RRepoGEO

REPOGEO REPORT · LITE

ageerle/ruoyi-ai

Default branch main · commit 7aa67cfc · scanned 6/20/2026, 5:17:54 AM

GitHub: 5,409 stars · 1,332 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface ageerle/ruoyi-ai, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README H1 to explicitly state it's an enterprise AI application development framework

    Why:

    CURRENT
    # RuoYi AI
    COPY-PASTE FIX
    # RuoYi AI: 一站式企业级AI应用开发框架 (One-stop Enterprise AI Application Development Framework)
  • mediumtopics#2
    Expand topics to include broader AI framework and orchestration terms

    Why:

    CURRENT
    agent, ai, knowledge, mcp, rag
    COPY-PASTE FIX
    agent, ai, knowledge, mcp, rag, ai-framework, llm-orchestration, enterprise-ai, multi-agent-system, workflow-orchestration, ai-platform
  • lowreadme#3
    Integrate key differentiators more explicitly into the README's '核心亮点' (Core Highlights) section

    Why:

    COPY-PASTE FIX
    Enhance the '核心亮点' (Core Highlights) section to explicitly state how its features (e.g., multi-vendor LLM integration, secure knowledge base, visual workflow orchestration, multi-agent collaboration, Agent Skill protocol compatibility) differentiate it from other solutions, rather than just listing capabilities.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface ageerle/ruoyi-ai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Microsoft Azure AI Studio · recommended 1×
  4. Azure OpenAI Service · recommended 1×
  5. Azure Cognitive Search · recommended 1×
  • CATEGORY QUERY
    Framework for building enterprise AI applications with secure knowledge base and multi-agent orchestration.
    you: not recommended
    AI recommended (in order):
    1. Microsoft Azure AI Studio
    2. Azure OpenAI Service
    3. Azure Cognitive Search
    4. Azure Functions
    5. Logic Apps
    6. Azure Kubernetes Service (AKS)
    7. Google Cloud Vertex AI
    8. Google Cloud Storage
    9. BigQuery
    10. Vertex AI Search
    11. Cloud Functions
    12. Cloud Run
    13. GKE (Google Kubernetes Engine)
    14. AWS Bedrock
    15. Amazon SageMaker
    16. Amazon S3
    17. Amazon OpenSearch Service
    18. Amazon Kendra
    19. AWS Lambda
    20. AWS Step Functions
    21. Amazon ECS/EKS
    22. LangChain
    23. Llama 3
    24. Kubernetes
    25. OpenShift
    26. Pinecone
    27. Weaviate
    28. Qdrant
    29. Hugging Face Ecosystem
    30. Hugging Face Enterprise Hub
    31. Transformers
    32. LlamaIndex
    33. PostgreSQL
    34. pgvector

    AI recommended 34 alternatives but never named ageerle/ruoyi-ai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to visually orchestrate AI agents and integrate multiple large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangServe
    3. LangSmith
    4. Microsoft Semantic Kernel
    5. FlowiseAI
    6. LlamaIndex
    7. OpenAI Assistants API
    8. Dust.tt
    9. D3.js
    10. React Flow
    11. Mermaid.js

    AI recommended 11 alternatives but never named ageerle/ruoyi-ai. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of ageerle/ruoyi-ai?
    pass
    AI did not name ageerle/ruoyi-ai — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts ageerle/ruoyi-ai in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ageerle/ruoyi-ai explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo ageerle/ruoyi-ai solve, and who is the primary audience?
    pass
    AI named ageerle/ruoyi-ai explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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ageerle/ruoyi-ai — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite