OpenAI Agents SDK vs LangGraph: Which to Use in 2026?

Two frameworks dominate the “how should I build this agent?” question in 2026, and they answer it in opposite ways. The OpenAI Agents SDK hides the agent loop so you write almost no plumbing. LangGraph exposes everything as a state graph you wire node by node. Same goal, very different philosophy.

This is a head-to-head on OpenAI Agents SDK vs LangGraph: the core difference, a side-by-side table, where each one wins, and a decision rule you can apply to your own project. If you want a broader field scan instead, the best AI agent frameworks in 2026 covers the whole landscape.

The verdict, up front#

Comparisons should not bury the answer. Pick the OpenAI Agents SDK for speed and simplicity on agent-centric apps; pick LangGraph when control over state and durability is the whole point. Most teams building a customer bot, a research assistant, or an internal tool are happier starting with the SDK. Teams building long-running, auditable, human-in-the-loop workflows usually end up wanting LangGraph’s graph. The rest of this post is why.

OpenAI Agents SDK vs LangGraph: the core difference#

The OpenAI Agents SDK runs the loop for you. You define an agent — instructions, tools, handoffs — and the Runner handles the model calls, tool calls, and delegation. You think in agents.

LangGraph is a state machineA model of computation where the app moves between explicit states along defined transitions — here, nodes connected by edges.. You define nodes (steps), edges (transitions), and a shared state object. You think in graphs. That extra structure is the cost and the payoff: more to write, but total control over what happens when. The SDK optimizes for getting an agent running; LangGraph optimizes for controlling exactly how it runs.

Side-by-side comparison#

Read the “State & persistence,” “Durable, long-running,” and “Best for” rows first — that is where the decision usually gets made.

A quick feel for each#

The gap is clearest in code. Here is a minimal agent in the OpenAI Agents SDK.

from agents import Agent, Runner
 
agent = Agent(name="Assistant", instructions="Answer concisely.")
result = Runner.run_sync(agent, "Summarize the OpenAI Agents SDK in one line.")
print(result.final_output)

Four lines and it runs. Now the same idea in LangGraph, where you declare the graph explicitly.

from typing import TypedDict
from langgraph.graph import StateGraph, START, END
 
class State(TypedDict):
    topic: str
    summary: str
 
def summarize(state: State) -> State:
    # call your LLM here
    return {"summary": f"A short take on {state['topic']}."}
 
builder = StateGraph(State)
builder.add_node("summarize", summarize)
builder.add_edge(START, "summarize")
builder.add_edge("summarize", END)
graph = builder.compile()
 
print(graph.invoke({"topic": "LangGraph"}))

More code — but every node and edge is yours to inspect, branch, and checkpoint. That verbosity is not overhead; it is the control you are paying for. For the full walkthrough, see the LangGraph tutorial and the OpenAI Agents SDK tutorial.

Where the OpenAI Agents SDK wins#

It wins on time-to-first-agent. There is no graph to design and no state schema to declare before you see output. Handoffs make multi-agent routing a one-liner, tracing is built in, and guardrails ship in the box. If your app is a set of specialists that pass work around — triage to billing to tech support — the SDK models that directly. When the shape of the problem is “agents handing off to agents,” the SDK is the shortest correct path.

Where LangGraph wins#

It wins the moment state and durability matter. LangGraph’s checkpointers persist state at every node, so a run survives a crash and resumes where it stopped. You can pause for human approval mid-run and continue later. You can even replay a past run with changed inputs to debug it.

For long-running, regulated, or auditable workflows, that is not a nice-to-have. When you need the agent to be resumable, inspectable, and pausable, LangGraph’s graph earns its complexity — the same durability theme covered in long-running AI workflows.

When to use each#

Use this rule. If the answer to “does this need durable, resumable, human-checkpointed state?” is no, start with the OpenAI Agents SDK and stay there until something forces a change. If the answer is yes — or the workflow is long-running, branchy, and must be auditable — start with LangGraph.

Two more nudges. If you are OpenAI-first and want built-in tracing with minimal code, that tilts toward the SDK. If you need model-agnosticism and tight control inside a larger LangChain stack, that tilts toward LangGraph. Comparing more than two frameworks? The LangGraph vs CrewAI vs AutoGen breakdown widens the field.

Common tradeoffs people get wrong#

Two mistakes show up again and again. The first is reaching for LangGraph’s graph on a simple app because it looks more “serious.” If there is no durable state to manage, the graph is just boilerplate — and the Agents SDK would have shipped the same feature in a fraction of the code.

The second is the reverse: forcing the Agents SDK into a long-running, human-checkpointed workflow, then rebuilding persistence by hand. That is re-implementing what LangGraph gives you for free. Match the tool to the state, not to the hype. In the OpenAI Agents SDK vs LangGraph choice, the wrong pick usually shows up as code you should not have had to write.

The bottom line#

There is no universal winner, only a fit. The OpenAI Agents SDK trades control for speed; LangGraph trades speed for control. Pick by the one axis that dominates your project — how much you need to own the state — and you will rarely regret it. Whichever you choose, add evals before you call it production. New to agents entirely? Start with what AI agents are.

So, which side is your current project on — speed or control? Tell me what you’re building and I’ll tell you which one I’d reach for.

Related: the OpenAI Agents SDK tutorial to build the SDK side hands-on, and the LangGraph tutorial for the graph side.