RRepoGEO

REPOGEO REPORT · LITE

SciPhi-AI/R2R

Default branch main · commit 9c5a94d1 · scanned 6/18/2026, 6:11:35 AM

GitHub: 7,890 stars · 635 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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
    Add a 'Why R2R?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '### Why R2R? (vs. LangChain, LlamaIndex, Pinecone, etc.)' with a sentence like: 'Unlike generic LLM orchestration frameworks or standalone vector databases, R2R provides an opinionated, end-to-end solution for production-grade agentic RAG, focusing on simplicity and robustness, with features like multimodal ingestion, hybrid search, and a Deep Research API.'
  • mediumhomepage#2
    Add Homepage URL to repository metadata

    Why:

    COPY-PASTE FIX
    https://r2r-docs.sciphi.ai/
  • mediumreadme#3
    Emphasize 'agentic' and 'production-ready' features in README's 'About' or 'Features' section

    Why:

    COPY-PASTE FIX
    In the 'About' or a new 'Features' section, explicitly state: 'R2R's **Deep Research API** powers its **agentic RAG capabilities**, enabling multi-step reasoning and context-aware answers for complex queries, making it ideal for **production-ready** deployments.'

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 5 of 2 queries
COMPETITOR LEADERBOARD
  1. FastAPI · recommended 5×
  2. Pinecone · recommended 3×
  3. Haystack · recommended 2×
  4. Elasticsearch · recommended 2×
  5. LlamaIndex · recommended 2×
  • CATEGORY QUERY
    I need a production-ready RAG system with a RESTful API for large language model applications.
    you: not recommended
    AI recommended (in order):
    1. Haystack
    2. FAISS
    3. Elasticsearch
    4. Pinecone
    5. OpenAI
    6. Hugging Face models
    7. FastAPI
    8. LlamaIndex
    9. FastAPI
    10. Flask
    11. LangChain
    12. FastAPI
    13. Elasticsearch
    14. ELSER
    15. FastAPI
    16. Spring Boot
    17. Pinecone
    18. Weaviate
    19. Qdrant
    20. FastAPI
    21. OpenAI API

    AI recommended 21 alternatives but never named SciPhi-AI/R2R. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are robust options for building agentic RAG pipelines with multimodal data ingestion capabilities?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. OpenAI Assistants API
    5. unstructured.io
    6. Faiss
    7. Weaviate
    8. Pinecone
    9. Qdrant
    10. Milvus
    11. Chroma
    12. CLIP
    13. BLIP
    14. OpenAI's `text-embedding-ada-002`
    15. transformers
    16. guidance
    17. Microsoft Semantic Kernel

    AI recommended 17 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