RRepoGEO

REPOGEO REPORT · LITE

Kocoro-lab/Shannon

Default branch main · commit 0190a842 · scanned 6/21/2026, 6:02:07 AM

GitHub: 2,023 stars · 316 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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
  • highabout#1
    Update repository description for clearer categorization and disambiguation

    Why:

    CURRENT
    A production-oriented multi-agent orchestration framework.
    COPY-PASTE FIX
    Shannon is a robust, production-oriented framework for building, orchestrating, and debugging reliable multi-agent AI systems, distinct from projects related to information theory or vector databases.
  • mediumtopics#2
    Add more specific topics related to production AI agents and their features

    Why:

    CURRENT
    agent, ai, multi-agent-systems
    COPY-PASTE FIX
    agent, ai, multi-agent-systems, ai-agents, agent-orchestration, production-ai, llm-ops, ai-debugging, token-management, ai-security
  • lowreadme#3
    Add a dedicated "Features" section to the README

    Why:

    COPY-PASTE FIX
    Add a new, prominent section titled 'Key Features' or 'Features at a Glance' with a bulleted list summarizing capabilities like 'Multi-strategy orchestration', 'Time-travel debugging', 'Hard token budgets', 'Real-time event streaming', 'WASI sandbox for code execution', 'Support for multiple LLM providers'.

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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LangSmith · recommended 1×
  3. LlamaIndex · recommended 1×
  4. LlamaCloud · recommended 1×
  5. LlamaParse · recommended 1×
  • CATEGORY QUERY
    How to build reliable AI agent systems with robust orchestration and debugging in production?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. LlamaIndex
    4. LlamaCloud
    5. LlamaParse
    6. Microsoft Semantic Kernel
    7. Haystack
    8. CrewAI
    9. OpenAI Assistants API
    10. FastAPI
    11. Flask
    12. OpenTelemetry
    13. Grafana
    14. Prometheus

    AI recommended 14 alternatives but never named Kocoro-lab/Shannon. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Framework for managing token costs and ensuring security in multi-agent AI applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. OpenAI Evals (openai/evals)
    4. Guardrails AI (guardrails-ai/guardrails)
    5. Haystack (deepset-ai/haystack)
    6. LiteLLM (BerriAI/litellm)

    AI recommended 6 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