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I built OLTP in my memory system. Looking for opinions

I built OLTP in my memory system. Looking for opinions

by Mnexium·Feb 16, 2026·1 point·2 comments
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AI Analysis

●●SolidSolve My ProblemSlick
The Take

The idea of putting a schema-driven, CRUD-able records layer alongside an LLM memory store and letting the AI create/update records from conversations is practical and useful — think calendar events, pipelines, tickets auto-created from chat. The blog and SDK snippets show they've thought about ergonomics, but the post skips over the difficult bits (concurrency/OLTP semantics, conflict resolution, audit trails, and scaling against dedicated DBs/vector stores), so it's promising but not yet a clear replacement for existing infra.

Category
AI/ML
Target Audience

AI/ML developers, backend engineers building conversational agents or AI-first apps, startups integrating structured data with memory systems

Post Description

I've spent the past couple of months building Mnexium.com

Originally it was supposed to just be a memory management system (and it still is very much core).

I was thinking with all of the talk of AI replacing SaaS etc etc... How?

Most (if not all) AI systems do not have a way to manage, store & maintain records. I thought it would be an interesting project and problem to solve.

I built it into the overall project more details here (https://www.mnexium.com/blogs/introducing-records).

I've realized it is nearly impossible to get some kind of feedback on these types of projects.

I really just want people to lend their opinion.

This is needed? This is not needed? Needed but implemented incorrectly. etc

Happy to answer any questions -

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