AI · head to head
LangGraph vs Semantic Kernel
Semantic Kernel
Machine Learning
Model-agnostic SDK for AI orchestration
- From
- Free
- Rated
- -
The short version
- Each has a real cost: LangGraph steeper learning curve compared to high-level abstractions; Semantic Kernel steep learning curve for advanced features
- They diverge on capability: LangGraph covers Human-in-the-loop controls, Semantic Kernel covers Multi-model support.
Where they differ
Only the attributes on which LangGraph and Semantic Kernel actually diverge.
| Attribute | LangGraph | Semantic Kernel |
|---|---|---|
| Pricing model | Open source and free, with optional managed platform | Open source, no pricing |
| Platforms | Python, JavaScript, Web | Python, .NET, Java |
| Category | AI | Machine Learning |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in LangGraph
- Human-in-the-loop controls
- Customizable workflows
- Memory management
- Token-by-token streaming
- Low-level control
- Multi-agent support
Only in Semantic Kernel
- Multi-model support
- Agent framework
- Multi-agent systems
- Plugin ecosystem
- Vector database integration
- Multimodal support
- Local model support
- Enterprise observability
What people use each for
The jobs each tool is most often brought in to do.
LangGraph
- Building production AI agents with auditable workflowsnot Semantic Kernel
- Designing multi-agent systems for complex tasksnot Semantic Kernel
- Implementing human oversight in autonomous systemsnot Semantic Kernel
- Creating reliable agentic applications at scalenot Semantic Kernel
Semantic Kernel
- Building enterprise AI applications with LLM integrationnot LangGraph
- Creating multi-agent systems for complex workflowsnot LangGraph
- Developing AI-powered chatbots and assistantsnot LangGraph
- Implementing RAG systems with vector databasesnot LangGraph
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
LangGraph
- Steeper learning curve compared to high-level abstractions
- Requires understanding of graph-based architecture
- Debugging complex workflows can be challenging
- Not optimized for simple, one-off use cases
Semantic Kernel
- Steep learning curve for advanced features
- Documentation focuses on Azure cloud services
- Configuration complexity for multi-model scenarios
- Requires understanding of AI/LLM concepts
Pricing, plan by plan
LangGraph
Free- Open SourceFree
- MIT-licensed framework
- Self-hosted deployment
- Full API access
- LangGraph Platform$35/month
- Managed hosting
- Enterprise deployment
- Integrated tooling
Semantic Kernel
Free- Open SourceFree
- MIT license
- Full framework access
- All language SDKs
Which should you pick?
Choose LangGraph if
- You need human-in-the-loop controls.
- You want to start without paying.
- You work on Python, JavaScript, Web.
- You also want customizable workflows.
Choose Semantic Kernel if
- You need multi-model support.
- You want to start without paying.
- You work on Python, .NET, Java.
- You also want agent framework.
Questions people ask
- Is LangGraph or Semantic Kernel better?
- Neither clearly leads. LangGraph starts at Free and Semantic Kernel at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, LangGraph or Semantic Kernel?
- LangGraph starts at Free and Semantic Kernel at Free.
- Does LangGraph or Semantic Kernel run on more platforms?
- LangGraph runs on Python, JavaScript, Web. Semantic Kernel runs on Python, .NET, Java.
- Can I use LangGraph for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is LangGraph best used for?
- LangGraph is most often used for building production ai agents with auditable workflows, designing multi-agent systems for complex tasks, implementing human oversight in autonomous systems, creating reliable agentic applications at scale. Of those, building production ai agents with auditable workflows and designing multi-agent systems for complex tasks are not what Semantic Kernel is typically brought in for.
- What can LangGraph do that Semantic Kernel cannot?
- LangGraph covers Human-in-the-loop controls, Customizable workflows, Memory management, Token-by-token streaming. Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem.
Answered from the vendors’ own pages
LangGraph: Is LangGraph free to use?
Yes. The core LangGraph framework is MIT-licensed and completely free. You only pay if you use the optional managed LangGraph Platform for hosting.
SourceSemantic Kernel: What LLM providers does Semantic Kernel support?
Semantic Kernel supports OpenAI, Azure OpenAI, Hugging Face, Nvidia, and other providers through extensible model implementations.
SourceLangGraph: What programming languages does LangGraph support?
LangGraph provides first-class support for Python and JavaScript, enabling cross-platform agent development.
SourceSemantic Kernel: Can I run Semantic Kernel locally?
Yes. Semantic Kernel supports local models through Ollama, LMStudio, and ONNX for complete data control and offline operation.
SourceLangGraph: Can I deploy LangGraph in production?
Yes. LangGraph can be self-hosted on your own infrastructure or deployed through LangGraph Platform with enterprise support and SLA guarantees.
SourceSemantic Kernel: Is Semantic Kernel free?
Yes. Semantic Kernel is MIT-licensed open source and completely free. You only pay for external LLM APIs you use.
SourceRelated pages
More on Semantic Kernel
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