RRepoGEO

REPOGEO REPORT · LITE

microsoft/lida

Default branch main · commit d892e20b · scanned 6/18/2026, 1:36:48 PM

GitHub: 3,260 stars · 379 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 microsoft/lida, 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 README opening to emphasize programmatic code generation

    Why:

    CURRENT
    LIDA is a library for generating data visualizations and data-faithful infographics. LIDA is grammar agnostic (will work with any programming language and visualization libraries e.g. matplotlib, seaborn, altair, d3 etc) and works with multiple large language model providers (OpenAI, Azure OpenAI, PaLM, Cohere, Huggingface).
    COPY-PASTE FIX
    LIDA is an open-source Python library for developers and data scientists to programmatically generate data visualizations and data-faithful infographics. It leverages large language models (LLMs) to create visualization *code* (e.g., for Matplotlib, Seaborn, Altair, D3) from natural language, making it grammar-agnostic and compatible with various LLM providers (OpenAI, Azure OpenAI, PaLM, Cohere, Huggingface).
  • hightopics#2
    Refine topics to be more specific to LLM-driven visualization code generation

    Why:

    CURRENT
    cohere, datavisualization, hacktoberfest, llm, openai, openai-api, palm2, visualization
    COPY-PASTE FIX
    cohere, datavisualization, llm, openai, openai-api, palm2, visualization, visualization-generation, code-generation, python-library, data-science-tools, infographics
  • mediumcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives
Unlike general LLM frameworks (e.g., LangChain) that provide broad LLM orchestration, LIDA specifically focuses on generating and manipulating visualization *code*. Compared to traditional BI tools (e.g., Tableau, Power BI) or design platforms (e.g., Canva), LIDA offers a programmatic, code-centric approach for developers to integrate AI-driven visualization generation directly into their applications and workflows, rather than a GUI-based end-user experience.

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 microsoft/lida
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pandas-dev/pandas
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pandas-dev/pandas · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. ChatGPT / GPT-4 · recommended 1×
  4. Google Gemini · recommended 1×
  5. Llama 2 · recommended 1×
  • CATEGORY QUERY
    How can I automate data visualization creation using large language models?
    you: not recommended
    AI recommended (in order):
    1. Pandas (pandas-dev/pandas)
    2. LangChain (langchain-ai/langchain)
    3. ChatGPT / GPT-4
    4. Google Gemini
    5. Llama 2
    6. Matplotlib (matplotlib/matplotlib)
    7. Seaborn (mwaskom/seaborn)
    8. Plotly (plotly/plotly.py)
    9. Altair (altair-viz/altair)
    10. Microsoft Copilot / GitHub Copilot

    AI recommended 10 alternatives but never named microsoft/lida. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools generate data-faithful infographics from raw data using AI?
    you: not recommended
    AI recommended (in order):
    1. Tableau CRM (formerly Einstein Analytics)
    2. Infogram
    3. Beautiful.ai
    4. Canva (with Magic Design/Magic Write)
    5. Microsoft Power BI
    6. Datawrapper

    AI recommended 6 alternatives but never named microsoft/lida. 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 microsoft/lida?
    pass
    AI named microsoft/lida explicitly

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

  • If a team adopts microsoft/lida in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/lida 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 microsoft/lida solve, and who is the primary audience?
    pass
    AI named microsoft/lida 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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HTML
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microsoft/lida — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite