RRepoGEO

REPOGEO REPORT · LITE

togethercomputer/MoA

Default branch main · commit 1b5cab0f · scanned 5/20/2026, 10:28:05 PM

GitHub: 2,897 stars · 378 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 togethercomputer/MoA, 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
  • highreadme#1
    Add a concise, category-defining tagline to the README's opening

    Why:

    COPY-PASTE FIX
    Add this sentence immediately after the H1 and navigation links:

"MoA is a novel framework for orchestrating multiple open-source LLM agents to achieve state-of-the-art performance, significantly outperforming GPT-4 Omni on benchmarks like AlpacaEval."
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://www.together.ai/

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 togethercomputer/MoA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. scikit-learn/scikit-learn · recommended 1×
  3. rasbt/mlxtend · recommended 1×
  4. mistralai/mistral-src · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How can I combine multiple open-source language models to achieve better performance?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Scikit-learn (scikit-learn/scikit-learn)
    3. MLxtend (rasbt/mlxtend)
    4. Mixtral 8x7B (mistralai/mistral-src)
    5. DeepSpeed (microsoft/DeepSpeed)
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)
    8. PyTorch (pytorch/pytorch)
    9. TensorFlow (tensorflow/tensorflow)

    AI recommended 9 alternatives but never named togethercomputer/MoA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework enables orchestrating several LLM agents for state-of-the-art reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGen
    4. CrewAI
    5. Haystack

    AI recommended 5 alternatives but never named togethercomputer/MoA. 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 togethercomputer/MoA?
    pass
    AI named togethercomputer/MoA explicitly

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

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

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

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togethercomputer/MoA — 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