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LangGraph vs Semantic Kernel

LangGraphSemantic Kernel

Bottom line: LangGraph for engineering teams building production agents; Semantic Kernel for enterprise .NET development teams.

LangGraphLangGraph

Graph-based orchestration for stateful, controllable LLM agents.

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Semantic KernelSemantic Kernel

Microsoft's enterprise SDK for orchestrating LLMs, plugins, and agents.

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Votes00
PricingFreemiumFree
CategoryAgent FrameworksAgent Frameworks
Tags
agentsorchestrationllm-frameworkopen-sourceworkflow
llm-frameworkagentsopen-sourcemicrosoftdotnet
Best for
  • Engineering teams building production agents
  • Developers needing controllable, resumable workflows
  • Teams already invested in LangChain
  • Enterprise .NET development teams
  • Microsoft/Azure-aligned organizations
  • Developers adding AI to existing apps
Pros
  • Explicit, debuggable control over agent state and flow
  • MIT-licensed core, free to self-host
  • Model-agnostic, not tied to one LLM vendor
  • Strong support for cycles, checkpointing, and human-in-the-loop
  • Backed by the widely used LangChain ecosystem
  • First-class C#/.NET support, rare among AI frameworks
  • Free and open source
  • Enterprise features: type safety, DI, telemetry
  • Multi-language (C#, Python, Java)
  • Backed by Microsoft with clear Azure integration
Cons
  • Steeper learning curve than high-level agent builders
  • Requires comfort with graph and state concepts
  • Paid platform and observability are separate products
  • Documentation and APIs have evolved quickly
  • Can be overkill for simple single-shot LLM tasks
  • Strategic focus shifting to Microsoft Agent Framework
  • Concepts like planners have changed across versions
  • Python support historically trailed .NET
  • Migration planning needed for long-term projects
  • Less multi-agent focus than AutoGen lineage

Comparison generated from each tool's listing. Add or remove tools above to change it.