Microsoft Research· Research / Phi

Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity

Today’s AI agents don’t remember past interactions. They must repeatedly be fed relevant information or retrieve it from external sources, which becomes less efficient as they handle longer and more complex tasks. To scale agent capabilities, we need a more efficient way to retain and access information over time. Memora is a scalable memory system that dramatically increases agent productivity on long-horizon tasks by decoupling what is stored (rich memory content) from how it’s retrieved (lightweight abstractions and cue anchors), balancing abstraction and specificity. Memora sets new state-of-the-art on LoCoMo and LongMemEval, outperforming Mem0, RAG, and full-context inference while using up to 98% fewer context tokens. Memora paper (opens in new tab) is published at ICML 2026. Memora code is available at https://github.com/microsoft/Memora (opens in new tab) . Imagine a workplace AI assistant helping you run a multi-month project. Over weeks of conversations, you share constraints, agree on milestones, revise deadlines, and surface dozens of stakeholder preferences. …

Announced

29 Jun 2026 21:14 UTC