REPOGEO REPORT · LITE

NVIDIA-BioNeMo/bionemo-framework

Default branch main · commit 534f7f1a · scanned 6/8/2026, 7:21:31 AM

GitHub: 763 stars · 155 forks

AI VISIBILITY SCORE

28 /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

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 NVIDIA-BioNeMo/bionemo-framework, 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 sentence to the README's introduction to clarify its specialized role.

Why:

CURRENT

NVIDIA BioNeMo Framework is a comprehensive suite of programming tools, libraries, and models designed for digital biology. It accelerates the most time-consuming and costly stages of building and adapting biomolecular AI models by providing domain-specific, optimized model recipes and tooling that are easily integrated into GPU-based computational resources with state-of-the-art performance.

COPY-PASTE FIX

NVIDIA BioNeMo Framework is a comprehensive suite of programming tools, libraries, and models designed for digital biology. It accelerates the most time-consuming and costly stages of building and adapting biomolecular AI models by providing domain-specific, optimized model recipes and tooling that are easily integrated into GPU-based computational resources with state-of-the-art performance. **Unlike general-purpose machine learning frameworks, BioNeMo is purpose-built to streamline AI development specifically for drug discovery and protein engineering.**

highlicense#2

Add a LICENSE file to the repository.

Why:

COPY-PASTE FIX

Create a `LICENSE` file in the repository root, specifying the chosen open-source license (e.g., Apache-2.0, MIT, or a specific NVIDIA license if applicable).

mediumtopics#3

Refine repository topics to emphasize specialization.

Why:

CURRENT

drug-discovery, gpu, machine-learning, pytorch

COPY-PASTE FIX

drug-discovery, gpu, biomolecular-ai, transformer-models, protein-engineering, generative-ai, large-language-models-for-biology

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 NVIDIA-BioNeMo/bionemo-framework

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

PyTorch

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

PyTorch · recommended 2×
TensorFlow · recommended 2×
JAX · recommended 2×
DeepChem · recommended 1×
MXNet · recommended 1×

CATEGORY QUERY
What are the best frameworks for developing AI models in drug discovery using GPUs?
you: not recommended
AI recommended (in order):
1. PyTorch
2. TensorFlow
3. JAX
4. DeepChem
5. MXNet
6. Theano
AI recommended 6 alternatives but never named NVIDIA-BioNeMo/bionemo-framework. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to efficiently train large transformer models on biological data with GPU acceleration?
you: not recommended
AI recommended (in order):
1. PyTorch
2. PyTorch Lightning
3. DeepSpeed
4. Hugging Face Transformers
5. Hugging Face Accelerate
6. NVIDIA NeMo Framework
7. JAX
8. Flax
9. Optax
10. TensorFlow
11. Keras
12. Horovod
13. tf.distribute.MirroredStrategy
14. MultiWorkerMirroredStrategy
AI recommended 14 alternatives but never named NVIDIA-BioNeMo/bionemo-framework. 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 NVIDIA-BioNeMo/bionemo-framework?
pass
AI named NVIDIA-BioNeMo/bionemo-framework explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts NVIDIA-BioNeMo/bionemo-framework in production, what risks or prerequisites should they evaluate first?
pass
AI named NVIDIA-BioNeMo/bionemo-framework 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 NVIDIA-BioNeMo/bionemo-framework solve, and who is the primary audience?
pass
AI did not name NVIDIA-BioNeMo/bionemo-framework — 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?

Embed your GEO score

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

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/NVIDIA-BioNeMo/bionemo-framework.svg)](https://repogeo.com/en/r/NVIDIA-BioNeMo/bionemo-framework)

HTML

<a href="https://repogeo.com/en/r/NVIDIA-BioNeMo/bionemo-framework"><img src="https://repogeo.com/badge/NVIDIA-BioNeMo/bionemo-framework.svg" alt="RepoGEO" /></a>

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NVIDIA-BioNeMo/bionemo-framework — 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