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

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 The agent kept recalling outdated facts, contradicting itself across sessions, and the context window was full of redundant near-duplicates.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.

How can you decide if something is a contradiction without having the context?

I'm incredibly interested in this as a product, but I think it makes too many assumptions about how to prune information. Sure, this looks amazing on an extremely simple facts, but most information is not reducible to simple facts.

"CEO is Alice" and "CEO is Bob" may or may not actually be contradictions and you simply cannot tell without understanding the broader context. How does your system account for that context?

Example: Alice and Bob can both be CEO in any of these cases:

* The company has two CEOs. Rare and would likely be called "co-CEO"

* The company has sub-organizations with CEOs. Matt Garman is the CEO of AWS. Andy Jassy is the CEO of Amazon. Amazon has multiple people named "CEO".

* Alice and Bob are CEOs of different companies (perhaps, this is only implicit)

* Alice is the current CEO. Bob is the previous CEO. Both statements are temporally true.

This is what I run into every time I try to do conflict detection and resolution. Pruning things down to facts doesn't provide sufficient context understand how/why that statement was made?

I have experienced similar degradation with vectors at scale.

I've experimented quite a bit with mem0 (which is similar in design) for my OpenClaw and stopped using it very soon. My impression is that "facts" are an incredibly dull and far too rigid tool for any actual job at hand and for me were a step back instead of forward in daily use. In the end, the extracted "facts database" was a complete mess of largely incomplete, invalid, inefficient and unhelpful sentences that didn't help any of my conversations, and after the third injected wrong fact I went back to QMD and prose / summarization. Sometimes it's slightly worse at updating stuck facts, but I'll take a 1000% better big picture and usefulness over working with "facts".

The failure modes were multiple: - Facts rarely exist in a vacuum but have lots of subtlety - Inferring facts from conversation has a gazillion failure modes, especially irony and sarcasm lead to hilarious outcomes (joking about a sixpack with a fat buddy -> "XYZ is interested in achieving an athletic form"), but even things as simple as extracting a concrete date too often go wrong - Facts are almost never as binary as they seem. "ABC has the flights booked for the Paris trip". Now I decided afterwards to continue to New York to visit a friend instead of going home and completely stumped the agent.

So soon our agents will need spaced repetition flash cards for the things we want them to remember! I eagerly await Anki for Agents.

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

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.

The half-life idea is interesting.

What's the loop behind consolidation? Random sampling and LLM to merge?

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

Did you check if this leads to any actual benefits? If so, how did you benchmark it?

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