Dify (2026 메이저 업데이트)
No-code platform for building LLM workflows with visual drag-and-drop functionality.
Dify is an open-source LLM app platform developed by LangGenius (formerly from the Tencent Cloud DevOps team). In short, it can be described as a "visual IDE that assembles LLM workflows and RAG using drag-and-drop, without coding."
Previously, creating LLM-based automation required coding directly with frameworks like LangChain and connecting vector databases, document ingestion tools, and observability tools individually. Dify integrates all these steps into a single canvas, allowing you to drag and drop node boxes to bundle LLM chains, conditional branching, RAG searches, and external API calls at once. In the major update of 2026-03, it began to support MCP (Model Context Protocol) bidirectionally, enabling the Dify workflow itself to be called as a function from external clients like Claude or Cursor.
From a practical perspective, it allows you to (1) build a RAG pipeline for searching thousands of internal PDFs/PPTs with a chatbot in just 30 minutes, (2) replace 100+ LLM providers (including GPT, Claude, Mistral, and self-hosted Ollama) with a single click to compare costs and quality, and (3) combine Function Calling/ReAct agents with 50+ built-in tools (such as Google Search and DALL·E) to create multi-step automation.
💻 System Requirements
Not required — LLM inference is delegated to an external API or a separate inference server (e.g., Ollama, vLLM). Dify itself operates using only the CPU.
Approximately 2-5GB total for the Docker image + vector database + PostgreSQL. This will increase depending on the scale of the RAG documents.
⚡ Installation
4-1. Quick Start (Docker Compose)
git clone https://github.com/langgenius/dify.git
cd dify/docker
cp .env.example .env
docker compose up -d
After installation, access http://localhost/install in your browser to perform the initial administrator setup.
4-2. Detailed Installation
# 1. Clone the repository (latest release)
git clone https://github.com/langgenius/dify.git
cd dify/docker
# 2. Configure environment variables
cp .env.example .env
# Customize `SECRET_KEY`, database password, etc., in the `.env` file.
# For advanced configuration, refer to the theme-specific environment files under `docker/envs/`.
# 3. Start the services (API, Worker, Web, Plugin Daemon + PostgreSQL, Redis, Weaviate, Nginx, etc.)
docker compose up -d
# 4. Initial access
# `http://localhost/install` (local) or `http://<server IP>/install` (remote)
Kubernetes Deployment: Several community Helm Charts are available (Douban Charts, Magicsong AI Charts, etc.). One-Click Cloud Deployment: Supported on AWS Marketplace (Dify Premium AMI), Alibaba Cloud Computing Nest, and Terraform (Azure, GCP).
FAQ
What is Dify (2026 메이저 업데이트)?
Dify is an open-source LLM app platform developed by LangGenius (formerly from the Tencent Cloud DevOps team). In short, it can be described as a "visual IDE that assembles LLM workflows and RAG using drag-and-drop, without coding." Previously, creating LLM-based automation required coding directly with frameworks like LangChain and connecting vector databases, document ingestion tools, and observability tools individually. Dify integrates all these steps into a single canvas, allowing you to drag and drop node boxes to bundle LLM chains, conditional branching, RAG searches, and external API calls at once. In the major update of 2026-03, it began to support MCP (Model Context Protocol) bidirectionally, enabling the Dify workflow itself to be called as a function from external clients like Claude or Cursor. From a practical perspective, it allows you to (1) build a RAG pipeline for searching thousands of internal PDFs/PPTs with a chatbot in just 30 minutes, (2) replace 100+ LLM providers (including GPT, Claude, Mistral, and self-hosted Ollama) with a single click to compare costs and quality, and (3) combine Function Calling/ReAct agents with 50+ built-in tools (such as Google Search and DALL·E) to create multi-step automation.
When should I use Dify (2026 메이저 업데이트)?
No-code platform for building LLM workflows with visual drag-and-drop functionality.
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