RRepoGEO

REPOGEO REPORT · LITE

Kocoro-lab/Shannon

Default branch main · commit 7bcf422f · scanned 5/11/2026, 3:12:02 AM

GitHub: 1,833 stars · 290 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 Kocoro-lab/Shannon, 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 sentence to explicitly state its framework identity

    Why:

    CURRENT
    Ship reliable AI agents to production. Multi-strategy orchestration, swarm collaboration, token budget control, human approval workflows, and time-travel debugging — all built in.
    COPY-PASTE FIX
    Shannon is a production-grade framework for building and deploying reliable multi-agent AI systems. It provides multi-strategy orchestration, swarm collaboration, token budget control, human approval workflows, and time-travel debugging — all built in.
  • mediumabout#2
    Enhance the 'About' description with more specific keywords and features

    Why:

    CURRENT
    A production-oriented multi-agent orchestration framework.
    COPY-PASTE FIX
    A robust, production-oriented framework for building, deploying, and managing reliable multi-agent AI systems with advanced orchestration, debugging, and cost control features.
  • mediumtopics#3
    Add more specific topics to improve category matching

    Why:

    CURRENT
    agent, ai, multi-agent-systems
    COPY-PASTE FIX
    agent, ai, multi-agent-systems, llm, orchestration, ai-agents, production-ai, framework, developer-tools, debugging, cost-control

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 Kocoro-lab/Shannon
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. AutoGen · recommended 2×
  3. Haystack · recommended 2×
  4. LlamaIndex · recommended 2×
  5. OpenAI Assistants API · recommended 2×
  • CATEGORY QUERY
    What framework helps deploy and manage reliable multi-agent AI systems in production?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGen
    3. CrewAI
    4. Haystack
    5. LlamaIndex
    6. OpenAI Assistants API
    7. Rasa

    AI recommended 7 alternatives but never named Kocoro-lab/Shannon. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an AI agent orchestration solution with strong debugging, cost control, and observability?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. Microsoft Semantic Kernel
    4. LlamaIndex
    5. Haystack
    6. OpenAI Assistants API
    7. AutoGen

    AI recommended 7 alternatives but never named Kocoro-lab/Shannon. 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 Kocoro-lab/Shannon?
    pass
    AI named Kocoro-lab/Shannon explicitly

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

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

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

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Kocoro-lab/Shannon — 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