Dify is an open source platform for building LLM applications, covering agents, agentic workflows and chatbots that use your own data, deployable as web applications or through an API. The documentation covers getting started, cloud and self hosted deployment, the REST API, a command line tool called difyctl, plugin development, and a set of tutorials that run from a first workflow through to complete example applications. As a learning resource it is better than the average open source project docs and better than most vendor documentation, and the reason is that self hosting changes the learning dynamic completely. When you can run the whole platform locally with Docker Compose and point it at whatever model you like, experimentation is free.
That matters more than people realise. Learning to build LLM applications requires making a lot of bad applications first, and doing that on a metered cloud tier where every generation costs credits makes you cautious in exactly the wrong way. The workflow documentation is the strongest section and the one I would send someone to first. Orchestration concepts, how you chain steps, branch on conditions, loop, handle errors, and pass state between nodes, are the actual substance of building anything non trivial.
Dify's visual workflow builder makes those concepts concrete and the docs explain them in terms that transfer. If you later move to writing orchestration in code with LangGraph or the OpenAI Agents SDK, you will already understand what you are building. The RAG documentation is also better than I expected. Most vendor docs treat retrieval as a checkbox: upload documents, get answers.
Dify's material is more honest about the parts that determine whether retrieval actually works, chunking strategy, embedding choice, retrieval configuration and reranking. That honesty is useful because retrieval quality is where the majority of RAG projects quietly fail, and a learner who understands that early is ahead of most. The plugin development documentation deserves a mention as the thing that stops this being a toy. Being able to extend the platform with your own tools and model providers means you do not hit an arbitrary ceiling six weeks in, which is the usual failure mode of low code platforms.
Now the criticisms. The translation is the first and most immediate. Dify's documentation originates in Chinese and the English is uneven. Most of it is perfectly clear, but there are passages where a technically important distinction becomes ambiguous, and when you are debugging a retrieval configuration ambiguity is expensive.
It is improving, and I would not let it stop anyone using the platform, but expect to occasionally read a sentence twice or check the Chinese original. Second, the visual builder problem, which applies to every tool in this category. You can build a working RAG chatbot in Dify without understanding what an embedding is, how similarity search works, or why your retrieval is returning the wrong chunks. That is a feature for someone who needs a working chatbot and a real risk for someone who thinks they are learning AI engineering.
The docs do not push you towards the underlying concepts and I wish they did. My advice is to build something in the visual builder, then rebuild the same thing in code, and notice everything the builder was doing for you. Third, evaluation is thin. This is a running theme across the whole category and Dify is no worse than most, but a platform that makes it this easy to ship an LLM application ought to make it equally easy to measure whether the application is any good.
Fourth, the cloud versus community edition distinction could be clearer. The self hosted path is documented well. Cloud pricing beyond the free Sandbox tier is vaguer, and feature differences between editions are not always flagged where you need them. On alternatives.
Against Flowise, Dify is the more complete platform with better documentation and a stronger plugin story. Against n8n, Dify is more focused on LLM applications while n8n is a general automation tool that also does AI. Against writing code directly with the Vercel AI SDK or LangChain, Dify gets you to something working much faster and teaches you less about the mechanics. My three point nine reflects a capable platform with above average documentation, held back by the translation unevenness and the usual low code gap between shipping something and understanding it.
For a free, self hostable way to learn workflow and retrieval concepts, it is one of the better options available.