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.

Build autonomous, resilient and observable AI agents with built-in workflow orchestration, security, statefulness and telemetry.

A minimalistic production ready agent with Dapr Agents

Explore Agentic AI with the Dapr Agentic Cloud Ascent (DACA) pattern, using OpenAI Agents SDK, MCP, A2A communication, knowledge graphs, and Dapr. Orchestrated via Rancher Desktop and Kubernetes, it enables scalable, resilient multi-agent AI systems.

All things about MCP experiments.⭐️ Star to support our work!

Building effective agents patterns implemented with Dapr Agents

A demo application employing domain driven design, Dapr (Distributed Application Runtime) and an Agentic architecture with micro agents.

No description available

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.

Template for agentic Chat & Memory Agents with Dapr runtime, UV simplicity, and OpenAI Agents SDK.

Building effective agents patterns implemented with Dapr Agents

This is a golden path (opinionated shortcut) to explore AI from local homelab to PROD using OSS for technological independence and local LLM for privacy. We explore concepts like AI agents, RAG and LLM using OSS technologies like Linux, Opentofu, microk8s, DAPR, Ollama, TimescaleDB & pgai Vectorizer, Flask and Python.

Event-Driven AI Agent Workflows with Dapr PyData Amsterdam 2025 Workshop

This is a golden path (opinionated shortcut) to explore AI from local homelab to PROD using OSS for technological independence and local LLM/SLM for privacy. We explore concepts like AI agents, RAG, LLM/SLM and MCP arch using OSS technologies like Linux, Opentofu, microk8s, DAPR, Ollama, TimescaleDB & pgai Vectorizer, Flask, Chainlit and Python.

Use Dapr Workflow and the Dapr Conversation API to run agentic AI applications reliably.

The Faux Foundation — autonomous AI agents on a Dapr platform. Star a repo, get a spec.

Demonstrates how to create durable agents with Dapr Agents and run them with Diagrid Catalyst

No description available

Multi-agent AI systems face challenges with state persistence, communication, and coordination when pods restart or fail. This pattern demonstrates how to build resilient, stateful AI agents using Dapr on Amazon EKS, using customer support ticket analysis as the example use case.

Notes for PIAIC Batch 61 - Quarter 3 by Wajahat Hussain, covering DACA (Dapr Agentic Cloud Ascent), Dapr microservices, agentic AI systems, multi-agent protocols, and scalable cloud-native architectures.

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

AI Agents Event Driven Communication as Virtual Actors using Actor Model with OpenAI Agents SDK as agentic engine and Dapr Virtual Actors for distributed runtime

🔥 Spec driven development hackathon GIAIC 2 | Evolution of todo from CLI to cloud , built using OpenAI agents SDK , FastAPI , Kubernetes and Dapr

Recipes for scalable, streaming-first multi-agent systems in cloud. It combines A2A protocol, OpenAI Agents SDK, and Dapr building blocks (PubSub + Virtual Actors) to show how AI agents can stream outputs and coordinate at scale.

Agentic AI-driven Todo app built using spec-driven AI engineering workflows. Evolves from Python console → Full-Stack (Next.js + FastAPI + Neon DB) → AI Chatbot with MCP. Scales to Kubernetes and cloud-native deployment with Docker, Kafka, Dapr, and DOKS.

Various demos related to dapr-agents and dapr-for-agents

Experiments and utilities for Dapr Agents framework

Go SDK for building Dapr Agents - durable, LLM-powered autonomous applications with MCP support

Development setup for building Dapr Agents

Repository for my Learn Dapr Agents series