Production-ready platform for agentic workflow development.
Dify is an open-source LLM application development platform built with Python and Next.js that lets developers and teams construct AI workflows, RAG pipelines, and multi-step agentic applications through a visual, low-code interface. It connects to Claude via Anthropic's API alongside dozens of other model providers, allowing Claude to serve as the backbone of chatbots, document-processing pipelines, or autonomous agent loops within the same environment. Core features include a drag-and-drop workflow builder, a built-in RAG pipeline for indexing and querying document collections, an agent orchestration layer that supports tool use and multi-agent coordination, and MCP compatibility for extending tool access. Observability integrations with Langfuse, Opik, and Arize Phoenix let teams monitor and trace model behavior in production. The platform deploys via Docker Compose and is also available as a hosted cloud service. It suits developers, product teams, and enterprises that want to move LLM prototypes into production without building orchestration infrastructure from scratch.
Production-ready open-source platform for building and orchestrating agentic LLM workflows.
- ✓License: NOASSERTION
- ✓Actively maintained (<30d)
- ✓Healthy fork ratio
- ✓Clear description
- ✓Topics declared
- ✓Mature repo (>1y old)
git clone https://github.com/langgenius/dify && cp dify/*.md ~/.claude/agents/7 items en este repositorio
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.
Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component --json` shows complexity > 50 or lineCount > 300, when the user asks for code splitting, hook extraction, or complexity reduction, or when `pnpm analyze-component` warns to refactor before testing; avoid for simple/well-structured components, third-party wrappers, or when the user explicitly wants testing without refactoring.
Write, update, or review Dify end-to-end tests under `e2e/` that use Cucumber, Gherkin, and Playwright. Use when the task involves `.feature` files, `features/step-definitions/`, `features/support/`, `DifyWorld`, scenario tags, locator/assertion choices, or E2E testing best practices for this repository.
Review Dify frontend code for correctness, accessibility, component design, dify-ui usage, data/query boundaries, performance, and tests. Trigger for `.tsx`, `.ts`, `.js`, UI, React, Next.js, pending-change, or focused frontend review requests.
Generate Vitest + React Testing Library tests for Dify frontend components, hooks, and utilities. Triggers on testing, spec files, coverage, Vitest, RTL, unit tests, integration tests, or write/review test requests.
React/TypeScript component style guide. Use when writing, refactoring, or reviewing React components, especially around props typing, state boundaries, shared local state with Jotai atoms, API types, query/mutation contracts, navigation, memoization, wrappers, and empty-state handling.
Lightweight coding guardrails for making focused, simple, and verifiable changes in this repo. Use for all coding work.
Resumen de Subagents
<p align="center">
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Self-hosting</a> ·
<a href="https://docs.dify.ai">Documentation</a> ·
<a href="https://dify.ai/pricing">Dify edition overview</a>
</p>
<p align="center">
<a href="https://dify.ai" target="_blank">
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<a href="./docs/zh-TW/README.md"><img alt="繁體中文文件" src="https://img.shields.io/badge/繁體中文-d9d9d9"></a>
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</p>
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](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:
## Quick start
> Before installing Dify, make sure your machine meets the following minimum system requirements:
>
> - CPU >= 2 Core
> - RAM >= 4 GiB
<br/>
The 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](https://docs.docker.com/compose/install/) are installed on your machine:
```bash
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](http://localhost/install) and start the initialization process.
#### Seeking help
Please 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.
> 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)
## 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](https://docs.dify.ai/getting-started/readme/model-providers).
**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 <br/>**
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.
- **Self-hosting Dify Community Edition<br/>**
Quickly get Dify running in your environment with this [starter guide](#quick-start).
Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.
- **Dify for enterprise / organizations<br/>**
We provide additional enterprise-centric features. [Send us an email](mailto:business@dify.ai?subject=%5BGitHub%5DBusiness%20License%20Inquiry) to discuss your enterprise needs. <br/>
> For startups and small businesses using AWS, check out [Dify Premium on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) and deploy it to your own AWS VPC with one click. It's an affordable AMI offering with the option to create apps with custom logo and branding.
## Staying ahead
Star Dify on GitHub and be instantly notified of new releases.
## Advanced Setup
### Custom configurations
If 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. YoLo que la gente pregunta sobre dify
¿Qué es langgenius/dify?
+
langgenius/dify es subagents para el ecosistema de Claude AI. Production-ready platform for agentic workflow development. Tiene 145k estrellas en GitHub y se actualizó por última vez today.
¿Cómo se instala dify?
+
Puedes instalar dify clonando el repositorio (https://github.com/langgenius/dify) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar langgenius/dify?
+
Nuestro agente de seguridad ha analizado langgenius/dify y le ha asignado un Trust Score de 97/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene langgenius/dify?
+
langgenius/dify es mantenido por langgenius. La última actividad registrada en GitHub es de today, con 764 issues abiertos.
¿Hay alternativas a dify?
+
Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.
Despliega dify en tu cloud
Lleva este repo a producción en minutos. Cada plataforma genera su propio entorno con variables de entorno editables.
¿Mantienes este repo? Añade un badge a tu README
Pega el badge en tu README de GitHub para mostrar que está auditado por ClaudeWave. Cada badge enlaza de vuelta a esta página y muestra el Trust Score actual.
[](https://claudewave.com/repo/langgenius-dify)<a href="https://claudewave.com/repo/langgenius-dify"><img src="https://claudewave.com/api/badge/langgenius-dify" alt="Featured on ClaudeWave: langgenius/dify" width="320" height="64" /></a>Más Subagents
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