REPOGEO REPORT · LITE

XGenerationLab/XiYan-SQL

Default branch main · commit 603dedac · scanned 6/19/2026, 11:07:41 PM

GitHub: 1,012 stars · 51 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 XGenerationLab/XiYan-SQL, 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 project definition at the top of the README

Why:

CURRENT

The README currently starts with a news section after initial alignment blocks.

COPY-PASTE FIX

XiYan-SQL is a state-of-the-art multi-generator ensemble framework designed for converting natural language requests into complex SQL queries. It achieves top performance on challenging text-to-SQL benchmarks like BIRD-CRITIC, offering a robust solution for building and training advanced text-to-SQL models using modern LLMs.

hightopics#2

Add relevant topics to the repository

Why:

COPY-PASTE FIX

text-to-sql, natural-language-to-sql, llm, generative-ai, ensemble-framework, sql-generation, deep-learning, nlp, bird-benchmark

mediumhomepage#3

Add a homepage URL to the repository metadata

Why:

COPY-PASTE FIX

Add the official project page, Arxiv paper link, or PapersWithCode link (e.g., 'https://arxiv.org/abs/XXXX.XXXXX' or 'https://paperswithcode.com/paper/xiyan-sql-a-multi-generator-ensemble') to the repository's homepage field.

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 XGenerationLab/XiYan-SQL

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

sqlflow-dev/sqlflow

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

sqlflow-dev/sqlflow · recommended 1×
vanna-ai/vanna · recommended 1×
dataherald/dataherald · recommended 1×
OpenAI API · recommended 1×
Azure OpenAI Service · recommended 1×

CATEGORY QUERY
What are effective tools for converting natural language requests into complex SQL queries?
you: not recommended
AI recommended (in order):
1. SQLFlow (sqlflow-dev/sqlflow)
2. Vanna.ai (vanna-ai/vanna)
3. Dataherald (dataherald/dataherald)
4. OpenAI API
5. Azure OpenAI Service
6. Anthropic Claude
7. GPT-4
8. GPT-3.5 Turbo
9. Claude 3
10. Hugging Face Transformers (huggingface/transformers)
11. DB-GPT (eosphoros-ai/DB-GPT)
12. AI2SQL
AI recommended 12 alternatives but never named XGenerationLab/XiYan-SQL. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I build and train a robust text-to-SQL model using modern LLMs?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. T5 Model
3. BART Model
4. OpenAI GPT-3.5 Turbo
5. OpenAI GPT-4
6. Google's PaLM 2
7. Google's Gemini
8. Google Cloud Vertex AI
9. Salesforce CodeT5
10. Salesforce CodeGen
11. Microsoft CodeBERT
12. Microsoft GraphCodeBERT
AI recommended 12 alternatives but never named XGenerationLab/XiYan-SQL. 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 XGenerationLab/XiYan-SQL?
pass
AI named XGenerationLab/XiYan-SQL explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts XGenerationLab/XiYan-SQL in production, what risks or prerequisites should they evaluate first?
pass
AI named XGenerationLab/XiYan-SQL 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 XGenerationLab/XiYan-SQL solve, and who is the primary audience?
pass
AI named XGenerationLab/XiYan-SQL 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 XGenerationLab/XiYan-SQL. 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/XGenerationLab/XiYan-SQL.svg)](https://repogeo.com/en/r/XGenerationLab/XiYan-SQL)

HTML

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

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XGenerationLab/XiYan-SQL — 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