Dify Platform: Deep Dive Tutorial

June 15, 2026 · View on GitHub

Project: Dify — An open-source LLM application development platform for building workflows, RAG pipelines, and AI agents with a visual interface.

Why This Track Matters

Dify provides a complete open-source platform for building LLM applications with a visual workflow editor, RAG pipeline, and agent framework — reducing the time from idea to deployed AI application.

This track focuses on:

building and deploying LLM workflows with Dify's drag-and-drop node system
implementing RAG pipelines with multi-stage document processing and vector search
orchestrating agents with tool-calling loops and reasoning chain management
operating Dify in production with Docker, monitoring, and security controls

Dify is an open-source LLM application platform that provides a visual interface for building AI workflows, RAG systems, and agent frameworks. It supports orchestrating complex LLM pipelines with a drag-and-drop node system and offers one-click deployment via Docker.

Feature	Description
Visual Workflows	Drag-and-drop node system for chaining LLM operations
RAG Pipeline	Multi-stage document processing with vector storage and retrieval
Agent Framework	Tool-calling loops and reasoning chain management
Multi-Model	OpenAI, Anthropic, Google, local models via Ollama
Plugin System	Extensible architecture for custom nodes and integrations
Deployment	One-click Docker Compose deployment

Mental Model

graph TB
    subgraph Frontend["React Frontend"]
        UI[Visual Workflow Editor]
        CHAT[Chat Interface]
        ADMIN[Admin Console]
    end

    subgraph Backend["Flask Backend"]
        WF[Workflow Engine]
        RAG[RAG Pipeline]
        AGENT[Agent Framework]
        API[REST API]
    end

    subgraph Storage["Storage"]
        PG[(PostgreSQL)]
        REDIS[(Redis)]
        VEC[(Vector Store)]
        S3[Object Storage]
    end

    subgraph LLM["LLM Providers"]
        OAI[OpenAI]
        CLAUDE[Anthropic]
        LOCAL[Ollama]
    end

    Frontend --> Backend
    Backend --> Storage
    Backend --> LLM

Chapter Guide

Chapter	Topic	What You'll Learn
1. System Overview	Architecture	Dify's place in the LLM ecosystem, core components
2. Core Architecture	Design	Components, data flow, service boundaries
3. Workflow Engine	Orchestration	Node system, visual workflows, execution pipeline
4. RAG Implementation	Retrieval	Document processing, embeddings, vector search
5. Agent Framework	Agents	Tool calling, reasoning loops, agent types
6. Custom Nodes	Extensibility	Building custom workflow nodes and plugins
7. Production Deployment	Operations	Docker, scaling, monitoring, security
8. Operations Playbook	Reliability	Incident response, SLOs, and cost controls

Tech Stack

Component	Technology
Backend	Python, Flask
Frontend	React, TypeScript
Database	PostgreSQL
Cache	Redis
Vector Store	Weaviate, Qdrant, pgvector
Deployment	Docker Compose

Ready to begin? Start with Chapter 1: System Overview.

Built with insights from the Dify repository and community documentation.

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Full Chapter Map

Current Snapshot (auto-updated)

repository: langgenius/dify
stars: about 145k
GitHub release reference: 1.14.2 (checked 2026-06-15; release metadata on GitHub)

What You Will Learn

how Dify's workflow engine executes node graphs and manages LLM pipeline state
how to implement multi-stage RAG with document processing, embeddings, and vector retrieval
how Dify's agent framework manages tool-calling loops and reasoning chains
how to deploy and operate Dify in production with Docker Compose and monitoring

Source References

Dify

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