RRepoGEO

REPOGEO REPORT · LITE

Tanuki/tanuki.py

Default branch master · commit 138fed16 · scanned 6/4/2026, 10:46:27 AM

GitHub: 695 stars · 27 forks

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 Tanuki/tanuki.py, 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 opening statement to explicitly define its LLM focus

    Why:

    CURRENT
    # Tanuki <span style="font-family:Papyrus; font-size:2em;">🦝</span>  
Easily build LLM-powered apps that get cheaper and faster over time.
    COPY-PASTE FIX
    # Tanuki <span style="font-family:Papyrus; font-size:2em;">🦝</span>  
A Python library for building reliable, type-safe LLM-powered functions that automatically optimize for cost and speed. Stop prompt-wrangling and integrate AI into your Python code like regular functions.
  • mediumreadme#2
    Add a prominent feature point about automatic distillation and optimization

    Why:

    COPY-PASTE FIX
    Add to 'Features' section:

**Automatic Distillation & Optimization:** Tanuki functions get up to 9-10x cheaper and faster over time through automatic model distillation, making your LLM applications inherently more cost-effective and performant without manual intervention.
  • mediumreadme#3
    Add a 'Comparison to Other Tools' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Other Tools
Tanuki provides an idiomatic Pythonic approach to defining AI tasks using standard type hints and docstrings for both input and structured output, while remaining provider-agnostic and emphasizing built-in observability. Unlike general LLM frameworks, Tanuki focuses on making individual LLM calls behave like reliable, testable Python functions, with unique capabilities for automatic cost and performance optimization through distillation. While tools like Pydantic and Instructor help with structured output, Tanuki integrates this directly into a function-patching paradigm, adding alignment testing and automatic optimization.

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 Tanuki/tanuki.py
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. Pydantic · recommended 1×
  3. Instructor · recommended 1×
  4. Guidance · recommended 1×
  5. Lmfit · recommended 1×
  • CATEGORY QUERY
    How to reliably integrate LLM responses into Python functions with type safety?
    you: not recommended
    AI recommended (in order):
    1. Pydantic
    2. Instructor
    3. LangChain
    4. Guidance
    5. Lmfit

    AI recommended 5 alternatives but never named Tanuki/tanuki.py. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for building cost-effective and performant LLM applications that improve over time?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI API
    4. Hugging Face Transformers
    5. Hugging Face Inference Endpoints
    6. Weights & Biases
    7. MLflow

    AI recommended 7 alternatives but never named Tanuki/tanuki.py. 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 Tanuki/tanuki.py?
    pass
    AI named Tanuki/tanuki.py explicitly

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

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

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

Embed your GEO score

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Tanuki/tanuki.py — 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