Dify: Open-Source AI Application Development Platform for Agentic Workflows and RAG

Introduction

Dify is an open-source AI application development platform launched by LangGenius. It supports visually orchestrating Agentic workflows and RAG pipelines on a unified canvas, integrating hundreds of large models and tools. Users can publish AI applications as web pages or APIs, moving from prototype to production quickly. The platform is available in three forms — cloud service, enterprise private deployment, and the Apache-2.0 community edition — and has been widely adopted by companies such as Volvo and Ricoh, helping teams build, deploy, and manage AI applications with a low barrier to entry.

Key Features

• Workflow / Chatflow orchestration: Define the reasoning, retrieval, branching, and tool-calling logic of AI applications on a visual canvas; testable and debuggable.
• Agent runtime: Configure an Agent's instructions, skills, and tools, supporting multi-step reasoning and autonomous task execution.
• RAG knowledge pipeline: Upload documents to build knowledge bases, with full RAG capabilities including chunking, indexing, and retrieval augmentation.
• Rich model integration: Connects to hundreds of mainstream large models (OpenAI, Anthropic, DeepSeek, etc.) that can be switched at any time.
• Plugin system: Extend tools, models, and data sources via the plugin marketplace or custom-built plugins.
• Multi-channel publishing: Publish with one click as a web app, API, and other channels for a fast launch.
• Monitoring and operations: Logs, usage statistics, and performance monitoring make it easy to track and optimize applications.
• Multiple deployment options: Cloud service, enterprise private deployment, and self-hosted Docker from the open-source community edition.

How to Use

1. Visit the Dify website at https://dify.ai/ and click "Get Started".
2. Register with your email and sign in to the Dify cloud service, or download the community edition for self-hosting.
3. After signing in, enter the console and create or join a team workspace.
4. Click create and choose an app type (chatbot, Agent, Workflow, or Chatflow).
5. Configure the model API Key in settings (e.g., OpenAI, DeepSeek).
6. Define the app's role, task instructions, and output requirements (prompt).
7. Drag in nodes to build the workflow, or upload documents to a knowledge base for RAG.
8. Add the external tools your Agent needs from the plugin marketplace.
9. Trial-run in the preview window, check outputs, and fix issues.
10. Publish with one click as a web app or API, then keep monitoring and iterating via logs and usage panels.

Core Advantages

• Low-code visual development: Drag-and-drop canvas orchestration that beginners can pick up easily, promoting digital development across the whole team.
• Model-neutral: Connects to hundreds of mainstream models with instant switching, avoiding vendor lock-in.
• All-in-one: Agent, RAG, Workflow, and plugin capabilities integrated on one platform — no need to stitch together multiple tools.
• Production-grade: From prototype to production without rework, with built-in logs, monitoring, and permission management.
• Flexible deployment: Cloud, enterprise private deployment, and Docker self-hosting to fit different compliance needs.
• Rich ecosystem: Plugin marketplace and an active community for quickly reusing third-party tools and app templates.

Use Cases

• Intelligent customer service: Answer user questions automatically with a RAG knowledge base for 24/7 online service.
• Enterprise knowledge assistant: Connect internal documents to workflows so employees can query company policies and materials in natural language.
• Content generation: Batch-generate marketing copy, reports, or social media content to boost productivity.
• Data analysis Agent: Call database and charting tools to automate metric calculations and visual analysis.
• Process automation: Use Agents to call external APIs for approvals, notifications, order processing, and other business automation.

Limitations

• The cloud service requires you to configure your own model API Keys; model usage costs depend on the connected provider.
• The enterprise edition is custom-quoted; specific features and SLAs are negotiated with Dify or its partners.
• Orchestrating and debugging complex workflows still involves a learning curve; start with simple apps.

Project and Pricing

• Website: https://dify.ai/
• GitHub repository: https://github.com/langgenius/dify (Apache-2.0 open source)
• Professional: the popular plan at $59/month for indie developers and small teams, with 5,000 messages/month, 3 team members, 50 apps, and 5GB knowledge base storage.
• Team: $159/month for mid-sized teams needing collaboration and higher throughput, with 10,000 messages/month, 50 team members, 200 apps, and 20GB knowledge base storage.
• Enterprise: custom pricing with scalable enterprise deployment, SSO, advanced security and control, and dedicated technical support.

FAQs

What is Dify? Dify is an open-source AI application development platform by LangGenius that supports visually orchestrating Agentic workflows and RAG pipelines on a unified canvas, integrating hundreds of models and tools, and publishing AI apps as web pages or APIs.

Can Dify be deployed privately? Yes. Dify offers cloud service, enterprise private deployment, and a Docker self-hosted Apache-2.0 community edition to fit different compliance and security needs.

Which large models does Dify support? Dify connects to hundreds of mainstream models, including OpenAI, Anthropic, and DeepSeek, which can be switched at any time to avoid vendor lock-in.

Who is Dify for? It suits teams of all sizes that want to build, deploy, and manage AI applications with a low barrier — from indie developers and small teams to enterprises like Volvo and Ricoh; beginners can get started quickly with the drag-and-drop canvas.