Built by one person, six AI agents, and a Doberman.

The Story

AI assistants forget everything between conversations. Every session starts from zero. Users repeat themselves. Context is lost. The assistant that helped you yesterday has no idea who you are today.

m3mory fixes this. A full knowledge extraction and retrieval pipeline that understands what matters, builds entity relationships, and retrieves the right context when it counts. Not a key-value store. Not just embeddings in a vector database.

m3mory scores 83.6% on LongMemEval-S, the standard benchmark for AI memory designed by researchers at UCLA, Tencent AI Lab, and UC San Diego, ahead of Supermemory (81.6% on GPT-4o) and every other memory-as-a-service on the leaderboard. We do it on GPT-4o-mini with an average context of just 688 tokens per query. Our retrieval pipeline is selective by design, returning only what matters. The result: highest accuracy, cheapest model, smallest context. Up to 40x cheaper as a memory layer than competitors.