[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-openclaw-headless-mode-ollama-en":3,"article-related-openclaw-headless-mode-ollama-en":31,"series-tools-3c939d79-8282-4818-aa82-af78d041658c":84},{"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":23,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"3c939d79-8282-4818-aa82-af78d041658c","openclaw-headless-mode-ollama-en","OpenClaw can now run headless in Ollama","\u003Cp data-speakable=\"summary\">Ollama now lets \u003Ca href=\"\u002Ftag\u002Fopenclaw\">OpenClaw\u003C\u002Fa> run headless for scripts, \u003Ca href=\"\u002Ftag\u002Fdocker\">Docker\u003C\u002Fa>, and CI jobs.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fdocs.ollama.com\u002Fintegrations\u002Fopenclaw\" target=\"_blank\" rel=\"noopener\">OpenClaw\u003C\u002Fa> is now part of \u003Ca href=\"https:\u002F\u002Follama.com\" target=\"_blank\" rel=\"noopener\">Ollama\u003C\u002Fa>’s assistant lineup, and the new headless mode makes it easy to launch without a prompt loop. The key command is short: \u003Ccode>ollama launch openclaw --model kimi-k2.5:cloud --yes\u003C\u002Fcode>, which auto-pulls the model and skips the interactive selectors.\u003C\u002Fp>\u003Cp>That matters because OpenClaw is not a toy demo. It connects messaging apps like WhatsApp, Telegram, Slack, Discord, and iMessage to \u003Ca href=\"\u002Ftag\u002Fai-coding\">AI coding\u003C\u002Fa> agents through a central gateway, so the difference between “works in a terminal” and “works in automation” is a real deployment issue.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Item\u003C\u002Fth>\u003Cth>Detail\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Headless command\u003C\u002Ftd>\u003Ctd>\u003Ccode>ollama launch openclaw --model kimi-k2.5:cloud --yes\u003C\u002Fcode>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Required flag\u003C\u002Ftd>\u003Ctd>\u003Ccode>--yes\u003C\u002Fcode> skips selectors and confirms prompts\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Model requirement\u003C\u002Ftd>\u003Ctd>\u003Ccode>--model\u003C\u002Fcode> must be specified in non-interactive mode\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Recommended local context\u003C\u002Ftd>\u003Ctd>At least 64k tokens\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Local model example\u003C\u002Ftd>\u003Ctd>\u003Ccode>gemma4\u003C\u002Fcode> at about 16 GB VRAM\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Local model example\u003C\u002Ftd>\u003Ctd>\u003Ccode>qwen3.5\u003C\u002Fcode> at about 11 GB VRAM\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>What Ollama changed for OpenClaw\u003C\u002Fh2>\u003Cp>OpenClaw used to be something you launched, configured, and then babysat through a few setup screens. Ollama now wraps that flow so it can install OpenClaw if needed, show the security notice on first launch, pick a model, set the provider, install the gateway daemon, and start the bundled web search provider.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779411951742-c423.png\" alt=\"OpenClaw can now run headless in Ollama\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The practical gain is simple: fewer manual steps before the assistant is usable. That matters in environments where you want a repeatable launch path, whether you are testing locally or wiring the tool into a larger automation stack.\u003C\u002Fp>\u003Cul>\u003Cli>Install happens automatically if OpenClaw is missing.\u003C\u002Fli>\u003Cli>The first launch shows a security notice about tool access.\u003C\u002Fli>\u003Cli>Ollama sets the selected model as primary and starts the gateway in the background.\u003C\u002Fli>\u003Cli>The bundled web search provider turns on during an Ollama launch.\u003C\u002Fli>\u003Cli>OpenClaw now has a headless path for Docker, CI\u002FCD, and scripts.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Why headless mode is the useful part\u003C\u002Fh2>\u003Cp>The new non-interactive mode is the feature that turns OpenClaw from a desktop assistant into something you can actually automate. If you are building a container image, running a CI job, or scripting a workflow, prompts and selectors get in the way fast.\u003C\u002Fp>\u003Cp>Ollama’s docs are explicit about the tradeoff: \u003Ccode>--yes\u003C\u002Fcode> auto-pulls the model, skips selectors, and requires \u003Ccode>--model\u003C\u002Fcode>. That combination removes the last bits of human input from the startup path, which is exactly what automation needs.\u003C\u002Fp>\u003Cblockquote>“The \u003Ccode>--yes\u003C\u002Fcode> flag auto-pulls the model, skips selectors, and requires \u003Ccode>--model\u003C\u002Fcode> to be specified.”\u003C\u002Fblockquote>\u003Cp>That line from the \u003Ca href=\"https:\u002F\u002Fdocs.ollama.com\u002Fintegrations\u002Fopenclaw\" target=\"_blank\" rel=\"noopener\">Ollama documentation\u003C\u002Fa> is the whole story in one sentence. The feature is not about making OpenClaw fancier; it is about making it predictable.\u003C\u002Fp>\u003Ch2>Model choice now matters more than the launcher\u003C\u002Fh2>\u003Cp>Ollama also uses the launch flow to steer users toward specific models. The docs group them into cloud and local options, and the recommendations are practical rather than abstract: pick a model that matches your hardware and the kind of \u003Ca href=\"\u002Ftag\u002Fagent\">agent\u003C\u002Fa> work you want.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779411953068-d7ev.png\" alt=\"OpenClaw can now run headless in Ollama\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>For cloud-backed runs, \u003Ca href=\"https:\u002F\u002Follama.com\u002Fsearch?q=kimi-k2.5\" target=\"_blank\" rel=\"noopener\">kimi-k2.5:cloud\u003C\u002Fa> is described as multimodal reasoning with subagents. \u003Ca href=\"https:\u002F\u002Follama.com\u002Fsearch?q=qwen3.5\" target=\"_blank\" rel=\"noopener\">qwen3.5:cloud\u003C\u002Fa> is aimed at reasoning, coding, and agentic tool use with vision. On the local side, \u003Ca href=\"https:\u002F\u002Follama.com\u002Fsearch?q=gemma4\" target=\"_blank\" rel=\"noopener\">gemma4\u003C\u002Fa> needs about 16 GB of VRAM, while \u003Ca href=\"https:\u002F\u002Follama.com\u002Fsearch?q=qwen3.5\" target=\"_blank\" rel=\"noopener\">qwen3.5\u003C\u002Fa> is listed at about 11 GB VRAM.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Ccode>kimi-k2.5:cloud\u003C\u002Fcode> is the default-looking choice for multimodal agent work.\u003C\u002Fli>\u003Cli>\u003Ccode>qwen3.5:cloud\u003C\u002Fcode> targets reasoning, coding, and vision.\u003C\u002Fli>\u003Cli>\u003Ccode>glm-5.1:cloud\u003C\u002Fcode> focuses on reasoning and code generation.\u003C\u002Fli>\u003Cli>\u003Ccode>minimax-m2.7:cloud\u003C\u002Fcode> is pitched as fast and efficient for productivity tasks.\u003C\u002Fli>\u003Cli>Local runs need enough memory and a context window of at least 64k tokens.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>How this fits into the older Clawdbot path\u003C\u002Fh2>\u003Cp>OpenClaw was previously known as Clawdbot, and Ollama still keeps that alias alive. The old command \u003Ccode>ollama launch clawdbot\u003C\u002Fcode> still works, which is a nice touch for anyone with scripts or notes that predate the rename.\u003C\u002Fp>\u003Cp>There is also a manual path for people who want to tweak settings without starting the gateway and TUI. You can run \u003Ccode>ollama launch openclaw --config\u003C\u002Fcode> to change the model, or use \u003Ccode>openclaw configure --section web\u003C\u002Fcode> to adjust web search behavior directly. For channel integrations, \u003Ccode>openclaw configure --section channels\u003C\u002Fcode> links WhatsApp, Telegram, Slack, Discord, or iMessage.\u003C\u002Fp>\u003Cp>That split between launcher and configuration is smart. It keeps the common path short while still giving power users a way to manage the parts that matter most: model selection, search, and messaging connections.\u003C\u002Fp>\u003Ch2>What developers should do next\u003C\u002Fh2>\u003Cp>If you want OpenClaw in automation, the next step is straightforward: test the headless command with the model you actually plan to use, then verify that the gateway starts cleanly and the model loads without prompts. If you are using local models, check memory headroom and context size before you bake the setup into a container or CI job.\u003C\u002Fp>\u003Cp>The real question is whether your workflow needs a chat assistant or an agent gateway. If you need the second one, Ollama’s headless OpenClaw launch finally gives you a command that fits scripts instead of fighting them.\u003C\u002Fp>\u003Cp>For related reading, see \u003Ca href=\"\u002Fnews\u002Follama-web-search-update\">Ollama web search updates\u003C\u002Fa> and \u003Ca href=\"\u002Fnews\u002Follama-cli-reference\">Ollama CLI reference notes\u003C\u002Fa>.\u003C\u002Fp>","Ollama added headless OpenClaw runs for Docker, CI\u002FCD, and scripts with a single --yes flag and a required model choice.","docs.ollama.com","https:\u002F\u002Fdocs.ollama.com\u002Fintegrations\u002Fopenclaw",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779411951742-c423.png","tools","en","3a2cfa8e-b508-4a86-b2fc-23a1d5f72cb1",[17,18,19,20,21,22],"OpenClaw","Ollama","headless mode","CI\u002FCD","Docker","AI assistant",[24,25,26],"OpenClaw can now start without prompts using `--yes` and `--model`.","The launch flow now covers install, security notice, model setup, gateway startup, and web search.","Local use needs more memory, with Ollama recommending at least 64k tokens for context.",5,"2026-05-22T01:05:29.719456+00:00","2026-05-22T01:05:29.71+00:00","a7343b93-37cc-4634-a2bc-707f6275bdb6",{"tags":32,"relatedLang":43,"relatedPosts":47},[33,35,37,39,41],{"name":19,"slug":34},"headless-mode",{"name":18,"slug":36},"ollama",{"name":17,"slug":38},"openclaw",{"name":21,"slug":40},"docker",{"name":20,"slug":42},"cicd",{"id":15,"slug":44,"title":45,"language":46},"openclaw-headless-mode-ollama-zh","Ollama 讓 OpenClaw 可無頭執行","zh",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"1e0d71a2-19ae-44f4-970b-d27f77ad5a8a","nvidia-lg-ai-collaboration-playbook-en","Nvidia and LG turn AI plans into a playbook","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781056992194-i3tx.png","2026-06-10T02:02:46.922181+00:00",{"id":55,"slug":56,"title":57,"cover_image":58,"image_url":58,"created_at":59,"category":13},"9db77f6f-0d31-4686-86d9-16eb9615633d","ollama-best-free-ai-path-2026-en","Ollama is the best free AI path in 2026 for real work","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781056075632-qzpq.png","2026-06-10T01:47:25.10989+00:00",{"id":61,"slug":62,"title":63,"cover_image":64,"image_url":64,"created_at":65,"category":13},"c12c0470-eb29-4e44-872d-c133a84a1bc8","awesome-production-ml-turns-chaos-into-stack-en","This MLOps list turns chaos into a stack","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781055237524-86fa.png","2026-06-10T01:33:15.495884+00:00",{"id":67,"slug":68,"title":69,"cover_image":70,"image_url":70,"created_at":71,"category":13},"58924f21-83f4-405d-8d9a-4af334e9d030","bentoml-turns-model-serving-into-python-apis-en","BentoML turns model serving into Python APIs","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781054304942-bxxs.png","2026-06-10T01:17:56.721066+00:00",{"id":73,"slug":74,"title":75,"cover_image":76,"image_url":76,"created_at":77,"category":13},"aa96e422-2b01-4480-b4ce-a646be8e0993","magenta-realtime-2-score-inside-daw-en","Magenta RealTime 2 lets you score in the DAW","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781046208039-ksdz.png","2026-06-09T23:02:56.428086+00:00",{"id":79,"slug":80,"title":81,"cover_image":82,"image_url":82,"created_at":83,"category":13},"c79bca38-50b2-4d80-9a48-7f4d1afd051a","open-source-ai-tools-beat-claude-paid-tiers-en","Open-source AI tools beat Claude’s paid tiers on value","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781045269190-a1ow.png","2026-06-09T22:47:20.7972+00:00",[85,90,95,100,105,110,115,120,125,130],{"id":86,"slug":87,"title":88,"created_at":89},"8008f1a9-7a00-4bad-88c9-3eedc9c6b4b1","surepath-ai-mcp-policy-controls-en","SurePath AI's New MCP Policy Controls Enhance AI Security","2026-03-26T01:26:52.222015+00:00",{"id":91,"slug":92,"title":93,"created_at":94},"27e39a8f-b65d-4f7b-a875-859e2b210156","mcp-standard-ai-tools-2026-en","MCP Standard in 2026: Integrating AI Tools","2026-03-26T01:27:43.127519+00:00",{"id":96,"slug":97,"title":98,"created_at":99},"165f9a19-c92d-46ba-b3f0-7125f662921d","rag-2026-transforming-enterprise-ai-en","How RAG in 2026 is Transforming Enterprise AI","2026-03-26T01:28:11.485236+00:00",{"id":101,"slug":102,"title":103,"created_at":104},"6a2a8e6e-b956-49d8-be12-cc47bdc132b2","mastering-ai-prompts-2026-guide-en","Mastering AI Prompts: A 2026 Guide for Developers","2026-03-26T01:29:07.835148+00:00",{"id":106,"slug":107,"title":108,"created_at":109},"3ab2c67e-4664-4c67-a013-687a2f605814","garry-tan-open-sources-claude-code-toolkit-en","Garry Tan Open-Sources a Claude Code Toolkit","2026-03-26T08:26:20.245934+00:00",{"id":111,"slug":112,"title":113,"created_at":114},"66a7cbf8-7e76-41d4-9bbf-eaca9761bf69","github-ai-projects-to-watch-in-2026-en","20 GitHub AI Projects to Watch in 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