What Is Dify?
Dify (previously known as LangGenius) is an open-source platform for building LLM-powered applications. Unlike pure code frameworks (LangChain) or pure no-code tools, Dify bridges both: a visual workflow builder for rapid prototyping, with code extensibility for advanced customization.
The platform covers the full LLM app lifecycle: model management, knowledge base (RAG), workflow orchestration, API deployment, and user analytics—all in one unified dashboard.
Key Features
- Visual Workflow Builder — Drag-and-drop nodes to compose LLM pipelines. Connect prompts, retrievers, code blocks, and API calls visually without writing application code.
- Knowledge Base (RAG) — Upload PDFs, docs, websites, or databases. Dify handles chunking, embedding, vector storage, and retrieval automatically. Powers document Q&A apps in minutes.
- Agent Mode — Build AI agents that use tools (web search, code execution, custom APIs) to complete tasks autonomously. Supports ReAct and Function Calling patterns.
- 100+ Model Providers — OpenAI, Anthropic, Google, Ollama, Groq, Azure, HuggingFace, and custom endpoints. Switch models without changing your application.
- API & SDK — Every app built in Dify automatically gets a REST API. Integrate into any application with a few lines of code using the Dify SDK.
- Observability — Built-in logging, conversation history, token usage tracking, and user analytics. Debug LLM chains with complete trace visibility.
Self-Hosting with Docker
cd dify/docker
cp .env.example .env
docker compose up -d
# Open http://localhost in your browser
The self-hosted instance includes: Dify API server, web frontend, PostgreSQL, Redis, Weaviate vector database, and Nginx proxy—all in one docker-compose file.
Use Cases
Customer Support Chatbots
Upload your product documentation to Dify's knowledge base and deploy a RAG-powered chatbot in minutes. The chatbot answers questions accurately from your docs and escalates to humans when needed.
Internal Knowledge Tools
Connect your company wiki, Confluence, Notion, or Google Drive. Build an internal AI assistant that employees can query without sending data to third-party cloud services (self-hosted).
Data Processing Pipelines
Build LLM-powered data pipelines: extract structured data from documents, classify text, translate content, or generate reports—all orchestrated visually in Dify's workflow builder.
Pros & Cons
Pros
- Full platform: model + RAG + agents + API
- Visual + code: accessible to all skill levels
- Self-hostable for complete data privacy
- 100+ model providers supported
- Built-in observability and analytics
- Active development, frequent updates
Cons
- Self-hosting requires Docker knowledge
- Less flexible than pure code frameworks
- Visual workflows can become complex
- Cloud pricing is relatively high ($59+/mo)
- Community support primary for free tier