LangGraph vs Semantic Kernel
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | LangGraph | Semantic Kernel |
|---|---|---|
| Pricing | Free (MIT license) | Free (MIT license) |
| Primary Language | Python, JavaScript | C#, Python, Java |
| Ecosystem Bias | Any LLM provider | Microsoft/Azure |
| State Management | Graph-based state (low-level) | Process Framework (stateful workflows) |
| Human-in-the-Loop | Built-in checks | Via security filters |
| Best For | Production agent builders needing fine control | Enterprise .NET devs integrating with M365 |
Choose Semantic Kernel if you're building AI copilots inside Microsoft 365 and prefer a plugin-based, high-level SDK. Choose LangGraph if you need granular control over agent workflows, multi-agent orchestration, and production features like human-in-the-loop with any LLM provider. LangGraph's recent prompt caching and memory enhancements (June 2026) make it stronger for stateful, cost-sensitive agents.
Open-source framework for building reliable, stateful AI agents with low-level control.
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Microsoft's open-source SDK for building production-grade AI agents with plugins and orchestration.
Visit WebsiteWho should pick which
- Enterprise .NET developer integrating with M365Pick: Semantic Kernel
Semantic Kernel's native C# support and deep Microsoft Graph/365 Copilot integration make it the obvious choice for adding AI to existing .NET enterprise apps.
- Production agent builder needing fine controlPick: LangGraph
LangGraph's graph-based state, human-in-the-loop, and model-agnostic support let you build and customize production-grade agents with full control.
- Multi-agent system architectPick: LangGraph
LangGraph supports single, multi-agent, and hierarchical workflows out of the box, with recent updates (prompt caching, memory) enhancing multi-agent efficiency.
- Solo founder prototyping AI on a budgetPick: LangGraph
LangGraph's free model-agnostic framework with Ollama support and prompt caching keeps costs low, while its low-level primitives allow rapid prototyping.
- Azure-centric team building copilotPick: Semantic Kernel
Direct integration with Azure OpenAI, Cognitive Search, and 365 Copilot provides a streamlined path for enterprise copilot development.
Frequently Asked Questions
LangGraph vs Semantic Kernel: which should you choose?
Choose Semantic Kernel if you're building AI copilots inside Microsoft 365 and prefer a plugin-based, high-level SDK. Choose LangGraph if you need granular control over agent workflows, multi-agent orchestration, and production features like human-in-the-loop with any LLM provider. LangGraph's recent prompt caching and memory enhancements (June 2026) make it stronger for stateful, cost-sensitive agents.
Which framework is easier to learn for a beginner?
Semantic Kernel has a higher-level, plugin-based API that may be simpler for .NET developers familiar with Microsoft tools. LangGraph's graph-based paradigm requires understanding nodes, edges, and state, which has a steeper learning curve but offers more flexibility.
Can I use LangGraph with Microsoft models?
Yes, LangGraph is model-agnostic and supports Azure OpenAI and any LLM via provider integrations. It does not have the deep Microsoft Graph or 365 Copilot plugins that Semantic Kernel offers.
Does Semantic Kernel support multi-agent systems?
Yes, Semantic Kernel has an Agent Framework for multi-agent systems, but it is more opinionated and tied to Microsoft's ecosystem, whereas LangGraph offers more granular control over agent hierarchies.
Which framework has better human-in-the-loop features?
LangGraph has built-in human-in-the-loop checks as a first-class feature, allowing approval gates mid-workflow. Semantic Kernel relies on security filters and policy enforcement, which are less interactive.
Are there any hidden costs for these tools?
Both are free and open-source. Indirect costs come from LLM API calls and optional services: Semantic Kernel may push toward Azure services; LangGraph can work with any provider and its new prompt caching reduces LLM costs.
Which framework is better for stateless chatbots?
Neither is optimized for simple, stateless chatbots. For such use cases, consider higher-level frameworks like LangChain or direct LLM API calls. LangGraph's stateful graph adds unnecessary complexity, and Semantic Kernel's plugin model is overkill.
Can I deploy Semantic Kernel outside Azure?
Yes, Semantic Kernel supports OpenAI and can run on any hosting platform. However, many of its best features (Microsoft Graph, Cognitive Search) are Azure-only, limiting portability.
Does LangGraph support prompt caching?
Yes, as of June 2026, LangGraph's Deep Agents support prompt caching to reduce costs and latency by reusing cached results across sessions.
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Last reviewed: May 12, 2026