RRepoGEO

REPOGEO REPORT · LITE

towhee-io/towhee

Default branch main · commit fe856301 · scanned 6/19/2026, 9:36:58 PM

GitHub: 3,448 stars · 261 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
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 towhee-io/towhee, 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
    Reposition the README's opening to clearly state its core purpose and category

    Why:

    CURRENT
    <h3 align="center"> <p style="text-align: center;"> <span style="font-weight: bold; font: Arial, sans-serif;">x</span>2vec, Towhee is all you need! </p> </h3>
    COPY-PASTE FIX
    <h3 align="center"> <p style="text-align: center;"> Towhee: LLM-powered Pipeline Orchestration for Neural Data Processing </p> </h3>
  • mediumtopics#2
    Add more specific topics to highlight pipeline orchestration and neural data processing

    Why:

    CURRENT
    computer-vision, convolutional-networks, embedding-vectors, embeddings, feature-extraction, feature-vector, image-processing, image-retrieval, llm, machine-learning, milvus, pipeline, towhee, transformer, unstructured-data, video-processing, vision-transformer, vit
    COPY-PASTE FIX
    computer-vision, convolutional-networks, embedding-vectors, embeddings, feature-extraction, feature-vector, image-processing, image-retrieval, llm, machine-learning, milvus, pipeline, towhee, transformer, unstructured-data, video-processing, vision-transformer, vit, data-pipeline-orchestration, neural-data-processing, llm-pipelines
  • lowreadme#3
    Add a 'Comparison' or 'Why Towhee?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why Towhee? (vs. Spark, TFX, Hugging Face)' or '## How Towhee Compares' that explains its focus on LLM-based neural data processing pipelines for unstructured data, contrasting it with broader data processing frameworks or pure model libraries.

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 towhee-io/towhee
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
apache/spark
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. apache/spark · recommended 1×
  2. Spark MLlib · recommended 1×
  3. JohnSnowLabs/spark-nlp · recommended 1×
  4. tensorflow/tfx · recommended 1×
  5. TensorFlow Transform · recommended 1×
  • CATEGORY QUERY
    How to build efficient data processing pipelines for unstructured data using deep learning models?
    you: not recommended
    AI recommended (in order):
    1. Apache Spark (apache/spark)
    2. Spark MLlib
    3. Spark NLP (JohnSnowLabs/spark-nlp)
    4. TensorFlow Extended (TFX) (tensorflow/tfx)
    5. TensorFlow Transform
    6. TensorFlow (tensorflow/tensorflow)
    7. PyTorch Lightning (Lightning-AI/lightning)
    8. PyTorch (pytorch/pytorch)
    9. Hugging Face Transformers (huggingface/transformers)
    10. torchvision (pytorch/vision)
    11. Kubeflow Pipelines (kubeflow/pipelines)
    12. Kubernetes (kubernetes/kubernetes)
    13. 🤗 Datasets (huggingface/datasets)
    14. 🤗 Accelerate (huggingface/accelerate)
    15. DVC (Data Version Control) (iterative/dvc)

    AI recommended 15 alternatives but never named towhee-io/towhee. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Framework for extracting features and generating embeddings from images and videos with LLMs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. 🤗 Transformers Agents
    3. Diffusers
    4. OpenAI API
    5. GPT-4V
    6. CLIP
    7. DALL-E 3
    8. LlamaIndex
    9. LangChain
    10. PyTorch
    11. TensorFlow
    12. BLIP
    13. DINOv2
    14. SAM

    AI recommended 14 alternatives but never named towhee-io/towhee. 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 towhee-io/towhee?
    pass
    AI named towhee-io/towhee explicitly

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

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

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

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towhee-io/towhee — 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