[{"data":1,"prerenderedAt":291},["ShallowReactive",2],{"/cn/mcp_agent/agent/guide":3,"surround-/cn/mcp_agent/agent/guide":283},{"id":4,"title":5,"avatar":6,"banner":6,"body":7,"category":6,"desc":276,"description":254,"extension":277,"links":6,"meta":278,"navigation":6,"path":279,"seo":280,"stem":281,"__hash__":282},"docs/cn/mcp_agent/agent/guide.md","使用指南",null,{"type":8,"value":9,"toc":267},"minimark",[10,15,20,32,35,39,43,60,62,65,70,159,164,235,237,241,245,255],[11,12,14],"h2",{"id":13},"coze平台插件工具","Coze平台插件工具",[16,17,19],"h3",{"id":18},"_1插件上架信息","1.插件上架信息",[21,22,23,24,31],"p",{},"MemOS云服务接口插件已在Coze商店上架！您可以直接",[25,26,30],"a",{"href":27,"rel":28},"https://www.coze.cn/store/plugin/7569918012912893995?from=store_search_suggestion",[29],"nofollow","前往工具链接","添加插件，实现零代码集成。",[33,34],"br",{},[16,36,38],{"id":37},"_2-插件描述","2. 插件描述",[40,41,42],"h4",{"id":42},"插件功能",[44,45,46,54],"ul",{},[47,48,49,53],"li",{},[50,51,52],"code",{},"search_memory","：该工具用于查询用户的记忆数据，可返回与输入最相关的片段。支持在用户与AI对话期间实时检索内存，也能在整个内存中进行全局搜索，可用于创建用户配置文件或支持个性化推荐，查询时需提供对话ID、用户ID、查询文本等参数，还可设置返回的记忆项数量。",[47,55,56,59],{},[50,57,58],{},"add_memory","：此工具可将一条或多条消息批量导入到MemOS记忆存储数据库，方便在未来对话中检索，从而支持聊天历史管理、用户行为跟踪和个性化交互，使用时需指定对话ID、消息内容、发送者角色、对话时间和用户ID等信息。",[33,61],{},[40,63,64],{"id":64},"接口描述",[44,66,67],{},[47,68,69],{},"search_memory接口",[71,72,73,92],"table",{},[74,75,76],"thead",{},[77,78,79,83,86,89],"tr",{},[80,81,82],"th",{},"参数名称",[80,84,85],{},"参数类型",[80,87,88],{},"描述",[80,90,91],{},"是否必填",[93,94,95,110,123,135,147],"tbody",{},[77,96,97,101,104,107],{},[98,99,100],"td",{},"memory_limit_number",[98,102,103],{},"string",[98,105,106],{},"限制返回的内存项数量，如果没有提供，则默认为6",[98,108,109],{},"否",[77,111,112,115,117,120],{},[98,113,114],{},"memos_key",[98,116,103],{},[98,118,119],{},"MemOS云服务的授权密钥",[98,121,122],{},"是",[77,124,125,128,130,133],{},[98,126,127],{},"memos_url",[98,129,103],{},[98,131,132],{},"MemOS云服务的URL地址",[98,134,122],{},[77,136,137,140,142,145],{},[98,138,139],{},"query",[98,141,103],{},[98,143,144],{},"用户输入",[98,146,122],{},[77,148,149,152,154,157],{},[98,150,151],{},"user_id",[98,153,103],{},[98,155,156],{},"与正在被查询的内存相关联的用户的唯一标识符",[98,158,122],{},[44,160,161],{},[47,162,163],{},"add_memory接口",[71,165,166,178],{},[74,167,168],{},[77,169,170,172,174,176],{},[80,171,82],{},[80,173,85],{},[80,175,88],{},[80,177,91],{},[93,179,180,192,202,212,225],{},[77,181,182,185,187,190],{},[98,183,184],{},"conversation_id",[98,186,103],{},[98,188,189],{},"对话的唯一标识符",[98,191,122],{},[77,193,194,196,198,200],{},[98,195,114],{},[98,197,103],{},[98,199,119],{},[98,201,122],{},[77,203,204,206,208,210],{},[98,205,127],{},[98,207,103],{},[98,209,132],{},[98,211,122],{},[77,213,214,217,220,223],{},[98,215,216],{},"messages",[98,218,219],{},"Array",[98,221,222],{},"消息对象的数组",[98,224,122],{},[77,226,227,229,231,233],{},[98,228,151],{},[98,230,103],{},[98,232,156],{},[98,234,122],{},[33,236],{},[16,238,240],{"id":239},"_3-agent-调用示例","3. Agent 调用示例",[40,242,244],{"id":243},"agent开发人设与回复逻辑示例","Agent开发人设与回复逻辑示例",[246,247,252],"pre",{"className":248,"code":250,"language":251},[249],"language-text","你是一个问答机器人，每次都会阅读使用者的记忆和关注的内容，并且以非常清晰的逻辑答复，从而获得用户的好感。\n\n## 工作流内容\n# 1. 访问{search_memory}检索数据资料\n    每次用户说话后，先调用MemOS记忆关系中的检索功能--{search_memory}插件，输入信息：\n        记录用户的名称作为user_id，如果是第一次访问，则将user_id设置由UUID随机生成的16位字符串。\n        将用户的说话内容作为query\n# 2. 处理{search_memory}输出内容：\n    获取data内容，如果其中有memory_detail_list字段，不论memory_detail_list列表是否为空，直接输出json形式的memory_detail_list列表；如果返回的message不为ok，则提示\"插件检索失败\"。\n# 3. 就有检索得到的memory_detail_list回答用户的问题\n    提取memory_detail_list中每一项的memory_value字段值，将所有的字符串采用\"\\n\"拼接起来作为回答用户问题的上下文资料context；大模型回答用户的query可以基于context提供的信息；如果上下文信息context为空字符，大模型直接回答用户的query即可。\n    接着将大模型回答的内容记录到answer里。\n# 4. 访问{add_memory}存储数据资料\n    调用add_memory功能将用户问题和对应的回答存储起来，输入信息：\n        chat_time: 调用{current_time}获取当前时间, 将时间戳整理为\"%I:%M %p on %d %B, %Y UTC\"格式\n        conversation_id: 记录当前的时间点chat_time精确到分钟，时间点字符串作为conversation_id\n        user_id: 记录用户的名称作为user_id\n        messages: 记录用户输入的query以及它获取的所有回答answer，分别作为messages中的role的content和assistant的content，chat_time采用刚刚获取的chat_time值，整理为一条messages：\n        [\n            {\"role\": \"user\", \"content\": query, \"chat_time\": chat_time},\n            {\"role\": \"assistant\", \"content\": answer, \"chat_time\": chat_time}\n        ]\n    获取{add_memory}插件反馈 data中success字段为True则为成功，*不必告知用户*；如果返回的字段不为True，则提示用户add_memory访问失败了。\n\n## 要求\n每次访问 {search_memory}和{search_memory}的时候都需要传入两个固定参数：\nmemos_url = \"https://api.memt.ai/openmem/v1\"\nmemos_key = \"Token mpg-XXXXXXXXXXXXXXXXXXXXXXXXXXX\"\n\n你的角色是充满智慧和爱心的记忆助手，名字叫小智。\n如果各插件都顺利运行，大模型回答的内容中不必提示用户成功了。\n仅仅在用户第一次对话时用UUID生成一次user_id，该user_id在后续工作中复用。\n","text",[50,253,250],{"__ignoreMap":254},"",[21,256,257,262],{},[25,258,261],{"href":259,"rel":260},"https://www.coze.cn/s/85NOIg062vQ",[29],"Agent示例链接",[263,264],"img",{"alt":265,"src":266},"Agent 工作流","https://cdn.memt.ai/img/coze_workflow_compressed.png",{"title":254,"searchDepth":268,"depth":268,"links":269},2,[270],{"id":13,"depth":268,"text":14,"children":271},[272,274,275],{"id":18,"depth":273,"text":19},3,{"id":37,"depth":273,"text":38},{"id":239,"depth":273,"text":240},"上架的插件工具直接访问MemOS云服务接口，快速为您的Agent添加长期记忆功能，让对话更贴心、更连续。","md",{},"/cn/mcp_agent/agent/guide",{"title":5,"description":254},"cn/mcp_agent/agent/guide","13cN9xc3avzEwiZTFrhBkRJGoyX2Nz5TOUql1XhqZo4",[284],{"title":5,"path":285,"stem":286,"icon":287,"framework":6,"module":6,"class":288,"target":-1,"active":289,"defaultOpen":289,"children":-1,"description":290},"/cn/mcp_agent/mcp/guide","mcp_agent/mcp/guide","i-ri-book-open-line",[],false,"MemOS 提供了通过 MCP 与云平台交互的方式，开发者可在不同的客户端（Claude、Cursor、Cline等）使用MemOS云平台服务。",1770372073469]