An AI knowledge base/agent built with .Net 9, AntBlazor, Semantic Kernel, and Kernel Memory, supporting local offline AI large models. It can run offline without an internet connection. Supports Aspire for monitoring application data.

This repo contains hands-on labs for building AI Agents using the Azure AI Agent Service SDK and Semantic Kernel. The Azure AI Agent Service is used to create AI agents and Semantic Kernel is used to orchestrate the agents in a multi-agent system.

Sample for context-aware Agentic RaG, Q&A with multi-source verification, and self-curating knowledge base. Powered by Azure AI Foundry Agent Service, Azure AI Search with agentic retrieval and query rewrite, Semantic Kernel and LangGraph agents running in Azure Container Apps, and ready for Copilot Studio

Learn about Semantic Kernel, Microsoft's AI orchestration library. Build and interact with agents, plugins, and plans using various providers.

11‑lesson course on building AI agents using Azure AI Foundry & Semantic Kernel

Simple demo using Semantic Kernel and AutoGen to create an airline booking app powered by AI agents

VexFS is a Linux kernel-native file system with built-in vector search and semantic memory. Designed for AI agents, RAG, and LLM workloads, it merges POSIX storage with vector-native indexing. Built in Rust, bootable via QEMU, and engineered for AGI infrastructure.

Demonstrates how to build a modern .NET web application that integrates with both Azure AI Foundry Agents and Semantic Kernel Agents.

Simple Agentic AI system that leverages Semantic Kernel to orchestrate end-to-end workflow between user (human) and an agent equipped with tools. Evaluate the system through built-in evaluation methods with the Azure AI Foundry SDK and the Azure AI Evaluation library.

Microsoft Semantic Kernel Quick-start is a 100% Microsoft starter kit for building agentic apps with C#, Blazor (Fluent UI), ASP.NET Core, and SQL Server—no JavaScript required. It delivers a Copilot-style chat client, built-in data tools, and automated Azure deployment for real-world AI on a familiar .NET stack.

This repo contains starter code for the learning path module exercises.

Building your own AI Agent using Semantic Kernel - Microsoft Learn Zero to Hero Community 2024

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This repository contains the example code from the Medium blog post titled Step-by-Step Guide to Develop AI Multi-Agent System Using Microsoft Semantic Kernel and GPT-4. The example demonstrates how to create an AI multi-agent system using .NET8 and Semantic Kernel SDK.

Demonstrates how to use Semantic Kernel's process framework to orchestrate multiple agents from Azure AI Agent Service together

Using Semantic Kernel to create an AI Agent that supports RAG enabled by Azure SQL DB vector support that is now in public preview!

This project implements an agentic app using Magentic Orchestration from Semantic Kernel deployed on Azure Kubernetes Service. The system receive alerts from Azure Monitor to automatically diagnose and resolve AKS cluster issues, demonstrating how AI agents enhance Kubernetes operations.

Multi-Agent AI Application(Python) that uses Semantic-Kernel along with Azure AI Agent Service in Azure Ai Foundry

This repository contains the code samples and practical implementations from the "CODE Presents" webinar "Building Intelligent Multi-Agent Systems with Microsoft's Semantic Kernel." The session demonstrates how to orchestrate collaborative AI agents in .NET, featuring persistent vector memory and MCP tool integration.

A multi-agent demo using Azure AI Foundry Agent Service and Semantic Kernel to build web applications. Playwright MCP is used for interactive testing and Azure Communication Services keeps the human in the loop with phone call integration.

🍔 An AI restaurant agent demonstrating the RAG pattern using Semantic Kernel for orchestration and Qdrant for vector memory.

Multi-Agents Demo using Semantic Kernel and Azure AI Foundry Agents Service

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A sample project to show how to create an AI Agent by Semantic Kernel and LLM API

Contains a collection of advanced C#/.NET/Python sample applications and tools for working with AI, Model Context Protocol (MCP), Semantic Kernel, Microsoft Agent Framework and Azure OpenAI. Each folder demonstrates a unique scenariofor building intelligent, multi-agent systems and testing model performance

Building your own AI Agent using Semantic Kernel - NDC Copenhagen 2025

An example of a multi-agent system built with Semantic Kernel

This repository illustrates the usage of Semantic Kernel in building Agents for Beginners

A powerful and extensible graph-based execution framework built on top of Microsoft's Semantic Kernel, designed for building sophisticated AI workflows, multi-agent systems, and intelligent applications.

🤖🤝🤖AI agent orchestration patterns with Semantic Kernel and AutoGen. 💸Investment advisor scenario.