RRepoGEO

REPOGEO REPORT · LITE

Mnehmos/mnehmos.multi-agent.framework

Default branch main · commit 57e1bcf7 · scanned 6/6/2026, 5:07:52 PM

GitHub: 537 stars · 67 forks

AI VISIBILITY SCORE
22 /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
1 / 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 Mnehmos/mnehmos.multi-agent.framework, 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
  • highabout#1
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    A multi-agent framework for LLMs, providing a biological architecture for artificial minds with sensation, reflex, memory, and action capabilities, evolving chatbots into autonomous organisms.
  • mediumreadme#2
    Reposition the README's opening to explicitly state its core function and category

    Why:

    CURRENT
    # Agentic Nervous System
> Your LLM is a brain in a jar. Give it a nervous system.
A biological architecture for artificial minds. Sensation, reflex, memory, and action—organized into coherent loops that turn chatbots into organisms.
    COPY-PASTE FIX
    # Agentic Nervous System: A Multi-Agent Framework for LLMs
> Your LLM is a brain in a jar. Give it a nervous system.
This framework provides a biological architecture for artificial minds, organizing sensation, reflex, memory, and action into coherent loops that evolve chatbots into autonomous organisms.

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 Mnehmos/mnehmos.multi-agent.framework
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. OpenAI Assistants API · recommended 2×
  4. Haystack · recommended 2×
  5. AutoGPT / BabyAGI · recommended 1×
  • CATEGORY QUERY
    How to build autonomous AI agents with memory, reflexes, and tool use capabilities?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT / BabyAGI
    4. AutoGen
    5. OpenAI Assistants API
    6. Haystack
    7. CrewAI

    AI recommended 7 alternatives but never named Mnehmos/mnehmos.multi-agent.framework. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to evolve LLM chatbots into more sophisticated, self-managing AI entities.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. Microsoft Guidance
    5. Haystack
    6. OpenAI Assistants API
    7. AgentVerse

    AI recommended 7 alternatives but never named Mnehmos/mnehmos.multi-agent.framework. 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 Mnehmos/mnehmos.multi-agent.framework?
    pass
    AI did not name Mnehmos/mnehmos.multi-agent.framework — 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 Mnehmos/mnehmos.multi-agent.framework in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Mnehmos/mnehmos.multi-agent.framework 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 Mnehmos/mnehmos.multi-agent.framework solve, and who is the primary audience?
    pass
    AI did not name Mnehmos/mnehmos.multi-agent.framework — 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?

Embed your GEO score

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Mnehmos/mnehmos.multi-agent.framework — 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