RRepoGEO

REPOGEO REPORT · LITE

RunLLM/aqueduct

Default branch main · commit 840f9177 · scanned 6/16/2026, 6:36:39 AM

GitHub: 519 stars · 20 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 RunLLM/aqueduct, 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
    Add a prominent note about the project's unmaintained status to the README

    Why:

    CURRENT
    The README currently starts with a banner and then 'Aqueduct is an MLOps framework...'
    COPY-PASTE FIX
    Add this line immediately after the main title/banner in the README: '📢 **Note: Aqueduct is no longer actively maintained.** This repository is provided for historical reference and existing users. For new projects, consider actively maintained alternatives.'
  • highabout#2
    Refine the repository description to clarify the unmaintained status with context

    Why:

    CURRENT
    Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.
    COPY-PASTE FIX
    Aqueduct is an MLOps framework for running LLM and ML workloads on any cloud infrastructure. **Note: This project is no longer actively maintained and is provided for historical reference and existing deployments.**
  • mediumtopics#3
    Add more specific pipeline-related topics

    Why:

    CURRENT
    ai, data, data-science, kubernetes, llm, llms, machine-learning, ml, ml-infrastructure, ml-monitoring, mlops, orchestration, python, python3
    COPY-PASTE FIX
    ai, data, data-science, kubernetes, llm, llms, machine-learning, ml, ml-infrastructure, ml-monitoring, mlops, orchestration, python, python3, ml-pipelines, llm-pipelines

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 RunLLM/aqueduct
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Azure Machine Learning
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure Machine Learning · recommended 2×
  2. kubeflow/kubeflow · recommended 1×
  3. mlflow/mlflow · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. huggingface/accelerate · recommended 1×
  • CATEGORY QUERY
    How to deploy and manage machine learning and LLM models across various cloud environments?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow (kubeflow/kubeflow)
    2. MLflow (mlflow/mlflow)
    3. Hugging Face Transformers (huggingface/transformers)
    4. 🤗 Accelerate (huggingface/accelerate)
    5. Inference Endpoints
    6. Sagemaker
    7. Vertex AI
    8. Azure Machine Learning
    9. Ray Serve (ray-project/ray)
    10. Triton Inference Server (triton-inference-server/server)

    AI recommended 10 alternatives but never named RunLLM/aqueduct. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python framework for orchestrating complex ML and LLM pipelines on diverse cloud platforms.
    you: not recommended
    AI recommended (in order):
    1. Kubeflow Pipelines
    2. MLflow
    3. Apache Airflow
    4. Prefect
    5. Metaflow
    6. Azure Machine Learning
    7. Google Cloud Vertex AI Pipelines

    AI recommended 7 alternatives but never named RunLLM/aqueduct. 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 RunLLM/aqueduct?
    pass
    AI named RunLLM/aqueduct explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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RunLLM/aqueduct — 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