返回目录
开源项目自动化与 Agent 类新手

GitHub - langgenius/dify: Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy

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 ,

0 次阅读2026/08/14 发布
GitHub - langgenius/dify: Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy 来源图片

社区作者 · zZz

它解决什么问题

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:

命令
cd dify
命令
cd docker
命令
cp .env.example .env
命令
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

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

  1. Workflow :

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 .

  1. Comprehensive model support :

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

  1. Prompt IDE :

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.

  1. RAG Pipeline :

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.

  1. Agent capabilities :

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

  1. LLMOps :

All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.

  1. Backend-as-a-Service :

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 references and more in-depth instructions.

- Dify for enterprise / organizations

We provide additional enterprise-centric features. Send us an email to discuss your enterprise needs.

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 , 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 .

Metrics Monitoring with Grafana

Import the dashboard to Grafana, using Dify's PostgreSQL database as data source, to monitor metrics in granularity of apps, tenants, messages, and more.

  • Grafana Dashboard by @bowenliang123

Deployment with Kubernetes

If you'd like to configure a highly available setup, there are community-contributed Helm Charts and YAML files which allow Dify to be deployed on Kubernetes.

可复制命令
Helm Chart by @LeoQuote
可复制命令
Helm Chart by @BorisPolonsky
可复制命令
Helm Chart by @magicsong
  • YAML file by @Winson-030
  • YAML file by @wyy-holding
  • 🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym

Using Terraform for Deployment

Deploy Dify to Cloud Platform with a single click using terraform

Azure Global

  • Azure Terraform by @nikawang

Google Cloud

  • Google Cloud Terraform by @sotazum

Using AWS CDK for Deployment

Deploy Dify to AWS with CDK

AWS

  • AWS CDK by @KevinZhao (EKS based)
  • AWS CDK by @tmokmss (ECS based)

Using Alibaba Cloud Computing Nest

Quickly deploy Dify to Alibaba cloud with Alibaba Cloud Computing Nest

Using Alibaba Cloud Data Management

One-Click deploy Dify to Alibaba Cloud with Alibaba Cloud Data Management

Deploy to AKS with Azure Devops Pipeline

One-Click deploy Dify to AKS with Azure Devops Pipeline Helm Chart by @LeoZhang

Contributing

For those who'd like to contribute code, see our Contribution Guide . At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.

We are looking for contributors to help translate Dify into languages other than Mandarin or English. If you are interested in helping, please see the i18n README for more information, and leave us a comment in the global-users channel of our Discord Community Server .

Community & contact

  • GitHub Discussion . Best for: sharing feedback and asking questions.
  • GitHub Issues . Best for: bugs you encounter using Dify.AI, and feature proposals. See our Contribution Guide .
  • Discord . Best for: sharing your applications and hanging out with the community.
  • X(Twitter) . Best for: sharing your applications and hanging out with the community.

Contributors

Star History

Security disclosure

To protect your privacy, please avoid posting security issues on GitHub. Instead, report issues to security@dify.ai , and our team will respond with detailed answer.

License

This repository is licensed under the Dify Open Source License , based on Apache 2.0 with additional conditions.

— 本文由 AI 根据公开来源辅助整理,命令、版本与许可证请在使用前到原始页面复核。

安装 / 开始使用

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

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:

  • CPU >= 2 Core
  • RAM >= 4 GiB
命令
cd dify
命令
cd docker
命令
cp .env.example .env
命令
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

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

  1. Workflow :

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 .

  1. Comprehensive model support :

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

  1. Prompt IDE :

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.

  1. RAG Pipeline :

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.

  1. Agent capabilities :

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

  1. LLMOps :

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 references and more in-depth instructions.

- Dify for enterprise / organizations We provide additional enterprise-centric features. Send us an email to discuss your enterprise needs. 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 , 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 .

Metrics Monitoring with Grafana Import the dashboard to Grafana, using Dify's PostgreSQL database as data source, to monitor metrics in granularity of apps, tenants, messages, and more.

  1. Backend-as-a-Service :

Deployment with Kubernetes If you'd like to configure a highly available setup, there are community-contributed Helm Charts and YAML files which allow Dify to be deployed on Kubernetes.

  • Grafana Dashboard by @bowenliang123
可复制命令
Helm Chart by @LeoQuote
可复制命令
Helm Chart by @BorisPolonsky
可复制命令
Helm Chart by @magicsong

Using Terraform for Deployment Deploy Dify to Cloud Platform with a single click using terraform Azure Global

Google Cloud

Using AWS CDK for Deployment Deploy Dify to AWS with CDK AWS

Using Alibaba Cloud Computing Nest Quickly deploy Dify to Alibaba cloud with Alibaba Cloud Computing Nest Using Alibaba Cloud Data Management One-Click deploy Dify to Alibaba Cloud with Alibaba Cloud Data Management Deploy to AKS with Azure Devops Pipeline One-Click deploy Dify to AKS with Azure Devops Pipeline Helm Chart by @LeoZhang Contributing For those who'd like to contribute code, see our Contribution Guide .

At the same time, please consider supporting Dify by sharing it on social media and at events and conferences. We are looking for contributors to help translate Dify into languages other than Mandarin or English.

If you are interested in helping, please see the i18n README for more information, and leave us a comment in the global-users channel of our Discord Community Server . Community & contact

Contributors Star History Security disclosure To protect your privacy, please avoid posting security issues on GitHub. Instead, report issues to security@dify.ai , and our team will respond with detailed answer.

License This repository is licensed under the Dify Open Source License , based on Apache 2.0 with additional conditions.

  • YAML file by @Winson-030
  • YAML file by @wyy-holding
  • 🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym
  • Azure Terraform by @nikawang
  • Google Cloud Terraform by @sotazum
  • AWS CDK by @KevinZhao (EKS based)
  • AWS CDK by @tmokmss (ECS based)
  • GitHub Discussion . Best for: sharing feedback and asking questions.
  • GitHub Issues . Best for: bugs you encounter using Dify.AI, and feature proposals. See our Contribution Guide .
  • Discord . Best for: sharing your applications and hanging out with the community.
  • X(Twitter) . Best for: sharing your applications and hanging out with the community.

来源教程配图

cover-v5-optimized
配图 1 · cover-v5-optimized查看原图
providers-v5
配图 2 · providers-v5查看原图
star-us
配图 3 · star-us查看原图
教程配图
配图 4 · 教程配图查看原图
Star History Chart
配图 5 · Star History Chart查看原图

适用场景

学习研究
开源项目实践