learn-agentic-ai MCP Server
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.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
panaversity/learn-agentic-ai is a Jupyter Notebook tool licensed under MIT. 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. 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.
Ranked #19938 out of 24840 indexed tools.
Has 4,378 GitHub stars.
Has 16 contributors.
Ecosystem
Score Breakdown
4,378 stars → well-known project
Last commit 11mo ago → stale
1/60 issues closed → many open issues
16 contributors → active community
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
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From the README
# Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern: From Start to Scale This repo is part of the [Panaversity Certified Agentic & Robotic AI Engineer](https://panaversity.org/) program. You can also review the certification and course details in the [program guide](https://docs.google.com/document/d/1BygAckkfc_NFQnTfEM6qqUvPdlIHpNItmRtvfRMGp38/edit?usp=sharing). This repo provides learning material for Agentic AI and Cloud courses. Here’s a polished, professional rewrite you can use as a one-pager or slide—tight on wording, clear on stakes, and just a touch playful so it doesn’t read like it was written by a committee (no offense to committees 😄). # Our Agentic Strategy for Pakistan: Four Working Hypotheses Pakistan must place smart, early bets on the technologies and talent that will define the agentic AI era—because we intend to train **millions** of agentic-AI developers across the country and abroad, and launch startups at scale (ambitious, yes—but coffeeRead full README on GitHub →
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