agent-framework/dotnet/samples/SemanticKernelMigration

Semantic Kernel to Agent Framework Migration Guide

What's Changed?

• Namespace Updates: From Microsoft.SemanticKernel.Agents to Microsoft.Extensions.AI.Agents
• Agent Creation: Single fluent API calls vs multi-step builder patterns
• Thread Management: Built-in thread management vs manual thread creation
• Tool Registration: Direct function registration vs plugin wrapper systems
• Dependency Injection: Simplified service registration patterns
• Invocation Patterns: Streamlined options and result handling

Benefits of Migration

• Simplified API: Reduced complexity and boilerplate code
• Better Performance: Optimized object creation and memory usage
• Unified Interface: Consistent patterns across different AI providers
• Enhanced Developer Experience: More intuitive and discoverable APIs

Key Changes

1. Namespace Updates

Semantic Kernel

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;

Agent Framework

Agent Framework namespaces are now under Microsoft.Extensions.AI.

• Microsoft.Extensions.AI for core AI types
• Microsoft.Extensions.AI.Agents for core agent types OR just
• Microsoft.Extensions.AI.Agents.Abstractions if your

using Microsoft.Extensions.AI;
using Microsoft.Agents.AI;

2. Agent Creation Simplification

Semantic Kernel

Every agent in Semantic Kernel depends on a Kernel instance and will have an empty Kernel if not provided.

 Kernel kernel = Kernel
    .AddOpenAIChatClient(modelId, apiKey)
    .Build();

 ChatCompletionAgent agent = new() { Instructions = ParrotInstructions, Kernel = kernel };

Azure AI Foundry requires a strong setup before creating an agent

PersistentAgentsClient azureAgentClient = AzureAIAgent.CreateAgentsClient(azureEndpoint, new AzureCliCredential());

PersistentAgent definition = await azureAgentClient.Administration.CreateAgentAsync(
    deploymentName,
    instructions: ParrotInstructions);

AzureAIAgent agent = new(definition, azureAgentClient);

Agent Framework

Agent creation in Agent Framework is made simpler with extensions provided by all main providers.

AIAgent openAIAgent = chatClient.CreateAIAgent(instructions: ParrotInstructions);
AIAgent azureFoundryAgent = persistentAgentsClient.CreateAIAgent(instructions: ParrotInstructions);
AIAgent openAIAssistantAgent = assistantClient.CreateAIAgent(instructions: ParrotInstructions);

Additionally for hosted agent providers you can also use the GetAIAgent to retrieve an agent from an existing hosted agent.

AIAgent azureFoundryAgent = await persistentAgentsClient.GetAIAgentAsync(agentId);

Agent Thread Creation

Semantic Kernel

The caller has to know the thread type and create it manually.

// Create a thread for the agent conversation.
AgentThread thread = new OpenAIAssistantAgentThread(this.AssistantClient);
AgentThread thread = new AzureAIAgentThread(this.Client);
AgentThread thread = new OpenAIResponseAgentThread(this.Client);

Agent Framework

The agent is responsible for creating the thread.

// New
AgentThread thread = agent.GetNewThread();

Hosted Agent Thread Cleanup

This case applies exclusively to a few AI providers that still provide hosted threads.

Semantic Kernel

Threads have a self deletion method

i.e: OpenAI Assistants Provider

await thread.DeleteAsync();

Agent Framework

Note

OpenAI Responses introduced a new conversation model that simplifies completely how conversations are handled avoiding any previous hosted thread management complexities that were initially introduced by the now deprecated OpenAI Assistants model well documented in https://platform.openai.com/docs/assistants/migration

Agent Framework doesn't have thread deletion API in the AgentThread type as not all providers require hosted thread cleanup and this will become more common as more providers shift to conversation based architectures.

When the provider allow thread deletion the caller should keep track of the created threads and delete them later when necessary.

i.e: OpenAI Assistants Provider

await assistantClient.DeleteThreadAsync(thread.ConversationId);

Tool Registration

Semantic Kernel

In semantic kernel to expose a function as a tool you must:

1. Decorate the function with [KernelFunction] attribute.
2. Have a Plugin class or use the KernelPluginFactory to wrap the function.
3. Have a Kernel to use add your plugin.
4. Pass the Kernel to the agent.

KernelFunction function = KernelFunctionFactory.CreateFromMethod(GetWeather);
KernelPlugin plugin = KernelPluginFactory.CreateFromFunctions("KernelPluginName", [function]);
Kernel kernel = ... // Create kernel
kernel.Plugins.Add(plugin); 

ChatCompletionAgent agent = new() { Kernel = kernel, ... };

Agent Framework

In agent framework in a single call you can register tools directly in the agent creation process.

AIAgent agent = chatClient.CreateAIAgent(tools: [AIFunctionFactory.Create(GetWeather)]);

Agent Non-Streaming Invocation

Key differences can be seen in the method names from Invoke to Run, return types and parameters AgentRunOptions.

Semantic Kernel

The Non-Streaming uses a streaming pattern IAsyncEnumerable<AgentResponseItem<ChatMessageContent>> for returning multiple agent messages.

await foreach (AgentResponseItem<ChatMessageContent> result in agent.InvokeAsync(userInput, thread, agentOptions))
{
    Console.WriteLine(result.Message);
}

Agent Framework

The Non-Streaming returns a single AgentRunResponse with the agent response that can contain multiple messages. The text result of the run is available in AgentRunResponse.Text or AgentRunResponse.ToString(). All intermediate messages that lead up to creating the result is returned in the AgentRunResponse.Messages list.

AgentRunResponse agentResponse = await agent.RunAsync(userInput, thread);

Agent Streaming Invocation

Key differences in the method names from Invoke to Run, return types and parameters AgentRunOptions.

Semantic Kernel

await foreach (StreamingChatMessageContent update in agent.InvokeStreamingAsync(userInput, thread))
{
    Console.Write(update);
}

Agent Framework

Similar streaming API pattern with the key difference being that it AgentRunResponseUpdate including more agent related information per update.

All updates produced by any service underlying the AIAgent is returned. The textual result of the agent is available by concatenating the AgentRunResponse.Text values.

await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(userInput, thread))
{
    Console.Write(update); // Update is ToString() friendly
}
``
### Tool Function Signatures
**Problem**: SK plugin methods need `[KernelFunction]` attributes
```csharp
public class MenuPlugin
{
    [KernelFunction] // Required for SK
    public static MenuItem[] GetMenu() => ...;
}

Solution: AF can use methods directly without attributes

public class MenuTools
{
    [Description("Get menu items")] // Only Description needed
    public static MenuItem[] GetMenu() => ...;
}

Options Configuration

Problem: Complex options setup in SK

OpenAIPromptExecutionSettings settings = new() { MaxTokens = 1000 };
AgentInvokeOptions options = new() { KernelArguments = new(settings) };

Solution: Simplified options in AF

ChatClientAgentRunOptions options = new(new() { MaxOutputTokens = 1000 });

Dependency Injection

Semantic Kernel

A Kernel registration is required in the service container to be able to create an agent as every agent abstractions needs to be initialized with a Kernel property.

Semantic Kernel uses Agent type as the lower level abstraction for agents.

services.AddKernel().AddProvider(...);
serviceContainer.AddKeyedSingleton<SemanticKernel.Agents.Agent>(
    TutorName,
    (sp, key) =>
        new ChatCompletionAgent()
        {
            // Passing the kernel is required
            Kernel = sp.GetRequiredService<Kernel>(),
        });

Agent Framework

Agent framework lower level agents abstraction are defined as AIAgent type to avoid potential type clashes with other Agent types not necessarily related to AI Agents.

services.AddKeyedSingleton<AIAgent>(() => client.CreateAIAgent(...));

Migration Samples

This folder contains separate console application projects demonstrating how to transition from Semantic Kernel (SK) to the new Agent Framework (AF).

Each project shows side-by-side comparisons of equivalent functionality in both frameworks and can be run independently.

Each sample code contains the following:

1. SK Agent (Semantic Kernel before)
2. AF Agent (Agent Framework after)

Running the samples from Visual Studio

Open the solution in Visual Studio and set the desired sample project as the startup project. Then, run the project using the built-in debugger or by pressing F5.

You will be prompted for any required environment variables if they are not already set.

Prerequisites

Before you begin, ensure you have the following:

• .NET 8.0 SDK or later
• For Azure AI Foundry samples: Azure OpenAI service endpoint and deployment configured
• For OpenAI samples: OpenAI API key
• For OpenAI Assistants samples: OpenAI API key with Assistant API access

Environment Variables

Set the appropriate environment variables based on the sample type you want to run:

For Azure AI Foundry projects:

$env:AZURE_FOUNDRY_PROJECT_ENDPOINT = "https://<your-project>-resource.services.ai.azure.com/api/projects/<your-project>"

For OpenAI and OpenAI Assistants projects:

$env:OPENAI_API_KEY = "sk-..."

For Azure OpenAI and Azure OpenAI Assistants projects:

$env:AZUREOPENAI_ENDPOINT = "https://<your-project>.cognitiveservices.azure.com/"
$env:AZUREOPENAI_DEPLOYMENT_NAME = "gpt-4o" # Optional, defaults to gpt-4o

Optional debug mode:

$env:AF_SHOW_ALL_DEMO_SETTING_VALUES = "Y"

If environment variables are not set, the demos will prompt you to enter values interactively.

Samples

The migration samples are organized into three categories, each demonstrating different AI service integrations:

Category	Description
AzureAIFoundry	Azure OpenAI service integration samples
AzureOpenAI	Direct Azure OpenAI API integration samples
AzureOpenAIAssistants	Azure OpenAI Assistants API integration samples
AzureOpenAIResponses	Azure OpenAI Responses API integration samples
OpenAI	Direct OpenAI API integration samples
OpenAIAssistants	OpenAI Assistants API integration samples
OpenAIResponses	OpenAI Responses API integration samples

Running the samples from the console

To run any migration sample, navigate to the desired sample directory:

# Azure AI Foundry Examples
cd "AzureAIFoundry\Step01_Basics"
dotnet run

# Azure OpenAI Examples
cd "AzureOpenAI\Step01_Basics"
dotnet run

# OpenAI Examples
cd "OpenAI\Step01_Basics"
dotnet run

# OpenAI Assistants Examples
cd "OpenAIAssistants\Step01_Basics"
dotnet run

# OpenAI Responses Examples
cd "OpenAIResponses\Step01_Basics"

# Azure OpenAI Examples
cd "AzureOpenAI\Step01_Basics"
dotnet run

# Azure OpenAI Assistants Examples
cd "AzureOpenAIAssistants\Step01_Basics"
dotnet run