Semantic Kernel
The latest news from the Semantic Kernel team for developers
Latest posts
The “Golden Triangle” of Agentic Development with Microsoft Agent Framework: AG-UI, DevUI & OpenTelemetry Deep Dive
In the explosive era of Agentic AI, we're not just seeking more powerful models—we're searching for a development experience that lets developers actually get some sleep. When building Agents locally, we've traditionally faced three major challenges: Today, I'll walk you through a classic case from Microsoft Agent Framework Samples—GHModel.AI—to reveal the "Golden Triangle" development stack that perfectly solves these pain points: DevUI, AG-UI, and OpenTelemetry. Let's explore how this powerful combination empowers the entire local development lifecycle. Phase 1: Creation — Standing on t...
Unlocking Enterprise AI Complexity: Multi-Agent Orchestration with the Microsoft Agent Framework
The Architectural Imperative: Why Multi-Agent Orchestration is Essential In modern enterprise AI systems, the scope and complexity of real-world business challenges quickly exceed the capabilities of a single, monolithic AI Agent. Facing tasks like end-to-end customer journey management, multi-source data governance, or deep human-in-the-loop review processes, the fundamental architectural challenge shifts: How do we effectively coordinate and manage a network of specialized, atomic AI capabilities? Much like a high-performing corporation relies on specialized departments, we must transition from a single-execu...
Semantic Kernel and Microsoft Agent Framework
Last week we announced Microsoft Agent Framework, you can find all the details: I'm immensely proud of the work the team that brought you AutoGen and Semantic Kernel have done to create Microsoft Agent Framework. We really think it's a great step forward in building AI agents and applications, building on all the learnings we've had from creating AutoGen and Semantic Kernel. Please give a try and give us your feedback, we think you'll like it! If you've been building and shipping on Semantic Kernel, I'm sure you have questions. I've answered the most common here but, as always, you...
Encoding Changes for Template Arguments in Semantic Kernel
In previous versions of the Semantic Kernel, the encoding of template arguments was performed automatically if the argument type was a . The encoding was not applied for custom types, anonymous types, or collections. With the latest changes, we've introduced stricter rules: if automatic encoding is enabled (the default behavior), an exception will now be thrown when complex types are used as arguments. This enforces more secure template rendering by requiring developers to handle encoding manually for complex types and explicitly disable automatic encoding for those variables. This change promotes best practic...
Azure Authentication Changes in Semantic Kernel Python
In previous versions of the Semantic Kernel Python, the default fallback authentication mechanism for Azure services like was from the Azure Identity library. This provided a convenient way to authenticate without explicitly passing credentials, especially during development. However, with the latest package version , this fallback is being removed to encourage more secure and explicit authentication practices. If your code relied on this default behavior, you may encounter errors after updating, and you'll need to make minor code adjustments to continue using credential-based authentication. This post expla...
Guest Blog: Building Multi-Agent Solutions with Semantic Kernel and A2A Protocol
In the rapidly evolving landscape of AI application development, the ability to orchestrate multiple intelligent agents has become crucial for building sophisticated, enterprise-grade solutions. While individual AI agents excel at specific tasks, complex business scenarios often require coordination between specialized agents running on different platforms, frameworks, or even across organizational boundaries. This is where the combination of Microsoft's Semantic Kernel orchestration capabilities and Agent-to-Agent (A2A) protocol creates a powerful foundation for building truly interoperable multi-agent systems. ...
Semantic Kernel Python Gets a Major Vector Store Upgrade
We're excited to announce a significant update to Semantic Kernel Python's vector store implementation. Version 1.34 brings a complete overhaul that makes working with vector data simpler, more intuitive, and more powerful. This update consolidates the API, improves developer experience, and adds new capabilities that streamline AI development workflows. What Makes This Release Special? The new vector store architecture consolidates everything under and delivers three key improvements: Let's explore what makes these changes valuable. Unified Field Model - Simplified Configuration We've repla...
Enhancing Plugin Metadata Management with SemanticPluginForge
In the world of software development, flexibility and adaptability are key. Developers often face challenges when it comes to updating plugin metadata dynamically without disrupting services or requiring redeployment. This is where SemanticPluginForge, an open-source project, steps in to improve the way we manage plugin metadata. LLM Function Calling Feature The function calling feature in LLMs allows developers to define a set of functions that the model can invoke during a conversation. These functions are described using metadata, which includes the function name, parameters, and their descriptions. The LL...
Smarter SK Agents with Contextual Function Selection
Smarter SK Agents with Contextual Function Selection In today's fast-paced AI landscape, developers are constantly seeking ways to make AI interactions more efficient and relevant. The new Contextual Function Selection feature in the Semantic Kernel Agent Framework is here to address this need. By dynamically selecting and advertising only the most relevant functions based on the current conversation context, this feature ensures that your AI agents are smarter, faster, and more effective than ever before. Why Contextual Function Selection Matters When dealing with a large number of available functions, AI mod...
Semantic Kernel and Microsoft.Extensions.AI: Better Together, Part 2
This is Part 2 of our series on integrating Microsoft.Extensions.AI with Semantic Kernel. In Part 1, we explored the relationship between these technologies and how they complement each other. Now, let's dive into practical examples showing how to use Microsoft.Extensions.AI abstractions with Semantic Kernel in non-agent scenarios. Getting Started with Microsoft.Extensions.AI and Semantic Kernel Before we dive into examples, let's understand what we'll be working with. Microsoft.Extensions.AI provides foundational abstractions like and , while Semantic Kernel builds upon these to provide higher-level functio...
Semantic Kernel: Multi-agent Orchestration
The field of AI is rapidly evolving, and the need for more sophisticated, collaborative, and flexible agent-based systems is growing. With this in mind, Semantic Kernel introduces a new multi-agent orchestration framework that enables developers to build, manage, and scale complex agent workflows with ease. This post explores the new orchestration patterns, their capabilities, and how you can leverage them in your own projects. Why Multi-agent Orchestration? Traditional single-agent systems are limited in their ability to handle complex, multi-faceted tasks. By orchestrating multiple agents, each with special...
Semantic Kernel and Microsoft.Extensions.AI: Better Together, Part 1
This is the start of a series highlighting the integration between Microsoft Semantic Kernel and Microsoft.Extensions.AI. Future parts will provide detailed examples of using Semantic Kernel with Microsoft.Extensions.AI abstractions. The most common questions are: This blog post will address these questions and offer guidance on when and how to use them. First, we will explore what Microsoft Extensions AI is and its relationship with Semantic Kernel. The Evolution of AI Integration in .NET with Microsoft Extensions AI Artificial Intelligence, or AI, is evolving at a rapid pace that many d...
Transitioning to new Extensions AI IEmbeddingGenerator interface
As Semantic Kernel shifts its foundational abstractions to Microsoft.Extensions.AI, we are obsoleting and moving away from our experimental embeddings interfaces to the new standardized abstractions that provide a more consistent and powerful way to work with AI services across the .NET ecosystem. The Evolution of Embedding Generation in Semantic Kernel Semantic Kernel has always aimed to provide a unified way to interact with AI services, including embedding generation. Our initial approach used the interface, which served us well during the experimental phase. However, as the AI landscape has matured, so...
Vector Data Extensions are now Generally Available (GA)
We’re excited to announce the release of Microsoft.Extensions.VectorData.Abstractions, a foundational library providing exchange types and abstractions for vector stores when working with vector data in AI-powered applications. This release is the result of a close collaboration between the Semantic Kernel and .NET teams, combining expertise in AI and developer tooling to deliver a robust, extensible solution for developers. What is Microsoft.Extensions.VectorData.Abstractions? Microsoft.Extensions.VectorData.Abstractions provides shared abstractions and utilities for working with vector data, enabling develope...
Semantic Kernel: Package previews, Graduations & Deprecations
Semantic Kernel: Package Previews, Graduations & Deprecations We are excited to share a summary of recent updates and continuous clean-up efforts across the Semantic Kernel .NET codebase. These changes focus on improving maintainability, aligning with the latest APIs, and ensuring a consistent experience for users. Below you’ll find details on package graduations, deprecations, and a few other improvements. Graduations Spring Cleaning – Deprecations Improvements & Updates These updates are part of our ongoing effort to keep the S...
RC1: Semantic Kernel for Java Agents API
We’re excited to announce the release candidate of the Semantic Kernel for Java Agents API! This marks a major step forward in bringing the power of intelligent agents to Java developers, enabling them to build rich, contextual, and interactive AI experiences using the Semantic Kernel framework. What Are Agents in Semantic Kernel? Agents are intelligent, autonomous components that can reason, plan, and act using natural language. They leverage large language models (LLMs) to interact with users, invoke tools, and maintain context over time. With this API, Java developers can now create agents that: ...
Guest Blog: Orchestrating AI Agents with Semantic Kernel Plugins: A Technical Deep Dive
Today we're excited to welcome Jarre Nejatyab as a guest blog to highlight a technical deep dive on orchestrating AI Agents with Semantic Kernel Plugins. In the rapidly evolving world of Large Language Models (LLMs), orchestrating specialized AI agents has become crucial for building sophisticated cognitive architectures capable of complex reasoning and task execution. While powerful, coordinating multiple agents—each with unique capabilities and data access—presents significant engineering challenges. Microsoft's Semantic Kernel (SK) offers a robust framework for managing this complexity through its intuitive p...
Guest Blog: Letting AI Help Make the World More Accessible – Analyzing Website Accessibility with Semantic Kernel and OmniParser
Today we're excited to welcome Jonathan David, as a guest author on the Semantic Kernel blog. We'll turn it over to Jonathan to dive into Letting AI Help Make the World More Accessible - Analyzing Website Accessibility with Semantic Kernel and OmniParser. With the European Accessibility Act and Germany's Barrierefreiheitsstärkungsgesetz (which translates to Barrier Freedom Strengthening Act) coming into force in July 2025, ensuring digital accessibility is no longer optional. This article explores the importance of accessibility and how AI-driven solutions using Semantic Kernel and OmniParser could strea...
Guest Blog: SemantiClip: A Practical Guide to Building Your Own AI Agent with Semantic Kernel
Today we’re excited to welcome Vic Perdana, as a guest author on the Semantic Kernel blog today to cover his work on a SemantiClip: A Practical Guide to Building Your Own AI Agent with Semantic Kernel. We’ll turn it over to Vic to dive in further. Everywhere you look lately, the buzz is about AI agents. But cutting through the noise—what does agentic AI really mean for developers and builders? How can we move from hype to building real, practical solutions that solve business problems, automate workflows, and, simply put, make our lives easier? I'm excited to share my journey and, as a little Easter egg...
Customer Case Study: Microsoft Store Assistant — bringing multi expert intelligence to Microsoft Store chat with Semantic Kernel and Azure AI
Introduction In October 2024 Microsoft replaced a legacy rule‑based chat bot on Microsoft Store with Microsoft Store Assistant, powered by Azure Open AI, Semantic Kernel, and real‑time page context. The transformation changed a scripted, button-driven experience into a conversation that comprehends the entire public Microsoft portfolio, including Surface and Xbox products, Microsoft 365 subscriptions, Azure services, and the Dynamics and Power Platform portfolio, and knows when to involve a human Sales Associate. Six months later, the assistant manages several millions of conversations annually, maintains a fo...
Guest Blog: Build an AI App That Can Browse the Internet Using Microsoft’s Playwright MCP Server & Semantic Kernel — in Just 4 Steps
Today we're excited to feature a returning guest author, Akshay Kokane to share his recent Medium article on Building an AI App That Can Browse the Internet Using Microsoft’s Playwright MCP Server & Semantic Kernel. We’ll turn it over to him to dive in! MCP! It’s the new buzzword in the AI world. So, I thought — why not be a part of this buzz myself? That’s why I wrote this blog on using the MCP server with Semantic Kernel and Azure AI Foundry. Let’s start by understanding: What is MCP? There are many blogs and videos that helped me grasp the MCP concept, and I’ll drop those links at the end. But for me,...
Integrating Semantic Kernel Python with Google’s A2A Protocol
Google's Agent-to-Agent (A2A) protocol is designed to enable seamless interoperability among diverse AI agents. Microsoft’s Semantic Kernel (SK), an open-source platform for orchestrating intelligent agent interactions, is now being integrated into the A2A ecosystem. In this blog, we demonstrate how Semantic Kernel agents can easily function as an A2A Server, efficiently routing agent calls to specialized services. You can read more about the A2A protocol in Google's technical documentation. Our Contribution to the A2A Ecosystem Our initial contribution to the A2A repository addresses the current absence of ...
Semantic Kernel adds Model Context Protocol (MCP) support for Python
We are excited to announce that Semantic Kernel (SK) now has first-class support for the Model Context Protocol (MCP) — a standard created by Anthropic to enable models, tools, and agents to share context and capabilities seamlessly. With this release, SK can act as both an MCP host (client) and an MCP server, and you can leverage these capabilities directly in your agents. This unlocks powerful new scenarios for tool interoperability, prompt sharing, and agent orchestration across local and remote boundaries. This requires Semantic Kernel Python version 1.28.1 or higher. What is MCP? MCP is a protocol that ...
Customer Case Study: Announcing the Neon Serverless Postgres Connector for Microsoft Semantic Kernel
Announcing the Neon Serverless Postgres Connector for Microsoft Semantic Kernel We’re excited to introduce the Neon Serverless Postgres Connector for Microsoft Semantic Kernel, enabling developers to seamlessly integrate Neon’s serverless Postgres capabilities with AI-driven vector search and retrieval workflows. By leveraging the pgvector extension in Neon and the existing Postgres Vector Store connector, this integration provides a high-performance, scalable solution for vector embeddings and performing vector similarity search in Postgres. Why Use Neon for Semantic Kernel? Neon is a fully managed Serverless...
Guest Blog: Bridging Business and Technology: Transforming Natural Language Queries into SQL with Semantic Kernel Part 2
Today we'd like to welcome back a team of internal Microsoft employees for part 2 of their guest blog series focused on Bridging Business and Technology: Transforming Natural Language Queries into SQL with Semantic Kernel. We'll turn it over to our authors - Samer El Housseini, Riccardo Chiodaroli, Daniel Labbe, Fabrizio Ruocco and Angel Sevillano Cabrera to dive in. Introduction In today's data-driven business landscape, access to information is critical for decision-making. However, a persistent challenge has been the technical barrier between business users who need data insights and the complex database sys...
Guest Blog: Revolutionize Business Automation with AI: A Guide to Microsoft’s Semantic Kernel Process Framework
Revolutionize Business Automation with AI: A Guide to Microsoft’s Semantic Kernel Process Framework Step-by-Step guide on creating your first process with AI Microsoft’s AI Framework, Semantic Kernel, is an easy-to-use C#, Java, and Python-based AI framework that helps you quickly build AI solutions or integrate AI capabilities into your existing app. Semantic Kernel provides various ways to integrate the power of LLM into your application. The two core sub-frameworks that Semantic Kernel offers are Agent-based and Process-based. In my previous blogs I have shared steps to create agents with Semantic Kernel’...
Announcing Hybrid Search with Semantic Kernel for .NET
Today we’re thrilled to announce support for Hybrid search with Semantic Kernel Vector Stores for .NET. What is Hybrid Search? Hybrid search performs two parallel searches on a vector database. The union of the results of these two searches are then returned to callers with a combined rank, based on the rankings from each of the constituent searches. The two searches typically consist of 1. a vector similarity search and 2. a keyword search over the source text of the vector from search 1. Using hybrid search typically results in much better RAG performance than just using regular vector similarity search....
Guest Blog: A Comprehensive Guide to Agentic AI with Semantic Kernel
Today we're excited to welcome Arafat Tehsin, who’s a Microsoft Most Valuable Professional (MVP) for AI. back as a guest author on the Semantic Kernel blog today to cover his work on a Comprehensive Guide to Agentic AI with Semantic Kernel. We'll turn it over to Arafat to dive in further. The world of AI is evolving rapidly and just two weeks back, the Semantic Kernel team rolled out several significant improvements to their Agent Framework for both .NET and Python SDKs. These updates pave the way for more dynamic and flexible applications across various industries. Therefore, I decided to come up with a compr...
Python Vector Store Connectors update: Faiss, Azure SQL Server and Pinecone
Announcing New Vector Stores: Faiss, SQL Server, and Pinecone We are thrilled to announce the availability of three new Vector Stores and Vector Store Record Collections: Faiss, SQL Server, and Pinecone. These new connectors will enable you to store and retrieve vector data efficiently, making it easier to work with your own data and data models. Faiss Faiss is a library for efficient similarity search and clustering of dense vectors. It builds on the built-in InMemoryCollection, by creating Faiss indexes on the side, which are then used for the actual vector search. Setup Install Semantic Kernel with ...
Guest Blog: Semantic Kernel and Copilot Studio Usage Series – Part 1
Today on the Semantic Kernel blog we're excited to welcome a group of guest authors from Microsoft. We'll turn it over to Riccardo Chiodaroli, Samer El Housseini, Daniel Labbe and Fabrizio Ruocco to dive into their use cases with Semantic Kernel and Copilot Studio. In today's fast-paced digital economy, intelligent automation is no longer optional—it's an essential capability for organizations striving to remain competitive and agile. Modern business success depends not merely on adopting advanced technologies, but on seamlessly integrating them into existing operations to enhance productivity, improve custo...
Semantic Kernel Agents are now Generally Available
The time is finally here, Semantic Kernel’s Agent framework is now Generally Available! Available today as part of Semantic Kernel 1.45 (.NET) and 1.27 (Python), the Semantic Kernel Agent framework makes it easier for agents to coordinate and dramatically reduces the code developers need to write to build amazing AI applications. What does Generally Available mean? When we mark an API as Generally Available it means that we have high confidence in the quality of the surface for building AI applications and that we can support and maintain the API going forward. We know that a stable and supported API is import...
Using OpenAI’s Audio-Preview Model with Semantic Kernel
OpenAI's gpt-4o-audio-preview is a powerful multimodal model that enables audio input and output capabilities, allowing developers to create more natural and accessible AI interactions. This model supports both speech-to-text and text-to-speech functionalities in a single API call through the Chat Completions API, making it suitable for building voice-enabled applications where turn-based interactions are appropriate. In this post, we'll explore how to use the audio-preview model with Semantic Kernel in both C# and Python to create voice-enabled AI applications. Best Use Cases Best for turn-based interaction...
Building a Model Context Protocol Server with Semantic Kernel
This is second MCP related blog post that is part of a series of blog posts that will cover how to use Semantic Kernel (SK) with the Model Context Protocol (MCP). This blog post demonstrates how to build an MCP server using MCP C# SDK and SK, expose SK plugins as MCP tools and call the tools from client side via SK. Here are a few reasons why you might want to build an MCP server with SK: For more information about MCP, please refer to the documentation. The sample described below uses the official ModelContextProtocol nuget package. Its runnable source code is available in the Semantic...
Semantic Kernel Agent Framework RC2
Three weeks ago we released the Release the Agents! SK Agents Framework RC1 | Semantic Kernel and we’ve been thrilled to see the momentum grow. Thank you to everyone who has shared feedback, filed issues, and started building with agents in Semantic Kernel—we’re seeing more developers try agents than ever before. Today, we’re declaring build 1.43 (.NET) and 1.26.1 (Python) as Release Candidate 2 of the Semantic Kernel Agent Framework. With this release, we’re introducing a small but impactful change to how agents handle chat message threads —one that sets the stage for powerful new capabilities coming soon. ...
Accelerating Agentic Workflows with NVIDIA AgentIQ, Azure AI Foundry and Semantic Kernel
Today, we're excited to announce our collaboration with NVIDIA. In Azure AI Foundry, we've integrated NVIDIA NIM microservices and the NVIDIA AgentIQ toolkit into Azure AI Foundry—unlocking unprecedented efficiency, performance, and cost optimization for your AI projects. Read more on the announcement here. Optimizing performance with NVIDIA AgentIQ and Semantic Kernel Once your NVIDIA NIM microservices are deployed, NVIDIA AgentIQ takes center stage. This open-source toolkit is designed to seamlessly connect, profile, and optimize teams of AI agents, enables your systems to run at peak performance. AgentIQ del...
Guest Blog: Build a Multi-Agent System Using Microsoft Azure AI Agent Service and Semantic Kernel in 3 Simple Steps!
Build a Multi-Agent System Using Microsoft Azure AI Agent Service and Semantic Kernel in 3 Simple Steps! Today we're thrilled to welcome back guest author, Akshay Kokane to share his recent Medium article on Build a Multi-Agent System Using Microsoft Azure AI Agent Service and Semantic Kernel in 3 Simple Steps. We’ll turn it over to him to dive in! In my previous blog, I introduced Microsoft’s Azure AI Agent Service, a fully managed platform that simplifies the process of building, deploying, and scaling AI agents. Unlike OpenAI Assistant, Azure AI Agent Service offers greater flexibility, supporting multip...
Customer Case Study: Announcing the Microsoft Semantic Kernel Couchbase Connector
We're thrilled to announce the launch of the Semantic Kernel Couchbase Vector Store Connector for .NET developers, created through our strategic partnership with Microsoft's Semantic Kernel team. This powerful out-of-the-box connector transforms how developers integrate vector search capabilities into their AI applications. What sets this connector apart is how it harnesses Couchbase's distributed NoSQL platform alongside Semantic Kernel's vector store abstractions, creating an integration that prioritizes both performance and developer experience. The Semantic Kernel Couchbase Vector Store Connector elimin...
The Future of AI: Customizing AI Agents with the Semantic Kernel Agent Framework
Today we're excited to promote a recent blog from AI Platform focused on customizing AI agents with the Semantic Kernel agent framework. Read the entire blog post: here The Future of AI blog series is an evolving collection of posts from the AI Futures team in collaboration with subject matter experts across Microsoft. In this series, we explore tools and technologies that will drive the next generation of AI. Explore more at: https://aka.ms/the-future-of-ai Customizing AI agents with the Semantic Kernel agent framework AI agents are autonomous entities designed to solve complex tasks for humans...
Using Azure AI Agents with Semantic Kernel in .NET and Python
Today we’re excited to dive into Semantic Kernel and Azure AI Agents. There are additional details about using an within Semantic Kernel covered in our documentation here. Azure AI Agents are powerful tools for developers seeking to integrate AI capabilities into their applications. In this blog post, we'll explore how to utilize Azure AI Agents alongside the Semantic Kernel in both .NET and Python, showcasing the potential of these technologies to create intelligent and responsive applications. What is an Azure AI Agent? An Azure AI Agent is a specialized agent within the Semantic Kernel framework, designe...
Customer Case Story: Creating a Semantic Kernel Agent for Automated GitHub Code Reviews
Today I want to welcome a guest author to our Semantic Kernel blog, Rasmus Wulff Jensen, to cover how he's created a Semantic Kernel agent for automated GitHub code review. We'll turn it over to Rasmus to dive in. Introduction If you work in software development, you know that Code reviews are an essential part of the software development process, ensuring quality, security, and maintainability. However, they also consume a significant amount of developers’ time, often delaying feature delivery and increasing workload. Recognizing this challenge, the company Relewise, a Search and Recommendation SASS Business, ...
Customer Case Study: INCM transforms legal accessibility with an AI Search Assistant
Customer Case Study: INCM transforms legal accessibility with an AI Search Assistant The Imprensa Nacional-Casa da Moeda (INCM) is responsible for managing and publishing Portugal’s Diário da República (Official Gazette of the Republic of Portugal), which includes essential information for understanding laws, regulations, and legal processes. The quantity of information and the complex language used in these documents present significant challenges for individuals seeking to access and understand them. This complexity often acts as an obstacle, limiting access to vital legal information. These challenges not onl...
Keeping the Conversation Flowing: Managing Context with Semantic Kernel Python
In the dynamic field of conversational AI, managing coherent and contextually meaningful interactions between humans and digital assistants poses increasingly complex challenges. As dialogue lengths extend, maintaining full conversational context becomes problematic due to token constraints and memory limitations inherent to large language models (LLMs). These constraints not only degrade conversational clarity but also compromise the system's ability to deliver accurate and relevant responses. Thus, effective solutions require strategies that intelligently balance context retention with efficient memory ma...
G3J Learn Semantic Kernel Show – A Deep Dive in Korean! | 세계로 뻗어갑니다: “G3J Learn Semantic Kernel” 쇼 – 한국어로 배우는 Semantic Kernel!
Global Expansion – "G3J Learn Semantic Kernel" Show – A Deep Dive in Korean! Localization Increases Demand Following the success of this multi-language delivery, we quickly noticed a surge in demand for localized content. Developers from different parts of the world have expressed interest in diving deeper into Semantic Kernel, and we couldn’t be more excited to expand on this momentum. To address this growing interest, we are thrilled to announce the recordings of a new initiative, starting with a deep dive into Semantic Kernel in Korean. The pilot session featured above is the first of many localized f...
Integration of AWS Bedrock Agents in Semantic Kernel
Overview of AWS Bedrock Agents AWS Bedrock Agents provide a managed service that facilitates the experimentation and rapid deployment of AI agents. Users can leverage proprietary AWS models as well as a diverse selection of models from various providers available on AWS Bedrock. Semantic Kernel's Integration with AWS Bedrock Semantic Kernel now integrates with AWS Bedrock Agents, enabling users to leverage kernel functions alongside features such as code interpretation and Retrieval-Augmented Generation (RAG) powered by the AWS knowledge base. If you manage resources on AWS and are exploring a multi-cloud AI s...
Talk to your agents! Introducing the Realtime API’s in Semantic Kernel
Introducing Realtime Agents in Semantic Kernel for Python With release 1.23.0 of the Python version of Semantic Kernel we are introducing a new set of clients for interacting with the realtime multi-modal API's of OpenAI and Azure OpenAI. They provide a abstracted approach to connecting to those services, adding your tools and running apps that leverage these very powerful and useful agents. The key addition that Semantic Kernel brings when you want to connect to these models is that we make the experience of using these models with function calling very easy, just create a Kernel and add your plugins as ...
Effortlessly Integrate xAI’s Grok with Semantic Kernel
For Semantic Kernel users, integrating xAI's Grok API using the OpenAI connector is a breeze thanks to its compatibility with OpenAI's API format. This tutorial focuses on setting up Grok in your Semantic Kernel projects with minimal fuss, using C# and Python examples. Why Grok? Grok, built by xAI, is a powerful AI model, offers a 128k context window and function-calling support, making it a solid choice for complex tasks in Semantic Kernel. With an API compatible with OpenAI, announced in November 2023 and now available via API access, with models like "grok-beta" available for developers and soon the ne...
AutoGen and Semantic Kernel, Part 2
Following on from our blog post a couple months ago: Microsoft’s Agentic AI Frameworks: AutoGen and Semantic Kernel, Microsoft’s agentic AI story is evolving at a steady pace. Both Azure AI Foundry’s Semantic Kernel and AI Frontier’s AutoGen are designed to empower developers to build advanced multi-agent systems. The AI Frontier’s team is charging ahead pushing the boundaries of multi-agent approaches, building new agentic patterns as well as a growing library of purpose-built agents, such as Magentic One, while the Semantic Kernel team builds on years of enterprise expertise to enable developers to build agents...
Integrating Model Context Protocol Tools with Semantic Kernel: A Step-by-Step Guide
This post describes how to use Model Context Protocol tools with Semantic Kernel. Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. MCP standardizes the connection between AI models and various data sources and tools. The Model Context Protocol is significant because it enhances the way AI models interface with data and tools, promoting interoperability, flexibility, and improved contextual understanding. Its potential applications span various domains including, data integration and knowledge management, making it a valuable component in the developme...
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=...
Guest Blog: Let your Copilot Declarative Agent think deep with DeepSeek-R1
Today we'd like to feature a guest author on our Semantic Kernel blog, Mahmoud Hassan, a Microsoft Valuable Professional (MVP) focused on AI. We'll turn it over to him to dive into his work below. In recent days, there has been significant attention in the AI community regarding DeepSeek-R1 and its capabilities. Many people are playing with it. For instance, Fabian Williams yesterday shared his experiment here: https://lnkd.in/dgZ8hjgB of running it locally. I thought, maybe today is my turn! However, I also thought it is an excellent opportunity to show a plugin design pattern I previously sha...
Using Azure OpenAI Chat Completion with data source and Function Calling
Azure OpenAI Chat Completion with data source provides powerful capabilities for integrating conversational AI into applications. However, using a data source and function calling in a single request is not supported yet. When both features are enabled, function calling is ignored, and only the data source is used. This presents a challenge when retrieving information, as a single request might not be sufficient to obtain an answer. This article shows how to address this limitation with custom retry mechanism. If a query remains unanswered, the system sequentially retries with different sources until the reque...
Semantic Kernel Roadmap H1 2025: Accelerating Agents, Processes, and Integration
As we move into the first half of 2025, I'm excited to share our ambitious roadmap that we hope will enable you to build even more sophisticated AI applications with Semantic Kernel. Agent Framework 1.0 By the end of Q1 2025, the SK Agent Framework will transition from preview to general availability (GA). This milestone marks a commitment to a stable, versioned API that minimizes breaking changes and supports production-grade applications. As highlighted in our recent community office hours meeting, our vision is to embrace an agent-first programming model. This approach has been shaped by extensive fe...
Using DeepSeek models in Microsoft Semantic Kernel
DeepSeek recently awed the AI community by open sourcing two new state-of-the-art models, the DeepSeek-V3 and a reasoning model, the DeepSeek-R1, that not only claim to be op-par with the most capable models from OpenAI but are also extremely cost-effective. We'd like to highlight the recent announcement from the Azure AI Foundry team highlighting DeepSeek R1 is now available on Azure AI Foundry and GitHub. DeepSeek is also available for use in Semantic Kernel, Azure AI Foundry and can be called via our inferencing connector as well. Semantic Kernel is thrilled to see such exciting development in...
Guest Blog: Creative Writing Assistant – a multi-agent app sample with Semantic Kernel & .NET Aspire
Today we’re featuring a guest author, Ricardo Niepel, who’s a Senior Technical Specialist at Microsoft. He’s written an article we’re sharing below; we’ll turn it over to Ricardo to share more! If “Creative Writing Assistant” sounds familiar, it might be because we've previously released a similar multi-agent application built in Python during our Ignite conference (Azure AI Foundry and the dev toolchain to infuse AI in all your apps | Ignite Session BRK119). Based on the invaluable feedback we received from all of you we are now excited to show a .NET version of this sample application. It illustrates how the...
Guest Blog: Leap Forward with Modern AI using Microsoft 365 Agents SDK and Semantic Kernel
Leap Forward with Modern AI using Microsoft 365 Agents SDK and Semantic Kernel Today we’re featuring a guest author, Arafat Tehsin, who’s a Microsoft Most Valuable Professional (MVP) for AI. He’s written an article we’re sharing below, focused on how to Leap Forward with Modern AI using Microsoft 365 Agents SDK and Semantic Kernel. We’ll turn it over to Arafat to share more! Happy new year. I know you’re excited to see this. It is real, it is happening, but you know what? It is still in preview and things will break. So, get ready for it. If you’re wondering what this SDK is all about then despite my ...