RRepoGEO

REPOGEO REPORT · LITE

panaversity/learn-agentic-ai

Default branch main · commit dd7426f6 · scanned 5/21/2026, 1:38:48 PM

GitHub: 4,169 stars · 969 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
35 /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
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 panaversity/learn-agentic-ai, 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's opening to highlight practical learning outcomes for cloud-native agentic AI

    Why:

    CURRENT
    This repo is part of the Panaversity Certified Agentic & Robotic AI Engineer program. You can also review the certification and course details in the program guide. This repo provides learning material for Agentic AI and Cloud courses.
    COPY-PASTE FIX
    This repository offers a structured, practical learning path for developers and AI practitioners to build scalable and resilient agentic AI applications. Dive into the Dapr Agentic Cloud Ascent (DACA) Design Pattern and master agent-native cloud technologies like OpenAI Agents SDK, Dapr, Kubernetes, and more, to develop robust AI agents from start to scale.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the URL for the Panaversity Certified Agentic & Robotic AI Engineer program guide or a dedicated project website to the 'Homepage' field in the repository settings.
  • lowreadme#3
    Reorganize README content to prioritize global learning path over regional strategy

    Why:

    CURRENT
    The section 'Our Agentic Strategy for Pakistan: Four Working Hypotheses' appears very early in the README.
    COPY-PASTE FIX
    Move the section 'Our Agentic Strategy for Pakistan: Four Working Hypotheses' and its sub-hypotheses further down the README, perhaps under a 'Project Vision' or 'Context' section, after the core learning path and technical overview.

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 panaversity/learn-agentic-ai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Redis
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Redis · recommended 2×
  2. Kubernetes · recommended 1×
  3. KubeFlow · recommended 1×
  4. LangChain · recommended 1×
  5. LlamaIndex · recommended 1×
  • CATEGORY QUERY
    How to build scalable and resilient agentic AI applications on a cloud-native infrastructure?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes
    2. KubeFlow
    3. LangChain
    4. LlamaIndex
    5. AWS Fargate
    6. ECS
    7. AWS Step Functions
    8. Google Cloud Run
    9. GKE
    10. Google Cloud Workflows
    11. Azure Container Apps
    12. AKS
    13. Azure Logic Apps
    14. Durable Functions
    15. Temporal.io
    16. Cadence
    17. Docker
    18. Podman
    19. Ray
    20. Ray Serve
    21. Ray Core

    AI recommended 21 alternatives but never named panaversity/learn-agentic-ai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What architectural patterns enable effective communication and state management for AI agents?
    you: not recommended
    AI recommended (in order):
    1. Akka
    2. Microsoft Orleans
    3. Erlang/OTP
    4. Pykka
    5. Apache Kafka
    6. RabbitMQ
    7. AWS SNS/SQS
    8. Google Cloud Pub/Sub
    9. Azure Event Hubs
    10. Redis
    11. Apache Ignite
    12. JADE (Java Agent Development Framework)
    13. SPADE (Python Agent Development Environment)
    14. NetLogo
    15. Apache Cassandra
    16. MongoDB
    17. Redis
    18. etcd
    19. Axon Framework
    20. NServiceBus
    21. EventStoreDB

    AI recommended 21 alternatives but never named panaversity/learn-agentic-ai. 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 panaversity/learn-agentic-ai?
    pass
    AI named panaversity/learn-agentic-ai explicitly

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

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

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

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panaversity/learn-agentic-ai — 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