RRepoGEO

REPOGEO REPORT · LITE

lamm-mit/SciAgentsDiscovery

Default branch main · commit c5c30451 · scanned 5/31/2026, 7:38:24 PM

GitHub: 612 stars · 109 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 lamm-mit/SciAgentsDiscovery, 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
  • highabout#1
    Add a concise 'About' description for better categorization

    Why:

    COPY-PASTE FIX
    An adaptive framework of LLM-powered multi-agents for automating scientific discovery, leveraging knowledge graphs to uncover hidden interdisciplinary relationships in scientific data.
  • mediumreadme#2
    Refine README's opening to highlight application vs. framework

    Why:

    CURRENT
    A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, we present SciAgents, an approach that leverages three core concepts: (1) the use of large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities.
    COPY-PASTE FIX
    SciAgents is an adaptive framework of LLM-powered multi-agents designed to autonomously advance scientific understanding. It addresses a key challenge in artificial intelligence by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. Our approach leverages three core concepts: (1) the use of large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning 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 lamm-mit/SciAgentsDiscovery
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 2×
  2. vaticle/typedb · recommended 1×
  3. apache/jena · recommended 1×
  4. Stardog · recommended 1×
  5. langchain-ai/langchain · recommended 1×
  • CATEGORY QUERY
    How can I use AI agents and knowledge graphs to automate scientific discovery?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. TypeDB (vaticle/typedb)
    3. Apache Jena (apache/jena)
    4. Stardog
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)
    7. AutoGPT (significant-gravitas/AutoGPT)
    8. BabyAGI (yoheinakajima/babyagi)
    9. RDKit (rdkit/rdkit)
    10. Open Babel (openbabel/openbabel)
    11. BioPython (biopython/biopython)
    12. SciPy (scipy/scipy)
    13. NumPy (numpy/numpy)
    14. Pandas (pandas-dev/pandas)

    AI recommended 14 alternatives but never named lamm-mit/SciAgentsDiscovery. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help uncover hidden interdisciplinary relationships in scientific data using large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. openai
    3. Hugging Face Transformers Library
    4. spaCy
    5. Google Cloud AI Platform (Vertex AI)
    6. Microsoft Azure OpenAI Service
    7. Neo4j
    8. Amazon Neptune
    9. LangChain
    10. LlamaIndex

    AI recommended 10 alternatives but never named lamm-mit/SciAgentsDiscovery. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 lamm-mit/SciAgentsDiscovery?
    pass
    AI did not name lamm-mit/SciAgentsDiscovery — likely talking about a different project

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

  • If a team adopts lamm-mit/SciAgentsDiscovery in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lamm-mit/SciAgentsDiscovery 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 lamm-mit/SciAgentsDiscovery solve, and who is the primary audience?
    pass
    AI named lamm-mit/SciAgentsDiscovery 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 lamm-mit/SciAgentsDiscovery. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/lamm-mit/SciAgentsDiscovery.svg)](https://repogeo.com/en/r/lamm-mit/SciAgentsDiscovery)
HTML
<a href="https://repogeo.com/en/r/lamm-mit/SciAgentsDiscovery"><img src="https://repogeo.com/badge/lamm-mit/SciAgentsDiscovery.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

lamm-mit/SciAgentsDiscovery — 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