LangGraph is a low-level, graph-based orchestration framework for building robust, stateful AI agents in Python. It provides core runtime capabilities like durable execution, human-in-the-loop support, and comprehensive memory, but requires explicit management of prompts and agent architecture. It integrates with LangChain components and LangSmith for observability and deployment.

The OpenAI Agents SDK provides a lightweight, Python-first framework for building agentic AI applications with built-in primitives for agents, tools, guardrails, and multi-agent coordination. It abstracts away common complexities like tool invocation, turn management, and state persistence, while offering customization. The SDK is designed for production-ready agent workflows that go beyond single LLM calls.

• +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 for deep debugging visibility, tracing execution paths, and capturing state transitions.
• +Designed for production-ready deployment of scalable, stateful, long-running agent systems via LangSmith.

• +Provides a built-in agent loop handling tool invocation and result processing until task completion.
• +Offers a Python-first approach, using language features for orchestration without new abstractions.
• +Supports multi-agent coordination through 'Agents as tools' (Handoffs) for task delegation.
• +Includes 'Sandbox agents' for running specialists in isolated workspaces with resumable sessions.
• +Implements 'Guardrails' for parallel input validation and safety checks, enabling fail-fast behavior.
• +Generates automatic schemas for Python functions, enabling them as Pydantic-validated tools.
• +Features built-in tracing for visualizing, debugging, and monitoring workflows, with support for OpenAI evaluation and fine-tuning.
• +Enables building low-latency voice agents with `gpt-realtime-2` for real-time interactions and context management.

• −Is a very low-level framework, focusing solely on orchestration and not abstracting prompts or agent architecture.
• −Requires familiarity with underlying components like models and tools, potentially increasing complexity for beginners.
• −Recommends higher-level abstractions like LangChain agents for those just starting or seeking simpler solutions.

• −Introduces a higher-level runtime that adds overhead for simple, short-lived API calls where direct model response is the only goal.
• −Less control over core model loop, tool dispatch, and state handling when compared to direct use of the OpenAI Responses API.

LangGraph manages state through a graph structure, supporting durable execution, persistence, comprehensive memory for both short-term and long-term context, and the ability to inspect and modify agent state.

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