REPOGEO REPORT · LITE

evo-design/evo

Default branch main · commit 6856bba4 · scanned 6/22/2026, 3:36:59 PM

GitHub: 1,519 stars · 178 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 evo-design/evo, 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

Add specific topics to improve categorization

Why:

COPY-PASTE FIX

biological-foundation-model, dna-sequencing, genomics, machine-learning, deep-learning, long-context-ai, sequence-modeling, synthetic-biology, computational-biology

highreadme#2

Reposition README's opening to prioritize this repo's identity

Why:

CURRENT

**We have developed a new model called Evo 2 that extends the Evo 1 model and its ideas to all domains of life. Please see https://github.com/arcinstitute/evo2 for more details.**

Evo is a biological foundation model capable of long-context modeling and design.
Evo uses the StripedHyena architecture to enable modeling of sequences at a single-nucleotide, byte-level resolution with near-linear scaling of compute and memory relative to context length.
Evo has 7 billion parameters and is trained on OpenGenome, a prokaryotic whole-genome dataset containing ~300 billion tokens.

COPY-PASTE FIX

Evo is a biological foundation model capable of long-context modeling and design, specifically for DNA sequence analysis from molecular to genome scale. It uses the StripedHyena architecture for single-nucleotide, byte-level resolution. Evo has 7 billion parameters and is trained on OpenGenome, a prokaryotic whole-genome dataset containing ~300 billion tokens.

We have also developed Evo 2, which extends the Evo 1 model and its ideas to all domains of life; please see https://github.com/arcinstitute/evo2 for more details.

mediumhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://arcinstitute.org/
```

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 evo-design/evo

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

DNABERT

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

DNABERT · recommended 2×
ESM-2 · recommended 1×
AlphaFold2 · recommended 1×
AlphaFold3 · recommended 1×
ProGen · recommended 1×

CATEGORY QUERY
What AI models are available for long-context biological sequence analysis and design?
you: not recommended
AI recommended (in order):
1. ESM-2
2. AlphaFold2
3. AlphaFold3
4. ProGen
5. OpenFold
6. Tranception
7. ProtGPT2
8. DNABERT
9. RNABERT
AI recommended 9 alternatives but never named evo-design/evo. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I generate synthetic DNA sequences using a large-scale genomic language model?
you: not recommended
AI recommended (in order):
1. HyenaDNA
2. GenSLMs
3. Nucleotide Transformer
4. DNABERT
5. Genomic Foundation Models
6. PyTorch
7. TensorFlow
8. GPT-2/GPT-3
9. Hugging Face Transformers
AI recommended 9 alternatives but never named evo-design/evo. 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 evo-design/evo?
pass
AI named evo-design/evo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts evo-design/evo in production, what risks or prerequisites should they evaluate first?
pass
AI named evo-design/evo 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 evo-design/evo solve, and who is the primary audience?
pass
AI named evo-design/evo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/evo-design/evo.svg)](https://repogeo.com/en/r/evo-design/evo)

HTML

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

Pro

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evo-design/evo — 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