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Code Presents Sk Agents

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 MC

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Scored 4 months ago · breakdown

About Code Presents Sk Agents

Code Presents Sk Agents is an MCP server published by philippjbauer in the Developer Tools category: 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 MC. It has been installed 0 times through Conduid.

The repository has 12 stars and 3 forks, with the last commit 8 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.

Install

Install
npx code-presents-sk-agents

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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README

AI Agents in Semantic Kernel – Webinar Code

Instructor: Philipp Bauer, Senior Software Architect at CODE Consulting
Webinar Date: December 10, 2025

Overview

This repository contains the code samples and practical implementations from the 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.

Target Audience:

  • C#/.NET developers
  • AI engineers interested in agent orchestration
  • Anyone looking to build production-ready multi-agent systems with Semantic Kernel

Key Topics Covered

  • Multi-agent orchestration patterns in .NET
  • Integrating persistent vector memory for contextual AI
  • Using MCP tools for agent collaboration
  • Practical, production-focused code examples

Repository Structure

  • Agents/ – C# agent implementations (AccountAgent, CategoryAgent, etc.)
  • actual-budget-mcp/ – MCP tool integration (TypeScript/Node.js)
  • ChatModes/ – Chat orchestration modes and logic
  • Configuration/ – App configuration files
  • Helpers/ – Utility and formatting helpers
  • Models/ – Data models for agents and chat
  • Plugins/ – Example plugin integrations
  • Program.cs – Main entry point for the .NET application

Getting Started

  1. .NET Solution:

    • Open CODE.Presents.SemanticKernel.sln in Visual Studio or VS Code.
    • Restore NuGet packages and build the solution.
    • Run Program.cs to start the agent system.
  2. MCP Tools (Node.js):

    • Navigate to actual-budget-mcp/.
    • Run npm install to install dependencies.
    • Use the scripts in src/tools/ for MCP integration.

Prerequisites

Learning Outcomes

  • Build and orchestrate multiple AI agents in C#
  • Integrate vector memory for persistent context
  • Connect agents to external tools (MCP) for real-world workflows
  • Apply best practices for scalable, production-ready agent systems

More Resources


For questions or feedback, please contact Philipp Bauer via CODE Consulting.

README mirrored from the source repository 4 months ago. The original is authoritative.

Questions

About Code Presents Sk Agents

How do I install Code Presents Sk Agents?

Run npx code-presents-sk-agents, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is Code Presents Sk Agents safe to use with an AI agent?

Its trust score is 53 out of 100 (fair). It passes 0 of 1 static security checks; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Code Presents Sk Agents still maintained?

The last commit was 8 months ago, with 0 open issues. That's long enough that you should check whether the maintainer is responding to issues before depending on it.