LangGraph vs Semantic Kernel

Semantic Kernel excels at plugin-based skill composition and memory management, allowing developers to combine AI with existing code and services, particularly within the Microsoft ecosystem (Azure OpenAI, Microsoft Graph, 365 Copilot). Its Process Framework enables stateful workflows with durable execution, and the Agent Framework supports multi-agent systems. LangGraph, on the other hand, offers low-level primitives for custom agent architectures with graph-based state management. Its standout features include human-in-the-loop checks for moderation, built-in cross-session memory, token-by-token streaming for real-time UX, and recent additions like retries, timeouts, and error handlers for fault tolerance. LangGraph also provides Rubrics for agent self-evaluation. While both support multi-agent setups, LangGraph provides more granular control over agent behavior and workflow structure, whereas Semantic Kernel emphasizes seamless integration with pre-built Microsoft connectors. A critical difference: LangGraph's RCE vulnerability (shared with LangFlow) was highlighted in June 2026, meaning teams must implement strict security auditing. Semantic Kernel doesn't have a similar public advisory, but its Azure-centric nature brings enterprise security via Microsoft's infrastructure.

Both Semantic Kernel and LangGraph are free and open-source, with no cost to use the frameworks themselves. Semantic Kernel is available under the MIT license (like LangGraph) but is developed by Microsoft, which may provide comfort for enterprise support. LangGraph is also MIT-licensed and can be used without any vendor lock-in. However, the real costs come from the underlying LLM APIs and infrastructure. Semantic Kernel is tightly integrated with Azure OpenAI, implying potential Azure consumption costs if using Azure services like Cognitive Search or Entra ID. LangGraph is model-agnostic, meaning you can choose OpenAI, Anthropic, Google, Mistral, or others, offering flexibility to optimize costs across providers. LangGraph also recommends LangSmith for observability, which has its own pricing tiers. There are no hidden framework costs for either, but the security advisory around LangGraph may impose additional auditing expenses for production deployments. Semantic Kernel's enterprise-grade integrations might require Azure subscriptions, adding cost for heavy Microsoft users.