Mnexium Integrations feel like one of the most important parts of the platform because they solve a different problem than memory. It also outlines the completion of the feature-set for the platform. I don't think any more features will offer any more utility.
Memory helps an assistant remember durable user context over time. Integrations let it work with live operational data from external systems right when a response is being generated.
We just published a new case study on Cartly, an iOS app that uses Mnexium to power a full receipt-tracking AI workflow. We really wanted to see what it would take to get a demo like this up and running.
In the post, we walk through how Cartly uses:
Memory for user preferences and continuity
Records for structured receipts and receipt_items storage
A single mnx runtime object to control identity, history, recall, and record sync
Request trace packets for auditability and debugging in production
Mnexium gives AI apps one shared memory layer across models and agents. Add persistent memory, chat history, user profiles, records, and live context with one API. Built for OpenAI, Anthropic, Gemini, and agent workflows, without managing vector DBs, sync jobs, or custom memory pipelines.
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.
Hi all - I've built @Mnexium AI and I thought the fastest way to get folks to try was it to build a chat plug-in for websites. I am providing free keys (however much usage it may be) to anyone who is willing to try it.
The plug-in can be found on NPM https://www.npmjs.com/package/@m...
Most automation workflows can call a model, but still need substantial glue code for memory, personalization, and structured data. The Mnexium connector makes those capabilities native in n8n.
We just shipped @mnexium/chat: a single npm package that adds a polished, production-ready AI chat widget to any website. React, Next.js, Express, or plain HTML it just works, and most importantly it remembers.
