Semantic Kernel
What is Semantic Kernel?
Semantic Kernel is a developer framework for building AI agents that turn prompts and existing APIs into function calls. It sits between the model and your code, using plugins and connectors to orchestrate workflows, with support for C#, Python, and Java plus observability, security, and filters. It integrates with GitHub, Logic Apps, and Azure Container Apps Dynamic Sessions.
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At a glance
- Semantic Kernel is best for developers who need to connect AI models to existing code and workflows.
- Yes — Semantic Kernel combines prompts with existing APIs and plugins to translate model requests into function calls.
It has been superseded by Microsoft's own successor SDK
As of this listing, the top of the Semantic Kernel GitHub README carries a flagged notice: "Semantic Kernel is now Microsoft Agent Framework! Microsoft Agent Framework (MAF) is the enterprise-ready successor to Semantic Kernel." Agent Framework reached a stable 1.0 release. Microsoft's Agent Framework devblog states it will "support Semantic Kernel v1.x for the foreseeable future" with critical bug fixes and security patches, and will bring some existing Semantic Kernel features to general availability, but says "the majority of new features" will be built for Agent Framework instead. The same devblog post recommends new projects start with Agent Framework. This isn't a hostile fork or a rumor — it's stated by Microsoft on the project's own primary channels.
What it actually is, for anyone still evaluating it
Semantic Kernel is a model-agnostic orchestration SDK for building AI agents and multi-agent systems, shipping as native packages for Python (PyPI: semantic-kernel), .NET (NuGet: Microsoft.SemanticKernel) and Java. It connects to OpenAI, Azure OpenAI, Hugging Face, NVIDIA NIM and others, supports local model runtimes (Ollama, LM Studio, ONNX), and includes a plugin system that accepts native code functions, prompt templates, OpenAPI specs, or MCP tools. It's the SDK Microsoft itself used internally before building Agent Framework on top of it, so the underlying orchestration concepts (kernel, plugins, planners) carry into the successor rather than being thrown away.
Development activity is real, not abandoned
The GitHub repo (microsoft/semantic-kernel) shows commits pushed within the last day, and merged PRs land daily — e.g. a .NET version bump to 1.80.0 merged 2026-08-18. NuGet's Microsoft.SemanticKernel package is on 1.80.0 (released 2026-08-18) with 15.4M total downloads; PyPI's semantic-kernel package is on 1.44.1 (uploaded 2026-08-06). The repo has 28,467 stars, 4,731 forks, and roughly 400 contributors. This activity is consistent with Microsoft's stated 'critical bugs and security patches' commitment — it is not a project that has gone quiet, even though most new feature work is going to Agent Framework instead.
Two critical CVEs, both patched
OSV/GitHub Security Advisories list two CRITICAL-severity vulnerabilities disclosed in February 2026: CVE-2026-25592 (GHSA-2ww3-72rp-wpp4), an arbitrary file write in the .NET SessionsPythonPlugin via AI agent function calling, fixed in Microsoft.SemanticKernel.Plugins.Core 1.71.0; and GHSA-xjw9-4gw8-4rqx, a remote code execution issue in the InMemoryVectorStore filter functionality, fixed in 1.39.4. Both predate the current 1.80.0 (.NET) / 1.44.1 (Python) releases, so current installs are past the fixed versions — but anyone running an older pinned version should check they're above these thresholds.
Cost model
Semantic Kernel itself is free and open source under the MIT licence — there is no vendor pricing tier to disclose. All cost is indirect: whatever LLM provider (OpenAI, Azure OpenAI, etc.) and vector store you connect it to.
Frequently asked questions
What is Semantic Kernel?
Semantic Kernel is a developer framework for building AI agents that turn prompts and existing APIs into function calls. It sits between the model and your code, using plugins and connectors to orchestrate workflows, with support for C#, Python, and Java plus observability, security, and filters. It integrates with GitHub, Logic Apps, and Azure Container Apps Dynamic Sessions.
What is Semantic Kernel used for? Who is it for?
Semantic Kernel is used for Getting Started, Quick Start, and Concepts. It's built for Application developers, Platform teams, and Enterprise architects.
Does Semantic Kernel have an API and what does it integrate with?
Semantic Kernel combines prompts with existing APIs and plugins to translate model requests into function calls. It integrates with GitHub, Logic Apps, Azure Container Apps Dynamic Sessions.
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