The Memory Genesis Competition 2026 is now underway, highlighting advancements in AI memory systems. Central to this is EverMemOS, a memory operating system designed for agentic AI. Unlike traditional AI, which struggles with context retention and personalization, EverMemOS aims to create a persistent memory that evolves with user interactions. It addresses the limitations of large language models (LLMs) that often suffer from "amnesia" and inconsistent behavior by implementing a structured memory architecture. The system employs a four-layer architecture that mirrors human memory processes. This includes an Agentic Layer for task execution, a Memory Layer for long-term storage, an Index Layer for organizing information, and an API layer to connect with external systems. Key features of EverMemOS include a Memory Processor that actively applies stored knowledge, hierarchical memory extraction for stable contextual understanding, and a modular framework that adapts to various applications, from enterprise tasks to companion AI. EverMemOS incorporates a three-phase memory lifecycle: Episodic Trace Formation, Semantic Consolidation, and Reconstructive Recollection. This structured approach allows for better long-term coherence without solely relying on expanding context windows, which can be costly and ineffective. The system is particularly well-suited for scenarios requiring deep personalization and consistent interaction, such as AI assistants and customer service applications. For developers looking to implement or explore EverMemOS, the source code is available on GitHub, allowing for easy access and experimentation. The focus on creating a digital brain through organized memory sets this system apart from traditional methods, promising a more sophisticated interaction model for future AI applications.