RAG Was Never Memory. This Is.

Most AI memory systems are still doing the same thing: chunk text embed it retrieve something vaguely similar later hope the model sorts it out That works for document search. It breaks when memory needs to track people, relationships, constraints, and state changes over time . If a user says: my budget is €5,000 actually now it’s €7,000 my daughter has a nut allergy we’re flying from Manchester you do not want “similar chunks”. You want the current state, the connected facts, and the ability to reason across them. That is the gap between retrieval and memory. And it is why we built MINNS . MINNS is a memory system for agents that builds a context graph from conversations and lets you query that graph directly. Not raw text retrieval. Not chunk stuffing. Structured memory that can handle multi-hop reasoning and state changes properly. The best part is how little setup it takes. Three lines to get started import { MinnsClient } from ' minns-sdk ' ; const client = new MinnsClient ({ apiK