The Agent SDK War: OpenAI vs Anthropic vs Google, and Why It Matters to No-Code Builders

Something quietly changed last year. All three frontier labs stopped just selling models and started selling the plumbing underneath them: agent SDKs. OpenAI shipped its Agents SDK in March 2025, and Google followed a month later. Anthropic came last, in September. For developers it was a welcome new toy. For everyone building on no-code platforms, it matters more than most of us have clocked yet, because the SDK your platform standardises on quietly becomes the shape of every agent you get to build.

The test nobody wanted to be a single winner

The best read I have found on this came from a developer who spent five days building the same five agents across all three SDKs and published the results on Stackademic in July. (A small caveat: that piece dates all three launches to 2026, but the vendors' own posts put them in 2025, and those are the dates I've used.) It is one person's five builds, not a benchmark, so treat the scoreboard as a useful anecdote rather than a verdict. They expected one clear winner. They got a draw that is far more interesting than a ranking.

Their scoreboard looked like this: OpenAI won multi-step automation and the data pipeline. Anthropic won the document and RAG agent, plus the multi-agent supervisor. Google won the debugging agent, mostly on the strength of its typed, structured output. Final tally: OpenAI 2, Anthropic 2, Google 1.

That is not a podium. It is a map. Each SDK was built to solve a different problem, and once you see that, every other result in the test suddenly makes sense.

There is a second table worth sitting with, because it explains the scoreboard. On setup speed, OpenAI was fastest and Google slowest. On tool calling, OpenAI was best. On multi-agent work, Anthropic was best and Google was effectively manual, you had to build the orchestration yourself. On structured output, Google was best. On reasoning depth, Anthropic was best. None of that is marketing copy. It is five days of one developer's real builds distilled into a spreadsheet, and it is the most useful thing I have read about the agent SDK race all year.

What each SDK actually is

OpenAI's Agents SDK launched in March 2025 as the production successor to Swarm, the experimental handoff framework. It is built around three primitives: agents, tools and handoffs. The goal is a working agent with as little code as possible. It is the fastest to start and the fastest to reach production when your workload is execution-heavy, and its tool calling was the best the tester touched.

Anthropic's Claude Agent SDK landed in September 2025, the same day Anthropic shipped Claude Sonnet 4.5. It leans on native MCP server integration and sub-agent coordination, which plays directly to Claude's long context and reliable reasoning. It takes longer to set up, but it hands you far more control over how agents think and work together. That trade shows up in the multi-agent test, where it was the only SDK that felt purpose-built for the job.

Google's ADK arrived in April 2025, announced at Google Cloud Next, with a different goal entirely: typed, structured workflows and deep integration into Google's own tools. Its stand-out strength is native typed output. You define the exact format you want and ADK enforces it. If your team already lives on Google Cloud, you reach production fast. Outside that ecosystem, the setup tax is real, and the tester felt it more than once.

Why this matters if you never touch an SDK

Here is the part no-code builders should care about. Most of us will never write an OpenAI, Anthropic or Google agent loop by hand. We pick a platform, tick a box that says AI agent, and inherit whatever that box is wired to underneath.

Which means the SDK war is not really a developer story. It is a supply-chain story. When your no-code platform's bring-your-own-agent lane routes to OpenAI, you inherit OpenAI's strengths, clean tool calling and fast pipelines, and its weaker spots, like multi-agent handoffs that can drop context. When it routes to Anthropic, you inherit the reverse. When it routes to Google, you get beautiful structured output and a lot of setup pain if you are not on Google Cloud.

You do not get to choose the SDK. You get to choose the platform, and the platform chose the SDK. So the practical question becomes: what are you actually building, and does the platform's wiring match it?

A rough translation table

For a no-code builder, the three-way split maps onto everyday decisions better than any benchmark. Here is how I read it.

If your agents do multi-step work, move data between systems or chain a dozen tool calls, you want a platform wired toward OpenAI's execution model. That is where the OpenAI win lives.

If your agents need to reason over a pile of documents, cite sources or coordinate several sub-agents without falling over, you want the Anthropic-flavoured platform. The multi-agent supervisor is exactly where most ambitious no-code agents die in production, and it was the one test that felt one-sided.

If your agents live or die on clean, schema-validated output, or you already run on Google Workspace and Cloud, a Google ADK-wired platform is the call. Structured output is boring right up until a malformed JSON payload breaks an automation at 2am, then it is the only thing that matters.

None of this is to say one lab is winning. The draw is the honest finding. What it tells me is that the no-code platforms that win in 2027 will be the ones that stop pretending an AI agent is one thing and start matching the model and the SDK to the job, or better, let you route between them.

What the platforms will not tell you

Here is the awkward bit. Very few no-code platforms publish which SDK sits under their agent feature. They will tell you they support AI agents, full stop, as if that were one thing. It is not. It is three different things with three different strengths, and the platform quietly picked one for you.

I would like to see this become a standard disclosure, the way data residency became one. It is a fair question: which SDK, which model provider, and can I route around it. Until then, treat an agent feature with no stated SDK as a feature with no stated warranty.

The takeaway

Do not pick a side in the SDK war. Pick the task first, then the platform whose inherited SDK matches it. The developer who ran the test put it plainly: trying to use one SDK for every problem is the wrong strategy in 2026. Swap the word platform in for SDK and you have the whole brief for a no-code buyer.

And if your platform will not tell you which SDK sits under its agent feature, ask anyway. A vendor that cannot answer that question is asking you to bet your stack on a black box.