RRepoGEO

REPOGEO REPORT · LITE

scaleapi/llm-engine

Default branch main · commit efbbe3cb · scanned 6/1/2026, 11:21:52 AM

GitHub: 828 stars · 75 forks

AI VISIBILITY SCORE
40 /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
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 scaleapi/llm-engine, 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
  • highabout#1
    Update the 'about' description to highlight self-hosting and Kubernetes

    Why:

    CURRENT
    Scale LLM Engine public repository
    COPY-PASTE FIX
    An open-source engine for fine-tuning and serving large language models on your own infrastructure, powered by Kubernetes.
  • mediumtopics#2
    Add more specific topics related to self-hosting and MLOps

    Why:

    CURRENT
    fine-tune, llm, python, scaleai
    COPY-PASTE FIX
    fine-tune, llm, python, scaleai, kubernetes, mlops, self-hosted, llm-deployment, foundation-models
  • lowreadme#3
    Refine the README's opening sentence to explicitly mention Kubernetes-native and self-hosted

    Why:

    CURRENT
    🚀 **The open source engine for fine-tuning and serving large language models**.
    COPY-PASTE FIX
    🚀 **The open source, Kubernetes-native engine for fine-tuning and serving large language models on your own infrastructure.**

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 scaleapi/llm-engine
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Accelerate · recommended 1×
  3. Triton Inference Server · recommended 1×
  4. vLLM · recommended 1×
  5. DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How can I fine-tune and serve large language models on my own infrastructure?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Accelerate
    3. Triton Inference Server
    4. vLLM
    5. DeepSpeed
    6. Hugging Face PEFT library
    7. Ray
    8. OpenVINO

    AI recommended 8 alternatives but never named scaleapi/llm-engine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good Python library for easily deploying and customizing foundation models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. OpenAI Python Library (openai/openai-python)
    3. LangChain (langchain-ai/langchain)
    4. LlamaIndex (run-llama/llama_index)
    5. Keras (keras-team/keras)
    6. PyTorch Lightning (Lightning-AI/lightning)
    7. MLflow (mlflow/mlflow)

    AI recommended 7 alternatives but never named scaleapi/llm-engine. 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 scaleapi/llm-engine?
    pass
    AI named scaleapi/llm-engine explicitly

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

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

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

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