Microsoft Semantic Kernel is an AI orchestration SDK for building multi-agent systems, process workflows, and LLM-powered applications. It provides a unified abstraction layer over multiple AI providers (OpenAI, Azure, Google, Mistral, Ollama, HuggingFace, etc.) and supports function calling, plugin systems, prompt templates, memory/vector stores, and content safety filters. The framework is implemented in both Python and .NET with consistent architectural patterns. In production, the system acts as a library that developers embed into their applications. The central Kernel class manages service registration, plugin loading, and function execution. Agents (ChatCompletionAgent, AzureAIAgent, etc.) provide conversational interfaces with tool-calling capabilities. The process framework enables event-driven, multi-step workflows with state persistence. The filter system allows cross-cutting concerns like logging, moderation, and quality checks to be injected into function invocation and prompt rendering pipelines. Key entry points include the Kernel class, agent classes, ProcessBuilder for workflows, and various sample scripts. Data flows typically involve user input being processed by an agent or kernel, which invokes AI services via connectors, optionally calls plugins/functions, and returns responses. The framework relies on external AI services, vector databases, and optionally Dapr for distributed process execution. While primarily an application framework, Semantic Kernel includes data/research-oriented components such as vector store integrations, embedding generation services, and quality evaluation filters, making it a hybrid tool suitable for both production AI applications and experimental research.
This repository is a comprehensive educational course titled 'AI Agents for Beginners', structured as a collection of Jupyter notebooks and code samples. It functions primarily as an ML notebook collection and tutorial suite rather than a deployable application or library. The content focuses on teaching the Microsoft Agent Framework (MAF) and Azure AI ecosystem, covering topics from basic agent concepts to advanced protocols like MCP and A2A. The codebase is organized into 18 lessons, each containing Python and .NET code samples, documentation, and images. It relies heavily on external cloud services (Azure AI Foundry, Azure OpenAI) and rapidly evolving AI standards, making it a high-quality learning resource but one with significant dependency volatility.
The repository is a comprehensive platform for AI red teaming education, hosting a series of Capture the Flag (CTF) challenges that target LLM vulnerabilities such as prompt injection, metaprompt extraction, and safety filter bypass. It is built as a modified version of Microsoft's Chat Copilot, integrating a multi-service architecture: a .NET Core chat backend with Semantic Kernel, a React frontend, a load balancer (Go), a scoring service (Flask/Celery), a picture submission service, and a challenge home page. The system is designed to be deployed via Docker Compose or Kubernetes, with each challenge running its own configured chat instance. While primarily an application, the repository also includes Jupyter notebooks using PyRIT for automated red teaming, blending application and research elements. The architecture is modular, with clear separation between services. The chat copilot backend provides real-time chat via SignalR, supports OpenAPI plugins, and integrates with Azure services (CosmosDB, Redis, Blob Storage, Content Safety). The scoring system supports both automatic (regex/LLM) and manual review, with CTFd flag submission. The platform is highly configurable through challenge JSON files, allowing different metaprompts, scorers, RAG inputs, and security settings. This makes it a flexible testbed for red teaming exercises. The repository demonstrates strong engineering practices with reusable components such as a generic repository pattern, authentication handlers, telemetry services, and a load balancer. However, it is tightly coupled to Azure and Semantic Kernel, and some features are intentionally insecure for educational purposes. The codebase is actively maintained and well-documented, making it suitable for both learning and running red teaming events.
This repository implements a multi-agent orchestrator system for healthcare workflows, specifically designed for cancer care/tumor board scenarios. The system coordinates specialized AI agents (Patient History, Radiology, Patient Status, Clinical Guidelines, Report Creation, Clinical Trials, Medical Research) via Semantic Kernel group chat, with an Orchestrator agent facilitating collaboration. It provides both Microsoft Teams integration and a React/Vite web interface for user interaction. The architecture includes a Python/FastAPI backend handling WebSocket connections and Bot Framework messages, agent-specific tools for data access and external API integration, and an evaluation framework for assessing agent performance. Infrastructure is provisioned via Azure Bicep templates deploying AI services, storage, compute, and networking resources.