RRepoGEO

REPOGEO REPORT · LITE

reflex-dev/reflex-llm-examples

Default branch main · commit 0aa66e00 · scanned 6/14/2026, 5:22:37 AM

GitHub: 847 stars · 149 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 reflex-dev/reflex-llm-examples, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise About description

    Why:

    COPY-PASTE FIX
    Practical examples of advanced AI applications built with the Reflex framework, showcasing LLMs, RAG, and AI agents in full-stack Python web apps.
  • mediumhomepage#2
    Add the Reflex homepage URL

    Why:

    COPY-PASTE FIX
    https://reflex.dev

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 reflex-dev/reflex-llm-examples
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. OpenAI Cookbook · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. DeepLearning.AI Courses · recommended 1×
  5. Awesome-LLM-Apps · recommended 1×
  • CATEGORY QUERY
    Where can I find practical examples for building advanced AI applications with large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Cookbook
    2. LangChain
    3. Hugging Face Transformers
    4. DeepLearning.AI Courses
    5. Awesome-LLM-Apps
    6. Gradio Demos

    AI recommended 6 alternatives but never named reflex-dev/reflex-llm-examples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Show me example implementations of RAG and AI agents for scalable LLM solutions.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. ChromaDB
    3. Pinecone
    4. OpenAI
    5. Anthropic
    6. LlamaIndex
    7. Weaviate
    8. Qdrant
    9. Azure OpenAI Service
    10. Haystack
    11. Elasticsearch
    12. OpenSearch
    13. Hugging Face Models
    14. GoogleSearchAPIWrapper
    15. CrewAI
    16. Serper.dev
    17. Browserless
    18. Autogen
    19. Docker
    20. Kubernetes

    AI recommended 20 alternatives but never named reflex-dev/reflex-llm-examples. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 reflex-dev/reflex-llm-examples?
    pass
    AI named reflex-dev/reflex-llm-examples explicitly

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

  • If a team adopts reflex-dev/reflex-llm-examples in production, what risks or prerequisites should they evaluate first?
    pass
    AI named reflex-dev/reflex-llm-examples 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 reflex-dev/reflex-llm-examples solve, and who is the primary audience?
    pass
    AI named reflex-dev/reflex-llm-examples 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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reflex-dev/reflex-llm-examples — 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