Experimental Features in Semantic Kernel

Semantic Kernel introduces experimental features to provide early access to new, evolving capabilities. These features allow users to explore cutting-edge functionality, but they are not yet stable and may be modified, deprecated, or removed in future releases.

Purpose of Experimental Features

The Experimental attribute serves several key purposes:

• Signals Instability – Indicates that a feature is still evolving and not yet production-ready.
• Encourages Early Feedback – Allows developers to test and provide input before a feature is fully stabilized.
• Manages Expectations – Ensures users understand that experimental features may have limited support or documentation.
• Facilitates Rapid Iteration – Enables the team to refine and improve features based on real-world usage.
• Guides Contributors – Helps maintainers and contributors recognize that the feature is subject to significant changes.

Implications for Users

Using experimental features comes with certain considerations:

• Potential Breaking Changes – APIs, behavior, or entire features may change without prior notice.
• Limited Support – The Semantic Kernel team may provide limited or no support for experimental features.
• Stability Concerns – Features may be less stable and prone to unexpected behavior or performance issues.
• Incomplete Documentation – Experimental features may have incomplete or outdated documentation.

Suppressing Experimental Feature Warnings in .NET

In the .NET SDK, experimental features generate compiler warnings. To suppress these warnings in your project, add the relevant diagnostic IDs to your .csproj file:

<PropertyGroup>
  <NoWarn>$(NoWarn);SKEXP0001,SKEXP0010</NoWarn>
</PropertyGroup>

Each experimental feature has a unique diagnostic code (SKEXPXXXX). The full list can be found in EXPERIMENTS.md.

Using Experimental Features in .NET

In .NET, experimental features are marked using the [Experimental] attribute:

using System;
using System.Diagnostics.CodeAnalysis;

[Experimental("SKEXP0101", "FeatureCategory")]
public class NewFeature 
{
    public void ExperimentalMethod() 
    {
        Console.WriteLine("This is an experimental feature.");
    }
}

Experimental Feature Support in Other SDKs

• Python and Java do not have a built-in experimental feature system like .NET.
• Experimental features in Python may be marked using warnings (e.g., warnings.warn).
• In Java, developers typically use custom annotations to indicate experimental features.

Developing and Contributing to Experimental Features

Marking a Feature as Experimental

• Apply the Experimental attribute to classes, methods, or properties:

[Experimental("SKEXP0101", "FeatureCategory")]
public class NewFeature { }

• Include a brief description explaining why the feature is experimental.
• Use meaningful tags as the second argument to categorize and track experimental features.

Coding and Documentation Best Practices

• Follow Coding Standards – Maintain Semantic Kernel's coding conventions.
• Write Unit Tests – Ensure basic functionality and prevent regressions.
• Document All Changes – Update relevant documentation, including EXPERIMENTS.md.
• Use GitHub for Discussions – Open issues or discussions to gather feedback.
• Consider Feature Flags – Where appropriate, use feature flags to allow opt-in/opt-out.

Communicating Changes

• Clearly document updates, fixes, or breaking changes.
• Provide migration guidance if the feature is evolving.
• Tag the relevant GitHub issues for tracking progress.

Future of Experimental Features

Experimental features follow one of three paths:

1. Graduation to Stable – If a feature is well-received and technically sound, it may be promoted to stable.
2. Deprecation & Removal – Features that do not align with long-term goals may be removed.
3. Continuous Experimentation – Some features may remain experimental indefinitely while being iterated upon.

The Semantic Kernel team strives to communicate experimental feature updates through release notes and documentation updates.

Getting Involved

The community plays a crucial role in shaping the future of experimental features. Provide feedback via:

• GitHub Issues – Report bugs, request improvements, or share concerns.
• Discussions & PRs – Engage in discussions and contribute directly to the codebase.

Summary

• Experimental features allow users to test and provide feedback on new capabilities in Semantic Kernel.
• They may change frequently, have limited support, and require caution when used in production.
• Contributors should follow best practices, use [Experimental] correctly, and document changes properly.
• Users can suppress warnings for experimental features but should stay updated on their evolution.

For the latest details, check EXPERIMENTS.md.