This article updates the comparison of LangGraph and Semantic Kernel for Python AI agents, outlines their recent releases, explains their core architectural models, shows side‑by‑side code examples, and provides concrete decision criteria for choosing the appropriate framework.
This article traces the rise of Microsoft AutoGen, explains its core design and v0.4 architecture, showcases code examples and benchmark results, examines its limitations, and details the transition to the Microsoft Agent Framework and its current state in 2026.
The article examines the shift from single-model calls to autonomous AI agents, outlines the seven most influential AI agent frameworks for 2025—including LangChain, LangGraph, CrewAI, AutoGen, and Semantic Kernel—compares their core strengths, learning curves, and ideal use cases, and offers a practical selection guide for developers and enterprises.
This article explains Microsoft’s open‑source Semantic Kernel SDK, describing its purpose, core components, workflow (Ask, Planner, Skills, Memory, Connectors, Pipeline), practical examples, and the advantages it brings to developers building Copilot‑style AI applications.
Microsoft’s newly released Semantic Kernel (SK) is an open‑source SDK that blends large language models with traditional programming languages, offering features like prompt chaining, recursive reasoning, memory management, and planner integration, while outlining a roadmap that includes vector database support and Azure Cognitive Search.
This article highlights six recent open‑source projects on GitHub—including Microsoft’s Semantic‑Kernel SDK, Shell GPT command‑line tool, Feishu GPT productivity bot, Bloop code‑search engine, a GPT‑4 PDF chatbot, and a VSCode ChatGPT extension—showcasing practical AI applications for developers.
