YantrikDB Launches Cognitive Memory Engine
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- YantrikDB is a cognitive memory engine that forgets, consolidates, and detects contradictions, addressing the scaling limits of traditional vector databases that only store embeddings.
- YantrikDB can be added to agents like Claude Code, Cursor, or Windsurf with a single pip install and MCP config block, providing persistent memory that auto‑recalls and flags conflicts without explicit prompts.
- YantrikDB uses multi‑signal scoring (semantic similarity, temporal decay, importance, graph proximity, retrieval feedback) to keep query token usage around ~70 tokens even with 5,000 memories, whereas file‑based memory exceeds context windows.
- YantrikDB runs as a Rust binary with an HTTP + binary wire protocol, supporting high‑availability clusters via Docker Compose or Kubernetes, with per‑tenant quotas, Prometheus metrics, AES‑256‑GCM encryption, and runtime deadlock detection.
- YantrikDB includes proactive triggers, derived personality, procedural memory, and temporal awareness that surface pending conflicts, decaying important memories, approaching deadlines, and cross‑domain patterns automatically.
- YantrikDB issued a correction notice that Phase 3 benchmark writeups originally used a Python simulator instead of the real engine, with corrected findings to be posted on 2026‑04‑20.
- YantrikDB completed a 42‑task hardening sprint covering mutex deadlock detection, Prometheus metrics, chaos‑tested failover, per‑tenant quotas, and 1,178 core tests plus fuzz and CRDT property tests.
Why it matters: AI developers and enterprises deploying LLM agents gain a memory system that stays efficient at scale, cutting query token usage to ~70 tokens even with thousands of memories and automatically surfacing conflicts and deadlines, while the correction notice flags that earlier benchmark results were simulated, prompting users to await verified performance data.
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