Supported Semantic Kernel languages

Semantic Kernel plans on providing support to the following languages:

While the overall architecture of the kernel is consistent across all languages, we made sure the SDK for each language follows common paradigms and styles in each language to make it feel native and easy to use.

Available SDK packages

C# packages

In C#, there are several packages to help ensure that you only need to import the functionality that you need for your project. The following table shows the available packages in C#.

There are other packages available (e.g., the memory connectors), but they are still experimental and are not yet recommended for production use.

To install any of these packages, you can use the following command:

dotnet add package <package-name>

Python packages

In Python, there's a single package that includes everything you need to get started with Semantic Kernel. To install the package, you can use the following command:

pip install semantic-kernel

On PyPI under Provides-Extra the additional extras you can install are also listed and when used that will install the packages needed for using SK with that specific connector or service, you can install those with the square bracket syntax for instance:

pip install semantic-kernel[azure]

This will install Semantic Kernel, as well as specific tested versions of: azure-ai-inference, azure-search-documents, azure-core, azure-identity, azure-cosmos and msgraph-sdk (and any dependencies of those packages). Similarly the extra hugging_face will install transformers and sentence-transformers.

Java packages

For Java, Semantic Kernel has the following packages; all are under the group Id com.microsoft.semantic-kernel, and can be imported from maven.

    <dependency>
        <groupId>com.microsoft.semantic-kernel</groupId>
        <artifactId>semantickernel-api</artifactId>
    </dependency>

A BOM is provided that can be used to define the versions of all Semantic Kernel packages.

    <dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>com.microsoft.semantic-kernel</groupId>
                <artifactId>semantickernel-bom</artifactId>
                <version>${semantickernel.version}</version>
                <scope>import</scope>
                <type>pom</type>
            </dependency>
        </dependencies>
    </dependencyManagement>

• semantickernel-bom – A Maven project BOM that can be used to define the versions of all Semantic Kernel packages.
• semantickernel-api – Package that defines the core public API for the Semantic Kernel for a Maven project.
• semantickernel-aiservices-openai –Provides a connector that can be used to interact with the OpenAI API.

Below is an example POM XML for a simple project that uses OpenAI.

<project>
    <dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>com.microsoft.semantic-kernel</groupId>
                <artifactId>semantickernel-bom</artifactId>
                <version>${semantickernel.version}</version>
                <scope>import</scope>
                <type>pom</type>
            </dependency>
        </dependencies>
    </dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>com.microsoft.semantic-kernel</groupId>
            <artifactId>semantickernel-api</artifactId>
        </dependency>
        <dependency>
            <groupId>com.microsoft.semantic-kernel</groupId>
            <artifactId>semantickernel-connectors-ai-openai</artifactId>
        </dependency>
    </dependencies>
</project>

Available features in each SDK

The following tables show which features are available in each language. The 🔄 symbol indicates that the feature is partially implemented, please see the associated note column for more details. The ❌ symbol indicates that the feature is not yet available in that language; if you would like to see a feature implemented in a language, please consider contributing to the project or opening an issue.

Core capabilities

Prompt template formats

When authoring prompts, Semantic Kernel provides a variety of template languages that allow you to embed variables and invoke functions. The following table shows which template languages are supported in each language.

Prompt serialization formats

Once you've created a prompt, you can serialize it so that it can be stored or shared across teams. The following table shows which serialization formats are supported in each language.

AI Services Modalities

AI Service Connectors

Memory Connectors (Experimental)

Vector Store Connectors (Experimental)

For the list of out of the box vector store connectors and the language support for each, refer to out of the box connectors.