We built a persistent agent memory layer on Elasticsearch with 0.89 recall • Hacker News Board

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#11

Post by reactordev » Thu, Jun 18, 2026, 1:44 PM UTC

itissid wrote: post #9

reactordev wrote: post #5

I built one into my agent using sqlite…

Especially for indie users/devs and smaller teams. I built a part of this(the retriever) in https://github.com/itissid/wiki for replacing deepwiki. I think the challenge is to teach how ranking works to people more effectively so that they can build it for themselves and host them on their own. Like the other day someone who has worked in search explained to me why you would care about using learning-to-rank(LTR) tec…

I don't treat memory like RAG. That's the key. I only track decisions, actions, and outcomes.

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#12

Post by jakevoytko » Thu, Jun 18, 2026, 1:46 PM UTC

stingraycharles wrote: post #4

This is such a basic thing nowadays, and ElasticSearch is massive overkill for it. Something like SQLite or LanceDB or basically any vector database is much more appropriate. This seems to be coming from the “we must make ElasticSearch AI-compatible” department more than anything.

Nah, "Any other vector DB" starts to fall apart once you need stuff like scripted scoring like OP uses. Then it starts to be a question of, "do you need ANN for performance?" since SQLite only does brute-force vector scoring. And granted, brute-force is performant for far more vectors than most people give it credit for, but it definitely hits a wall well below 1 million if you want it to have webpage-type latency.

Maintaining Elasticsearch isn't free, but picking an underpowered db and having to port to the right one is also quite time consuming.

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#13

Post by itissid » Thu, Jun 18, 2026, 1:52 PM UTC

reactordev wrote: post #11

itissid wrote: post #9

Earlier quoted context omitted.

Especially for indie users/devs and smaller teams. I built a part of this(the retriever) in https://github.com/itissid/wiki for replacing deepwiki. I think the challenge is to teach how ranking works to people more effectively so that they can build it for themselves and host them on their own. Like the other day someone who has worked in search explained to me why you would care about using learning-to-rank(LTR) tec…

I don't treat memory like RAG. That's the key. I only track decisions, actions, and outcomes.

Ah so you extract decisions, actions and outcomes and you index and search over them?

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#14

Post by voidUpdate » Thu, Jun 18, 2026, 2:19 PM UTC

For someone who isn't super familiar, what is "R@10", and is 0.89 good? It's impossible to google for

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#15

Post by [deleted] » Thu, Jun 18, 2026, 2:25 PM UTC

itissid wrote: post #3

I have a request: can this text be even more AI generated?

[deleted]

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#16

Post by 0xbadcafebee » Thu, Jun 18, 2026, 2:26 PM UTC

Summary of the article (https://pastebin.com/aawJfrF6) since the original one is like reading an academic paper filtered through an LLM that hates human readers.

It seems like a cool approach. Don't know if it's novel but it's much smarter than "shove markdown files into directories".

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#17

Post by Hexcles » Thu, Jun 18, 2026, 2:26 PM UTC

voidUpdate wrote: post #14

For someone who isn't super familiar, what is "R@10", and is 0.89 good? It's impossible to google for

89% chance the thing you want is among the 10 items returned by the system

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#18

Post by [deleted] » Thu, Jun 18, 2026, 2:31 PM UTC

stingraycharles wrote: post #4

This is such a basic thing nowadays, and ElasticSearch is massive overkill for it. Something like SQLite or LanceDB or basically any vector database is much more appropriate. This seems to be coming from the “we must make ElasticSearch AI-compatible” department more than anything.

[deleted]

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#19

Post by 0xbadcafebee » Thu, Jun 18, 2026, 2:36 PM UTC

stingraycharles wrote: post #4

This is such a basic thing nowadays, and ElasticSearch is massive overkill for it. Something like SQLite or LanceDB or basically any vector database is much more appropriate. This seems to be coming from the “we must make ElasticSearch AI-compatible” department more than anything.

The design they talk about includes 3 different types of memory. They store those kinds of memory separately, so that if there's 10 users, all 10 access memories that are more general ("what bulbs work with this kind of light fixture"), and user-specific memories are segregated ("sarah has three lightbulbs"). The different memory types are ranked together leading to a different result. So this is a novel design and use of ElasticSearch-specific features

Re: We built a persistent agent memory layer on Elasticsearch with 0.89 recall

#20

Post by tuo-lei » Thu, Jun 18, 2026, 2:51 PM UTC

so the 11% miss rate - do users actually notice when the agent drops a memory? like if someone already said they tried X and the agent suggests it again.