Dify AI应用开发平台
dify 是一款MCP 工具，可直接接入 Claude 等 AI 客户端（GitHub 141k⭐）。开源MCP工具：Production-ready platform for agentic workflow development.。⭐141.1k · TypeScript。配置方法：将 MCP 服务器条目添加至 claude_desktop_config.json，重启 Claude Desktop 即可使用。AI Skill Hub 编辑推荐，适合希望提升 AI 工作流效率的开发者和运营者。
Dify AI应用开发平台 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。
在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 8.5 分，是同类 Agent 工作流中的精选推荐。
Dify AI应用开发平台 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
Dify AI应用开发平台 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
# 方式一：npm 全局安装 npm install -g dify # 方式二：npx 直接运行（无需安装） npx dify --help # 方式三：项目依赖安装 npm install dify # 方式四：从源码运行 git clone https://github.com/langgenius/dify cd dify npm install npm start
- 访问 GitHub 仓库获取工作流文件
- 在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
- 上传工作流文件
- 按照提示配置必要的环境变量和 API Key
- 运行测试确认流程正常后投入使用
# 命令行使用
dify --help
# 基本用法
dify [options] <input>
# Node.js 代码中使用
const dify = require('dify');
const result = await dify.run(options);
console.log(result);
# dify 配置说明 # 查看配置选项 dify --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export DIFY_CONFIG="/path/to/config.yml"
简介
<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>
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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:
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.
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.
Advanced Setup
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.
- Helm Chart by @LeoQuote
- Helm Chart by @BorisPolonsky
- 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
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
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. Before running Dify with the following commands, make sure that Docker and Docker Compose are installed on your machine:
cd dify
cd docker
cp .env.example .env
docker compose up -dAfter 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
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.
Dify是成熟的开源智能体平台，14万+Stars体现高人气。MCP集成和可视化工作流设计领先业界，文档完整维护活跃，是企业级智能工作流首选。
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
- +GitHub 141.1k Star，社区高度认可
- +大幅减少重复性人工操作
- +可视化流程，清晰直观
- +可扩展性强，支持复杂场景
- −初始配置和调试需投入一定时间
- −强依赖外部服务的稳定性
- −复杂场景需具备一定技术基础
该工具使用 NOASSERTION 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
总体来看，Dify AI应用开发平台 是一款质量优秀的Agent工作流，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | dify |
| 原始描述 | 开源MCP工具：Production-ready platform for agentic workflow development.。⭐141.1k · TypeScript |
| Topics | 智能体框架工作流编排MCP工具AI开发平台开源 |
| GitHub | https://github.com/langgenius/dify |
| License | NOASSERTION |
| 语言 | TypeScript |
收录时间：2026-05-13 · 更新时间：2026-05-26 · License：NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。