Which is more popular, MCP Python SDK or Semantic Kernel?

Semantic Kernel has more GitHub stars (28,620 vs 24,469).

Which is more actively developed, MCP Python SDK or Semantic Kernel?

MCP Python SDK had more commits in the last 90 days (120 vs 59).

Should I use MCP Python SDK 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.

MCP Python SDK vs Semantic Kernel

Side-by-side comparison of two AI agent tools

Short answer

MCP Python SDK is growing faster: +332 GitHub stars in the last 30 days vs +165 for Semantic Kernel.
Pick MCP Python SDK for: the official Python SDK for Model Context Protocol servers and clients. Pick Semantic Kernel for: integrate cutting-edge LLM technology quickly and easily into your apps.

From GitHub data refreshed daily.

MCP Python SDKopen-source

The official Python SDK for Model Context Protocol servers and clients

Semantic Kernelopen-source

Integrate cutting-edge LLM technology quickly and easily into your apps

Metrics

	MCP Python SDK	Semantic Kernel
Stars	24.5k	28.6k
Star velocity /mo	332.2105263157895	165
Commits (90d)	120	59
Releases (6m)	10	10
Downloads (30d, npm + PyPI)	219.0M	287.7K
Overall score	0.7280604269497934	0.661646916269183

Pros

+Official implementation with comprehensive MCP protocol support including resources, tools, prompts, and structured output capabilities
+Multiple deployment options from development mode to production ASGI server integration with Claude Desktop compatibility
+Advanced features like context management, authentication, elicitation, sampling, and streamable HTTP transport for flexible AI integration

+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

-Currently in version transition with v2 being pre-alpha and in development, potentially causing breaking changes
-Complexity may be overkill for simple AI tool integrations that don't need full MCP protocol compliance

-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 MCP servers to connect AI assistants to databases, APIs, or file systems with standardized security
•Creating AI-enabled applications that need structured tool calling and resource access capabilities
•Integrating existing ASGI web applications with MCP protocol support for AI assistant connectivity

•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

View MCP Python SDK Details View Semantic Kernel Details

FAQ

Which is more popular, MCP Python SDK or Semantic Kernel?: Semantic Kernel has more GitHub stars (28,620 vs 24,469).
Which is more actively developed, MCP Python SDK or Semantic Kernel?: MCP Python SDK had more commits in the last 90 days (120 vs 59).
Should I use MCP Python SDK 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.