Dify

Dify is an open-source platform for building and operating applications powered by large language models (LLMs), providing a visual workflow builder, a built-in retrieval-augmented generation (RAG) pipeline, agent capabilities, and support for dozens of model providers in a single interface. First released in 2023 by the company LangGenius, it became one of the most widely used open-source LLM application frameworks, popular with developers who want to ship AI features without building the surrounding infrastructure from scratch.^[1][2]

History

Dify was created by a team led by Zhang Luqi, a former employee of ByteDance and Tencent, to lower the barrier to building LLM applications. The project was open-sourced on GitHub under the Apache 2.0 licence and grew rapidly in popularity, becoming one of the most-starred artificial intelligence repositories on the platform as interest in generative AI expanded through 2024 and 2025.^[2]

The platform evolved from a prompt-orchestration tool into a fuller application development environment, adding visual workflow orchestration, an agent node system, a built-in RAG engine with multiple retrieval strategies, and observability features such as tracing and annotation. A hosted cloud offering, Dify Cloud, was introduced alongside the self-hosted version, and an enterprise edition added deployment and governance features for larger organisations.^[1][3]

Key Concepts

Dify's core abstraction is the application, which developers assemble from a visual canvas of nodes rather than writing glue code. A typical Dify application chains together nodes for model invocation, prompt templates, knowledge retrieval, tool calls, and conditional logic, and the resulting graph can be exposed as a chatbot interface or an API endpoint.^[3]

The platform includes a RAG pipeline with document ingestion, chunking, embedding, and hybrid retrieval, plus a built-in vector database option, allowing teams to build question-answering systems over their own documents. Dify also supports agentic workflows, in which a language model decides which tools to invoke, and it can connect to external tools and services through the Model Context Protocol (MCP) and standard API integrations.^[3]

A defining characteristic of Dify is model provider integration: a single configuration panel connects to models from OpenAI, Anthropic, Google, DeepSeek, and many other providers, including locally served models through Ollama. Because the platform is self-hostable, organisations can keep their application code, prompts, and data within their own infrastructure.^[2][3]

Applications and Impact

Dify is used to build a wide range of LLM applications: customer-support chatbots grounded in knowledge bases, internal document search and summarisation tools, lead-qualification assistants, and multi-step agent systems that query databases or call external APIs. Its combination of a visual builder and an API backend lets non-specialists prototype AI features while still giving engineering teams a deployment path.^[1][3]

The project's open-source model has made it a common choice for small and medium enterprises, startups, and public-sector teams that want control over their AI applications without committing to a single commercial platform. Its documentation and community provide templates and examples that have been reused across industries, and the hosted cloud offering serves teams that prefer a managed service.^[1][2]

>See Also

• LangChain
• Retrieval-Augmented Generation
• AI Agents
• Model Context Protocol
• Ollama
• Vector Database

References