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learn agentic ai

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Multi-Agent Platforms & Frameworks
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Basic Information

Learn Agentic AI is a demonstration and learning repository focused on exploring Agentic AI using the Dapr Agentic Cloud Ascent (DACA) design pattern. It documents an approach for building microservice-based intelligent applications that integrate Dapr for sidecar-based service invocation and pub-sub, the OpenAI Agents SDK for agent logic, memory management techniques, and cloud-native infrastructure. The README outlines supported components such as knowledge graphs, MCP (Microservices Communication Protocol), A2A (Agent-to-Agent Communication), Docker and docker-compose for local setup, Kubernetes for production orchestration, and data stores like PostgreSQL and Redis. The repo is organized to help users create agents, configure Dapr, deploy on Kubernetes, and monitor agent performance. It targets practitioners who want a modular, event-driven reference architecture for agentic systems and covers real-time streaming with Kafka and RabbitMQ as part of that stack.

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App URL
https://github.com/bilal0399/learn-agentic-ai
Github Repository
https://github.com/bilal0399/learn-agentic-ai/blob/main/README.md

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App Details

Features
The project emphasizes an event-driven architecture using Dapr"s pub-sub model and sidecar pattern for decoupled microservices. It integrates the OpenAI Agents SDK to define agent behaviors and supports memory management and knowledge graphs for stateful agent interactions. Messaging and streaming are supported through RabbitMQ and Kafka for real-time data processing. Persistent storage and caching are handled via PostgreSQL and Redis. The repo documents MCP and A2A communication patterns for microservice and agent-to-agent exchanges. Containerization and local environment setup are addressed with Docker and docker-compose, while Kubernetes and Rancher Desktop are recommended for scalable deployments. The README also includes a high-level architecture diagram and practical steps for creating agents in an agents directory and configuring Dapr.
Use Cases
This repository serves as a practical guide and reference architecture for developers and architects learning to build agentic applications. It provides step-by-step setup instructions for a local environment using docker-compose and notes on accessing the application at localhost:8080. Users are shown how to create new agents using the OpenAI SDK, add them to the agents directory, and update Dapr configuration so messages route correctly. The README explains deployment on Kubernetes, use of messaging systems for real-time streams, and storage options for persistence and caching. By documenting MCP, A2A, memory management, and knowledge graph usage, the repo helps teams design scalable, observable, and modular agentic systems without prescribing proprietary tooling.

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