RRepoGEO

REPOGEO REPORT · LITE

LHRLAB/Graph-R1

Default branch main · commit e44dbff7 · scanned 6/9/2026, 3:28:33 PM

GitHub: 563 stars · 73 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 LHRLAB/Graph-R1, 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
  • highabout#1
    Refine GitHub repository description for LLM focus

    Why:

    CURRENT
    [ICML 2026] Official resources of "Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning".
    COPY-PASTE FIX
    [ICML 2026] Graph-R1: An agentic GraphRAG framework using end-to-end reinforcement learning to enhance LLM reasoning with graph-structured knowledge.
  • highreadme#2
    Add explicit statement about project's research status

    Why:

    COPY-PASTE FIX
    This repository provides the official research resources for the ICML 2026 paper "Graph-R1", focusing on experimental results and reproducibility.
  • mediumtopics#3
    Expand repository topics to include LLM and RAG terms

    Why:

    CURRENT
    chain-of-thought, graphrag, hypergraph, reinforcement-learning
    COPY-PASTE FIX
    chain-of-thought, graphrag, hypergraph, reinforcement-learning, large-language-models, llms, retrieval-augmented-generation, rag, agentic-ai

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 LHRLAB/Graph-R1
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. Neo4j · recommended 2×
  3. Amazon Neptune · recommended 2×
  4. Apache TinkerPop · recommended 1×
  5. LlamaIndex · recommended 1×
  • CATEGORY QUERY
    Seeking an end-to-end framework to enhance LLM reasoning with graph-structured knowledge.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Neo4j
    3. Apache TinkerPop
    4. LlamaIndex
    5. Kuzu
    6. GraphRAG
    7. Amazon Neptune
    8. Neo4j AuraDB
    9. RelationalAI

    AI recommended 9 alternatives but never named LHRLAB/Graph-R1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to leverage reinforcement learning for iterative reasoning in graph-based RAG systems?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. Amazon Neptune
    3. ArangoDB
    4. PyTorch Geometric (PyG)
    5. Deep Graph Library (DGL)
    6. StellarGraph
    7. RLlib (Ray)
    8. Stable Baselines3
    9. Tianshou
    10. Hugging Face Transformers
    11. OpenAI API
    12. LangChain

    AI recommended 12 alternatives but never named LHRLAB/Graph-R1. 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 LHRLAB/Graph-R1?
    pass
    AI named LHRLAB/Graph-R1 explicitly

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

  • If a team adopts LHRLAB/Graph-R1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name LHRLAB/Graph-R1 — 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?

  • In one sentence, what problem does the repo LHRLAB/Graph-R1 solve, and who is the primary audience?
    pass
    AI named LHRLAB/Graph-R1 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 LHRLAB/Graph-R1. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/LHRLAB/Graph-R1.svg)](https://repogeo.com/en/r/LHRLAB/Graph-R1)
HTML
<a href="https://repogeo.com/en/r/LHRLAB/Graph-R1"><img src="https://repogeo.com/badge/LHRLAB/Graph-R1.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

LHRLAB/Graph-R1 — 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