Dify
by langgenius
Dify: Production-Ready AI Agent & Workflow Platform
Open-source low-code platform for building, deploying, and managing AI agents and agentic workflows with enterprise-grade features.
- 126,716+ GitHub stars
- Built with Python
- Visual workflow builder with low-code/no-code interface for rapid AI application development
- Other license
About This Project
Dify is a comprehensive development platform that bridges the gap between AI capabilities and production applications. It provides developers and teams with a visual interface and robust backend to orchestrate complex AI workflows, deploy intelligent agents, and integrate multiple LLM providers without writing extensive boilerplate code.
Built for real-world deployment, Dify combines the flexibility of code with the speed of visual development. The platform supports RAG (Retrieval-Augmented Generation) pipelines, multi-agent orchestration, and seamless integration with OpenAI, Gemini, and other leading AI models. Its architecture handles everything from prompt engineering to production monitoring.
What sets Dify apart is its production-ready infrastructure that includes built-in observability, version control for prompts, and enterprise security features. Whether you're building customer support bots, document analysis systems, or complex multi-step AI workflows, Dify provides the scaffolding to go from prototype to production quickly.
The platform leverages a modern tech stack with Python for backend orchestration and Next.js for the frontend, making it both powerful and maintainable. With extensive API support and MCP (Model Context Protocol) integration, developers can customize and extend the platform to fit specific business requirements.
Key Features
- Visual workflow builder with low-code/no-code interface for rapid AI application development
- Multi-LLM support including OpenAI, Gemini, and custom models with unified API abstraction
- Built-in RAG engine with vector database integration for knowledge-enhanced AI responses
- Production-grade observability with logging, monitoring, and prompt version control
- Agent orchestration framework for building collaborative multi-agent systems with defined roles
How You Can Use It
Building intelligent customer service chatbots with context-aware responses and knowledge base integration
Creating document processing pipelines that extract, analyze, and summarize information from large datasets
Developing multi-agent systems where specialized AI agents collaborate to solve complex business problems
Orchestrating RAG applications that combine proprietary data with LLM capabilities for domain-specific answers
Who Is This For?
AI/ML engineers, full-stack developers, product teams, and enterprises looking to rapidly deploy production-grade AI applications without building infrastructure from scratch