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

Of all named tools, what % are you?

Top rival

Amazon Neptune

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Amazon Neptune · recommended 2×
Neo4j · recommended 1×
LangChain · recommended 1×
LlamaIndex · recommended 1×
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

Drop this badge into the README of TencentCloudADP/youtu-graphrag. 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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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/TencentCloudADP/youtu-graphrag.svg)](https://repogeo.com/en/r/TencentCloudADP/youtu-graphrag)

HTML

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TencentCloudADP/youtu-graphrag — 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