RRepoGEO

REPOGEO REPORT · LITE

Klavis-AI/klavis

Default branch main · commit 669e95dc · scanned 5/22/2026, 10:56:54 AM

GitHub: 5,742 stars · 547 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
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 Klavis-AI/klavis, 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 README's opening to clearly state core purpose

    Why:

    CURRENT
    The current README starts with visual elements and then '🎯 Choose Your Solution' without a clear introductory sentence.
    COPY-PASTE FIX
    Insert the following sentence as the very first line of the README, before any visual elements or section headers: 'Klavis is an open-source MCP integration platform designed to empower AI agents to reliably use external tools and APIs at any scale.'
  • mediumreadme#2
    Add a 'Why Klavis?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Create a new section in the README titled 'Why Klavis?' or 'Klavis vs. X' that explicitly addresses how Klavis helps AI agents reliably use external tools and APIs at scale, and how it differentiates from or complements other popular LLM agent frameworks.
  • lowabout#3
    Ensure GitHub 'About' description is impactful and consistent

    Why:

    CURRENT
    Klavis AI: MCP integration platforms that let AI agents use tools reliably at any scale
    COPY-PASTE FIX
    Confirm the GitHub 'About' section is set to: 'Klavis AI: MCP integration platforms that let AI agents use tools reliably at any scale.' This reinforces the core message for AI models that scan repository metadata.

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 Klavis-AI/klavis
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. Microsoft Semantic Kernel · recommended 1×
  4. OpenAI Function Calling · recommended 1×
  5. RabbitMQ · recommended 1×
  • CATEGORY QUERY
    How can I make my AI agents reliably use external tools and APIs at scale?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Microsoft Semantic Kernel
    4. OpenAI Function Calling
    5. RabbitMQ
    6. Apache Kafka
    7. Prefect
    8. Apache Airflow

    AI recommended 8 alternatives but never named Klavis-AI/klavis. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source platforms help LLM agents integrate tools and manage context efficiently?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/Haystack)
    4. AutoGPT
    5. CrewAI
    6. Open Interpreter

    AI recommended 6 alternatives but never named Klavis-AI/klavis. 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 Klavis-AI/klavis?
    pass
    AI did not name Klavis-AI/klavis — 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 Klavis-AI/klavis in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Klavis-AI/klavis 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 Klavis-AI/klavis solve, and who is the primary audience?
    pass
    AI named Klavis-AI/klavis explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite