An open-source framework from Microsoft Research for building multi-agent applications, where agents converse to solve tasks together.
Agent Development comparison
Microsoft AutoGen vs Semantic Kernel
Pricing, pros, cons, and ideal use cases — side by side.
Semantic KernelFree
An open-source SDK from Microsoft for integrating LLMs into applications, with a focus on enterprise-grade orchestration in C#, Python, and Java.
At a glance
| Microsoft AutoGen | Semantic Kernel | |
|---|---|---|
| Pricing | FreeOpen-source (MIT), free to use. You pay only for the underlying model API calls. | FreeOpen-source (MIT), free. Costs come from the model APIs you call. |
| Category | Agent Development | Agent Development |
| Ideal for | Engineering teams prototyping multi-agent systemsResearch and innovation groupsTeams already in the Microsoft ecosystem | .NET and Java enterprise development teamsOrgs embedding AI into existing applicationsMicrosoft-ecosystem shops |
Pros & cons
Microsoft AutoGen
Pros
- Mature multi-agent conversation patterns
- Backed by Microsoft Research
- Flexible high-level and low-level APIs
- Strong fit for experimentation
Cons
- Multi-agent conversations are hard to evaluate and debug
- Token costs can escalate without guardrails
- APIs have changed significantly between versions
Semantic Kernel
Pros
- First-class C#, Python, and Java support
- Designed to fit into existing enterprise apps
- Backed and maintained by Microsoft
- Clean plugin and function abstractions
Cons
- Smaller ecosystem than Python-first frameworks
- Roadmap is converging with Microsoft Agent Framework
- Less suited to rapid Python prototyping
Which should you choose?
Microsoft AutoGen is built around engineering teams prototyping multi-agent systems; Semantic Kernel leans more toward .net and java enterprise development teams. Shortlist the one whose strengths line up with your biggest constraint.
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