--- title: "ruoyi-ai vs Prompt-Engineering-Guide" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ageerle-ruoyi-ai-vs-dair-ai-prompt-engineering-guide" tools: ["ageerle-ruoyi-ai", "dair-ai-prompt-engineering-guide"] --- # ruoyi-ai vs Prompt-Engineering-Guide *GraphCanon updated Jul 15, 2026* ## Verdict Pick ruoyi-ai when ruoyi-ai is primarily Java; Prompt-Engineering-Guide is MDX; pick Prompt-Engineering-Guide when prompt-Engineering-Guide is primarily MDX; ruoyi-ai is Java. [ruoyi-ai](https://doc.ruoyiai.chat) reports 5.5k GitHub stars, 1.4k forks, and 8 open issues, last pushed Jul 15, 2026. [Prompt-Engineering-Guide](https://www.promptingguide.ai/) has 76k stars, 8.4k forks, and 274 open issues, last pushed Mar 11, 2026. Figures are from public GitHub metadata via [ruoyi-ai's repository](https://github.com/ageerle/ruoyi-ai) and [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide). | | [ruoyi-ai](/tools/ageerle-ruoyi-ai.md) | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | | --- | --- | --- | | Tagline | 面向企业级市场的一站式AI应用开发框架，支持多厂商大模型统一接入与管理，具备安全可控的企业知识库与高精度检索优化能力，提供可视化流程编排、自主决策智能体与多智能体协同调度，兼容主流 Agent Skill 协议，帮助企业与开发者零门槛快速构建安全、高效、可落地的AI智能体应用与行业解决方案。 | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | | Stars | 5,514 | 76,349 | | Forks | 1,353 | 8,361 | | Open issues | 8 | 274 | | Language | Java | MDX | | Adopt for | - | Decision-critical facts for Prompt-Engineering-Guide | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Computer Vision, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ruoyi-ai](/tools/ageerle-ruoyi-ai.md) | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 121d | | Open issues (now) | 8 | 274 | | Owner type | User | Organization | | Full report | [trust report](/tools/ageerle-ruoyi-ai/trust.md) | [trust report](/tools/dair-ai-prompt-engineering-guide/trust.md) | ## Decision facts: Prompt-Engineering-Guide - **Adopt for:** Decision-critical facts for Prompt-Engineering-Guide ## Choose when ### Choose ruoyi-ai if… - ruoyi-ai is primarily Java; Prompt-Engineering-Guide is MDX. - Tags unique to ruoyi-ai: ai, java, knowledge, mcp. - Also covers Computer Vision. ### Choose Prompt-Engineering-Guide if… - Prompt-Engineering-Guide is primarily MDX; ruoyi-ai is Java. - Tags unique to Prompt-Engineering-Guide: agents, ai-agents, chatgpt, deep-learning. - When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ## When NOT to use ruoyi-ai - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use Prompt-Engineering-Guide - Avoid using if your focus is entirely on deep-learning frameworks without a need for detailed instructions or examples related to prompt crafting. - Not suitable when you require tools that go beyond guiding materials, such as custom prompts or direct software plugins provided by competitors focused more on practical implementation over learning. ## Common questions ### What is the difference between ruoyi-ai and Prompt-Engineering-Guide? ruoyi-ai: 面向企业级市场的一站式AI应用开发框架，支持多厂商大模型统一接入与管理，具备安全可控的企业知识库与高精度检索优化能力，提供可视化流程编排、自主决策智能体与多智能体协同调度，兼容主流 Agent Skill 协议，帮助企业与开发者零门槛快速构建安全、高效、可落地的AI智能体应用与行业解决方案。. Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose ruoyi-ai over Prompt-Engineering-Guide? Choose ruoyi-ai over Prompt-Engineering-Guide when ruoyi-ai is primarily Java; Prompt-Engineering-Guide is MDX; Tags unique to ruoyi-ai: ai, java, knowledge, mcp; Also covers Computer Vision. ### When should I choose Prompt-Engineering-Guide over ruoyi-ai? Choose Prompt-Engineering-Guide over ruoyi-ai when Prompt-Engineering-Guide is primarily MDX; ruoyi-ai is Java; Tags unique to Prompt-Engineering-Guide: agents, ai-agents, chatgpt, deep-learning; When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ### When should I avoid ruoyi-ai? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid Prompt-Engineering-Guide? Avoid using if your focus is entirely on deep-learning frameworks without a need for detailed instructions or examples related to prompt crafting. Not suitable when you require tools that go beyond guiding materials, such as custom prompts or direct software plugins provided by competitors focused more on practical implementation over learning. ### Is ruoyi-ai or Prompt-Engineering-Guide more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 5,514). Stars measure visibility, not whether either tool fits your constraints. ### Are ruoyi-ai and Prompt-Engineering-Guide open source? Yes - both are open-source projects on GitHub (ruoyi-ai: MIT, Prompt-Engineering-Guide: MIT). ### Where can I find alternatives to ruoyi-ai or Prompt-Engineering-Guide? GraphCanon lists graph-backed alternatives at [ruoyi-ai alternatives](/tools/ageerle-ruoyi-ai/alternatives) and [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) ([ruoyi-ai markdown twin](/tools/ageerle-ruoyi-ai/alternatives.md), [Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/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-dair-ai-prompt-engineering-guide.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ruoyi-ai or Prompt-Engineering-Guide? ruoyi-ai: Very active. Prompt-Engineering-Guide: Slowing. 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 Prompt-Engineering-Guide? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ruoyi-ai trust report](/tools/ageerle-ruoyi-ai/trust); [Prompt-Engineering-Guide trust report](/tools/dair-ai-prompt-engineering-guide/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/_