LangGraph is a low-level orchestration framework for constructing stateful, long-running AI agents. It offers fine-grained control to combine deterministic and LLM-driven steps, providing durable execution, persistence, and human-in-the-loop capabilities. This framework is ideal for bespoke agent workflows, but it is not a high-level abstraction.

Semantic Kernel is a lightweight, open-source development kit for building AI agents and integrating AI models into C#, Python, or Java codebases. It acts as middleware, translating AI model requests into calls to existing APIs and passing results back. Designed for enterprise use, it emphasizes flexibility, modularity, and future-proofing.

• +Provides fine-grained control to mix deterministic, hand-coded steps with LLM-driven agentic steps in a single graph.
• +Enables durable execution, allowing agents to persist through failures and resume from where they left off.
• +Supports human-in-the-loop functionality for inspecting and modifying agent state at any point.
• +Offers comprehensive memory capabilities for both short-term working memory and long-term memory across sessions.
• +Integrates with LangSmith for deep visibility into complex agent behavior, tracing execution paths, and capturing state transitions.
• +Provides low-level infrastructure supporting production-ready deployment of stateful workflows.

• +Lightweight and open-source development kit.
• +Facilitates rapid integration of AI models and agent building.
• +Acts as efficient middleware for enterprise-grade solutions.
• +Flexible, modular, and observable architecture.
• +Includes security-enhancing capabilities like telemetry, hooks, and filters.
• +Offers stable V1.0+ support with commitment to non-breaking changes.
• +Allows easy swapping of AI models without code rewrites.
• +Integrates prompts with existing APIs using OpenAPI specifications.
• +Supports expanding existing APIs to additional modalities like voice and video.

• −Operates at a very low level of abstraction, requiring more manual configuration than higher-level frameworks.
• −Does not abstract prompts or architectural patterns, placing responsibility on the developer.
• −Requires familiarity with underlying components like models and tools before effective use.
• −Higher-level abstractions, such as LangChain's agents, are recommended for beginners or simpler use cases.

LangGraph manages state through its `StateGraph` mechanism, enabling durable execution, persistence, and comprehensive memory for long-running, stateful agents.

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