Overview
Semantic Kernel (SK) is an enterprise-ready orchestration framework that lets developers weave cutting-edge large-language-model (LLM) capabilities into conventional codebases written in C#, Python or Java. Acting as a lightweight “middleware” layer, SK abstracts away model differences, unifies prompts with native functions, and provides building blocks—kernels, plugins, planners and memories—for rapidly delivering AI-powered solutions that can run on OpenAI, Azure OpenAI, Hugging Face or local models.
Key Capabilities
- Model-agnostic connectivity with first-class adapters for OpenAI, Azure OpenAI, Hugging Face and more.
- Agent framework for composing modular agents that share tools, memory and planning components.
- Multi-agent orchestration to coordinate specialist agents in complex workflows.
- Plugin ecosystem supporting prompt templates, native code, OpenAPI specs and Model Context Protocol (MCP) function calling.
- Vector-store integrations for Azure AI Search, Elasticsearch, Chroma and other databases.
- Multimodal I/O enabling text, image and audio processing where supported by underlying models.
- Enterprise-grade features such as telemetry, observability, secure credential handling and stable, versioned APIs.
