Microsoft Agent Framework
The latest news from the Microsoft Agent Framework team for developers
Latest posts
Announcing the GA Release of Filters for .NET and Python in Semantic Kernel
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 building enterprise-ready solutions that you can feel confident deploying. What Are Filters? Filters in Semantic Kernel provide a mechanism for developers to manage how and when functions operate within their applications. For example, validation filters can be employed...
Customer Case Study: Announcing the Microsoft Semantic Kernel Elasticsearch Connector
Today we're excited to feature the Elastic team to share more about their Semantic Kernel Elasticsearch connector. Read the entire announcement here. I'll turn it over to Srikanth Manvi to dive into it. In collaboration with the Microsoft Semantic Kernel team, we are announcing the availability of Semantic Kernel Elasticsearch Vector Store connector for Microsoft Semantic Kernel (.NET) users. Semantic Kernel simplifies building enterprise-grade AI agents, including the capability to enhance large language models (LLMs) with more relevant, data-driven responses from a vector store. Semantic Kern...
Announcing the GA Release of the OpenAPI Plugin for .NET and Python in Semantic Kernel
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 OpenAPI Plugins Matter In many enterprises, a robust set of APIs already exists, facilitating critical functions for automation services and front-end applications. Semantic Kernel now allows you to harness these APIs—transforming them into plugins that your agents can ...
Leveraging Microsoft 365 Agents SDK with Semantic Kernel for Enhanced Multichannel AI
As the landscape of AI continues to evolve, meeting users where they are is vital. We want to spotlight the newly released Microsoft 365 Agents SDK and its seamless integration with Semantic Kernel. This powerful combination empowers developers to create robust AI agents capable of operating across multiple channels, engaging users wherever they are active. With the Microsoft 365 Agents SDK, you can extend the reach of your AI agents beyond a single platform, ensuring they are accessible across various environments and always available to connect with users. What is Microsoft 365 Agents SDK The Mi...
Customer Case Study: Zipp by Sticos – Powered by Semantic Kernel
Today we're featuring Sticos on our Semantic Kernel blog to highlight their customer journey with AI and Semantic Kernel. Who is Sticos Our Vision: Sticos is determined to become the most customer driven company in the world. Together we make incomprehensible legislation easy and practical! What We Do: We combine legal expertise and technology to make it easy for businesses to comply with regulations and to make right and smart decisions. Sticos’ history in AI In February 2017, before any LLM we launched a HR Chatbot. It could answer HR related questions, and you could even say ...
Customer Case Study: Fujitsu Kozuchi AI Agent Powered by Semantic Kernel
Customer Case Study: Fujitsu Kozuchi AI Agent Powered by Semantic Kernel Japanese multinational Fujitsu, a pioneer of information and communications technology, has been transforming industries since 1935. With a workforce of 124,000 dedicated professionals across 50 countries, Fujitsu’s mission is to “make the world more sustainable by building trust in society through innovation.” After introducing their groundbreaking Composite AI earlier this year, Fujitsu is back with another innovative business solution. The Fujitsu Kozuchi AI Agent is an autonomous system that functions as a full team member, making pr...
Allow users to talk and listen to your chatbot using Semantic Kernel Python
Until now, Semantic Kernel Python only allowed for the development of text-based AI applications. However, this is no longer the case, as we have expanded its capabilities to include audio as one of the supported modalities. In this article, I will provide a detailed, step-by-step guide on how to create a chatbot that can both speak to and listen to your users. As of the time of this blog post, OpenAI has announced the release of the Realtime API. For further details, you can find more information here. Please note that this blog post is not intended as a tutorial on the Realtime API. The Semantic Kernel team ...
Microsoft’s Agentic AI Frameworks: AutoGen and Semantic Kernel
Microsoft’s agentic AI frameworks, Semantic Kernel and AutoGen are deeply collaborating to provide the best-in-class agentic developer experience. With Semantic Kernel’s enterprise ready AI capabilities, customers can already use and get support for building agent applications and, moving forward, we’ll align the multi-agent runtime in AutoGen (called autogen-core) with Semantic Kernel, allowing customers to create enterprise-ready multi-agent solutions. Our two teams will be working quickly and diligently to get to this alignment, with the goal of making it available in early 2025. Meanwhile, for customers that...
Working with Audio in Semantic Kernel Python
We are pleased to announce the arrival of audio support in Semantic Kernel Python. This new audio functionality will enable you to create more interactive and accessible user experiences. In this blog post, I will detail the new interface, the existing connectors, and provide samples. Please continue reading for more information. Audio-to-Text The first feature we are introducing is the ability to transcribe audio into text. The foundational class for this feature is , which includes two public methods: and . The former returns a list of possible transcriptions based on the number requested, while the latter r...