RRepoGEO

REPOGEO REPORT · LITE

apecloud/ApeRAG

Default branch main · commit a6bb55cd · scanned 5/19/2026, 10:37:30 PM

GitHub: 1,167 stars · 129 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 apecloud/ApeRAG, 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
    Emphasize 'enterprise' in the README's opening sentence

    Why:

    CURRENT
    ApeRAG is a production-ready RAG (Retrieval-Augmented Generation) platform that combines Graph RAG, vector search, and full-text search with advanced AI agents.
    COPY-PASTE FIX
    ApeRAG is a production-ready **enterprise RAG platform** that combines Graph RAG, vector search, and full-text search with advanced AI agents.
  • hightopics#2
    Add specific topics for 'enterprise', 'platform', and 'Kubernetes'

    Why:

    CURRENT
    agents, context-engineering, graphrag, knowledge-graph, mcp
    COPY-PASTE FIX
    agents, context-engineering, graphrag, knowledge-graph, mcp, kubernetes, enterprise-ai, rag-platform
  • mediumhomepage#3
    Add the project's homepage URL

    Why:

    COPY-PASTE FIX
    https://archestra.ai/mcp-catalog/apecloud__aperag

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 apecloud/ApeRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Weaviate · recommended 2×
  4. Neo4j · recommended 1×
  5. Amazon Kendra · recommended 1×
  • CATEGORY QUERY
    How to build a production-ready RAG system with knowledge graphs and intelligent AI agents?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Neo4j
    4. Amazon Kendra
    5. Amazon Bedrock
    6. Amazon SageMaker
    7. Haystack
    8. Weaviate

    AI recommended 8 alternatives but never named apecloud/ApeRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What platforms offer scalable GraphRAG with multimodal indexing and AI agents for enterprise?
    you: not recommended
    AI recommended (in order):
    1. Neo4j Graph Data Science (GDS)
    2. Azure OpenAI Service
    3. AWS Bedrock
    4. Google Cloud Vertex AI
    5. Azure AI Search
    6. Amazon OpenSearch Service
    7. Google Cloud Vector Search
    8. DataStax Astra DB
    9. LangChain
    10. LlamaIndex
    11. OpenAI
    12. Anthropic
    13. Google
    14. TigerGraph Cloud
    15. Pinecone
    16. Weaviate
    17. Amazon Neptune
    18. Google Cloud Knowledge Graph
    19. BigQuery
    20. Dataproc
    21. Vertex AI Vector Search

    AI recommended 21 alternatives but never named apecloud/ApeRAG. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 apecloud/ApeRAG?
    pass
    AI named apecloud/ApeRAG explicitly

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

  • If a team adopts apecloud/ApeRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named apecloud/ApeRAG 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 apecloud/ApeRAG solve, and who is the primary audience?
    pass
    AI named apecloud/ApeRAG explicitly

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

Embed your GEO score

Drop this badge into the README of apecloud/ApeRAG. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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apecloud/ApeRAG — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite