RRepoGEO

REPOGEO REPORT · LITE

GiovanniPasq/agentic-rag-for-dummies

Default branch main · commit 8b3e5ff0 · scanned 6/24/2026, 2:01:54 AM

GitHub: 3,535 stars · 464 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
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 GiovanniPasq/agentic-rag-for-dummies, 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 the README's opening to clarify its role as a LangGraph learning resource

    Why:

    CURRENT
    <p align="center"><strong>Build a modular Agentic RAG system with LangGraph, conversation memory, and human-in-the-loop query clarification</strong></p>
    COPY-PASTE FIX
    <p align="center"><strong>A practical guide and example for building a modular Agentic RAG system using LangGraph, complete with conversation memory and human-in-the-loop query clarification. Learn to implement advanced RAG agents step-by-step.</strong></p>
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://giovannipasq.github.io/agentic-rag-for-dummies/
  • lowcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., `## Comparison to Alternatives`, explaining that this project is a *guide/example* for building with frameworks like LangGraph, not a new framework itself. Highlight how it complements existing tools by providing a concrete, modular implementation for learning.

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 GiovanniPasq/agentic-rag-for-dummies
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Assistants API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Assistants API · recommended 2×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. deepset/Haystack · recommended 1×
  5. Microsoft/AutoGen · recommended 1×
  • CATEGORY QUERY
    How to build a modular retrieval-augmented generation agent system with conversation memory?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/Haystack)
    4. AutoGen (Microsoft/AutoGen)
    5. OpenAI Assistants API
    6. FastAPI
    7. Flask
    8. Faiss
    9. Chroma
    10. Weaviate
    11. OpenAI
    12. Anthropic
    13. Hugging Face Transformers
    14. PostgreSQL
    15. Redis

    AI recommended 15 alternatives but never named GiovanniPasq/agentic-rag-for-dummies. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a quick way to implement retrieval-augmented generation agents with human-in-the-loop features.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. Guardrails AI (guardrails-ai/guardrails)
    5. OpenAI Assistants API
    6. Flask (pallets/flask)
    7. Streamlit (streamlit/streamlit)
    8. transformers (huggingface/transformers)
    9. openai (openai/openai-python)
    10. faiss (facebookresearch/faiss)
    11. chroma (chroma-core/chroma)

    AI recommended 11 alternatives but never named GiovanniPasq/agentic-rag-for-dummies. 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 GiovanniPasq/agentic-rag-for-dummies?
    pass
    AI did not name GiovanniPasq/agentic-rag-for-dummies — 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 GiovanniPasq/agentic-rag-for-dummies in production, what risks or prerequisites should they evaluate first?
    pass
    AI named GiovanniPasq/agentic-rag-for-dummies 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 GiovanniPasq/agentic-rag-for-dummies solve, and who is the primary audience?
    pass
    AI did not name GiovanniPasq/agentic-rag-for-dummies — 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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GiovanniPasq/agentic-rag-for-dummies — 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