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Dify

The visual platform for LLM apps and agentic workflows — canvas-built chatflows, RAG pipeline, agent nodes with 50+ tools, and LLMOps, self-hosted via Docker.

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Updated Jun 11, 2026
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Dify (~145k stars) is the visual answer to LLM app building: a workflow canvas for chatflows and agents, a built-in RAG pipeline from ingestion to retrieval, agent nodes with 50+ tools, hundreds of models via any provider, a prompt IDE, and LLMOps — self-hosted with one docker compose or cloud freemium. License caveat: it's a modified Apache-2.0 with conditions.

Dify is the heavyweight of visual LLM-app platforms — ~145k GitHub stars and a self-description that tracks the era: "production-ready platform for agentic workflow development." Its proposition: everything between a model API and a shipped AI product — RAG, agents, prompts, ops — on one canvas, deployable with one docker compose up.

Highlights

  • Visual workflow canvas — chatflows and agentic workflows assembled from nodes, debugged visually, versioned.
  • RAG pipeline included — ingestion through retrieval (PDF/PPT and common formats out of the box) without assembling a vector stack by hand.
  • Agent nodes — Function-calling or ReAct agents with 50+ built-in tools (search, image generation, WolframAlpha…) plus a plugin marketplace of strategies.
  • Every model, one panel — hundreds of proprietary and open models across dozens of providers, including any OpenAI-compatible endpoint (so local models plug in).
  • Prompt IDE + LLMOps — compare models on prompts, then log, annotate, and improve from production traffic.
  • Backend-as-a-Service — your Dify apps expose APIs, so the visual build embeds into real products.

In an AI-assisted workflow

git clone https://github.com/langgenius/dify.git && cd dify/docker
cp .env.example .env && docker compose up -d    # → http://localhost/install

The fit: teams that want the AI app built more than they want to own its plumbing — prototypes that become products, internal tools, and the "let domain experts iterate on prompts" workflow code-first frameworks can't offer.

WARNING

Read the license before building a business on it: Dify's modified Apache-2.0 forbids multi-tenant operation without a commercial license and requires keeping frontend branding. Internal single-tenant self-hosting is the cleanly-free case; SaaS-on-Dify is a conversation with LangGenius.

Good to know

Cloud is freemium (a sandbox tier with message credits, then per-workspace plans); self-hosting is a real multi-service stack (API, worker, Postgres, Redis, vector DB) — budget ops accordingly. v1.0 landed February 2025 with the plugin architecture; the 1.x line iterates steadily. Versus the automation-first alternative: n8n vs Dify; versus code-first frameworks: Agent Frameworks in 2026.

Frequently asked questions

What do people build with Dify?
Production LLM apps without writing the orchestration: support chatbots over company knowledge (the built-in RAG pipeline), agentic workflows that chain models and 50+ tools on a visual canvas, and internal AI apps exposed via Dify's backend-as-a-service APIs — with logging and annotation (LLMOps) closing the improvement loop.
Is Dify really open source?
Source-available with conditions, not OSI open source: the Dify Open Source License is Apache-2.0 plus two restrictions — you can't operate it as a multi-tenant service without a commercial license, and the frontend logo/copyright must remain. For single-tenant internal self-hosting (the common case), it behaves like free software.
How does Dify compare to n8n?
Center of gravity. Dify is AI-native — the canvas exists to build LLM apps, with RAG and prompt tooling first-class. n8n is automation-native — 400+ app integrations with AI nodes added to a general workflow engine. Building an AI product: Dify. Automating business processes that include AI steps: n8n. Full comparison in our n8n vs Dify guide.

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