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Basic Information

MemOS is an open source operating system for Large Language Models designed to add long-term memory capabilities so models can store, retrieve, and manage information across interactions. It provides a unified Memory-Augmented Generation (MAG) API and a higher-level orchestration layer called MOS to manage multiple MemCubes and user memory state. The project targets developers and researchers who want to integrate persistent, modular memory into chat and reasoning workflows, enabling more context-aware, consistent, and personalized LLM behavior. The README documents end-to-end evaluation on the LOCOMO benchmark and shows substantial gains over baseline memory solutions, highlights versioned releases and community resources, and includes installation instructions, optional feature groups, example data and code, and API references for integrating with LLMs and memory backends.

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App Details

Features
MemOS exposes Memory-Augmented Generation (MAG) as a unified API and a modular MemCube architecture for flexible memory management. It supports multiple memory types including textual memory for unstructured or structured knowledge, activation memory for KV cache acceleration, and parametric memory for storing model adaptation parameters such as LoRA weights. The system is extensible, allowing custom memory modules, data sources, and LLM integrations. A higher-level MOS component orchestrates multiple MemCubes and user management. The README also documents optional dependency groups for tree memory, memory reader, and memory scheduler, integration guidance for Transformers and Ollama, example scripts and data, and performance benchmark results demonstrating improved reasoning and temporal accuracy.
Use Cases
MemOS helps developers and researchers build LLM applications that remember and reason over long-term context by providing reusable memory primitives, storage types, and orchestration tools. It simplifies common tasks such as initializing MemCubes from directories, registering cubes per user, adding conversation traces to memory, and searching retrieved memories. The modular design enables swapping or extending memory backends and using optional components like schedulers or readers. Benchmark results in the README indicate substantial improvements in multi-hop and temporal reasoning, which can lead to more consistent assistant behavior. Installation via pip and included examples accelerate prototyping and evaluation for both research and production scenarios.

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