RRepoGEO

REPOGEO REPORT · LITE

nibzard/awesome-agentic-patterns

Default branch main · commit 50f446aa · scanned 6/19/2026, 5:08:14 AM

GitHub: 4,707 stars · 423 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 nibzard/awesome-agentic-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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    agentic-ai, ai-agents, design-patterns, awesome-list, patterns, ai-architecture, generative-ai, llm-agents
  • highreadme#2
    Reposition the README's main heading and opening sentence

    Why:

    CURRENT
    # Awesome Agentic Patterns
    
    A curated catalogue of **agentic AI patterns** — real‑world tricks, workflows, and mini‑architectures that help autonomous or semi‑autonomous AI agents get useful work done in production.
    COPY-PASTE FIX
    # Awesome Agentic Patterns: A Curated Catalogue of AI Agent Design Patterns
    
    This repository is an **awesome list** and curated catalogue of **agentic AI patterns** — real‑world tricks, workflows, and mini‑architectures that help autonomous or semi‑autonomous AI agents get useful work done in production.
  • mediumreadme#3
    Clarify the repo's role in relation to the external website

    Why:

    CURRENT
    ## 🌐 Explore the Website
    
    **Visit:** https://agentic-patterns.com
    
    The website offers powerful discovery tools beyond this README:
    COPY-PASTE FIX
    ## 🌐 Explore the Website (and this Repo's Relationship to it)
    
    **Visit the companion website:** https://agentic-patterns.com
    
    This repository serves as the primary source for the patterns themselves, while the website offers powerful discovery tools beyond this README, such as filtering and comparison. The website's source code is located in [`apps/web/`](./apps/web/).

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 nibzard/awesome-agentic-patterns
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytransitions
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pytransitions · recommended 1×
  2. SHOP2 · recommended 1×
  3. Apache Kafka · recommended 1×
  4. RabbitMQ · recommended 1×
  5. AWS SNS/SQS · recommended 1×
  • CATEGORY QUERY
    What are effective design patterns for building robust autonomous AI agents in production?
    you: not recommended
    AI recommended (in order):
    1. pytransitions
    2. SHOP2
    3. Apache Kafka
    4. RabbitMQ
    5. AWS SNS/SQS
    6. Google Cloud Pub/Sub
    7. PyPubSub
    8. Redis
    9. MongoDB
    10. PostgreSQL
    11. SPADE
    12. JADE
    13. Ray RLlib
    14. Stable Baselines3
    15. OpenAI Gym
    16. Docker
    17. Kubernetes
    18. AWS ECS/EKS
    19. Google Kubernetes Engine (GKE)
    20. Azure Kubernetes Service (AKS)
    21. React
    22. Angular
    23. Vue.js
    24. Qt
    25. Electron

    AI recommended 25 alternatives but never named nibzard/awesome-agentic-patterns. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical workflows and mini-architectures to improve AI agent performance and reliability.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI Function Calling
    3. LlamaIndex
    4. Haystack
    5. Auto-GPT
    6. BabyAGI
    7. CrewAI
    8. Guardrails AI

    AI recommended 8 alternatives but never named nibzard/awesome-agentic-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
    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 nibzard/awesome-agentic-patterns?
    pass
    AI did not name nibzard/awesome-agentic-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?

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