Dify is an open-source agentic workflow platform that enables developers and non-technical users to build, deploy, and manage LLM-powered applications through a visual workflow designer and programmatic APIs. With over 134,000 GitHub stars, Dify has become one of the most popular platforms for orchestrating AI agents and RAG pipelines.

• Visual Workflow Studio — Drag-and-drop interface for designing AI workflows, training agents, and configuring RAG systems
• Multi-Model Support — Access, switch, and compare performance across dozens of LLM providers including OpenAI, Anthropic, open-source models
• RAG Pipeline — Built-in retrieval-augmented generation engine that extracts, transforms, and indexes data from various sources into vector databases
• Agent Node System — Autonomous decision-making nodes within workflows using ReAct, Function Calling, Chain-of-Thought, Tree-of-Thought, and custom strategies
• Prompt IDE — Dedicated prompt orchestration interface for configuring and managing prompts
• MCP Integration — Native Model Context Protocol support for accessing external APIs, databases, and services
• Backend-as-a-Service — One-click deployment as APIs, chatbots, or internal business tools

Dify employs a Beehive modular architecture where each component can be developed, tested, and deployed independently. The platform comprises three core operational layers:

• LLM Orchestration Layer — Manages connections and switching between multiple large language models
• Visual Studio Layer — Drag-and-drop interface for designing workflows and configuring agents
• Deployment Hub — Enables publishing as APIs, chatbots, or internal tools

Model suppliers and models are configured declaratively using YAML-based DSL, standardizing the process of adding new models while maintaining API consistency across integration points.

The Agent Node functions as a decision center within workflows, supporting multiple reasoning strategies:

• ReAct — Chain of Think-Act-Observe cycles
• Function Calling — Precise function-based tool invocation
• Chain-of-Thought (CoT) — Step-by-step reasoning
• Tree-of-Thought (ToT) — Branching exploration of reasoning paths
• Graph-of-Thought (GoT) — Graph-based reasoning structures

Developers can create custom strategy plugins using CLI tools and customize configuration forms.

import requests
 
DIFY_API_KEY = "app-your-api-key"
BASE_URL = "https://api.dify.ai/v1"
 
def run_workflow(inputs, user_id="default"):
    response = requests.post(
        f"{BASE_URL}/workflows/run",
        headers={
            "Authorization": f"Bearer {DIFY_API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "inputs": inputs,
            "response_mode": "blocking",
            "user": user_id
        }
    )
    return response.json()
 
result = run_workflow({"query": "Summarize this document"})
print(result["data"]["outputs"])

• Dify GitHub Repository
• Dify Official Website
• Dify Documentation
• Dify Architecture Blog Post

• RAGFlow — RAG engine with deep document understanding
• Langfuse — LLM observability and tracing
• Mem0 — Memory layer for AI agents
• MCP Servers — Model Context Protocol implementations