AutoGen vs Semantic Kernel: Microsoft's Two Agent Frameworks

Microsoft has placed two significant bets on AI agent infrastructure, and they're not the same bet. AutoGen and Semantic Kernel both come from Redmond, both support Python, and both can build agents — but they were designed with different problems in mind. If you've looked at both and wondered which one belongs in your next project, this is the breakdown I wish I'd had when I started.

I've built production systems with both. The short version: AutoGen wins for multi-agent research and conversation workflows, Semantic Kernel wins for enterprise .NET integrations and plugin-based skill composition. But the full picture is more nuanced, and the right answer depends heavily on your team's background.

For broader context on the agent landscape, AI agents explained covers the foundational concepts both frameworks implement.

The Origin Story

AutoGen started as a Microsoft Research project focused on a specific question: what happens when multiple LLM agents talk to each other? Its core primitive is the conversation — agents pass messages back and forth, call tools, and collectively solve problems. The framework was open-sourced in 2023 and has gone through a significant rewrite (AutoGen 0.4, also called AgentChat) that cleaned up the API considerably.

Semantic Kernel came from the Microsoft engineering side rather than research. It was built to answer a different question: how do you integrate LLM capabilities into existing enterprise applications? Its core primitive is the plugin (formerly "skill") — discrete, reusable functions that an orchestrator can compose. It has always had strong .NET support because that's where Microsoft's enterprise customers live.

Feature Comparison Table

AutoGen Deep Dive

AutoGen's signature is the ConversableAgent. Every participant in an AutoGen workflow is an agent that can send and receive messages. This model makes multi-agent coordination feel natural because it is natural — you're literally writing out who talks to whom.

import autogen

llm_config = {
    "config_list": [{"model": "gpt-4o", "api_key": "YOUR_KEY"}]
}

# Research agent specializes in finding information
researcher = autogen.AssistantAgent(
    name="Researcher",
    system_message="You are a research specialist. Find accurate, cited information.",
    llm_config=llm_config,
)

# Writer agent specializes in turning research into prose
writer = autogen.AssistantAgent(
    name="Writer",
    system_message="You are a technical writer. Turn research into clear explanations.",
    llm_config=llm_config,
)

# User proxy manages the conversation
user_proxy = autogen.UserProxyAgent(
    name="Manager",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=5,
)

# Create a group chat
groupchat = autogen.GroupChat(
    agents=[researcher, writer, user_proxy],
    messages=[],
    max_round=10,
)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)

user_proxy.initiate_chat(
    manager,
    message="Research the current state of quantum computing and write a 500-word summary.",
)

This is where AutoGen shines — three agents with different roles, collaborating through conversation, with the GroupChatManager deciding who speaks next. Building this in Semantic Kernel requires significantly more boilerplate.

Semantic Kernel Deep Dive

Semantic Kernel thinks in terms of plugins and planners. You define functions as plugins, and the kernel figures out how to compose them to achieve a goal.

// C# example — Semantic Kernel's native language
using Microsoft.SemanticKernel;

var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(
        deploymentName: "gpt-4o",
        endpoint: "https://your-resource.openai.azure.com/",
        apiKey: "YOUR_KEY"
    )
    .Build();

// Define a plugin
var plugin = kernel.CreateFunctionFromPrompt(
    "Summarize the following text in {{$wordCount}} words: {{$input}}",
    functionName: "Summarize",
    description: "Summarizes text to a target word count"
);
kernel.Plugins.AddFromFunctions("TextPlugin", [plugin]);

// Invoke directly
var result = await kernel.InvokeAsync(
    "TextPlugin", "Summarize",
    new KernelArguments {
        ["input"] = longText,
        ["wordCount"] = "100"
    }
);
Console.WriteLine(result);

The Python version:

import asyncio
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.functions import kernel_function

kernel = Kernel()
kernel.add_service(OpenAIChatCompletion(
    service_id="openai",
    ai_model_id="gpt-4o",
    api_key="YOUR_KEY"
))

class ResearchPlugin:
    @kernel_function(
        name="summarize",
        description="Summarize text to a target word count"
    )
    async def summarize(self, input: str, word_count: str = "100") -> str:
        # The kernel routes this to the LLM
        return f"Summary of {len(input)} chars to {word_count} words requested"

kernel.add_plugin(ResearchPlugin(), plugin_name="Research")

async def main():
    result = await kernel.invoke(
        "Research", "summarize",
        input="Your long text here...",
        word_count="150"
    )
    print(result)

asyncio.run(main())

The plugin architecture is clean for compositions where you have well-defined, reusable functions. The planner system means the kernel can automatically sequence plugins to achieve a goal without you wiring up the steps manually.

.NET vs Python: The Real Difference

This is where many comparisons stop at the surface. Let me give you the real breakdown.

Semantic Kernel .NET: First-class citizen. The C# SDK is feature-complete, well-documented, and has enterprise support through Microsoft. If your team writes .NET and you're building enterprise software, this is the path of least resistance.

AutoGen Python: Primary and best-supported. The documentation, examples, and community are overwhelmingly Python-focused. If you're in the Python AI/ML ecosystem, AutoGen feels native.

AutoGen .NET: Exists but lags behind. The .NET port (AutoGen.Net) covers core functionality but has fewer examples and community resources. Use it if you must, but expect to fill gaps yourself.

Semantic Kernel Python: Solid but secondary. It works well and has all major features, but .NET gets features first and the community skews toward C# examples.

Bottom line: Python team → AutoGen. .NET enterprise team → Semantic Kernel.

Memory and State Management

Memory is where the frameworks diverge most visibly.

AutoGen has a basic in-memory conversation history by default. For persistent memory you need to integrate a vector store yourself — the Vector database guide covers options. Community packages like autogen-ext add memory capabilities.

Semantic Kernel has a built-in memory abstraction with pluggable backends:

from semantic_kernel.memory import SemanticTextMemory, VolatileMemoryStore

memory = SemanticTextMemory(
    storage=VolatileMemoryStore(),
    embeddings_generator=embedding_service
)

# Save to memory
await memory.save_information(
    collection="facts",
    id="fact1",
    text="The company was founded in 2019"
)

# Search memory
results = await memory.search("facts", "when was the company founded")

This built-in memory system is a genuine advantage for Semantic Kernel in RAG-heavy applications.

When to Use AutoGen

• You need multiple agents collaborating on a task through conversation
• Your team is Python-first
• You want human-in-the-loop approval integrated into agent workflows
• You're building research agents, debate simulations, or code generation pipelines
• You need a lightweight framework that doesn't prescribe too much

Practical examples: research pipelines, code review bots, customer support with escalation, AI research agent build style systems.

When to Use Semantic Kernel

• Your team is .NET/C# focused
• You're integrating AI into an existing enterprise application
• You need reliable plugin composition with built-in planning
• You want Azure AI services deeply integrated
• Memory management and RAG are central to your use case

Practical examples: enterprise chatbots in .NET, Microsoft 365 integrations, Azure Function-hosted AI services, Copilot extensions.

Can You Use Both?

Yes, and the integration is getting better. Microsoft has published examples of using Semantic Kernel plugins as tools in AutoGen agents. The pattern looks like this:

# Use Semantic Kernel plugin as AutoGen tool
from semantic_kernel.functions import KernelFunction

def create_autogen_tool_from_sk_function(sk_function: KernelFunction):
    """Wrap a Semantic Kernel function as an AutoGen tool."""
    async def tool_wrapper(**kwargs) -> str:
        result = await kernel.invoke(sk_function, **kwargs)
        return str(result)
    
    tool_wrapper.__name__ = sk_function.name
    tool_wrapper.__doc__ = sk_function.description
    return tool_wrapper

This hybrid approach lets you use SK's plugin ecosystem while leveraging AutoGen's multi-agent conversation patterns. It's complex to set up but powerful for enterprise use cases that need both.

Performance and Cost Considerations

Both frameworks produce similar API costs since they both ultimately call LLMs. The differences are:

• AutoGen's multi-agent conversations can multiply token usage — each agent message goes through the LLM, so complex group chats get expensive
• Semantic Kernel's planners can make multiple LLM calls to construct a plan before execution
• Semantic Kernel's prompt templates tend to be more token-efficient by default
• AutoGen 0.4 improved token efficiency significantly vs 0.3

For production deployments, Deploy AI model to production covers cost optimization strategies that apply to both frameworks.

The Honest Verdict

If you're starting fresh in Python and want to build agents that talk to each other, AutoGen is the faster path. The community is active, the examples are plentiful, and the conversation-first model maps naturally to most agent use cases. For CrewAI tutorial style multi-agent workflows, AutoGen is similarly capable with a more Microsoft-aligned interface.

If you're a .NET enterprise developer or you're building something that lives inside the Microsoft ecosystem, Semantic Kernel is the right choice. The Azure integration is first-class, the .NET SDK is production-hardened, and it has a clearer story for enterprise compliance and support.

Don't feel forced to choose permanently. Both are MIT licensed, both are actively developed, and the integration paths between them are improving. Start with the one that matches your team's current stack.

FAQ

Can I use Semantic Kernel and AutoGen together? Yes. Microsoft has built integration bridges between the two. You can use Semantic Kernel plugins as tools within AutoGen agents, or use AutoGen's multi-agent orchestration while Semantic Kernel handles memory and skill management.

Which framework is better for .NET developers? Semantic Kernel has the most mature .NET SDK and is the better choice for enterprise .NET teams. AutoGen has a .NET port but Python is its primary language and has significantly more documentation and community support.

Is AutoGen production-ready in 2026? AutoGen 0.4+ (AgentChat) is stable enough for production with proper error handling and monitoring. Semantic Kernel has a longer track record in enterprise settings. Both are actively maintained by Microsoft.

Does Semantic Kernel support multi-agent conversations? Yes, via the Agent Framework added in Semantic Kernel 1.x. However, AutoGen's multi-agent conversation patterns are more mature and have more community examples. For complex agent-to-agent workflows, AutoGen is still ahead.

What LLMs do AutoGen and Semantic Kernel support? Both support OpenAI, Azure OpenAI, and most major LLM providers through their respective connector systems. Semantic Kernel has a broader set of first-party connectors including Hugging Face and Google Gemini. AutoGen relies on litellm for non-OpenAI models.