EverMind today introduced EverOS, a Long-Term Memory Operating System designed to address one of artificial intelligence's most persistent limitations: the inability to retain and learn from prior interactions. The platform transforms stateless large language models into agents capable of learning from experience rather than processing each interaction from scratch.
Current large language models operate as stateless systems—powerful tools that process each query independently without accumulating knowledge across sessions. EverOS addresses this architectural constraint by providing a structured memory infrastructure that enables AI agents to remember, adapt, and evolve through continuous interaction.
At the core of EverOS is EverCore, an underlying engine that achieves state-of-the-art performance across industry-standard memory benchmarks. The system records 93.05% accuracy on LoCoMo, 83.00% on LongMemEval, and 90.04% on HaluMem through continual learning technology that strengthens memory retention over time.
"We are not merely adding another layer to the AI stack," said the EverMind team. "EverOS represents the foundational memory operating system that will serve as the core data infrastructure for the next era of intelligent agents. Our mission is to solve AI's core weakness: its inability to remember."
The EverOS architecture operates through a sophisticated four-layer system: the Agentic Layer handles task understanding and execution; the Memory Layer provides long-term storage and retrieval; the Index Layer manages embeddings and knowledge graph indexing; and the API/MCP Interface Layer enables integration with external enterprise systems.
Unlike conventional memory systems, EverOS features native multimodal memory ingestion, parsing diverse data types—including PDFs, images, and URLs—through a single API. This capability is driven by mRAG, a multimodal retrieval strategy that enables cross-modal search. The system also records agent trajectories as Cases and distills repeated patterns into reusable Skills.
At the heart of EverOS lies a three-phase memory lifecycle that transforms raw interactions into structured, evolving knowledge. The process begins with Episodic Trace Formation, where conversational streams are converted into MemCells—atomic memory units containing episodes, atomic facts, foresight with validity intervals, and metadata. These MemCells then undergo Semantic Consolidation, where online incremental clustering organizes them into MemScenes (thematic memory clusters) while simultaneously updating compact User Profiles. Finally, Reconstructive Recollection retrieves only what is necessary and sufficient to answer queries effectively.
Users access transparent control via the Memory Bank interface, providing visibility into user, group, and agent memories. EverOS is available as a managed Cloud service or open-source solution, ensuring behavioral consistency and temporal tracking across days and sessions for companions, enterprise knowledge bases, and multi-agent systems.
"The future of long-term agents depends more on structured memory organization than on brute-force context expansion," the team noted. "Real assistants must handle conflicting preferences, stable personalization, time-bounded states, and proactive foresight—capabilities that EverOS explicitly builds into its memory representations."
EverOS is designed as a system-level foundation that attaches to different agent stacks while maintaining a consistent lifecycle contract for building and using memory. Developers can access EverOS Cloud at everos.evermind.ai or deploy the open-source version available on GitHub. The platform supports integration with existing LLM APIs and embedding services.
About EverMind: EverMind is dedicated to solving AI's fundamental memory limitation by architecting next-generation data infrastructure for intelligent agents. The company empowers AI with expanded context capabilities and the ability to grow continuously from every interaction. For more information, visit evermind.ai or explore the open-source EverOS repository on GitHub.
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