[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-openclaw-multi-agent-deployment-app-platform-zh":3,"tags-openclaw-multi-agent-deployment-app-platform-zh":33,"related-lang-openclaw-multi-agent-deployment-app-platform-zh":47,"related-posts-openclaw-multi-agent-deployment-app-platform-zh":51,"series-ai-agent-72fb07e7-8c8d-4dd0-aeac-304b03cd0493":88},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":21,"translated_content":10,"views":22,"is_premium":23,"created_at":24,"updated_at":24,"cover_image":11,"published_at":25,"rewrite_status":26,"rewrite_error":10,"rewritten_from_id":27,"slug":28,"category":29,"related_article_id":30,"status":31,"google_indexed_at":32,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":10,"topic_cluster_id":10,"embedding":10,"is_canonical_seed":23},"72fb07e7-8c8d-4dd0-aeac-304b03cd0493","OpenClaw 多智能体上雲更省事","\u003Cp>\u003Ca href=\"\u002Fnews\u002Fopenclaw-ai-worker-privacy-security-costs-zh\">Open\u003C\u002Fa>Claw 已經不是只拿來做 demo 的框架。它能接 Slack、微信、飛書，還能把多個智能體放在同一套流程裡跑。講白了，重點早就不是「能不能做出助手」，而是「能不能穩穩活著」。\u003C\u002Fp>\u003Cp>DigitalOcean 這次把 \u003Ca href=\"https:\u002F\u002Fopenclaw.ai\" target=\"_blank\" rel=\"noopener\">OpenClaw\u003C\u002Fa> 直接放到 \u003Ca href=\"https:\u002F\u002Fwww.digitalocean.com\u002Fproducts\u002Fapp-platform\" target=\"_blank\" rel=\"noopener\">DigitalOcean App Platform\u003C\u002Fa> 上跑。這件事看起來很務實。它把容器、網路、擴容、部署流程，全部收進同一個托管環境。\u003C\u002Fp>\u003Cp>如果你只在本機玩過一個聊天機器人，可能感受不深。但一旦它開始接真實訊息、呼叫 API、保存狀態，麻煩就會冒出來。這時候，部署方式比模型名字還重要。\u003C\u002Fp>\u003Ch2>從能跑，變成能長跑\u003C\u002Fh2>\u003Cp>\u003Ca href=\"\u002Fnews\u002Fopenclaw-13-chinese-tech-giants-race-zh\">Open\u003C\u002Fa>Claw 的賣點很直接。它把智能體、訊息渠道、模型選擇做成可配置項。開發者不用從零拼一套系統，就能先把助手跑起來。這對個人開發者很友善，對小團隊也省很多時間。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775057607855-cg82.png\" alt=\"OpenClaw 多智能体上雲更省事\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>但開源專案常見的問題也很現實。demo 階段很順，正式上線就開始卡。重啟會掉狀態，升級會中斷，權限會亂，日誌也常常不夠看。說真的，很多 AI 專案不是死在模型，而是死在維運。\u003C\u002Fp>\u003Cp>DigitalOcean 的做法，是把 \u003Ca href=\"\u002Fnews\u002Fopenclaw-3-28-approval-gates-updates-zh\">Open\u003C\u002Fa>Claw 放進 App Platform。這樣一來，服務可以常駐，部署可以走 Git 流程，容器也能比較乾淨地更新。你不用一直手動 SSH 進去修東修西，這點真的省事很多。\u003C\u002Fp>\u003Cul>\u003Cli>支援 Slack、微信、飛書等訊息平台\u003C\u002Fli>\u003Cli>App Platform 負責容器、網路、日誌與擴容\u003C\u002Fli>\u003Cli>可用 Git 驅動更新，減少停機時間\u003C\u002Fli>\u003Cli>狀態可同步到 \u003Ca href=\"https:\u002F\u002Fwww.digitalocean.com\u002Fproducts\u002Fspaces\" target=\"_blank\" rel=\"noopener\">DigitalOcean Spaces\u003C\u002Fa>\u003C\u002Fli>\u003Cli>可用私有 worker 模式，不必暴露公網入口\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>多智能體為什麼更難管\u003C\u002Fh2>\u003Cp>單一助手已經不輕鬆了。多智能體會把問題放大。你可能有客服智能體、銷售智能體、個人助理智能體，甚至是家庭自動化智能體。每個角色都不同，權限也不同。\u003C\u002Fp>\u003Cp>如果你把這些全丟在本機，系統很容易變脆。重開一次，記憶就不見。加一個渠道，設定就亂。想多跑一個智能體，架構又要重拉。這種東西看起來很自由，實際上很折騰。\u003C\u002Fp>\u003Cp>OpenClaw 在 App Platform 上的思路，是把這些智能體放進同一套部署模型。你用設定管理它們，而不是手工拼伺服器。這種方式雖然沒那麼炫，但對長期運作很重要。\u003C\u002Fp>\u003Cblockquote>“The future of AI is not about replacing humans, it's about augmenting human capabilities.” — \u003Ca href=\"https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fpeople\u002Fjohndo\u002F\" target=\"_blank\" rel=\"noopener\">Jordi Ribas\u003C\u002Fa>\u003C\u002Fblockquote>\u003Cp>這句話放在這裡很合適。多智能體不是要把人全換掉。它比較像把不同任務拆開，交給不同助手處理。有人管訊息，有人管資料，有人管流程，分工會清楚很多。\u003C\u002Fp>\u003Cp>我覺得這才是多智能體真正的價值。不是「一個模型什麼都能做」，而是「一組助手各做各的」。\u003C\u002Fp>\u003Ch2>App Platform 解掉哪些硬問題\u003C\u002Fh2>\u003Cp>DigitalOcean 這套方案最實際的地方，不是宣傳詞，而是幾個很土但很重要的點。第一，服務要能一直在線。第二，更新不要老是中斷。第三，成本要能先估出來。第四，網路暴露要能收斂。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775057629375-cq1t.png\" alt=\"OpenClaw 多智能体上雲更省事\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>App Platform 是以實例為單位算資源。這對常駐型 AI 助手很友善。你加一個智能體，或把容量拉高，成本怎麼變，通常比較好抓。這比某些按請求計費的平台更容易做預算。\u003C\u002Fp>\u003Cp>還有一個很關鍵的地方是狀態。OpenClaw 的設定、會話、記憶，可以同步到 \u003Ca href=\"https:\u002F\u002Fwww.digitalocean.com\u002Fproducts\u002Fspaces\" target=\"_blank\" rel=\"noopener\">DigitalOcean Spaces\u003C\u002Fa>。容器可以換，資料不用跟著消失。這種切法很合理。\u003C\u002Fp>\u003Cul>\u003Cli>以實例為單位擴容，成本較好預估\u003C\u002Fli>\u003Cli>可用 Git push 更新部署\u003C\u002Fli>\u003Cli>後台 worker 模式預設沒有公網 URL\u003C\u002Fli>\u003Cli>Web UI 可透過 \u003Ca href=\"https:\u002F\u002Ftailscale.com\" target=\"_blank\" rel=\"noopener\">Tailscale\u003C\u002Fa> 私有存取\u003C\u002Fli>\u003Cli>也能用 \u003Ca href=\"https:\u002F\u002Fdocs.digitalocean.com\u002Freference\u002Fdoctl\u002F\" target=\"_blank\" rel=\"noopener\">doctl\u003C\u002Fa> 管理部署與日誌\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>安全與存取，這次處理得比較像樣\u003C\u002Fh2>\u003Cp>AI 助手一旦接上真實業務，安全就不能只靠「先測試看看」。OpenClaw 在 App Platform 上的預設設計偏私有。後台 worker 不需要公開入口，也不必把入站埠口開給外面。\u003C\u002Fp>\u003Cp>這種設計很適合常駐服務。少一個公網入口，就少一個出包點。少一點手動改設定，就少一點環境漂移。對要長期跑的智能體來說，這些細節很值錢。\u003C\u002Fp>\u003Cp>文章裡把部署分成兩種模式，也很貼近現實。你要看 Web UI，就用 Tailscale。你只想跑訊息流，就用無頭模式。前者適合除錯，後者適合自動化。兩種都很實用。\u003C\u002Fp>\u003Cul>\u003Cli>Tailscale 模式提供私有 Web UI\u003C\u002Fli>\u003Cli>無頭模式沒有入站端口\u003C\u002Fli>\u003Cli>兩種模式都可同步狀態到 Spaces\u003C\u002Fli>\u003Cli>可從 GitHub 模板一鍵部署\u003C\u002Fli>\u003Cli>也能直接從 Git 倉庫部署\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>和其他平台比，差在哪\u003C\u002Fh2>\u003Cp>如果拿來跟一般雲端平台比，App Platform 的優勢在簡化。你不用自己管太多 VM，也不用每次都手動處理反向代理和部署腳本。對很多開發者來說，這比功能多還重要。\u003C\u002Fp>\u003Cp>如果拿來跟超大雲服務比，它的定位又更清楚。它不是要把一切都做滿，而是把常見的部署需求收好。對 OpenClaw 這種要長跑的 AI 助手，這種定位反而剛好。\u003C\u002Fp>\u003Cp>如果拿來跟本機或單機 Docker 比，差距更明顯。本機很快，但不穩。Docker 很彈性，但你還是得自己顧網路、更新、監控和資料持久化。App Platform 則把這些事情包起來，讓你少踩坑。\u003C\u002Fp>\u003Cul>\u003Cli>比本機部署更穩定\u003C\u002Fli>\u003Cli>比手動 Docker 維運更省事\u003C\u002Fli>\u003Cli>比大型雲服務更容易上手\u003C\u002Fli>\u003Cli>比臨時腳本更適合常駐 AI 助手\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>這類方案代表什麼\u003C\u002Fh2>\u003Cp>我覺得這背後其實是一個很明確的趨勢。AI 助手正在從「玩具」變成「服務」。一旦變成服務，部署、監控、權限、備份、擴容，就會比模型參數更重要。\u003C\u002Fp>\u003Cp>這也是為什麼 OpenClaw 這種框架值得看。它不只在講智能體怎麼互動，也在講它們怎麼活下去。對開發者來說，這比單純秀 prompt 更接近真實工作。\u003C\u002Fp>\u003Cp>更現實一點說，很多團隊其實不缺模型 API。缺的是一套能長期跑的應用層。OpenClaw 上 App Platform，就是在補這一塊。\u003C\u002Fp>\u003Cp>如果你的 OpenClaw 目前還在 demo 階段，先別急著搬上雲。但如果它已經開始接真實訊息，甚至準備跑第二個智能體，那就該認真看部署了。下一步不只是加功能，而是把職責、權限、狀態切清楚。\u003C\u002Fp>\u003Cp>我會直接給一個判斷：當一個助手開始需要 24 小時在線時，雲端托管就不是加分項，而是基本盤。你現在要問的不是「能不能跑」，而是「出了問題，誰來收拾」。\u003C\u002Fp>","OpenClaw 可直接跑在 DigitalOcean App Platform，上雲後支援多智能體、私有網路與較好預估的成本，適合常駐 AI 助手。","zhuanlan.zhihu.com","https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F2020915366835044740",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775057607855-cg82.png",[13,14,15,16,17,18,19,20],"OpenClaw","DigitalOcean App Platform","多智能體","AI 助手","雲端部署","私有網路","Spaces","Tailscale","zh",1,false,"2026-04-01T09:51:37.270953+00:00","2026-04-01T09:51:37.068+00:00","done","1a446d78-a6ac-47f4-b485-47987bd6da11","openclaw-multi-agent-deployment-app-platform-zh","ai-agent","dab40f99-9820-4325-9322-d6dbc3097372","published","2026-04-09T09:00:54.636+00:00",[34,35,37,39,41,43,44,46],{"name":18,"slug":18},{"name":19,"slug":36},"spaces",{"name":14,"slug":38},"digitalocean-app-platform",{"name":20,"slug":40},"tailscale",{"name":13,"slug":42},"openclaw",{"name":17,"slug":17},{"name":16,"slug":45},"ai-助手",{"name":15,"slug":15},{"id":30,"slug":48,"title":49,"language":50},"openclaw-multi-agent-deployment-app-platform-en","OpenClaw 多智能体上云，运维更轻了","en",[52,58,64,70,76,82],{"id":53,"slug":54,"title":55,"cover_image":56,"image_url":56,"created_at":57,"category":29},"e7874ed9-592f-4e06-b7b7-ab733fe779db","claude-agent-dreaming-outcomes-multiagent-zh","Claude 幫 Agent 加了做夢功能","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778868642412-7woy.png","2026-05-15T18:10:24.427608+00:00",{"id":59,"slug":60,"title":61,"cover_image":62,"image_url":62,"created_at":63,"category":29},"38406a12-f833-4c69-ae22-99c31f03dd52","switch-ai-outputs-markdown-to-html-zh","怎麼把 AI 輸出改成 HTML","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778743243861-8901.png","2026-05-14T07:20:21.545364+00:00",{"id":65,"slug":66,"title":67,"cover_image":68,"image_url":68,"created_at":69,"category":29},"c7c69fe4-97e3-4edf-a9d6-a79d0c4495b4","anthropic-cat-wu-proactive-ai-assistants-zh","Cat Wu 談 Claude 的主動式 AI","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778735455993-gnw7.png","2026-05-14T05:10:30.453046+00:00",{"id":71,"slug":72,"title":73,"cover_image":74,"image_url":74,"created_at":75,"category":29},"e1d6acda-fa49-4514-aa75-709504be9f93","how-to-run-hermes-agent-on-discord-zh","如何在 Discord 執行 Hermes Agent","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778724655796-cjul.png","2026-05-14T02:10:34.362605+00:00",{"id":77,"slug":78,"title":79,"cover_image":80,"image_url":80,"created_at":81,"category":29},"4104fa5f-d95f-45c5-9032-99416cf0365c","why-ragflow-is-the-right-open-source-rag-engine-to-self-host-zh","為什麼 RAGFlow 是最適合自架的開源 RAG 引擎","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778674262278-1630.png","2026-05-13T12:10:23.762632+00:00",{"id":83,"slug":84,"title":85,"cover_image":86,"image_url":86,"created_at":87,"category":29},"7095f05c-34f5-469f-a044-2525d2010ce9","how-to-add-temporal-rag-in-production-zh","如何在正式環境加入 Temporal RAG","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778667053844-osvs.png","2026-05-13T10:10:30.930982+00:00",[89,94,99,104,109,114,119,124,129,134],{"id":90,"slug":91,"title":92,"created_at":93},"4ae1e197-1d3d-4233-8733-eafe9cb6438b","claude-now-uses-your-pc-to-finish-tasks-zh","Claude 開始幫你操作電腦","2026-03-26T07:20:48.457387+00:00",{"id":95,"slug":96,"title":97,"created_at":98},"5bede67f-e21c-413d-9ab8-54a3c3d26227","googles-2026-ai-agent-report-decoded-zh","Google 2026 AI Agent 報告解讀","2026-03-26T11:15:22.651956+00:00",{"id":100,"slug":101,"title":102,"created_at":103},"2987d097-563f-46c7-b76f-b558d8ef7c2b","kimi-k25-review-stronger-still-not-legend-zh","Kimi K2.5 評測：更強，但還不是神作","2026-03-27T07:15:55.277513+00:00",{"id":105,"slug":106,"title":107,"created_at":108},"95c9053b-e3f4-4cb5-aace-5c54f4c9e044","claude-code-controls-mac-desktop-zh","Claude Code 也能操控 Mac 了","2026-03-28T03:01:58.58121+00:00",{"id":110,"slug":111,"title":112,"created_at":113},"dc58e153-e3a8-4c06-9b96-1aa64eabbf5f","cloudflare-100x-faster-ai-agent-sandbox-zh","Cloudflare 的 AI 沙箱跑超快","2026-03-28T03:09:44.142236+00:00",{"id":115,"slug":116,"title":117,"created_at":118},"1c8afc56-253f-47a2-979f-1065ff072f2a","openai-backs-isara-agent-swarm-bet-zh","OpenAI 挺 Isara 的 agent swarm …","2026-03-28T03:15:27.513155+00:00",{"id":120,"slug":121,"title":122,"created_at":123},"7379b422-576e-45df-ad5a-d57a0d9dd467","openai-plan-automated-ai-researcher-zh","OpenAI 想做自動化 AI 研究員","2026-03-28T03:17:42.090548+00:00",{"id":125,"slug":126,"title":127,"created_at":128},"48c9889e-86df-450b-a356-e4a4b7c83c5b","harness-engineering-ai-agent-reliability-2026-zh","駕馭工程：從「馬具」到「作業系統」，AI Agent 可靠性的終極密碼","2026-03-31T06:42:53.556721+00:00",{"id":130,"slug":131,"title":132,"created_at":133},"e41546b8-ba9e-455f-9159-88d4614ad711","openai-codex-plugin-claude-code-zh","OpenAI 把 Codex 放進 Claude Code","2026-04-01T09:21:54.687617+00:00",{"id":135,"slug":136,"title":137,"created_at":138},"96d8e8c8-1edd-475d-9145-b1e7a1b02b65","mcp-explained-from-prompts-to-production-zh","MCP 怎麼把提示詞變工作流","2026-04-01T09:24:39.321274+00:00"]