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 ...

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...

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 ...

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 ...

AI Connectors in Semantic Kernel are components that facilitate communication between the Kernel's core functionalities and various AI services. They abstract the intricate details of service-specific protocols, allowing developers to seamlessly interact with AI services for tasks like text generation, chat interactions, and more.   ...

ONNX GenAI Connector for Python (Experimental)  With the latest update we added support for running models locally with the onnxruntime-genai. The onnxruntime-genai package is powered by the ONNX Runtime in the background, but first let’s clarify what ONNX, ONNX Runtime and ONNX Runtime-GenAI are.  ONNX ONNX is an open-source format for AI model...

We previously introduced observability within the Semantic Kernel. For further insights, please refer to our previous blog post, and you can also explore our learn site for additional details. To summarize, observability is an essential aspect of your application stack, particularly in today's landscape where AI plays a significant role in numer...

Semantic Kernel (SK) 是由微软开发的一款开源开发工具包，旨在帮助开发者和企业利用最新的人工智能技术构建更智能和复杂的AI驱动解决方案，例如检索增强生成 (RAG) 和智能体 (Agent)。 核心概念 内核（Kernel） 您可以将SK的内核视为一个容器，该容器包含您所需的所有与AI相关的组件，如提示语模板、AI服务和插件。如果将所有的服务和插件提供给内核，则AI会根据需求自动使用它们。SK还提供多项企业级功能，使您可以保证您的AI符合安全要求且在部署后能够持续监控AI的表现。 提示语（Prompt Engineering） 提示语在使用大型语言模型中至关重要。优质的提示语能够显著提升用户体验。SK通过提示语模板（template）的方式来实现自然语言与...

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 t...

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 Th...