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.

The OpenAI Agents SDK is a lightweight Python framework for building production-ready agentic AI applications. It provides primitives like agents, tools, and guardrails, managing complex multi-step workflows with built-in tracing and state management. The SDK prioritizes ease of use while allowing extensive customization for intricate agent coordination.

• +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.

• +Offers a lightweight, easy-to-use package with few abstractions for building agentic AI applications.
• +Provides built-in tracing for visualizing, debugging, evaluating, and fine-tuning agentic workflows.
• +Supports complex multi-agent coordination through 'Agents as tools' (handoffs) and isolated 'Sandbox agents'.
• +Includes Guardrails for input validation and safety checks, failing fast on non-compliance.
• +Manages persistent memory across turns using 'Sessions' to maintain working context.
• +Facilitates turning any Python function into a tool with automatic schema generation and Pydantic validation.

• −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.

• −Higher-level abstraction limits direct control over the LLM interaction loop, tool dispatch, and state handling.
• −Less optimal for short-lived workflows that only require a single model response, due to its managed runtime overhead.

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

The SDK provides a persistent memory layer called Sessions for maintaining working context across agent turns and within an agent loop.