YantrikDB – persistent memory for AI agents
Bundled 7MB embedder means zero network calls or model downloads for agent memory.
Cognitive memory database for AI agents — consolidates duplicates, detects contradictions, fades stale memories via temporal decay. Rust, AGPL, ships as library / MCP server / HTTP cluster.
Vector DBs store memories; this one forgets, consolidates, and flags contradictions like human memory.
AI agent developers building long-term memory systems
Pinecone · Weaviate · Chroma
YantrikDB is a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores: consolidation collapses duplicate memories, contradiction detection flags incompatible facts, temporal decay with configurable half-life lets unimportant memories fade like human memory does.
Single Rust binary. HTTP + binary wire protocol. 2-voter + 1-witness HA cluster via Docker Compose or Kubernetes. Chaos-tested failover, runtime deadlock detection (parking_lot), per-tenant quotas, Prometheus metrics. Ran a 42-task hardening sprint last week — 1178 core tests, cargo-fuzz targets, CRDT property tests, 5 ops runbooks.
Live on a 3-node Proxmox homelab cluster with multiple tenants. Alpha — primary user is me, looking for the second one.
Bundled 7MB embedder means zero network calls or model downloads for agent memory.
Applies sheaf cohomology to catch graph contradictions that schema validation misses.
Biologically-inspired memory consolidation that prunes unused facts and strengthens associations overnight.
Temporal memory with contradiction detection—Claude finally remembers job changes.
Memory deduplication and contradiction detection, but vector DBs already do semantic search.
Hippocampal memory model for Claude runs entirely in-browser, no API—genuinely novel architecture.