RRepoGEO

REPOGEO REPORT · LITE

MeetKai/functionary

Default branch main · commit aa3dbdd6 · scanned 5/22/2026, 6:08:08 PM

GitHub: 1,595 stars · 119 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 MeetKai/functionary, 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 to explicitly state its core differentiator

    Why:

    CURRENT
    Functionary is a language model that can interpret and execute functions/plugins.
    COPY-PASTE FIX
    Functionary is an **open-source, self-hostable language model specifically fine-tuned for robust function calling and tool use**, designed to be compatible with the OpenAI function calling API.
  • mediumhomepage#2
    Add the homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://functionary.meetkai.com
  • lowtopics#3
    Add more specific topics to reinforce its role as a fine-tuned model

    Why:

    CURRENT
    agents, ai, ai-agents, function-calling, llm, ml, python
    COPY-PASTE FIX
    agents, ai, ai-agents, function-calling, llm, ml, python, tool-use, fine-tuned-llm, open-source-llm

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 MeetKai/functionary
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 · recommended 1×
  2. Mistral Large / Mixtral 8x22B Instruct · recommended 1×
  3. CodeLlama · recommended 1×
  4. Gemma 2 · recommended 1×
  5. Phi-3-medium-4k-instruct / Phi-3-small-8k-instruct · recommended 1×
  • CATEGORY QUERY
    Looking for an open-source language model capable of robust function calling and tool use.
    you: not recommended
    AI recommended (in order):
    1. Llama 3
    2. Mistral Large / Mixtral 8x22B Instruct
    3. CodeLlama
    4. Gemma 2
    5. Phi-3-medium-4k-instruct / Phi-3-small-8k-instruct
    6. Zephyr

    AI recommended 6 alternatives but never named MeetKai/functionary. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build AI agents that autonomously decide when to use external tools?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Microsoft AutoGen
    4. OpenAI Assistants API
    5. Haystack
    6. CrewAI
    7. Marvin AI

    AI recommended 7 alternatives but never named MeetKai/functionary. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 MeetKai/functionary?
    pass
    AI named MeetKai/functionary explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of MeetKai/functionary. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/MeetKai/functionary.svg)](https://repogeo.com/en/r/MeetKai/functionary)
HTML
<a href="https://repogeo.com/en/r/MeetKai/functionary"><img src="https://repogeo.com/badge/MeetKai/functionary.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

MeetKai/functionary — 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