Mastering n8n
If Directus is the first thing I stand up on a new project, n8n is the second. It's the workflow automation tool I reach for whenever something needs to talk to something else — webhooks, scheduled syncs, API orchestration, and increasingly, AI agents that actually do work instead of just chatting. The pitch is simple: a visual canvas where each node is a step, the data flows down the chain, and you can drop into real JavaScript any time the visual builder runs out of road. Self-host it on a single box, point it at Postgres and Redis, and you've got a durable automation engine you fully own — no per-task pricing, no vendor watching your data go by. This is the guide I'd hand a developer who's tired of gluing services together by hand: how to self-host n8n properly with Docker and queue mode, how the data model and expressions actually work, how to handle errors like you mean it, how to build real AI agents with the LangChain nodes, and how to wire it up to Directus so the two cover each other's weak spots. _This is a living document and will be updated as n8n updates._
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