RRepoGEO

REPOGEO REPORT · LITE

knownsec/aipyapp

Default branch main · commit 49086c0f · scanned 6/29/2026, 6:27:08 AM

GitHub: 3,983 stars · 400 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
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 knownsec/aipyapp, 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 H1 and opening to emphasize direct LLM Python execution

    Why:

    CURRENT
    # Python-Use: A New AI Agent Paradigm (Agent 2.0)

**AI-Powered Python & Python-Powered AI**

Python-Use is a task-driven, result-oriented intelligent execution paradigm. It tightly integrates LLMs with a Python interpreter to establish a complete loop:
    COPY-PASTE FIX
    # Python-Use: Direct Python Execution for LLM Agents (Agent 2.0)

**AI-Powered Python & Python-Powered AI: A New Paradigm for LLMs to Directly Execute Code**

Python-Use is a novel AI agent framework that empowers Large Language Models (LLMs) to directly interact with and execute a full Python environment, bypassing traditional function calling and tool-based limitations. It establishes a complete loop:
  • hightopics#2
    Add specific topics for AI agent frameworks and LLM Python execution

    Why:

    COPY-PASTE FIX
    llm, ai-agent, python, interpreter, code-execution, generative-ai, agent-framework, python-use, large-language-models
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example:

## License
This project is licensed under a custom license. Please refer to the `LICENSE` file in the repository root for complete details.

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 knownsec/aipyapp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. AutoGPT · recommended 2×
  3. Open Interpreter · recommended 2×
  4. BabyAGI · recommended 1×
  5. Docker · recommended 1×
  • CATEGORY QUERY
    How can I give an LLM direct Python interpreter access for task execution, bypassing function calls?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGPT
    3. BabyAGI
    4. Open Interpreter
    5. Docker
    6. Jupyter Kernel Gateway
    7. Papermill

    AI recommended 7 alternatives but never named knownsec/aipyapp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are frameworks for building AI agents that execute Python code directly, not just call tools?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGPT
    3. CrewAI
    4. Open Interpreter
    5. GPT-Engineer
    6. LlamaIndex

    AI recommended 6 alternatives but never named knownsec/aipyapp. 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 knownsec/aipyapp?
    pass
    AI named knownsec/aipyapp explicitly

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

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

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

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knownsec/aipyapp — 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