Authority AI Editorial
Dify: a visual builder for agents and workflows
Dify is an open-source platform for building AI applications on a visual canvas rather than in code. It targets developers and technical product teams who want to assemble an agent, a multi-step workflow, or a retrieval-augmented chatbot by connecting nodes on a screen, then expose the result as a web app or a REST API without standing up their own backend for it.
What it does
At the center of Dify is a drag-and-drop workflow builder. Dify's own documentation frames it as connecting "nodes that each handle a specific step, such as calling a model, retrieving knowledge, running code, or branching on conditions," so the model is doing the reasoning inside boundaries you define on the canvas rather than improvising the whole task. Dify actually offers two flavors of this: a Workflow app runs once end to end, taking an input, moving it through the connected nodes, and returning an output, which suits batch jobs like generating a report from a document. A Chatflow keeps that same node structure but wraps it in a chat interface, so each message a user sends triggers the flow before a response comes back, which suits a conversational assistant that still needs structured, multi-step logic behind it. Either app type can call an LLM node, retrieve from a knowledge base, run custom code, branch on a condition, or call an external tool. Dify also includes a retrieval-augmented generation pipeline that ingests documents such as PDFs and slide decks and indexes them for a model to query, a prompt IDE for comparing model outputs side by side, an agent framework capable of running commands and handling files inside a sandbox, and LLMOps features for monitoring an application once it is live. Model access runs through a single management layer that Dify's documentation says connects to hundreds of proprietary and open-source LLMs across dozens of inference providers.
How you use it
You can start on Dify Cloud, which offers a free sandbox tier and needs no installation, or self-host with Docker Compose on your own infrastructure. Either way the workflow is the same: build your pipeline on the canvas, wire in a knowledge base if the app needs retrieval, pick a model provider, and publish. Once published, an app can be embedded as a chat widget, run as a standalone web app, or called through Dify's REST API from another product. Developers can extend the platform with custom plugins for tools and model providers, and there is a CLI for running Dify workflows from scripts or CI.
What to know before adopting it
Dify's own source repository states it is licensed under a modified Apache License 2.0: the modification requires that anyone using the source code to run a multi-tenant service, where a tenant means a separate workspace, obtain a commercial license from the maintainers, and it also restricts removing the Dify logo from the console. Plain self-hosted, single-tenant use is unrestricted under Apache 2.0 terms. The stack is built in Python on the backend with a Next.js frontend, deployed with Docker Compose. Recent releases have added an agent sandbox with stronger isolation, workspace-level skill management with a draft-to-publish lifecycle, and support for a newer version of the Model Context Protocol, according to the project's GitHub release notes.
Where it fits next to similar tools
Dify sits closer to a low-code platform than a code-first agent library: where a terminal or IDE-embedded coding agent edits your own repository, Dify is meant for building and hosting a separate AI application, chatbot, or workflow that other people or systems call. That makes it a fit for teams that want a shared canvas non-engineers can also read, rather than a tool an individual developer runs against their own codebase. Anyone assembling instructions for agents across a stack that includes tools like Dify can also draw on the AI Ultimate Skill Collection, 3,029 ready-made skills organized into 12 packs, each a written procedure an agent follows step by step.