// rag · langgenius/dify

Dify

Dify es un proyecto de RAG y conocimiento de código abierto de langgenius (langgenius/dify en GitHub). Tiene 157.773 estrellas en GitHub y 24.895 bifurcaciones, se publica bajo la licencia Custom (check repo) y se actualizó por última vez en GitHub el 3 de octubre de 2026.

Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…

★ 157,8k estrellas⑂ 24,9k bifurcacionesCustom (check repo)actualizado oct 2026
▦ visual: el README de este proyecto no incluye ninguna captura incrustable,
así que aquí va un marcador. La guía de abajo está completa de todos modos.
// datos del proyecto

Datos del proyecto

Repositorio
langgenius/dify
Estrellas en GitHub
157.773
Bifurcaciones
24.895
Licencia
Custom (check repo)
Última actualización en GitHub
3 de octubre de 2026
Tipo de ficha
Guía de instalación completa
Sitio oficial
Official site
// cómo funciona

Cómo funciona

  1. Connect your apps

    Link the services you already use — chat, email, sheets, CRMs, databases — through built-in integrations or generic webhooks and API calls.

  2. Build the flow visually

    Drag triggers and actions onto a canvas: when this happens, do that. Branch on conditions, loop over items, and transform data between steps.

  3. Add AI where it helps

    Pipe steps through language models for classification, extraction, summarising, or drafting — most platforms here treat AI as just another node.

  4. Deploy and monitor

    Turn the workflow on, watch executions in the run history, and get alerted when something fails.

// características

Por qué gusta tanto

Visual workflow builder

Design automations on a canvas instead of writing glue code for every integration.

Hundreds of integrations

Prebuilt connectors for popular apps plus HTTP, webhooks, and database nodes for everything else.

Self-hosted & private

Your credentials and execution data stay on your infrastructure — no third-party middleman.

AI-native steps

Call LLMs mid-workflow for decisions, extraction, and content generation.

Scheduling & triggers

Cron schedules, webhooks, and event triggers keep flows running around the clock.

No per-run pricing

Open-source means the automation itself is free; you only pay for your own server.

Advertisement
// ejecútalo

Ejecútalo tú mismo

  1. Step 1 — Clone the repo

    Download the source and move into the project folder.

    git clone https://github.com/langgenius/dify.git
    cd dify
  2. Step 2 — Install dependencies

    Use the package manager the project specifies (check for pnpm or yarn lockfiles).

    npm install
  3. Step 3 — Start it

    Launch the dev server or build, per the README.

    npm run dev
// casos de uso

Qué puedes crear

Also on this shelf: ai-dev-tools-zoomcamp — free hands-on course on using AI dev tools without losing engineering discipline. The Automation shelf also holds rpaframework — open-source RPA libraries for automating browsers, documents, and desktops. Readers working through the Automation shelf also stop at x-tweet-fetcher, Fetch X tweets, replies, and timelines with no login or API keys required.

Advertisement
// preguntas frecuentes

Preguntas frecuentes

Is it really free?

The core software is open-source and free to self-host. Managed cloud versions exist for most of these if you would rather not run a server.

Do I need to code?

Most flows are built visually. Light expressions or code nodes help for advanced logic, but simple automations need none.

Who maintains Dify?

It is an open-source project at langgenius/dify with 24,895 forks. Check the repo's contributors and recent commits to see how active maintenance is.

How does it compare to Zapier?

Same idea — triggers and actions across apps — but self-hosted, with AI steps first-class and no per-task pricing.

What do I need to run it?

Typically Docker and a small VPS or home server; several offer one-command installs.

Can it call AI models?

Yes — LLM nodes are standard, working with hosted APIs or your own local models.

// sigue explorando

Herramientas similares

Automatización

n8n

Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+…

★ 206,6k estrellasCustom (check repo)
Ver la guía →
Automatización

Automatisch

The open source Zapier alternative. Build workflow automation without spending time and money

★ 14,0k estrellasCustom (check repo)
Ver la guía →
Automatización

Activepieces

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI…

★ 24,9k estrellasCustom (check repo)
Ver la guía →
RAG y conocimiento

txtai

All-in-one AI framework for semantic search, LLM orchestration and language model workflows.

★ 13,0k estrellasApache-2.0
Ver la guía →
RAG y conocimiento

MinerU

MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms

★ 82,5k estrellasApache-2.0 + terms
Ver la guía →
RAG y conocimiento

llm-app

Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data

★ 58,8k estrellasMIT
Ver la guía →
// alternativas

Alternativas en RAG y conocimiento

Los demás proyectos de RAG y conocimiento con más estrellas del catálogo de LibreAI: la lista realista de alternativas si Dify no es lo que buscas.

// más en rag

Más en RAG y conocimiento

Proyectos de RAG y conocimiento cercanos en el catálogo, los más próximos en estrellas a Dify: sigue explorando a ambos lados.

Consigue Dify

Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.