Handling Long-Running Operations with Background Responses AI agents powered by reasoning models can take minutes to work through complex problems — deep research, multi-step analysis, lengthy content generation. In a traditional request-response pattern, that means your client sits idle waiting for a connection that may time out, or worse, fails ...

Agent harness is the layer where model reasoning connects to real execution: shell and filesystem access, approval flows, and context management across long-running sessions. With Agent Framework, these patterns can now be built consistently in both Python and .NET. In this post, we’ll look at three practical building blocks for production agent...

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 work...

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 capabili...

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...

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 le...

We are thrilled to announce the General Availability (GA) of our Memory packages for .NET, Java, and Python! The Semantic Kernel team and our partners have been working hard to allow you to quickly connect to vector stores and make it easy for you to do embeddings and data retrieval between stores. Transforming Data Management with Vector Stores...

We are excited to announce the General Availability (GA) release of Filters for our .NET and Python Semantic Kernel! This vital enhancement strengthens the control and visibility developers have when running functions, ensuring responsible AI practices are instilled from the ground up. Filters not only improve security but also contribute to buildi...

We are thrilled to announce the release of the long-awaited Semantic Kernel Python SDK 1.0.0! This milestone version brings a plethora of enhancements and new features designed to empower AI and app developers with more robust and versatile tools. Exciting New Feature: Shared Prompts Across Languages One of the groundbreaking features of this rel...

We are excited to showcase a Semantic Kernel sample across all 3 languages: .NET, Python and Java! Today we’re going to dive into the Python version of the TimePlugin sample which is featured here. Prerequisites for the Azure OpenAI key or OpenAI key: As covered in our Python README.md or DEV_SETUP.md, Python leverages Pydantic settings. This...