Semantic Kernel

What is Semantic Kernel?

Semantic Kernel is a model-agnostic SDK from Microsoft that developers use to create, coordinate and ship AI agents, including multi-agent systems. You connect it to a language model such as OpenAI, Azure OpenAI, Hugging Face or NVIDIA models, then extend it with plugins, which can be Model Context Protocol tools, OpenAPI specs, prompt templates or functions in native code. It includes an agent framework with memory and planning, a process framework for structured business workflows, vector database connectors for Azure AI Search, Elasticsearch and Chroma, multimodal input, and local deployment with Ollama, LMStudio or ONNX. SDKs exist for Python, .NET and Java, and the code is released under the MIT license, so there is no price to pay beyond the model services you call. Microsoft now points developers to Microsoft Agent Framework, which it calls the successor to Semantic Kernel, and publishes a migration guide for existing projects.

Use cases

• Build AI agents with plugins
• Orchestrate multi-agent systems
• Add LLMs to existing apps
• Connect agents to vector databases
• Model business processes as workflows
• Run models locally with Ollama
• Return structured output from agents
• Migrate to Agent Framework

Who it's for

• Software developers
• AI engineers
• Enterprise dev teams
• Python developers
• .NET developers

Why use Semantic Kernel

• A single SDK works across Python, .NET and Java codebases.
• Model-agnostic design lets you change providers without rewriting agent logic.
• Plugins and MCP support let agents call your own code and external tools.
• Open source under the MIT license, with no license fee.
• A migration guide helps existing projects move to Microsoft Agent Framework.

Semantic Kernel features

• Model-agnostic connectors for OpenAI, Azure OpenAI, Hugging Face and NVIDIA
• Agent framework with tools, memory and planning
• Multi-agent orchestration
• Plugins from native code, prompt templates, OpenAPI specs and MCP
• Vector database support including Azure AI Search, Elasticsearch and Chroma
• Multimodal text, vision and audio input
• Local deployment through Ollama, LMStudio and ONNX
• Process framework for structured workflows
• Observability and stable APIs for enterprise use

Semantic Kernel integrations

• OpenAI
• Azure OpenAI
• Hugging Face
• NVIDIA
• Azure AI Search
• Elasticsearch
• Chroma
• Ollama
• LMStudio
• ONNX
• Model Context Protocol

How to get started with Semantic Kernel

1. Set an environment variable with your Azure OpenAI or OpenAI API key.
2. Install the package with pip for Python or dotnet add package for .NET, or follow the Java build instructions.
3. Create a chat completion agent with a name and instructions.
4. Add plugins, such as kernel functions, so the agent can call your code.
5. Combine several specialist agents to handle larger workflows.
6. Review the migration guide if you plan to move to Microsoft Agent Framework.

Semantic Kernel limitations

• Microsoft Agent Framework is named as the successor, so new work may belong there
• Needs Python 3.10+, .NET 10.0+ or JDK 17+
• You pay separately for the model services you connect to

Semantic Kernel pricing

Semantic Kernel is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

Semantic Kernel alternatives

The main Semantic Kernel alternatives listed on Debuly are LangChain, LlamaIndex, AutoGen, Microsoft Agent Framework, CrewAI and Haystack. The table compares pricing and platforms; order is A–Z.

1. 

   AutoGen

   Microsoft's MIT-licensed Python and .NET framework for multi-agent AI apps, now archived; new projects are pointed to Microsoft Agent Framework.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit AutoGen (official site)
2. 

   CrewAI

   Multi-agent framework and platform for building AI agent crews in Python or a visual editor, then deploying and monitoring them.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit CrewAI (official site)
3. 

   Haystack

   Open-source Python framework from deepset for building RAG pipelines, AI agents and multimodal apps from modular components.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Haystack (official site)
4. 

   LangChain

   Open-source framework for building LLM agents, paired with LangSmith for tracing, evals and agent deployment.

   Developer platformsAutomation and agentsCoding
   FreemiumVisit LangChain (official site)
5. 

   LlamaIndex

   Open-source framework plus the LlamaParse platform for parsing, extracting and indexing documents so LLM apps can use them.

   Developer platformsResearch and searchAutomation and agents
   FreemiumVisit LlamaIndex (official site)
6. 

   Microsoft Agent Framework

   Open, multi-language framework from Microsoft for building, orchestrating and running AI agents and multi-agent workflows in Python, .NET and Go.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit Microsoft Agent Framework (official site)

Semantic Kernel FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

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