REPOGEO REPORT · LITE

lucidrains/x-transformers

Default branch main · commit 03ca3be7 · scanned 5/18/2026, 2:56:55 AM

GitHub: 5,861 stars · 509 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

22 /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

1 / 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 lucidrains/x-transformers, 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

Reposition the README's opening statement to clarify its niche

Why:

CURRENT

A concise but fully-featured transformer, complete with a set of promising experimental features from various papers.

COPY-PASTE FIX

x-transformers is a PyTorch-native library for rapidly implementing and experimenting with advanced, full-attention transformer architectures and novel attention mechanisms from recent research papers. It provides concise, modular building blocks for both encoder-decoder and decoder-only models, designed for researchers and practitioners exploring cutting-edge transformer designs.

mediumhomepage#2
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://github.com/lucidrains/x-transformers
```

mediumtopics#3

Add more specific topics to highlight experimental and advanced features

Why:

CURRENT

artificial-intelligence, attention-mechanism, deep-learning, transformers

COPY-PASTE FIX

artificial-intelligence, attention-mechanism, deep-learning, transformers, pytorch-transformers, experimental-ai, advanced-transformers, neural-network-architectures

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 lucidrains/x-transformers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 1×
PyTorch · recommended 1×
Keras · recommended 1×
TensorFlow · recommended 1×
JAX · recommended 1×

CATEGORY QUERY
How can I quickly implement a full attention transformer model in Python?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. PyTorch
3. Keras
4. TensorFlow
5. JAX
6. Flax
7. Haiku
AI recommended 7 alternatives but never named lucidrains/x-transformers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a Python library for building both encoder-decoder and decoder-only transformer models.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. PyTorch (pytorch/pytorch)
3. TensorFlow (tensorflow/tensorflow)
4. Keras (keras-team/keras)
5. fairseq (facebookresearch/fairseq)
AI recommended 5 alternatives but never named lucidrains/x-transformers. 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 lucidrains/x-transformers?
pass
AI did not name lucidrains/x-transformers — 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 lucidrains/x-transformers in production, what risks or prerequisites should they evaluate first?
pass
AI named lucidrains/x-transformers 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 lucidrains/x-transformers solve, and who is the primary audience?
pass
AI did not name lucidrains/x-transformers — 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 lucidrains/x-transformers. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/lucidrains/x-transformers.svg)](https://repogeo.com/en/r/lucidrains/x-transformers)

HTML

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

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite