Semantic Kernel

Semantic Kernel is an open-source SDK and orchestration framework for developers who want to build AI agents and multi-agent systems into their applications. According to the repository, it is positioned as an enterprise-ready way to integrate LLMs quickly while keeping the architecture flexible across different model providers.

You use it from your own codebase rather than through a standalone app. The project supports Python, .NET, and Java, and the README shows setup via package managers like pip and dotnet. It can connect to cloud models such as OpenAI and Azure OpenAI, and it also supports local model setups like Ollama, LM Studio, and ONNX.

Semantic Kernel is strongest as a developer framework, not as a finished end-user agent product. It gives you building blocks for agents, plugins, workflows, memory, and multi-agent coordination, but the actual behavior and safety controls depend on how you implement it. That makes it useful for teams building custom agent systems, but less suitable if you want a no-code assistant or a turnkey automation platform.

The README describes it as enterprise-ready and mentions observability, security, and stable APIs, but pricing is not publicly listed because the project is open source under MIT. It explicitly supports Model Context Protocol (MCP) and multiple model backends, including local models. If you want a simple chat app with minimal engineering, this is probably more framework than you need.