RRepoGEO

REPOGEO REPORT · LITE

snap-stanford/Biomni

Default branch main · commit 400c1f36 · scanned 5/24/2026, 2:07:11 AM

GitHub: 3,115 stars · 580 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 snap-stanford/Biomni, 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
  • hightopics#1
    Expand repository topics to include core functionalities

    Why:

    CURRENT
    agent, ai, biomedicine
    COPY-PASTE FIX
    agent, ai, biomedicine, research-automation, llm-agent, hypothesis-generation, task-execution
  • mediumreadme#2
    Strengthen README's opening to emphasize active agent capabilities

    Why:

    CURRENT
    Biomni is a general-purpose biomedical AI agent designed to autonomously execute a wide range of research tasks across diverse biomedical subfields. By integrating cutting-edge large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, Biomni helps scientists dramatically enhance research productivity and generate testable hypotheses.
    COPY-PASTE FIX
    Biomni is an **active AI agent** that **automates complex research tasks** across diverse biomedical subfields, going beyond passive LLMs by autonomously executing experiments, analyzing data, and generating testable hypotheses. By integrating cutting-edge large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, Biomni helps scientists dramatically enhance research productivity and generate testable hypotheses.
  • lowabout#3
    Refine the repository's 'About' description

    Why:

    CURRENT
    Biomni: a general-purpose biomedical AI agent
    COPY-PASTE FIX
    Biomni: an autonomous AI agent for biomedical research automation and hypothesis generation.

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 snap-stanford/Biomni
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BioGPT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. BioGPT · recommended 2×
  2. ChatGPT · recommended 2×
  3. AlphaFold · recommended 2×
  4. GPT-4 · recommended 2×
  5. Elicit · recommended 1×
  • CATEGORY QUERY
    Need an AI agent to automate research tasks across diverse biomedical subfields.
    you: not recommended
    AI recommended (in order):
    1. BioGPT
    2. Elicit
    3. Scispace
    4. ChatGPT
    5. Perplexity AI
    6. Semantic Scholar
    7. AlphaFold

    AI recommended 7 alternatives but never named snap-stanford/Biomni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which AI tools use LLMs and code execution for biomedical hypothesis generation?
    you: not recommended
    AI recommended (in order):
    1. BioGPT
    2. Med-PaLM 2
    3. AlphaFold
    4. GPT-4
    5. Claude
    6. ChatGPT
    7. GPT-4
    8. Code Interpreter (Advanced Data Analysis)
    9. LangChain
    10. LlamaIndex
    11. GPT-3.5 API
    12. GPT-4 API
    13. PubChem
    14. NCBI
    15. DrugBank
    16. SciPy
    17. R
    18. AlphaMissense

    AI recommended 18 alternatives but never named snap-stanford/Biomni. 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 snap-stanford/Biomni?
    pass
    AI named snap-stanford/Biomni explicitly

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

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

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

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