RRepoGEO

REPOGEO REPORT · LITE

neural-maze/agentic-patterns-course

Default branch main · commit 9c4b70a3 · scanned 6/25/2026, 9:42:07 AM

GitHub: 1,726 stars · 329 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
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 neural-maze/agentic-patterns-course, 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 paragraph to emphasize 'course'

    Why:

    CURRENT
    <p align="center">
    
    <h1 align="center">Agentic Patterns</h1>
    <h3 align="center">Implementing the agentic patterns using Groq</h3>
</p>

<p align="center">
    
</p>

> No LangChain, no LangGraph, no LlamaIndex, no CrewAI. Pure and simple API calls to Groq.

## Table of Contents
    COPY-PASTE FIX
    <p align="center">
    
    <h1 align="center">Agentic Patterns Course</h1>
    <h3 align="center">Learn to Implement Agentic Patterns from Scratch using Groq</h3>
</p>

<p align="center">
    
</p>

> This course teaches you to implement the 4 agentic patterns (Reflection, Tool Use, Planning, Multi-agent) from scratch, using pure API calls to Groq. No LangChain, no LangGraph, no LlamaIndex, no CrewAI.

## Table of Contents
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    agentic-patterns, llms, groq, ai-agents, reflection, tool-use, planning, multi-agent-systems, deeplearning-ai, andrew-ng, educational, course, python
  • mediumhomepage#3
    Add a homepage URL linking to the DeepLearning.AI article series

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://www.deeplearning.ai/the-batch/agentic-patterns-for-llm-applications/

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 neural-maze/agentic-patterns-course
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. Anthropic Claude · recommended 1×
  3. Google Gemini · recommended 1×
  4. Requests · recommended 1×
  5. Axios · recommended 1×
  • CATEGORY QUERY
    How to build custom AI agents and implement core patterns without relying on large frameworks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude
    3. Google Gemini
    4. Requests
    5. Axios
    6. HttpClient
    7. Pydantic
    8. Zod
    9. Faiss
    10. Hnswlib
    11. OpenAI Embeddings
    12. Sentence Transformers
    13. SQLite
    14. DuckDB
    15. Redis
    16. json
    17. os
    18. logging
    19. asyncio

    AI recommended 19 alternatives but never named neural-maze/agentic-patterns-course. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking examples for developing reflection, tool use, and multi-agent systems using direct API calls?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude API
    3. Google Gemini API
    4. Llama 3 API
    5. Mistral API
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)

    AI recommended 7 alternatives but never named neural-maze/agentic-patterns-course. 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 neural-maze/agentic-patterns-course?
    pass
    AI did not name neural-maze/agentic-patterns-course — 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 neural-maze/agentic-patterns-course in production, what risks or prerequisites should they evaluate first?
    pass
    AI named neural-maze/agentic-patterns-course 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 neural-maze/agentic-patterns-course solve, and who is the primary audience?
    pass
    AI named neural-maze/agentic-patterns-course explicitly

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

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neural-maze/agentic-patterns-course — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite