RRepoGEO

REPOGEO REPORT · LITE

mlc-ai/xgrammar

Default branch main · commit 557becfb · scanned 6/28/2026, 8:16:50 AM

GitHub: 1,765 stars · 162 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
    Elevate the core value proposition in the README

    Why:

    CURRENT
    The current README structure where the 'Overview' section is below 'News'.
    COPY-PASTE FIX
    Move the entire 'Overview' section content to immediately follow the initial tagline, before the 'News' section. Ensure the first sentence explicitly mentions 'Large Language Models' or 'LLMs', for example, by changing 'XGrammar is an open-source library for efficient, flexible, and portable structured generation.' to 'XGrammar is an open-source library for efficient, flexible, and portable structured generation for Large Language Models.'
  • mediumreadme#2
    Add a dedicated section highlighting key differentiators

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why XGrammar?' or 'Key Features' near the top of the README (after the repositioned overview). Include text like: 'XGrammar stands out with its **high performance and cross-platform compatibility**, achieved through its efficient implementation in Rust and compilation to WebAssembly. This enables seamless integration across various environments, from local development to production-grade LLM inference systems.'
  • lowtopics#3
    Expand repository topics for finer-grained categorization

    Why:

    CURRENT
    large-language-models, structured-generation
    COPY-PASTE FIX
    large-language-models, structured-generation, constrained-decoding, grammar-parsing, json-generation, regex-generation, llm-inference, rust, webassembly

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
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. pydantic/pydantic · recommended 1×
  3. colinhacks/zod · recommended 1×
  4. microsoft/guidance · recommended 1×
  5. jxnl/instructor · recommended 1×
  • CATEGORY QUERY
    How can I ensure large language model output adheres to a specific grammar or schema?
    you: not recommended
    AI recommended (in order):
    1. Pydantic (pydantic/pydantic)
    2. Zod (colinhacks/zod)
    3. Guidance (microsoft/guidance)
    4. Instructor (jxnl/instructor)
    5. LangChain (langchain-ai/langchain)

    AI recommended 5 alternatives but never named mlc-ai/xgrammar. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient tools for generating structured data from language models, like JSON?
    you: not recommended
    AI recommended (in order):
    1. Instructor (instructor-ai/instructor)
    2. OpenAI Function Calling
    3. LMQL (eth-sri/lmql)
    4. Outlines (outlines-dev/outlines)
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)
    7. Guardrails AI (guardrails-ai/guardrails)

    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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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