pragmatic open-source solution for reliable semantic caching of LLM calls (reducing API cost/latency while avoiding incorrect cached replies). Useful to teams operating LLM-backed products but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Kmemo is an open-source semantic cache for LLM calls that uses similarity plus a chain of lexical guards to avoid false positive cache hits.
- Kmemo core is released as version 1.0.0 on Maven Central: io.github.nacode-studios:kmemo-core:1.0.0 and is Apache-2.0 licensed.
- The guard chain checks for concrete differences (swapped numbers, mismatched units, negation, different entities, time references, etc.) and prefers abstention over guessing.
- Kmemo ships store adapters for in-memory, Redis (RediSearch KNN), and Postgres (pgvector), plus an optional in-process HNSW store, and integrations for Spring Boot, Spring AI, LangChain4j, and Ktor.
- On a blind validation split, Kmemo's guards rejected 67% of labelled near misses and kept 88% of paraphrases.
Connected Companies & Entities
3 Entities mapped“val cache = SemanticCache( embedder = Embedder { text -> openAi.embed(text) }, store = InMemoryStore(maxEntries = 10_000, ttl = 1.ho...”
“Redis (RediSearch KNN) and Postgres (pgvector) stores ship, plus an opt-in in-process HNSW store for when the exact scan stops scaling....”
“Redis (RediSearch KNN) and Postgres (pgvector) stores ship, plus an opt-in in-process HNSW store for when the exact scan stops scaling....”
Ontology Mapping & Concepts
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