Dify — Open-Source LLMOps Platform

What it is

Dify is a visual platform for building production AI applications without deep coding. It provides a drag-and-drop workflow editor, RAG pipeline, agent builder, prompt IDE, and automatic API deployment. Every app gets a REST API endpoint automatically.

Dify works with OpenAI, Anthropic, Google, Azure, Ollama, HuggingFace, and 50+ model providers. It is self-hostable via Docker with full data control.

How it saves time or tokens

Dify lets non-engineers build AI applications that would otherwise require a full-stack developer. The visual workflow builder connects LLM calls, conditions, loops, and tools as nodes on a canvas. The RAG engine handles document upload, chunking, embedding, and retrieval with citation. Instead of writing custom orchestration code, you design workflows visually and deploy them as APIs. The prompt IDE lets you test and iterate prompts with variable injection before deploying.

How to use

1. Deploy Dify with Docker:

git clone https://github.com/langgenius/dify.git
cd dify/docker
cp .env.example .env
docker compose up -d

1. Open http://localhost/install to create your admin account.

1. Create your first AI app:

• Choose a template (chatbot, text generation, workflow)
• Configure your LLM provider (API key)
• Design the workflow with visual nodes
• Publish and get your API endpoint

Example

Using the Dify API to interact with your published workflow:

import requests

response = requests.post(
    'http://localhost/v1/chat-messages',
    headers={
        'Authorization': 'Bearer app-your-api-key',
        'Content-Type': 'application/json'
    },
    json={
        'inputs': {},
        'query': 'What are the key features of our product?',
        'response_mode': 'streaming',
        'user': 'user-123'
    },
    stream=True
)

for line in response.iter_lines():
    if line:
        print(line.decode())

Related on TokRepo

• AI tools for no-code — More visual AI building tools on TokRepo.
• RAG tools — Browse retrieval-augmented generation tools.

Common pitfalls

• Running Dify without configuring persistent storage means uploaded documents and conversation history are lost on restart. Configure volume mounts in Docker Compose.
• Not setting rate limits on published API endpoints exposes your LLM API keys to abuse. Configure API key authentication and rate limiting before sharing endpoints.
• The default Docker setup uses an embedded database. For production, configure an external PostgreSQL and Redis instance.