RRepoGEO

REPOGEO REPORT · LITE

alphasecio/langchain-examples

Default branch main · commit a71c7443 · scanned 6/5/2026, 8:02:58 PM

GitHub: 550 stars · 153 forks

AI VISIBILITY SCORE
33 /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
2 / 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 alphasecio/langchain-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

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 sentence to clarify purpose

    Why:

    CURRENT
    # langchain-examples

This repository contains a collection of apps powered by LangChain.
    COPY-PASTE FIX
    # LangChain Examples: Practical LLM Applications & Patterns

This repository offers a curated collection of runnable, end-to-end applications and patterns built with the LangChain framework, designed for developers seeking practical implementations of large language models (LLMs) for various use cases like summarization, Q&A, and interactive dashboards.
  • mediumabout#2
    Enhance the 'About' description for clarity

    Why:

    CURRENT
    A collection of apps powered by the LangChain LLM framework.
    COPY-PASTE FIX
    A curated collection of practical, runnable applications and patterns built with the LangChain LLM framework, demonstrating real-world use cases for developers.
  • lowtopics#3
    Add more specific topics to highlight application type

    Why:

    CURRENT
    chromadb, jupyter-notebook, langchain, llm, notebook, openai, pinecone, python, streamlit, vectordb
    COPY-PASTE FIX
    chromadb, jupyter-notebook, langchain, llm, notebook, openai, pinecone, python, streamlit, vectordb, llm-applications, generative-ai-examples, langchain-apps

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 alphasecio/langchain-examples
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Streamlit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Streamlit · recommended 1×
  2. Gradio · recommended 1×
  3. Panel · recommended 1×
  4. LangChain · recommended 1×
  5. FastAPI · recommended 1×
  • CATEGORY QUERY
    How can I quickly build interactive applications leveraging large language models in Python?
    you: not recommended
    AI recommended (in order):
    1. Streamlit
    2. Gradio
    3. Panel
    4. LangChain
    5. FastAPI
    6. React
    7. Vue

    AI recommended 7 alternatives but never named alphasecio/langchain-examples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical examples for document summarization and Q&A using vector stores and generative AI.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. OpenAI
    3. Anthropic
    4. Pinecone
    5. ChromaDB (chroma-core/chroma)
    6. LlamaIndex (run-llama/llama_index)
    7. Hugging Face
    8. Weaviate (weaviate/weaviate)
    9. Qdrant (qdrant/qdrant)
    10. Haystack (deepset-ai/haystack)
    11. Elasticsearch (elastic/elasticsearch)
    12. OpenSearch (opensearch-project/OpenSearch)
    13. Hugging Face Transformers (huggingface/transformers)
    14. Gradio (gradio-app/gradio)
    15. Streamlit (streamlit/streamlit)
    16. FAISS (facebookresearch/faiss)
    17. Llama.cpp (ggerganov/llama.cpp)
    18. Ollama (ollama/ollama)
    19. Mistral
    20. Llama 2
    21. Azure AI Search
    22. Azure OpenAI Service
    23. AWS Kendra
    24. Amazon Bedrock

    AI recommended 24 alternatives but never named alphasecio/langchain-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
    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 alphasecio/langchain-examples?
    pass
    AI named alphasecio/langchain-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 alphasecio/langchain-examples in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alphasecio/langchain-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 alphasecio/langchain-examples solve, and who is the primary audience?
    pass
    AI did not name alphasecio/langchain-examples — 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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  • Brand-free category queries5 vs 2 in Lite
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