RRepoGEO

REPOGEO REPORT · LITE

SciPhi-AI/R2R

Default branch main · commit 9c5a94d1 · scanned 5/8/2026, 2:31:26 PM

GitHub: 7,812 stars · 629 forks

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 SciPhi-AI/R2R, 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 R2R's positioning as an end-to-end RAG system in the README intro

    Why:

    CURRENT
    <h3 align="center">
The most advanced AI retrieval system.

Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.
</h3>
    COPY-PASTE FIX
    <h3 align="center">
The most advanced, **end-to-end** AI retrieval system for production.

Agentic Retrieval-Augmented Generation (RAG) with a RESTful API, **not just a toolkit.**
</h3>
  • mediumhomepage#2
    Add the project's homepage URL

    Why:

    COPY-PASTE FIX
    https://r2r-docs.sciphi.ai/
  • lowreadme#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

R2R is designed as an **end-to-end, API-first RAG system** for production deployment and scalability. Unlike modular RAG toolkits such as LangChain or LlamaIndex, which provide building blocks, R2R offers a comprehensive, integrated solution for multimodal content ingestion, hybrid search, knowledge graphs, and document management, ready for immediate deployment.

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 SciPhi-AI/R2R
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FastAPI
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. FastAPI · recommended 3×
  2. LangChain · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Haystack · recommended 2×
  5. Django REST Framework · recommended 1×
  • CATEGORY QUERY
    Seeking a production-ready RAG system with agentic capabilities and a robust RESTful API.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. FastAPI
    3. LlamaIndex
    4. FastAPI
    5. Haystack
    6. FastAPI
    7. Django REST Framework
    8. OpenAI Assistants API
    9. Flask
    10. Django
    11. Hugging Face Transformers
    12. Faiss

    AI recommended 12 alternatives but never named SciPhi-AI/R2R. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best Python frameworks for building advanced RAG with multimodal search and knowledge graphs?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. GraphRAG
    5. PyG
    6. DGL
    7. Neo4j Python Driver
    8. PyOrient
    9. ArangoDB Python Driver

    AI recommended 9 alternatives but never named SciPhi-AI/R2R. 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 SciPhi-AI/R2R?
    pass
    AI named SciPhi-AI/R2R explicitly

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

  • If a team adopts SciPhi-AI/R2R in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SciPhi-AI/R2R 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 SciPhi-AI/R2R solve, and who is the primary audience?
    pass
    AI named SciPhi-AI/R2R explicitly

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

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SciPhi-AI/R2R — 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