[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-openclaw-multi-agent-deployment-app-platform-en":3,"article-related-openclaw-multi-agent-deployment-app-platform-en":25,"series-ai-agent-dab40f99-9820-4325-9322-d6dbc3097372":70},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":11,"views":22,"created_at":23,"published_at":24,"topic_cluster_id":11},"dab40f99-9820-4325-9322-d6dbc3097372","openclaw-multi-agent-deployment-app-platform-en","OpenClaw 多智能体上云，运维更轻了","\u003Cp>OpenClaw 正在变成很多人搭个人 AI 助手时会先想到的开源框架之一。它能接 Slack、微信、飞书等消息平台，而这篇文章讨论的重点已经不是“能不能做出一个助手”，而是“怎么让它长期跑下去”。\u003C\u002Fp>\u003Cp>DigitalOcean 给出的答案是把 \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> 上运行。这个组合的目标很明确：让单个助手和多智能体系统都能持续在线，同时把服务器维护、网络暴露和扩容这些麻烦事压到更低。\u003C\u002Fp>\u003Cp>如果你只是在本地跑过一个 demo，这种变化可能不明显。但一旦助手开始处理真实消息、调用 API、保存状态、接入多个工作流，运维成本会迅速抬头。OpenClaw 在 App Platform 上的部署方式，正是为这个阶段准备的。\u003C\u002Fp>\u003Ch2>从“能跑”到“能长期跑”\u003C\u002Fh2>\u003Cp>OpenClaw 的吸引力在于它把智能体、消息渠道和模型选择都做成了可配置项，开发者可以很快搭出一个能聊天、能办事的助手。问题在于，很多开源智能体项目停在“演示可用”，真正上线后就开始碰到重启、漂移、升级和权限控制问题。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775057602681-6fgm.png\" alt=\"OpenClaw 多智能体上云，运维更轻了\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>DigitalOcean 这次把 OpenClaw 和 App Platform 组合起来，重点解决的是生产阶段的几个现实问题：服务要一直在线，更新要尽量少中断，状态要能保留，访问要尽量收紧，成本要能提前算清楚。\u003C\u002Fp>\u003Cp>这套思路对 AI 助手尤其重要。助手一旦进了常驻模式，它就不再像一个临时脚本，而更像一个需要被托管的在线服务。你要管的不只是模型输出，还包括容器、端口、网络、日志、备份和扩缩容。\u003C\u002Fp>\u003Cul>\u003Cli>OpenClaw 支持连接 Slack、微信、飞书等消息平台\u003C\u002Fli>\u003Cli>App Platform 负责容器运行、网络和可观测性\u003C\u002Fli>\u003Cli>更新可以通过 Git 驱动，支持零停机发布\u003C\u002Fli>\u003Cli>状态可同步到 \u003Ca href=\"https:\u002F\u002Fwww.digitalocean.com\u002Fproducts\u002Fspaces\" target=\"_blank\" rel=\"noopener\">DigitalOcean Spaces\u003C\u002Fa>\u003C\u002Fli>\u003Cli>部署可选择私有后台 worker 模式，不暴露公网入口\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>为什么多智能体部署会更难\u003C\u002Fh2>\u003Cp>单个助手已经够麻烦了，多智能体会把问题再放大一层。你可能会有客服智能体、销售智能体、个人助理智能体，甚至家庭助手。每个智能体的职责不同，权限不同，依赖的模型和工具也不同。\u003C\u002Fp>\u003Cp>如果这些都放在本地机器上，系统很容易变成“能用但很脆”的状态。重启一次，状态丢了；扩一个智能体，架构要重做；加一个消息渠道，配置又乱了。OpenClaw 在 App Platform 上的做法，是把这些智能体放进同一套部署模型里，用声明式配置管理它们，而不是让你手工拼服务器。\u003C\u002Fp>\u003Cp>这也是为什么文章里反复强调“从单一助手扩展到多个智能体”这件事。真正难的不是写出第二个智能体，而是让第二个、第三个、第四个智能体都能在同一套运维框架下稳定工作。\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>这句话放在 OpenClaw 这里很合适。多智能体系统的价值，不在于把每件事都自动化，而在于把不同任务拆给不同助手，让它们各自处理自己擅长的工作流。\u003C\u002Fp>\u003Ch2>App Platform 带来的几个硬指标\u003C\u002Fh2>\u003Cp>DigitalOcean 在这套方案里最强调的不是“更聪明”，而是“更可控”。它给 OpenClaw 的是容器化运行环境、私有网络、实例级扩展和相对清晰的定价方式。对团队来说，这些比花哨的宣传词更实在。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775057629540-s5kt.png\" alt=\"OpenClaw 多智能体上云，运维更轻了\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>和一些按请求计费、使用量一上来就难以预估的平台相比，App Platform 走的是基于实例的定价。文章里明确提到，团队可以看清楚增加智能体或提升容量时，成本会怎么变化。对于长期常驻的 AI 助手，这种可预测性很重要。\u003C\u002Fp>\u003Cp>另一个细节是状态处理。OpenClaw 的配置、会话和记忆可以通过用户配置同步到 DigitalOcean Spaces。容器本身是可替换的，但助手的状态不必跟着一起消失。这让“临时容器”与“长期记忆”可以分开处理。\u003C\u002Fp>\u003Cul>\u003Cli>App Platform 以实例为单位扩容，成本更容易估算\u003C\u002Fli>\u003Cli>OpenClaw 可通过 Git push 更新镜像，减少停机时间\u003C\u002Fli>\u003Cli>后台 worker 模式默认没有公网 URL\u003C\u002Fli>\u003Cli>Web UI 可通过 \u003Ca href=\"https:\u002F\u002Ftailscale.com\" target=\"_blank\" rel=\"noopener\">Tailscale\u003C\u002Fa> 私有访问\u003C\u002Fli>\u003Cli>状态可选同步到 \u003Ca href=\"https:\u002F\u002Fwww.digitalocean.com\u002Fproducts\u002Fspaces\" target=\"_blank\" rel=\"noopener\">DigitalOcean Spaces\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>安全、访问控制和两种部署方式\u003C\u002Fh2>\u003Cp>AI 助手一旦接入真实业务，安全就不再是附加项。OpenClaw 在 App Platform 上的默认设计是私有的，后台 worker 不暴露公共 URL，也不会开放公网入站端口。访问路径主要有两种：通过 Tailscale 打开 Web UI，或者直接用 \u003Ca href=\"https:\u002F\u002Fdocs.digitalocean.com\u002Freference\u002Fdoctl\u002F\" target=\"_blank\" rel=\"noopener\">doctl\u003C\u002Fa> 进入控制台查看日志和执行命令。\u003C\u002Fp>\u003Cp>这种设计的好处很直接。第一，减少误暴露风险。第二，减少长期运行服务器带来的配置漂移。第三，每次部署都从干净状态开始，更容易控制环境一致性。对于多智能体系统，这些细节会直接影响故障排查速度。\u003C\u002Fp>\u003Cp>文章把部署方式分成两类，也很贴近实际需求。你要 Web 界面，就启用 Tailscale；你只要消息网关，就用无头模式。前者适合需要频繁调试和观察的团队，后者适合更偏自动化、只关心消息流转的场景。\u003C\u002Fp>\u003Cul>\u003Cli>Tailscale 模式提供私有 Web UI 地址，例如 tailnet 域名\u003C\u002Fli>\u003Cli>无头模式没有入站端口，默认更收敛\u003C\u002Fli>\u003Cli>两种模式都支持状态同步到 Spaces\u003C\u002Fli>\u003Cli>OpenClaw 可通过 GitHub 模板仓库一键部署\u003C\u002Fli>\u003Cli>也可以从 Git 仓库用 doctl 部署\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>这套方案适合谁\u003C\u002Fh2>\u003Cp>如果你还在本地调试一个聊天机器人，App Platform 这套方案可能有点重。但如果你的 OpenClaw 已经开始接消息、跑工作流、保存上下文，或者你已经在考虑第二个、第三个智能体，那它就很对路。\u003C\u002Fp>\u003Cp>我更愿意把它看成一个“从实验走向长期运行”的过渡层。它没有强迫你重写 OpenClaw，也没有逼你换一套完全不同的架构，而是把部署、扩展和访问控制这些常见痛点收拢到一个更省心的托管环境里。\u003C\u002Fp>\u003Cp>对个人开发者来说，这意味着你可以把时间花在智能体行为、提示词、工具调用和消息路由上。对小团队来说，这意味着你不用为了一个助手再养一套复杂基础设施。对已经开始做多智能体系统的人来说，这意味着扩容时不必总想着重构。\u003C\u002Fp>\u003Cp>我会给这个方案一个很明确的判断：如果你的 OpenClaw 还停留在 demo 阶段，先别急着上云；如果它已经开始处理真实消息流，那现在就是把它迁到可控托管环境的好时机。接下来真正值得关注的问题不是“能不能扩”，而是“你愿不愿意把每个智能体的职责和权限都切得更清楚”。\u003C\u002Fp>\u003Cp>如果你正在做一个常驻 AI 助手，下一步不妨先问自己一个问题：当第二个智能体上线时，你希望它只是多一段代码，还是一个能独立运维、独立扩容、独立控制权限的服务？\u003C\u002Fp>","OpenClaw 现在可跑在 DigitalOcean App Platform，上云后支持多智能体、私有网络和更可预测的成本。","zhuanlan.zhihu.com","https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F2020915366835044740",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775057602681-6fgm.png","ai-agent","en","72fb07e7-8c8d-4dd0-aeac-304b03cd0493",[17,18,19,20,21],"OpenClaw","DigitalOcean App Platform","多智能体","Tailscale","容器部署",9,"2026-04-01T09:51:37.947376+00:00","2026-04-01T09:51:37.849+00:00",{"tags":26,"relatedLang":29,"relatedPosts":33},[27],{"name":17,"slug":28},"openclaw",{"id":15,"slug":30,"title":31,"language":32},"openclaw-multi-agent-deployment-app-platform-zh","OpenClaw 多智能体上雲更省事","zh",[34,40,46,52,58,64],{"id":35,"slug":36,"title":37,"cover_image":38,"image_url":38,"created_at":39,"category":13},"697af300-a6ed-47c9-93cc-4c3227a4d862","llm-wikis-beat-raw-rag-knowledge-work-en","LLM wikis beat raw RAG for real knowledge work","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782760670241-gdea.png","2026-06-29T19:17:21.2178+00:00",{"id":41,"slug":42,"title":43,"cover_image":44,"image_url":44,"created_at":45,"category":13},"6c32d3c9-f5b9-4f47-8786-b6e8efd2660a","mcps-new-primitives-make-agent-middleware-obsolete-en","MCP’s new primitives make agent middleware obsolete","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782748973197-wvm6.png","2026-06-29T16:02:25.212097+00:00",{"id":47,"slug":48,"title":49,"cover_image":50,"image_url":50,"created_at":51,"category":13},"8c46d754-431a-4c64-a11d-d1978ee1d948","mcp-servers-ai-workflows-explained-en","MCP servers turn AI tools into connected workflows","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782747182218-n3ml.png","2026-06-29T15:32:33.962535+00:00",{"id":53,"slug":54,"title":55,"cover_image":56,"image_url":56,"created_at":57,"category":13},"d6956b2a-b5fb-44f5-b316-9b6dddb3ca47","openmontage-open-source-ai-video-production-en","OpenMontage proves open-source should own AI video production","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782685070172-081n.png","2026-06-28T22:17:23.291322+00:00",{"id":59,"slug":60,"title":61,"cover_image":62,"image_url":62,"created_at":63,"category":13},"38c66c8f-d8b7-493b-9816-8f03cd180db9","gemini-35-flash-computer-use-safeguards-en","Gemini 3.5 Flash lets you script computer use","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782681499817-t4xj.png","2026-06-28T21:17:57.418998+00:00",{"id":65,"slug":66,"title":67,"cover_image":68,"image_url":68,"created_at":69,"category":13},"936eafb7-dbcb-42e4-9b4c-0abc46a58ca7","design-md-bridge-taste-to-ui-scaffolds-en","DESIGN.md is the missing bridge from taste to UI scaffolds","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782586070432-887m.png","2026-06-27T18:47:24.354308+00:00",[71,76,81,86,91,96,101,106,111,116],{"id":72,"slug":73,"title":74,"created_at":75},"03db8de8-8dc2-4ac1-9cf7-898782efbb1f","anthropic-claude-ai-agent-task-automation-en","Anthropic's Claude AI Agent: A New Era of Task Automation","2026-03-25T16:25:06.513026+00:00",{"id":77,"slug":78,"title":79,"created_at":80},"045d1abc-190d-4594-8c95-91e2a26f0c5a","googles-2026-ai-agent-report-decoded-en","Google’s 2026 AI Agent Report, Decoded","2026-03-26T11:15:23.046616+00:00",{"id":82,"slug":83,"title":84,"created_at":85},"e64aba21-254b-4f93-aa21-837484bb52ec","kimi-k25-review-stronger-still-not-legend-en","Kimi K2.5 review: stronger, still not a legend","2026-03-27T07:15:55.385951+00:00",{"id":87,"slug":88,"title":89,"created_at":90},"30dfb781-a1b2-4add-aebe-b3df40247c37","claude-code-controls-mac-desktop-en","Claude Code now controls your Mac desktop","2026-03-28T03:01:59.384091+00:00",{"id":92,"slug":93,"title":94,"created_at":95},"254405b6-7833-4800-8e13-f5196deefbe6","cloudflare-100x-faster-ai-agent-sandbox-en","Cloudflare’s 100x Faster AI Agent Sandbox","2026-03-28T03:09:44.356437+00:00",{"id":97,"slug":98,"title":99,"created_at":100},"04f29b7f-9b91-4306-89a7-97d725e6e1ba","openai-backs-isara-agent-swarm-bet-en","OpenAI backs Isara’s agent-swarm bet","2026-03-28T03:15:27.849766+00:00",{"id":102,"slug":103,"title":104,"created_at":105},"3b0bf479-e4ae-4703-9666-721a7e0cdb91","openai-plan-automated-ai-researcher-en","OpenAI’s plan for an automated AI researcher","2026-03-28T03:17:42.312819+00:00",{"id":107,"slug":108,"title":109,"created_at":110},"fe91bce0-b85d-4efa-a207-24ae9939c29f","harness-engineering-ai-agent-reliability-2026","Harness Engineering: From Bridle to Operating System, The Missing Link in AI Agent Reliability","2026-03-31T06:36:55.648751+00:00",{"id":112,"slug":113,"title":114,"created_at":115},"7a09007d-820f-43b3-8607-8ad1bfcb94c8","mcp-explained-from-prompts-to-production-en","MCP Explained: From Prompts to Production","2026-04-01T09:24:40.089177+00:00",{"id":117,"slug":118,"title":119,"created_at":120},"116d5ee9-a4f1-4b5a-aac5-5d035dd22bbe","amazon-bedrock-agents-multi-agent-workflows-en","Amazon Bedrock Agents Gets Multi-Agent Workflows","2026-04-01T09:30:30.197685+00:00"]