langgraph vs semantic-kernel
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
semantic-kernelopen-source
Integrate cutting-edge LLM technology quickly and easily into your apps
Metrics
| langgraph | semantic-kernel | |
|---|---|---|
| Stars | 27.7k | 27.6k |
| Star velocity /mo | 2.3k | 2.3k |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.7586411782605156 | 0.7604232031722189 |
Pros
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
- +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
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
- -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
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions
- •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