A new AI memory infrastructure is being introduced by Evermind AI as the company launches EverMemOS, a system designed to give artificial intelligence durable, evolving memory. The platform is intended to address a persistent limitation in modern AI: despite advanced reasoning abilities, most systems are unable to retain knowledge across interactions. With EverMemOS, long-term memory is positioned as a foundational layer for agentic AI that can adapt over time rather than reset with each conversation.
Until now, AI tools have largely functioned as stateless systems. As a result, personalization has been limited, behavioral continuity has been difficult to sustain, and learning from experience has remained constrained. By contrast, EverMemOS is being positioned as infrastructure that allows AI agents to remember, refine behavior, and improve through accumulated interaction history.
Why AI Memory Infrastructure Has Been Missing
Although large language models can analyze complex prompts, their knowledge is typically locked at training time. Each session begins without awareness of prior exchanges. Consequently, user preferences are not retained, and insights gained from earlier tasks cannot be applied later.
According to Evermind AI, this absence of persistent memory has prevented artificial intelligence from becoming a long-term collaborator. Instead, systems have been confined to short-lived interactions. With the introduction of EverMemOS, memory is treated as a core operating function rather than an optional add-on.
Jason Deng, cofounder of Evermind AI, said the company is focused on building a memory operating system rather than another software layer. He explained that the goal is to enable AI agents to grow with each interaction and provide continuity over time.
EverMemOS and Its Layered Memory Architecture
At the center of EverMemOS is what Evermind describes as a Layered Memory Extraction framework. This architecture is structured around four distinct layers, each modeled on cognitive functions observed in human memory systems.
An interface layer connects the system with enterprise tools through APIs and the Model Context Protocol. Beneath that, an indexing layer organizes embeddings, key-value stores, and knowledge graphs, functioning in a role similar to memory indexing in the brain. A dedicated memory layer is used for long-term storage and retrieval, while an agentic layer is responsible for planning, task execution, and reasoning.
Through this design, memory is not stored passively. Instead, stored information actively shapes how responses are generated and decisions are made.
Turning Conversations Into Structured Memory
Rather than relying on similarity search alone, EverMemOS converts interaction data into structured semantic components known as MemUnits. These units are then arranged into adaptive memory graphs that evolve as new information is added.
This hierarchical approach allows context to be preserved over long periods. As a result, limitations associated with short context windows and static retrieval methods are reduced. Contextual understanding is therefore maintained across extended timelines, even as tasks change.
Flexible Memory Strategies for Different Use Cases
Because EverMemOS is modular, memory behavior can be tailored to specific applications. In enterprise environments, configurations can prioritize precision, compliance, and repeatability. Meanwhile, consumer-facing or companion AI systems can emphasize emotional continuity and personal history.
This adaptability allows the same AI memory infrastructure to support varied use cases without sacrificing performance. Consequently, developers are not locked into a single memory strategy.
Benchmarks Show Strong Performance
The effectiveness of EverMemOS has been demonstrated through independent benchmarks. On the LoCoMo dataset, which measures long-term contextual reasoning, the system recorded 92.3 percent accuracy using fully open and reproducible methods.
Similarly, an 82 percent accuracy score was achieved on the LongMemEval-S benchmark. In both cases, performance exceeded that of other memory solutions evaluated under the same conditions.
Expanding the AI Memory Stack
In addition to EverMemOS, Evermind AI has developed supporting technologies. The EverMemModel is approaching a 100 million token context length and has shown strong results on tasks such as NQ320k. Meanwhile, the EverMemReRank module more than doubled question-answering performance on the 2Wiki benchmark when compared with baseline models.
To further advance the field, Evermind has also released a unified evaluation framework. This system enables consistent testing of AI memory tools, including Mem0, MemOS, Zep, and MemU, using shared datasets and metrics that mirror real-world production environments.
Open-Source Release Targets Industry Adoption
EverMemOS has been released as open-source software, allowing developers to integrate persistent memory into their own AI applications. Deployment can be handled through Docker, and compatibility is provided for multiple large language model APIs and embedding services.
By making the platform publicly available, Evermind AI aims to encourage experimentation and collaboration across the industry. In doing so, AI memory infrastructure is being positioned as a core layer of future systems, comparable in importance to operating systems in traditional computing.
