RRepoGEO

REPOGEO REPORT · LITE

HazyResearch/minions

Default branch main · commit a87d0ee9 · scanned 5/11/2026, 11:03:25 AM

GitHub: 1,308 stars · 152 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 HazyResearch/minions, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-orchestration, on-device-llm, cloud-llm, cost-reduction, distributed-llm, ai-collaboration, language-models, communication-protocol
  • highreadme#2
    Strengthen the README's opening statement to clarify purpose

    Why:

    CURRENT
    _What is this?_ Minions is a communication protocol that enables small on-device models to collaborate with frontier models in the cloud. By only reading long contexts locally, we can reduce cloud costs with minimal or no quality degradation.
    COPY-PASTE FIX
    Minions is a **Python framework and communication protocol** for **cost-efficient, collaborative intelligence between small on-device LLMs and large cloud LLMs.** It enables local models to process long contexts, significantly reducing cloud API costs with minimal or no quality degradation, unlike general RAG or data pipeline solutions.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://hazyresearch.stanford.edu/blog/2025-02-24-minions

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 HazyResearch/minions
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ollama/ollama
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ollama/ollama · recommended 1×
  2. meta-llama/llama3 · recommended 1×
  3. mistralai/mistral-src · recommended 1×
  4. microsoft/Phi-3-Mini · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    How to reduce cloud LLM API costs by leveraging local models for context processing?
    you: not recommended
    AI recommended (in order):
    1. Ollama (ollama/ollama)
    2. Llama 3 (meta-llama/llama3)
    3. Mistral (mistralai/mistral-src)
    4. Phi-3 (microsoft/Phi-3-Mini)
    5. Hugging Face Transformers (huggingface/transformers)
    6. PyTorch (pytorch/pytorch)
    7. TensorFlow (tensorflow/tensorflow)
    8. LiteLLM (BerriAI/litellm)
    9. text-generation-webui (oobabooga/text-generation-webui)
    10. Llama.cpp (ggerganov/llama.cpp)
    11. llama-cpp-python (abetlen/llama-cpp-python)
    12. OpenVINO (openvinotoolkit/openvino)
    13. MLflow (mlflow/mlflow)
    14. scikit-learn (scikit-learn/scikit-learn)

    AI recommended 14 alternatives but never named HazyResearch/minions. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to orchestrate small local and large cloud language models for optimal performance.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Microsoft Semantic Kernel
    5. LiteLLM

    AI recommended 5 alternatives but never named HazyResearch/minions. 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 HazyResearch/minions?
    pass
    AI named HazyResearch/minions explicitly

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

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

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

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HazyResearch/minions — 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