LangChain vs Semantic Kernel: Head-to-Head Comparison

Quick Verdict

LangChain is the better pick for general-purpose LLM apps and agents. Semantic Kernel is the better pick for .NET and enterprise teams on Azure.

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At a Glance

Feature	LangChain	Semantic Kernel
Best For	General-purpose LLM apps and agents	.NET and enterprise teams on Azure
Pricing	Free and open source	Free and open source
Free to Start	Yes	Yes
License	Open source	Open source
Deployment	Library / framework in your stack	Library / framework in your stack
Link	Visit LangChain	Visit Semantic Kernel

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Detailed Breakdown

LangChain

Framework for building LLM applications and agents

Pros:

• Huge integration catalog
• LangGraph for agent workflows
• Python and JavaScript versions

Cons:

• Abstractions can feel heavy
• Frequent API changes

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Semantic Kernel

Microsoft SDK for building AI agents

Pros:

• C#, Python and Java SDKs
• Enterprise focus
• Azure integration

Cons:

• Microsoft-centric
• Microsoft is consolidating it into the newer Agent Framework

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Key Differences

• Positioning: LangChain — framework for building LLM applications and agents. Semantic Kernel — Microsoft SDK for building AI agents.
• Both share the same licensing model (open source), so the decision comes down to features and workflow fit.
• Pricing: LangChain — free and open source. Semantic Kernel — free and open source.
• Signature strength: LangChain — huge integration catalog. Semantic Kernel — C#, Python and Java SDKs.

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Frequently Asked Questions

Is LangChain better than Semantic Kernel?

It depends on your requirements. LangChain is a strong fit for general-purpose LLM apps and agents, while Semantic Kernel suits .NET and enterprise teams on Azure.

Is LangChain free to use?

Yes, you can start with LangChain for free. Pricing model: Free and open source.

Is Semantic Kernel free to use?

Yes, you can start with Semantic Kernel for free. Pricing model: Free and open source.

Can I self-host LangChain or Semantic Kernel?

LangChain runs inside your own stack: library / framework in your stack. Semantic Kernel runs inside your own stack: library / framework in your stack.

What are the main drawbacks of LangChain and Semantic Kernel?

LangChain: abstractions can feel heavy; frequent API changes. Semantic Kernel: Microsoft-centric; Microsoft is consolidating it into the newer Agent Framework.