RRepoGEO

REPOGEO REPORT · LITE

chaitanyagiri/munder-difflin

Default branch main · commit 7eed9393 · scanned 6/16/2026, 11:11:30 PM

GitHub: 533 stars · 56 forks

AI VISIBILITY SCORE
33 /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
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 chaitanyagiri/munder-difflin, 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
  • highabout#1
    Update the repository's 'about' description for clarity

    Why:

    CURRENT
    local multi-agent harness
    COPY-PASTE FIX
    Desktop app for orchestrating local multi-agent AI systems (Claude, Gemini, OpenAI) with memory, communication, and visual coordination.
  • mediumtopics#2
    Add more specific topics for multi-agent systems and desktop apps

    Why:

    CURRENT
    agents, claude-code, free, harness, harness-engineering, memory
    COPY-PASTE FIX
    agents, claude-code, free, harness, harness-engineering, memory, multi-agent-systems, agent-orchestration, llm-agents, desktop-app, electron
  • lowreadme#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a section or line to your README, for example: "This project is released under the terms specified in the [LICENSE](LICENSE) file. Please refer to the file for full details on the applicable license(s)."

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 chaitanyagiri/munder-difflin
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. Haystack · recommended 2×
  3. LlamaIndex · recommended 2×
  4. CrewAI · recommended 1×
  5. AutoGen · recommended 1×
  • CATEGORY QUERY
    How to build and orchestrate a local multi-agent system for various AI models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. CrewAI
    3. Haystack
    4. AutoGen
    5. LlamaIndex
    6. Open Interpreter
    7. BabyAGI
    8. Auto-GPT

    AI recommended 8 alternatives but never named chaitanyagiri/munder-difflin. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a tool to give autonomous AI agents memory, communication, and visual coordination.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Assistants API
    5. AutoGPT / BabyAGI
    6. Rasa
    7. Microsoft Semantic Kernel

    AI recommended 7 alternatives but never named chaitanyagiri/munder-difflin. 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 chaitanyagiri/munder-difflin?
    pass
    AI named chaitanyagiri/munder-difflin explicitly

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

  • If a team adopts chaitanyagiri/munder-difflin in production, what risks or prerequisites should they evaluate first?
    pass
    AI named chaitanyagiri/munder-difflin 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 chaitanyagiri/munder-difflin solve, and who is the primary audience?
    pass
    AI did not name chaitanyagiri/munder-difflin — 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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chaitanyagiri/munder-difflin — 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