RRepoGEO

REPOGEO REPORT · LITE

Bessouat40/RAGLight

Default branch main · commit 99cd5e34 · scanned 5/28/2026, 12:02:10 PM

GitHub: 663 stars · 101 forks

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 Bessouat40/RAGLight, 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 opening to emphasize flexibility and tool integration

    Why:

    CURRENT
    **RAGLight** is a lightweight and modular Python library for implementing **Retrieval-Augmented Generation (RAG)**. It enhances the capabilities of Large Language Models (LLMs) by combining document retrieval with natural language inference. Designed for simplicity and flexibility, RAGLight provides modular components to easily integrate various LLMs, embeddings, and vector stores, making it an ideal tool for building context-aware AI solutions.
    COPY-PASTE FIX
    **RAGLight** is a lightweight and modular Python library for building flexible **Retrieval-Augmented Generation (RAG)** applications. It empowers developers to easily integrate custom components, various LLMs, embeddings, and vector stores, and now includes seamless **MCP integration** to connect external tools and diverse data sources. Designed for simplicity and flexibility, RAGLight is an ideal tool for building context-aware AI solutions, from rapid prototyping to scalable deployments.
  • mediumreadme#2
    Prominently feature MCP integration in README

    Why:

    COPY-PASTE FIX
    Add a new bullet point under 'Features' like: '- **Seamless MCP Integration:** Easily connect to external tools and diverse data sources, extending RAG capabilities beyond local documents.' Also, ensure a dedicated section further down, e.g., 'MCP: Connecting External Tools & Data', provides detailed examples and setup instructions.
  • lowtopics#3
    Expand topics with broader RAG and AI framework terms

    Why:

    CURRENT
    agentic-ai, agentic-rag, agentic-workflow, artificial-intelligence, data-science, framework, huggingface, lmstudio, mcp, mcp-tools, mistral-api, mistralai, ollama, openai, openai-api, rag, retrieval-augmented, retrieval-augmented-generation, vector-database
    COPY-PASTE FIX
    agentic-ai, agentic-rag, agentic-workflow, artificial-intelligence, data-science, framework, huggingface, lmstudio, mcp, mcp-tools, mistral-api, mistralai, ollama, openai, openai-api, rag, retrieval-augmented, retrieval-augmented-generation, vector-database, llm-framework, ai-framework, custom-rag-components, rag-pipeline

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 Bessouat40/RAGLight
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 1×
  2. LangChain · recommended 1×
  3. Haystack · recommended 1×
  4. Ragas · recommended 1×
  5. DSPy · recommended 1×
  • CATEGORY QUERY
    What are good Python frameworks for building flexible RAG applications with custom components?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Ragas
    5. DSPy

    AI recommended 5 alternatives but never named Bessouat40/RAGLight. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I integrate external tools and data sources into a RAG system and deploy it?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (run-llama/llama_index)
    2. LangChain (langchain-ai/langchain)
    3. Unstructured.io (Unstructured-IO/unstructured)
    4. Apache Airflow (apache/airflow)
    5. Prefect (PrefectHQ/prefect)
    6. Pinecone
    7. Weaviate (weaviate/weaviate)
    8. Qdrant (qdrant/qdrant)
    9. Chroma (chroma-core/chroma)
    10. Haystack (deepset.ai) (deepset-ai/haystack)
    11. Hugging Face Spaces
    12. Streamlit (streamlit/streamlit)
    13. Gradio (gradio-app/gradio)
    14. Docker (docker/docker-ce)
    15. Kubernetes (kubernetes/kubernetes)
    16. Google Kubernetes Engine
    17. Amazon EKS
    18. Azure Kubernetes Service
    19. AWS SageMaker
    20. Google Cloud Vertex AI
    21. Azure Machine Learning
    22. Render
    23. Vercel
    24. Hugging Face Transformers (huggingface/transformers)

    AI recommended 24 alternatives but never named Bessouat40/RAGLight. 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 Bessouat40/RAGLight?
    pass
    AI named Bessouat40/RAGLight explicitly

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

  • If a team adopts Bessouat40/RAGLight in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Bessouat40/RAGLight 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 Bessouat40/RAGLight solve, and who is the primary audience?
    pass
    AI named Bessouat40/RAGLight explicitly

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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