RRepoGEO

REPOGEO REPORT · LITE

SakanaAI/treequest

Default branch main · commit 96047d71 · scanned 6/10/2026, 4:11:37 AM

GitHub: 538 stars · 69 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 SakanaAI/treequest, 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
    Reposition the README's opening statement to clarify its core function

    Why:

    CURRENT
    A flexible answer tree search library featuring **AB-MCTS**, useful for (but not limited to) LLM inference-time scaling.
    COPY-PASTE FIX
    TreeQuest is a Python library providing advanced tree search algorithms, including **AB-MCTS**, specifically designed to accelerate and enhance large language model (LLM) inference-time scaling and complex decision processes.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://sakana.ai/ab-mcts/

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 SakanaAI/treequest
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. Hugging Face Transformers · recommended 1×
  4. OpenNMT-py · recommended 1×
  5. Fairseq · recommended 1×
  • CATEGORY QUERY
    How can I accelerate large language model inference using advanced tree search methods?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. OpenNMT-py
    3. Fairseq
    4. Ray
    5. PyTorch
    6. TensorFlow
    7. DeepMind's Acme

    AI recommended 7 alternatives but never named SakanaAI/treequest. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries offer flexible tree search implementations for complex decision processes?
    you: not recommended
    AI recommended (in order):
    1. Anytree
    2. MCTS
    3. NetworkX
    4. PyTorch
    5. TensorFlow
    6. SimpleAI

    AI recommended 6 alternatives but never named SakanaAI/treequest. 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 SakanaAI/treequest?
    pass
    AI named SakanaAI/treequest explicitly

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

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

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

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SakanaAI/treequest — 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