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.
What is Learn Agentic AI?
Learn Agentic AI is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to 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...
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.
This server falls under the Developer Tools category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Desi
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx learn-agentic-aiConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Learn Agentic AI
Learn Agentic AI is an educational repository from the Panaversity Certified Agentic & Robotic AI Engineer program that teaches you to build production-scale agentic AI systems using the Dapr Agentic Cloud Ascent (DACA) design pattern. It covers the full stack of agent-native cloud technologies — OpenAI Agents SDK, MCP, A2A, Dapr (actors, workflows, pub/sub), Kafka, Kubernetes, and Rancher Desktop — through structured course material across three progressive tiers. Developers who want to go beyond chatbots and build durable, observable, horizontally scalable multi-agent swarms will find this a comprehensive curriculum from fundamentals to planet-scale deployment.
Prerequisites
- Python 3.11+ and Node.js 18+ installed locally
- Docker Desktop or Rancher Desktop for local Kubernetes
- An OpenAI API key (or compatible provider) for running agent examples
- Basic familiarity with Python async programming and REST APIs
- An MCP-compatible client such as Claude Desktop or Cursor
Clone the repository
Clone the learn-agentic-ai repository to your local machine. The repo is organized into numbered folders (00 through 18) that follow the course progression from agentic AI fundamentals to cloud-native deployment.
git clone https://github.com/panaversity/learn-agentic-ai.git
cd learn-agentic-aiInstall Rancher Desktop for local Kubernetes
Rancher Desktop provides a local Kubernetes cluster and container runtime needed for many of the DACA examples. Download and install it from rancherdesktop.io, then verify your cluster is running.
kubectl get nodesSet up your Python environment
Most course modules use a Python virtual environment with uv. Install uv and sync the project dependencies before running any module examples.
pip install uv
uv syncInstall and configure Dapr CLI
Dapr is the distributed runtime underpinning the DACA pattern. Install the Dapr CLI and initialize it on your local Kubernetes cluster or in standalone mode for early-course examples.
# macOS / Linux
brew install dapr/tap/dapr-cli
dapr init --kubernetesSet your API key environment variable
Most agent examples in the repo require an OpenAI-compatible API key. Export it in your shell or add it to a .env file in each module folder.
export OPENAI_API_KEY=sk-your-key-hereWork through the numbered course modules
Start from folder 00 (foundations) and progress through the modules. Each folder contains Jupyter notebooks and Python scripts. Run notebooks with Jupyter Lab or execute scripts directly.
cd 00_intro
jupyter labAdd as an MCP resource in Claude Desktop
The repository discusses MCP as a connectivity standard. You can reference local agent scripts as MCP tool servers by pointing Claude Desktop at the module being studied.
{
"mcpServers": {
"learn-agentic-ai": {
"command": "npx",
"args": ["learn-agentic-ai"]
}
}
}Learn Agentic AI Examples
Client configuration
Minimal Claude Desktop configuration to run the learn-agentic-ai MCP entry point.
{
"mcpServers": {
"learn-agentic-ai": {
"command": "npx",
"args": ["learn-agentic-ai"]
}
}
}Prompts to try
Prompts for exploring agentic AI patterns and course concepts.
- "Explain the DACA design pattern and how Dapr actors fit into it"
- "Show me a minimal OpenAI Agents SDK example with a tool call"
- "How do I set up Dapr pub/sub between two FastAPI microservices?"
- "What is A2A protocol and how does it differ from MCP?"Troubleshooting Learn Agentic AI
Dapr sidecar fails to inject in Kubernetes pods
Ensure dapr-system components are running with `dapr status -k` and that the namespace is annotated with `dapr.io/enabled: "true"` in the pod spec.
OpenAI API key not picked up by agent examples
Check that OPENAI_API_KEY is exported in the shell running the script. For Jupyter notebooks, restart the kernel after setting the env var, or use a .env file with `python-dotenv`.
Rancher Desktop Kubernetes cluster not reachable via kubectl
Open Rancher Desktop preferences, confirm Kubernetes is enabled, then run `kubectl config use-context rancher-desktop` to switch to the correct context.
Frequently Asked Questions about Learn Agentic AI
What is Learn Agentic AI?
Learn Agentic AI is a Model Context Protocol (MCP) server that 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. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Learn Agentic AI?
Follow the installation instructions on the Learn Agentic AI GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Learn Agentic AI?
Learn Agentic AI works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Learn Agentic AI free to use?
Yes, Learn Agentic AI is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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