EverMind recently released an open-source Memory Operating System, EverMemOS, designed for equipping machines with scalable, long-term memory. For years this has been a problematic issue for AI.
Large language models (LLMs) have experienced “forgetfulness” in long-term tasks because of fixed context windows resulting in broken context, factual inconsistencies, and lack of deep personalization or maintaining knowledge coherence.
EverMind explains memory “forgetfulness” is inconsistent and problematic for AI powered systems:
An entity without memory cannot exhibit behavioral consistency or initiative, let alone achieve self-evolution. Personalization, consistency, and proactivity, which are considered the hallmarks of intelligence, all depend on a robust memory system.
Earlier AI technology such as Retrieval-Augmented Generation (RAG) and fragmented memory systems, are inferior with failure to support both 1-on-1 companion use cases and complex multi-agent enterprise collaboration. Large models enabled with a high-performance, pluggable memory module is a coveted goal of current AI applications.
Discoverative Intelligence, discovered in 2025 by entrepreneur and philanthropist Chen Tianqiao is differentiated from generative AI, mimicking human output by processing existing data. Discoverative Intelligence is a form of AI form that asks questions, forms testable hypotheses, and finds new scientific principles.
Based on the “Structural Path,” which relies on the “cognitive anatomy” of intelligence and how systems operate over time, Discoverative Intelligence is built on a cognitive model called Structured Temporal Intelligence (STI) which relies on five core capabilities :
- Neural dynamics (sustained, self-organizing activity to keep systems “alive”)
- Long-term memory (storing and selectively forgetting experiences to build knowledge)
- Causal reasoning (inferring “why” events occur)
- World modeling (an internal simulation of reality for prediction)
- metacognition & intrinsic motivation (curiosity-driven exploration
Long-term memory serves as vital factor in achieving Artificial General Intelligence (AGI). Discoverative Intelligence promotes understanding causality and underlying principles over statistical patterns.
Enter EverMind’s EverMemOS, an open-source Memory Operating System built as a foundation technology for developing Discoverative Intelligence:
Inspired by the hierarchical organization of the human memory system, EverMemOS features a four-layer architecture analogous to key brain regions: an Agentic Layer (task planning, mirroring the prefrontal cortex), a Memory Layer (long-term storage, like cortical networks), an Index Layer (associative retrieval, drawing from the hippocampus), and an API/MCP Interface Layer (external integration, serving as AI’s “sensory interface”).
EverMemOS is the first memory system capable of both 1-on-1 conversation use cases and complex multi-agent enterprise collaboration. EverMemOS scored 92.3% accuracy on LoCoMo (a long-context memory evaluation) and 82% on LongMemEval-S (a suite for assessing long-term memory retention), setting a new industry standard.
EverMemOS is currently available on GitHub, and with a cloud service version available later this year.
Memoryless AI
- Frequent “amnesia” and broken context
- Unable to achieve deep personalization
- Lacks long-term behavioral consistency
- Cannot evolve or self-improve
EverMemOS
- A persistent and coherent memory system
- Deep understanding of user preferences
- Temporal continuity and consistency
- An evolvable “soul” for intelligent agents
About EverMind
EverMind is redefining the future of AI by solving one of its most fundamental limitations: long-term memory. Its flagship platform, EverMemOS, introduces a breakthrough architecture for scalable and customizable memory systems, enabling AI to operate with extended context, maintain behavioral consistency, and improve through continuous interaction.
