REPOGEO REPORT · LITE

eth-sri/lmql

Default branch main · commit fc8edd44 · scanned 5/22/2026, 2:22:25 PM

GitHub: 4,178 stars · 224 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 eth-sri/lmql, 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 README opening to emphasize programmatic LLM control and structured output

Why:

CURRENT

<p align="center">A programming language for large language models.</p>
LMQL is a programming language for large language models (LLMs) based on a *superset of Python*.

COPY-PASTE FIX

<p align="center">A programming language for fine-grained, programmatic control of large language model outputs, enabling structured generation and efficient execution.</p>
LMQL is a programming language that extends Python, offering a novel way to interweave traditional programming with the ability to call LLMs in your code, providing token-level control for structured generation and efficient execution.

mediumtopics#2

Add specific topics for LLM programming, structured output, and constraints

Why:

CURRENT

chatgpt, huggingface, language-model, programming-language

COPY-PASTE FIX

chatgpt, huggingface, language-model, programming-language, llm-programming, structured-output, constraint-programming, prompt-engineering

lowreadme#3

Add a sentence to the README emphasizing declarative constraints and advanced search strategies

Why:

CURRENT

It goes beyond traditional templating languages by integrating LLM interaction natively at the level of your program code.

COPY-PASTE FIX

It goes beyond traditional templating languages by integrating LLM interaction natively at the level of your program code. This declarative approach enables developers to define complex constraints and advanced search strategies (like `WHERE` clauses) for LLM generation directly within their Python code.

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 eth-sri/lmql

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Instructor

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Instructor · recommended 2×
Pydantic · recommended 2×
LangChain · recommended 2×
Guidance · recommended 1×
LiteLLM · recommended 1×

CATEGORY QUERY
How can I programmatically control large language model outputs with Python syntax?
you: not recommended
AI recommended (in order):
1. Instructor
2. Pydantic
3. Guidance
4. LangChain
5. LiteLLM
6. Outlines
7. OpenAI Function Calling
AI recommended 7 alternatives but never named eth-sri/lmql. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help build LLM applications with structured output and performance constraints?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Pydantic
4. Instructor
5. FastAPI
6. vLLM
7. OpenAI API
AI recommended 7 alternatives but never named eth-sri/lmql. 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 eth-sri/lmql?
pass
AI named eth-sri/lmql explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts eth-sri/lmql in production, what risks or prerequisites should they evaluate first?
pass
AI named eth-sri/lmql 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 eth-sri/lmql solve, and who is the primary audience?
pass
AI named eth-sri/lmql 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 eth-sri/lmql. 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/eth-sri/lmql.svg)](https://repogeo.com/en/r/eth-sri/lmql)

HTML

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

Pro

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eth-sri/lmql — 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