Semantic Kernel is a model-agnostic SDK designed to help developers build, orchestrate, and deploy AI agents and multi-agent systems. It offers plugin support, memory and planning capabilities, and integrations with multiple LLMs and vector databases, suitable for scenarios from simple chatbots to complex workflow automation.
Key Features
- Model flexibility: built-in connectors for OpenAI, Azure OpenAI, Hugging Face, and more.
- Agent framework: modular agents with tool/plugin access, memory, and planning.
- Multi-agent orchestration: coordinate specialist agents to solve complex tasks.
- Extensible plugin ecosystem: native functions, prompt templates, OpenAPI specs, and MCP support.
Use Cases
- Enterprise-grade assistants with memory and tool invocation capabilities.
- Automating complex business workflows using multi-agent orchestration.
- Rapidly integrating LLM capabilities into existing applications (customer support, search augmentation, QA).
Technical Highlights
- Multi-language SDKs: Python, .NET, and Java implementations.
- Plugin & function model: register business logic as callable plugins.
- Vector DB integration: seamless support for Chroma, Elasticsearch, Azure, etc.
- Designed for observability and security in production environments.
Note: This is a concise overview — check the project homepage and docs for the latest installation and examples.