docs: both api keys, example model gpt-5.4-nano

examples/folio/README.md

@@ -81,19 +81,21 @@ Traces land in `./sanderling/runs/<timestamp>/`.
 ## Run with the LLM action backend
 
 `sanderling/spec-llm.ts` reuses the same properties and login setup but swaps the
-seeded fuzzer for `llm({ model })`: an OpenRouter vision model chooses which of
-the already-enumerated candidates to act on each step, from the screenshot.
+seeded fuzzer for `llm({ model })`: a vision model chooses which of the
+already-enumerated candidates to act on each step, from the screenshot.
 
 ```sh
-export OPENROUTER_API_KEY=sk-or-...
+export OPENROUTER_API_KEY=sk-or-...   # or OPENAI_API_KEY=sk-... for OpenAI direct
 sanderling test --spec sanderling/spec-llm.ts --bundle-id app.folio --duration 2m
 ```
 
-The model must support image input **and** strict `json_schema` structured
-outputs (e.g. `openai/gpt-4o`); edit the `model` string in `spec-llm.ts` to pick
-another. Each step is one multimodal call, so keep the duration / step budget
-modest. The trace records the model's reasoning and `source: "llm"` on each
-chosen action, so the replay UI shows why each pick was made.
+OpenRouter wins when both keys are set. With a plain OpenAI key, drop the vendor
+prefix from the model id in `spec-llm.ts` (`gpt-5.4-nano`, not
+`openai/gpt-5.4-nano`). The model must support image input **and** strict
+`json_schema` structured outputs. Each step is one multimodal call, so keep the
+duration / step budget modest. The trace records the model's reasoning and
+`source: "llm"` on each chosen action, so the replay UI shows why each pick was
+made.
 
 ## Run a sanderling test (iOS)
 

examples/folio/sanderling/spec-llm.ts

@@ -2,17 +2,19 @@
 //
 // The properties and the login `setup` are reused verbatim; only the action
 // generator changes. Instead of the seeded fuzzer drawing a random candidate,
-// `llm({ model })` hands selection to an OpenRouter vision model: each step it
-// sees the screenshot plus the candidate list the system already enumerates and
-// returns which candidate to act on. The candidate set, the typed input values,
-// action execution, and the trace are all identical to the seeded run.
+// `llm({ model })` hands selection to a vision model: each step it sees the
+// screenshot plus the candidate list the system already enumerates and returns
+// which candidate to act on. The candidate set, the typed input values, action
+// execution, and the trace are all identical to the seeded run.
 //
 // Requirements:
-//   - OPENROUTER_API_KEY in the environment.
+//   - OPENROUTER_API_KEY (OpenRouter) or OPENAI_API_KEY (OpenAI) in the
+//     environment; OpenRouter wins when both are set. With a plain OpenAI key,
+//     drop the vendor prefix from the model id ("gpt-5.4-nano").
 //   - A model that supports image input AND strict json_schema structured
-//     outputs (e.g. "openai/gpt-4o"). A model lacking either fails clearly.
+//     outputs. A model lacking either fails clearly.
 import { llm } from "@sanderling/spec";
 
 export { properties, setup } from "./spec";
 
-export const actionsRoot = llm({ model: "openai/gpt-4o" });
+export const actionsRoot = llm({ model: "openai/gpt-5.4-nano" });

pkg/spec/src/actions.ts

@@ -35,10 +35,11 @@ export function actions(generator: () => Action[]): GeneratorNode {
 }
 
 // llm selects the LLM action backend: instead of the seeded picker drawing a
-// random candidate, Go drives an OpenRouter model that chooses which candidate
-// to act on from the screenshot + the candidate list. The returned marker is
-// inert on the JS picker (pick.ts walks it to null); Go reads its config.model
-// off globalThis.actions. API key comes from OPENROUTER_API_KEY.
+// random candidate, Go drives an OpenAI-compatible model that chooses which
+// candidate to act on from the screenshot + the candidate list. The returned
+// marker is inert on the JS picker (pick.ts walks it to null); Go reads its
+// config.model off globalThis.actions. API key comes from OPENROUTER_API_KEY
+// (OpenRouter) or OPENAI_API_KEY (OpenAI); OpenRouter wins when both are set.
 export function llm(config: { model: string }): GeneratorNode {
   return { kind: "llm", config };
 }