AutoGen vs Semantic Kernel

Side-by-side comparison of features, pricing, and ratings

Analysis generated Live tool data as of 2026-11-13
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AutoGen or Semantic Kernel: which should you choose?

DimensionAutoGenSemantic Kernel
PricingFreeFree
Best forDevelopers building multi-agent apps in Python or .NET, Researchers studying multi-agent collaboration and conversation topologies.NET developers building copilots and AI agents, Enterprise teams standardized on Microsoft Azure
Standout featuresAutoGen Studio web UI for prototyping agents without writing code (pip install, port 8080) · AgentChat Python framework for conversational single- and multi-agent apps, Python 3.10+ · Core event-driven framework for scalable multi-agent systemsPlugin-based composition of reusable functions · Memory layer for supplying context to models · Process Framework for stateful, durable workflows
Viability score73/10073/100
APIYesYes

AutoGen is the stronger pick for developers building multi-agent apps in python or .net; Semantic Kernel fits better for .net developers building copilots and ai agents.

Built from live tool data, last verified 2026-11-13.

AutoGen
AutoGen

AutoGen is Microsoft's open-source framework for building conversational and event-driven multi-agent AI apps in Python and .NET.

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Semantic Kernel
Semantic Kernel

Microsoft's open-source SDK for building AI agents and copilots with plugins, memory, and multi-agent orchestration.

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Pricing
Free
Free
Plans
Open SourceFree
Open SourceFree
Popularity
5.3k views
3.0k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
—
API
Categories
🕸️ Agent Frameworks & Orchestration
🕸️ Agent Frameworks & Orchestration
FeaturesAutoGenSemantic Kernel
AutoGen Studio web UI for prototyping agents without writing code (pip install, port 8080)
AgentChat Python framework for conversational single- and multi-agent apps, Python 3.10+
Core event-driven framework for scalable multi-agent systems
Deterministic and dynamic agentic workflows for business processes
Distributed multi-language agents via GrpcWorkerAgentRuntime over gRPC
AssistantAgent with model clients such as OpenAIChatCompletionClient
Customizable conversation patterns including group chat and sequential turns
McpWorkbench for connecting agents to Model Context Protocol (MCP) servers
OpenAIAssistantAgent for using the OpenAI Assistant API
DockerCommandLineCodeExecutor for running model-generated code in a Docker container
Community extensions you can discover, use, or publish
Human-in-the-loop approval and intervention inside agent workflows
Parallel .NET API surface alongside Python
Documented migration path for projects moving off AutoGen 0.2
MIT-licensed source you can self-host and modify
Plugin-based composition of reusable functions
Memory layer for supplying context to models
Process Framework for stateful, durable workflows
Agent Framework for multi-agent coordination
Group chat pattern for multi-agent systems
Security filters for AI policy enforcement
Observability and telemetry hooks
Multi-language SDK support (C#, Python, Java)
Azure OpenAI connectivity
OpenAI connectivity
Microsoft 365 Copilot extension
Microsoft Graph data access
Kernel extensibility through middleware
Semantic functions and plans
Connectors for external data sources
IntegrationsAutoGenSemantic Kernel
OpenAI
Model Context Protocol (MCP) servers
Docker
gRPC
OpenAI Assistant API
Azure OpenAI
Microsoft 365 Copilot
Microsoft Graph
Azure Cognitive Search
Entity Framework
ASP.NET Core
Blazor
Power Platform
Microsoft Entra ID

Frequently Asked Questions

Which is better, AutoGen or Semantic Kernel?

The best choice between AutoGen and Semantic Kernel depends on your specific use case — we compare them independently on features, current pricing, integrations, and real-world signals (with an on-demand sentiment scan available for each). See the side-by-side breakdown above to match them to your needs.

What are the main differences between AutoGen and Semantic Kernel?

The key differences include pricing model, feature set, platform support, and skill level requirements. Review the full comparison on RightAIChoice for a detailed breakdown.

Is there a free version of AutoGen or Semantic Kernel?

Check the pricing section in the comparison for the latest pricing details on both tools, including free tiers, trial options, and paid plans.

What else are AutoGen and Semantic Kernel compared with?

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If you're engineering complex agents that must run reliably in production and you need deep debugging, evaluation, and autonomous issue diagnosis, choose LangChain. If you're a developer or researcher

LangGraph vs CrewAI vs AutoGen comparison
LangGraph vs Semantic Kernel comparison
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LangChain vs Semantic Kernel comparison

If you're a .NET shop on Azure building production copilots, Semantic Kernel is the no-brainer——it's free, deeply integrated with Microsoft's stack, and the process framework handles durable workflows

Where can you read more about AutoGen and Semantic Kernel?

AutoGen
View AutoGen reviewAutoGen alternatives
Semantic Kernel
View Semantic Kernel reviewSemantic Kernel alternatives

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Analysis generated by our model pipeline from live data: