Semantic Kernel vs LangChain: Which AI Framework to Choose?

The AI Development Landscape: Semantic Kernel vs. LangChain in 2026

Companies are rapidly developing generative AI services for applications ranging from custom chatbots to business automation agents. Choosing the right development platform from the multitude of options available, however, can be a significant challenge.

Two popular choices are Microsoft’s open-source Software Development Kit (SDK), Semantic Kernel, which allows users to integrate various AI models into their builds, and LangChain, an open-source framework for building applications based on Large Language Models (LLMs). Both frameworks share similarities and differences in their features, strengths, weaknesses, and surrounding ecosystems, requiring teams to weigh the costs and benefits of each when embarking on a generative AI project.

When to Choose Semantic Kernel and When to Choose LangChain?

The choice between Semantic Kernel and LangChain depends on project requirements, preferred programming language, and the desired level of integration, and flexibility.

Compared to Semantic Kernel, LangChain boasts a much larger community, an ecosystem of third-party tools, and a wider range of integrations. This can be advantageous for developers seeking comprehensive out-of-the-box functionality and active support. However, many of LangChain’s integrations are based on open-source contributions, meaning they may not be regularly updated or maintained.

In contrast, Semantic Kernel’s data sources and available integrations are much more aligned with Microsoft, offering tight integration with Azure services and the .NET ecosystem. While it offers fewer integrations these are more likely to be reliable and consistently maintained.

• LangChain is better suited for projects requiring extensive tools and integrations, as well as building generative AI services offline.
• Semantic Kernel is preferred for projects within the .NET ecosystem or those requiring a lightweight framework with stronger Microsoft-aligned integrations.

Semantic Kernel vs. LangChain: Features and Capabilities

*This list represents a selection of the most popular options at the time of publication. For a comprehensive and up-to-date list, refer to the documentation of each tool.

Semantic Kernel: Microsoft’s .NET-Focused SDK

Semantic Kernel is a Microsoft SDK available in Java, C#, and Python, with C# offering the most comprehensive functionality.

Key features of Semantic Kernel include:

• AI connectors for integrating with generative AI models
• Services for telemetry and logging
• Orchestration of function calls
• Plugins, or groups of functions
• Vector stores for storing embeddings and performing similarity searches.

A central feature is Semantic Kernel’s framework for creating AI agents: the Microsoft open-source project AutoGen. This framework was previously available separately – and itself a popular choice for AI application development – but Microsoft’s goal for the future is to merge its functionality with Semantic Kernel.

Semantic Kernel provides documentation through Microsoft Learn and hosts community web meetings. However, much of the available documentation and tutorials use C#, while other languages receive less support.

LangChain: The Community-Driven Framework

LangChain is a popular framework for building generative AI applications with Python or JavaScript, with additional support for Java through LangChain4j.

The founders of LangChain have also developed other frameworks, such as LangGraph for agent-based workflows and LangSmith for monitoring and debugging generative AI applications. Despite an increased focus on AI agents and agent workflows, the founders of LangChain continue to add new LangGraph features.

LangChain encompasses the following main features:

• Chat models for LLM integration
• Document loaders for processing data in the LangChain data format
• Retrievers for querying knowledge bases
• Vector stores for embedding data and similarity search

LangChain supports numerous language models, vector stores, document loaders, and SaaS services, including Google, Microsoft 365, and Slack. However, due to the speed of framework development, many integrations are not compatible with newer versions of LangChain – meaning developers sometimes need to use an older version of LangChain to utilize a specific version of an integration.

LangChain has an impressively active community with many tutorials on YouTube and eLearning platforms, as well as an active Discord server. However, due to the rapid pace of new version releases, official documentation can sometimes lag.

Frequently Asked Questions

What is the primary difference between Semantic Kernel and LangChain?

Semantic Kernel is a Microsoft SDK with strong .NET integration, while LangChain is a more flexible, community-driven framework with broader language support.

Which framework is better for beginners?

LangChain’s larger community and extensive documentation may make it easier for beginners to get started, but Semantic Kernel’s focused approach can be beneficial for those familiar with the Microsoft ecosystem.

Are these frameworks open source?

Yes, both Semantic Kernel and LangChain are open-source projects.

Which framework has more integrations?

LangChain currently offers a significantly larger number of integrations, but Semantic Kernel’s integrations are generally more reliable within the Microsoft ecosystem.

Pro Tip: Consider your existing tech stack and team expertise when making your decision. If you’re heavily invested in Microsoft technologies, Semantic Kernel might be a natural fit. If you need maximum flexibility and a wide range of integrations, LangChain could be the better choice.

What are your experiences with Semantic Kernel and LangChain? Share your thoughts in the comments below!