Vector databases frequently degrade recall quality past ten thousand entries because they lack essential memory management functions. YantrikDB aims to solve this by acting as a cognitive memory engine, consolidating duplicates, flagging factual contradictions, and implementing temporal decay to fade unimportant memories. This approach moves beyond simple retrieval to true memory organization.

Built as a single Rust binary, YantrikDB offers multiple deployment modes: embedded library, network server with HTTP/binary protocols, or as an MCP server. The architecture incorporates five indexes—Vector (HNSW), Graph, Temporal, Decay Heap, and Key-Value—to manage complex relationships and time-based relevance scoring effectively.

Recent hardening sprints included extensive chaos testing and 1178 core tests, ensuring stability under load, including failover scenarios on its current 3-node Proxmox cluster. Benchmarks demonstrate that YantrikDB keeps context size minimal—around 70 tokens per query even with thousands of memories—offering massive token savings versus naive file-based context stuffing.

Unlike standard vector stores that only offer nearest-neighbor lookup, YantrikDB actively manages knowledge via operations like `db.think()` to process and refine stored data. The project currently sits in alpha, seeking a second user to test its advanced memory handling capabilities in production environments.