RRepoGEO

REPOGEO REPORT · LITE

1517005260/graph-rag-agent

Default branch master · commit 4296b7c6 · scanned 5/29/2026, 8:13:21 AM

GitHub: 2,189 stars · 303 forks

AI VISIBILITY SCORE
40 /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
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 1517005260/graph-rag-agent, 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
    Clarify the README's opening as a comprehensive framework/solution

    Why:

    CURRENT
    # GraphRAG + DeepSearch 实现与问答系统(Agent)构建
    本项目聚焦于结合 **GraphRAG** 与 **私域 Deep Search** 的方式,实现可解释、可推理的智能问答系统,同时结合多 Agent 协作与知识图谱增强,构建完整的 RAG 智能交互解决方案。
    COPY-PASTE FIX
    # GraphRAG + DeepSearch: A Comprehensive Multi-Agent RAG Framework for Explainable Knowledge Graph Q&A
    本项目提供了一个完整的解决方案,聚焦于结合 **GraphRAG** 与 **私域 Deep Search** 的方式,实现可解释、可推理的智能问答系统,同时结合多 Agent 协作与知识图谱增强,构建完整的 RAG 智能交互解决方案。
  • mediumtopics#2
    Add more specific topics to improve query matching

    Why:

    CURRENT
    agentic-rag, chain-of-exploration, deepresearch, deepsearch, evaluation, graphrag, graphsearch, kg, lightrag, reasoning, think-on-graph
    COPY-PASTE FIX
    agentic-rag, chain-of-exploration, deepresearch, deepsearch, evaluation, graphrag, graphsearch, kg, lightrag, reasoning, think-on-graph, multi-agent, explainable-ai, knowledge-graph, rag-framework, rag-evaluation
  • lowcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a section titled 'Comparison with Alternatives' or 'Why GraphRAG + DeepSearch?' that briefly explains how this project differs from general RAG frameworks (e.g., LangChain, LlamaIndex) by focusing on explainable, multi-agent GraphRAG with DeepSearch and custom evaluation, and how it leverages but is distinct from pure graph databases (e.g., Neo4j).

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 1517005260/graph-rag-agent
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 1×
  3. Amazon Neptune · recommended 1×
  4. Grakn (Vaticle Ascent) · recommended 1×
  5. LlamaIndex (formerly GPT Index) · recommended 1×
  • CATEGORY QUERY
    How to build an intelligent Q&A system using knowledge graphs and multi-agent RAG?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. Amazon Neptune
    3. Grakn (Vaticle Ascent)
    4. LangChain
    5. LlamaIndex (formerly GPT Index)
    6. AutoGen (Microsoft)
    7. OpenAI GPT-4 / GPT-3.5 Turbo
    8. Anthropic Claude 3 (Opus/Sonnet/Haiku)
    9. Google Gemini (Pro/Ultra)
    10. OpenAI Embeddings (text-embedding-ada-002)
    11. Hugging Face Sentence Transformers
    12. Pinecone
    13. Weaviate
    14. Chroma

    AI recommended 14 alternatives but never named 1517005260/graph-rag-agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for explainable RAG with deep search and custom evaluation capabilities.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. LangSmith
    4. Haystack
    5. Ragas
    6. DSPy

    AI recommended 6 alternatives but never named 1517005260/graph-rag-agent. 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 1517005260/graph-rag-agent?
    pass
    AI named 1517005260/graph-rag-agent explicitly

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

  • If a team adopts 1517005260/graph-rag-agent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 1517005260/graph-rag-agent 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 1517005260/graph-rag-agent solve, and who is the primary audience?
    pass
    AI named 1517005260/graph-rag-agent explicitly

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

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1517005260/graph-rag-agent — 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