AI infrastructure company EverMind has unveiled EverMemOS, a new open-source Memory Operating System that aims to tackle one of artificial intelligence’s most persistent limitations scalable, long-term memory. With this release, the company is positioning memory not as an add-on feature, but as a foundational pillar required for the next phase of AI evolution.
For years, large language models (LLMs) have struggled with fixed context windows, a constraint that often leads to memory loss during extended interactions. As a result, AI systems frequently lose track of earlier information, introduce factual inconsistencies, and fail to maintain coherent personalization over time. More importantly, this realization highlights a deeper challenge. Without durable memory, machines cannot display consistent behavior, proactive decision-making, or meaningful learning over extended periods. In essence, memory is not just a technical requirement it is a prerequisite for intelligence itself.
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Across the AI industry, consensus is forming around the idea that memory will become the defining competitive advantage for future systems. However, existing approaches such as Retrieval-Augmented Generation (RAG) and fragmented memory layers have not fully met real-world demands. While these tools can enhance short-term recall, they struggle to scale across both personalized, one-on-one interactions and complex, multi-agent enterprise environments. As a result, the industry still lacks a high-performance, adaptable, and truly pluggable memory system that can support advanced AI workflows at scale.
This gap connects directly to a broader vision of intelligence. In late 2025, entrepreneur and philanthropist Chen Tianqiao introduced the concept of “Discoverative Intelligence,” describing a form of AI that goes beyond pattern replication. Unlike generative AI, which reassembles existing data, Discoverative Intelligence actively formulates questions, builds hypotheses, and uncovers new principles. It emphasizes causal understanding over statistical prediction, a shift Chen believes is essential to reaching Artificial General Intelligence (AGI).
Chen also distinguished between two major development paths shaping AI today. The first, known as the “Scaling Path,” relies on ever-larger models, datasets, and compute power. The second, the “Structural Path,” focuses on the internal architecture of intelligence and how systems evolve over time. Discoverative Intelligence belongs to this structural approach and is built on a brain-inspired framework called Structured Temporal Intelligence (STI).
Within this framework, five interconnected capabilities form a closed loop: sustained neural dynamics, long-term memory, causal reasoning, world modeling, and metacognition driven by intrinsic motivation. Among these elements, long-term memory plays a central role by linking past experience with future decision-making, reinforcing its importance in the pursuit of true AGI.
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EverMind’s EverMemOS directly addresses this need. Designed as foundational infrastructure for Discoverative Intelligence, the system draws inspiration from the hierarchical structure of the human brain. It features a four-layer architecture that includes an Agentic Layer for task planning, a Memory Layer for long-term knowledge storage, an Index Layer for associative retrieval, and an API/MCP Interface Layer that connects AI systems to external tools and environments.
Beyond its architectural design, EverMemOS delivers strong technical performance. It is the first memory system capable of supporting both individualized conversational experiences and large-scale, multi-agent enterprise collaboration. In benchmark evaluations, EverMemOS achieved 92.3% accuracy on LoCoMo and 82% on LongMemEval-S, significantly outperforming previous state-of-the-art memory solutions.
The open-source release of EverMemOS is now available on GitHub, while a cloud-based version is scheduled to launch later this year. By combining open collaboration with managed cloud services, EverMind aims to accelerate innovation in long-term memory systems and invite developers, enterprises, and researchers to help shape the future of intelligent AI.
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