Microsoft Agent Framework now integrates with the Claude Agent SDK, enabling you to build AI agents powered by Claude's full agentic capabilities. This integration brings together the Agent Framework's consistent agent abstraction with Claude's powerful features, including file editing, code execution, function calling, streaming responses, multi-t...

Microsoft Agent Framework now integrates with the GitHub Copilot SDK, enabling you to build AI agents powered by GitHub Copilot. This integration brings together the Agent Framework's consistent agent abstraction with GitHub Copilot's capabilities, including function calling, streaming responses, multi-turn conversations, shell command execution, f...

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 wi...

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 remov...

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 c...

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 re...

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 General Availability (GA) release of the OpenAPI package for Semantic Kernel, available for both .NET and Python! With this significant milestone, developers can easily integrate existing APIs as plugins, enhancing the capabilities of their AI agents while making them more versatile in real-world applications. Why O...

In one of the previous posts, we demonstrated how to use JSON Schema to get Structured Output with OpenAI and Python version of Semantic Kernel: Using JSON Schema for Structured Output in Python for OpenAI Models. In this post, we will explore how to implement a JSON Schema-based structured output using .NET version of Semantic Kernel. For mo...

Hello, everyone! AI planning is a powerful tool that allows to generate and execute complex workflows in applications based on specified goal. In Semantic Kernel, it's possible to implement planning logic using FunctionCallingStepwisePlanner class. Today we want to introduce a new way how to achieve the same results by using Automatic Function C...