Most AI apps eventually hit the same wall. They forget users unless you build a ton of infrastructure first. This means every AI dev eventually will end up building this infra to provide the best user experience needs for their agent and app.
What rolling your own really means:
Vector DBs + embeddings + tuning
Extracting memories from conversations (and resolving conflicts)
Designing user profile schemas and keeping them in sync
Managing long chat history + summarization pipelines
Juggling different formats across OpenAI, Claude, etc.
๐ง ๐๐ง๐๐ฑ๐ข๐ฎ๐ฆ = persistent memory for LLM apps.
Add one ๐ฆ๐ง๐ฑ object and get chat history, semantic recall, and user profiles that follow users across sessions and providers.
๐ Works with ๐๐ก๐๐ญ๐๐๐ and ๐๐ฅ๐๐ฎ๐๐ โ same memories, any model. Switch mid-conversation without losing context.
โ๏ธ No vector DBs or pipelines. A/B test, fail over, and route by cost โ your memory layer stays consistent.
In this new getting-started guide, you will learn how to build a ChatGPT-style application that includes persistent memory, conversation history, and semantic recall all using a single API from Mnexium.
The guide walks through how Mnexium simplifies AI memory by replacing complex setups such as:
