OpenAI Agents SDK vs LangGraph: Runtime or Graph?

⚡The verdict up front

This is not a feature-count comparison, because on features the two frameworks have largely converged: both support tool calling, multi-agent patterns, streaming, human-in-the-loop and tracing. The real difference is philosophical, and philosophy is what you will be living with in month eight.

The OpenAI Agents SDK says: the runtime should be managed, the primitives should be few, and the platform should carry as much of the operational weight as possible. LangGraph says: orchestration is application logic, application logic should be explicit, and you should be able to see and control every state transition. The first philosophy optimizes for speed and simplicity. The second optimizes for control and portability.

So the recommendation is situational and we will not hedge it. All-in on OpenAI models, small team, speed to production matters most: Agents SDK. Multi-model strategy, complex stateful flows, compliance-driven self-hosting, or a platform team that wants full control: LangGraph. The sections below unpack why.

🧠Two philosophies: managed runtime vs explicit graph

The OpenAI Agents SDK, released in 2025 as the production evolution of the earlier Swarm experiment, is deliberately small. An agent is a model plus instructions plus tools. Multi-agent behavior is expressed as handoffs — one agent passing the conversation to another — and safety as guardrails that run alongside. You can read the core concepts in an afternoon, which is the point: the framework makes the common case nearly configuration-free and lets the OpenAI platform handle the plumbing.

LangGraph, from the LangChain team, models an agent system as a graph: nodes are units of work, edges are transitions, and a shared state object flows through the whole thing. Nothing is hidden. The loop that the Agents SDK manages for you is, in LangGraph, a graph you drew — which means you can inspect it, branch it, put a human approval node in the middle of it, and persist it at any point.

Neither philosophy is wrong; they optimize for different failure modes. Managed runtimes fail by constraining you when your flow stops matching the convention. Explicit graphs fail by making you build and maintain structure that a convention would have given you for free. Pick the failure mode your team handles better.

🔗The vendor lock-in trade, stated without drama

The Agents SDK is more portable than its name suggests — the library itself runs anywhere, tool definitions use standard function schemas, and support for non-OpenAI model providers exists through the model interface and community adapters. But honesty requires the follow-up sentence: the deepest value — the tightest Responses API integration, the managed conveniences, the native tracing experience — accumulates when you run it the way it was designed to be run, against the OpenAI platform.

LangGraph is model-agnostic as a design principle, not an adapter. Swapping providers is normal usage, and the same graph can run OpenAI, Anthropic, Google or self-hosted models per node. For teams with a deliberate multi-model strategy — routing hard steps to strong models and easy steps to cheap ones — this is structural, not cosmetic.

Frame the trade as a bet, not a sin. Choosing the Agents SDK bets that OpenAI remains your best model source for the system lifetime, and collects speed as the payoff. Choosing LangGraph pays a small complexity premium up front to keep every provider option open. Both bets are defensible; pretending the bet does not exist is not.

🔭Tracing and evals: the ecosystems around each

Production agents live or die on observability, and both frameworks take it seriously. The Agents SDK ships with tracing built in — runs are captured with spans for model calls, tool calls and handoffs, viewable in the OpenAI platform, and the tracing pipeline supports exporting to external processors if you want the data elsewhere.

LangGraph connects to LangSmith, which is the more mature dedicated observability and evaluation product: detailed traces, dataset management, evaluators that run on every deploy, and annotation queues for human review. Notably, LangSmith is framework-agnostic — you can use it with the Agents SDK, raw API calls or anything else — so the observability choice and the framework choice are separable, which many buyers miss.

The practical guidance: whichever framework you choose, budget for evals from week one, not from the first incident. Both ecosystems support it. The teams that regret their framework choice almost always regret skipping evals first.

🏭Production maturity, as of writing

Both frameworks are used in real production systems at meaningful scale, and both are young by infrastructure standards. LangGraph has a longer public track record — it has been generally available longer, its persistence and checkpointing model has been exercised by long-running workflow use cases, and the LangGraph Platform adds managed deployment for teams that do not want to operate the runtime themselves. The Agents SDK, despite arriving later, carries the operational weight of the OpenAI platform behind it and has seen fast adoption for exactly the teams it targets.

What this means practically: neither choice is a bet on an unproven research project, and neither is a bet on a decade-stable foundation. Expect breaking changes less often than in 2024 but more often than in your web framework. Pin versions, keep the orchestration layer thin, and treat framework upgrades as a scheduled activity rather than a surprise.

Because both projects ship quickly, verify the current release status, deprecation policy and platform pricing for each before committing to either. Anything more specific printed here would be stale by the time you read it.

🔄What migrating between them actually costs

The good news: the expensive parts of an agent system do not live in the framework. Tool implementations, retrieval pipelines, prompts, eval datasets and guardrail logic all port between frameworks with modest adaptation, because both speak the same underlying language of function schemas and message lists. If you build those assets cleanly, you own them regardless of the orchestration layer.

The part that does not port is the orchestration model itself. Agents SDK handoffs are conversational — agent A passes the thread to agent B. LangGraph edges are structural — state moves from node to node along paths you defined. Translating one mental model into the other means rethinking control flow, not just retyping it, and for a mature system that is measured in weeks, not afternoons.

The mitigation is the same advice as everywhere else in this article: keep the framework layer thin. Agents that are 90 percent tools, prompts and evals with 10 percent orchestration migrate cheaply in either direction. Agents where business logic leaked into the orchestration glue migrate painfully, and that is true no matter which framework the glue belongs to.

📏How to choose, stated as rules

Choose the OpenAI Agents SDK when these hold together: your models are OpenAI today and you have no concrete plan to diversify; the team is small and values convention over configuration; the workflows are conversational agent loops rather than long-running stateful processes; and speed to production is the metric you are being measured on.

Choose LangGraph when any of these hold: you run or plan to run multiple model providers; your workflows are long-running, resumable processes where durability and checkpointing are requirements rather than features; you need fine-grained control over state and transitions; or your deployment must live inside your own infrastructure boundary.

And if you genuinely cannot tell which list describes you, start with the philosophy question rather than the feature list: does your team want the framework to manage the loop, or to show the loop? Teams that want managed rarely regret the Agents SDK. Teams that want visible rarely regret LangGraph.

🛠️Whichever you pick: the disciplines that do not change

Framework choice is maybe twenty percent of what determines whether an agent succeeds in production. The other eighty percent is identical on both: tools with strict schemas and good error messages, an eval suite that gates every prompt or model change, tracing wired up before launch, budget caps on runs, and a named owner who reads the traces every week.

It is also worth saying plainly that the framework is the replaceable part. Teams that invest in their tools, evals and data pipelines can survive a framework migration in a quarter. Teams that invested only in framework-specific structure are locked in regardless of which logo is on the SDK — portability is a property of your architecture, not theirs.

We build production agents on both stacks, and the first conversation is always the same: workload shape, model strategy, control requirements — then framework. If you want that conversation with a team that has shipped 500+ projects since 2018, it is a free call.

OpenAI Agents SDK development services→LangGraph development services→