RRepoGEO

REPOGEO REPORT · LITE

guidance-ai/llguidance

Default branch main · commit 0384f3f6 · scanned 6/11/2026, 5:26:53 PM

GitHub: 785 stars · 67 forks

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 guidance-ai/llguidance, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening statement for category recognition

    Why:

    CURRENT
    # Low-level Guidance (llguidance)
    COPY-PASTE FIX
    # llguidance: Super-fast Constrained Decoding for LLMs (Structured Outputs)

`llguidance` is a high-performance library for enforcing arbitrary context-free grammars, including JSON schemas and regular expressions, on Large Language Model outputs.
  • mediumhomepage#2
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://guidance.ai/llguidance

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 guidance-ai/llguidance
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LMQL
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LMQL · recommended 2×
  2. Guidance · recommended 2×
  3. Outlines · recommended 2×
  4. Instructor · recommended 1×
  5. OpenAI's `response_format={"type": "json_object"}` · recommended 1×
  • CATEGORY QUERY
    How to enforce strict JSON schema output from LLMs with high performance?
    you: not recommended
    AI recommended (in order):
    1. Instructor
    2. OpenAI's `response_format={"type": "json_object"}`
    3. Pydantic
    4. LMQL
    5. Guidance
    6. jsonschema
    7. Outlines

    AI recommended 7 alternatives but never named guidance-ai/llguidance. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are fast methods for constrained decoding and grammar enforcement in LLM generation?
    you: not recommended
    AI recommended (in order):
    1. Outlines
    2. LMQL
    3. Guidance
    4. JSONFormer
    5. SGL
    6. LiteLLM

    AI recommended 6 alternatives but never named guidance-ai/llguidance. 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 guidance-ai/llguidance?
    pass
    AI named guidance-ai/llguidance explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts guidance-ai/llguidance in production, what risks or prerequisites should they evaluate first?
    pass
    AI named guidance-ai/llguidance 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 guidance-ai/llguidance solve, and who is the primary audience?
    pass
    AI named guidance-ai/llguidance explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite