Dify: Open Source AI Development Platforms

What is Dify?

What it is

Dify is an open-source LLM application development platform built in TypeScript. It provides a visual interface for designing, testing, and deploying AI workflows, RAG pipelines, and agentic systems without writing extensive code. The platform lives in the generative AI development ecosystem and targets teams needing to move from prototype to production using consistent tooling across environments.

Dify solves the fragmentation and complexity of building LLM-powered applications by unifying workflow orchestration, model integration, retrieval-augmented generation, agent tooling, and observability into a single collaborative workspace. Teams avoid rebuilding stacks when migrating from local development to cloud or on-premises deployments.

Key capabilities

Visual workflow builder for chaining LLM calls, tools, conditionals, and memory nodes
Full RAG pipeline support including document ingestion (PDF, PPT, etc.), chunking, embedding, and retrieval
50+ built-in agent tools such as Google Search, DALL·E, Stable Diffusion, and WolframAlpha
Integration with hundreds of LLMs via OpenAI-compatible APIs, including GPT, Claude, DeepSeek, and Llama3
LLMOps features: log monitoring, prompt analytics, and production data annotation
Backend-as-a-Service with REST APIs for all core functionalities
Observability integrations with Opik, Langfuse, and Arize Phoenix

Who uses it and how

Product teams prototype AI features quickly using the visual canvas before integrating via APIs into production services
Enterprises deploy Dify in VPC or on-premises environments to maintain data governance while building internal RAG applications
Developers extend Dify with custom tools and models, then deploy the full stack using Docker Compose or source builds

Getting started

Install via Docker Compose using the docker-compose.yaml in the repo after copying .env.example to .env, then access http://localhost/install. A hosted Dify Cloud option is available at https://dify.ai with a sandbox plan offering 200 free GPT-4 calls.

When to use it — and when not to

Use Dify when you need a unified, visual platform for building and deploying agentic or RAG-based LLM apps with minimal infrastructure overhead. Avoid it if you require only raw model inference without orchestration, or if your environment cannot meet the minimum 2 CPU core and 4 GiB RAM requirement. Self-hosting requires managing PostgreSQL, vector store (e.g., Weaviate, Chroma), and optional SMTP for notifications.

project readme (upstream, from github) — read inline

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Dify Cloud · Self-hosting · Documentation · Dify edition overview

Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (including Opik, Langfuse, and Arize Phoenix) and more, letting you quickly go from prototype to production. Here's a list of the core features:

Quick start

Before installing Dify, make sure your machine meets the following minimum system requirements:

CPU >= 2 Core

RAM >= 4 GiB

The easiest way to start the Dify server is through Docker Compose. Before running Dify with the following commands, make sure that Docker and Docker Compose v2.24.0 or later are installed on your machine:

cd dify
cd docker
cp .env.example .env
docker compose up -d

After running, you can access the Dify dashboard in your browser at http://localhost/install and start the initialization process.

Seeking help

Please refer to our FAQ if you encounter problems setting up Dify. Reach out to the community and us if you are still having issues.

If you'd like to contribute to Dify or do additional development, refer to our guide to deploying from source code

Key features

1. Workflow: Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.

2. Comprehensive model support: 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 API-compatible models. A full list of supported model providers can be found here.

providers-v5

3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.

4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.

5. Agent capabilities: You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.

6. LLMOps: Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.

7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.

Using Dify

Cloud
We host a Dify Cloud service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan. If you run into issues with Dify Cloud, contact our Cloud support team.
Self-hosting Dify Community Edition
Quickly get Dify running in your environment with this starter guide. Use our documentation for further refere

Frequently asked questions

Is Dify free to use?

Dify is open source. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does Dify do?

Build and deploy AI agents and RAG pipelines visually

What is Dify written in?

Dify is primarily written in TypeScript. Its source is publicly available at https://github.com/langgenius/dify, and it has 156,167 GitHub stars.