RRepoGEO

REPOGEO REPORT · LITE

graphistry/pygraphistry

Default branch master · commit 592e34a6 · scanned 5/17/2026, 11:51:20 AM

GitHub: 2,486 stars · 229 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
63 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
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 graphistry/pygraphistry, 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
  • highhomepage#1
    Add the project homepage URL

    Why:

    COPY-PASTE FIX
    https://www.graphistry.com
  • highreadme#2
    Strengthen README's opening statement for interactive data science workflows

    Why:

    CURRENT
    PyGraphistry is an open source Python library for data scientists and developers to leverage the power of graph visualization, analytics, AI, including with native GPU acceleration:
    COPY-PASTE FIX
    PyGraphistry is an open source Python library for data scientists and developers, enabling **interactive graph visualization and analysis directly within data science workflows** (like Jupyter notebooks), leveraging the power of graphs, AI, and native GPU acceleration:
  • mediumtopics#3
    Add specific topics for interactive data science visualization

    Why:

    CURRENT
    csv, cudf, cugraph, gpu, graph, graph-visualization, graphistry, igraph, jupyter, neo4j, network-analysis, network-visualization, networkx, pandas, python, rapids, splunk, tigergraph, visualization, webgl
    COPY-PASTE FIX
    interactive-visualization, data-science-workflow, jupyter-notebooks

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
1 / 2
50% of queries surface graphistry/pygraphistry
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
rapidsai/cugraph
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. rapidsai/cugraph · recommended 1×
  2. rapidsai/rapids · recommended 1×
  3. pyg-team/pytorch_geometric · recommended 1×
  4. dglai/dgl · recommended 1×
  5. networkx/networkx · recommended 1×
  • CATEGORY QUERY
    How can I visualize and analyze large graphs using Python with GPU acceleration?
    you: #3
    AI recommended (in order):
    1. cuGraph (rapidsai/cugraph)
    2. NVIDIA RAPIDS (rapidsai/rapids)
    3. Graphistry (graphistry/pygraphistry) ← you
    4. PyTorch Geometric (PyG) (pyg-team/pytorch_geometric)
    5. Deep Graph Library (DGL) (dglai/dgl)
    6. NetworkX (networkx/networkx)
    7. Plotly (plotly/plotly.py)
    8. Dash (plotly/dash)
    9. pydeck (visgl/pydeck)
    Show full AI answer
  • CATEGORY QUERY
    What are good Python libraries for interactive graph visualization within a data science workflow?
    you: not recommended
    AI recommended (in order):
    1. Plotly Dash
    2. Pyvis
    3. NetworkX
    4. Plotly
    5. Bokeh
    6. Matplotlib
    7. Graphistry
    8. Altair

    AI recommended 8 alternatives but never named graphistry/pygraphistry. 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 graphistry/pygraphistry?
    pass
    AI named graphistry/pygraphistry explicitly

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

  • If a team adopts graphistry/pygraphistry in production, what risks or prerequisites should they evaluate first?
    pass
    AI named graphistry/pygraphistry 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 graphistry/pygraphistry solve, and who is the primary audience?
    pass
    AI named graphistry/pygraphistry 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 graphistry/pygraphistry. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/graphistry/pygraphistry.svg)](https://repogeo.com/en/r/graphistry/pygraphistry)
HTML
<a href="https://repogeo.com/en/r/graphistry/pygraphistry"><img src="https://repogeo.com/badge/graphistry/pygraphistry.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

graphistry/pygraphistry — 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