RRepoGEO

REPOGEO REPORT · LITE

PySpur-Dev/pyspur

Default branch main · commit 97b9856e · scanned 7/1/2026, 10:31:50 AM

GitHub: 5,743 stars · 427 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 PySpur-Dev/pyspur, 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 statement to clarify PySpur's identity

    Why:

    CURRENT
    Iterate over your agents 10x faster. AI engineers use PySpur to iterate over AI agents visually without reinventing the wheel.
    COPY-PASTE FIX
    PySpur: The visual playground for AI engineers to iterate on LLM agent workflows 10x faster. (Not a gear design library or microservices framework.)
  • mediumtopics#2
    Add more specific topics related to visual agent debugging and iteration

    Why:

    CURRENT
    agent, agents, ai, builder, deepseek, framework, gemini, graph, human-in-the-loop, llm, llms, loops, multimodal, ollama, python, rag, reasoning, tool, trace, workflow
    COPY-PASTE FIX
    agent, agents, ai, builder, deepseek, framework, gemini, graph, human-in-the-loop, llm, llms, loops, multimodal, ollama, python, rag, reasoning, tool, trace, workflow, agent-debugging, visual-debugger, prompt-engineering-tool, llm-ops, agent-orchestration, workflow-visualization
  • lowreadme#3
    Add a dedicated 'What is PySpur?' section to reinforce its purpose

    Why:

    COPY-PASTE FIX
    ## ✨ What is PySpur?
PySpur is a visual development environment designed specifically for AI engineers to build, test, and debug complex LLM agent workflows. It provides a graphical interface to observe agent execution, inspect intermediate steps, and rapidly iterate on prompts and logic.

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 PySpur-Dev/pyspur
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain Playground (LangSmith)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain Playground (LangSmith) · recommended 1×
  2. Humanloop · recommended 1×
  3. Weights & Biases (W&B Prompts) · recommended 1×
  4. Steamship · recommended 1×
  5. OpenPipe · recommended 1×
  • CATEGORY QUERY
    How can I visually debug and iterate on my LLM agent workflows more efficiently?
    you: not recommended
    AI recommended (in order):
    1. LangChain Playground (LangSmith)
    2. Humanloop
    3. Weights & Biases (W&B Prompts)
    4. Steamship
    5. OpenPipe
    6. Portkey.ai

    AI recommended 6 alternatives but never named PySpur-Dev/pyspur. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help accelerate AI agent development and manage complex prompt engineering loops?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Assistants API
    4. AutoGen
    5. PromptLayer
    6. Weights & Biases (W&B) Prompts
    7. Guidance

    AI recommended 7 alternatives but never named PySpur-Dev/pyspur. 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 PySpur-Dev/pyspur?
    pass
    AI named PySpur-Dev/pyspur explicitly

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

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

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

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PySpur-Dev/pyspur — 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