Why I Stopped Using n8n in 2026
Summary
In this video, the hosts explain why they stopped using n8n — a popular visual workflow automation tool — after over a year of use, in favor of running automations through OpenClaw skills and Claude Code. You get an honest, experience-based breakdown of what n8n does well and where it falls short in a real production environment. n8n excels at mapping out automated pipelines visually. You can see every step, connect different AI models like Gemini, schedule cron jobs, and get clear visibility into past executions — including exactly where and why something failed. It's also cost-efficient because it only calls AI when a specific step requires it, meaning a failure early in the chain saves you money on downstream steps. But here's where the hosts ran into trouble: maintaining those pipelines over time became a serious time sink. Whenever something changed — a database schema, an API endpoint, a workflow dependency — the whole pipeline could break, and debugging it manually was a multi-hour ordeal. With complex workflows involving many interconnected nodes, a single small change could cascade into hours of troubleshooting. There's no way to tell an AI 'go fix all of this' the way you can with Claude Code. That's the core reason they switched. With Claude Code managing OpenClaw skills, you can describe a change in plain language and have it update, test, and validate everything across your entire skill set at once. Yes, AI-driven skills carry some unpredictability — sometimes they behave unexpectedly — but the time saved on setup, maintenance, and debugging far outweighs the occasional inconsistency. The hosts are clear that n8n still makes sense for you if your workflows are stable, non-dynamic, and unlikely to change. It offers webhooks, API creation, scheduled triggers, and strong visual oversight. But if you're constantly iterating, building on top of other skills, or working in a fast-moving environment, the maintenance overhead of n8n starts to cost more in time than it saves in compute costs.
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