RRepoGEO

REPOGEO REPORT · LITE

DjangoPeng/openai-quickstart

Default branch main · commit 5c2a5ab3 · scanned 5/13/2026, 2:52:48 AM

GitHub: 1,739 stars · 1,152 forks

AI VISIBILITY SCORE
22 /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
1 / 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 DjangoPeng/openai-quickstart, 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 the repository

    Why:

    COPY-PASTE FIX
    generative-ai, llm, large-language-models, langchain, openai, python, ai-applications, quickstart, tutorial, guide
  • highreadme#2
    Clarify the README's English positioning and scope in the opening

    Why:

    CURRENT
    The current `README.md` starts with a Chinese title and introductory paragraph, with an English link.
    COPY-PASTE FIX
    Add the following English summary at the very top of `README.md`, before the existing Chinese title:
`This repository is a comprehensive guide and quickstart for developing generative AI applications with large language models (LLMs), featuring practical examples using LangChain and OpenAI.`
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/DjangoPeng/openai-quickstart

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 DjangoPeng/openai-quickstart
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. Generative AI with Large Language Models · recommended 1×
  3. Building Systems with the ChatGPT API · recommended 1×
  4. Hugging Face Transformers Library · recommended 1×
  5. openai/openai-cookbook · recommended 1×
  • CATEGORY QUERY
    Looking for a comprehensive guide to develop generative AI applications with large language models.
    you: not recommended
    AI recommended (in order):
    1. Generative AI with Large Language Models
    2. Building Systems with the ChatGPT API
    3. LangChain
    4. Hugging Face Transformers Library
    5. OpenAI Cookbook (openai/openai-cookbook)
    6. Practical Deep Learning for Coders
    7. From Data to Products with LLMs

    AI recommended 7 alternatives but never named DjangoPeng/openai-quickstart. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for building LLM-powered applications using Python and modern frameworks?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Python Library
    5. Hugging Face Transformers
    6. LiteLLM
    7. Pinecone
    8. Chroma
    9. Weaviate
    10. Qdrant
    11. FastAPI
    12. Streamlit
    13. Gradio
    14. LangSmith
    15. Weights & Biases
    16. Helicone

    AI recommended 16 alternatives but never named DjangoPeng/openai-quickstart. 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 DjangoPeng/openai-quickstart?
    pass
    AI did not name DjangoPeng/openai-quickstart — likely talking about a different project

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

  • If a team adopts DjangoPeng/openai-quickstart in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DjangoPeng/openai-quickstart 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 DjangoPeng/openai-quickstart solve, and who is the primary audience?
    pass
    AI did not name DjangoPeng/openai-quickstart — likely talking about a different project

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

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