EverMemOS
The Memory OS for Agentic AI

We have released our latest research on EverMemOS, now available on arXiv! Large Language Models are quickly evolving from “single turn chatbots” into long-term interactive agents. But as soon as an agent is expected to stay coherent across weeks of conversations, it runs into a practical ceiling: a limited context window and fragmented memory. Even with retrieval, many systems still behave like they are pulling isolated snippets—often missing conflicts, failing to update user state, or giving inconsistent guidance over time. In our latest research, we introduce EverMemOS, a self-organizing memory operating system that treats memory not as a flat store, but as a lifecycle—inspired by biological “engram” principles—so agents can continuously transform raw interactions into structured, evolving knowledge.

Using a unified, production-grade evaluation framework, we benchmarked leading memory systems — EverMemOS, Mem0, MemOS, Zep, and MemU — under the same datasets, metrics, and answer model. This framework provides a fair, transparent, and reproducible standard for evaluating real-world memory performance in the Agentic Era. And EverMemOS delivered best-in-class results across LoCoMo and LongMemEval.

Part of the EverMemModel modules, the ReRankModel achieves SOTA performance on 2wiki and Hotpotqa.

The EverMemModel has achieved SOTA performance both on retrieval task and QA task.

EverMemOS is an intelligent memory operating system designed to give AI the ability not just to remember, but to understand, reason, and evolve. On the LoCoMo benchmark, our approach built upon EverMemOS achieved a 92.3% reasoning accuracy (evaluated by LLM-Judge), outperforming comparable methods in our internal evaluation.