Dify: Building LLM Applications Without Writing the Plumbing
Dify is for building applications on top of language models rather than chatting with them. It provides the pieces every such application needs so you are not reimplementing them.
What it gives you
A workflow editor for chaining model calls, conditionals, code steps, and tool invocations visually.
A RAG pipeline with document ingestion, chunking, embedding, and retrieval, configurable per knowledge base rather than fixed.
Prompt management with versioning, so a prompt change is a tracked edit rather than an untracked one.
Agent capabilities, letting a model call tools and iterate.
An API for whatever you build, so the application you assemble is consumable from your own code.
Why not just call the API yourself
For a simple call, you should. Dify earns its place when the application involves several steps, retrieval over your own documents, or non-engineers who need to adjust prompts without a deployment.
The observability is the underrated part. When a multi-step chain gives a bad answer, seeing each step’s input and output is the difference between debugging and guessing.
Models
It supports hosted providers and local models through Ollama or any OpenAI-compatible endpoint such as LocalAI or vLLM.
Being able to develop against a cheap local model and switch to a larger one for production is a genuine cost saving during iteration.
Running it
Docker Compose, with PostgreSQL, Redis, and a vector database. It is a real application stack rather than a single container, so give it real resources and put it behind a reverse proxy.
Alternatives
Flowise and Langflow cover similar ground with different emphases: both are more node-graph oriented, Dify is more application oriented with better multi-tenant management.
License
Dify is released under a modified Apache 2.0 License with some commercial restrictions.