LangGraph vs Semantic Kernel
Side-by-side comparison of two AI agent tools
Short answer
- LangGraph is growing faster: +2,370 GitHub stars in the last 30 days vs +166 for Semantic Kernel.
- Pick LangGraph for: build resilient language agents as graphs. Pick Semantic Kernel for: integrate cutting-edge LLM technology quickly and easily into your apps.
From GitHub data refreshed daily.
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 | 42.7k | 28.6k |
| Star velocity /mo | 2.4k | 166.19047619047618 |
| Commits (90d) | 132 | 59 |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8220244037908294 | 0.6825043139380368 |
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
FAQ
- Which is more popular, LangGraph or Semantic Kernel?
- LangGraph has more GitHub stars (42,656 vs 28,620).
- Which is more actively developed, LangGraph or Semantic Kernel?
- LangGraph had more commits in the last 90 days (132 vs 59).
- Should I use LangGraph 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.