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Show HN: A memory database that forgets, consolidates, and detects contradiction

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Show HN: A memory database that forgets, consolidates, and detects contradiction

#1

Post by pranabsarkar »

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.

Show HN: A memory database that forgets, consolidates, and detects contradiction
github.com

Re: Show HN: A memory database that forgets, consolidates, and detects contradiction

#2

Post by pranabsarkar »

Author here. I built this because I was using ChromaDB for an AI agent's memory and recall quality went to garbage at ~5k memories. The agent kept recalling outdated facts, contradicting itself across sessions, and the context window was full of redundant near-duplicates.

I tried to write the consolidation/conflict-detection logic on top of ChromaDB. It didn't work — the operations need to be transactional with the vector index, and they need an HLC for ordering across nodes. So I built it as a database.

The cognitive operations (think, consolidate, detect_conflicts, derive_personality) are the actual differentiator. The clustered server is what made me confident enough to ship — I needed to know the data was safe before I'd put real work on it.

What I genuinely want to know: is this solving a problem you're hitting with your AI agent's memory, or did I build a really polished thing for my own narrow use case? Honest reactions help more than encouragement.

Re: Show HN: A memory database that forgets, consolidates, and detects contradiction

#4

Post by polotics »

In this day and age, without serious evidence that the software presented has seen some real usage, or at least has a good reviewable regression test suite, sadly the assumption may be that this is a slopcoded brainwave. The ascii-diagram doesn't help. Also maybe explain the design more.

Re: Show HN: A memory database that forgets, consolidates, and detects contradiction

#7

Post by tcdent »

I appreciate the effort you put into mapping semantics so language constructs can be incorporated into this. You’re probably already seeing that the amount of terminology, how those terms interact with each other, and the way you need to model it have ballooned into a fairly complex system.

The fundamental breakthrough with LLMs is that they handle semantic mapping for you and can (albeit non-deterministically) interpret the meaning and relationships between concepts with a pretty high degree of accuracy, in context.

It just makes me wonder if you could dramatically simplify the schema and data modeling by incorporating more of these learnings.

I have a simple experiment along these lines that’s especially relevant given the advent of one-million-token context windows, although I don’t consider it a scientifically backed or production-ready concept, just an exploration: https://github.com/tcdent/wvf

Re: Show HN: A memory database that forgets, consolidates, and detects contradiction

#8

Post by 6r17 »

polotics wrote: post #4

In this day and age, without serious evidence that the software presented has seen some real usage, or at least has a good reviewable regression test suite, sadly the assumption may be that this is a slopcoded brainwave. The ascii-diagram doesn't help. Also maybe explain the design more.

I kind of agree with the comment here that a lot of stuff happening around comes out from an idea without proof that the project has a meaningful result. A compacting memory bench is not something difficult to put off but I'm also having difficulties understanding what would be the outcome on a running system

Re: Show HN: A memory database that forgets, consolidates, and detects contradiction

#10

Post by pranabsarkar »

hazelnut wrote: post #9

Congrats, looking promising. How does it compare to supermemory.ai?

Fair question. Supermemory is a hosted SaaS built around embedding + ranking. YantrikDB is self-hosted and adds three things Supermemory doesn't do as first-class operations:

think() — consolidates similar memories into canonical ones (not just deduplication, actual collapse of redundant facts) Contradiction detection — when "CEO is Alice" and "CEO is Bob" both exist in memory, it flags the pair as a conflict the agent can resolve Temporal decay with configurable half-life — memories fade, so old unimportant stuff stops polluting recall Supermemory does more on the cloud side (team sharing, permissions, integrations). YantrikDB does more on the "actively manage my agent's memory" side. Different optimization points — no dig at Supermemory.

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