Agent Development Kit (ADK) vs Semantic Kernel
Side-by-side comparison of two AI agent tools
A
Agent Development Kit (ADK)open-source
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
Semantic Kernelopen-source
Integrate cutting-edge LLM technology quickly and easily into your apps
Metrics
| Agent Development Kit (ADK) | Semantic Kernel | |
|---|---|---|
| Stars | 21.7k | 28.6k |
| Star velocity /mo | 1.8k | 166.6844919786096 |
| Commits (90d) | 1.3k | 56 |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8546977073741404 | 0.6504666256577387 |
Pros
- +Model-agnostic design supports multiple LLM providers including OpenAI, Azure OpenAI, Hugging Face, and local models
- +Enterprise-ready with built-in observability, security features, and stable APIs for production deployments
- +Multi-language support (Python, .NET, Java) with comprehensive agent orchestration and multi-agent system capabilities
Cons
- -Requires significant programming knowledge and understanding of AI agent concepts
- -Complex setup and configuration for advanced multi-agent workflows
- -Learning curve for mastering the framework's extensive feature set and architectural patterns
Use Cases
- •Building enterprise chatbots and conversational AI applications with reliable LLM integration
- •Creating complex multi-agent systems where specialized AI agents collaborate on business processes
- •Developing AI applications that need flexibility to switch between different LLM providers and deployment environments
FAQ
- Which is more popular, Agent Development Kit (ADK) or Semantic Kernel?
- Semantic Kernel has more GitHub stars (28,614 vs 21,688).
- Which is more actively developed, Agent Development Kit (ADK) or Semantic Kernel?
- Agent Development Kit (ADK) had more commits in the last 90 days (1,322 vs 56).
- Should I use Agent Development Kit (ADK) or Semantic Kernel?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.