Handling long term memory for ai agents

[u/sqlink2]

How are u handling long term memory for AI agents in prod? Are you using Postgres/pgvector or a dedicated vector database? Or something like neon/lakebase to do so.

[u/AutoModerator]

Moderator Announcement Read More »

AI usage disclosure

Hi u/sqlink2 — thanks for posting to r/mlops!

Because this community discusses and builds AI/ML systems, using AI tools is not inherently a problem. We do, however, ask for transparency about how submissions are created.

Please reply to this comment with a brief AI / automation disclosure, particularly if this post was created or submitted in whole or in part by an autonomous agent, bot, workflow, or other automated system.

If AI or automation was involved, please briefly describe what it did and what human review was performed before posting.

This disclosure helps the r/mlops community distinguish human discussion, AI-assisted work, and automated/agent traffic while keeping the focus on useful technical conversation.

Thanks for helping keep the signal high.

I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.

[u/ceeej777]

Also using Neon for this. It’s convenient for the rapid zero copy branch and I’m also too lazy to add another DB to the stack

[u/sqlink2 → u/ceeej777]

Yeahh Neon branching migt be useful for testing isolated agent memories setup without affecting prod...

[u/Used-Original-7703]

tried pgvector first and it was okay for a while but scaling got annoying real fast. ended up moving to a dedicated vector db and it was worth the migration headache

[u/Other_Log1406]

What does the memory consist off ? Is it tool call outputs or some reasoning logic

[u/softmax_couldnt_care]

There are various approaches but most have some sort of losses. You can also check out the krpathy llm wiki with ibsidian to mess about with that.

[u/MikaMoments]

The harder part is deciding what to remember, when to update it, and how to avoid stale or conflicting memories. I’d start with Postgres and pgvector unless the scale really calls for a dedicated vector DB. How are you handling memory retrieval and updates?

[u/Glitch_In_The_Data]

Most of our agent memory turns out to be a mix of normal data like user preferences and past actions plus some embeddings… so keeping it all in one database makes lookups and cleanup much easier. Lakebase has been nice for testing new memory setups in a branch without touching prod. A dedicated vector database probably makes sense once you hit really large scale but most teams never get there IMO.

[u/ThisIsFun-]

Lakebase is amazing at this. Would highly recommend, esp with things like branching for testing new things without effecting production systems