OpenAI Agents SDK vs LangGraph: which should you pick?
Pick the OpenAI Agents SDK when you want to ship a focused agent fast, and pick LangGraph when your workflow needs durable state, human approvals, and explicit branching control. Both are legitimate production choices in 2026. The decision is not about which framework is "better" in the abstract — it is about the shape of the workload you are building and how much control you need over the agent's execution path.
If you are still deciding whether agents are even the right tool, start with the fundamentals in our business guide to agentic AI. If you already know you need an agent and are choosing the runtime, read on.
What is the OpenAI Agents SDK?
The OpenAI Agents SDK is a lightweight, open-source framework, released in early 2026, for building agents around a small set of primitives. It ships in Python and JavaScript/TypeScript, and it is deliberately minimal.
It is built on four ideas:
- Agents — an LLM configured with instructions, tools, and a model.
- Tools — Python or TypeScript functions the agent can call, plus hosted tools like web and file search.
- Handoffs — one agent transfers control of the conversation to another. A handoff is not a function call that returns; once the triage agent hands off to the billing agent, the billing agent owns the loop.
- Guardrails — input and output validation that runs in parallel with the agent and fails fast when a check does not pass.
Tracing and structured outputs are built in. Despite the name, the SDK is provider-agnostic: it supports 100+ non-OpenAI models through the LiteLLM extension and any-llm integrations, so you can point it at Anthropic, Bedrock, Azure, or Vertex. Worth knowing: those adapters are best-effort beta layers, and any model you use must support both structured output and tool calling.
The headline is speed. If your team already knows the OpenAI API, the SDK takes minutes to learn. The whole surface is small enough to hold in your head.
What is LangGraph?
LangGraph models your agent as a stateful directed graph — nodes do work, edges decide what happens next, and cycles let the agent loop until a condition is met. It sits in the LangChain ecosystem and adds a durable execution model on top.
The features that matter for production are the ones the OpenAI SDK does not ship:
- Checkpointing — the entire execution state is serialized at every node, so a run can pause and resume reliably.
- Human-in-the-loop interrupts — an
interrupt()stops the graph, waits for a human decision, and resumes cleanly, even hours later. - Time-travel debugging — rewind to any past checkpoint, edit the state, and re-run from there to test "what-if" branches.
- Persistent, thread-scoped memory — state carries forward across turns and sessions without you wiring up a database by hand.
The trade-off is the mental model. The graph/state-machine way of thinking is not how most application developers frame problems, and teams typically need one to two weeks to get genuinely comfortable. For a deeper walkthrough of the architecture, see our explainer on what LangGraph is and how it works.
How do the OpenAI Agents SDK and LangGraph compare?
Here is the honest side-by-side on the dimensions that actually drive the decision:
| Dimension | OpenAI Agents SDK | LangGraph |
|---|---|---|
| Core abstraction | Agent loop with handoffs | Stateful directed graph |
| Learning curve | Minutes if you know the OpenAI API | 1–2 weeks for the graph model |
| State persistence | Not built in — you build it | Checkpointing built in |
| Human-in-the-loop | Roll your own pause/resume | Native interrupt() and resume |
| Control over flow | Higher-level, more implicit | Explicit, node-by-node |
| Model support | OpenAI-first, 100+ via LiteLLM (beta) | Model-agnostic |
| Best fit | Prototype-to-production agents | Long-running, regulated, stateful workflows |
The one-line summary: the OpenAI SDK optimizes for how fast you can ship an agent; LangGraph optimizes for how much you can control and audit what the agent does.
When should you choose the OpenAI Agents SDK?
Reach for the OpenAI Agents SDK when the workflow is broadly linear and you value velocity:
- Customer support or triage bots that route between a handful of specialist agents.
- Internal automation and data-research agents where a run starts and finishes in one pass.
- Prototypes you want in front of users this week, not next month.
- Teams already fluent in the OpenAI API who do not want to learn a new execution model.
The risk to watch: the SDK lacks checkpointing and durable state. The moment your product needs to pause for a human approval and resume later, you are building that persistence infrastructure yourself — and that is exactly the surface where LangGraph earns its keep.
When should you choose LangGraph?
Reach for LangGraph when the workflow itself is the product — when state, control, and auditability are non-negotiable:
- Regulated domains — finance, healthcare, insurance — where every step must be inspectable and reproducible.
- Hybrid human/AI processes where an agent proposes and a person approves before anything commits.
- Long-lived agents that run over hours or days, survive restarts, and carry explicit state forward.
- Complex multi-agent orchestration with cycles, retries, and conditional branching you need to reason about precisely.
LangGraph is not the only graph or crew-style framework in this space. If you are weighing orchestration options more broadly, our comparison of LangGraph vs CrewAI vs AutoGen covers where each one fits before you commit.
Can you migrate between the two agent SDKs later?
Yes — and this is why the choice is lower-stakes than it feels. If you design cleanly, migration is real work but not a rewrite.
The parts that port well are the parts that should never have been framework-specific: your tool functions, your prompts and instructions, and your evaluation harness. Keep tools as plain, well-typed functions with no dependency on the runtime, and either framework can call them.
The parts that do not port are the orchestration and state layers. OpenAI-style handoffs do not map one-to-one onto LangGraph nodes and edges, and any pause/resume logic you hand-rolled on the OpenAI side gets replaced by native checkpointing on the LangGraph side. A common, pragmatic path is to prototype on the OpenAI Agents SDK to validate the agent's behavior, then graduate the flows that need durability to LangGraph once the product demands it.
One more option worth naming: not every "agent" needs a code SDK at all. For workflow-shaped automation with human checkpoints, a visual orchestrator can be faster to ship and easier to hand to non-engineers — see our n8n AI agent tutorial for that route.
How do you decide? A quick agent SDK selection checklist
Run your use case through these questions in order:
- Does a run need to pause for a human and resume later? Yes → LangGraph. No → either.
- Is the workflow mostly linear, or full of cycles and conditional branches? Linear → OpenAI SDK. Branchy → LangGraph.
- Do you need to audit, replay, or time-travel through past runs? Yes → LangGraph.
- Is time-to-first-demo your biggest constraint? Yes → OpenAI SDK.
- Is your team already fluent in the OpenAI API? Yes → OpenAI SDK lowers the ramp.
If your answers split, default to the OpenAI Agents SDK for the prototype and keep your tools framework-agnostic so LangGraph stays an option. The one thing you should not do is pick the heavier framework "just in case" — the graph model has a real cost, and you should pay it only when the workload demands durable state.
How ILMTEC helps
ILMTEC builds production AI applications and agents on both stacks, so the framework recommendation follows your workload rather than our preference. In a short architecture consult we map your use case to the right runtime, design tools and state layers that survive a future migration, and deliver a working agent in a fixed six-week cycle. Whether you are choosing an SDK for a greenfield build or untangling a prototype that outgrew its first framework, let's talk through the architecture before you write the orchestration code — that one conversation is usually what saves the rewrite.
