chore: generate

packages/llm/src/protocols/bedrock-converse.ts

@@ -225,10 +225,7 @@ const lowerToolSpec = (tool: ToolDefinition): BedrockToolSpec => ({
   },
 })
 
-const lowerTools = (
-  breakpoints: BedrockCache.Breakpoints,
-  tools: ReadonlyArray<ToolDefinition>,
-): BedrockTool[] => {
+const lowerTools = (breakpoints: BedrockCache.Breakpoints, tools: ReadonlyArray<ToolDefinition>): BedrockTool[] => {
   const result: BedrockTool[] = []
   for (const tool of tools) {
     result.push(lowerToolSpec(tool))

packages/llm/test/provider/bedrock-converse.test.ts

@@ -464,9 +464,7 @@ describe("Bedrock Converse route", () => {
       const prepared = yield* LLMClient.prepare(
         LLM.request({
           model,
-          tools: [
-            { name: "lookup", description: "lookup", inputSchema: { type: "object", properties: {} }, cache },
-          ],
+          tools: [{ name: "lookup", description: "lookup", inputSchema: { type: "object", properties: {} }, cache }],
           messages: [
             LLM.user("What's the weather?"),
             LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: {} })]),

packages/llm/test/recorded-scenarios.ts

@@ -12,8 +12,7 @@ export const weatherToolName = "get_weather"
 // a fixed sentence — the cassette replays bit-for-bit, so the exact text matters
 // only when re-recording with `RECORD=true`.
 export const LARGE_CACHEABLE_SYSTEM = (() => {
-  const sentence =
-    "You are a concise, factual assistant. Answer precisely and avoid filler. Cite numbers when known. "
+  const sentence = "You are a concise, factual assistant. Answer precisely and avoid filler. Cite numbers when known. "
   // ~100 chars per sentence × 250 repeats ≈ 25,000 chars ≈ 5k+ tokens, safely
   // above every provider's threshold.
   return sentence.repeat(250)