[{"data":1,"prerenderedAt":574},["ShallowReactive",2],{"\u002Fcn\u002Fmemos_cloud\u002Fintroduction\u002Fmem_production":3,"surround-\u002Fcn\u002Fmemos_cloud\u002Fintroduction\u002Fmem_production":559},{"id":4,"title":5,"avatar":6,"banner":6,"body":7,"category":6,"desc":552,"description":137,"extension":553,"links":6,"meta":554,"navigation":6,"path":555,"seo":556,"stem":557,"__hash__":558},"docs\u002Fcn\u002Fmemos_cloud\u002Fintroduction\u002Fmem_production.md","记忆生产",null,{"type":8,"value":9,"toc":547},"minimark",[10,34,36,41,48,57,60,82,84,91,94,123,125,131,168,194,200,202,205,236,249,251,258,264,320,326,328,332,373,399,404,406,409,429,431,433,437,447,514,516,520,523,543],[11,12,13,29],"warning",{},[14,15,16,24,27],"p",{},[17,18,19],"strong",{},[20,21,23],"a",{"href":22},"\u002Fapi_docs\u002Fcore\u002Fadd_message","直接看 API文档 点这里哦",[25,26],"br",{},[25,28],{},[14,30,31],{},[17,32,33],{},"本文聚焦于功能说明，详细接口字段及限制请点击上方文字链接查看",[25,35],{},[37,38,40],"h2",{"id":39},"_1-能力介绍为什么要将原始消息加工为记忆","1. 能力介绍：为什么要将原始消息加工为记忆",[14,42,43,44,47],{},"在 MemOS 里，你提交的是 ",[17,45,46],{},"原始信息","（用户与 AI 的对话、用户在APP上的操作日志 \u002F 行动轨迹、知识库文档等），系统会自动完成“记忆化”的过程。",[49,50,52],"note",{"icon":51},"ri:triangular-flag-fill",[14,53,54],{},[17,55,56],{},"为什么要进行记忆加工？",[14,58,59],{},"如果只是简单地把原始信息全部保存，再次使用时直接丢给大模型，会出现几个问题：",[61,62,63,70,76],"ul",{},[64,65,66,69],"li",{},[17,67,68],{},"上下文过长","：原始信息通常冗长且包含大量重复与无关内容，整段传入模型会显著拉长上下文窗口，处理效率低且浪费 Token；",[64,71,72,75],{},[17,73,74],{},"检索不精准","：未经结构化与抽取的原始文本难以突出对话中的关键信息，检索时容易召回大量噪音内容，影响回答质量；",[64,77,78,81],{},[17,79,80],{},"体验不连续","：原始对话是一次性的静态文本，无法感知用户偏好与情感、业务背景与规则的变化，造成连续对话的体验偏差。",[25,83],{},[49,85,86],{},[14,87,88],{},[17,89,90],{},"MemOS 加工后的记忆是什么？",[14,92,93],{},"MemOS 会把原始消息转化为结构化记忆单元，自动提炼：",[61,95,96,107],{},[64,97,98,101,102],{},[17,99,100],{},"关键事实","：\n",[61,103,104],{},[64,105,106],{},"提炼用户对话中的事实信息，如“用户计划在2025年暑假期间前往广州旅游。”；",[64,108,109,101,112],{},[17,110,111],{},"用户偏好",[61,113,114,117,120],{},[64,115,116],{},"提炼用户对话中显式的偏好表述，例如“用户提到喜欢全家出游”；",[64,118,119],{},"提炼用户对话中隐含的推理范式，例如“用户可能偏好性价比高的酒店选择”；",[64,121,122],{},"MemOS 会保留用户的偏好模式，引导模型在后续回答中保持一致性。例如，当用户在使用 AI 辅助写作时，展现出“偏向逻辑清晰的写法”，那么 MemOS 会继续引导模型在其他任务中，保持“逻辑性的写作手法”。",[25,124],{},[14,126,127,130],{},[17,128,129],{},"例子","：",[132,133,138],"pre",{"className":134,"code":135,"language":136,"meta":137,"style":137},"language-json shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","User: 我暑假定好去广州旅游，住宿的话有哪些连锁酒店可选？\nAssistant: 您可以考虑【七天、全季、希尔顿】等等\nUser: 我选七天\nAssistant: 好的，有其他问题再问我。\n","json","",[139,140,141,150,156,162],"code",{"__ignoreMap":137},[142,143,146],"span",{"class":144,"line":145},"line",1,[142,147,149],{"class":148},"sTEyZ","User: 我暑假定好去广州旅游，住宿的话有哪些连锁酒店可选？\n",[142,151,153],{"class":144,"line":152},2,[142,154,155],{"class":148},"Assistant: 您可以考虑【七天、全季、希尔顿】等等\n",[142,157,159],{"class":144,"line":158},3,[142,160,161],{"class":148},"User: 我选七天\n",[142,163,165],{"class":144,"line":164},4,[142,166,167],{"class":148},"Assistant: 好的，有其他问题再问我。\n",[132,169,171],{"className":134,"code":170,"language":136,"meta":137,"style":137},"事实记忆: 用户计划在暑假期间前往广州旅游，并选择了七天连锁酒店作为住宿选项。\n\n偏好记忆: 用户可能偏好性价比较高的酒店选择\nReasoning: 七天酒店通常以经济实惠著称，而用户选择七天酒店可能表明其在住宿方面倾向于选择性价比较高的选项。虽然用户没有明确提到预算限制或具体酒店偏好，但在提供的选项中选择七天可能反映了对价格和实用性的重视。\n",[139,172,173,178,184,189],{"__ignoreMap":137},[142,174,175],{"class":144,"line":145},[142,176,177],{"class":148},"事实记忆: 用户计划在暑假期间前往广州旅游，并选择了七天连锁酒店作为住宿选项。\n",[142,179,180],{"class":144,"line":152},[142,181,183],{"emptyLinePlaceholder":182},true,"\n",[142,185,186],{"class":144,"line":158},[142,187,188],{"class":148},"偏好记忆: 用户可能偏好性价比较高的酒店选择\n",[142,190,191],{"class":144,"line":164},[142,192,193],{"class":148},"Reasoning: 七天酒店通常以经济实惠著称，而用户选择七天酒店可能表明其在住宿方面倾向于选择性价比较高的选项。虽然用户没有明确提到预算限制或具体酒店偏好，但在提供的选项中选择七天可能反映了对价格和实用性的重视。\n",[195,196,197],"blockquote",{},[14,198,199],{},"对你来说，这意味着：只要存原始对话，不必自己写“关键词提取”或“意图识别”的逻辑，就能得到可被长期调用的用户偏好。",[25,201],{},[14,203,204],{},"除了文本对话消息，针对 Agent 任务执行的过程，MemOS 适配了工具记忆、技能：",[61,206,207,220],{},[64,208,209,101,215],{},[17,210,211],{},[20,212,214],{"href":213},"\u002Fmemos_cloud\u002Ffeatures\u002Fadvanced\u002Ftool_calling","工具记忆（tool memory）",[61,216,217],{},[64,218,219],{},"提炼 Agent 任务执行过程中的工具调用信息为记忆，记录工具类型、使用场景与调用结果特征，用于后续相似任务的工具优先选择。",[64,221,222,101,228],{},[17,223,224],{},[20,225,227],{"href":226},"\u002Fmemos_cloud\u002Ffeatures\u002Fadvanced\u002Fskill","技能（Skills）",[61,229,230,233],{},[64,231,232],{},"提炼用户对话，生成可复用的执行能力。",[64,234,235],{},"例如，从多轮关于“生成旅行规划”的对话中，提炼出包含目的地分析、行程拆分、预算约束的可执行“旅行规划技能”，而不仅仅再是“用户喜欢特种兵旅行”这样供模型推理参考的记忆。",[14,237,238,239,243,244,248],{},"此外，MemOS 还支持基于",[20,240,242],{"href":241},"\u002Fmemos_cloud\u002Ffeatures\u002Fadvanced\u002Fknowledge_base","知识库","、",[20,245,247],{"href":246},"\u002Fmemos_cloud\u002Ffeatures\u002Fbasic\u002Fmultimodal","多模态消息","的记忆加工哦！",[25,250],{},[49,252,253],{},[14,254,255],{},[17,256,257],{},"记忆是如何被加工的？",[14,259,260,261,263],{},"MemOS 始终认为记忆不是静态的记录，而是随时间演化的对象，它需要快速、准确、连续地记住用户。\n",[25,262],{},"\n为了更好地处理记忆的三角问题：实时性、准确性、一致性，MemOS 把记忆加工的全流程拆分为以下三个阶段：",[265,266,267,283],"table",{},[268,269,270],"thead",{},[271,272,273,277,280],"tr",{},[274,275,276],"th",{},"阶段",[274,278,279],{},"目标",[274,281,282],{},"特点",[284,285,286,298,309],"tbody",{},[271,287,288,292,295],{},[289,290,291],"td",{},"Fast",[289,293,294],{},"不丢记忆",[289,296,297],{},"原文简单处理并快速入库，耗时毫秒级，下一轮对话即可检索到。",[271,299,300,303,306],{},[289,301,302],{},"Fine",[289,304,305],{},"近程组织",[289,307,308],{},"回溯当前上下文及历史记忆，如发现冲突，会在原 Memory ID 上生成新版本。",[271,310,311,314,317],{},[289,312,313],{},"Offline",[289,315,316],{},"全局组织",[289,318,319],{},"定期长程回溯，修正早期未识别的冲突，保持整体一致性。",[14,321,322,323],{},"形成的结果不是两条冲突记忆，而是：",[17,324,325],{},"同一个 Memory ID 的 V1 \u002F V2 \u002F V3… 时间演化链",[25,327],{},[14,329,330,130],{},[17,331,129],{},[132,333,335],{"className":134,"code":334,"language":136,"meta":137,"style":137},"# 假设是2025年\nUser: 我是小忆，现在在上海居住，不太能吃辣。\n\n# 假设是2026年\nUser: 最近我搬去成都了，突然爱上四川火锅，喜欢辣锅！\n",[139,336,337,349,354,358,367],{"__ignoreMap":137},[142,338,339,342,346],{"class":144,"line":145},[142,340,341],{"class":148},"# 假设是",[142,343,345],{"class":344},"sbssI","2025",[142,347,348],{"class":148},"年\n",[142,350,351],{"class":144,"line":152},[142,352,353],{"class":148},"User: 我是小忆，现在在上海居住，不太能吃辣。\n",[142,355,356],{"class":144,"line":158},[142,357,183],{"emptyLinePlaceholder":182},[142,359,360,362,365],{"class":144,"line":164},[142,361,341],{"class":148},[142,363,364],{"class":344},"2026",[142,366,348],{"class":148},[142,368,370],{"class":144,"line":369},5,[142,371,372],{"class":148},"User: 最近我搬去成都了，突然爱上四川火锅，喜欢辣锅！\n",[132,374,376],{"className":134,"code":375,"language":136,"meta":137,"style":137},"V1版本记忆：用户叫小忆，在上海居住，不能吃辣\nV2版本记忆：用户叫小忆，在成都居住，喜欢吃辣，喜欢四川火锅。\n",[139,377,378,389],{"__ignoreMap":137},[142,379,380,383,386],{"class":144,"line":145},[142,381,382],{"class":148},"V",[142,384,385],{"class":344},"1",[142,387,388],{"class":148},"版本记忆：用户叫小忆，在上海居住，不能吃辣\n",[142,390,391,393,396],{"class":144,"line":152},[142,392,382],{"class":148},[142,394,395],{"class":344},"2",[142,397,398],{"class":148},"版本记忆：用户叫小忆，在成都居住，喜欢吃辣，喜欢四川火锅。\n",[195,400,401],{},[14,402,403],{},"这样，同一条记忆能够随时间演化，既能给出“可依赖的当前认知”，同时保留完整的历史轨迹。",[25,405],{},[14,407,408],{},"这样一来，我们解决了最初提到的问题：",[61,410,411,417,423],{},[64,412,413,416],{},[17,414,415],{},"调用更高效","：拼接给大模型时只需传递精炼记忆，减少 Token 消耗。",[64,418,419,422],{},[17,420,421],{},"检索更快更准","：直接定位到事实 \u002F 偏好 \u002F 工具 \u002F 技能记忆，而不是一整段原始消息。",[64,424,425,428],{},[17,426,427],{},"体验更稳定","：模型能持续保持对用户习惯的理解，不会因上下文丢失而偏离。",[25,430],{},[25,432],{},[37,434,436],{"id":435},"_2-进阶如果你想做深度定制","2. 进阶：如果你想做深度定制",[14,438,439,440,442,443,446],{},"在 MemOS 中，",[17,441,5],{},"是将原始输入加工为可调度、可检索的记忆单元的全过程。具体的流水线细节（如抽取方式、嵌入模型、存储后端）会随着版本和社区实践不断演进——因此，本节不提供固定的唯一流程，而是说明 ",[17,444,445],{},"可扩展的环节","，你可以根据需求在这些位置上动手。",[265,448,449,468],{},[268,450,451],{},[271,452,453,458,463],{},[274,454,455],{},[17,456,457],{},"可扩展点举例",[274,459,460],{},[17,461,462],{},"默认行为",[274,464,465],{},[17,466,467],{},"可定制方式",[284,469,470,481,492,503],{},[271,471,472,475,478],{},[289,473,474],{},"抽取与结构化",[289,476,477],{},"默认会生成 MemoryItem（包含内容、时间戳、来源等）",[289,479,480],{},"可以替换抽取模型或模板，或在 schema 中增加领域字段",[271,482,483,486,489],{},[289,484,485],{},"切分与嵌入",[289,487,488],{},"系统会对长文本切分并送入嵌入模型",[289,490,491],{},"可以调整切分粒度，或替换为更适合的 embedding 模型（如 bge、e5）",[271,493,494,497,500],{},[289,495,496],{},"存储后端",[289,498,499],{},"默认使用向量数据库（如 Qdrant）",[289,501,502],{},"可切换为图数据库，或混合使用两者",[271,504,505,508,511],{},[289,506,507],{},"合并与治理",[289,509,510],{},"系统会自动处理重复与冲突",[289,512,513],{},"可以编写自定义规则（如时间优先、来源优先）等",[25,515],{},[37,517,519],{"id":518},"_3-下一步行动","3. 下一步行动",[14,521,522],{},"了解MemOS更多核心能力",[61,524,525,531,537],{},[64,526,527],{},[20,528,530],{"href":529},"\u002Fmemos_cloud\u002Fintroduction\u002Fmem_schedule","记忆调度",[64,532,533],{},[20,534,536],{"href":535},"\u002Fmemos_cloud\u002Fintroduction\u002Fmem_recall","记忆召回",[64,538,539],{},[20,540,542],{"href":541},"\u002Fmemos_cloud\u002Fintroduction\u002Fmem_lifecycle","记忆生命周期管理",[544,545,546],"style",{},"html pre.shiki code .sTEyZ, html code.shiki .sTEyZ{--shiki-light:#90A4AE;--shiki-default:#EEFFFF;--shiki-dark:#BABED8}html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .sbssI, html code.shiki .sbssI{--shiki-light:#F76D47;--shiki-default:#F78C6C;--shiki-dark:#F78C6C}",{"title":137,"searchDepth":152,"depth":152,"links":548},[549,550,551],{"id":39,"depth":152,"text":40},{"id":435,"depth":152,"text":436},{"id":518,"depth":152,"text":519},"记忆生产模块将原始消息、事件或知识转化为可存储、可检索的记忆单元，作为MemOS全流程的起点。","md",{},"\u002Fcn\u002Fmemos_cloud\u002Fintroduction\u002Fmem_production",{"title":5,"description":137},"cn\u002Fmemos_cloud\u002Fintroduction\u002Fmem_production","Qsnyv6uCxoynbzBUWzxjt6aHoqYN9O3xjDxvBuDIFZo",[560,568],{"title":561,"path":562,"stem":563,"icon":564,"framework":6,"module":6,"class":565,"target":-1,"active":566,"defaultOpen":566,"children":-1,"description":567},"快速开始","\u002Fcn\u002Fmemos_cloud\u002Fquick_start","memos_cloud\u002Fquick_start","i-ri-rocket-line",[],false,"欢迎访问 MemOS 云平台，可参考本新手指南，快速接入记忆能力。",{"title":530,"path":569,"stem":570,"icon":571,"framework":6,"module":6,"class":572,"target":-1,"active":566,"defaultOpen":566,"children":-1,"description":573},"\u002Fcn\u002Fmemos_cloud\u002Fintroduction\u002Fmem_schedule","memos_cloud\u002Fintroduction\u002Fmem_schedule","i-ri-calendar-line",[],"记忆调度就像大脑的注意力机制，动态决定在合适的时刻调用合适的记忆。",1774339745431]