LangGraph vs OpenAI Agents SDK

OpenAI Agents SDK excels in rapid prototyping with features like multi-agent handoffs, agent-as-tool delegation (v0.15.0+), and Realtime Agents for voice (v0.17.6+). Its Sandbox Agents provide containerized environments for safe code execution and file inspection. The SDK is provider-agnostic via LiteLLM (100+ LLMs) and includes built-in tracing, session management, and guardrails. However, it lacks mature third-party integrations and is early-stage with frequent API changes.

LangGraph offers low-level graph-based control over agent workflows, supporting single, multi-agent, and hierarchical architectures. Key features include human-in-the-loop checks, built-in cross-session memory, token-by-token streaming, and fault tolerance (retries, timeouts). Latest news (June 2026) highlights prompt caching in Deep Agents and dedicated memory guidance. LangGraph integrates deeply with LangSmith for observability and evaluation, and works with any LLM provider. It is trusted by enterprises like Lyft and United Airlines. LangGraph's flexibility comes with a steeper learning curve but greater control for complex stateful agents.

Both tools are free and open-source under the MIT license, with no usage limits or paid tiers. OpenAI Agents SDK is a straightforward Python package; costs come solely from API calls to OpenAI or other providers via LiteLLM. LangGraph also carries no direct cost, but requires API keys for LLM providers. However, LangGraph’s ecosystem includes LangSmith (offering a free tier plus paid plans for advanced observability and evaluation at scale). For production deployments, LangGraph may incur additional infrastructure costs for graph persistence and state storage. Overall, both tools are cost-effective for development, but LangGraph might lead to higher indirect costs in production due to observability and scaling needs.