Dify

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.

Repository	github.com/langgenius/dify
License	Apache 2.0
Language	Python, TypeScript
Stars	134K+
Category	Agentic Workflow Platform

Key Features

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

Architecture

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.

graph TB subgraph Client["Client Layer"] WebUI[Web UI] API[REST API] MCP[MCP Server] end subgraph Core["Core Engine"] WF[Workflow Engine] Agent[Agent Node] Prompt[Prompt IDE] end subgraph Models["LLM Orchestration"] OpenAI[OpenAI] Claude[Anthropic] OSS[Open Source Models] end subgraph Data["Data Layer"] RAG[RAG Pipeline] VDB[(Vector DB)] KB[Knowledge Base] end Client --> Core Core --> Models Core --> Data RAG --> VDB RAG --> KB Agent --> WF

Agent Strategies

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.

Code Example

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"])

Table of Contents

Dify

Key Features

Architecture

Agent Strategies

Code Example

References

See Also