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.
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 AIPersistent, structured memory for AI Agents
Mnexium AI
