I’m Saksham, the co-founder of Ryumem — thanks for stopping by! We built Ryumem because while working with agentic systems, one thing kept breaking over and over: memory. Agents could retrieve embeddings, but they couldn’t reason over state, decisions, or why something happened before. Everything reset the moment context got messy. Ryumem is our attempt to fix that — a deterministic, queryable memory layer for AI agents that captures state, decisions, and relationships over time. Think of it as giving agents a system of record for “what actually happened,” not just what was retrieved. We’re already using this in production with a few teams, and today’s launch is about opening it up, learning in public, and getting honest feedback from builders who are wrestling with the same problems. Would love to hear: How are you handling agent memory today? What breaks first in your workflows as agents get more complex? Happy to answer anything — and thanks for checking us out 🚀 PS: We’re building this in the open — if you like what we’re doing, feel free to ⭐ the repo and follow along.

RyuMem

Context that compounds. Costs that don't.

Context that compounds. Costs that don't.