RRepoGEO

REPOGEO REPORT · LITE

TencentCloudADP/youtu-graphrag

Default branch main · commit d982b5a8 · scanned 5/15/2026, 1:07:13 PM

GitHub: 1,177 stars · 177 forks

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 TencentCloudADP/youtu-graphrag, 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 introduction to highlight unique agentic GraphRAG framework

    Why:

    CURRENT
    Youtu-GraphRAG is a vertically unified agentic paradigm that jointly connects the entire framework as an intricate integration based on graph schema. We allow seamless domain transfer with minimal intervention on the graph schema, providing insights of the next evolutionary GraphRAG paradigm for real-world applications with remarkable adaptability.
    COPY-PASTE FIX
    Youtu-GraphRAG is a vertically unified agentic paradigm for Graph Retrieval-Augmented Generation (GraphRAG), specifically designed for complex reasoning over graph-structured information. Unlike general RAG frameworks (e.g., LangChain, LlamaIndex) or standalone graph databases (e.g., Neo4j, Amazon Neptune), Youtu-GraphRAG provides an end-to-end, agentic solution that jointly connects the entire framework based on graph schema, allowing seamless domain transfer with remarkable adaptability.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    agent, graph, graphrag, llm, rag
    COPY-PASTE FIX
    agent, graph, graphrag, llm, rag, agentic-framework, knowledge-graph-rag, complex-reasoning-llm, multi-hop-reasoning
  • lowlicense#3
    Clarify existing license(s) in the README

    Why:

    COPY-PASTE FIX
    Add a section to the README, e.g., '## License
This project is licensed under [SPECIFY LICENSE NAME(S) HERE, e.g., Apache-2.0 AND MIT]. Please refer to the [LICENSE](LICENSE) file for complete details.'

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 TencentCloudADP/youtu-graphrag
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 · recommended 1×
  3. LangChain · recommended 1×
  4. LlamaIndex · recommended 1×
  5. TypeDB · recommended 1×
  • CATEGORY QUERY
    How can I enhance LLM complex reasoning using graph-based retrieval augmented generation?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. LangChain
    3. LlamaIndex
    4. TypeDB
    5. Amazon Neptune
    6. ArangoDB
    7. Apache Jena
    8. Stardog
    9. PyTorch Geometric
    10. DGL

    AI recommended 10 alternatives but never named TencentCloudADP/youtu-graphrag. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Comparing agent-based RAG solutions for complex reasoning over graph-structured information.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Neo4j (neo4j/neo4j)
    3. Memgraph (memgraph/memgraph)
    4. LlamaIndex (run-llama/llama_index)
    5. GraphRAG (microsoft/GraphRAG)
    6. ArangoDB (arangodb/arangodb)
    7. OpenAI APIs (openai/openai-python)
    8. Anthropic APIs (anthropics/anthropic-sdk-python)
    9. Amazon Neptune

    AI recommended 9 alternatives but never named TencentCloudADP/youtu-graphrag. 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 TencentCloudADP/youtu-graphrag?
    pass
    AI named TencentCloudADP/youtu-graphrag explicitly

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

  • If a team adopts TencentCloudADP/youtu-graphrag in production, what risks or prerequisites should they evaluate first?
    pass
    AI named TencentCloudADP/youtu-graphrag 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 TencentCloudADP/youtu-graphrag solve, and who is the primary audience?
    pass
    AI did not name TencentCloudADP/youtu-graphrag — 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?

Embed your GEO score

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