Dify is an open-source LLM application platform that gathers AI workflows, RAG knowledge bases, agent capabilities, model management and observability into a single visual interface. It sits between "write code against an API" and "pure low-code tool": build the prototype in the UI, and keep the API and SDKs for wiring it into your own systems.
What It Does
- Workflow orchestration: chain questions, retrieval, model calls, branches and tools into a visual flow.
- Knowledge bases (RAG): upload documents, get chunking, embedding and retrieval as the grounding for answers.
- Agents: let a model call tools to finish multi-step tasks.
- Prompt management: variables, conditions, versioning and side-by-side model tests a team can collaborate on.
- Model access: international providers, Chinese providers and locally deployed models all plug in.
Why It Earns a Listing
Most teams' first AI feature involves the same grunt work: chunking documents, storing vectors, assembling prompts, logging runs and giving business users something clickable. Dify standardizes that layer. What it saves is the two weeks of scaffolding, not the capability of the model itself.
It does not remove the code — complex logic still lands in custom nodes or external services, and the limits of visual orchestration show up as flows grow. Treating it as "scaffolding for the first 80%" is more accurate than treating it as a complete replacement.
Deployment and Licensing
The community edition is MIT licensed and self-hostable with Docker, keeping data on your own infrastructure; there are also cloud and enterprise offerings. For teams whose data can't leave the network, self-hosting is the practical draw — with Docker, vector store and upgrade operations as the cost you take on.
Using It From China
It can be deployed on servers inside China and pointed at domestic model APIs, which makes an end-to-end local setup straightforward — one reason it spread faster among Chinese teams than comparable overseas products.