AutoGen vs Semantic Kernel
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | AutoGen | Semantic Kernel |
|---|---|---|
| Price | Free (open-source, MIT license) | Free (open-source, MIT license) |
| Primary Use | Multi-agent conversation systems for research & flexible LLM backends | Enterprise copilot & workflow orchestration on .NET/Azure |
| Language Support | Python (primary), extensible via API | C#, Python, Java |
| Latest Notable Feature (2025) | N/A (no recent news) | MCP Support (Jan 2025) |
| Ecosystem Bias | Model-agnostic (OpenAI, Hugging Face, Claude) | Strong Azure/Microsoft 365 focus |
| Best for Production | Experimental multi-agent simulations & prototyping with AutoGen Studio | Enterprise-grade process workflows with stateful orchestration |
Choose Semantic Kernel if you're building enterprise copilots on .NET/Azure and need stateful workflows, memory management, and tight Microsoft integration. Choose AutoGen if you want multi-agent conversations with flexible LLM backends, rapid prototyping via AutoGen Studio, and minimal vendor lock-in.
Microsoft's open-source SDK for building production-grade AI agents with plugins and orchestration.
Visit WebsiteWho should pick which
- Enterprise .NET developer building a copilotPick: Semantic Kernel
SK's native C# support, deep Azure integration, and Process Framework for stateful workflows match enterprise needs. MCP support adds future flexibility.
- AI researcher simulating multi-agent debatesPick: AutoGen
AutoGen's flexible conversation patterns, role definition, and model-agnostic design enable rapid experimentation. AutoGen Studio speeds up iteration.
- Startup building a no-code agent builderPick: AutoGen
AutoGen's extensible tool use and human-in-the-loop features allow custom workflows without vendor lock-in. AutoGen Studio lowers UI development cost.
- DevOps team deploying stateful AI pipelinesPick: Semantic Kernel
SK's durable execution, security filters, and telemetry are production-ready for long-running workflows on Azure.
- Python developer prototyping multi-agent code generationPick: AutoGen
AutoGen's Python-first API, code execution sandboxing (Docker), and integration with local models (Ollama) suit iterative development.
Frequently Asked Questions
AutoGen vs Semantic Kernel: which should you choose?
Choose Semantic Kernel if you're building enterprise copilots on .NET/Azure and need stateful workflows, memory management, and tight Microsoft integration. Choose AutoGen if you want multi-agent conversations with flexible LLM backends, rapid prototyping via AutoGen Studio, and minimal vendor lock-in.
Which framework is better for production enterprise apps?
Semantic Kernel, due to its security filters, telemetry, stateful Process Framework, and Azure-native integration.
Can I use AutoGen with non-Microsoft LLMs?
Yes, AutoGen integrates with OpenAI, Hugging Face, LLaMA, Mistral, Claude, and more via an abstract LLM interface.
Does Semantic Kernel support multi-agent systems?
Yes, its Agent Framework (released Mar 2024) supports group chat and multi-agent orchestration, though more structured than AutoGen.
Which is easier for rapid prototyping?
AutoGen Studio provides a visual UI for quick prototyping; Semantic Kernel requires coding and .NET setup.
Is there a cost to use Semantic Kernel or AutoGen?
Both are free (MIT license). Costs come from backend API calls (e.g., OpenAI, Azure OpenAI) and infrastructure.
Can I use Semantic Kernel without Azure?
Yes, SK supports OpenAI and other providers, but its best features (Graph, 365, Cognitive Search) are optimized for Azure.
Which framework has better documentation and support?
Semantic Kernel, backed by Microsoft's enterprise documentation and Azure support; AutoGen has active community but less formal docs.
Does AutoGen support stateful workflows?
Not natively; it's designed for stateless multi-agent conversations. For durable state, Semantic Kernel's Process Framework is better.
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Last reviewed: May 12, 2026