LangGraph vs OpenAI Agents SDK (Python)

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	LangGraph Agents	OpenAI Agents SDK (Python) Agents
Tagline	Stateful, graph-based agent orchestration from LangChain.	A lightweight Python framework for building multi-agent LLM applications with handoffs, guardrails, and built-in tracing.
Category	Agents	Agents
Pricing	Freemium· Developer: $0 / seat per month · Plus: $39 / seat per month · Enterprise: Custom pricing	Free· SDK is free and open source (MIT-licensed). Cost is pass-through to whichever model provider you point it at — OpenAI API pricing applies when using GPT models; third-party providers billed separately.
Model	BYO (Claude / GPT / open)	GPT-4o / GPT-4.1 / gpt-realtime (provider-pluggable via LiteLLM)
Editorial score	8.8 / 10	—
Use cases	stateful agentshuman-in-loopproduction	Multi-agent customer support with specialist handoffsVoice agents using gpt-realtime for phone or in-app callsResearch agents that plan, call tools, and summariseStructured data extraction pipelines with Pydantic outputsGuardrail-protected content moderation flowsCoding assistants with sandboxed executionInternal ops copilots that call company APIs as toolsRetrieval-augmented question answering over vector stores
Pros	Reliable, debuggable agent graphs Built-in persistence + HITL Production-grade Tight LangSmith integration	Very small surface area — a handful of primitives means the whole SDK fits in your head in an afternoon First-class handoffs and guardrails make multi-agent and safety patterns idiomatic instead of bolt-on Automatic function-to-tool conversion from Python type hints and docstrings, with structured Pydantic outputs Built-in tracing viewer surfaces every model call, tool call, and handoff without extra instrumentation Realtime voice agent support (gpt-realtime) is unusually mature for an agent framework Provider-pluggable — you can point it at non-OpenAI models via the LiteLLM integration or a custom model provider Open source under MIT, actively maintained by OpenAI itself, so it tracks new Responses API features quickly
Cons	Steeper learning curve than CrewAI Verbose to set up	Strongly Python-first — the JS/TS sibling exists but lags in features, and there is no other-language support Optimised for the OpenAI Responses API; using it with Anthropic, Gemini, or local models works but loses some ergonomics (built-in tools, tracing fidelity) Tracing dashboard is hosted by OpenAI, which some regulated teams cannot use without extra work Fewer high-level building blocks than LangGraph/CrewAI — you write more of the orchestration and state logic yourself Documentation assumes fluency in async Python and the Responses API; not a great first agent framework for beginners
Website	www.langchain.com	openai.github.io

Pick LangGraph if

✅ Reliable, debuggable agent graphs
✅ Built-in persistence + HITL
✅ Production-grade
✅ Tight LangSmith integration

Pick OpenAI Agents SDK (Python) if

✅ Very small surface area — a handful of primitives means the whole SDK fits in your head in an afternoon
✅ First-class handoffs and guardrails make multi-agent and safety patterns idiomatic instead of bolt-on
✅ Automatic function-to-tool conversion from Python type hints and docstrings, with structured Pydantic outputs
✅ Built-in tracing viewer surfaces every model call, tool call, and handoff without extra instrumentation

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