RRepoGEO

REPOGEO REPORT · LITE

answeryt/Fat-Cat

Default branch main · commit 3583915c · scanned 6/8/2026, 7:27:54 PM

GitHub: 723 stars · 36 forks

AI VISIBILITY SCORE
28 /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
2 / 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 answeryt/Fat-Cat, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, agent-framework, context-management, multi-stage-reasoning, generative-ai, ai-agents
  • highreadme#2
    Strengthen the README's opening statement to prevent miscategorization

    Why:

    CURRENT
    A next-generation Agent framework based on global document context and multi-stage reasoning
    COPY-PASTE FIX
    Fat-Cat is an LLM-native operating system and agent framework designed to solve the "quagmire of context management" and "fragile control flow" in LLM agent development. It enables multi-stage reasoning and dynamic tool use by treating context as a global document, making it as simple as reading chat history.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Your project's official website or documentation URL here]

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 answeryt/Fat-Cat
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. LlamaIndex · recommended 2×
  3. Haystack · recommended 2×
  4. Instructor · recommended 1×
  5. Guidance · recommended 1×
  • CATEGORY QUERY
    How to manage LLM agent context without complex JSON parsing for better reasoning?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Instructor
    5. Guidance
    6. Pydantic

    AI recommended 6 alternatives but never named answeryt/Fat-Cat. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an LLM agent framework for dynamic tool use and multi-stage reasoning.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGen
    4. CrewAI
    5. Haystack
    6. Marvin

    AI recommended 6 alternatives but never named answeryt/Fat-Cat. 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 answeryt/Fat-Cat?
    pass
    AI did not name answeryt/Fat-Cat — likely talking about a different project

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

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

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

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answeryt/Fat-Cat — 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