# panaversity/learn-agentic-ai

Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.

Repository: https://github.com/panaversity/learn-agentic-ai
Canonical: https://ross.abutalabs.com/products/learn-agentic-ai
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: agentic-ai, langmem, mcp, openai-api, openai, docker, kubernetes, dapr, a2a, dapr-pub-sub, dapr-service-invocation, dapr-sidecar, dapr-workflow, kafka, openai-agents-sdk, postgresql-database, rabbitmq, rancher-desktop, redis, serverless-containers
Last push: 2025-10-26T01:09:51+00:00

## Health v2 (maintenance only)
Score: 45/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 48, release rhythm 35, longevity 58
- inputs: {"age_days": 812, "days_push": 312, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4347, forks 1008 (observed 2026-08-28T04:08:46.212724+00:00)

## What it is
An open-source curriculum teaching Agentic AI development using the Dapr Agentic Cloud Ascent (DACA) design pattern, covering OpenAI Agents SDK, MCP, A2A, knowledge graphs, Dapr, and Kubernetes. It is part of the Panaversity Certified Agentic & Robotic AI Engineer program and is delivered as Jupyter Notebook learning material.

## Use cases
- learn agentic ai from scratch
- study the dapr agentic cloud ascent design pattern
- learn openai agents sdk with tutorials
- understand mcp and a2a protocols for agents
- learn to scale ai agents on kubernetes and dapr
- follow a certified agentic ai engineer curriculum
- learn agent-native cloud technologies like dapr and kafka

## When to choose
- you want structured, hands-on course material for building and scaling AI agents
- you want to learn the OpenAI Agents SDK alongside cloud-native tooling like Dapr and Kubernetes
- you are following the Panaversity certification program
- you prefer learning via Jupyter notebooks with an MIT license

## When to avoid
- you need production-ready agent framework code rather than educational material
- you want a framework-agnostic course that avoids the OpenAI ecosystem
- you are looking for a deployable application rather than a curriculum

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, mcp, llm-inference, container-orchestration, developer-tools
- domain: large-language-models, tutorials, cloud-computing
- platform: python, cloud
- tags: daca-design-pattern, openai-agents-sdk, dapr, a2a, agentic-ai, course-material, jupyter-notebooks, rancher-desktop, ai-agents, containers, kubernetes, docker

## Member repositories
- panaversity/learn-agentic-ai (main) score 45

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:46.212724+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:34.247630+00:00, confidence not recorded.
  - readme: https://github.com/panaversity/learn-agentic-ai (fetched 2026-08-28T04:08:46.212724+00:00, sha a09635bef6ca)
- Data as of 2026-08-30T08:39:29.467469+00:00.