Semantic Kernel to Agent Framework Migration Guide

The following sections summarize the key differences between Semantic Kernel Agent Framework and Microsoft Agent Framework to help you migrate your code.

1. Namespace Updates

Semantic Kernel

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

Agent Framework

Agent Framework namespaces are under Microsoft.Agents.AI. Agent Framework uses the core AI message and content types from Microsoft.Extensions.AI for communication between components.

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 has an empty Kernel if not provided.

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

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

Azure AI Foundry requires an agent resource to be created in the cloud before creating a local agent class that uses it.

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 = await persistentAgentsClient.CreateAIAgentAsync(instructions: ParrotInstructions);
AIAgent openAIAssistantAgent = await assistantClient.CreateAIAgentAsync(instructions: ParrotInstructions);

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

AIAgent azureFoundryAgent = await persistentAgentsClient.GetAIAgentAsync(agentId);

3. 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();

4. 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.

OpenAI Assistants Provider:

await thread.DeleteAsync();

Agent Framework

Agent Framework doesn't have a thread deletion API in the AgentThread type as not all providers support hosted threads or thread deletion. This design will become more common as more providers shift to responses-based architectures.

If you require thread deletion and the provider allows it, the caller should keep track of the created threads and delete them later when necessary via the provider's SDK.

OpenAI Assistants Provider:

await assistantClient.DeleteThreadAsync(thread.ConversationId);

5. Tool Registration

Semantic Kernel

To expose a function as a tool, you must:

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

6. 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 messages created as part of the response are returned in the AgentRunResponse.Messages list. This might include tool call messages, function results, reasoning updates, and final results.

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

7. Agent Streaming Invocation

The key differences are 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

Agent Framework has a similar streaming API pattern, with the key difference being that it returns AgentRunResponseUpdate objects that include more agent-related information per update.

All updates produced by any service underlying the AIAgent are 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
}

8. Tool Function Signatures

Problem: Semantic Kernel plugin methods need [KernelFunction] attributes.

public class MenuPlugin
{
    [KernelFunction] // Required.
    public static MenuItem[] GetMenu() => ...;
}

Solution: Agent Framework can use methods directly without attributes.

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

9. Options Configuration

Problem: Complex options setup in Semantic Kernel.

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

Solution: Simplified options in Agent Framework.

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

10. Dependency Injection

Semantic Kernel

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

Semantic Kernel uses the Agent type as the base abstraction class 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 provides the AIAgent type as the base abstraction class.

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

11. Agent Type Consolidation

Semantic Kernel

Semantic Kernel provides specific agent classes for various services, for example:

• ChatCompletionAgent for use with chat-completion-based inference services.
• OpenAIAssistantAgent for use with the OpenAI Assistants service.
• AzureAIAgent for use with the Azure AI Foundry Agents service.

Agent Framework

Agent Framework supports all the mentioned services via a single agent type, ChatClientAgent.

ChatClientAgent can be used to build agents using any underlying service that provides an SDK that implements the IChatClient interface.

Key differences

Here is a summary of the key differences between the Semantic Kernel Agent Framework and Microsoft Agent Framework to help you migrate your code.

1. Package and import updates

Semantic Kernel

Semantic Kernel packages are installed as semantic-kernel and imported as semantic_kernel. The package also has a number of extras that you can install to install the different dependencies for different AI providers and other features.

from semantic_kernel import Kernel
from semantic_kernel.agents import ChatCompletionAgent

Agent Framework

Agent Framework package is installed as agent-framework and imported as agent_framework. Agent Framework is built up differently, it has a core package agent-framework-core that contains the core functionality, and then there are multiple packages that rely on that core package, such as agent-framework-azure-ai, agent-framework-mem0, agent-framework-copilotstudio, etc. When you run pip install agent-framework it will install the core package and all packages, so that you can get started with all the features quickly. When you are ready to reduce the number of packages because you know what you need, you can install only the packages you need, so for instance if you only plan to use Azure AI Foundry and Mem0 you can install only those two packages: pip install agent-framework-azure-ai agent-framework-mem0, agent-framework-core is a dependency to those two, so will automatically be installed.

Even though the packages are split up, the imports are all from agent_framework, or it's modules. So for instance to import the client for Azure AI Foundry you would do:

from agent_framework.azure import AzureAIAgentClient

Many of the most commonly used types are imported directly from agent_framework:

from agent_framework import ChatMessage, ChatAgent

2. Agent Type Consolidation

Semantic Kernel

Semantic Kernel provides specific agent classes for various services, for example, ChatCompletionAgent, AzureAIAgent, OpenAIAssistantAgent, etc. See Agent types in Semantic Kernel.

Agent Framework

In Agent Framework, the majority of agents are built using the ChatAgent which can be used with all the ChatClient based services, such as Azure AI Foundry, OpenAI ChatCompletion, and OpenAI Responses. There are two additional agents: CopilotStudioAgent for use with Copilot Studio and A2AAgent for use with A2A.

All the built-in agents are based on the BaseAgent (from agent_framework import BaseAgent). And all agents are consistent with the AgentProtocol (from agent_framework import AgentProtocol) interface.

3. Agent Creation Simplification

Semantic Kernel

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

from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

agent = ChatCompletionAgent(
    service=OpenAIChatCompletion(),
    name="Support",
    instructions="Answer in one sentence.",
)

Agent Framework

Agent creation in Agent Framework can be done in two ways, directly:

from agent_framework.azure import AzureAIAgentClient
from agent_framework import ChatMessage, ChatAgent

agent = ChatAgent(chat_client=AzureAIAgentClient(credential=AzureCliCredential()), instructions="You are a helpful assistant")

Or, with the convenience methods provided by chat clients:

from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential
agent = AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(instructions="You are a helpful assistant")

The direct method exposes all possible parameters you can set for your agent. While the convenience method has a subset, you can still pass in the same set of parameters, because it calls the direct method internally.

4. Agent Thread Creation

Semantic Kernel

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

from semantic_kernel.agents import ChatHistoryAgentThread

thread = ChatHistoryAgentThread()

Agent Framework

The agent can be asked to create a new thread for you.

agent = ...
thread = agent.get_new_thread()

A thread is then created in one of three ways:

1. If the agent has a thread_id (or conversation_id or something similar) set, it will create a thread in the underlying service with that ID. Once a thread has a service_thread_id, you can no longer use it to store messages in memory. This only applies to agents that have a service-side thread concept. such as Azure AI Foundry Agents and OpenAI Assistants.
2. If the agent has a chat_message_store_factory set, it will use that factory to create a message store and use that to create an in-memory thread. It can then no longer be used with a agent with the store parameter set to True.
3. If neither of the previous settings is set, it's considered uninitialized and depending on how it is used, it will either become a in-memory thread or a service thread.

Agent Framework

Agent Framework doesn't have a thread deletion API in the AgentThread type as not all providers support hosted threads or thread deletion and this will become more common as more providers shift to responses based architectures.

If you require thread deletion and the provider allows this, the caller should keep track of the created threads and delete them later when necessary via the provider's sdk.

OpenAI Assistants Provider:

# OpenAI Assistants threads have self-deletion method in Semantic Kernel
await thread.delete_async()

5. Tool Registration

Semantic Kernel

To expose a function as a tool, you must:

1. Decorate the function with a @kernel_function decorator.
2. Have a Plugin class or use the kernel plugin factory to wrap the function.
3. Have a Kernel to add your plugin to.
4. Pass the Kernel to the agent.

from semantic_kernel.functions import kernel_function

class SpecialsPlugin:
    @kernel_function(name="specials", description="List daily specials")
    def specials(self) -> str:
        return "Clam chowder, Cobb salad, Chai tea"

agent = ChatCompletionAgent(
    service=OpenAIChatCompletion(),
    name="Host",
    instructions="Answer menu questions accurately.",
    plugins=[SpecialsPlugin()],
)

Agent Framework

In a single call, you can register tools directly in the agent creation process. Agent Framework doesn't have the concept of a plugin to wrap multiple functions, but you can still do that if desired.

The simplest way to create a tool is just to create a Python function:

def get_weather(location: str) -> str:
    """Get the weather for a given location."""
    return f"The weather in {location} is sunny."

agent = chat_client.create_agent(tools=get_weather)

The name of the function will then become the name of the tool, and the docstring will become the description of the tool, you can also add a description to the parameters:

from typing import Annotated

def get_weather(location: Annotated[str, "The location to get the weather for."]) -> str:
    """Get the weather for a given location."""
    return f"The weather in {location} is sunny."

Finally, you can use the decorator to further customize the name and description of the tool:

from typing import Annotated
from agent_framework import ai_function

@ai_function(name="weather_tool", description="Retrieves weather information for any location")
def get_weather(location: Annotated[str, "The location to get the weather for."])
    """Get the weather for a given location."""
    return f"The weather in {location} is sunny."

This also works when you create a class with multiple tools as methods.

When creating the agent, you can now provide the function tool to the agent by passing it to the tools parameter.

class Plugin:

    def __init__(self, initial_state: str):
        self.state: list[str] = [initial_state]

    def get_weather(self, location: Annotated[str, "The location to get the weather for."]) -> str:
        """Get the weather for a given location."""
        self.state.append(f"Requested weather for {location}. ")
        return f"The weather in {location} is sunny."

    def get_weather_details(self, location: Annotated[str, "The location to get the weather details for."]) -> str:
        """Get detailed weather for a given location."""
        self.state.append(f"Requested detailed weather for {location}. ")
        return f"The weather in {location} is sunny with a high of 25°C and a low of 15°C."

plugin = Plugin("Initial state")
agent = chat_client.create_agent(tools=[plugin.get_weather, plugin.get_weather_details])

... # use the agent

print("Plugin state:", plugin.state)

This mechanism is also useful for tools that need additional input that cannot be supplied by the LLM, such as connections, secrets, etc.

6. Agent Non-Streaming Invocation

Key differences can be seen in the method names from invoke to run, return types (for example, AgentRunResponse) and parameters.

Semantic Kernel

The Non-Streaming invoke uses an async iterator pattern for returning multiple agent messages.

async for response in agent.invoke(
    messages=user_input,
    thread=thread,
):
    print(f"# {response.role}: {response}")
    thread = response.thread

And there was a convenience method to get the final response:

response = await agent.get_response(messages="How do I reset my bike tire?", thread=thread)
print(f"# {response.role}: {response}")

Agent Framework

The Non-Streaming run returns a single AgentRunResponse with the agent response that can contain multiple messages. The text result of the run is available in response.text or str(response). All messages created as part of the response are returned in the response.messages list. This might include tool call messages, function results, reasoning updates and final results.

agent = ...

response = await agent.run(user_input, thread)
print("Agent response:", response.text)

7. Agent Streaming Invocation

Key differences in the method names from invoke to run_stream, return types (AgentRunResponseUpdate) and parameters.

Semantic Kernel

async for update in agent.invoke_stream(
    messages="Draft a 2 sentence blurb.",
    thread=thread,
):
    if update.message:
        print(update.message.content, end="", flush=True)

Agent Framework

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

All contents produced by any service underlying the Agent are returned. The final result of the agent is available by combining the update values into a single response.

from agent_framework import AgentRunResponse
agent = ...
updates = []
async for update in agent.run_stream(user_input, thread):
    updates.append(update)
    print(update.text)

full_response = AgentRunResponse.from_agent_run_response_updates(updates)
print("Full agent response:", full_response.text)

You can even do that directly:

from agent_framework import AgentRunResponse
agent = ...
full_response = AgentRunResponse.from_agent_response_generator(agent.run_stream(user_input, thread))
print("Full agent response:", full_response.text)

8. Options Configuration

Problem: Complex options setup in Semantic Kernel

from semantic_kernel.connectors.ai.open_ai import OpenAIPromptExecutionSettings

settings = OpenAIPromptExecutionSettings(max_tokens=1000)
arguments = KernelArguments(settings)

response = await agent.get_response(user_input, thread=thread, arguments=arguments)

Solution: Simplified options in Agent Framework

Agent Framework allows the passing of all parameters directly to the relevant methods, so that you don't have to import anything extra, or create any options objects, unless you want to. Internally, it uses a ChatOptions object for ChatClients and ChatAgents, which you can also create and pass in if you want to. This is also created in a ChatAgent to hold the options and can be overridden per call.

agent = ...

response = await agent.run(user_input, thread, max_tokens=1000, frequency_penalty=0.5)