RRepoGEO

REPOGEO REPORT · LITE

apecloud/ApeRAG

Default branch main · commit a6bb55cd · scanned 7/1/2026, 8:42:06 AM

GitHub: 1,210 stars · 135 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
    Reposition README's core value proposition to emphasize AI agents and GraphRAG

    Why:

    CURRENT
    ApeRAG is the best choice for building your own Knowledge Graph, Context Engineering, and deploying intelligent AI agents that can autonomously search and reason across your knowledge base.
    COPY-PASTE FIX
    ApeRAG empowers you to deploy intelligent AI agents that autonomously search and reason across your knowledge base, making it the best choice for advanced Context Engineering and leveraging Graph RAG for sophisticated AI applications.
  • mediumhomepage#2
    Add homepage URL to repository metadata

    Why:

    COPY-PASTE FIX
    https://archestra.ai/mcp-catalog/apecloud__aperag
  • lowreadme#3
    Add explicit distinction from general-purpose graph databases in README

    Why:

    COPY-PASTE FIX
    Unlike general-purpose graph databases, ApeRAG is purpose-built as a production-ready GraphRAG platform focused on AI agent deployment and advanced context engineering, not just data storage or graph analytics.

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
Amazon Neptune
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Amazon Neptune · recommended 2×
  2. Neo4j AuraDS · recommended 1×
  3. TigerGraph Cloud · recommended 1×
  4. Memgraph Enterprise · recommended 1×
  5. DataStax Astra DB · recommended 1×
  • CATEGORY QUERY
    What are the best production-ready GraphRAG platforms for deploying intelligent AI agents?
    you: not recommended
    AI recommended (in order):
    1. Neo4j AuraDS
    2. TigerGraph Cloud
    3. Memgraph Enterprise
    4. Amazon Neptune
    5. DataStax Astra DB
    6. ArangoDB Oasis

    AI recommended 6 alternatives but never named apecloud/ApeRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build a scalable knowledge graph with multimodal indexing and advanced context engineering?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. Graph Data Science Library (GDS)
    3. Bloom
    4. AuraDB
    5. Weaviate
    6. Pinecone
    7. LangChain
    8. LlamaIndex
    9. Amazon Neptune
    10. Azure Cosmos DB
    11. Google Cloud Knowledge Graph
    12. TypeDB
    13. Apache Jena
    14. Stardog
    15. Virtuoso
    16. ArangoDB
    17. DGL
    18. PyTorch Geometric (PyG)

    AI recommended 18 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?

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apecloud/ApeRAG — 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