RRepoGEO

REPOGEO REPORT · LITE

katanemo/plano

Default branch main · commit 5a4487fc · scanned 5/12/2026, 7:17:15 PM

GitHub: 6,468 stars · 413 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 katanemo/plano, 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
  • mediumreadme#1
    Add a concise 'Key Features' section early in the README

    Why:

    COPY-PASTE FIX
    ## Key Features
- **Agent Orchestration & Routing:** Centralized logic for directing requests to the right agent.
- **Guardrail Filters:** Built-in safety and moderation for agentic applications.
- **Observability & Evaluation:** Rich signals and traces for continuous learning and improvement.
- **Smart LLM Routing:** APIs for model agility and optimal LLM selection.
- **Framework Agnostic:** Use with any language or AI framework, decoupling you from brittle abstractions.
  • lowreadme#2
    Explicitly state 'proxy' and 'data plane' in the README's 'Overview' section

    Why:

    CURRENT
    Building agentic demos is easy. Shipping agentic applications safely, reliably, and repeatably to production is hard. After the thrill of a quick hack, you end up building the “hidden middleware” to reach production: routing logic to reach the right agent, guardrail hooks for safety and moderation, evaluation and observability glue for continuous learning, and model/provider quirks scattered across frameworks and application code. Plano solves this by moving core delivery concerns into a unified, out-of-process dat
    COPY-PASTE FIX
    ## Overview
Building agentic demos is easy. Shipping agentic applications safely, reliably, and repeatably to production is hard. Plano addresses this by providing an **AI-native proxy and data plane** that centralizes core delivery concerns like routing logic, guardrail hooks for safety and moderation, evaluation and observability glue, and smart LLM routing, moving them out-of-process from your application code.

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 katanemo/plano
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. Guardrails AI · recommended 1×
  3. Arize AI · recommended 1×
  4. OpenAI Evals · recommended 1×
  5. Vellum · recommended 1×
  • CATEGORY QUERY
    How to manage LLM routing, safety, and observability for agentic applications in production?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Guardrails AI
    3. Arize AI
    4. OpenAI Evals
    5. Vellum
    6. LlamaIndex

    AI recommended 6 alternatives but never named katanemo/plano. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an AI-native proxy or data plane for robust LLM orchestration and guardrails.
    you: not recommended
    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 katanemo/plano?
    pass
    AI named katanemo/plano explicitly

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

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

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

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katanemo/plano — 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