{"repo":"langgenius/dify","free":true,"listed":false,"github":"https://github.com/langgenius/dify","clone":"git clone https://github.com/langgenius/dify.git","description":"Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.","language":"TypeScript","stars":152618,"topics":["agent","agentic-ai","agentic-framework","agentic-workflow","ai","automation","claude","genai","gpt","llm","low-code","mcp","nextjs","no-code","openai","orchestration","python","rag","skills","workflow"],"license":null,"category":"llm_platform","readme_excerpt":"![cover-v5-optimized](./images/GitHub_README_if.png)\n\n

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

\n\n

\n \n \"Static\n \n \"Static\n \n \"chat\n \n \"join\n \n \"follow\n \n \"follow\n \n \"Docker\n \n \"Commits\n \n \"Issues\n \n \"Discussion\n \n \"LFX\n \n \"LFX\n \n \"LFX\n

\n\n

\n \"README\n \"繁體中文文件\"\n \"简体中文文件\"\n \"日本語のREADME\"\n \"README\n \"README\n \"README\n \"README\n \"README\n \"Türkçe\n \"README\n \"README\n \"README\n \"README\n \"README\n \"README\n \"README\n

\n\nDify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (including [Opik](https://www.comet.com/docs/opik/integrations/dify), [Langfuse](https://docs.langfuse.com), and [Arize Phoenix](https://docs.arize.com/phoenix)) and more, letting you quickly go from prototype to production. Here's a list of the core features:\n\n## Quick start\n\n> Before installing Dify, make sure your machine meets the following minimum system requirements:\n>\n> - CPU >= 2 Core\n> - RAM >= 4 GiB\n\n
\n\nThe easiest way to start the Dify server is through [Docker Compose](docker/docker-compose.yaml). Before running Dify with the following commands, make sure that [Docker](https://docs.docker.com/get-docker/) and Docker Compose v2.24.0 or later are installed on your machine:\n\n```bash\ncd dify\ncd docker\ncp .env.example .env\ndocker compose up -d\n```\n\nAfter running, you can access the Dify dashboard in your browser at [http://localhost/install](http://localhost/install) and start the initialization process.\n\n#### Seeking help\n\nPlease refer to our [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs) if you encounter problems setting up Dify. Reach out to [the community and us](#community--contact) if you are still having issues.\n\n> If you'd like to contribute to Dify or do additional development, refer to our [guide to deploying from source code](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)\n\n## Key features\n\n**1. Workflow**:\nBuild and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.\n\n**2. Comprehensive model support**:\nSeamless 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](https://docs.dify.ai/getting-started/readme/model-providers).\n\n![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)\n\n**3. Prompt IDE**:\nIntuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.\n\n**4. RAG Pipeline**:\nExtensive 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.\n\n**5. Agent capabilities**:\nYou 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.\n\n**6. LLMOps**:\nMonitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.\n\n**7. Backend-as-a-Service**:\nAll of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.\n\n## Using Dify\n\n- **Cloud
**\n We host a [Dify Cloud](https://dify.ai) 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](mailto:cloud@dify.ai?subject=%5BGitHub%5DDify%20Cloud%20Support).\n\n- **Self-hosting Dify Community Edition
**\n Quickly get Dify running in your environment with this [starter guide](#quick-start).\n Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.\n\n- **Dify for enterprise / organizations
**\n We provide additional enterprise-centric features. [Send us an email](mailto:business@dify.ai?subject=%5BGitHub%5DBusiness%20License%20Inquiry) to discuss your enterprise needs.
\n\n## Staying ahead\n\nStar Dify on GitHub and be instantly notified of new releases.\n\n![star-us](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)\n\n## Advanced Setup\n\n### Custom configurations\n\nIf you need to customize the configuration, edit `docker/.env`. The essential startup defaults live in [`docker/.env.example`](docker/.env.example), and optional advanced variables are split under `docker/envs/` by theme. After making any changes, re-run `docker compose up -d` from the `docker` directory. You can find the full list of available environment variables [here](https://docs.dify.ai/getting-started/install-self-hosted/environments).\n\n### Metrics Monitoring with Grafana\n\nImport the 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