RRepoGEO

REPOGEO REPORT · LITE

NVlabs/Eagle

Default branch main · commit 3af39904 · scanned 6/4/2026, 5:08:06 AM

GitHub: 1,993 stars · 166 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 NVlabs/Eagle, 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 clear, concise opening sentence to the README's first paragraph

    Why:

    COPY-PASTE FIX
    Add a sentence like: "Eagle is a cutting-edge family of Vision-Language Models (VLMs) designed for advanced image understanding and multimodal reasoning, leveraging data-centric strategies for superior performance."
  • highreadme#2
    Explicitly state the project's license in the README

    Why:

    COPY-PASTE FIX
    Add a line in the 'Resources' or 'About' section of the README: "This project is released under the Apache-2.0 License."
  • mediumreadme#3
    Add a 'Comparison' or 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Key Differentiators' or 'Comparison with Other VLMs' that briefly explains how Eagle stands out from models like LLaVA, CogVLM, or Fuyu-8B in terms of architecture, data strategies, or performance.

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 NVlabs/Eagle
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LLaVA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LLaVA · recommended 1×
  2. CogVLM · recommended 1×
  3. Fuyu-8B · recommended 1×
  4. MiniGPT-4 / MiniGPT-v2 · recommended 1×
  5. BakLLaVA · recommended 1×
  • CATEGORY QUERY
    What are the best open-source multimodal large language models for advanced image understanding tasks?
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. CogVLM
    3. Fuyu-8B
    4. MiniGPT-4 / MiniGPT-v2
    5. BakLLaVA
    6. Qwen-VL

    AI recommended 6 alternatives but never named NVlabs/Eagle. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking frameworks for developing efficient vision-language models with strong data-centric performance.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. OpenMMLab
    4. TensorFlow (with Keras)
    5. Detectron2

    AI recommended 5 alternatives but never named NVlabs/Eagle. 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 NVlabs/Eagle?
    pass
    AI named NVlabs/Eagle explicitly

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

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

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

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NVlabs/Eagle — 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