Comparison Preset
LangGraph is the recommended choice for an enterprise context, primarily due to its lower security risk profile, with one moderate vulnerability compared to Semantic Kernel's critical one. Its design for durable execution and human-in-the-loop workflows provides the auditability and control necessary for mission-critical systems. While Semantic Kernel's multi-language support and focus on integrating with existing code are appealing, the security risk is a significant concern. LangGraph's integration with LangSmith offers the deep tracing and observability required for long-term maintenance and support. Both frameworks have a permissive MIT license and strong bus factor scores, but LangGraph presents a more justifiable choice from a risk management perspective.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
LangGraph is a low-level Python framework for orchestrating stateful, long-running AI agents using a graph-based approach. It focuses on capabilities like durable execution, comprehensive memory, and human-in-the-loop interactions. While it integrates with LangChain components, it provides fine-grained control for complex agent designs.
Semantic Kernel is a lightweight, open-source development kit for building AI agents and integrating AI models into C#, Python, or Java applications. It functions as efficient middleware, enabling rapid delivery of enterprise-grade solutions by orchestrating existing APIs and supporting future model advancements.
Best For
Building and deploying complex, stateful, long-running agent workflows requiring granular control.
Building AI agents, integrating AI models with existing code/APIs, automating business processes.
Avoid If
Starting with agents, or requiring a higher-level abstraction than a graph-based runtime.
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Strengths
- +Provides durable execution, allowing agents to persist through failures and resume from where they left off.
- +Supports human-in-the-loop workflows, enabling inspection and modification of agent state at any point.
- +Offers comprehensive memory capabilities for both short-term working memory and long-term memory across sessions.
- +Integrates with LangSmith Observability for deep visibility into agent behavior, tracing, and debugging.
- +Designed for production-ready deployment of scalable, stateful, long-running agent systems.
- +Can be used standalone without relying on other LangChain components.
- +Lightweight, open-source development kit for AI agents and model integration.
- +Efficient middleware enabling rapid delivery of enterprise-grade AI solutions.
- +Flexible, modular, and observable architecture for responsible AI at scale.
- +Includes security-enhancing capabilities like telemetry support, hooks, and filters.
- +Offers Version 1.0+ support across C#, Python, and Java with commitment to non-breaking changes.
- +Allows easy expansion of existing chat-based APIs to support modalities like voice and video.
- +Designed to be future-proof, allowing new AI models to be swapped without extensive code rewrites.
- +Combines prompts with existing APIs to perform actions and automate business processes.
- +Utilizes OpenAPI specifications for easily sharing extensions with other developers.
Weaknesses
- −It is a very low-level orchestration framework, requiring developers to understand underlying agent components.
- −It does not abstract prompts or architecture, demanding manual definition of these elements.
- −Not recommended for beginners or those seeking a higher-level abstraction, as LangChain's agents offer prebuilt architectures.
- −Requires familiarity with components like models and tools before effective use.
Project Health
Is this project alive, well-maintained, and safe to bet on long-term?
Bus Factor Score
Maintainers
Open Issues
Fit
Does it support the workflows, patterns, and capabilities your team actually needs?
State Management
LangGraph manages state through its `StateGraph` abstraction, enabling the definition of custom state objects like `MessagesState` for persisting agent progress and memory across executions.
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Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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