Microsoft Agent Framework
The latest news from the Microsoft Agent Framework team for developers
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Guest Blog: LLMAgentOps Toolkit for Semantic Kernel
Today the Semantic Kernel team is excited to welcome a guest author, Prabal Deb to share his work. LLMAgentOps Toolkit is repository that contains basic structure of LLM Agent based application built on top of the Semantic Kernel Python version. The toolkit is designed to be a starting point for data scientists and developers for experimentation to evaluation and finally deploy to production their own LLM Agent based applications. Architecture The LLMAgentOps architecture might be constructed using the following key components divided into two phases like DevOps / MLOps / LLMOps development and deploym...
Release the Agents! SK Agents Framework RC1
Semantic Kernel Agent Framework Reaches Release Candidate 1 We're excited to announce that with the release of Semantic Kernel 1.40 (.NET) and 1.22.0 (Python), we're elevating the Semantic Kernel Agent Framework to Release Candidate 1. This marks a significant milestone in our journey toward providing a robust, versatile framework for building AI agents for enterprise applications. Code Sample: Creating a Chat Agent with tool plugins Creating an agent with Semantic Kernel is easy! Let's look at a simple Python example that demonstrates how to create a chat completion agent that answers questions about a menu u...
Compatibility of PostgreSQL Connector with AWS and GCP
As AI-driven applications continue to evolve, the need for efficient vector-based search capabilities is greater than ever. Microsoft Semantic Kernel makes it easy to integrate these capabilities with PostgreSQL databases using the connector. Whether you're leveraging cloud-hosted PostgreSQL instances on Amazon Web Services or Google Cloud, this connector enables seamless interaction, allowing you to store and query vectorized data for tasks like recommendation systems, semantic search, and more. Compatible Databases Semantic Kernel Postgres Connector is compatible with PostgreSQL instances hosted locally or i...
Hybrid Model Orchestration
Hybrid model orchestration is a powerful technique that AI applications can use to intelligently select and switch between multiple models based on various criteria, all while being transparent to the calling code. This technique not only allows for model selection based on factors such as the prompt's input token size and each model's min/max token capacity, or data sensitivity - where sensitive inference is done against local models and the others against cloud models - returning either the fastest response, the most relevant response, or the first available model's response, but also provides a robust fallbac...
Guest Blog: Revolutionizing AI Workflows: Multi-Agent Group Chat with Copilot Agent Plugins in Microsoft Semantic Kernel
Revolutionizing AI Workflows: Multi-Agent Group Chat with Copilot Agent Plugins in Microsoft Semantic Kernel Copilot Agent Plugins (CAPs) are revolutionizing how developers interact with Microsoft 365 data. By transforming natural language into seamless CRUD actions using Microsoft Graph and Semantic Kernel, CAPs enable the creation of intelligent, AI-driven solutions. This sample demonstrates a multi-agent group chat system where AI-powered agents collaborate across Contacts, Calendar, and Email— with a standout Legal Secretary Agent ensuring compliance and multilingual support. 📺 Watch the full video: htt...
AI Agents for Beginners Course: 10 Lessons teaching you how to start building AI Agents
10 Lessons teaching everything you need to know to start building AI Agents Today we want to highlight the AI Agents For Beginners course that was released. 🔗https://github.com/microsoft/ai-agents-for-beginners/tree/main Start learning and building in the exciting world of AI Agents today! From the Semantic Kernel team, we look forward to seeing what you build!
Using OpenAI’s o3-mini Reasoning Model in Semantic Kernel
OpenAI’s o3-mini is a newly released small reasoning model (launched January 2025) that delivers advanced problem-solving capabilities at a fraction of the cost of previous models. It excels in STEM domains (science, math, coding) while maintaining low latency and cost similar to the earlier o1-mini model. This model is also available as Azure OpenAI Service, emphasizing its efficiency gains and new features like reasoning effort control and tool use. Throughout this post We'll explore how to use  and other reasoning models with Semantic Kernel in both C# and Python. Key Features of OpenAI o3-mini: ...
Guest Blog: Step-by-Step Guide to Building a Portfolio Manager: A Multi-Agent System with Microsoft Semantic Kernel and Azure OpenAI
Today the Semantic Kernel team is excited to welcome back a guest author, Akshay Kokane to share his recent Medium article using Semantic Kernel and Azure OpenAI, showcasing a step-by-step guide to building a Portfolio Manager. We’ll turn it over to him to dive into his work below. In my previous blog, we went over how Semantic Kernel can be used to create a multi-agent system. Link. However, agent collaboration was really challenging, as we were not able to control how agents collaborated. We could set the termination strategy to decide when to stop collaboration between agents, but not how agents would pa...
Customer Case Study: How preezie’s AI shopping assistant is reshaping Blue Bungalow’s online store
Introduction Blue Bungalow, one of Australia’s leading fashion retailers, faced a common challenge in eCommerce—how to create a more engaging, seamless, and personalised shopping experience for customers online. They wanted to implement AI-powered assistance to provide personalised product recommendations, accurate sizing guidance, product comparisons, and instant answers to customer questions—replicating the ease and support of an in-store shopping experience. To bring this vision to life, preezie developed an AI shopping assistant, built on Semantic Kernel and Elasticsearch. https://www.youtube.com/watch?v=...