RRepoGEO

REPOGEO REPORT · LITE

evoiz/Agentic-Design-Patterns

Default branch main · commit 11835e4e · scanned 5/8/2026, 5:37:46 AM

GitHub: 1,446 stars · 258 forks

AI VISIBILITY SCORE
23 /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
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 evoiz/Agentic-Design-Patterns, 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 repository description

    Why:

    COPY-PASTE FIX
    A hands-on guide and code repository for 'Agentic Design Patterns', covering foundational to advanced techniques for building intelligent AI agent systems.
  • mediumlicense#2
    Add a standard open-source license file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, for example, using the MIT License, to clearly define usage terms for the code.

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 evoiz/Agentic-Design-Patterns
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Docker
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Docker · recommended 1×
  2. kubernetes/kubernetes · recommended 1×
  3. akka/akka · recommended 1×
  4. ray-project/ray · recommended 1×
  5. apache/kafka · recommended 1×
  • CATEGORY QUERY
    How to implement robust design patterns for building intelligent AI agent systems?
    you: not recommended
    AI recommended (in order):
    1. Docker
    2. Kubernetes (kubernetes/kubernetes)
    3. Akka (akka/akka)
    4. Ray (ray-project/ray)
    5. Apache Kafka (apache/kafka)
    6. RabbitMQ (rabbitmq/rabbitmq-server)
    7. OpenAI Gym (openai/gym)
    8. Stable Baselines3 (DLR-RM/stable-baselines3)
    9. Apache Jena (apache/jena)
    10. Protégé (protegeproject/protege)
    11. JADE
    12. Mesa (projectmesa/mesa)

    AI recommended 12 alternatives but never named evoiz/Agentic-Design-Patterns. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a practical guide to build intelligent systems using advanced AI agent patterns.
    you: not recommended
    AI recommended (in order):
    1. Designing Autonomous Agents: A Practical Guide to Building Intelligent Systems
    2. Reinforcement Learning: An Introduction
    3. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
    4. Artificial Intelligence: A Modern Approach
    5. Deep Reinforcement Learning Hands-On
    6. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

    AI recommended 6 alternatives but never named evoiz/Agentic-Design-Patterns. 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 evoiz/Agentic-Design-Patterns?
    pass
    AI named evoiz/Agentic-Design-Patterns explicitly

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

  • If a team adopts evoiz/Agentic-Design-Patterns in production, what risks or prerequisites should they evaluate first?
    pass
    AI named evoiz/Agentic-Design-Patterns 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 evoiz/Agentic-Design-Patterns solve, and who is the primary audience?
    pass
    AI did not name evoiz/Agentic-Design-Patterns — 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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evoiz/Agentic-Design-Patterns — 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