langgenius/dify

Project overview

Dify is an open-source platform for teams building, testing, and deploying agentic AI workflows and LLM applications through a visual interface, RAG features, agent tools, model management, and observability. It can be self-hosted or used as a managed service, and exposes APIs for integration with custom business logic.

Project type

AI Agent · RAG · Workflow

Use cases

Knowledge Q&A · Coding & Development · Automation

Deployment

Docker / Docker Compose

License

License pending

Best for

• Developers, AI engineers, and enterprise teams that need to build and operate agentic AI workflows or LLM applications through a visual interface, with RAG, agents, model connections, observability, and APIs available in the same platform.

Key capabilities

• Build and test AI workflows on a visual canvas.
• Connect hundreds of proprietary and open-source LLMs from dozens of inference providers and self-hosted solutions, including GPT, Mistral, Llama3, and OpenAI API-compatible models.
• Craft prompts, compare model performance, and add features such as text-to-speech to a chat-based application.
• Handle document ingestion and retrieval, with built-in text extraction for PDFs, PPTs, and other common document formats.
• Define agents using LLM Function Calling or ReAct and attach pre-built or custom tools.
• Monitor and analyze application logs and performance over time to improve prompts, datasets, and models using production data and annotations.
• Use APIs corresponding to Dify offerings to integrate AI application functionality into custom business logic.

Limitations and risks

• GPU and operating-system requirements are not documented in the available information; verify them against the intended deployment environment.
• Model capabilities can use proprietary or open-source models through inference providers or self-hosted solutions. External API use is optional, and paid services are optional, so confirm the selected model and service arrangement before deployment.

Getting started

• For the documented first path, change into the repository and its docker directory, copy `.env.example` to `.env`, run `docker compose up -d`, then open `http://localhost/install` in a browser. Docker Compose is presented as the easiest method, and the visual interface supports no-code configuration.
• Plan for at least a 2-core CPU and 4 GiB of RAM. GPU requirements are not documented in the available information.

Evidence and sources

• README: Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (incl…
• GitHub project description: Production-ready platform for agentic workflow development.
• Release: 1.15.0
• README: Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
• README: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI AP…

Reviewed at: 2026-07-15T15:12:13Z