[{"data":1,"prerenderedAt":330},["ShallowReactive",2],{"docs-page-cn-\u002Fcn\u002Fopen_source\u002Fhome\u002Fcore_concepts":3,"surround-cn-\u002Fcn\u002Fopen_source\u002Fhome\u002Fcore_concepts":314},{"id":4,"title":5,"avatar":6,"banner":6,"body":7,"category":6,"desc":307,"description":289,"extension":308,"links":6,"meta":309,"navigation":6,"path":310,"seo":311,"stem":312,"__hash__":313},"docs\u002Fcn\u002Fopen_source\u002Fhome\u002Fcore_concepts.md","核心概念",null,{"type":8,"value":9,"toc":288},"minimark",[10,14,43,46,54,60,63,68,73,75,78,139,142,147,152,154,159,165,170,181,183,188,202,207,210,213,224,239,258,260,263,266,269,272,282,285],[11,12,13],"h2",{"id":13},"概述",[15,16,17,25,31,37],"ul",{},[18,19,20],"li",{},[21,22,24],"a",{"href":23},"#mos-%E8%AE%B0%E5%BF%86%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F","MOS (记忆操作系统)",[18,26,27],{},[21,28,30],{"href":29},"#memcube","MemCube",[18,32,33],{},[21,34,36],{"href":35},"#%E8%AE%B0%E5%BF%86%E7%B1%BB%E5%9E%8B","记忆类型",[18,38,39],{},[21,40,42],{"href":41},"#%E6%A8%AA%E5%88%87%E6%A6%82%E5%BF%B5","横切概念",[11,44,24],{"id":45},"mos-记忆操作系统",[47,48,49,53],"p",{},[50,51,52],"strong",{},"定义","\nMOS 是 MemOS 的编排调度层，负责协调多个 MemCube 与各类记忆操作。它作为中间件，将 LLM 与结构化、可解释的记忆系统连接起来，以支持复杂的推理与规划任务。",[47,55,56,59],{},[50,57,58],{},"使用场景","\n当您需要在用户、会话或智能体之间建立一致、可审计且可追溯的记忆工作流时，应使用 MOS 进行统一调度。",[11,61,30],{"id":62},"memcube",[47,64,65,67],{},[50,66,52],{},"\nMemCube 是 MemOS 中的可插拔、可扩展记忆容器。每个用户、会话或任务均可分配独立的 MemCube，其中可承载一种或多种类型的记忆。",[47,69,70,72],{},[50,71,58],{},"\n随着系统规模增长，可通过配置不同的 MemCube 实现记忆的隔离、复用与水平扩展。",[11,74,36],{"id":36},[47,76,77],{},"MemOS 将记忆视为动态演化的知识系统，而非静态数据存储。其核心记忆类型如下：",[79,80,81,96],"table",{},[82,83,84],"thead",{},[85,86,87,90,93],"tr",{},[88,89,36],"th",{},[88,91,92],{},"描述",[88,94,95],{},"何时使用",[97,98,99,113,126],"tbody",{},[85,100,101,107,110],{},[102,103,104],"td",{},[50,105,106],{},"参数记忆",[102,108,109],{},"内化至模型权重的知识",[102,111,112],{},"常青技能、稳定领域专业知识",[85,114,115,120,123],{},[102,116,117],{},[50,118,119],{},"激活记忆",[102,121,122],{},"可复用的 KV 缓存与隐藏状态",[102,124,125],{},"对话中的快速重用、多轮会话",[85,127,128,133,136],{},[102,129,130],{},[50,131,132],{},"明文记忆",[102,134,135],{},"文本、文档、图节点、工具或偏好等",[102,137,138],{},"可搜索、可检查、演进知识",[140,141,106],"h3",{"id":106},[47,143,144,146],{},[50,145,52],{},"\n参数记忆指固化在模型权重中的知识，可视为模型的“长期记忆”。它始终在线，为推理任务提供零延迟的知识支持。",[47,148,149,151],{},[50,150,58],{},"\n适用于稳定的领域知识、经过提炼的通用问题解法以及不易变化的操作技能。",[140,153,119],{"id":119},[47,155,156,158],{},[50,157,52],{},"\n激活记忆是模型可复用的“工作记忆”，包括预先计算的键值缓存（KV Cache）与隐藏状态，可直接注入注意力机制中，避免对重复内容的重复编码。",[47,160,161,164],{},[50,162,163],{},"为什么重要：","\n将稳定的上下文信息（如产品说明、操作指南）以 KV Cache 形式存储，可大幅降低首词元延迟（TTFT），并提升多轮对话与检索增强生成（RAG）的吞吐效率。",[47,166,167],{},[50,168,169],{},"何时使用：",[15,171,172,175,178],{},[18,173,174],{},"在连续查询中复用背景知识",[18,176,177],{},"加速基于固定上下文的对话系统",[18,179,180],{},"配合 MemScheduler 将高频明文记忆自动转换为 KV 缓存",[140,182,132],{"id":132},[47,184,185,187],{},[50,186,52],{},"\n结构化或非结构化的知识单元，具有用户可见性和可解释性。除了传统的文档、聊天日志、图节点和向量嵌入外，MemOS 还将以下内容视为明文记忆：",[15,189,190,196],{},[18,191,192,195],{},[50,193,194],{},"工具记忆 (Tool Memory)","：包括工具的定义 (Schema) 和使用轨迹 (Trajectory)，用于增强智能体（Agent）的工具调用能力。",[18,197,198,201],{},[50,199,200],{},"偏好记忆 (Preference Memory)","：显式或隐式的用户偏好，用于个性化推荐和响应。",[47,203,204,206],{},[50,205,58],{},"\n适用于语义搜索、个性化体验构建、复杂任务的工具增强以及随时间演进的可追溯事实。支持标签、来源追踪和完整的生命周期管理。",[11,208,209],{"id":209},"它们如何协同工作",[47,211,212],{},"MemOS 让您在生命周期循环中调度所有三种记忆类型：",[15,214,215,218,221],{},[18,216,217],{},"提炼过程：高频使用的明文记忆可被蒸馏为参数记忆，提升推理效率。",[18,219,220],{},"缓存优化：常见推理路径可固化为可复用的 KV 模板，减少重复计算。",[18,222,223],{},"降级归档：使用频率降低的参数或激活记忆可降级为明文存储，便于审计与再训练。",[47,225,226,227,230,231,234,235,238],{},"借助 MemOS，您的 AI 系统不仅能存储信息，更能实现持续",[50,228,229],{},"记忆","、",[50,232,233],{},"深度理解","和",[50,236,237],{},"自主进化","。",[240,241,242,250],"note",{},[47,243,244,247],{},[50,245,246],{},"系统洞察",[248,249],"br",{},[15,251,252,255],{},[18,253,254],{},"随着时间的推移，频繁使用的明文记忆可以提炼为参数记忆。",[18,256,257],{},"低频参数或缓存则可归档为明文，形成可审计、可再训练的知识闭环。",[11,259,42],{"id":42},[140,261,262],{"id":262},"混合检索",[47,264,265],{},"结合向量相似性检索和图遍历算法，实现稳健且具有上下文感知能力的混合搜索。",[140,267,268],{"id":268},"治理与生命周期",[47,270,271],{},"每个记忆单元都具备完整的生命周期状态（激活、合并、归档），并支持来源跟踪和细粒度访问控制，这对满足审计和数据合规性要求至关重要。",[240,273,274],{},[47,275,276,279,281],{},[50,277,278],{},"合规提示",[248,280],{},"\n请确保对每个记忆单元的来源与状态变更进行完整记录，以符合数据治理与审计规范。",[11,283,284],{"id":284},"关键要点",[47,286,287],{},"MemOS 为您的 LLM 应用提供结构化、可演进、可治理的记忆系统，使智能体能够进行长远规划、复杂推理与持续自适应，释放下一代 AI 应用的真正潜力。",{"title":289,"searchDepth":290,"depth":290,"links":291},"",2,[292,293,294,295,301,302,306],{"id":13,"depth":290,"text":13},{"id":45,"depth":290,"text":24},{"id":62,"depth":290,"text":30},{"id":36,"depth":290,"text":36,"children":296},[297,299,300],{"id":106,"depth":298,"text":106},3,{"id":119,"depth":298,"text":119},{"id":132,"depth":298,"text":132},{"id":209,"depth":290,"text":209},{"id":42,"depth":290,"text":42,"children":303},[304,305],{"id":262,"depth":298,"text":262},{"id":268,"depth":298,"text":268},{"id":284,"depth":290,"text":284},"MemOS 将记忆视为一等资源。其核心设计围绕如何为您的 LLM 应用程序组织、存储、检索和治理记忆。","md",{},"\u002Fcn\u002Fopen_source\u002Fhome\u002Fcore_concepts",{"title":5,"description":289},"cn\u002Fopen_source\u002Fhome\u002Fcore_concepts","H5hB9dfOIPdk_2kYfLGc8uhpg-8Azx1vbEPVS1TxYl8",[315,323],{"title":316,"path":317,"stem":318,"icon":319,"framework":6,"module":6,"class":320,"target":-1,"active":321,"defaultOpen":321,"children":-1,"description":322},"创建你的第一个记忆","\u002Fcn\u002Fopen_source\u002Fgetting_started\u002Fyour_first_memory","open_source\u002Fgetting_started\u002Fyour_first_memory","i-ri-bookmark-line",[],false,"动手实战！我们将带您使用 SimpleStructMemReader 从对话中提取记忆，并把它存进 TreeTextMemory 进行管理与检索。",{"title":324,"path":325,"stem":326,"icon":327,"framework":6,"module":6,"class":328,"target":-1,"active":321,"defaultOpen":321,"children":-1,"description":329},"架构设计","\u002Fcn\u002Fopen_source\u002Fhome\u002Farchitecture","open_source\u002Fhome\u002Farchitecture","i-ri-building-2-line",[],"MemOS 采用模块化设计，各核心组件协同工作，将传统 LLM 升级为具备完整记忆生命周期管理能力的记忆增强系统。",1781576387202]