REPOGEO REPORT · LITE

mlc-ai/xgrammar

Default branch main · commit 41dbbb18 · scanned 5/17/2026, 6:41:44 AM

GitHub: 1,695 stars · 148 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 mlc-ai/xgrammar, 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 initial description to explicitly state core purpose and mechanism

Why:

CURRENT

Efficient, Flexible and Portable Structured Generation

COPY-PASTE FIX

XGrammar is an open-source library for **constrained decoding**, enabling **100% structurally correct generation** from LLMs for formats like **JSON, regex, and custom grammars**. It offers efficient, flexible, and portable structured generation.

mediumtopics#2

Add more specific topics related to constrained decoding and output formats

Why:

CURRENT

large-language-models, structured-generation

COPY-PASTE FIX

large-language-models, structured-generation, constrained-decoding, json-generation, regex-generation, llm-output-control, grammar-parsing

lowreadme#3

Add a concise 'Key Differentiators' section near the top of the README

Why:

COPY-PASTE FIX

## Key Differentiators
- **High performance** for real-time applications.
- **Broad compatibility** with major LLM inference frameworks (e.g., vLLM, MLC-LLM, SGLang, TensorRT-LLM, OpenVINO GenAI, Mirai, MAX).
- **Flexible support** for any context-free grammar.

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 mlc-ai/xgrammar

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI API

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenAI API · recommended 2×
jxnl/instructor · recommended 2×
microsoft/guidance · recommended 2×
run-llama/llama_index · recommended 2×
langchain-ai/langchain · recommended 2×

CATEGORY QUERY
How can I enforce specific output formats and structures from large language models?
you: not recommended
AI recommended (in order):
1. OpenAI API
2. Instructor (jxnl/instructor)
3. Pydantic (pydantic/pydantic)
4. Guidance (microsoft/guidance)
5. OpenAI
6. Google Gemini
7. Anthropic Claude 3
8. LlamaIndex (run-llama/llama_index)
9. LangChain (langchain-ai/langchain)
10. Hugging Face
11. Llama 2
12. Mistral
AI recommended 12 alternatives but never named mlc-ai/xgrammar. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help guide LLMs to generate valid JSON, XML, or other structured data?
you: not recommended
AI recommended (in order):
1. Outlines (outlines-dev/outlines)
2. Instructor (jxnl/instructor)
3. Guidance (microsoft/guidance)
4. LiteLLM (BerriAI/litellm)
5. OpenAI API
6. LangChain (langchain-ai/langchain)
7. LlamaIndex (run-llama/llama_index)
AI recommended 7 alternatives but never named mlc-ai/xgrammar. 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 mlc-ai/xgrammar?
pass
AI named mlc-ai/xgrammar explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts mlc-ai/xgrammar in production, what risks or prerequisites should they evaluate first?
pass
AI named mlc-ai/xgrammar 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 mlc-ai/xgrammar solve, and who is the primary audience?
pass
AI named mlc-ai/xgrammar explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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HTML

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

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mlc-ai/xgrammar — 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