Semantic Kernel
Open-source SDK to add LLMs to your .NET and Python apps
Semantic Kernel is an open-source Microsoft framework for integrating large language models into your applications. It provides SDKs and tooling for .NET and Python so you can wire LLM capabilities directly into your existing codebases. Best suited for developers who want fine-grained control rather than a hosted, no-code agent platform.
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About
Semantic Kernel is an open-source framework from Microsoft designed to help developers integrate large language models (LLMs) into their applications. The project lives on GitHub and is distributed under the MIT license, so you can use and modify it freely in both personal and commercial projects.
The repository is organized around language-specific SDKs, most notably for .NET (via the dotnet folder) and Python (referenced in recent commits). It’s aimed squarely at software engineers who want to embed LLM-powered features—such as chat-style interactions or text generation—directly into their own services, backends, or apps, rather than relying on a standalone AI product.
To get started, you clone the GitHub repo and follow the documentation in the docs directory and language-specific folders. There’s support for running the project in a dev container (e.g., via VS Code), and commits reference integration with Azure AI Inference chat completions, indicating that it’s built to work with external LLM APIs.
Semantic Kernel is infrastructure, not a turnkey assistant or no-code tool. It works best if you’re comfortable with .NET or Python and want direct programmatic control over how your app calls LLMs and handles responses. The active commit history and language-specific folders suggest an evolving, actively maintained codebase with ongoing platform updates (for example, upgrades to newer .NET versions).
However, many implementation details—such as which specific LLM providers are supported out of the box, how memory or orchestration is handled, and what production patterns are recommended—require reading the official docs, which aren’t fully visible in the snippet provided here. You should not expect built-in, end-user-facing autonomous agents; instead, you use Semantic Kernel as a foundation to build your own agentic or LLM-driven workflows. Pricing is straightforward: it’s MIT-licensed open source, but you’ll still pay separately for any external AI services (such as Azure AI) you connect it to.
Significant autonomy on complex workflows
Semantic Kernel is an agent-building framework rather than a single agent. It enables strong action capability and high autonomy in multi-step workflows via skills, plugins, and planners, but the actual level of autonomy and safety depends heavily on how developers use it. Based on the described features, it supports complex tool use and orchestration with moderate adaptation and state handling, and relies on host applications for most advanced safety controls. Overall, it falls into the category of an advanced agentic platform with significant but not fully self-directed autonomy.
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- Free: MIT-licensed open-source framework; free to use and modify for personal and commercial projects (AI API usage billed separately by providers)
- Pro: Not available; project is fully open source with no paid upgrade tier
- Enterprise: Not available as a separate edition; enterprises can adopt the open-source project under the MIT license or arrange custom support separately
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