LangGraph is a low-level orchestration framework and runtime for building and deploying long-running, stateful agents. It provides core capabilities like durable execution, human-in-the-loop interactions, and comprehensive memory management. While it integrates seamlessly with LangChain components, it can be used independently for fine-grained control over agent workflows.

The OpenAI Agents SDK is a lightweight, Python-first framework for building production-ready agentic AI applications with minimal abstractions. It provides core primitives like agents, handoffs, and guardrails, along with built-in tracing and session management. The SDK prioritizes ease of learning while offering customization, making it suitable for rapid development and debugging of AI-driven workflows.

• +Provides durable execution, allowing agents to persist through failures and resume from where they left off.
• +Supports human-in-the-loop interactions by enabling inspection and modification of agent state at any point.
• +Offers comprehensive memory capabilities for short-term reasoning and long-term state persistence across sessions.
• +Integrates with LangSmith for deep visibility, debugging, tracing, and evaluation of agent behavior.
• +Designed for production-ready deployment of scalable, stateful, and long-running agent systems.

• +Offers a lightweight, easy-to-use package with few abstractions for rapid development.
• +Provides a built-in agent loop that handles tool invocation and LLM interaction until task completion.
• +Enables Python-first orchestration and chaining of agents using native language features.
• +Features 'Agents as tools' (Handoffs) for powerful coordination and delegation across multiple agents.
• +Includes Guardrails for parallel input validation, safety checks, and fail-fast execution.
• +Automatically generates schemas and provides Pydantic-powered validation for Python function tools.
• +Offers a persistent memory layer via Sessions for maintaining working context across agent turns.
• +Supports built-in mechanisms for involving human users across agent runs (Human in the loop).
• +Comes with built-in tracing for visualizing, debugging, and monitoring workflows, supporting OpenAI evaluation and fine-tuning.
• +Allows building real-time voice agents with features like interruption detection and context management using `gpt-realtime-1.5`.

• −LangGraph is very low-level and does not abstract prompts or architecture, requiring more manual configuration.
• −It is not recommended for users just getting started with agents or those seeking a higher-level abstraction.
• −Requires familiarity with components like models and tools before effective use.

• −Optimal functionality, especially for tracing, evaluation, and real-time features, is tied to the OpenAI ecosystem.
• −Limited to Python applications, making it unsuitable for polyglot environments or teams focused on other languages.
• −The 'very few abstractions' design may limit flexibility for highly complex or specialized agent architectures beyond its core primitives.

LangGraph manages state through a comprehensive memory system for long-running agents, supporting both short-term working memory and long-term memory across sessions, allowing state inspection and modification.

The SDK provides 'Sessions', a persistent memory layer designed to maintain working context across turns within an agent loop.