Both vendors now ship an official agent SDK, both are open source, both are at a confident 0.x, and both will let you stand up a working agent in a dozen lines. So the comparison gets filed as a feature bake-off — tracing, guardrails, model support, lines of code to "hello agent." That framing produces a tidy table and the wrong decision, because the two SDKs are not competing implementations of the same idea. They sit at different layers of the stack and bet on different hard parts. Pick by the table and you will adopt the one whose defaults fight your problem.

Two different things wearing the same noun#

The OpenAI Agents SDK is an orchestration library. Its whole surface is a small set of primitives — Agents, Handoffs, Guardrails, Sessions — designed, in its own words, to be "enough features to be worth using, but few enough primitives to make it quick to learn." An Agent is an LLM with instructions and tools. A Handoff lets one agent delegate to another. The unit of thought is the graph: you wire specialists together and decide who routes to whom. PyPI describes the package plainly as "a lightweight yet powerful framework for building multi-agent workflows." It is multi-agent-first by construction, and this lineage is honest — it is the production successor to Swarm, which was always about choreographing many small agents.

The Claude Agent SDK is a harness. Anthropic's framing is that it "gives you the same tools, agent loop, and context management that power Claude Code, programmable in Python and TypeScript." The unit of thought is not a graph — it is one capable agent with a computer. Out of the box it ships Read, Write, Edit, Bash, Glob, Grep, WebSearch, and WebFetch. The official docs draw the contrast against their own API client: the plain SDK "gives you direct API access: you send prompts and implement tool execution yourself. The Agent SDK gives you Claude with built-in tool execution." This is the harness-versus-thin-wrapper bet, made by the people who run the loop in production.

So the real question is not "which is better at multi-agent." It is: what do you want to own?

Who designs the control flow#

OpenAI hands you the primitives and assumes you design the control flow. You decide the triage agent hands off to the refund agent, you place the guardrail, you choose the session backend. The model fills each node; you draw the edges. That is exactly right when the workflow is known — a supervisor or routed pipeline of specialists where the structure is the value and you want it explicit, inspectable, and testable.

Claude hands you a loop and assumes the model designs the control flow. The agent gathers context, takes an action, verifies the result, and repeats until it is done — and because it has a real filesystem and a shell, "an action" can be running your test suite and reading the failure. You do not draw edges; you grant tools and set permissions. That is right when the task is open-ended and the steps are not known in advance — the thing you cannot express as a static graph because the graph is what you wanted the agent to discover.

You are not choosing which framework orchestrates agents better. You are choosing whether you own the graph or the model owns the loop.

The tell is in the delegation primitive#

If you want one detail that exposes the whole philosophy, compare how each SDK delegates.

An OpenAI handoff is, to the model, a tool call: hand off to an agent named "Refund Agent" and the SDK exposes a tool called transfer_to_refund_agent. The receiving agent "takes over the conversation, and gets to see the entire previous conversation history." It is a lateral transfer of one shared context between peers — perfect for a routed pipeline where the next specialist needs everything that came before.

A Claude subagent is the opposite move. It runs in "its own fresh conversation," and "only its final message returns to the parent." It is delegation into a clean, isolated context — which is also, not coincidentally, a context-management tool: the parent's window stays uncluttered while the subagent burns tokens out of sight. The docs are explicit that this is for a few subtasks per turn; for coordinating dozens of agents they point you at a separate Workflow tool. Shared-context handoff versus isolated-context delegation is not a cosmetic API difference. It is the multi-agent worldview and the single-agent worldview, compiled into a primitive.

Defaults are destiny#

The dimension that will actually bite you is the defaults, because the default path is the one that is a single line of code and everything else is a project.

The Claude SDK is Claude-only. You can route through Bedrock, Vertex, or Azure, but every path is a Claude model; there is no LiteLLM escape hatch. In exchange you get automatic context compaction — when the window approaches its limit the SDK summarizes older history for you — which is the single feature that most distinguishes a harness from a wrapper for long-running agents.

The OpenAI SDK is provider-agnostic — its own docs claim 100+ models through LiteLLM, "included on a best-effort, beta basis" — and leaves context management to you. Sessions persist and replay history automatically, but nobody compacts it; that is your problem when the conversation grows.

One honest caveat against my own framing: the two are converging from opposite ends. OpenAI's April 2026 release added native sandbox execution across providers, durable execution by snapshot-and-rehydrate, and subagents — harness features grafted onto an orchestration library. Anthropic, from the other side, added subagents and a Workflow tool for when one agent is not enough. They are meeting in the middle. But a 0.x SDK's defaults still encode the bet it started from: OpenAI defaults to "you compose, any provider," Claude defaults to "the model drives a computer, Claude only." Choose the one whose default is the project you are actually building — not the one with the longer feature table. If you are weighing open versus closed more broadly, that same question — own the graph or rent the loop — is the one under everything else.