Dify Beginner Guide: Build Your First AI Workflow

<p>Dify is an open-source LLM app development platform with drag-and-drop workflow orchestration. Perfect if you do not want to send all data to OpenAI or Anthropic.</p><h2>Why Dify?</h2><ul><li><strong>Fully open source</strong>: Free on GitHub, no third-party data transmission</li><li><strong>Visual orchestration</strong>: Drag-and-drop components, no coding required</li><li><strong>Multi-model support</strong>: OpenAI, Claude, local Ollama all supported</li><li><strong>One-click API publish</strong>: Created AI apps can be called via API immediately</li></ul><h2>Quick Deployment</h2><p>Prerequisites: Docker Desktop (Windows/Mac) or Docker (Linux). One-command startup: git clone Dify, cd docker, cp .env.example .env, docker-compose up -d. Visit localhost:80 to open Dify interface.</p><h2>Create Your First Chatbot</h2><p>1. Click Create App, choose Chat Assistant; 2. Name your app, select AI model (add API Key in Settings first); 3. Write system instructions in prompt template; 4. Publish, get API address.</p><h2>Build Automated Workflows</h2><p>Difys real power is in Workflows. Example: Article Analysis Assistant - add LLM component to summarize key points, add conditional node to categorize by length, add template generation node to output structured report.</p><h2>Local Models with Ollama</h2><p>Zero-cost local LLMs: Install Ollama, run ollama serve and ollama pull llama3.2, add Ollama as model provider in Dify. Local models are free but lower quality, great for internal tools and testing.</p><h2>Use Cases</h2><ul><li>Customer service bot (website/WeChat integration)</li><li>Content moderation workflow</li><li>Data cleaning and formatting</li><li>Automatic meeting summary</li><li>Competitive analysis report automation</li></ul>