GH Repository · 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.
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rabbitmqredisopenai-agents-sdkserverless-containerskafkaagentic-airancher-desktopopenaipostgresql-databasekuberneteslangmemmcp-serverdaprJupyter Notebooka2aopenai-apidockerdapr-service-invocationmcpdapr-workflowdapr-pub-subdapr-sidecar
A GitHub repository from Panaversity that teaches Agentic AI through the Dapr Agentic Cloud Ascent (DACA) design pattern, built around OpenAI Agents SDK, MCP, A2A, Dapr, and Kubernetes. It is a Jupyter Notebook-based learning resource, MIT licensed, and also tagged as an MCP server.
You want a structured, hands-on curriculum for building and deploying agents on agent-native cloud infrastructure.
Use it to
- Learn the DACA design pattern for agentic systems
- Practice with the OpenAI Agents SDK in notebooks
- Study Dapr workflows, pub-sub, and service invocation for agents
- Work with MCP, A2A, and knowledge graph concepts
- Deploy agents locally with Rancher Desktop and Kubernetes
For Developers learning to build and deploy agentic AI systems
- Role
- mcp-server
- Language
- Jupyter Notebook
- Licence
- MIT
- Forks
- 1,010
- Open issues
- 12
- Last push
- 2025-10-26
topicsagentic-aimcpopenai-agents-sdkdaprkuberneteslearning