CogniMemo

CogniMemo solves a problem every LLM app eventually hits: the blank-slate session. A support bot that cannot remember the ticket it resolved yesterday, a sales agent that re-asks for details it already collected, a tutor that restarts the learner from zero — these are the failures a memory layer exists to prevent. CogniMemo's answer is not a bigger context window, it is a separate store you write to and read from, organized as Memories, Entities, People, and a Graph that shows how the stored records relate. That structure is the interesting part. Handing a model a longer transcript is easy and cheap to build; deciding what is worth remembering, attaching it to a person or entity, and linking memories to each other is the work that compounds. A People view implies per-user recall as a first-class concept, which is exactly what a multi-tenant assistant needs and exactly what a raw vector store makes you bolt on yourself. The Playground exists so you can test retrieval against your own data before wiring the API — that is the right gate to put in front of adoption. The operational surface is also more built out than the seed profile suggested. Usage & cost, Audit log, and Billing all sit in the workspace, so once you are live you can watch consumption per call and see what the agent remembered and when. For teams where an agent's recall decisions are auditable — regulated support flows, anything a customer might dispute — a built-in audit log saves you from writing your own. The gaps are real and worth naming plainly. There is no public API reference documentation, so you are integrating against what the Connect page and Playground show you rather than against a written spec. No rate limits, context sizes, or data volume caps are published, which means your capacity planning is guesswork until you measure. No security, retention, or data residency certification is visible, and for a product whose entire job is holding your users' personal context, that is the missing piece most likely to block a serious procurement conversation. Both Mem0 and Zep publish open-source paths you can self-host and inspect, which converts directly into a due-diligence advantage when your legal team asks where the data lives. Where CogniMemo fits: a developer or small SaaS team that wants memory live in days, is comfortable being early on a young product, and can validate retrieval quality empirically before scaling it. Where it does not: buyers who need signed security terms before integration, teams that must model per-request memory cost up front, and anyone already self-hosting Mem0 or Zep who values control of the memory data more than the operational savings. It is also not a chatbot — there is no end-user UI here, just an API and a workspace for the people building on it.