RRepoGEO

REPOGEO REPORT · LITE

ljquan/opentu

Default branch main · commit f57c2123 · scanned 6/13/2026, 7:17:41 PM

GitHub: 615 stars · 134 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 ljquan/opentu, 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 improve categorization

    Why:

    COPY-PASTE FIX
    ai, ai-platform, generative-ai, canvas, workflow, low-code, no-code, visual-programming, multi-modal, tools, agent, application-development
  • highreadme#2
    Strengthen README's initial description for precise AI understanding

    Why:

    CURRENT
    <h3>开图 · 以画布为核心的 AI 应用平台</h3><p>连接多模型生成、工具、素材与知识流，让 AI 任务在同一工作区持续执行。</p>
    COPY-PASTE FIX
    <h3>Opentu (开图): 开源的、以画布为核心的 AI 应用开发与管理平台</h3><p>Opentu 是一个强大的开源平台，旨在构建和管理各类 AI 应用。它提供一个可视化画布工作区，连接多模型生成（文本、图像、视频）、AI 工具、素材与知识流，让 AI 任务在同一工作区持续、高效地执行。</p>
  • mediumreadme#3
    Add a concise 'Why Opentu?' or 'Key Features' summary near the top

    Why:

    COPY-PASTE FIX
    ## Opentu 的核心优势 (Why Opentu?)
Opentu 不仅仅是一个 AI 工具，更是一个以画布为核心的集成平台，旨在简化 AI 应用的开发与管理。它独特地结合了：
- **多模型统一调度**：无缝集成图片、视频、音频、文本与 Agent 流程。
- **可视化画布工作区**：直观承载 AI 任务、素材、工具与知识库。
- **高效任务与素材管理**：通过队列、素材库和历史记录复用生成结果。
- **强大的工具箱与扩展性**：支持内部 React 工具、iframe 工具、Skill/Agent 和插件化运行时。
- **全面的内容工作流**：支持 Frame 幻灯片、PPT 导出、Markdown/Mermaid 转换和多媒体编辑。

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 ljquan/opentu
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
KNIME Analytics Platform
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. KNIME Analytics Platform · recommended 1×
  2. apache/hop · recommended 1×
  3. biolab/orange3 · recommended 1×
  4. mlflow/mlflow · recommended 1×
  5. iterative/cml · recommended 1×
  • CATEGORY QUERY
    Looking for an open-source AI platform with a visual canvas workspace for various tasks.
    you: not recommended
    AI recommended (in order):
    1. KNIME Analytics Platform
    2. Apache Hop (apache/hop)
    3. Orange (biolab/orange3)
    4. MLflow (mlflow/mlflow)
    5. CML (iterative/cml)
    6. OpenCV (opencv/opencv)

    AI recommended 6 alternatives but never named ljquan/opentu. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I build a unified AI application platform for text, image, and video generation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Diffusers (huggingface/diffusers)
    3. Hugging Face Hub
    4. accelerate (huggingface/accelerate)
    5. PyTorch (pytorch/pytorch)
    6. TensorFlow (tensorflow/tensorflow)
    7. Keras (keras-team/keras)
    8. OpenAI API
    9. RunwayML
    10. Replicate
    11. DeepMotion
    12. AWS
    13. Google Cloud
    14. Vertex AI
    15. Azure
    16. Azure Machine Learning
    17. FastAPI (tiangolo/fastapi)
    18. Flask (pallets/flask)
    19. Kubernetes (kubernetes/kubernetes)
    20. React (facebook/react)
    21. Vue (vuejs/core)
    22. Svelte (sveltejs/svelte)

    AI recommended 22 alternatives but never named ljquan/opentu. 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 ljquan/opentu?
    pass
    AI named ljquan/opentu explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of ljquan/opentu. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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ljquan/opentu — 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