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Machine Learning · head to head

Semantic Kernel vs AutoGen

Semantic Kernel logo

Semantic Kernel

Machine Learning

Model-agnostic SDK for AI orchestration

From
Free
Rated
-
Full reviewWebsite
AutoGen logo

AutoGen

AI

Programming framework for multi-agent agentic AI

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Semantic Kernel steep learning curve for advanced features; AutoGen framework now in maintenance mode, no new features planned
  • They diverge on capability: Semantic Kernel covers Multi-model support, AutoGen covers Multi-agent orchestration.

Where they differ

Only the attributes on which Semantic Kernel and AutoGen actually diverge.

Attributes where Semantic Kernel and AutoGen differ
AttributeSemantic KernelAutoGen
PlatformsPython, .NET, JavaPython, .NET
CategoryMachine LearningAI

Identical on both: starting price (Free), pricing model (Open source, no pricing), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Semantic Kernel

  • Multi-model support
  • Agent framework
  • Multi-agent systems
  • Plugin ecosystem
  • Vector database integration
  • Multimodal support
  • Local model support
  • Enterprise observability

Only in AutoGen

  • Multi-agent orchestration
  • Message passing API
  • AgentChat API
  • Extensions API
  • MCP server support
  • AutoGen Studio
  • Cross-language support
  • Observable agent networks

What people use each for

The jobs each tool is most often brought in to do.

Semantic Kernel

  • Building enterprise AI applications with LLM integrationnot AutoGen
  • Creating multi-agent systems for complex workflowsnot AutoGen
  • Developing AI-powered chatbots and assistantsnot AutoGen
  • Implementing RAG systems with vector databasesnot AutoGen

AutoGen

  • Building multi-agent conversational systemsnot Semantic Kernel
  • Rapid prototyping of agent applicationsnot Semantic Kernel
  • Research on agentic AI patterns and architecturesnot Semantic Kernel
  • Distributed agent networks across boundariesnot Semantic Kernel

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Semantic Kernel

  • Steep learning curve for advanced features
  • Documentation focuses on Azure cloud services
  • Configuration complexity for multi-model scenarios
  • Requires understanding of AI/LLM concepts

AutoGen

  • Framework now in maintenance mode, no new features planned
  • Steeper learning curve for advanced use cases
  • Microsoft recommends new projects use Agent Framework instead
  • Limited to Python and .NET platforms

Pricing, plan by plan

Semantic Kernel

Free
  • Open SourceFree
    • MIT license
    • Full framework access
    • All language SDKs

AutoGen

Free
  • Open SourceFree
    • MIT and CC-BY-4.0 licenses
    • Full framework access
    • Community support

Which should you pick?

Choose Semantic Kernel if

  • You need multi-model support.
  • You want to start without paying.
  • You work on Python, .NET, Java.
  • You also want agent framework.
Semantic Kernel review

Choose AutoGen if

  • You need multi-agent orchestration.
  • You want to start without paying.
  • You work on Python, .NET.
  • You also want message passing api.
AutoGen review

Questions people ask

Is Semantic Kernel or AutoGen better?
Neither clearly leads. Semantic Kernel starts at Free and AutoGen at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Semantic Kernel or AutoGen?
Semantic Kernel starts at Free and AutoGen at Free.
Does Semantic Kernel or AutoGen run on more platforms?
Semantic Kernel runs on Python, .NET, Java. AutoGen runs on Python, .NET.
Can I use Semantic Kernel for free?
Both have a free tier, so you can try either at no cost before committing.
What is Semantic Kernel best used for?
Semantic Kernel is most often used for building enterprise ai applications with llm integration, creating multi-agent systems for complex workflows, developing ai-powered chatbots and assistants, implementing rag systems with vector databases. Of those, building enterprise ai applications with llm integration and creating multi-agent systems for complex workflows are not what AutoGen is typically brought in for.
What can Semantic Kernel do that AutoGen cannot?
Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem. AutoGen covers Multi-agent orchestration, Message passing API, AgentChat API, Extensions API.

Answered from the vendors’ own pages

Semantic Kernel: What LLM providers does Semantic Kernel support?

Semantic Kernel supports OpenAI, Azure OpenAI, Hugging Face, Nvidia, and other providers through extensible model implementations.

Source
AutoGen: Is AutoGen still actively developed?

As of March 2026, AutoGen is in maintenance mode and will not receive new features. Microsoft recommends new projects use the Microsoft Agent Framework instead.

Source
Semantic Kernel: Can I run Semantic Kernel locally?

Yes. Semantic Kernel supports local models through Ollama, LMStudio, and ONNX for complete data control and offline operation.

Source
AutoGen: Can I still use AutoGen for new projects?

While AutoGen is stable and maintained for existing projects, Microsoft recommends using the Microsoft Agent Framework for new development.

Source
Semantic Kernel: Is Semantic Kernel free?

Yes. Semantic Kernel is MIT-licensed open source and completely free. You only pay for external LLM APIs you use.

Source
AutoGen: What LLM providers does AutoGen support?

AutoGen includes extensions for OpenAI and Azure OpenAI through its Extensions API, with community support for other providers.

Source
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