Agno vs Semantic Kernel
Side-by-side comparison of two AI agent tools
Short answer
- Agno is growing faster: +560 GitHub stars in the last 30 days vs +165 for Semantic Kernel.
- Pick Agno for: build, run, manage agentic software at scale. Pick Semantic Kernel for: integrate cutting-edge LLM technology quickly and easily into your apps.
From GitHub data refreshed daily.
Agnoopen-source
Build, run, manage agentic software at scale.
Semantic Kernelopen-source
Integrate cutting-edge LLM technology quickly and easily into your apps
Metrics
| Agno | Semantic Kernel | |
|---|---|---|
| Stars | 42.5k | 28.6k |
| Star velocity /mo | 560.0526315789474 | 165 |
| Commits (90d) | 351 | 59 |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | 1.7M | 287.7K |
| Overall score | 0.7960554542558297 | 0.661646916269183 |
Pros
- +Production-ready runtime with built-in scalability, session isolation, and native tracing capabilities
- +Comprehensive monitoring and management through AgentOS UI for testing, debugging, and production oversight
- +Simple development experience - build sophisticated agents with memory and tools in approximately 20 lines of Python code
- +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
- -Python-focused platform with limited examples for other programming languages
- -Requires multiple dependencies and proper configuration of API keys and database connections
- -May have a learning curve for implementing complex multi-agent workflows and team coordination
- -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 production AI agents with persistent state, memory, and custom tool integrations for customer service or automation
- •Creating multi-agent teams and workflows for complex business processes that require coordination between specialized agents
- •Enterprise deployment of AI agents with comprehensive monitoring, user session management, and production-grade reliability requirements
- •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, Agno or Semantic Kernel?
- Agno has more GitHub stars (42,524 vs 28,620).
- Which is more actively developed, Agno or Semantic Kernel?
- Agno had more commits in the last 90 days (351 vs 59).
- Should I use Agno 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.