RRepoGEO

REPOGEO REPORT · LITE

cheshire-cat-ai/core

Default branch main · commit f8025c94 · scanned 5/25/2026, 4:57:56 AM

GitHub: 3,037 stars · 399 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 cheshire-cat-ai/core, 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 README's opening to emphasize 'deployable AI agent microservice'

    Why:

    CURRENT
    ## AI agent as a microservice

The Cheshire Cat is a framework to build custom AI agents:
    COPY-PASTE FIX
    ## Cheshire Cat AI: Your Deployable AI Agent Microservice

The Cheshire Cat is an API-first, full-stack framework designed to build and deploy custom, conversational AI agents as a standalone microservice, easily integrating into any application.
  • mediumtopics#2
    Add more specific topics to highlight deployable microservice nature

    Why:

    CURRENT
    ag-ui-protocol, agent, ai, assistant, chatbot, conversational, docker, framework, function-calling, llm, mcp, mcp-client, plugin, python, vector-search
    COPY-PASTE FIX
    ag-ui-protocol, agent, ai, assistant, chatbot, conversational, docker, framework, function-calling, llm, mcp, mcp-client, plugin, python, vector-search, microservice, deployable-ai, full-stack-ai, api-first, self-hosted
  • mediumabout#3
    Expand the 'About' description to include key differentiators

    Why:

    CURRENT
    AI agent microservice
    COPY-PASTE FIX
    An API-first, full-stack framework to build and deploy custom, conversational AI agents as a self-hostable microservice, featuring RAG, function calling, and multi-user support.

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 cheshire-cat-ai/core
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. OpenAI API · recommended 1×
  5. Microsoft Semantic Kernel · recommended 1×
  • CATEGORY QUERY
    How can I build a custom conversational AI agent with RAG and function calling?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI API
    5. Microsoft Semantic Kernel
    6. LiteLLM

    AI recommended 6 alternatives but never named cheshire-cat-ai/core. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good frameworks for deploying a customizable, multi-user AI assistant as a microservice?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. FastAPI
    5. Flask
    6. Django REST Framework (DRF)

    AI recommended 6 alternatives but never named cheshire-cat-ai/core. 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 cheshire-cat-ai/core?
    pass
    AI named cheshire-cat-ai/core explicitly

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

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

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

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cheshire-cat-ai/core — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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