REPOGEO REPORT · LITE

AkariAsai/OpenScholar

Default branch main · commit 0e9b8fb9 · scanned 5/17/2026, 2:07:49 AM

GitHub: 1,499 stars · 164 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

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

OVERALL DIRECTION

highreadme#1

Reposition the README H1 and opening sentence to specify category

Why:

CURRENT

# OpenScholar 

This repository contains the code bases of OpenScholar.

COPY-PASTE FIX

# OpenScholar: An Open-Source AI Assistant for Scientific Literature Synthesis 

This repository provides the official implementation of OpenScholar, an open-source and self-hostable AI assistant designed to help researchers efficiently synthesize scientific literature.

hightopics#2

Add relevant topics to the repository

Why:

COPY-PASTE FIX

scientific-literature, retrieval-augmented-generation, llm, nlp, research-assistant, ai-assistant, literature-review, open-source, self-hosted

mediumreadme#3

Add a sentence highlighting OpenScholar's open-source differentiator in the Overview

Why:

COPY-PASTE FIX

Add this sentence to the 'Overview of OpenScholar' section, perhaps after 'Try open-scholar.allen.ai/ and check our paper for more detail.':

"Unlike proprietary platforms, OpenScholar is designed to be open-source and self-hostable, giving researchers full control over their data and privacy while providing powerful literature synthesis capabilities."

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

Of all named tools, what % are you?

Top rival

Elicit

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Elicit · recommended 1×
Scite.ai · recommended 1×
Semantic Scholar · recommended 1×
Connected Papers · recommended 1×
ResearchRabbit · recommended 1×

CATEGORY QUERY
What AI tools are available for synthesizing information from scientific literature efficiently?
you: not recommended
AI recommended (in order):
1. Elicit
2. Scite.ai
3. Semantic Scholar
4. Connected Papers
5. ResearchRabbit
6. Iris.ai
7. ChatGPT
8. GPT-4
AI recommended 8 alternatives but never named AkariAsai/OpenScholar. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to build a system that answers research questions using retrieval-augmented LLMs?
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. LangChain
3. Hugging Face Transformers
4. Hugging Face Datasets
5. OpenAI API
6. Anthropic Claude API
7. Google Gemini API
8. Pinecone
9. Weaviate
10. Chroma
11. FastAPI
12. Streamlit
13. Gradio
14. Docker
15. Kubernetes
AI recommended 15 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?

Embed your GEO score

Drop this badge into the README of AkariAsai/OpenScholar. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/AkariAsai/OpenScholar.svg)](https://repogeo.com/en/r/AkariAsai/OpenScholar)

HTML

<a href="https://repogeo.com/en/r/AkariAsai/OpenScholar"><img src="https://repogeo.com/badge/AkariAsai/OpenScholar.svg" alt="RepoGEO" /></a>

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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