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General

As LLMs evolve from chatbots into long-term agents, they hit a practical “cognitive wall” driven by:
  • Limited context windows: You can’t keep weeks or months of history in the prompt.
  • Fragmented memory: Even with retrieval, systems often pull isolated snippets without proper integration, conflict handling, or stable user modeling.
EverMemOS argues that the next leap comes from structured memory organization, not just longer context. It addresses the inability to integrate fragmented experiences into higher-level semantic structures, enabling agents to maintain consistent personas and reason consistently across time.
The core difference lies in EverMemOS’s Lifecycle-based architecture versus the traditional Flat Storage + Fragment Retrieval model.
  • Others (Mem0, MemOS, Zep): Often treat memory as isolated records or focus on storage optimization and fact management.
  • EverMemOS: Simulates a complete biological memory lifecycle: Episodic Trace Formation, Semantic Consolidation, and Reconstructive Recollection. This allows it to actively transform fragmented dialogues into structured knowledge (MemScenes) and dynamic User Profiles, rather than passively storing and retrieving snippets.
EverMemOS is ideal for applications requiring long-term consistency and deep user understanding:
  • Long-term AI Companions: Maintaining coherent personas and evolving user models over weeks or months.
  • Personalized Health & Lifestyle Management: Leveraging Experience-grounded Foresight to make safe recommendations (e.g., suggesting a mocktail because the system knows the user is currently on antibiotics, despite a past preference for IPA).
  • Professional Collaboration: Ensuring context consistency across complex, multi-turn interactions.

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Technical & Performance

EverMemOS leverages its three-stage lifecycle to handle complex reasoning:
  • Multi-hop Reasoning: It employs Semantic Consolidation to organize MemCells into thematic MemScenes. During retrieval (Reconstructive Recollection), it uses MemScene-guided retrieval and episode re-ranking to effectively link dispersed information, significantly outperforming baselines.
  • Temporal Reasoning: It introduces Prospections with Validity Intervals. The system infers future states (e.g., “flu” is temporary, “graduation” is permanent) and uses Prospection Filtering during retrieval to retain only currently valid information (tnow∈[tstart,tend]t_{now} \in [t_{start}, t_{end}]), ensuring precise temporal reasoning.
EverMemOS achieves State-of-the-Art (SOTA) performance on major long-context and memory benchmarks:
  • LoCoMo: 93.05% overall accuracy (with GPT-4.1-mini), significantly outperforming Zep (85.22%). It shows massive gains in Multi-hop (91.84%) and Temporal (89.72%) tasks.
  • LongMemEval: 83.00% overall accuracy, surpassing MemOS (77.80%).
  • PersonaMem v2: In user profiling tasks, incorporating the consolidated Profile improved accuracy by over 9%, validating its effectiveness in personalization.
For our monthly subscription plans, the quota is calculated based on the number of MemCells generated from your input messages.
  • Generation Ratio: On average, the ratio is approximately 10 raw messages to 1 MemCell.
  • Dynamic Segmentation: EverMemOS uses Semantic Boundary Detection to identify topic shifts and group related turns into a single MemCell.
  • Efficiency: This structured approach ensures that each memory unit is contextually coherent and provides a more predictable usage model for long-term agents.
For a deep dive into how Semantic Boundary Detection outperforms fixed heuristics, please refer to our research paper: ArXiv:2601.02163.