DeepYard

AutoGen vs Semantic Kernel

Side-by-side comparison built from DeepYard's structured catalog. Content updated Aug 21, 2026.

Direct Answer

AutoGen suits teams needing open-source access, conversation focus, and 60,568 GitHub stars in current listing data. Semantic Kernel suits teams needing open-source access, csharp focus, and 28,477 GitHub stars in current listing data. This summary reflects catalog metadata only for decision support, not independent testing.

What this comparison weighs

  • Pricing and free-tier availability
  • License model and deployment fit
  • GitHub adoption and contributor depth
  • Catalog tags, integrations, and supported workflows
A

AutoGen

Microsoft's framework for building multi-agent AI systems

OSSFree
60.6K18w ago444
Visit GitHub
S

Semantic Kernel

Microsoft's SDK for integrating LLMs into applications — 24K+ stars

OSSFree
28.5Ktoday400
Visit GitHub
MetricAutoGenSemantic Kernel
GitHub Stars60.6K28.5K
Contributors444400
Last CommitApr 15, 2026Aug 21, 2026
Open Issues997250
Licenseopen-sourceopen-source
Pricingopen-sourceopen-source
Free TierYesYes
Categoryframeworksframeworks
TrendingNoNo

Choose AutoGen if you need

  • • AutoGen is the stronger pick when Conversation matters because that capability is listed only on its profile.
  • • AutoGen shows broader GitHub adoption with 60,568 stars versus 28,477 for Semantic Kernel.
  • • AutoGen has a larger visible contributor base (444 vs 400).

Choose Semantic Kernel if you need

  • • Semantic Kernel stands out for Csharp workflows that are not listed for AutoGen.
  • • Semantic Kernel is the stronger pick when Plugins matters because that capability is listed only on its profile.
  • • Semantic Kernel is the cleaner fit if you specifically need Enterprise workflows from the catalog tags.

Meaningful differences

  • • Workflow emphasis: AutoGen highlights Python, while Semantic Kernel highlights Csharp.
  • • GitHub adoption: AutoGen shows 60,568 stars versus 28,477 for Semantic Kernel.
  • • Contributor count: AutoGen lists 444 contributors and Semantic Kernel lists 400.

Shared capabilities

  • • Multi Agent
  • • Microsoft
  • • Python

Shared Tags

multi-agentmicrosoftpython

Only in AutoGen

conversationcode-executiongroup-chat

Only in Semantic Kernel

enterprisecsharpjavapluginsopen-source

Limitations and evidence

  • • DeepYard compares structured public metadata; this is not an independent benchmark unless a test record is shown.
  • • Signals such as stars, contributors, and last commit indicate public activity, not purchase fit or runtime quality.
  • • Pricing and feature coverage reflect the stored listing snapshot and may lag vendor changes between refreshes.

Source links

Manifest content date: 2026-08-21

About AutoGen

AutoGen is Microsoft's open-source framework for building multi-agent conversational systems where agents can converse with each other and with humans to solve complex tasks. Agents are highly customizable and can use LLMs, tools, and human input in flexible combinations. The framework supports group chats, nested conversations, and code execution sandboxes, making it well-suited for coding assistants, research automation, and enterprise agentic workflows. AutoGen Studio provides a no-code UI for prototyping agent systems visually.

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About Semantic Kernel

Semantic Kernel is Microsoft's open-source SDK for building AI agents and integrating LLMs into enterprise applications. It supports C#, Python, and Java with a plugin architecture for extending agent capabilities. Semantic Kernel provides planning, memory, and function-calling abstractions that work with OpenAI, Azure OpenAI, and Hugging Face models.

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