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LangGraph vs Semantic Kernel
Side-by-side comparison of LangGraph and Semantic Kernel — features, pricing, performance scores, and which to choose for your AI agents.
View All Framework ComparisonsQuick Verdict
| Dimension | LangGraph | Semantic Kernel | Winner |
|---|---|---|---|
Ease of Use | 3/5 | 4/5 | Semantic Kernel |
Scalability | 5/5 | 4/5 | LangGraph |
Documentation | 4/5 | 4/5 | Tie |
Community | 4/5 | 4/5 | Tie |
Performance | 5/5 | 4/5 | LangGraph |
Overall: LangGraph wins 2 categories, Semantic Kernel wins 1, 2 tied
Feature Comparison
| Feature | LangGraph | Semantic Kernel |
|---|---|---|
| Primary Language | Python | C# |
| License | MIT | MIT |
| Pricing | Open Source + Commercial | Open Source |
| GitHub Stars | 19,900 | 26,300 |
| Difficulty | Advanced | Intermediate |
| Enterprise Ready | ||
| Community Size | Large | Large |
| Category | Multi-Agent Systems | Enterprise Integration |
Pros & Cons
LangGraph
Advantages
Full control over agent behavior with low-level primitives
Excellent for complex non-linear workflows
Built-in state persistence and memory management
Production-proven by major companies (Klarna Uber LinkedIn)
Strong streaming and observability features
Human-in-the-loop support is first-class
Can be used standalone or with LangChain
MIT licensed with commercial platform option
Limitations
Steeper learning curve than LangChain
Requires understanding of graph theory concepts
May be overkill for simple linear workflows
Smaller community than LangChain (but growing)
Some advanced features require LangGraph Platform
Documentation still maturing compared to LangChain
More complex setup for basic use cases
Semantic Kernel
Advantages
Strong Microsoft ecosystem integration
Multi-language support (C# Python Java)
Enterprise-grade quality and support
Good for .NET developers
Plugin architecture is flexible
MIT license
Limitations
Optimized primarily for Azure ecosystem
Less community adoption than LangChain
Documentation can be overwhelming
C# focus may alienate Python-first developers
Smaller integration ecosystem
Best Use Cases
LangGraph
Complex customer support workflows with escalation
Multi-agent research and analysis systems
Task management and orchestration
Long-running business process automation
Interactive assistants with memory
Decision support systems with conditional logic
Semantic Kernel
Enterprise application integration
Plugin-based AI systems
Multi-language AI applications
Microsoft ecosystem integration
Enterprise chatbots and assistants
Getting Started
LangGraph
Installation
pip install langgraph
Semantic Kernel
Installation
Install via NuGet or pip
Learn More
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