What Is Dify?
Dify is an open-source LLM application development platform that consolidates five things that typically require separate tools: dataset/RAG management, workflow orchestration, agent building, model provider management, and API generation. It's designed to replace the custom glue code most teams write to stitch these together.
The architecture is opinionated and production-oriented. Where Flowise prioritizes simplicity, Dify prioritizes completeness.
Self-Hosting
git clone https://github.com/langgenius/dify
cd dify/docker
cp .env.example .env
# Edit .env with your API keys
docker compose up -d
Dify runs as a set of Docker services (API, web, worker, db, redis, vector store). The web UI is available at http://localhost.
Dataset Management
Dify's Knowledge base handles the full RAG pipeline:
Chunking strategies:
- Fixed size (token count + overlap)
- Paragraph-based (split on double newline)
- Q&A format (GPT-4o generates Q&A pairs from each chunk for better retrieval)
Indexing modes:
- High quality (embedding model + vector search)
- Economy (keyword search, no embedding cost)
You upload files through the UI or via API, and Dify handles chunking, embedding, and indexing.
Visual Workflow Editor
The workflow editor handles branching logic, loops, and conditional paths that are awkward to express in pure chain-based tools:
[User Input]
↓
[Classify Intent] — branch on classification
├── "FAQ" → [Retrieve from Knowledge] → [Generate Answer]
├── "Complaint" → [Extract Details] → [Create Ticket] → [Send Confirmation]
└── "Other" → [Default Response]
Each node type (LLM, Code, HTTP Request, Knowledge Retrieval, If-Else, Loop) is configured in a side panel.
Model Provider Hub
Dify connects to 30+ model providers through a unified interface: OpenAI, Anthropic, Google Gemini, Mistral, Bedrock, Azure OpenAI, Ollama (local), and hosted open-source models. You configure provider credentials once in Settings and reference models by name in any workflow.
Prompt Management with A/B Testing
Dify stores prompts as versioned assets. You can run A/B tests by routing a percentage of traffic to a new prompt version and comparing response quality metrics side-by-side before promoting.
API and Webhook Integration
Every Dify application exposes an API endpoint. The response format is consistent regardless of whether the app is a chatbot, workflow, or agent:
import requests
response = requests.post(
"http://localhost/v1/chat-messages",
headers={"Authorization": "Bearer YOUR_API_KEY"},
json={
"inputs": {},
"query": "What is the cancellation policy?",
"response_mode": "blocking",
"conversation_id": "",
"user": "user-123",
},
)
print(response.json()["answer"])
Dify vs Flowise
Flowise is simpler and maps more directly to LangChain concepts — good for developers who think in chains. Dify has better built-in RAG management, more robust dataset tooling, and prompt A/B testing — better for teams building production customer-facing applications where content management matters.
