REPOGEO REPORT · LITE

SwanHubX/SwanLab

Default branch main · commit 0d8bbf02 · scanned 5/21/2026, 11:26:56 AM

GitHub: 3,946 stars · 207 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

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 SwanHubX/SwanLab, 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 prominent English summary to the README's top section

Why:

CURRENT

The README currently starts with Chinese text and links to an English version.

COPY-PASTE FIX

Add the following text prominently at the very top of the `README.md` (before any Chinese text or language selectors):

"**SwanLab is an open-source, modern-design AI training tracking and visualization tool.** It supports Cloud and Self-hosted use, and integrates seamlessly with PyTorch, Transformers, LLaMA Factory, and many other popular ML frameworks, making it ideal for model training teams."

mediumreadme#2

Add a 'Comparison with Alternatives' section to the README

Why:

COPY-PASTE FIX

Create a new section in the README titled "Comparison with Alternatives" or "Why SwanLab?" that directly compares SwanLab to MLflow, Weights & Biases, and TensorBoard, emphasizing its strengths such as modern design, open-source nature, self-hostability, and specific integrations.

lowtopics#3

Add more specific MLOps-related topics

Why:

CURRENT

ai-infra, data-science, deep-learning, llm, logging, machine-learning, mlops, model-versioning, python, pytorch, tensorboard, tensorflow, tracking, training, transformers, visualization

COPY-PASTE FIX

ai-infra, data-science, deep-learning, llm, logging, machine-learning, mlops, model-versioning, python, pytorch, tensorboard, tensorflow, tracking, training, transformers, visualization, experiment-tracking, hyperparameter-tuning

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 SwanHubX/SwanLab

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

MLflow

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

MLflow · recommended 2×
TensorBoard · recommended 2×
Comet ML · recommended 2×
Neptune.ai · recommended 2×
Weights & Biases · recommended 1×

CATEGORY QUERY
What are good open-source tools for tracking and visualizing deep learning model training?
you: not recommended
AI recommended (in order):
1. Weights & Biases
2. MLflow
3. TensorBoard
4. Comet ML
5. ClearML
6. Neptune.ai
AI recommended 6 alternatives but never named SwanHubX/SwanLab. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I effectively log and compare machine learning experiment runs across different frameworks?
you: not recommended
AI recommended (in order):
1. MLflow
2. Weights & Biases (W&B)
3. Comet ML
4. Neptune.ai
5. TensorBoard
6. DVC (Data Version Control)
7. DVC Studio
AI recommended 7 alternatives but never named SwanHubX/SwanLab. 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 SwanHubX/SwanLab?
pass
AI named SwanHubX/SwanLab explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts SwanHubX/SwanLab in production, what risks or prerequisites should they evaluate first?
pass
AI named SwanHubX/SwanLab 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 SwanHubX/SwanLab solve, and who is the primary audience?
pass
AI named SwanHubX/SwanLab 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 SwanHubX/SwanLab. 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/SwanHubX/SwanLab.svg)](https://repogeo.com/en/r/SwanHubX/SwanLab)

HTML

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

Pro

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SwanHubX/SwanLab — 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