--- title: "ruoyi-ai vs dify" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ageerle-ruoyi-ai-vs-langgenius-dify" tools: ["ageerle-ruoyi-ai", "langgenius-dify"] --- # ruoyi-ai vs dify *GraphCanon updated Sep 20, 2026* ## Verdict Pick ruoyi-ai if ruoyi-ai is an enterprise-focused all-in-one AI app development framework with support for model management, multi-agent collaboration, and RAG technology; pick dify if dify is a platform for creating agentic workflows and RAG pipelines, supporting various AI models and tools, deployable on cloud, VPC, or self-hosted environments. [ruoyi-ai](https://doc.ruoyiai.chat) reports 5.7k GitHub stars, 1.4k forks, and 4 open issues, last pushed Sep 5, 2026. [dify](https://dify.ai) has 156k stars, 25k forks, and 1.1k open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [ruoyi-ai's repository](https://github.com/ageerle/ruoyi-ai) and [dify's repository](https://github.com/langgenius/dify). | | [ruoyi-ai](/tools/ageerle-ruoyi-ai.md) | [dify](/tools/langgenius-dify.md) | | --- | --- | --- | | Tagline | 一站式AI应用开发框架 | Build Agentic workflows and RAG pipelines with rich AI model and tool support on one collaborative workspace. | | Stars | 5,683 | 156,243 | | Forks | 1,403 | 24,675 | | Open issues | 4 | 1,103 | | Language | Java | TypeScript | | Adopt for | Ruoyi-ai is an enterprise-focused all-in-one AI app development framework with support for model management, multi-agent collaboration, and RAG technology. | Dify is a platform for creating agentic workflows and RAG pipelines, supporting various AI models and tools, deployable on cloud, VPC, or self-hosted environments. | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | AI Agents, Data & Retrieval, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ruoyi-ai](/tools/ageerle-ruoyi-ai.md) | [dify](/tools/langgenius-dify.md) | | --- | --- | --- | | Days since push | 2d | 0d | | Open issues (now) | 4 | 1.1k | | Stars delta | +73 (30d) | +4.5k (30d) | | Open issues delta | +3 (30d) | +172 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/ageerle-ruoyi-ai/trust.md) | [trust report](/tools/langgenius-dify/trust.md) | ## Decision facts: ruoyi-ai - **Adopt for:** Ruoyi-ai is an enterprise-focused all-in-one AI app development framework with support for model management, multi-agent collaboration, and RAG technology. ## Decision facts: dify - **Adopt for:** Dify is a platform for creating agentic workflows and RAG pipelines, supporting various AI models and tools, deployable on cloud, VPC, or self-hosted environments. ## Choose when ### Choose ruoyi-ai if… - ruoyi-ai is primarily Java; dify is TypeScript. - License: ruoyi-ai is MIT, dify is Other. - Tags unique to ruoyi-ai: knowledge, mcp, rag. - Also covers Evaluation & Observability, Inference & Serving, Model Training. - When you need to integrate multiple vendor models into a single platform ### Choose dify if… - dify is primarily TypeScript; ruoyi-ai is Java. - License: dify is Other, ruoyi-ai is MIT. - Tags unique to dify: agentic-ai, agentic-framework, agentic-workflow, automation. - Also covers AI Agents. - When you need a collaborative workspace that supports rich AI model and tool integration for agentic workflows and RAG pipelines. ## When NOT to use ruoyi-ai - Avoid if only simple AI functionalities are needed without complex model integration or management - Not recommended for teams preferring non-Java ecosystems as the platform is Java-centric - If immediate deployment and setup speed are critical, due to its enterprise-grade extensive features ## When NOT to use dify - If your project does not require agentic workflows or RAG pipelines, and you are looking for a more general-purpose AI development tool. - When you need a platform that does not require Docker and Docker Compose for setup, as Dify's quick start guide assumes these prerequisites. - If your team prefers a platform with a different licensing model, as Dify uses a modified Apache 2.0 license with additional conditions. ## Common questions ### What is the difference between ruoyi-ai and dify? ruoyi-ai: 一站式AI应用开发框架. dify: Build Agentic workflows and RAG pipelines with rich AI model and tool support on one collaborative workspace.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ruoyi-ai over dify? Choose ruoyi-ai over dify when ruoyi-ai is primarily Java; dify is TypeScript; License: ruoyi-ai is MIT, dify is Other; Tags unique to ruoyi-ai: knowledge, mcp, rag; Also covers Evaluation & Observability, Inference & Serving, Model Training; When you need to integrate multiple vendor models into a single platform. ### When should I choose dify over ruoyi-ai? Choose dify over ruoyi-ai when dify is primarily TypeScript; ruoyi-ai is Java; License: dify is Other, ruoyi-ai is MIT; Tags unique to dify: agentic-ai, agentic-framework, agentic-workflow, automation; Also covers AI Agents; When you need a collaborative workspace that supports rich AI model and tool integration for agentic workflows and RAG pipelines. ### When should I avoid ruoyi-ai? Avoid if only simple AI functionalities are needed without complex model integration or management Not recommended for teams preferring non-Java ecosystems as the platform is Java-centric If immediate deployment and setup speed are critical, due to its enterprise-grade extensive features ### When should I avoid dify? If your project does not require agentic workflows or RAG pipelines, and you are looking for a more general-purpose AI development tool. When you need a platform that does not require Docker and Docker Compose for setup, as Dify's quick start guide assumes these prerequisites. If your team prefers a platform with a different licensing model, as Dify uses a modified Apache 2.0 license with additional conditions. ### Is ruoyi-ai or dify more popular on GitHub? dify has more GitHub stars (156,243 vs 5,683). Stars measure visibility, not whether either tool fits your constraints. ### Are ruoyi-ai and dify open source? Yes - both are open-source projects on GitHub (ruoyi-ai: MIT, dify: Other). ### Where can I find alternatives to ruoyi-ai or dify? GraphCanon lists graph-backed alternatives at [ruoyi-ai alternatives](/tools/ageerle-ruoyi-ai/alternatives) and [dify alternatives](/tools/langgenius-dify/alternatives) ([ruoyi-ai markdown twin](/tools/ageerle-ruoyi-ai/alternatives.md), [dify markdown twin](/tools/langgenius-dify/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/ageerle-ruoyi-ai-vs-langgenius-dify.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ruoyi-ai or dify? ruoyi-ai: Very active. dify: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for ruoyi-ai and dify? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ruoyi-ai trust report](/tools/ageerle-ruoyi-ai/trust); [dify trust report](/tools/langgenius-dify/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ageerle-ruoyi-ai`](/api/graphcanon/graph?tool=ageerle-ruoyi-ai) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_