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Most repository guides show the shortest path to a running demo. This site focuses on the questions people actually need before using a project: what to prepare, whether it fits, what the architecture hides, and where to look when the first run fails.

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DifyDify is attractive because it gives you a finished-looking AI app platform quickly. I would slow down before deploying it: the hard part is not opening the console, it is proving that workers, model keys, datasets, files, queues, and backups behave correctly after the first demo.

LangGraphLangGraph is worth reading when your agent has to stop, resume, branch, retry, or ask a human before continuing. I would treat it less like a smarter chatbot framework and more like a state machine for work that can fail halfway.

OllamaOllama makes local models feel easy, but the real decision is hardware, model size, disk, latency, and what other tools expect from its API. I would test small and measure before building anything around it.

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Check the project before you deploy it.

Not another quick-start copy. Preparation, fit checks, failure paths, and the first safe use case.

Check the
project before
you deploy it.