Microsoft Agent Framework
The latest news from the Microsoft Agent Framework team for developers
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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...