LangChain vs Semantic Kernel: Agent Frameworks Comparison
Compare LangChain and Semantic Kernel side by side. Both are tools in the Agent Frameworks category.
Quick Comparison
| Category | Agent Frameworks | Agent Frameworks |
| Pricing | Open Source | Open Source |
| Best For | Developers building complex LLM applications who need a comprehensive orchestration framework | Enterprise .NET developers building AI applications on Microsoft infrastructure |
| Website | langchain.com | learn.microsoft.com |
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When to Choose LangChain vs Semantic Kernel
- Building RAG applications
- Multi-agent orchestration with LangGraph
- Tool-using conversational agents
- Enterprise AI on Microsoft stack
- Copilot-style applications
- Integrating AI into .NET applications
How to Choose a Agent Frameworks Tool
Key criteria to evaluate when comparing Agent Frameworks solutions:
About LangChain
LangChain is the most widely adopted framework for building LLM-powered applications and AI agents. It provides abstractions for chains, agents, tools, memory, and retrieval that make it easy to compose complex AI systems. LangGraph, its agent orchestration layer, enables building stateful, multi-actor workflows with human-in-the-loop capabilities. LangSmith provides tracing, evaluation, and monitoring. The LangChain ecosystem is the largest in the AI application development space.
About Semantic Kernel
Semantic Kernel is Microsoft's enterprise SDK for integrating AI into applications. It provides planners for multi-step task execution, plugin architectures for tool use, memory systems, and connectors for all major LLM providers. Available in C#, Python, and Java, Semantic Kernel is designed for enterprise .NET shops building AI-powered features into existing applications.
What is Agent Frameworks?
Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.
Browse all Agent Frameworks tools →