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A memory database that forgets, consolidates, and detects contradiction

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Vector databases store memories. They don't manage them. After 10k memories, recall quality degrades because there's no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier.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.

HN · launched Apr 14, 2026 · 48 upvotes (top 16%) · 0 views

Niche: ai agent memory infrastructure · 350 launches, 11 in the last 7 days ▼ -58% · Explore this niche →

Category and tags
  • Horizontal
  • Data infrastructure
  • Developer
  • AI-native
  • Commercial product

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More like this: all 350 launches in ai agent memory infrastructure →

Could you build this?

Partial A basic vector store is easy to wrap, but implementing automated memory consolidation, decay/forgetting curves, and contradiction detection requires non-trivial cognitive architecture and graph engineering.

What it would actually take: The engine requires a hybrid data store combining vector search (e.g., HNSW), graph structures (for entity relations), and temporal indexing. The complex components are the asynchronous background jobs that compute memory decay curves, cluster and merge related facts into synthesized long-term knowledge, and detect logical contradictions across new and existing assertions. Building a reliable implementation requires deep expertise in knowledge graphs, NLP logic systems, and database internals.

Discussion

20 comments analyzed.

Competitors mentioned: mem0 (similar memory system design), Vector stores (for document retrieval at scale), QMD and prose summarization (for agent context)

Concerns raised: Conflict detection fails on raw text without explicit entity extraction, Cosine similarity at 0.85 threshold can't differentiate between similar concepts (AWS vs Azure, love vs hate), Facts extracted from conversation lose critical context and nuance, Irony and sarcasm in conversation lead to incorrect fact extraction, Temporal contradictions not resolved (current vs previous CEO, temporal validity not tracked)

Feature requests: Auto-extraction of entities and relations to surface contradictions without explicit relate() calls, Time-of-validity schema to handle temporal supersession, not just decay, Typed relations support for modeling scope (subsidiaries, co-CEOs, sub-organizations), Context preservation in memory storage, not just atomic facts, Open source or self-hosted local options

Competitors

Other products that read as similar to this one : 154 launches clear the similarity bar, closest 8 shown.

Attention rank: #22 of 155 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 164 days after the earliest competitor.

Related data

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