RRepoGEO

REPOGEO REPORT · LITE

togethercomputer/together-cookbook

Default branch main · commit 9aaa1dde · scanned 5/27/2026, 11:57:56 PM

GitHub: 1,134 stars · 206 forks

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/together-cookbook, 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
    Clarify README opening to emphasize 'cookbook' and 'Together AI examples'

    Why:

    CURRENT
    The Together Cookbook is a collection of code and guides designed to help developers build with open source models using Together AI. The best way to use the recipes is to copy code snippets and integrate them into your own projects!
    COPY-PASTE FIX
    The Together Cookbook is your go-to resource for practical, copy-pasteable code examples and how-to guides, specifically designed to help developers build with open-source models using Together AI. This repository provides ready-to-use recipes to integrate Together AI's platform into your projects, rather than being a library or framework itself.
  • mediumhomepage#2
    Add Together AI platform URL as homepage

    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/together-cookbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Ollama · recommended 2×
  3. LangChain · recommended 2×
  4. Llama.cpp · recommended 1×
  5. LiteLLM · recommended 1×
  • CATEGORY QUERY
    How can I integrate open-source large language models into my Python application?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Llama.cpp
    3. Ollama
    4. LangChain
    5. LiteLLM
    6. vLLM
    7. TensorRT-LLM

    AI recommended 7 alternatives but never named togethercomputer/together-cookbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical code examples and recipes for building AI agents with open models.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. OpenAI Cookbook
    5. Ollama
    6. AutoGPT
    7. BabyAGI

    AI recommended 7 alternatives but never named togethercomputer/together-cookbook. 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/together-cookbook?
    pass
    AI named togethercomputer/together-cookbook 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/together-cookbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named togethercomputer/together-cookbook 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/together-cookbook solve, and who is the primary audience?
    pass
    AI named togethercomputer/together-cookbook 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
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