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langgenius / Subagents

dify

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.

  • 159.3k estrellas
  • 25.2k forks
  • 1136 issues abiertos
  • Lenguaje: Python
  • Licencia: NOASSERTION
  • actualizado

Instalación

Terminal
git clone https://github.com/langgenius/dify && cp dify/*.md ~/.claude/agents/
  1. Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
  2. Start a new Claude Code session to load the agents.
  3. Delegate work to them with the Task/Agent tool or by name.

Nota editorial

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.

Items instalables

7 items en este repositorio

Use only when the user explicitly requests a review or audit of backend code under `api/`. Supports pending-change, file-focused, and pasted-diff reviews. Do not use for implementation-only requests, diagnosis without review intent, frontend code, or backend code outside `api/`.

Instalar

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.

Instalar

Use when writing, changing, or reviewing Cucumber and Playwright tests under `e2e/`, including feature files, step definitions, support code, scenario tags, locators, and assertions. Do not use for Vitest, React Testing Library, backend tests, or generic browser automation outside the E2E suite.

Instalar

Use only when the user explicitly requests a review or audit of frontend code under `web/` or `packages/dify-ui/`. Supports pending-change, file-focused, and pasted-diff reviews. Do not use for implementation-only requests, diagnosis without review intent, or backend-only code.

Instalar

Use when writing or changing Vitest or React Testing Library tests under `web/` or `packages/dify-ui/`, or when the user explicitly requests frontend test strategy, including evaluation of an existing strategy. Do not use for frontend code-review-only requests, general testability discussion, Python tests, or Cucumber/Playwright E2E.

Instalar

Use when implementing or refactoring React/TypeScript components and the task requires decisions about component ownership, feature boundaries, state, data flow, effects, or interaction ownership. Do not use for review-only requests, test-only work, copy-only edits, or styling-only changes.

Instalar

Lightweight coding guardrails for making focused, simple, and verifiable changes in this repo. Use for all coding work.

Instalar
README

Resumen de Subagents

README completo en GitHub

cover-v5-optimized

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

alt="chat on Discord"> alt="join Reddit"> alt="follow on X(Twitter)"> alt="follow on LinkedIn">

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: Autonomous agents with a sandbox of their own: they run commands, install software, and handle files to get open-ended tasks done. Describe the agent you want and it builds itself. Give it skills, connect tools from Dify Marketplace, MCP servers, or your own APIs, and put it to work as a chat app or as a step in any workflow.

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 Dify Cloud so you can try Dify without managing a server. See the pricing page for current plans and usage allowances. If you run into issues, 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 references and more in-depth instructions.

  • Dify Enterprise For organizations requiring self-hosting, SSO, RBAC Security and Enterprise Support SLAs, please fill out this form to speak to a solution representative.

Staying ahead

Star Dify on GitHub and be instantly notified of new releases.

Advanced Setup

For custom configuration, observability, and deployment options, see Advanced Setup.

Contributing

Dify welcomes contributions of all kinds:

Topics
agentagentic-aiagentic-frameworkagentic-workflowaiautomationclaudedeepseekgenaigptllmlow-codemcpnextjsno-codeopenaipythonskillsworkflow
Preguntas frecuentes

Lo que la gente pregunta sobre dify

¿Qué es langgenius/dify?

langgenius/dify es subagents para el ecosistema de Claude AI. 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. Tiene 159.3k estrellas en GitHub y su última actualización registrada es del 2026-10-23.

¿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 95/100 (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 del 2026-10-23, con 1136 issues abiertos.

¿Hay alternativas a dify?

Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.

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Web del proyecto
https://dify.ai