RRepoGEO

REPOGEO REPORT · LITE

AkariAsai/OpenScholar

Default branch main · commit 0e9b8fb9 · scanned 6/28/2026, 2:42:21 AM

GitHub: 1,549 stars · 167 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 AkariAsai/OpenScholar, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumreadme#1
    Strengthen README's opening sentence to immediately state core value

    Why:

    CURRENT
    # OpenScholar 

This repository contains the code bases of OpenScholar.
    COPY-PASTE FIX
    # OpenScholar 

OpenScholar is an open-source retrieval-augmented language model (LM) for synthesizing scientific literature, designed to help researchers answer queries by grounding responses in relevant papers.
  • lowcomparison#2
    Add a 'Comparison with Alternatives' section to README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

Unlike many proprietary, cloud-based research assistants, OpenScholar is an open-source and self-hostable retrieval-augmented language model, providing researchers with full control and transparency over their scientific literature synthesis.

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 AkariAsai/OpenScholar
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Semantic Scholar
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Semantic Scholar · recommended 2×
  2. Scite.ai · recommended 2×
  3. Elicit AI · recommended 1×
  4. Connected Papers · recommended 1×
  5. Zotero · recommended 1×
  • CATEGORY QUERY
    How to efficiently synthesize information from a vast collection of scientific papers?
    you: not recommended
    AI recommended (in order):
    1. Elicit AI
    2. Semantic Scholar
    3. Connected Papers
    4. Zotero
    5. Mendeley
    6. Obsidian
    7. Dataview
    8. Zotero Integration
    9. Scite.ai

    AI recommended 9 alternatives but never named AkariAsai/OpenScholar. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best retrieval-augmented language models for scientific literature review?
    you: not recommended
    AI recommended (in order):
    1. Elicit
    2. Semantic Scholar
    3. Scite.ai
    4. ChatGPT Plus
    5. ScholarAI
    6. AskYourPDF
    7. Litmaps
    8. Perplexity AI
    9. ResearchRabbit
    10. Consensus

    AI recommended 10 alternatives but never named AkariAsai/OpenScholar. 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 AkariAsai/OpenScholar?
    pass
    AI named AkariAsai/OpenScholar explicitly

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

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

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

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AkariAsai/OpenScholar — 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