fix(core): trigger compaction at 85% of the input window

fix(core): apply GPT verbosity defaults at request time (#51166 )
refactor(ai): one media client shape and route types erased over options (#51226 )
2026-09-25 18:17:35 +00:00 · 2026-09-24 17:36:58 -05:00 · 2026-09-24 17:13:39 -05:00 · 2026-09-25 03:15:45 +05:30 · 2026-09-24 16:34:46 -05:00 · 2026-09-24 16:27:30 -05:00
105 changed files with 2411 additions and 1005 deletions
@@ -10,7 +10,7 @@

 ## Conventions

-Per-type constructors live on the type, not as top-level re-exports. Use `Message.system(...)`, `Message.user(...)`, `Message.assistant(...)`, `Message.tool(...)`, `Message.media(...)`, `LanguageModel.make(...)`, `ToolDefinition.make(...)`, `ToolCallPart.make(...)`, `ToolResultPart.make(...)`, `ToolChoice.make(...)`, `ToolChoice.named(...)`, `SystemPart.make(...)`, and `GenerationOptions.make(...)` directly. The top-level `LLM` namespace is reserved for request-shaped call APIs: `LLM.request`, `LLM.generate`, `LLM.stream`, and `LLM.generateObject`. Use `LLMRequest.update(...)` when deriving canonical request data; do not add a duplicate `LLM.updateRequest(...)` path. Two ways to construct the same thing is one too many.
+Per-type constructors live on the type, not as top-level re-exports. Use `Message.system(...)`, `Message.user(...)`, `Message.assistant(...)`, `Message.tool(...)`, `Message.media(...)`, `LanguageModel.make(...)`, `ToolDefinition.make(...)`, `ToolCallPart.make(...)`, `ToolResultPart.make(...)`, `ToolChoice.make(...)`, `ToolChoice.named(...)`, `SystemPart.make(...)`, and `GenerationOptions.make(...)` directly. The top-level `LLM` namespace is reserved for request-shaped call APIs: `LLM.request`, `LLM.generate`, `LLM.stream`, and `LLM.generateObject`. `LLM.generate`/`LLM.stream` and Promise `ai.llm.generate`/`ai.llm.stream` accept ergonomic input or an `LLMRequest`; both paths use the same canonical request. Core still builds, logs, replays, and updates that durable `LLMRequest` boundary. Use `LLMRequest.update(...)` when deriving canonical request data; do not add a duplicate `LLM.updateRequest(...)` path.

 Modality namespaces mirror `LLM` exactly: `Image.request`, `Image.generate`, `Image.stream` (later `Video`, `Speech`, `Transcription`). Common request fields (`images`, `mask`, `n`, `size`, `aspectRatio`, `seed`, `format`) lower natively or fail with a typed `AIError`; provider-native controls always live under `providerOptions`, never under a modality-specific `options` key.

@@ -102,7 +102,7 @@ Media does not fit the SSE-frames-to-event-state-machine LLM route. `MediaRoute.

 `MediaProtocol.stream` is the incremental kind every speech route uses, with the same discipline as LLM protocols. `MediaRoute.stream` submits the caller's request as `MediaProtocol.Addressed<Request>` (`{ ...request, mode }`, `mode: "generate" | "stream"`), so one provider stays one protocol: `body.from`, the endpoint path, and `frames` read `request.mode` to pick the body, path, and framing. `frames(bytes, context)` returns frames — `Framing.sse`, `Framing.lines`, `Framing.document` (a single-document response shaped like a streamed record), or the raw `bytes` for chunked audio. `initial()` is fresh per-response parser state; `step` folds each frame into it and emits modality events; `finish(state, context)` runs once after the last frame with the request, body, and observed `http` (header-only usage lives there) and emits exactly one terminal event or fails with `route.incomplete()`. Keep parser state to real accumulators and derive anything the request or body determines in `finish`. `generate` runs the same stream and folds it with the modality's `collect`. Request-derived URL parameters go on the body's `query` (array values repeat the parameter), applied before route and caller `http.query`. Decode frames with `route.decodeFrame` and raise stream-time failures with `route.frameError` (the frame stays on `reason.body`); protocols never thread HTTP context, because the route fills `reason.http` on stream errors that lack it. Speech protocols share `protocols/utils/speech-stream.ts` for deltas, timestamps, voice ids, PCM and container descriptions, and the terminal asset.

-Transcription uses all three kinds (OpenAI and Gemini stream, Deepgram is inline, AssemblyAI is queued): every route carries its `kind` and `TranscriptionClient` dispatches on it. `ImageRoute` is the same union; both clients dispatch through `MediaRoute.dispatch` and models compose through `composeAnyRoute`, and fal queue protocols come from `protocols/utils/fal-queue.ts`. Bodies are `json`, `multipart`, or `binary` (a raw upload), and a queued protocol that must upload media before submitting implements `start.prepare` (`MediaProtocol.Prepare`; AssemblyAI `/v2/upload`).
+Every modality route is the inline | stream | queued union (transcription uses all three: OpenAI and Gemini stream, Deepgram is inline, AssemblyAI is queued), every client is `MediaClient.make(Service, { modality, responseEvents })` (`src/media-client.ts`), which dispatches on the route's `kind`, and every model composes through `composeRoute`. fal queue protocols come from `protocols/utils/fal-queue.ts`, bodies are `json`, `multipart`, or `binary` (a raw upload), and a queued protocol that must upload media before submitting implements `start.prepare` (`MediaProtocol.Prepare`; AssemblyAI `/v2/upload`).

 ### URL Construction

@@ -275,6 +275,7 @@ Use this order for every protocol module:
 ### Rules

 - Keep protocol files focused on the protocol. Move provider-specific projection, signing, media normalization, or other bulky transformations into `src/protocols/utils/*`.
+- Send `tool.inputSchema` as given. `prepareRequest` applies the tool schema rules (`ToolSchemaProjection.tools`) once per request, including tools in namespaces. A protocol whose API needs a model family's rules for every model declares `sanitizer` instead of transforming schemas itself.
 - Use `Effect.fn("Provider.fromRequest")` for request body construction entrypoints. Use `Effect.fn(...)` for event handlers that yield effects; keep purely synchronous handlers as plain functions returning a `StepResult` that the dispatcher lifts via `Effect.succeed(...)`.
 - Parser state owns terminal information. The state machine records finish reason, usage, and pending tool calls; emit one terminal `finish` event (or `provider-error`) for each completed response. If a provider splits reason and usage across events, merge them in parser state before flushing.
 - Emit exactly one terminal `finish` event for a completed response, normally after a matching `step-finish`. Use `stream.terminal` to stop reading when the provider has a completion sentinel; use `stream.onHalt` when the final event must be flushed after the framed stream ends.
@@ -9,15 +9,13 @@ import { OpenAI } from "@opencode/ai/providers"

 const openai = OpenAI.configure({ apiKey: process.env.OPENAI_API_KEY })

-const request = LLM.request({
-  model: openai.responses("gpt-4o-mini"), // `.chat(...)` selects the Chat Completions API instead
-  system: "You are concise.",
-  prompt: "Say hello in one short sentence.",
-  generation: { maxTokens: 40 },
-})
-
 const program = Effect.gen(function* () {
-  const response = yield* LLM.generate(request)
+  const response = yield* LLM.generate({
+    model: openai.responses("gpt-4o-mini"), // `.chat(...)` selects the Chat Completions API instead
+    system: "You are concise.",
+    prompt: "Say hello in one short sentence.",
+    generation: { maxTokens: 40 },
+  })
  console.log(response.text)
 })

@@ -25,7 +23,8 @@ const program = Effect.gen(function* () {
 await Effect.runPromise(program.pipe(Effect.provide(AIClient.layer)))
 ```

-Run `LLM.stream(request)` instead of `generate` when you want incremental `LLMEvent`s. The event stream is provider-neutral — same shape across OpenAI Chat, OpenAI Responses,
+Run `LLM.stream(...)` instead of `generate` when you want incremental `LLMEvent`s. Both accept input or a prebuilt
+`LLM.request(...)`. The event stream is provider-neutral — same shape across OpenAI Chat, OpenAI Responses,
 Anthropic Messages, Gemini, Bedrock Converse, and any OpenAI-compatible deployment.

 The same configured facade names image, video, speech, and transcription models. `Image.generate` resolves the
@@ -72,10 +71,11 @@ helpers; `ai.file` and `ai.write` load `node:fs/promises` on first use, so no Ef
 import { AI } from "@opencode/ai/promise"

 const ai = AI.make()
-const text = await ai.llm.generate({ model: openai.responses("gpt-4o-mini"), prompt: "Say hello." })
+const input = { model: openai.responses("gpt-4o-mini"), prompt: "Say hello." }
+const text = await ai.llm.generate(input)
 const generated = await ai.image.generate({ model: openai.image("gpt-image-2"), prompt: "A lighthouse" })
 await ai.write(generated.image, "./lighthouse.png") // also ai.file(path), ai.bytes(asset), ai.base64(asset), ai.materialize(asset)
-for await (const event of ai.llm.stream({ model: openai.responses("gpt-4o-mini"), prompt: "Stream hello." })) {
+for await (const event of ai.llm.stream(ai.llm.request(input))) {
  // LLMEvent
 }
 await ai.dispose()
@@ -936,7 +936,7 @@ const transcript = await generation.await({ poll: { interval: 3_000 } })
 ## Public API

 - **`LLM.request({...})`** — build a provider-neutral `LLMRequest`. Accepts ergonomic inputs (`system: string`, `prompt: string`) that normalize into the canonical Schema classes.
- **`LLM.generate` / `LLM.stream`** — re-exported from `LLMClient` for one-import use.
+- **`LLM.generate` / `LLM.stream`** — run direct input or an `LLMRequest` through `LLMClient` for one-import use.
 - **`Message.user(...)` / `Message.assistant(...)` / `Message.tool(...)`** — message constructors from the canonical schema model.
 - **`LanguageModel.make(...)` / `ToolCallPart.make(...)` / `ToolResultPart.make(...)` / `ToolDefinition.make(...)`** — model and tool-related constructors from the canonical schema model.
 - **`LLMEvent.is.*`** — typed guards (`is.textDelta`, `is.toolCall`, `is.finish`, …) for filtering streams.
@@ -140,7 +140,7 @@ portability matrix.

 Editing is not a separate function; `images`/`mask` on the request select the edit path in the route (OpenAI `/images/edits`, Gemini multimodal parts, xAI `/images/edits`). Routes that cannot honor `mask` fail with `Unsupported`.

-`ImageRoute` is the inline | stream | queued union, dispatched on `route.kind`. `Image.stream` on a streaming route emits `image-partial` previews before each `image`; on a queued route it emits `generation-queued` / `generation-progress` observations, then the result's `image` and `finish` events.
+`ImageRoute` is the inline | stream | queued union, dispatched on `route.kind`, like every modality route. `Image.stream` on a streaming route emits `image-partial` previews before each `image`; on a queued route it emits `generation-queued` / `generation-progress` observations, then the result's `image` and `finish` events.

 #### Video

@@ -215,7 +215,7 @@ const request = Speech.request({
 })

 const response = yield* Speech.generate(request)   // SpeechResponse: audio: Media.Asset, timestamps?, usage?, providerMetadata?
-yield* Speech.stream(request)                      // Stream<SpeechEvent>: audio-delta { chunk } | timestamps { items } | finish { audio, usage? }
+yield* Speech.stream(request)                      // Stream<SpeechEvent>: generation-queued | generation-progress | audio-delta { chunk } | timestamps { items } | finish { audio, usage? }
 ```

 Execution is `MediaProtocol.stream` for every provider: one request whose body is framed and folded by a `step`
@@ -389,7 +389,8 @@ for await (const event of generation.events({ poll: { interval: 10_000 } })) {
 const video = await generation.await({ poll: { interval: 10_000 }, signal })
 const resumed = await ai.video.resume(model, JSON.parse(saved)) // persist provider + model ID with the token

-const text = await ai.llm.generate({ model, prompt })
+const request = ai.llm.request({ model, prompt })
+const text = await ai.llm.generate(request)
 for await (const event of ai.llm.stream(request)) { … }

 await ai.dispose()
@@ -419,7 +420,7 @@ Existing facades gain per-modality selectors; the modality routes each facade pr

 New facades follow the existing one-file-per-provider rule. The facade selector is the public path for media models; modality-specific package entrypoints (for example `@opencode/ai/providers/openai/images`) are deferred until Core has a modality-aware model resolver.

-`ImageModel<Options>` gives typed `providerOptions` per model; `VideoModel`, `SpeechModel`, and `TranscriptionModel` follow the same generic. They share an internal `MediaModel` base class (ids, route, `http` overlays) that is not part of the public exports; `Generation` and the promise client work with the concrete modality models.
+`ImageModel<Options>` gives typed `providerOptions` per model; `VideoModel`, `SpeechModel`, and `TranscriptionModel` follow the same generic. As with `LanguageModel`, the route type does not carry `Options`, so `ImageModel<OpenAIImageOptions>` is an `ImageModel` and client methods take plain `ImageRequestFor`. They share an internal `MediaModel` base class (ids, route, `http` overlays) that is not part of the public exports; `Generation` and the promise client work with the concrete modality models.

 ### Routes and protocols

@@ -1,99 +1,30 @@
-import { Context, Effect, Layer, Stream } from "effect"
-import type { AwaitOptions, Generation } from "./generation.js"
-import { RequestExecutor } from "./route/executor.js"
-import { MediaRoute } from "./route/media.js"
-import type { AIError } from "./schema/index.js"
+import { Context } from "effect"
+import { MediaClient } from "./media-client.js"
 import {
-  responseEvents,
+  ImageOutputEvent,
+  ImageFinishEvent,
  type ImageEvent,
-  type ImageModel,
-  type ImageOptions,
  type ImageRequestFor,
  type ImageResponse,
 } from "./image.js"

-export interface Interface {
-  readonly generate: <Options extends ImageOptions>(
-    request: ImageRequestFor<Options>,
-    options?: AwaitOptions,
-  ) => Effect.Effect<ImageResponse, AIError>
-  readonly stream: <Options extends ImageOptions>(
-    request: ImageRequestFor<Options>,
-    options?: AwaitOptions,
-  ) => Stream.Stream<ImageEvent, AIError>
-  readonly start: <Options extends ImageOptions>(
-    request: ImageRequestFor<Options>,
-  ) => Effect.Effect<Generation<ImageResponse>, AIError>
-  readonly resume: <Options extends ImageOptions>(
-    model: ImageModel<Options>,
-    token: unknown,
-  ) => Effect.Effect<Generation<ImageResponse>, AIError>
-}
+export type Interface = MediaClient.Interface<ImageRequestFor, ImageEvent, ImageResponse>

 export class ImageClientService extends Context.Service<ImageClientService, Interface>()("@opencode/ImageClient") {}
 export const Service = ImageClientService
 export type Service = ImageClientService

-export const generate = <Options extends ImageOptions>(
-  request: ImageRequestFor<Options>,
-  options?: AwaitOptions,
-): Effect.Effect<ImageResponse, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.generate(request, options)
-  })
-
-export const stream = <Options extends ImageOptions>(
-  request: ImageRequestFor<Options>,
-  options?: AwaitOptions,
-): Stream.Stream<ImageEvent, AIError, Service> =>
-  Stream.unwrap(
-    Effect.gen(function* () {
-      const client = yield* Service
-      return client.stream(request, options)
-    }),
-  )
-
-export const start = <Options extends ImageOptions>(
-  request: ImageRequestFor<Options>,
-): Effect.Effect<Generation<ImageResponse>, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.start(request)
-  })
-
-export const resume = <Options extends ImageOptions>(
-  model: ImageModel<Options>,
-  token: unknown,
-): Effect.Effect<Generation<ImageResponse>, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.resume(model, token)
-  })
-
-export const layer: Layer.Layer<Service, never, RequestExecutor.Service> = Layer.effect(
-  Service,
-  Effect.gen(function* () {
-    const executor = yield* RequestExecutor.Service
-    const dispatch = MediaRoute.dispatch<ImageEvent, ImageResponse>({
-      modality: "image",
-      execute: executor.execute,
-      responseEvents,
-    })
-    return Service.of({
-      start: (request) => dispatch.start(request.model.route, request),
-      resume: (model, token) => dispatch.resume(model.route, model, token),
-      generate: (request, options) => dispatch.generate(request.model.route, request, options),
-      stream: (request, options) => dispatch.stream(request.model.route, request, options),
-    })
-  }),
-)
-
 export const ImageClient = {
  Service,
-  layer,
-  generate,
-  stream,
-  start,
-  resume,
+  ...MediaClient.make(Service, {
+    modality: "image",
+    responseEvents: (response: ImageResponse) => [
+      ...response.images.map((image, index) => ImageOutputEvent.make({ index, image })),
+      ImageFinishEvent.make({
+        usage: response.usage,
+        notices: response.notices,
+        providerMetadata: response.providerMetadata,
+      }),
+    ],
+  }),
 } as const
@@ -1,9 +1,8 @@
 import { Effect, Schema, Stream } from "effect"
 import { Generation, ProgressEvent, QueuedEvent, type AwaitOptions } from "./generation.js"
 import { Media } from "./media.js"
-import { MediaModel, composeAnyRoute, tryRequest } from "./media-model.js"
+import { MediaModel, composeRoute, tryRequest } from "./media-model.js"
 import { MediaRoute } from "./route/media.js"
-import type { MediaProtocol } from "./route/media-protocol.js"
 import { AIError, HttpOptions, MediaUsage, ProviderMetadata, type OpenString } from "./schema/index.js"
 import { ImageClient, Service } from "./image-client.js"

@@ -11,75 +10,39 @@ import { ImageClient, Service } from "./image-client.js"
 // Model
 // ---------------------------------------------------------------------------

-export type ImageOptions = Record<string, unknown>
+export type ImageOptions = MediaModel.Options

-export type ImageRoute<Options extends ImageOptions = ImageOptions> = MediaRoute.AnyRoute<
-  ImageRequestFor<Options>,
-  ImageEvent,
-  ImageResponse
->
+export type ImageRoute = MediaRoute.AnyRoute<ImageRequestFor, ImageEvent, ImageResponse>

-export class ImageModel<Options extends ImageOptions = ImageOptions> extends MediaModel<ImageRoute<Options>, Options> {
+export class ImageModel<Options extends ImageOptions = ImageOptions> extends MediaModel<ImageRoute, Options> {
  declare protected readonly _ImageModel: void

-  static make<Options extends ImageOptions = ImageOptions>(input: MediaModel.Input<ImageRoute<Options>>) {
-    return new ImageModel<Options>(input)
-  }
-
  /** The number of type arguments selects the kind: `<Options>`, `<Options, Frame, State>`, or `<Options, Token>`. */
  static fromRoute<Options extends ImageOptions>(
-    route: ImageModel.InlineRouteInput<Options>,
+    route: MediaModel.InlineRouteInput<ImageRequestFor<Options>, ImageResponse>,
    input: MediaRoute.ModelInput,
  ): ImageModel<Options>
  static fromRoute<Options extends ImageOptions, Frame, State>(
-    route: ImageModel.StreamRouteInput<Options, Frame, State>,
+    route: MediaModel.StreamRouteInput<ImageRequestFor<Options>, ImageEvent, Frame, State>,
    input: MediaRoute.ModelInput,
  ): ImageModel<Options>
  static fromRoute<Options extends ImageOptions, Token>(
-    route: ImageModel.QueuedRouteInput<Options, Token>,
+    route: MediaModel.QueuedRouteInput<ImageRequestFor<Options>, ImageResponse, Token>,
    input: MediaRoute.ModelInput,
  ): ImageModel<Options>
  static fromRoute<Options extends ImageOptions, Frame, State, Token>(
-    route: ImageModel.RouteInput<Options, Frame, State, Token>,
+    route: MediaModel.AnyRouteInput<ImageRequestFor<Options>, ImageEvent, ImageResponse, Frame, State, Token>,
    input: MediaRoute.ModelInput,
  ) {
    return new ImageModel<Options>({
      id: input.id,
      provider: route.protocol.provider,
      http: input.http,
-      route: composeAnyRoute(route, input, collectResponse),
+      route: composeRoute(route, input, collectResponse) as ImageRoute,
    })
  }
 }

-export namespace ImageModel {
-  export type InlineRouteInput<Options extends ImageOptions = ImageOptions> = MediaModel.RouteInput<
-    ImageRequestFor<Options>,
-    MediaProtocol.Inline<ImageRequestFor<Options>, ImageResponse>
-  >
-
-  export type StreamRouteInput<
-    Options extends ImageOptions = ImageOptions,
-    Frame = unknown,
-    State = unknown,
-  > = MediaModel.RouteInput<
-    MediaProtocol.Addressed<ImageRequestFor<Options>>,
-    MediaProtocol.Streamed<ImageRequestFor<Options>, ImageEvent, Frame, State>
-  >
-
-  export type QueuedRouteInput<Options extends ImageOptions = ImageOptions, Token = unknown> = MediaModel.RouteInput<
-    ImageRequestFor<Options>,
-    MediaProtocol.Queued<ImageRequestFor<Options>, ImageResponse, Token>
-  >
-
-  export type RouteInput<
-    Options extends ImageOptions = ImageOptions,
-    Frame = unknown,
-    State = unknown,
-    Token = unknown,
-  > = MediaModel.AnyRouteInput<ImageRequestFor<Options>, ImageEvent, ImageResponse, Frame, State, Token>
-}
-
 export const ImageModelSchema = Schema.declare((value): value is ImageModel => value instanceof ImageModel, {
  expected: "Image.Model",
 })
@@ -190,15 +153,6 @@ export const ImageEvent = Object.assign(imageEventTagged, {
 })
 export type ImageEvent = Schema.Schema.Type<typeof imageEventTagged>

-export const responseEvents = (response: ImageResponse): ReadonlyArray<ImageEvent> => [
-  ...response.images.map((image, index) => ImageOutputEvent.make({ index, image })),
-  ImageFinishEvent.make({
-    usage: response.usage,
-    notices: response.notices,
-    providerMetadata: response.providerMetadata,
-  }),
-]
-
 const collectResponse = (events: ReadonlyArray<ImageEvent>): Effect.Effect<ImageResponse> => {
  const finish = events.find(ImageEvent.is.finish)
  // Every image protocol's `finish` emits the terminal event or fails, so a completed stream always has one.
@@ -232,36 +186,31 @@ export function request(input: ImageRequest | ImageRequestInput) {
 const requestEffect = (input: ImageRequest | ImageRequestInput) => tryRequest(() => request(input))

 export function generate<const Model extends ImageModel>(
-  input: ImageRequestInput<Model>,
+  input: ImageRequest | ImageRequestInput<Model>,
  options?: AwaitOptions,
 ): Effect.Effect<ImageResponse, AIError, Service>
-export function generate(input: ImageRequest, options?: AwaitOptions): Effect.Effect<ImageResponse, AIError, Service>
 export function generate(input: ImageRequest | ImageRequestInput, options?: AwaitOptions) {
  return requestEffect(input).pipe(Effect.flatMap((request) => ImageClient.generate(request, options)))
 }

 export function stream<const Model extends ImageModel>(
-  input: ImageRequestInput<Model>,
+  input: ImageRequest | ImageRequestInput<Model>,
  options?: AwaitOptions,
 ): Stream.Stream<ImageEvent, AIError, Service>
-export function stream(input: ImageRequest, options?: AwaitOptions): Stream.Stream<ImageEvent, AIError, Service>
 export function stream(input: ImageRequest | ImageRequestInput, options?: AwaitOptions) {
  return Stream.unwrap(requestEffect(input).pipe(Effect.map((request) => ImageClient.stream(request, options))))
 }

 /** Inline and streaming routes fail with `UnsupportedOperation`. */
 export function start<const Model extends ImageModel>(
-  input: ImageRequestInput<Model>,
+  input: ImageRequest | ImageRequestInput<Model>,
 ): Effect.Effect<Generation<ImageResponse>, AIError, Service>
-export function start(input: ImageRequest): Effect.Effect<Generation<ImageResponse>, AIError, Service>
 export function start(input: ImageRequest | ImageRequestInput) {
  return requestEffect(input).pipe(Effect.flatMap((request) => ImageClient.start(request)))
 }

-export const resume = <Options extends ImageOptions>(
-  model: ImageModel<Options>,
-  token: unknown,
-): Effect.Effect<Generation<ImageResponse>, AIError, Service> => ImageClient.resume(model, token)
+export const resume = (model: ImageModel, token: unknown): Effect.Effect<Generation<ImageResponse>, AIError, Service> =>
+  ImageClient.resume(model, token)

 export const Image = {
  request,
@@ -1,5 +1,6 @@
-import { Effect, JsonSchema, Schema } from "effect"
-import { LLMClient, Service } from "./route/client.js"
+import { Effect, JsonSchema, Schema, Stream } from "effect"
+import { tryRequest } from "./media-model.js"
+import { LLMClient, Service, type StreamOptions } from "./route/client.js"
 import {
  GenerationOptions,
  HttpOptions,
@@ -35,9 +36,26 @@ export type RequestInput<SelectedLanguageModel extends LanguageModel = LanguageM
  readonly http?: HttpOptions.Input
 }

-export const generate = LLMClient.generate
+export function generate<const Model extends LanguageModel>(
+  input: RequestInput<Model>,
+  options?: StreamOptions,
+): Effect.Effect<LLMResponse, AIError, Service>
+export function generate(input: LLMRequest, options?: StreamOptions): Effect.Effect<LLMResponse, AIError, Service>
+export function generate(input: RequestInput | LLMRequest, options?: StreamOptions) {
+  return requestEffect(input).pipe(Effect.flatMap((request) => LLMClient.generate(request, options)))
+}

-export const stream = LLMClient.stream
+export function stream<const Model extends LanguageModel>(
+  input: RequestInput<Model>,
+  options?: StreamOptions,
+): Stream.Stream<LLMEvent, AIError, Service>
+export function stream(input: LLMRequest, options?: StreamOptions): Stream.Stream<LLMEvent, AIError, Service>
+export function stream(input: RequestInput | LLMRequest, options?: StreamOptions) {
+  return Stream.unwrap(requestEffect(input).pipe(Effect.map((request) => LLMClient.stream(request, options))))
+}
+
+const requestEffect = (input: RequestInput | LLMRequest) =>
+  input instanceof LLMRequest ? Effect.succeed(input) : tryRequest(() => request(input))

 export const request = <const SelectedLanguageModel extends LanguageModel>(
  input: RequestInput<SelectedLanguageModel>,
@@ -0,0 +1,77 @@
+import { type Context, Effect, Layer, Stream } from "effect"
+import { resultEvents, type AwaitOptions, type Generation, type Observation } from "./generation.js"
+import { RequestExecutor } from "./route/executor.js"
+import type { MediaRoute } from "./route/media.js"
+import { AIError, UnsupportedOperationError } from "./schema/index.js"
+
+/** A media request whose model carries the route that executes it. */
+export interface RoutedRequest<Self extends MediaRoute.MediaRequest, Event, Response> extends MediaRoute.MediaRequest {
+  readonly model: MediaRoute.MediaRequest["model"] & { readonly route: MediaRoute.AnyRoute<Self, Event, Response> }
+}
+
+/** `start` and `resume` fail with `UnsupportedOperation` on inline and stream routes. */
+export interface Interface<Req extends RoutedRequest<Req, Event, Response>, Event, Response> {
+  readonly generate: (request: Req, options?: AwaitOptions) => Effect.Effect<Response, AIError>
+  readonly stream: (request: Req, options?: AwaitOptions) => Stream.Stream<Event | Observation, AIError>
+  readonly start: (request: Req) => Effect.Effect<Generation<Response>, AIError>
+  readonly resume: (model: Req["model"], token: unknown) => Effect.Effect<Generation<Response>, AIError>
+}
+
+/** One modality's layer and service accessors, dispatching each request on its route's `kind`. */
+export const make = <Self, Req extends RoutedRequest<Req, Event, Response>, Event, Response>(
+  service: Context.Service<Self, Interface<Req, Event, Response>>,
+  input: {
+    readonly modality: string
+    /** A completed response expanded into the streaming event shape. */
+    readonly responseEvents: (response: Response) => ReadonlyArray<Event>
+  },
+) => ({
+  layer: Layer.effect(
+    service,
+    Effect.gen(function* () {
+      const executor = yield* RequestExecutor.Service
+      const notQueued = (route: MediaRoute.AnyRoute<Req, Event, Response>, operation: string) =>
+        new AIError({
+          reason: new UnsupportedOperationError({
+            operation: `${input.modality}.${operation}`,
+            provider: route.provider,
+            route: route.id,
+            message: `${route.provider}/${route.id} is not a queued route; use generate or stream`,
+          }),
+        })
+      const start = (request: Req) => {
+        const route = request.model.route
+        if (route.kind !== "queued") return Effect.fail(notQueued(route, "start"))
+        return route.start(request, executor.execute)
+      }
+      return service.of({
+        start,
+        resume: (model, token) => {
+          if (model.route.kind !== "queued") return Effect.fail(notQueued(model.route, "resume"))
+          return model.route.resume(model, token, executor.execute)
+        },
+        generate: (request, options) => {
+          const route = request.model.route
+          if (route.kind !== "queued") return route.generate(request, executor.execute)
+          return start(request).pipe(Effect.flatMap((generation) => generation.await(options)))
+        },
+        stream: (request, options) => {
+          const route = request.model.route
+          if (route.kind === "stream") return route.stream(request, executor.execute)
+          if (route.kind === "queued")
+            return Stream.unwrap(
+              start(request).pipe(Effect.map((generation) => resultEvents(generation, input.responseEvents, options))),
+            )
+          return Stream.fromIterableEffect(Effect.map(route.generate(request, executor.execute), input.responseEvents))
+        },
+      })
+    }),
+  ),
+  generate: (request: Req, options?: AwaitOptions) => service.use((client) => client.generate(request, options)),
+  stream: (request: Req, options?: AwaitOptions) =>
+    Stream.unwrap(service.useSync((client) => client.stream(request, options))),
+  start: (request: Req) => service.use((client) => client.start(request)),
+  resume: (model: Req["model"], token: unknown) => service.use((client) => client.resume(model, token)),
+})
+
+export * as MediaClient from "./media-client.js"
@@ -6,11 +6,13 @@ import { AIError, HttpOptions, InvalidRequestError, ModelID, ProviderID } from "

 /**
 * What every media model carries: ids, the configured route, and deployment `http` overlays. Modality classes
- * (`ImageModel`, `VideoModel`, `SpeechModel`) extend it with their route type and a nominal marker so one cannot stand
- * in for the other in requests.
+ * (`ImageModel`, `VideoModel`, `SpeechModel`, `TranscriptionModel`) extend it with their route type and a nominal
+ * marker so one cannot stand in for the other in requests.
 */
 export class MediaModel<Route, Options> {
-  declare protected readonly _Options: (options: Options) => Options
+  // As with `LanguageModel`, the route type is erased over `Options`; `fromRoute` and the constructor trust that the
+  // route accepts every request this model's `Options` admit.
+  declare protected readonly _Options: Options
  readonly id: ModelID
  readonly provider: ProviderID
  readonly route: Route
@@ -25,6 +27,8 @@ export class MediaModel<Route, Options> {
 }

 export namespace MediaModel {
+  export type Options = Record<string, unknown>
+
  export interface Input<Route> {
    readonly id: string | ModelID
    readonly provider: string | ProviderID
@@ -41,48 +45,56 @@ export namespace MediaModel {
    readonly headers?: Record<string, string>
  }

+  export type InlineRouteInput<Request extends MediaRoute.MediaRequest, Response> = RouteInput<
+    Request,
+    MediaProtocol.Inline<Request, Response>
+  >
+
+  export type StreamRouteInput<Request extends MediaRoute.MediaRequest, Event, Frame, State> = RouteInput<
+    MediaProtocol.Addressed<Request>,
+    MediaProtocol.Streamed<Request, Event, Frame, State>
+  >
+
+  export type QueuedRouteInput<Request extends MediaRoute.MediaRequest, Response, Token> = RouteInput<
+    Request,
+    MediaProtocol.Queued<Request, Response, Token>
+  >
+
  export type AnyRouteInput<Request extends MediaRoute.MediaRequest, Event, Response, Frame, State, Token> =
-    | RouteInput<Request, MediaProtocol.Inline<Request, Response>>
-    | RouteInput<MediaProtocol.Addressed<Request>, MediaProtocol.Streamed<Request, Event, Frame, State>>
-    | RouteInput<Request, MediaProtocol.Queued<Request, Response, Token>>
+    | InlineRouteInput<Request, Response>
+    | StreamRouteInput<Request, Event, Frame, State>
+    | QueuedRouteInput<Request, Response, Token>
 }

 /** Compose a protocol route input with one deployment through `MediaRoute.inline`, `queued`, or `stream`. */
-export const composeRoute = <Request extends MediaRoute.MediaRequest, Protocol, Route>(
-  compose: (input: MediaRoute.Composition<Request> & { readonly protocol: Protocol }) => Route,
-  route: MediaModel.RouteInput<Request, Protocol>,
-  input: MediaRoute.ModelInput,
-): Route =>
-  compose({
-    protocol: route.protocol,
-    endpoint: Endpoint.path(route.path, { baseURL: input.baseURL ?? route.baseURL }),
-    auth: input.auth,
-    headers:
-      route.headers === undefined && input.headers === undefined ? undefined : { ...route.headers, ...input.headers },
-  })
-
-export const composeAnyRoute = <Request extends MediaRoute.MediaRequest, Event, Response, Frame, State, Token>(
+export const composeRoute = <Request extends MediaRoute.MediaRequest, Event, Response, Frame, State, Token>(
  route: MediaModel.AnyRouteInput<Request, Event, Response, Frame, State, Token>,
  input: MediaRoute.ModelInput,
  collect: (events: ReadonlyArray<Event>) => Effect.Effect<Response, AIError>,
 ): MediaRoute.AnyRoute<Request, Event, Response> => {
-  if (isStreamInput(route))
-    return composeRoute((composition) => MediaRoute.stream({ ...composition, collect }), route, input)
-  if (isQueuedInput(route)) return composeRoute(MediaRoute.queued, route, input)
-  return composeRoute(MediaRoute.inline, route, input)
+  if (isStreamInput(route)) return MediaRoute.stream({ ...composition(route, input), collect })
+  if (isQueuedInput(route)) return MediaRoute.queued(composition(route, input))
+  return MediaRoute.inline(composition(route, input))
 }

+const composition = <Request extends MediaRoute.MediaRequest, Protocol>(
+  route: MediaModel.RouteInput<Request, Protocol>,
+  input: MediaRoute.ModelInput,
+): MediaRoute.Composition<Request> & { readonly protocol: Protocol } => ({
+  protocol: route.protocol,
+  endpoint: Endpoint.path(route.path, { baseURL: input.baseURL ?? route.baseURL }),
+  auth: input.auth,
+  headers:
+    route.headers === undefined && input.headers === undefined ? undefined : { ...route.headers, ...input.headers },
+})
+
 const isStreamInput = <Request extends MediaRoute.MediaRequest, Event, Response, Frame, State, Token>(
  route: MediaModel.AnyRouteInput<Request, Event, Response, Frame, State, Token>,
-): route is MediaModel.RouteInput<
-  MediaProtocol.Addressed<Request>,
-  MediaProtocol.Streamed<Request, Event, Frame, State>
-> => route.protocol.kind === "stream"
+): route is MediaModel.StreamRouteInput<Request, Event, Frame, State> => route.protocol.kind === "stream"

 const isQueuedInput = <Request extends MediaRoute.MediaRequest, Event, Response, Frame, State, Token>(
  route: MediaModel.AnyRouteInput<Request, Event, Response, Frame, State, Token>,
-): route is MediaModel.RouteInput<Request, MediaProtocol.Queued<Request, Response, Token>> =>
-  route.protocol.kind === "queued"
+): route is MediaModel.QueuedRouteInput<Request, Response, Token> => route.protocol.kind === "queued"

 /** Lift a synchronous Schema-class constructor into a typed `InvalidRequest` failure. */
 export const tryRequest = <A>(make: () => A): Effect.Effect<A, AIError> =>
@@ -1,23 +1,22 @@
 import { Effect, Layer, ManagedRuntime, Stream } from "effect"
 import { AIClient } from "./ai-client.js"
 import type { AwaitOptions, Event, Generation, Snapshot } from "./generation.js"
-import { Image, ImageModel, ImageRequest, type ImageOptions, type ImageRequestInput } from "./image.js"
+import { Image, type ImageModel, type ImageRequest, type ImageRequestInput } from "./image.js"
 import { LLM } from "./index.js"
 import { Media } from "./media.js"
 import { tryRequest } from "./media-model.js"
 import { RequestExecutor } from "./route/executor.js"
 import { AIError, InvalidRequestError, LanguageModel, LLMRequest } from "./schema/index.js"
 import type { RequestInput } from "./llm.js"
-import { Speech, SpeechModel, SpeechRequest, type SpeechRequestInput } from "./speech.js"
+import { Speech, type SpeechModel, type SpeechRequest, type SpeechRequestInput } from "./speech.js"
 import {
  Transcription,
-  TranscriptionModel,
-  TranscriptionRequest,
-  type TranscriptionOptions,
+  type TranscriptionModel,
+  type TranscriptionRequest,
  type TranscriptionRequestInput,
 } from "./transcription.js"
 import { fileMediaType } from "./utils/media-type.js"
-import { Video, VideoModel, VideoRequest, type VideoOptions, type VideoRequestInput } from "./video.js"
+import { Video, type VideoModel, type VideoRequest, type VideoRequestInput } from "./video.js"

 /**
 * Promise-first entrypoint for scripts and non-Effect callers. One `ManagedRuntime` hosts the LLM, image, video, speech,
@@ -93,17 +92,8 @@ export const make = (options: Options = {}) => {
    cancel: (options) => run(generation.cancel(), options),
  })

-  // The typed `generate`/`stream` overloads take a concrete input or a request, not the union; normalize once here.
  const llmRequest = (input: RequestInput | LLMRequest) =>
    input instanceof LLMRequest ? Effect.succeed(input) : tryRequest(() => LLM.request(input))
-  const imageRequest = (input: ImageRequestInput | ImageRequest) =>
-    input instanceof ImageRequest ? input : Image.request(input)
-  const videoRequest = (input: VideoRequestInput | VideoRequest) =>
-    input instanceof VideoRequest ? input : Video.request(input)
-  const speechRequest = (input: SpeechRequestInput | SpeechRequest) =>
-    input instanceof SpeechRequest ? input : Speech.request(input)
-  const transcriptionRequest = (input: TranscriptionRequestInput | TranscriptionRequest) =>
-    input instanceof TranscriptionRequest ? input : Transcription.request(input)

  return {
    run,
@@ -155,61 +145,58 @@ export const make = (options: Options = {}) => {
      generate: <const Model extends ImageModel>(
        input: ImageRequestInput<Model> | ImageRequest,
        options?: AwaitOptions & RunOptions,
-      ) => run(Image.generate(imageRequest(input), { poll: options?.poll }), options),
+      ) => run(Image.generate(input, { poll: options?.poll }), options),
      stream: <const Model extends ImageModel>(
        input: ImageRequestInput<Model> | ImageRequest,
        options?: AwaitOptions & RunOptions,
-      ) => iterate(Image.stream(imageRequest(input), { poll: options?.poll }), options),
+      ) => iterate(Image.stream(input, { poll: options?.poll }), options),
      start: <const Model extends ImageModel>(input: ImageRequestInput<Model> | ImageRequest, options?: RunOptions) =>
-        run(Image.start(imageRequest(input)), options).then(handle),
-      resume: <Options extends ImageOptions>(model: ImageModel<Options>, token: unknown, options?: RunOptions) =>
+        run(Image.start(input), options).then(handle),
+      resume: (model: ImageModel, token: unknown, options?: RunOptions) =>
        run(Image.resume(model, token), options).then(handle),
    },
    video: {
      request: Video.request,
      start: <const Model extends VideoModel>(input: VideoRequestInput<Model> | VideoRequest, options?: RunOptions) =>
-        run(Video.start(videoRequest(input)), options).then(handle),
+        run(Video.start(input), options).then(handle),
      generate: <const Model extends VideoModel>(
        input: VideoRequestInput<Model> | VideoRequest,
        options?: AwaitOptions & RunOptions,
-      ) => run(Video.generate(videoRequest(input), { poll: options?.poll }), options),
-      resume: <Options extends VideoOptions>(model: VideoModel<Options>, token: unknown, options?: RunOptions) =>
+      ) => run(Video.generate(input, { poll: options?.poll }), options),
+      resume: (model: VideoModel, token: unknown, options?: RunOptions) =>
        run(Video.resume(model, token), options).then(handle),
      stream: <const Model extends VideoModel>(
        input: VideoRequestInput<Model> | VideoRequest,
        options?: AwaitOptions & RunOptions,
-      ) => iterate(Video.stream(videoRequest(input), { poll: options?.poll }), options),
+      ) => iterate(Video.stream(input, { poll: options?.poll }), options),
    },
    speech: {
      request: Speech.request,
      generate: <const Model extends SpeechModel>(
        input: SpeechRequestInput<Model> | SpeechRequest,
        options?: RunOptions,
-      ) => run(Speech.generate(speechRequest(input)), options),
+      ) => run(Speech.generate(input), options),
      stream: <const Model extends SpeechModel>(
        input: SpeechRequestInput<Model> | SpeechRequest,
        options?: RunOptions,
-      ) => iterate(Speech.stream(speechRequest(input)), options),
+      ) => iterate(Speech.stream(input), options),
    },
    transcription: {
      request: Transcription.request,
      generate: <const Model extends TranscriptionModel>(
        input: TranscriptionRequestInput<Model> | TranscriptionRequest,
        options?: AwaitOptions & RunOptions,
-      ) => run(Transcription.generate(transcriptionRequest(input), { poll: options?.poll }), options),
+      ) => run(Transcription.generate(input, { poll: options?.poll }), options),
      stream: <const Model extends TranscriptionModel>(
        input: TranscriptionRequestInput<Model> | TranscriptionRequest,
        options?: AwaitOptions & RunOptions,
-      ) => iterate(Transcription.stream(transcriptionRequest(input), { poll: options?.poll }), options),
+      ) => iterate(Transcription.stream(input, { poll: options?.poll }), options),
      start: <const Model extends TranscriptionModel>(
        input: TranscriptionRequestInput<Model> | TranscriptionRequest,
        options?: RunOptions,
-      ) => run(Transcription.start(transcriptionRequest(input)), options).then(handle),
-      resume: <Options extends TranscriptionOptions>(
-        model: TranscriptionModel<Options>,
-        token: unknown,
-        options?: RunOptions,
-      ) => run(Transcription.resume(model, token), options).then(handle),
+      ) => run(Transcription.start(input), options).then(handle),
+      resume: (model: TranscriptionModel, token: unknown, options?: RunOptions) =>
+        run(Transcription.resume(model, token), options).then(handle),
    },
    dispose: () => runtime.dispose(),
  }
@@ -70,7 +70,11 @@ export const protocol = Protocol.make({
      return {
        ...(yield* OpenAIChat.protocol.body.from(req)),
        enable_thinking: opts.enableThinking,
-        thinking_budget: opts.thinkingBudget,
+        // Alibaba also rejects an explicit budget that is not below `max_completion_tokens`.
+        thinking_budget:
+          opts.thinkingBudget === undefined
+            ? undefined
+            : ProviderShared.fitThinkingBudget(opts.thinkingBudget, req.generation?.maxTokens),
        preserve_thinking: opts.preserveThinking,
        clear_thinking: opts.clearThinking,
        thinking: opts.thinking,
@@ -26,18 +26,21 @@ export const protocol = Protocol.make({
    from: Effect.fn("AlibabaMessages.fromRequest")(function* (req) {
      const opts = yield* ProviderShared.validateWith(Schema.decodeUnknownEffect(Options))(req.providerOptions ?? {})
      // Model Studio accepts enabled thinking without Anthropic's mandatory token budget.
+      const body = yield* AnthropicMessages.protocol.body.from(
+        LLMRequest.update(req, {
+          providerOptions: { ...req.providerOptions, thinking: undefined },
+        }),
+      )
+      const budget = opts.thinking?.budgetTokens ?? opts.thinking?.budget_tokens
      return {
-        ...(yield* AnthropicMessages.protocol.body.from(
-          LLMRequest.update(req, {
-            providerOptions: { ...req.providerOptions, thinking: undefined },
-          }),
-        )),
+        ...body,
        thinking:
          opts.thinking === undefined
            ? undefined
            : {
                type: opts.thinking.type,
-                budget_tokens: opts.thinking.budgetTokens ?? opts.thinking.budget_tokens,
+                budget_tokens:
+                  budget === undefined ? undefined : ProviderShared.fitThinkingBudget(budget, body.max_tokens),
              },
      }
    }),
@@ -18,7 +18,6 @@ import {
  type CacheHint,
  type FinishReasonDetails,
  type FinishReason,
-  type JsonSchema,
  type MediaPart,
  type ProviderMetadata,
  type ProviderOptions,
@@ -31,13 +30,13 @@ import { classifyProviderFailure } from "../provider-error.js"
 import { effortUpdate, resolveEffortUpdates } from "../effort-updates.js"
 import * as Cache from "./utils/cache.js"
 import { Lifecycle } from "./utils/lifecycle.js"
-import { ToolSchemaProjection } from "./utils/tool-schema.js"
 import { ToolStream } from "./utils/tool-stream.js"

 const ADAPTER = "anthropic-messages"
 export const DEFAULT_BASE_URL = "https://api.anthropic.com/v1"
 export const PATH = "/messages"
 export const DEFAULT_MAX_TOKENS = 32_000
+const MIN_THINKING_BUDGET = 1_024
 const DEFAULT_EFFORT = "high"

 const SSE_EVENTS = new Set([
@@ -524,10 +523,10 @@ const redactedDataFromMetadata = (metadata: ProviderMetadata | undefined, key: s
  return typeof provider.redactedData === "string" ? provider.redactedData : undefined
 }

-const lowerTool = (breakpoints: Cache.Breakpoints, tool: ToolDefinition, inputSchema: JsonSchema): AnthropicTool => ({
+const lowerTool = (breakpoints: Cache.Breakpoints, tool: ToolDefinition): AnthropicTool => ({
  name: tool.name,
  description: tool.description,
-  input_schema: inputSchema,
+  input_schema: tool.inputSchema,
  cache_control: cacheControl(breakpoints, tool.cache),
 })

@@ -1027,6 +1026,15 @@ const applyThinkingBindingDefault = (model: LLMRequest["model"], thinking: Anthr
  }
 }

+// Anthropic also requires an explicit thinking budget below `max_tokens` and at or above its minimum.
+const fitThinking = (thinking: AnthropicThinking | undefined, maxTokens: number) =>
+  thinking?.type === "enabled"
+    ? {
+        ...thinking,
+        budget_tokens: ProviderShared.fitThinkingBudget(thinking.budget_tokens, maxTokens, MIN_THINKING_BUDGET),
+      }
+    : thinking
+
 const fromRequest = Effect.fn("AnthropicMessages.fromRequest")(function* (request: LLMRequest) {
  const options = yield* decodeOptions(request.providerOptions ?? {})
  const management = options.contextManagement
@@ -1039,12 +1047,7 @@ const fromRequest = Effect.fn("AnthropicMessages.fromRequest")(function* (reques
  // over-mark we keep their tool hints and shed the message-tail ones first.
  const breakpoints = Cache.newBreakpoints(ANTHROPIC_BREAKPOINT_CAP)
  const flattened = ProviderShared.flattenToolRequest(updates.request)
-  const tools =
-    flattened.tools.length === 0
-      ? undefined
-      : flattened.tools.map((tool) =>
-          lowerTool(breakpoints, tool, ToolSchemaProjection.modelCompatibility(tool.inputSchema, request.model)),
-        )
+  const tools = flattened.tools.length === 0 ? undefined : flattened.tools.map((tool) => lowerTool(breakpoints, tool))
  // Anthropic rejects tool_choice when tools are absent; "none" is only meaningful with tools present.
  const toolChoice = tools === undefined || !request.toolChoice ? undefined : yield* lowerToolChoice(request.toolChoice)
  const systemParts = request.system.filter((part) => part.text.length > 0)
@@ -1064,6 +1067,7 @@ const fromRequest = Effect.fn("AnthropicMessages.fromRequest")(function* (reques
  }
  const output_config =
    updates.effort === undefined && format === undefined ? undefined : { effort: updates.effort, format }
+  const maxTokens = generation?.maxTokens ?? DEFAULT_MAX_TOKENS
  const body = {
    model: request.model.id,
    system,
@@ -1071,12 +1075,12 @@ const fromRequest = Effect.fn("AnthropicMessages.fromRequest")(function* (reques
    tools,
    tool_choice: toolChoice,
    stream: true as const,
-    max_tokens: generation?.maxTokens ?? DEFAULT_MAX_TOKENS,
+    max_tokens: maxTokens,
    temperature: generation?.temperature,
    top_p: generation?.topP,
    top_k: generation?.topK,
    stop_sequences: generation?.stop,
-    thinking: applyThinkingBindingDefault(request.model, options.thinking),
+    thinking: applyThinkingBindingDefault(request.model, fitThinking(options.thinking, maxTokens)),
    output_config,
    // top-level passthrough per SDK MessageCreateParamsBase:4638,4643,4649,4654,4670
    cache_control: options.cache_control ?? options.cacheControl,
@@ -9,7 +9,6 @@ import {
  type CacheHint,
  type FinishReason,
  type FinishReasonDetails,
-  type JsonSchema,
  type LLMRequest,
  type LanguageModel,
  type ProviderMetadata,
@@ -26,7 +25,6 @@ import { BedrockCache } from "./utils/bedrock-cache.js"
 import { BedrockMedia } from "./utils/bedrock-media.js"
 import { Lifecycle } from "./utils/lifecycle.js"
 import { MistralToolID } from "./utils/mistral-tool-id.js"
-import { ToolSchemaProjection } from "./utils/tool-schema.js"
 import { ToolStream } from "./utils/tool-stream.js"
 import { concatBytes } from "../utils/bytes.js"

@@ -221,22 +219,18 @@ type BedrockEvent = Schema.Schema.Type<typeof BedrockEvent>
 // =============================================================================
 // Request Lowering
 // =============================================================================
-const lowerToolSpec = (tool: ToolDefinition, inputSchema: JsonSchema): BedrockToolSpec => ({
+const lowerToolSpec = (tool: ToolDefinition): BedrockToolSpec => ({
  toolSpec: {
    name: tool.name,
    ...(tool.description.trim().length > 0 ? { description: tool.description } : {}),
-    inputSchema: { json: inputSchema },
+    inputSchema: { json: tool.inputSchema },
  },
 })

-const lowerTools = (
-  model: LanguageModel,
-  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, ToolSchemaProjection.modelCompatibility(tool.inputSchema, model)))
+    result.push(lowerToolSpec(tool))
    const cachePoint = BedrockCache.block(breakpoints, tool.cache)
    if (cachePoint) result.push(cachePoint)
  }
@@ -441,19 +435,39 @@ const isHighReasoningEffort = Schema.is(
  }),
 )

+const Options = Schema.Struct({
+  thinking: Schema.optional(Schema.Struct({ type: Schema.Literal("enabled"), budgetTokens: Schema.Number })),
+})
+export type OptionsInput = typeof Options.Type
+const decodeOptions = ProviderShared.validateWith(Schema.decodeUnknownEffect(Options))
+// Claude on Bedrock requires the thinking budget below `maxTokens`, with a minimum of 1,024.
+const MIN_THINKING_BUDGET = 1_024
+
 const fromRequest = Effect.fn("BedrockConverse.fromRequest")(function* (request: LLMRequest) {
  const toolChoice = request.toolChoice ? yield* lowerToolChoice(request.toolChoice) : undefined
  const flattened = ProviderShared.flattenToolRequest(request)
  const generation = request.generation
+  const options = yield* decodeOptions(request.providerOptions ?? {})
  const maxTokens =
    isNova2(request.model) && isHighReasoningEffort(request.http?.body) ? undefined : generation?.maxTokens
+  const thinking =
+    options.thinking === undefined
+      ? undefined
+      : {
+          type: "enabled",
+          budget_tokens: ProviderShared.fitThinkingBudget(
+            options.thinking.budgetTokens,
+            maxTokens,
+            MIN_THINKING_BUDGET,
+          ),
+        }
  // Bedrock-Claude shares Anthropic's 4-breakpoint cap. Spend the budget in
  // tools → system → messages order to favour the highest-impact prefixes.
  const breakpoints = BedrockCache.breakpoints(request.model.id)
  const toolConfig = (() => {
    if (flattened.tools.length === 0) return undefined
    return {
-      tools: lowerTools(request.model, breakpoints, flattened.tools),
+      tools: lowerTools(breakpoints, flattened.tools),
      // Converse has no native "none". Keep definitions stable for prompt
      // caching and omit only the unsupported choice.
      toolChoice,
@@ -487,9 +501,15 @@ const fromRequest = Effect.fn("BedrockConverse.fromRequest")(function* (request:
    system,
    inferenceConfig,
    toolConfig,
-    // Converse's base inferenceConfig has no topK; Anthropic/Nova accept it
-    // as a model-specific field, so it goes through additionalModelRequestFields.
-    additionalModelRequestFields: generation?.topK === undefined ? undefined : { top_k: generation.topK },
+    // Converse's base inferenceConfig has no topK or thinking; Anthropic/Nova accept them
+    // as model-specific fields, so they go through additionalModelRequestFields.
+    additionalModelRequestFields:
+      generation?.topK === undefined && thinking === undefined
+        ? undefined
+        : {
+            ...(generation?.topK === undefined ? {} : { top_k: generation.topK }),
+            ...(thinking === undefined ? {} : { thinking }),
+          },
  }
 })

@@ -11,7 +11,6 @@ import {
  Usage,
  type FinishReason,
  type LLMRequest,
-  type LanguageModel,
  type MediaPart,
  type ProviderMetadata,
  type ProviderOptions,
@@ -24,11 +23,12 @@ import { Media } from "../media.js"
 import { JsonObject, knownString, lenient, optionalArray, optionalNull, ProviderShared } from "./shared.js"
 import { GeminiGenerateContent } from "./utils/gemini-generate-content.js"
 import { Lifecycle } from "./utils/lifecycle.js"
-import { ToolSchemaProjection } from "./utils/tool-schema.js"

 const ADAPTER = "gemini"
 // Google documents this sentinel for replaying Gemini 3 function calls after their original signature was lost.
 const SKIP_THOUGHT_SIGNATURE_VALIDATOR = "skip_thought_signature_validator"
+// Gemini 2.5 rejects a budget under the model's minimum: 512 on Flash-Lite, the highest, and 128 on Pro.
+const MIN_THINKING_BUDGET = 512
 export const DEFAULT_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"

 // Gemini 3 rejects replayed function calls without a thought signature. Google's SDKs avoid that in normal chats by
@@ -268,12 +268,11 @@ interface ParserState {
 // =============================================================================
 // Request Lowering
 // =============================================================================
-// Tool schemas go in `parametersJsonSchema`, which accepts standard JSON Schema. Gemini's schema
-// rules are this API's default, including for tuned endpoints whose IDs do not name Gemini.
-const lowerTool = (tool: ToolDefinition, model: LanguageModel) => ({
+// Tool schemas go in `parametersJsonSchema`, which accepts standard JSON Schema.
+const lowerTool = (tool: ToolDefinition) => ({
  name: tool.name,
  description: tool.description,
-  parametersJsonSchema: ToolSchemaProjection.modelCompatibility(tool.inputSchema, model, "gemini"),
+  parametersJsonSchema: tool.inputSchema,
 })

 const lowerToolConfig = (toolChoice: NonNullable<LLMRequest["toolChoice"]>) =>
@@ -452,10 +451,22 @@ const fromRequest = Effect.fn("Gemini.fromRequest")(function* (request: LLMReque
    presencePenalty: generation?.presencePenalty,
    seed: generation?.seed,
    stopSequences: generation?.stop,
+    // Gemini accepts a budget above `maxOutputTokens`, but thinking then leaves the answer empty.
    thinkingConfig:
      options.thinkingConfig === undefined
        ? undefined
-        : { ...options.thinkingConfig, includeThoughts: options.thinkingConfig.includeThoughts ?? true },
+        : {
+            ...options.thinkingConfig,
+            includeThoughts: options.thinkingConfig.includeThoughts ?? true,
+            thinkingBudget:
+              options.thinkingConfig.thinkingBudget === undefined
+                ? undefined
+                : ProviderShared.fitThinkingBudget(
+                    options.thinkingConfig.thinkingBudget,
+                    generation?.maxTokens,
+                    MIN_THINKING_BUDGET,
+                  ),
+          },
  }

  return {
@@ -468,7 +479,7 @@ const fromRequest = Effect.fn("Gemini.fromRequest")(function* (request: LLMReque
    tools: hasTools
      ? [
          {
-            functionDeclarations: flattened.tools.map((tool) => lowerTool(tool, request.model)),
+            functionDeclarations: flattened.tools.map(lowerTool),
          },
        ]
      : undefined,
@@ -804,6 +815,8 @@ export const protocol = Protocol.make({
    schema: GeminiBody,
    from: fromRequest,
  },
+  // Gemini's schema rules are this API's default, including for tuned endpoints whose IDs do not name Gemini.
+  sanitizer: "gemini",
  stream: {
    event: Protocol.jsonEvent(GeminiEvent),
    initial: (request) => ({
@@ -5,7 +5,6 @@ import { LLMEvent, LLMRequest, Message, ToolResultPart } from "../schema/index.j
 import { OpenResponses } from "./open-responses.js"
 import { JsonObject, optionalArray, optionalNull, ProviderShared } from "./shared.js"
 import { ResponsesHostedTools } from "./utils/responses-hosted-tools.js"
-import { ToolSchemaProjection } from "./utils/tool-schema.js"
 import { detectMediaType } from "../utils/media-type.js"

 const ADAPTER = "meta-responses"
@@ -103,12 +102,7 @@ const fromRequest = Effect.fn("MetaResponses.fromRequest")(function* (request: L
        ? undefined
        : yield* Effect.forEach(projected.tools, (tool) =>
            Effect.gen(function* () {
-              if (tool.native === undefined)
-                return yield* OpenResponses.lowerTool(
-                  NAME,
-                  tool,
-                  ToolSchemaProjection.modelCompatibility(tool.inputSchema, request.model),
-                )
+              if (tool.native === undefined) return yield* OpenResponses.lowerTool(NAME, tool)
              return yield* ProviderShared.validateWith(Schema.decodeUnknownEffect(NativeTool))(tool.native.meta)
            }),
          ),
@@ -13,7 +13,6 @@ import {
  UnknownProviderError,
  Usage,
  type FinishReasonDetails,
-  type JsonSchema,
  type LLMRequest,
  type MediaPart,
  type ToolCallPart,
@@ -23,7 +22,6 @@ import { classifyProviderFailure } from "../provider-error.js"
 import { JsonObject, optionalArray, optionalNull, ProviderShared } from "./shared.js"
 import { Lifecycle } from "./utils/lifecycle.js"
 import { MistralToolID } from "./utils/mistral-tool-id.js"
-import { ToolSchemaProjection } from "./utils/tool-schema.js"
 import { ToolStream } from "./utils/tool-stream.js"

 const ADAPTER = "mistral-chat"
@@ -368,9 +366,9 @@ const lowerMessages = Effect.fn("MistralChat.lowerMessages")(function* (request:
  return messages
 })

-const lowerTool = (tool: ToolDefinition, inputSchema: JsonSchema): MistralTool => ({
+const lowerTool = (tool: ToolDefinition): MistralTool => ({
  type: "function",
-  function: { name: tool.name, description: tool.description, parameters: inputSchema, strict: false },
+  function: { name: tool.name, description: tool.description, parameters: tool.inputSchema, strict: false },
 })

 export const fromRequest = Effect.fn("MistralChat.fromRequest")(function* (request: LLMRequest) {
@@ -396,12 +394,7 @@ export const fromRequest = Effect.fn("MistralChat.fromRequest")(function* (reque
  return {
    model: request.model.id,
    messages: yield* lowerMessages(flattened.request),
-    tools:
-      flattened.tools.length > 0
-        ? flattened.tools.map((tool) =>
-            lowerTool(tool, ToolSchemaProjection.modelCompatibility(tool.inputSchema, request.model)),
-          )
-        : undefined,
+    tools: flattened.tools.length > 0 ? flattened.tools.map(lowerTool) : undefined,
    tool_choice: toolChoice,
    stream: true as const,
    max_tokens: request.generation?.maxTokens,
@@ -8,7 +8,6 @@ import {
  ProviderInternalError,
  Usage,
  type FinishReason,
-  type JsonSchema,
  type LLMRequest,
  type MediaPart,
  type ProviderMetadata,
@@ -24,7 +23,6 @@ import { classifyProviderFailure } from "../provider-error.js"
 import { effortUpdate } from "../effort-updates.js"
 import { OpenResponsesOptions } from "./utils/open-responses-options.js"
 import { Lifecycle } from "./utils/lifecycle.js"
-import { ToolSchemaProjection } from "./utils/tool-schema.js"
 import { ToolStream } from "./utils/tool-stream.js"

 const ADAPTER = "open-responses"
@@ -443,23 +441,24 @@ interface ReasoningStreamItem {
 // =============================================================================
 // Request Lowering
 // =============================================================================
-export const lowerTool = Effect.fn("OpenResponses.lowerTool")(function* (
-  protocolName: string,
-  tool: ToolDefinition,
-  inputSchema: JsonSchema,
-) {
+export const lowerTool = Effect.fn("OpenResponses.lowerTool")(function* (protocolName: string, tool: ToolDefinition) {
  if (tool.native !== undefined)
    return yield* ProviderShared.invalidRequest(`${protocolName} does not support provider-native tool ${tool.name}`)
  return {
    type: "function" as const,
    name: tool.name,
    description: tool.description,
-    parameters: inputSchema,
+    parameters: tool.inputSchema,
    // The common tool definition does not currently express Responses strict-schema policy.
    strict: false,
  }
 })

+export const lowerTools = (tools: ReadonlyArray<ToolDefinition>, adapter: ProviderAdapter) =>
+  Effect.forEach(tools, (tool) =>
+    tool.native !== undefined && adapter.nativeTool ? adapter.nativeTool(tool.native) : lowerTool(adapter.name, tool),
+  )
+
 export const lowerToolChoice = (protocolName: string, toolChoice: NonNullable<LLMRequest["toolChoice"]>) =>
  ProviderShared.matchToolChoice(protocolName, toolChoice, {
    auto: () => "auto" as const,
@@ -821,14 +820,7 @@ export const fromRequestWithAdapter = Effect.fn("OpenResponses.fromRequestWithAd
  return {
    ...(yield* lowerConversation(projected.request, adapter)),
    ...lowerGeneration(request),
-    tools:
-      projected.tools.length === 0
-        ? undefined
-        : yield* Effect.forEach(projected.tools, (tool) =>
-            tool.native !== undefined && adapter.nativeTool
-              ? adapter.nativeTool(tool.native)
-              : lowerTool(adapter.name, tool, ToolSchemaProjection.modelCompatibility(tool.inputSchema, request.model)),
-          ),
+    tools: projected.tools.length === 0 ? undefined : yield* lowerTools(projected.tools, adapter),
    tool_choice:
      allowedToolChoice(request) ??
      (request.toolChoice ? yield* lowerToolChoice(adapter.name, request.toolChoice) : undefined),
@@ -17,7 +17,6 @@ import {
  type FinishReason,
  type FinishReasonDetails,
  type CacheHint,
-  type JsonSchema,
  type LLMRequest,
  type MediaPart,
  type ReasoningPart,
@@ -29,7 +28,6 @@ import { classifyProviderFailure } from "../provider-error.js"
 import { isRecord, JsonObject, optionalArray, optionalNull, ProviderShared } from "./shared.js"
 import { OpenAIOptions } from "./utils/openai-options.js"
 import { Lifecycle } from "./utils/lifecycle.js"
-import { ToolSchemaProjection } from "./utils/tool-schema.js"
 import { ToolStream } from "./utils/tool-stream.js"

 const ADAPTER = "openai-chat"
@@ -330,17 +328,12 @@ interface LoweringOptions {
  readonly toolCallID?: (id: string) => string
 }

-const lowerTool = (
-  tool: ToolDefinition,
-  inputSchema: JsonSchema,
-  options: LoweringOptions,
-  supportsStrictMode: boolean,
-): OpenAIChatTool => ({
+const lowerTool = (tool: ToolDefinition, options: LoweringOptions, supportsStrictMode: boolean): OpenAIChatTool => ({
  type: "function",
  function: {
    name: tool.name,
    description: tool.description,
-    parameters: inputSchema,
+    parameters: tool.inputSchema,
    ...(supportsStrictMode ? { strict: false } : {}),
  },
  cache_control: options.cacheControl?.(tool.cache),
@@ -825,14 +818,7 @@ export const fromRequest = Effect.fn("OpenAIChat.fromRequest")(function* (
        ? hasHistory
          ? []
          : undefined
-        : flattened.tools.map((tool) =>
-            lowerTool(
-              tool,
-              ToolSchemaProjection.modelCompatibility(tool.inputSchema, request.model),
-              options,
-              supportsStrictMode,
-            ),
-          ),
+        : flattened.tools.map((tool) => lowerTool(tool, options, supportsStrictMode)),
    tool_choice: hasActiveTools && request.toolChoice ? yield* lowerToolChoice(request.toolChoice) : undefined,
    stream: true as const,
    ...(supportsUsageInStreaming ? { stream_options: { include_usage: true } } : {}),
@@ -5,20 +5,12 @@ import { Auth } from "../route/auth.js"
 import { Endpoint } from "../route/endpoint.js"
 import { Protocol } from "../route/protocol.js"
 import { HttpTransport } from "../route/transport/index.js"
-import {
-  LLMRequest,
-  mergeJsonRecords,
-  type JsonSchema,
-  type LanguageModel,
-  type ToolDefinition,
-  type ToolEntry,
-} from "../schema/index.js"
+import { LLMRequest, type ToolDefinition, type ToolEntry } from "../schema/index.js"
 import { resolveEffortUpdates } from "../effort-updates.js"
 import { OpenResponses } from "./open-responses.js"
 import { OpenResponsesOptions } from "./utils/open-responses-options.js"
 import { JsonObject, optionalArray, optionalNull, ProviderShared } from "./shared.js"
 import { ResponsesHostedTools } from "./utils/responses-hosted-tools.js"
-import { ToolSchemaProjection } from "./utils/tool-schema.js"
 import { OpenResponsesChannel } from "./open-responses-channel.js"
 import { ResponsesCompaction } from "./utils/responses-compaction.js"
 import { ResponsesCheckpoint } from "./utils/responses-checkpoint.js"
@@ -143,11 +135,6 @@ export const CompactionTrigger = Schema.Struct({ type: Schema.Literal("compactio
 const CheckpointBody = Schema.Struct({
  ...OpenAIResponsesBody.fields,
  input: Schema.Array(Schema.Union([OpenAIResponsesInputItem, CompactionTrigger])),
-  store: Schema.Literal(false),
-  prompt_cache_retention: optionalNull(Schema.String),
-  prompt_cache_options: optionalNull(
-    Schema.Struct({ mode: Schema.optional(Schema.String), ttl: Schema.optional(Schema.String) }),
-  ),
 })

 const adapter = {
@@ -174,20 +161,19 @@ const nativeImageTool = (tool: ToolDefinition) => {
  return Schema.is(OpenAIResponsesImageGenerationTool)(native) ? native : undefined
 }

-const lowerTool = Effect.fn("OpenAIResponses.lowerTool")(function* (tool: ToolDefinition, inputSchema: JsonSchema) {
+const lowerTool = Effect.fn("OpenAIResponses.lowerTool")(function* (tool: ToolDefinition) {
  const native = nativeImageToolInput(tool)
  if (native !== undefined) {
    if (Schema.is(OpenAIResponsesImageGenerationTool)(native)) return native
    return yield* ProviderShared.invalidRequest("OpenAI Responses image generation tool options are invalid")
  }
-  return yield* OpenResponses.lowerTool(NAME, tool, inputSchema)
+  return yield* OpenResponses.lowerTool(NAME, tool)
 })

 // Native namespaces hold only function tools, so deeper levels flatten into
 // the leaf names the same way non-native protocols flatten the whole tree.
-const lowerToolEntry = Effect.fn("OpenAIResponses.lowerToolEntry")(function* (tool: ToolEntry, model: LanguageModel) {
-  if (tool.type === "tool")
-    return yield* lowerTool(tool, ToolSchemaProjection.modelCompatibility(tool.inputSchema, model))
+const lowerToolEntry = Effect.fn("OpenAIResponses.lowerToolEntry")(function* (tool: ToolEntry) {
+  if (tool.type === "tool") return yield* lowerTool(tool)
  // OpenAI requires a namespace description; fall back to a generic one so a
  // missing description never blocks the request.
  return {
@@ -195,11 +181,13 @@ const lowerToolEntry = Effect.fn("OpenAIResponses.lowerToolEntry")(function* (to
    name: tool.name,
    description: tool.description ?? `Tools in the ${tool.name} namespace.`,
    tools: yield* Effect.forEach(ProviderShared.flattenTools(tool.tools), (leaf) =>
-      OpenResponses.lowerTool(NAME, leaf, ToolSchemaProjection.modelCompatibility(leaf.inputSchema, model)),
+      OpenResponses.lowerTool(NAME, leaf),
    ),
  }
 })

+const lowerTools = (request: LLMRequest) => Effect.forEach(request.tools, lowerToolEntry)
+
 const lowerToolChoice = (toolChoice: NonNullable<LLMRequest["toolChoice"]>, tools: ReadonlyArray<ToolEntry>) =>
  ProviderShared.matchToolChoice(NAME, toolChoice, {
    auto: () => "auto" as const,
@@ -223,10 +211,7 @@ const fromRequest = Effect.fn("OpenAIResponses.fromRequest")(function* (request:
    ...(yield* OpenResponses.lowerConversation(updates.request, adapter)),
    ...OpenResponses.lowerGeneration(request, { ...options, reasoningEffort: updates.effort }),
    context_management: management?.map((edit) => ({ type: edit.type, compact_threshold: edit.compactThreshold })),
-    tools:
-      request.tools.length === 0
-        ? undefined
-        : yield* Effect.forEach(request.tools, (tool) => lowerToolEntry(tool, request.model)),
+    tools: request.tools.length === 0 ? undefined : yield* lowerTools(request),
    tool_choice:
      request.tools.length === 0
        ? undefined
@@ -238,7 +223,6 @@ const fromRequest = Effect.fn("OpenAIResponses.fromRequest")(function* (request:
 const checkpointBody = {
  schema: CheckpointBody,
  from: Effect.fn("OpenAIResponses.checkpointBody")(function* (request: LLMRequest) {
-    const native = yield* fromRequest(LLMRequest.update(request, { toolChoice: undefined }))
    const overlay = request.http?.body
    // Complete history is required for stateless replay and SSE recovery. Raw input overrides bypass that contract.
    if (
@@ -249,18 +233,13 @@ const checkpointBody = {
      return yield* ProviderShared.invalidRequest(
        "Trigger compaction requires complete canonical history, not an input or continuation override",
      )
-    return yield* ProviderShared.validateWith(Schema.decodeUnknownEffect(CheckpointBody))({
-      ...mergeJsonRecords(native, overlay),
-      input: [...native.input, { type: "compaction_trigger" }],
-      stream: true,
-      store: false,
-      parallel_tool_calls: true,
-      tool_choice: undefined,
-      context_management: undefined,
-      text: undefined,
-      max_output_tokens: undefined,
-      max_tool_calls: undefined,
-    })
+    if (overlay?.stream !== undefined && overlay.stream !== true)
+      return yield* ProviderShared.invalidRequest("Trigger compaction requires a streamed response")
+    const native = yield* fromRequest(request)
+    return {
+      ...native,
+      input: [...native.input, { type: "compaction_trigger" as const }],
+    }
  }),
 }

@@ -342,7 +321,10 @@ export const transport = channelTransport({
 })

 export const route = Route.make({
-  compact: { endpoint: ResponsesCompaction.make(adapter), trigger: ResponsesCheckpoint.make(checkpointBody) },
+  compact: {
+    endpoint: ResponsesCompaction.make(adapter, lowerTools),
+    trigger: ResponsesCheckpoint.make(checkpointBody),
+  },
  id: ADAPTER,
  provider: "openai",
  providerMetadataKey: "openai",
@@ -110,6 +110,14 @@ export const sumTokens = (...values: ReadonlyArray<number | undefined>): number
  return values.reduce((acc: number, value) => acc + (value ?? 0), 0)
 }

+/**
+ * Caps an explicit thinking budget at half the output limit. Thinking counts against the output limit, so a budget
+ * near it leaves the answer, a tool call, or a summary without room. Smaller budgets, special values such as `-1` and
+ * `0`, and requests without an output limit pass through unchanged.
+ */
+export const fitThinkingBudget = (budget: number, maxTokens: number | undefined, minimum = 1) =>
+  maxTokens === undefined || budget <= maxTokens / 2 ? budget : Math.max(minimum, Math.floor(maxTokens / 2))
+
 export const eventError = (route: string, message: string, body?: string, cause?: unknown) =>
  new AIError({
    reason: new InvalidProviderOutputError({ route, message, body, cause }),
@@ -1,7 +1,7 @@
 import { Effect, Schema, Stream } from "effect"
 import { Route, type RouteBody, type TriggerCompactOperation } from "../../route/client.js"
 import { Protocol } from "../../route/protocol.js"
-import { CompactionCheckpointResponse, HttpOptions, LLMEvent, LLMRequest } from "../../schema/index.js"
+import { CompactionCheckpointResponse, LLMEvent, LLMRequest } from "../../schema/index.js"
 import { OpenResponses } from "../open-responses.js"
 import { ProviderShared } from "../shared.js"

@@ -109,12 +109,8 @@ export const make = <Body>(body: RouteBody<Body>): TriggerCompactOperation =>
      transport: source.transport,
    })
    const native = yield* body.from(request)
-    // The body builder already applied and validated overlays. Do not let transport reapply them.
-    const preparedRequest = LLMRequest.update(request, {
-      http: request.http === undefined ? undefined : new HttpOptions({ ...request.http, body: undefined }),
-    })
-    const prepared = yield* route.prepareTransport(native, preparedRequest, options)
-    yield* route.streamPrepared(prepared, preparedRequest, { http: executor }, options).pipe(Stream.runDrain)
+    const prepared = yield* route.prepareTransport(native, request, options)
+    yield* route.streamPrepared(prepared, request, { http: executor }, options).pipe(Stream.runDrain)
    if (!result) return yield* ProviderShared.eventError(source.id, "Compaction response ended without a checkpoint")
    return result
  })
@@ -19,12 +19,18 @@ import { OpenResponses } from "../open-responses.js"
 import { JsonObject, optionalNull, ProviderShared } from "../shared.js"
 import { Media } from "../../media.js"

+// /compact has a smaller wire contract than /responses; keep the request controls it accepts.
 const Body = Schema.Struct({
  model: Schema.String,
  input: Schema.Array(Schema.Unknown),
  instructions: optionalNull(Schema.String),
  previous_response_id: optionalNull(Schema.String),
  service_tier: optionalNull(Schema.String),
+  reasoning: Schema.optional(JsonObject),
+  text: Schema.optional(JsonObject),
+  include: OpenResponses.coreFields.include,
+  parallel_tool_calls: OpenResponses.coreFields.parallel_tool_calls,
+  tools: Schema.optional(Schema.Array(JsonObject)),
  prompt_cache_key: optionalNull(Schema.String),
  prompt_cache_retention: optionalNull(Schema.String),
  prompt_cache_options: optionalNull(
@@ -74,17 +80,27 @@ const Response = Schema.Struct({
  usage: Schema.optional(Schema.StructWithRest(OpenResponses.OpenResponsesUsage, [JsonObject])),
 })

-export const make = (adapter: OpenResponses.ProviderAdapter): CompactOperation =>
+export const make = (
+  adapter: OpenResponses.ProviderAdapter,
+  lowerTools: (request: LLMRequest) => Effect.Effect<ReadonlyArray<Record<string, unknown>>, AIError>,
+): CompactOperation =>
  Effect.fn("ResponsesCompaction.execute")(function* (request, executor, options) {
    const route = request.model.route
    // The standalone compaction endpoint rejects histories containing configuration updates.
    const native = yield* OpenResponses.lowerConversation(stripEffortUpdates(request), adapter)
+    const generation = OpenResponses.lowerGeneration(request)
+    const tools = request.tools.length === 0 ? undefined : yield* lowerTools(request)
    const body = yield* ProviderShared.validateWith(Schema.decodeUnknownEffect(Body))(
      mergeJsonRecords(
        {
          ...native,
-          service_tier: request.providerOptions?.serviceTier,
-          prompt_cache_key: ProviderShared.promptCacheKey(request),
+          service_tier: generation.service_tier,
+          reasoning: generation.reasoning,
+          text: generation.text,
+          include: generation.include,
+          parallel_tool_calls: generation.parallel_tool_calls,
+          tools,
+          prompt_cache_key: generation.prompt_cache_key,
        },
        request.http?.body,
      ),
@@ -1,4 +1,4 @@
-import type { JsonSchema, LanguageModel, LanguageModelSanitizerCompatibility } from "../../schema/index.js"
+import { ToolDefinition, type JsonSchema, type LanguageModel, type LLMRequest } from "../../schema/index.js"
 import { isRecord } from "../../utils/record.js"
 import { GeminiJsonSchema } from "./gemini-json-schema.js"

@@ -70,13 +70,13 @@ const objectRoot = (schema: JsonSchema): JsonSchema => {
 // Otherwise the protocol's own default applies (the Gemini API always uses Gemini's rules), then the
 // model name selects the family's rules so models reached through gateways and OpenAI-compatible
 // endpoints get the same handling.
-const modelCompatibility = (
-  schema: JsonSchema,
-  model: LanguageModel,
-  protocolDefault?: LanguageModelSanitizerCompatibility,
-): JsonSchema => {
+const modelCompatibility = (schema: JsonSchema, model: LanguageModel): JsonSchema => {
  const root = objectRoot(schema)
-  switch (model.compatibility?.sanitizer ?? protocolDefault ?? MODEL_NAMES.find(([name]) => name.test(model.id))?.[1]) {
+  switch (
+    model.compatibility?.sanitizer ??
+    model.route.sanitizer ??
+    MODEL_NAMES.find(([name]) => name.test(model.id))?.[1]
+  ) {
    case "gemini":
      return gemini(root)
    case "moonshot":
@@ -87,10 +87,18 @@ const modelCompatibility = (
  }
 }

+// Applied once to every request before any protocol builds its body, including tools in namespaces.
+const tools = (entries: LLMRequest["tools"], model: LanguageModel): LLMRequest["tools"] =>
+  entries.map((tool) =>
+    tool.type === "tool"
+      ? new ToolDefinition({ ...tool, inputSchema: modelCompatibility(tool.inputSchema, model) })
+      : { ...tool, tools: tools(tool.tools, model) },
+  )
+
 export const ToolSchemaProjection = {
  gemini,
-  modelCompatibility,
  moonshot,
  openAI,
  responses,
+  tools,
 } as const
@@ -50,7 +50,8 @@ const fromRequest = Effect.fn("XAIResponses.fromRequest")(function* (request: LL
      operation: "in-band-compaction",
      provider: request.model.provider,
      route: request.model.route.id,
-      message: "xAI requires explicit compaction through LLMClient.compact; automatic context management is not supported",
+      message:
+        "xAI requires explicit compaction through LLMClient.compact; automatic context management is not supported",
    })
  return yield* decodeBody(yield* OpenResponses.fromRequestWithAdapter(request, adapter))
 })
@@ -93,6 +94,8 @@ export const protocol = Protocol.make({
  },
 })

-export const compact = ResponsesCompaction.make(adapter)
+export const compact = ResponsesCompaction.make(adapter, (request) =>
+  OpenResponses.lowerTools(ProviderShared.flattenTools(request.tools), adapter),
+)

 export * as XAIResponses from "./xai-responses.js"
@@ -80,7 +80,13 @@ const SERVER_CODES = new Set([
  "slow_down",
  "serviceunavailableexception",
 ])
-const INVALID_REQUEST_CODES = new Set(["invalid_prompt", "invalid_request_error", "validationexception"])
+// `invalid_request` is the Vercel AI Gateway's code for an upstream request rejection.
+const INVALID_REQUEST_CODES = new Set([
+  "invalid_prompt",
+  "invalid_request",
+  "invalid_request_error",
+  "validationexception",
+])
 // Azure OpenAI reports `content_filter` with `innererror.code` ResponsibleAIPolicyViolation.
 // OpenRouter tags provider failures with a typed `error_type`; its Responses skin also
 // emits `image_content_policy_violation` as the native code.
@@ -31,6 +31,7 @@ export interface Settings extends ProviderPackage.Settings {
  readonly profile?: string
  readonly region?: string
  readonly topP?: number
+  readonly thinking?: BedrockConverse.OptionsInput["thinking"]
 }
 export const routes = [BedrockConverse.route]

@@ -71,6 +72,7 @@ export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, se
    generation: settings.topP === undefined ? undefined : { topP: settings.topP },
    headers: settings.headers === undefined ? undefined : { ...settings.headers },
    http: settings.body === undefined ? undefined : { body: { ...settings.body } },
+    providerOptions: settings.thinking === undefined ? undefined : { thinking: settings.thinking },
    profile: settings.profile,
    region: settings.region,
  }).model(modelID)
@@ -32,10 +32,7 @@ const openAIProviderOptions = (options: OpenAIOptionsInput | undefined): Provide
  return result
 }

-export const gpt5DefaultOptions = (
-  modelID: string,
-  options: { readonly textVerbosity?: boolean } = {},
-): ProviderOptions | undefined => {
+export const gpt5DefaultOptions = (modelID: string): ProviderOptions | undefined => {
  const id = modelID.toLowerCase()
  if (!id.includes("gpt-5") || id.includes("gpt-5-chat") || id.includes("gpt-5-pro")) return undefined
  return openAIProviderOptions({
@@ -47,27 +44,19 @@ export const gpt5DefaultOptions = (
    // this, callers using the default model facade get reasoning summaries
    // they cannot replay statelessly.
    include: ["reasoning.encrypted_content"],
-    textVerbosity:
-      options.textVerbosity === true && id.includes("gpt-5.") && !id.includes("codex") && !id.includes("-chat")
-        ? "low"
-        : undefined,
  })
 }

-export const openAIDefaultOptions = (
-  modelID: string,
-  options: { readonly textVerbosity?: boolean } = {},
-): ProviderOptions | undefined =>
-  mergeProviderOptions(openAIProviderOptions({ store: false }), gpt5DefaultOptions(modelID, options))
+export const openAIDefaultOptions = (modelID: string): ProviderOptions | undefined =>
+  mergeProviderOptions(openAIProviderOptions({ store: false }), gpt5DefaultOptions(modelID))

 export const withOpenAIOptions = <Options extends { readonly providerOptions?: OpenAIProviderOptionsInput }>(
  modelID: string,
  options: Options,
-  defaults: { readonly textVerbosity?: boolean } = {},
 ): Omit<Options, "providerOptions"> & { readonly providerOptions?: ProviderOptions } => {
  return {
    ...options,
-    providerOptions: mergeProviderOptions(openAIDefaultOptions(modelID, defaults), options.providerOptions),
+    providerOptions: mergeProviderOptions(openAIDefaultOptions(modelID), options.providerOptions),
  }
 }

@@ -100,7 +100,7 @@ export const configure = (input: Config = {}) => {
  const modelDefaults = defaults(input)
  const responses = (id: string | ModelID) =>
    responsesRoute
-      .with(withOpenAIOptions(id, modelDefaults, { textVerbosity: true }))
+      .with(withOpenAIOptions(id, modelDefaults))
      .model<OpenAIProviderOptionsInput>({ id })
  const chat = (id: string | ModelID) =>
    chatRoute.with(withOpenAIOptions(id, modelDefaults)).model<OpenAIProviderOptionsInput>({
@@ -8,7 +8,7 @@ import type { ProviderPackage } from "../provider-package.js"
 import { SystemOne } from "../experimental/system-one.js"
 import { OpenAIChat } from "../protocols/openai-chat.js"
 import { newBreakpoints, ttlBucket } from "../protocols/utils/cache.js"
-import { isRecord } from "../protocols/shared.js"
+import { isRecord, ProviderShared } from "../protocols/shared.js"

 export const id = ProviderID.make("openrouter")
 const baseURL = "https://openrouter.ai/api/v1"
@@ -123,7 +123,7 @@ export const protocol = Protocol.make({
          return {
            ...body,
            messages,
-            ...bodyOptions(request.providerOptions),
+            ...bodyOptions(request.providerOptions, request.generation?.maxTokens),
          } as OpenRouterBody
        }),
      ),
@@ -143,7 +143,14 @@ const cacheControl = () => {
  }
 }

-const bodyOptions = (input: unknown) => {
+// OpenRouter forwards `reasoning.max_tokens` as the upstream thinking budget. Upstreams such as Anthropic and Alibaba
+// reject one that is not below the output limit; 1,024 is Anthropic's minimum budget.
+const fitReasoning = (reasoning: Record<string, unknown>, maxTokens: number | undefined) =>
+  typeof reasoning.max_tokens === "number"
+    ? { ...reasoning, max_tokens: ProviderShared.fitThinkingBudget(reasoning.max_tokens, maxTokens, 1_024) }
+    : reasoning
+
+const bodyOptions = (input: unknown, maxTokens: number | undefined) => {
  const openrouter = isRecord(input) ? input : {}
  const { usage, models, provider, plugins, web_search_options, debug, user, reasoning, promptCacheKey, ...options } =
    openrouter
@@ -162,7 +169,7 @@ const bodyOptions = (input: unknown) => {
    ...(isRecord(web_search_options) ? { web_search_options } : {}),
    ...(isRecord(debug) ? { debug } : {}),
    ...(typeof user === "string" ? { user } : {}),
-    ...(isRecord(reasoning) ? { reasoning } : {}),
+    ...(isRecord(reasoning) ? { reasoning: fitReasoning(reasoning, maxTokens) } : {}),
  }
 }

@@ -11,7 +11,8 @@ import { applyEffortUpdates } from "../effort-updates.js"
 import { normalizeToolHistory } from "../tool-history.js"
 import { sanitizeSurrogates } from "../utils/sanitize.js"
 import * as ProviderShared from "../protocols/shared.js"
-import type { ProtocolID, ProviderOptions } from "../schema/index.js"
+import { ToolSchemaProjection } from "../protocols/utils/tool-schema.js"
+import type { LanguageModelSanitizerCompatibility, ProtocolID, ProviderOptions } from "../schema/index.js"
 import {
  AIError,
  CompactionResponse,
@@ -57,6 +58,7 @@ export interface Route<
  readonly defaults: RouteDefaults
  readonly body: RouteBody<Body>
  readonly supportsEffortUpdates?: (request: LLMRequest) => boolean
+  readonly sanitizer?: LanguageModelSanitizerCompatibility
  readonly with: {
    <Next extends CompactionOperations | undefined>(
      patch: RoutePatch<Body, Prepared> & { readonly compact: Next },
@@ -388,6 +390,7 @@ function makeFromTransport<Body, Prepared, Frame, Event, State>(
      defaults: routeInput.defaults ?? {},
      body: protocol.body,
      supportsEffortUpdates: protocol.supportsEffortUpdates,
+      sanitizer: protocol.sanitizer,
      with: (patch: RoutePatch<Body, Prepared>) => {
        const { compact, id, provider, providerMetadataKey, auth, transport, endpoint, ...defaults } = patch
        return build({
@@ -559,7 +562,9 @@ const prepareRequest = (request: LLMRequest) => {
      tool.type === "tool" ? tool : { ...tool, tools: dedupe(tool.tools) },
    )
  const resolved = applyCachePolicy(
-    applyEffortUpdates(LLMRequest.update(sanitized, { tools: dedupe(sanitized.tools) })),
+    applyEffortUpdates(
+      LLMRequest.update(sanitized, { tools: ToolSchemaProjection.tools(dedupe(sanitized.tools), sanitized.model) }),
+    ),
  )
  const headers = resolved.model.route.headers?.({ request: resolved })
  return headers === undefined
@@ -5,7 +5,7 @@ import { Endpoint } from "./endpoint.js"
 import { RequestExecutorService, type Interface } from "./executor-service.js"
 import { RequestExecutor } from "./executor.js"
 import { MediaProtocol } from "./media-protocol.js"
-import { Generation, resultEvents, type AwaitOptions, type Observation } from "../generation.js"
+import { Generation } from "../generation.js"
 import type { Media } from "../media.js"
 import {
  AIError,
@@ -52,7 +52,7 @@ export const deployment = (
 // ---------------------------------------------------------------------------

 /** One request, one response. */
-export interface Route<Request extends MediaRequest, Response> {
+export interface InlineRoute<Request extends MediaRequest, Response> {
  readonly kind: "inline"
  readonly id: string
  readonly provider: ProviderID
@@ -86,7 +86,7 @@ export interface StreamRoute<Request extends MediaRequest, Event, Response> {
 }

 export type AnyRoute<Request extends MediaRequest, Event, Response> =
-  | Route<Request, Response>
+  | InlineRoute<Request, Response>
  | StreamRoute<Request, Event, Response>
  | QueuedRoute<Request, Response>

@@ -119,7 +119,7 @@ export interface StreamInput<Request extends MediaRequest, Event, Response, Fram
 */
 export const inline = <Request extends MediaRequest, Response>(
  input: InlineInput<Request, Response>,
-): Route<Request, Response> => {
+): InlineRoute<Request, Response> => {
  const transport = makeTransport(input)
  return {
    kind: "inline",
@@ -267,59 +267,6 @@ export const stream = <Request extends MediaRequest, Event, Response, Frame, Sta
  }
 }

-export const dispatch = <Event, Response>(input: {
-  readonly modality: string
-  readonly execute: Execute
-  readonly responseEvents: (response: Response) => ReadonlyArray<Event>
-}) => {
-  const notQueued = (route: { readonly provider: ProviderID; readonly id: string }, operation: string) =>
-    new AIError({
-      reason: new UnsupportedOperationError({
-        operation: `${input.modality}.${operation}`,
-        provider: route.provider,
-        route: route.id,
-        message: `${route.provider}/${route.id} is not a queued route; use generate or stream`,
-      }),
-    })
-  const start = <Request extends MediaRequest>(route: AnyRoute<Request, Event, Response>, request: Request) => {
-    if (route.kind !== "queued") return Effect.fail(notQueued(route, "start"))
-    return route.start(request, input.execute)
-  }
-  return {
-    start,
-    resume: <Request extends MediaRequest>(
-      route: AnyRoute<Request, Event, Response>,
-      model: MediaRequest["model"],
-      token: unknown,
-    ) => {
-      if (route.kind !== "queued") return Effect.fail(notQueued(route, "resume"))
-      return route.resume(model, token, input.execute)
-    },
-    generate: <Request extends MediaRequest>(
-      route: AnyRoute<Request, Event, Response>,
-      request: Request,
-      options?: AwaitOptions,
-    ) => {
-      if (route.kind !== "queued") return route.generate(request, input.execute)
-      return start(route, request).pipe(Effect.flatMap((generation) => generation.await(options)))
-    },
-    stream: <Request extends MediaRequest>(
-      route: AnyRoute<Request, Event, Response>,
-      request: Request,
-      options?: AwaitOptions,
-    ): Stream.Stream<Event | Observation, AIError> => {
-      if (route.kind === "stream") return route.stream(request, input.execute)
-      if (route.kind === "queued")
-        return Stream.unwrap(
-          start(route, request).pipe(
-            Effect.map((generation) => resultEvents(generation, input.responseEvents, options)),
-          ),
-        )
-      return Stream.fromIterableEffect(Effect.map(route.generate(request, input.execute), input.responseEvents))
-    },
-  }
-}
-
 // ---------------------------------------------------------------------------
 // Transport plumbing shared by every kind
 // ---------------------------------------------------------------------------
@@ -1,5 +1,5 @@
 import { Schema, type Effect } from "effect"
-import type { AIError, LLMEvent, LLMRequest, ProtocolID } from "../schema/index.js"
+import type { AIError, LanguageModelSanitizerCompatibility, LLMEvent, LLMRequest, ProtocolID } from "../schema/index.js"

 /**
 * The semantic API contract of one model server family.
@@ -43,6 +43,8 @@ export interface Protocol<Body, Frame, Event, State> {
  readonly stream: ProtocolStream<Frame, Event, State>
  /** Whether `body.from` lowers `Message.effort(...)` markers; wrappers around another `body.from` must forward it. */
  readonly supportsEffortUpdates?: (request: LLMRequest) => boolean
+  /** Tool schema sanitizer for every model on this protocol unless the model's compatibility sets one; wrappers around another `body.from` must forward it. */
+  readonly sanitizer?: LanguageModelSanitizerCompatibility
 }

 export interface ProtocolBody<Body> {
@@ -1,53 +1,31 @@
-import { Context, Effect, Layer, Stream } from "effect"
-import { RequestExecutor } from "./route/executor.js"
-import type { AIError } from "./schema/index.js"
-import type { SpeechEvent, SpeechOptions, SpeechRequestFor, SpeechResponse } from "./speech.js"
+import { Context } from "effect"
+import { MediaClient } from "./media-client.js"
+import {
+  SpeechTimestampsEvent,
+  SpeechFinishEvent,
+  type SpeechEvent,
+  type SpeechRequestFor,
+  type SpeechResponse,
+} from "./speech.js"

-export interface Interface {
-  readonly generate: <Options extends SpeechOptions>(
-    request: SpeechRequestFor<Options>,
-  ) => Effect.Effect<SpeechResponse, AIError>
-  readonly stream: <Options extends SpeechOptions>(
-    request: SpeechRequestFor<Options>,
-  ) => Stream.Stream<SpeechEvent, AIError>
-}
+export type Interface = MediaClient.Interface<SpeechRequestFor, SpeechEvent, SpeechResponse>

 export class SpeechClientService extends Context.Service<SpeechClientService, Interface>()("@opencode/SpeechClient") {}
 export const Service = SpeechClientService
 export type Service = SpeechClientService

-export const generate = <Options extends SpeechOptions>(
-  request: SpeechRequestFor<Options>,
-): Effect.Effect<SpeechResponse, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.generate(request)
-  })
-
-export const stream = <Options extends SpeechOptions>(
-  request: SpeechRequestFor<Options>,
-): Stream.Stream<SpeechEvent, AIError, Service> =>
-  Stream.unwrap(
-    Effect.gen(function* () {
-      const client = yield* Service
-      return client.stream(request)
-    }),
-  )
-
-export const layer: Layer.Layer<Service, never, RequestExecutor.Service> = Layer.effect(
-  Service,
-  Effect.gen(function* () {
-    const executor = yield* RequestExecutor.Service
-    return Service.of({
-      generate: (request) => request.model.route.generate(request, executor.execute),
-      stream: (request) => request.model.route.stream(request, executor.execute),
-    })
-  }),
-)
-
 export const SpeechClient = {
  Service,
-  layer,
-  generate,
-  stream,
+  ...MediaClient.make(Service, {
+    modality: "speech",
+    responseEvents: (response: SpeechResponse) => [
+      ...(response.timestamps === undefined ? [] : [SpeechTimestampsEvent.make({ items: response.timestamps })]),
+      SpeechFinishEvent.make({
+        audio: response.audio,
+        usage: response.usage,
+        notices: response.notices,
+        providerMetadata: response.providerMetadata,
+      }),
+    ],
+  }),
 } as const
@@ -1,8 +1,8 @@
 import { Effect, Schema, Stream } from "effect"
+import { ProgressEvent, QueuedEvent } from "./generation.js"
 import { Media } from "./media.js"
 import { MediaModel, composeRoute, tryRequest } from "./media-model.js"
 import { MediaRoute } from "./route/media.js"
-import type { MediaProtocol } from "./route/media-protocol.js"
 import { AIError, HttpOptions, MediaUsage, ProviderMetadata, type OpenString } from "./schema/index.js"
 import { SpeechClient, Service } from "./speech-client.js"

@@ -10,53 +10,39 @@ import { SpeechClient, Service } from "./speech-client.js"
 // Model
 // ---------------------------------------------------------------------------

-export type SpeechOptions = Record<string, unknown>
+export type SpeechOptions = MediaModel.Options

-export type SpeechRoute<Options extends SpeechOptions = SpeechOptions> = MediaRoute.StreamRoute<
-  SpeechRequestFor<Options>,
-  SpeechEvent,
-  SpeechResponse
->
+export type SpeechRoute = MediaRoute.AnyRoute<SpeechRequestFor, SpeechEvent, SpeechResponse>

-export class SpeechModel<Options extends SpeechOptions = SpeechOptions> extends MediaModel<
-  SpeechRoute<Options>,
-  Options
-> {
+export class SpeechModel<Options extends SpeechOptions = SpeechOptions> extends MediaModel<SpeechRoute, Options> {
  declare protected readonly _SpeechModel: void

-  static make<Options extends SpeechOptions = SpeechOptions>(input: MediaModel.Input<SpeechRoute<Options>>) {
-    return new SpeechModel<Options>(input)
-  }
-
-  /** Compose a streaming speech protocol with its canonical path into a model for one deployment. */
-  static fromRoute<Options extends SpeechOptions = SpeechOptions, Frame = unknown, State = unknown>(
-    route: SpeechModel.RouteInput<Options, Frame, State>,
+  /** The number of type arguments selects the kind: `<Options>`, `<Options, Frame, State>`, or `<Options, Token>`. */
+  static fromRoute<Options extends SpeechOptions>(
+    route: MediaModel.InlineRouteInput<SpeechRequestFor<Options>, SpeechResponse>,
+    input: MediaRoute.ModelInput,
+  ): SpeechModel<Options>
+  static fromRoute<Options extends SpeechOptions, Frame, State>(
+    route: MediaModel.StreamRouteInput<SpeechRequestFor<Options>, SpeechEvent, Frame, State>,
+    input: MediaRoute.ModelInput,
+  ): SpeechModel<Options>
+  static fromRoute<Options extends SpeechOptions, Token>(
+    route: MediaModel.QueuedRouteInput<SpeechRequestFor<Options>, SpeechResponse, Token>,
+    input: MediaRoute.ModelInput,
+  ): SpeechModel<Options>
+  static fromRoute<Options extends SpeechOptions, Frame, State, Token>(
+    route: MediaModel.AnyRouteInput<SpeechRequestFor<Options>, SpeechEvent, SpeechResponse, Frame, State, Token>,
    input: MediaRoute.ModelInput,
  ) {
    return new SpeechModel<Options>({
      id: input.id,
      provider: route.protocol.provider,
      http: input.http,
-      route: composeRoute(
-        (composition) => MediaRoute.stream({ ...composition, collect: collectResponse }),
-        route,
-        input,
-      ),
+      route: composeRoute(route, input, collectResponse) as SpeechRoute,
    })
  }
 }

-export namespace SpeechModel {
-  export type RouteInput<
-    Options extends SpeechOptions = SpeechOptions,
-    Frame = unknown,
-    State = unknown,
-  > = MediaModel.RouteInput<
-    MediaProtocol.Addressed<SpeechRequestFor<Options>>,
-    MediaProtocol.Streamed<SpeechRequestFor<Options>, SpeechEvent, Frame, State>
-  >
-}
-
 export const SpeechModelSchema = Schema.declare((value): value is SpeechModel => value instanceof SpeechModel, {
  expected: "Speech.Model",
 })
@@ -148,11 +134,17 @@ export const SpeechFinishEvent = Schema.Struct({
  providerMetadata: Schema.optional(ProviderMetadata),
 }).annotate({ identifier: "Speech.Event.Finish" })

-const speechEventTagged = Schema.Union([SpeechAudioDeltaEvent, SpeechTimestampsEvent, SpeechFinishEvent]).pipe(
-  Schema.toTaggedUnion("type"),
-)
+const speechEventTagged = Schema.Union([
+  QueuedEvent,
+  ProgressEvent,
+  SpeechAudioDeltaEvent,
+  SpeechTimestampsEvent,
+  SpeechFinishEvent,
+]).pipe(Schema.toTaggedUnion("type"))
 export const SpeechEvent = Object.assign(speechEventTagged, {
  is: {
+    generationQueued: speechEventTagged.guards["generation-queued"],
+    generationProgress: speechEventTagged.guards["generation-progress"],
    audioDelta: speechEventTagged.guards["audio-delta"],
    timestamps: speechEventTagged.guards.timestamps,
    finish: speechEventTagged.guards.finish,
@@ -195,17 +187,15 @@ export function request(input: SpeechRequest | SpeechRequestInput) {
 const requestEffect = (input: SpeechRequest | SpeechRequestInput) => tryRequest(() => request(input))

 export function generate<const Model extends SpeechModel>(
-  input: SpeechRequestInput<Model>,
+  input: SpeechRequest | SpeechRequestInput<Model>,
 ): Effect.Effect<SpeechResponse, AIError, Service>
-export function generate(input: SpeechRequest): Effect.Effect<SpeechResponse, AIError, Service>
 export function generate(input: SpeechRequest | SpeechRequestInput) {
  return requestEffect(input).pipe(Effect.flatMap((request) => SpeechClient.generate(request)))
 }

 export function stream<const Model extends SpeechModel>(
-  input: SpeechRequestInput<Model>,
+  input: SpeechRequest | SpeechRequestInput<Model>,
 ): Stream.Stream<SpeechEvent, AIError, Service>
-export function stream(input: SpeechRequest): Stream.Stream<SpeechEvent, AIError, Service>
 export function stream(input: SpeechRequest | SpeechRequestInput) {
  return Stream.unwrap(requestEffect(input).pipe(Effect.map((request) => SpeechClient.stream(request))))
 }
@@ -1,34 +1,13 @@
-import { Context, Effect, Layer, Stream } from "effect"
-import type { AwaitOptions, Generation } from "./generation.js"
-import { RequestExecutor } from "./route/executor.js"
-import { MediaRoute } from "./route/media.js"
-import type { AIError } from "./schema/index.js"
+import { Context } from "effect"
+import { MediaClient } from "./media-client.js"
 import {
-  responseEvents,
+  TranscriptionFinishEvent,
  type TranscriptionEvent,
-  type TranscriptionModel,
-  type TranscriptionOptions,
  type TranscriptionRequestFor,
  type TranscriptionResponse,
 } from "./transcription.js"

-export interface Interface {
-  readonly generate: <Options extends TranscriptionOptions>(
-    request: TranscriptionRequestFor<Options>,
-    options?: AwaitOptions,
-  ) => Effect.Effect<TranscriptionResponse, AIError>
-  readonly stream: <Options extends TranscriptionOptions>(
-    request: TranscriptionRequestFor<Options>,
-    options?: AwaitOptions,
-  ) => Stream.Stream<TranscriptionEvent, AIError>
-  readonly start: <Options extends TranscriptionOptions>(
-    request: TranscriptionRequestFor<Options>,
-  ) => Effect.Effect<Generation<TranscriptionResponse>, AIError>
-  readonly resume: <Options extends TranscriptionOptions>(
-    model: TranscriptionModel<Options>,
-    token: unknown,
-  ) => Effect.Effect<Generation<TranscriptionResponse>, AIError>
-}
+export type Interface = MediaClient.Interface<TranscriptionRequestFor, TranscriptionEvent, TranscriptionResponse>

 export class TranscriptionClientService extends Context.Service<TranscriptionClientService, Interface>()(
  "@opencode/TranscriptionClient",
@@ -36,66 +15,10 @@ export class TranscriptionClientService extends Context.Service<TranscriptionCli
 export const Service = TranscriptionClientService
 export type Service = TranscriptionClientService

-export const generate = <Options extends TranscriptionOptions>(
-  request: TranscriptionRequestFor<Options>,
-  options?: AwaitOptions,
-): Effect.Effect<TranscriptionResponse, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.generate(request, options)
-  })
-
-export const stream = <Options extends TranscriptionOptions>(
-  request: TranscriptionRequestFor<Options>,
-  options?: AwaitOptions,
-): Stream.Stream<TranscriptionEvent, AIError, Service> =>
-  Stream.unwrap(
-    Effect.gen(function* () {
-      const client = yield* Service
-      return client.stream(request, options)
-    }),
-  )
-
-export const start = <Options extends TranscriptionOptions>(
-  request: TranscriptionRequestFor<Options>,
-): Effect.Effect<Generation<TranscriptionResponse>, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.start(request)
-  })
-
-export const resume = <Options extends TranscriptionOptions>(
-  model: TranscriptionModel<Options>,
-  token: unknown,
-): Effect.Effect<Generation<TranscriptionResponse>, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.resume(model, token)
-  })
-
-export const layer: Layer.Layer<Service, never, RequestExecutor.Service> = Layer.effect(
-  Service,
-  Effect.gen(function* () {
-    const executor = yield* RequestExecutor.Service
-    const dispatch = MediaRoute.dispatch<TranscriptionEvent, TranscriptionResponse>({
-      modality: "transcription",
-      execute: executor.execute,
-      responseEvents,
-    })
-    return Service.of({
-      start: (request) => dispatch.start(request.model.route, request),
-      resume: (model, token) => dispatch.resume(model.route, model, token),
-      generate: (request, options) => dispatch.generate(request.model.route, request, options),
-      stream: (request, options) => dispatch.stream(request.model.route, request, options),
-    })
-  }),
-)
-
 export const TranscriptionClient = {
  Service,
-  layer,
-  generate,
-  stream,
-  start,
-  resume,
+  ...MediaClient.make(Service, {
+    modality: "transcription",
+    responseEvents: (response: TranscriptionResponse) => [TranscriptionFinishEvent.make({ ...response })],
+  }),
 } as const
@@ -1,9 +1,8 @@
 import { Effect, Schema, Stream } from "effect"
 import { Generation, ProgressEvent, QueuedEvent, type AwaitOptions } from "./generation.js"
 import { Media } from "./media.js"
-import { MediaModel, composeAnyRoute, tryRequest } from "./media-model.js"
+import { MediaModel, composeRoute, tryRequest } from "./media-model.js"
 import { MediaRoute } from "./route/media.js"
-import type { MediaProtocol } from "./route/media-protocol.js"
 import { AIError, HttpOptions, MediaUsage, ProviderMetadata } from "./schema/index.js"
 import { TranscriptionClient, Service } from "./transcription-client.js"

@@ -11,90 +10,49 @@ import { TranscriptionClient, Service } from "./transcription-client.js"
 // Model
 // ---------------------------------------------------------------------------

-export type TranscriptionOptions = Record<string, unknown>
+export type TranscriptionOptions = MediaModel.Options

-export type TranscriptionRoute<Options extends TranscriptionOptions = TranscriptionOptions> = MediaRoute.AnyRoute<
-  TranscriptionRequestFor<Options>,
-  TranscriptionEvent,
-  TranscriptionResponse
->
+export type TranscriptionRoute = MediaRoute.AnyRoute<TranscriptionRequestFor, TranscriptionEvent, TranscriptionResponse>

 export class TranscriptionModel<Options extends TranscriptionOptions = TranscriptionOptions> extends MediaModel<
-  TranscriptionRoute<Options>,
+  TranscriptionRoute,
  Options
 > {
  declare protected readonly _TranscriptionModel: void

-  static make<Options extends TranscriptionOptions = TranscriptionOptions>(
-    input: MediaModel.Input<TranscriptionRoute<Options>>,
-  ) {
-    return new TranscriptionModel<Options>(input)
-  }
-
  /** The number of type arguments selects the kind: `<Options>`, `<Options, Frame, State>`, or `<Options, Token>`. */
  static fromRoute<Options extends TranscriptionOptions>(
-    route: TranscriptionModel.InlineRouteInput<Options>,
+    route: MediaModel.InlineRouteInput<TranscriptionRequestFor<Options>, TranscriptionResponse>,
    input: MediaRoute.ModelInput,
  ): TranscriptionModel<Options>
  static fromRoute<Options extends TranscriptionOptions, Frame, State>(
-    route: TranscriptionModel.StreamRouteInput<Options, Frame, State>,
+    route: MediaModel.StreamRouteInput<TranscriptionRequestFor<Options>, TranscriptionEvent, Frame, State>,
    input: MediaRoute.ModelInput,
  ): TranscriptionModel<Options>
  static fromRoute<Options extends TranscriptionOptions, Token>(
-    route: TranscriptionModel.QueuedRouteInput<Options, Token>,
+    route: MediaModel.QueuedRouteInput<TranscriptionRequestFor<Options>, TranscriptionResponse, Token>,
    input: MediaRoute.ModelInput,
  ): TranscriptionModel<Options>
  static fromRoute<Options extends TranscriptionOptions, Frame, State, Token>(
-    route: TranscriptionModel.RouteInput<Options, Frame, State, Token>,
+    route: MediaModel.AnyRouteInput<
+      TranscriptionRequestFor<Options>,
+      TranscriptionEvent,
+      TranscriptionResponse,
+      Frame,
+      State,
+      Token
+    >,
    input: MediaRoute.ModelInput,
  ) {
    return new TranscriptionModel<Options>({
      id: input.id,
      provider: route.protocol.provider,
      http: input.http,
-      route: composeAnyRoute(route, input, collectResponse),
+      route: composeRoute(route, input, collectResponse) as TranscriptionRoute,
    })
  }
 }

-export namespace TranscriptionModel {
-  export type InlineRouteInput<Options extends TranscriptionOptions = TranscriptionOptions> = MediaModel.RouteInput<
-    TranscriptionRequestFor<Options>,
-    MediaProtocol.Inline<TranscriptionRequestFor<Options>, TranscriptionResponse>
-  >
-
-  export type StreamRouteInput<
-    Options extends TranscriptionOptions = TranscriptionOptions,
-    Frame = unknown,
-    State = unknown,
-  > = MediaModel.RouteInput<
-    MediaProtocol.Addressed<TranscriptionRequestFor<Options>>,
-    MediaProtocol.Streamed<TranscriptionRequestFor<Options>, TranscriptionEvent, Frame, State>
-  >
-
-  export type QueuedRouteInput<
-    Options extends TranscriptionOptions = TranscriptionOptions,
-    Token = unknown,
-  > = MediaModel.RouteInput<
-    TranscriptionRequestFor<Options>,
-    MediaProtocol.Queued<TranscriptionRequestFor<Options>, TranscriptionResponse, Token>
-  >
-
-  export type RouteInput<
-    Options extends TranscriptionOptions = TranscriptionOptions,
-    Frame = unknown,
-    State = unknown,
-    Token = unknown,
-  > = MediaModel.AnyRouteInput<
-    TranscriptionRequestFor<Options>,
-    TranscriptionEvent,
-    TranscriptionResponse,
-    Frame,
-    State,
-    Token
-  >
-}
-
 export const TranscriptionModelSchema = Schema.declare(
  (value): value is TranscriptionModel => value instanceof TranscriptionModel,
  { expected: "Transcription.Model" },
@@ -212,10 +170,6 @@ export const TranscriptionEvent = Object.assign(transcriptionEventTagged, {
 })
 export type TranscriptionEvent = Schema.Schema.Type<typeof transcriptionEventTagged>

-export const responseEvents = (response: TranscriptionResponse): ReadonlyArray<TranscriptionEvent> => [
-  TranscriptionFinishEvent.make({ ...response }),
-]
-
 const collectResponse = (events: ReadonlyArray<TranscriptionEvent>): Effect.Effect<TranscriptionResponse> => {
  const finish = events.find(TranscriptionEvent.is.finish)
  // Every transcription protocol's `finish` emits the terminal event or fails, so a completed stream always has one.
@@ -243,11 +197,7 @@ export function request(input: TranscriptionRequest | TranscriptionRequestInput)
 const requestEffect = (input: TranscriptionRequest | TranscriptionRequestInput) => tryRequest(() => request(input))

 export function generate<const Model extends TranscriptionModel>(
-  input: TranscriptionRequestInput<Model>,
-  options?: AwaitOptions,
-): Effect.Effect<TranscriptionResponse, AIError, Service>
-export function generate(
-  input: TranscriptionRequest,
+  input: TranscriptionRequest | TranscriptionRequestInput<Model>,
  options?: AwaitOptions,
 ): Effect.Effect<TranscriptionResponse, AIError, Service>
 export function generate(input: TranscriptionRequest | TranscriptionRequestInput, options?: AwaitOptions) {
@@ -255,11 +205,7 @@ export function generate(input: TranscriptionRequest | TranscriptionRequestInput
 }

 export function stream<const Model extends TranscriptionModel>(
-  input: TranscriptionRequestInput<Model>,
-  options?: AwaitOptions,
-): Stream.Stream<TranscriptionEvent, AIError, Service>
-export function stream(
-  input: TranscriptionRequest,
+  input: TranscriptionRequest | TranscriptionRequestInput<Model>,
  options?: AwaitOptions,
 ): Stream.Stream<TranscriptionEvent, AIError, Service>
 export function stream(input: TranscriptionRequest | TranscriptionRequestInput, options?: AwaitOptions) {
@@ -268,15 +214,14 @@ export function stream(input: TranscriptionRequest | TranscriptionRequestInput,

 /** Inline and streaming routes fail with `UnsupportedOperation`. */
 export function start<const Model extends TranscriptionModel>(
-  input: TranscriptionRequestInput<Model>,
+  input: TranscriptionRequest | TranscriptionRequestInput<Model>,
 ): Effect.Effect<Generation<TranscriptionResponse>, AIError, Service>
-export function start(input: TranscriptionRequest): Effect.Effect<Generation<TranscriptionResponse>, AIError, Service>
 export function start(input: TranscriptionRequest | TranscriptionRequestInput) {
  return requestEffect(input).pipe(Effect.flatMap((request) => TranscriptionClient.start(request)))
 }

-export const resume = <Options extends TranscriptionOptions>(
-  model: TranscriptionModel<Options>,
+export const resume = (
+  model: TranscriptionModel,
  token: unknown,
 ): Effect.Effect<Generation<TranscriptionResponse>, AIError, Service> => TranscriptionClient.resume(model, token)

@@ -1,98 +1,30 @@
-import { Context, Effect, Layer, Stream } from "effect"
-import { resultEvents, type AwaitOptions, type Generation } from "./generation.js"
-import { RequestExecutor } from "./route/executor.js"
-import type { AIError } from "./schema/index.js"
+import { Context } from "effect"
+import { MediaClient } from "./media-client.js"
 import {
-  responseEvents,
+  VideoOutputEvent,
+  VideoFinishEvent,
  type VideoEvent,
-  type VideoModel,
-  type VideoOptions,
  type VideoRequestFor,
  type VideoResponse,
 } from "./video.js"

-export interface Interface {
-  readonly start: <Options extends VideoOptions>(
-    request: VideoRequestFor<Options>,
-  ) => Effect.Effect<Generation<VideoResponse>, AIError>
-  readonly resume: <Options extends VideoOptions>(
-    model: VideoModel<Options>,
-    token: unknown,
-  ) => Effect.Effect<Generation<VideoResponse>, AIError>
-  readonly generate: <Options extends VideoOptions>(
-    request: VideoRequestFor<Options>,
-    options?: AwaitOptions,
-  ) => Effect.Effect<VideoResponse, AIError>
-  readonly stream: <Options extends VideoOptions>(
-    request: VideoRequestFor<Options>,
-    options?: AwaitOptions,
-  ) => Stream.Stream<VideoEvent, AIError>
-}
+export type Interface = MediaClient.Interface<VideoRequestFor, VideoEvent, VideoResponse>

 export class VideoClientService extends Context.Service<VideoClientService, Interface>()("@opencode/VideoClient") {}
 export const Service = VideoClientService
 export type Service = VideoClientService

-export const start = <Options extends VideoOptions>(
-  request: VideoRequestFor<Options>,
-): Effect.Effect<Generation<VideoResponse>, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.start(request)
-  })
-
-export const resume = <Options extends VideoOptions>(
-  model: VideoModel<Options>,
-  token: unknown,
-): Effect.Effect<Generation<VideoResponse>, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.resume(model, token)
-  })
-
-export const generate = <Options extends VideoOptions>(
-  request: VideoRequestFor<Options>,
-  options?: AwaitOptions,
-): Effect.Effect<VideoResponse, AIError, Service> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.generate(request, options)
-  })
-
-export const stream = <Options extends VideoOptions>(
-  request: VideoRequestFor<Options>,
-  options?: AwaitOptions,
-): Stream.Stream<VideoEvent, AIError, Service> =>
-  Stream.unwrap(
-    Effect.gen(function* () {
-      const client = yield* Service
-      return client.stream(request, options)
-    }),
-  )
-
-export const layer: Layer.Layer<Service, never, RequestExecutor.Service> = Layer.effect(
-  Service,
-  Effect.gen(function* () {
-    const executor = yield* RequestExecutor.Service
-    const start = <Options extends VideoOptions>(request: VideoRequestFor<Options>) =>
-      request.model.route.start(request, executor.execute)
-    return Service.of({
-      start,
-      resume: (model, token) => model.route.resume(model, token, executor.execute),
-      generate: (request, options) => start(request).pipe(Effect.flatMap((generation) => generation.await(options))),
-      stream: (request, options) =>
-        Stream.unwrap(
-          start(request).pipe(Effect.map((generation) => resultEvents(generation, responseEvents, options))),
-        ),
-    })
-  }),
-)
-
 export const VideoClient = {
  Service,
-  layer,
-  start,
-  resume,
-  generate,
-  stream,
+  ...MediaClient.make(Service, {
+    modality: "video",
+    responseEvents: (response: VideoResponse) => [
+      ...response.videos.map((video, index) => VideoOutputEvent.make({ index, video })),
+      VideoFinishEvent.make({
+        usage: response.usage,
+        notices: response.notices,
+        providerMetadata: response.providerMetadata,
+      }),
+    ],
+  }),
 } as const
@@ -3,7 +3,6 @@ import { Generation, ProgressEvent, QueuedEvent, type AwaitOptions } from "./gen
 import { Media } from "./media.js"
 import { MediaModel, composeRoute, tryRequest } from "./media-model.js"
 import { MediaRoute } from "./route/media.js"
-import type { MediaProtocol } from "./route/media-protocol.js"
 import { AIError, HttpOptions, MediaUsage, ProviderMetadata, type OpenString } from "./schema/index.js"
 import { VideoClient, Service } from "./video-client.js"

@@ -11,41 +10,39 @@ import { VideoClient, Service } from "./video-client.js"
 // Model
 // ---------------------------------------------------------------------------

-export type VideoOptions = Record<string, unknown>
+export type VideoOptions = MediaModel.Options

-export type VideoRoute<Options extends VideoOptions = VideoOptions> = MediaRoute.QueuedRoute<
-  VideoRequestFor<Options>,
-  VideoResponse
->
+export type VideoRoute = MediaRoute.AnyRoute<VideoRequestFor, VideoEvent, VideoResponse>

-export class VideoModel<Options extends VideoOptions = VideoOptions> extends MediaModel<VideoRoute<Options>, Options> {
+export class VideoModel<Options extends VideoOptions = VideoOptions> extends MediaModel<VideoRoute, Options> {
  declare protected readonly _VideoModel: void

-  static make<Options extends VideoOptions = VideoOptions>(input: MediaModel.Input<VideoRoute<Options>>) {
-    return new VideoModel<Options>(input)
-  }
-
-  /** Compose a queued video protocol with its canonical start path into a model for one deployment. */
-  static fromRoute<Options extends VideoOptions = VideoOptions, Token = unknown>(
-    route: VideoModel.RouteInput<Options, Token>,
+  /** The number of type arguments selects the kind: `<Options>`, `<Options, Frame, State>`, or `<Options, Token>`. */
+  static fromRoute<Options extends VideoOptions>(
+    route: MediaModel.InlineRouteInput<VideoRequestFor<Options>, VideoResponse>,
+    input: MediaRoute.ModelInput,
+  ): VideoModel<Options>
+  static fromRoute<Options extends VideoOptions, Frame, State>(
+    route: MediaModel.StreamRouteInput<VideoRequestFor<Options>, VideoEvent, Frame, State>,
+    input: MediaRoute.ModelInput,
+  ): VideoModel<Options>
+  static fromRoute<Options extends VideoOptions, Token>(
+    route: MediaModel.QueuedRouteInput<VideoRequestFor<Options>, VideoResponse, Token>,
+    input: MediaRoute.ModelInput,
+  ): VideoModel<Options>
+  static fromRoute<Options extends VideoOptions, Frame, State, Token>(
+    route: MediaModel.AnyRouteInput<VideoRequestFor<Options>, VideoEvent, VideoResponse, Frame, State, Token>,
    input: MediaRoute.ModelInput,
  ) {
    return new VideoModel<Options>({
      id: input.id,
      provider: route.protocol.provider,
      http: input.http,
-      route: composeRoute(MediaRoute.queued, route, input),
+      route: composeRoute(route, input, collectResponse) as VideoRoute,
    })
  }
 }

-export namespace VideoModel {
-  export type RouteInput<Options extends VideoOptions = VideoOptions, Token = unknown> = MediaModel.RouteInput<
-    VideoRequestFor<Options>,
-    MediaProtocol.Queued<VideoRequestFor<Options>, VideoResponse, Token>
-  >
-}
-
 export const VideoModelSchema = Schema.declare((value): value is VideoModel => value instanceof VideoModel, {
  expected: "Video.Model",
 })
@@ -149,15 +146,19 @@ export const VideoEvent = Object.assign(videoEventTagged, {
 })
 export type VideoEvent = Schema.Schema.Type<typeof videoEventTagged>

-/** A completed response expanded into the streaming event shape. */
-export const responseEvents = (response: VideoResponse): ReadonlyArray<VideoEvent> => [
-  ...response.videos.map((video, index) => VideoOutputEvent.make({ index, video })),
-  VideoFinishEvent.make({
-    usage: response.usage,
-    notices: response.notices,
-    providerMetadata: response.providerMetadata,
-  }),
-]
+const collectResponse = (events: ReadonlyArray<VideoEvent>): Effect.Effect<VideoResponse> => {
+  const finish = events.find(VideoEvent.is.finish)
+  // A streaming video protocol's `finish` emits the terminal event or fails, so a completed stream always has one.
+  if (finish === undefined) return Effect.die(new Error("The video stream completed without a finish event"))
+  return Effect.succeed(
+    new VideoResponse({
+      videos: events.filter(VideoEvent.is.video).map((event) => event.video),
+      usage: finish.usage,
+      notices: finish.notices,
+      providerMetadata: finish.providerMetadata,
+    }),
+  )
+}

 // ---------------------------------------------------------------------------
 // Request-shaped call API
@@ -178,33 +179,28 @@ export function request(input: VideoRequest | VideoRequestInput) {
 const requestEffect = (input: VideoRequest | VideoRequestInput) => tryRequest(() => request(input))

 export function start<const Model extends VideoModel>(
-  input: VideoRequestInput<Model>,
+  input: VideoRequest | VideoRequestInput<Model>,
 ): Effect.Effect<Generation<VideoResponse>, AIError, Service>
-export function start(input: VideoRequest): Effect.Effect<Generation<VideoResponse>, AIError, Service>
 export function start(input: VideoRequest | VideoRequestInput) {
  return requestEffect(input).pipe(Effect.flatMap((request) => VideoClient.start(request)))
 }

 export function generate<const Model extends VideoModel>(
-  input: VideoRequestInput<Model>,
+  input: VideoRequest | VideoRequestInput<Model>,
  options?: AwaitOptions,
 ): Effect.Effect<VideoResponse, AIError, Service>
-export function generate(input: VideoRequest, options?: AwaitOptions): Effect.Effect<VideoResponse, AIError, Service>
 export function generate(input: VideoRequest | VideoRequestInput, options?: AwaitOptions) {
  return requestEffect(input).pipe(Effect.flatMap((request) => VideoClient.generate(request, options)))
 }

 /** Rebuild a generation handle from a persisted `Generation.token`, refreshing its status once. */
-export const resume = <Options extends VideoOptions>(
-  model: VideoModel<Options>,
-  token: unknown,
-): Effect.Effect<Generation<VideoResponse>, AIError, Service> => VideoClient.resume(model, token)
+export const resume = (model: VideoModel, token: unknown): Effect.Effect<Generation<VideoResponse>, AIError, Service> =>
+  VideoClient.resume(model, token)

 export function stream<const Model extends VideoModel>(
-  input: VideoRequestInput<Model>,
+  input: VideoRequest | VideoRequestInput<Model>,
  options?: AwaitOptions,
 ): Stream.Stream<VideoEvent, AIError, Service>
-export function stream(input: VideoRequest, options?: AwaitOptions): Stream.Stream<VideoEvent, AIError, Service>
 export function stream(input: VideoRequest | VideoRequestInput, options?: AwaitOptions) {
  return Stream.unwrap(requestEffect(input).pipe(Effect.map((request) => VideoClient.stream(request, options))))
 }
@@ -54,6 +54,19 @@ import { TestLLM } from "@opencode/ai/testing"
 import { Evaluation, EvaluationClient } from "@opencode/ai/experimental"

 describe("public exports", () => {
+  test("modality, provider, and protocol entrypoints load first in a fresh process", async () => {
+    const results = await Promise.all(
+      ["image", "video", "speech", "transcription", "providers", "protocols"].map(async (entry) => {
+        const child = Bun.spawn(
+          [process.execPath, "-e", `await import(${JSON.stringify(`${import.meta.dir}/../src/${entry}.ts`)})`],
+          { stderr: "pipe" },
+        )
+        return { entry, exitCode: await child.exited, stderr: await new Response(child.stderr).text() }
+      }),
+    )
+    expect(results.filter((result) => result.exitCode !== 0)).toEqual([])
+  })
+
  test("root exposes app-facing runtime APIs", () => {
    expect(LLM.request).toBeFunction()
    expect(LLMClient.Service).toBeFunction()
@@ -30,7 +30,7 @@
        {
          "direction": "client",
          "kind": "text",
-          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"What is the weather in Paris?\"}]}],\"instructions\":\"Call get_weather once, then reply exactly: Paris is sunny.\",\"tools\":[{\"type\":\"function\",\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":50}"
+          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"What is the weather in Paris?\"}]}],\"instructions\":\"Call get_weather once, then reply exactly: Paris is sunny.\",\"tools\":[{\"type\":\"function\",\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"max_output_tokens\":50}"
        },
        {
          "direction": "server",
@@ -525,7 +525,7 @@
        {
          "direction": "client",
          "kind": "text",
-          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"function_call_output\",\"call_id\":\"call_hejtTYDa1IfLyNIzb3fq9gJs\",\"output\":\"{\\\"temperature\\\":22,\\\"condition\\\":\\\"sunny\\\"}\"}],\"instructions\":\"Call get_weather once, then reply exactly: Paris is sunny.\",\"tools\":[{\"type\":\"function\",\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":50,\"previous_response_id\":\"resp_0d9a44b6df400533016aa8c8e36de887d1be260913d131b2ca\"}"
+          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"function_call_output\",\"call_id\":\"call_hejtTYDa1IfLyNIzb3fq9gJs\",\"output\":\"{\\\"temperature\\\":22,\\\"condition\\\":\\\"sunny\\\"}\"}],\"instructions\":\"Call get_weather once, then reply exactly: Paris is sunny.\",\"tools\":[{\"type\":\"function\",\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"max_output_tokens\":50,\"previous_response_id\":\"resp_0d9a44b6df400533016aa8c8e36de887d1be260913d131b2ca\"}"
        },
        {
          "direction": "server",
@@ -30,7 +30,7 @@
        {
          "direction": "client",
          "kind": "text",
-          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Alpha.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":30}"
+          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Alpha.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"max_output_tokens\":30}"
        },
        {
          "direction": "server",
@@ -109,7 +109,7 @@
        {
          "direction": "client",
          "kind": "text",
-          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Alpha.\"}]},{\"type\":\"reasoning\",\"id\":\"rs_0a6277dd90b94da1016aa8c946e33487d1b725d8e9dc874d82\",\"summary\":[],\"encrypted_content\":\"gAAAAABqqMlHtG6yltzgW_UjUcYxvl2hoMkk7cSEH5SJMe9CR5gKIaCwCh4peUo8XZrd-EU-tPCXthv7JzHxYXGVG1fIgTJ7BjBoOh-jgd_oGlyCHj2jVPj7nVoB763tZHdyw_ovL3V7GpJ6VeLGIsRGqNTnBz47ZuKikTKrbTDupn6fT2wOM_69zwdg4-UVnoF2J9E_jIS0E5XRtRwOidqANl61HCwo7LV-Ut1aqCbXb-59vkrVGWPxD_8n4smf7Qjywc0FCH9zwuEDX4cPdqj3MmjvYPrn0jand9LKkAy9rblaKFJSQfVuritbHrdQrnF7hLxu7QCpehOzNXOkpNEqTmXnAZjfMc53hq-ahH6_KoyYlZpompwyGPngFeI2fKRqP8rlpDilD1BHBsh-bl7kXzI8HQ_jameXwPZ1La6gjtFkThXp53BD6BOx11SB9Nypqolu2at5rR32UYcGrYeGtTu-HmYGp0oHVFkHumTNuHKmGXV14dI9swgryfygLhX8EAJrWrjm3e8rvAkHKpAZ0IkmlCcp15UCFDKNeS560fQVRaKXWnQ7m0Ih3C1xG0ifJ89j27c8GHo1kAhEJk-lSB1-FZr9Ls_w7N772kmZ2a7LLswu3kNW78kPas_CtcOnBOHwE1DhcVh5YpxwNftOHZnK1v8NPNF8EWqio4ArZy1thMCzH5zXBNcFzgd8tePMFukBblSP8QGQoVYRqTne2sFoOZXjslXnDvEe-Ycj8X38zWgRiwAk7guOloFC7Se36KCDP357773Vah86gWCt55mSEyhVW_GF1oTuHvJ18GZsXcyN21scF1PSr8YaCM_jR7ZkU1GYXbQK2Y4oTAV9XDptQA5YzEREnn7muC_6v5ZTAglZF1lhn9Q0NwmylZEAXJdSGHaqXt1Hv-vQlprA_9m22vrreBOTLPnVK946J8absKrwfe-jK_1n_9YQR43uwH8XwFBFND0c4lICCQGbxwM8pX4ACWR0c19aORCYm-M5FrJsxmG29_aDVNhcvkoQ3mlP7ITQeqzkrjfytSwLb2BYpXYZKjEHNfV9j3JoxJobUkK5hrxXBhTvZzbVBnE0LSXQNwR-JAcOliP_jXBEeQ-28B-aGW8TI1vEP2i260QKgzOzPC-pOoFp0-vCvxojNO0kE16ECVTLfDNLNGSMZs9vekdLJP2akBp6PsUXsQUTbWO_wr1E2oNU7ctfMRxoh-yP0ZW2_xz_NjE72O5LF_6zuDVV7Q==\"},{\"type\":\"message\",\"id\":\"msg_0a6277dd90b94da1016aa8c947253887d184c150fcbcbcd8da\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\",\"text\":\"Alpha.\"}],\"phase\":\"final_answer\",\"status\":\"completed\"},{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Beta.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":30}"
+          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Alpha.\"}]},{\"type\":\"reasoning\",\"id\":\"rs_0a6277dd90b94da1016aa8c946e33487d1b725d8e9dc874d82\",\"summary\":[],\"encrypted_content\":\"gAAAAABqqMlHtG6yltzgW_UjUcYxvl2hoMkk7cSEH5SJMe9CR5gKIaCwCh4peUo8XZrd-EU-tPCXthv7JzHxYXGVG1fIgTJ7BjBoOh-jgd_oGlyCHj2jVPj7nVoB763tZHdyw_ovL3V7GpJ6VeLGIsRGqNTnBz47ZuKikTKrbTDupn6fT2wOM_69zwdg4-UVnoF2J9E_jIS0E5XRtRwOidqANl61HCwo7LV-Ut1aqCbXb-59vkrVGWPxD_8n4smf7Qjywc0FCH9zwuEDX4cPdqj3MmjvYPrn0jand9LKkAy9rblaKFJSQfVuritbHrdQrnF7hLxu7QCpehOzNXOkpNEqTmXnAZjfMc53hq-ahH6_KoyYlZpompwyGPngFeI2fKRqP8rlpDilD1BHBsh-bl7kXzI8HQ_jameXwPZ1La6gjtFkThXp53BD6BOx11SB9Nypqolu2at5rR32UYcGrYeGtTu-HmYGp0oHVFkHumTNuHKmGXV14dI9swgryfygLhX8EAJrWrjm3e8rvAkHKpAZ0IkmlCcp15UCFDKNeS560fQVRaKXWnQ7m0Ih3C1xG0ifJ89j27c8GHo1kAhEJk-lSB1-FZr9Ls_w7N772kmZ2a7LLswu3kNW78kPas_CtcOnBOHwE1DhcVh5YpxwNftOHZnK1v8NPNF8EWqio4ArZy1thMCzH5zXBNcFzgd8tePMFukBblSP8QGQoVYRqTne2sFoOZXjslXnDvEe-Ycj8X38zWgRiwAk7guOloFC7Se36KCDP357773Vah86gWCt55mSEyhVW_GF1oTuHvJ18GZsXcyN21scF1PSr8YaCM_jR7ZkU1GYXbQK2Y4oTAV9XDptQA5YzEREnn7muC_6v5ZTAglZF1lhn9Q0NwmylZEAXJdSGHaqXt1Hv-vQlprA_9m22vrreBOTLPnVK946J8absKrwfe-jK_1n_9YQR43uwH8XwFBFND0c4lICCQGbxwM8pX4ACWR0c19aORCYm-M5FrJsxmG29_aDVNhcvkoQ3mlP7ITQeqzkrjfytSwLb2BYpXYZKjEHNfV9j3JoxJobUkK5hrxXBhTvZzbVBnE0LSXQNwR-JAcOliP_jXBEeQ-28B-aGW8TI1vEP2i260QKgzOzPC-pOoFp0-vCvxojNO0kE16ECVTLfDNLNGSMZs9vekdLJP2akBp6PsUXsQUTbWO_wr1E2oNU7ctfMRxoh-yP0ZW2_xz_NjE72O5LF_6zuDVV7Q==\"},{\"type\":\"message\",\"id\":\"msg_0a6277dd90b94da1016aa8c947253887d184c150fcbcbcd8da\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\",\"text\":\"Alpha.\"}],\"phase\":\"final_answer\",\"status\":\"completed\"},{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Beta.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"max_output_tokens\":30}"
        },
        {
          "direction": "server",
@@ -30,7 +30,7 @@
        {
          "direction": "client",
          "kind": "text",
-          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Ready.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":30}"
+          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Ready.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"max_output_tokens\":30}"
        },
        {
          "direction": "server",
@@ -109,7 +109,7 @@
        {
          "direction": "client",
          "kind": "text",
-          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Recovered.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":30,\"previous_response_id\":\"resp_01cc0cda24c36acf016aa8ca3c1d3c87d1853283f43675e411\"}"
+          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Recovered.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"max_output_tokens\":30,\"previous_response_id\":\"resp_01cc0cda24c36acf016aa8ca3c1d3c87d1853283f43675e411\"}"
        },
        {
          "direction": "server",
@@ -119,7 +119,7 @@
        {
          "direction": "client",
          "kind": "text",
-          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Ready.\"}]},{\"type\":\"reasoning\",\"id\":\"rs_01cc0cda24c36acf016aa8ca3ced8c87d1a14c5c6a2ced8544\",\"summary\":[],\"encrypted_content\":\"gAAAAABqqMo9M9B15LsV1CLsXpNJpI08GiwCAK97hTheQ2s1lAkKgARMs1HIIXVeAr-tbUq88yN51fiQE8HvGwdF9ZcNI6bZle8D2PS7m8O7UE7C1Z_HX_fz2f0pRHyRfWpVAT2gYQWIDeZu6UdgxbKPBW0lXWzlk_relHG5x6nXkYzZoEeVasqavWoyMMSX7cexe-IYJh6e_3DgRpOchueS8Z-70P0w5R83Ea7UXZQNhMA0yDEYu_td2PmE2Pd2PUTOB3mxF2pb1z7-2t6S0UryhHx0az7Gh2eT60GGUqz9CIZzNE_FX--tszeuO0eI92Cen5tirOUHBTyyDqE0eG26DRl_p_U-xDZwaQONbtYbvkvrj-G7FA2oZXxjJPHuQZsNgBslXS-H0KT1lx3Y8XJ9QMVjFLFaucFG64wCmXPfCH8dYtX_YqfYQR4lwNfiSbyJEX2oDvTVVD_aCJ9NRo7c0aCTtmKBvr6fvvAy3MAFxAp_Sm2nMx4P5GYO4qAmJDByywKw-VK1vHlv3NRmVsAgbArIFgm-axoCs2PLpvZjDqeQGPavaq8zKWTyZYqBsEzKZUtGOZfYjD4mud0Z08I4i2H4K-L00ccVauode3548ZipOIuslbhJxonQXsF6TFdW2Hj8E5JjoEr5IbmwHyI0PBcDWW5AmkjHLwr9v08mFppRoD-2wzPAd5igROAuUbvJhiQd2A-uOaohwMjdpFxrjyUqgGTlI5g7tmI1ceeQWms0bKm8Pd0wIqVM3Nq6YvV7XfEyeogfRMVUQezr_lES42ZMVAoKBSFzMysDwCFkhVNVclcTUpcUUbVp21FChG7Ag-xuq8Cl6OGLA8nWX1C0aCf2HNa-n3dkYr1DtUziurh1MD-UIs5jdGiq3ptrc0VaVZwNdD4jVfAoHB_Ws7GiISXuclfpqsG3DTJEfzlbukI1vxXrt3FArsHiQvQjW5UM7gGel32M6p8AlXRxnez9PgIuU1WrtBUJetk7m39AZwp_aqbqC-AJ-MF70xP1VJZwFN-GeNL3VZsRHePFG4h7Pj---CCZRGlmzuE1-b-sIE7Bn_gbue_qHFMZJhAY55MO25vfZbSoLWHZCGmLMjJVHOYCPoy6l7zyxNmoIcC58QILNWal31KLCDmCsASmZC-xQRjyFwt-kvLbvk38Dc02IKcP3ujhf6WRRr1A0hh1K-gXmv_XI_MUFDcOVIjjhB-rXgjWSCoKfaKI9GCIsb9mnNaBq65BWg==\"},{\"type\":\"message\",\"id\":\"msg_01cc0cda24c36acf016aa8ca3d4a0087d1950121aed4802434\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\",\"text\":\"Ready.\"}],\"phase\":\"final_answer\",\"status\":\"completed\"},{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Recovered.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":30}"
+          "body": "{\"type\":\"response.create\",\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Ready.\"}]},{\"type\":\"reasoning\",\"id\":\"rs_01cc0cda24c36acf016aa8ca3ced8c87d1a14c5c6a2ced8544\",\"summary\":[],\"encrypted_content\":\"gAAAAABqqMo9M9B15LsV1CLsXpNJpI08GiwCAK97hTheQ2s1lAkKgARMs1HIIXVeAr-tbUq88yN51fiQE8HvGwdF9ZcNI6bZle8D2PS7m8O7UE7C1Z_HX_fz2f0pRHyRfWpVAT2gYQWIDeZu6UdgxbKPBW0lXWzlk_relHG5x6nXkYzZoEeVasqavWoyMMSX7cexe-IYJh6e_3DgRpOchueS8Z-70P0w5R83Ea7UXZQNhMA0yDEYu_td2PmE2Pd2PUTOB3mxF2pb1z7-2t6S0UryhHx0az7Gh2eT60GGUqz9CIZzNE_FX--tszeuO0eI92Cen5tirOUHBTyyDqE0eG26DRl_p_U-xDZwaQONbtYbvkvrj-G7FA2oZXxjJPHuQZsNgBslXS-H0KT1lx3Y8XJ9QMVjFLFaucFG64wCmXPfCH8dYtX_YqfYQR4lwNfiSbyJEX2oDvTVVD_aCJ9NRo7c0aCTtmKBvr6fvvAy3MAFxAp_Sm2nMx4P5GYO4qAmJDByywKw-VK1vHlv3NRmVsAgbArIFgm-axoCs2PLpvZjDqeQGPavaq8zKWTyZYqBsEzKZUtGOZfYjD4mud0Z08I4i2H4K-L00ccVauode3548ZipOIuslbhJxonQXsF6TFdW2Hj8E5JjoEr5IbmwHyI0PBcDWW5AmkjHLwr9v08mFppRoD-2wzPAd5igROAuUbvJhiQd2A-uOaohwMjdpFxrjyUqgGTlI5g7tmI1ceeQWms0bKm8Pd0wIqVM3Nq6YvV7XfEyeogfRMVUQezr_lES42ZMVAoKBSFzMysDwCFkhVNVclcTUpcUUbVp21FChG7Ag-xuq8Cl6OGLA8nWX1C0aCf2HNa-n3dkYr1DtUziurh1MD-UIs5jdGiq3ptrc0VaVZwNdD4jVfAoHB_Ws7GiISXuclfpqsG3DTJEfzlbukI1vxXrt3FArsHiQvQjW5UM7gGel32M6p8AlXRxnez9PgIuU1WrtBUJetk7m39AZwp_aqbqC-AJ-MF70xP1VJZwFN-GeNL3VZsRHePFG4h7Pj---CCZRGlmzuE1-b-sIE7Bn_gbue_qHFMZJhAY55MO25vfZbSoLWHZCGmLMjJVHOYCPoy6l7zyxNmoIcC58QILNWal31KLCDmCsASmZC-xQRjyFwt-kvLbvk38Dc02IKcP3ujhf6WRRr1A0hh1K-gXmv_XI_MUFDcOVIjjhB-rXgjWSCoKfaKI9GCIsb9mnNaBq65BWg==\"},{\"type\":\"message\",\"id\":\"msg_01cc0cda24c36acf016aa8ca3d4a0087d1950121aed4802434\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\",\"text\":\"Ready.\"}],\"phase\":\"final_answer\",\"status\":\"completed\"},{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Reply exactly: Recovered.\"}]}],\"instructions\":\"Follow the user's exact reply instruction.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"max_output_tokens\":30}"
        },
        {
          "direction": "server",
@@ -26,7 +26,7 @@
        "headers": {
          "content-type": "application/json"
        },
-        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Think briefly, then reply exactly with: Hello!\"}]}],\"instructions\":\"Show concise reasoning when the provider supports visible reasoning summaries.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"low\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":120,\"stream\":true}"
+        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Think briefly, then reply exactly with: Hello!\"}]}],\"instructions\":\"Show concise reasoning when the provider supports visible reasoning summaries.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"low\",\"summary\":\"auto\"},\"max_output_tokens\":120,\"stream\":true}"
      },
      "response": {
        "status": 200,
@@ -44,7 +44,7 @@
        "headers": {
          "content-type": "application/json"
        },
-        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Think briefly, then reply exactly with: Hello!\"}]},{\"type\":\"reasoning\",\"id\":\"rs_052e7ec551f55289016aa8c8d63eac87d19d6b921611f123e7\",\"summary\":[],\"encrypted_content\":\"gAAAAABqqMjWZ2Eei8_Gf-5FeEFAYp-gSzFL4D4lQBKL_fyyTYXv5iJ-2jql1mOq0wZpqHL8O9MWxebQGW56Ahd-p21qrDD52CyUBqKKlF87eC1d-cTgjXQlFMsPxvwyQeuU6A2l8tanTtJ48sKtzZtHrDuBXZ35u-lONnovjFGMX3Q83xoqG_um_w5rT420TA_SyU4fGt7oiQvOPS1q4PNo97O824oRnI7n_BC1jPYCaJhl2I1rPJg4afuOpjG-u7JcXRD4JPwZdqMa5o2d0uDKHuUYwP25qiPKKDqTTqFka5cDJjZNPF3ZkVHR-cagjZGvMnizXXxgUpPJ9j83gqY4QJLKCkzcaBj9H7mAL-v9yl4I5kn_9_DhpMILs2SZkC8AvIYNgmel3sDV_BG4XZ2JXciZz86ukQ6DwXgQqS4HOB91g-sGHOWMU1ohsZlEvvJBjGkJ_rAdXVMqAbvi2zvE3_NI4sTAGUrIugGJePrQYTe8gqL8f9NsYac6pzHNQL1e_jQNUvp49bu7EsPzCP3KPYVvZCFohDdwe7sMd6wrztrCJwM4CLdAQK61A7sYzU0HyglLtPidmS5QkSmV6U_xgih7JbKnY0oAeCyYw4ADYqdNTi0axmBErh-lbh-XKNG_TnoMa-2IS3X041N8OsDfSdQp3QsAm53fF8seQuLFa27Iaq2etMj3yGeqWVjA-Mae3K34mt2YPGjQ-HbIOMVmYXBLzNr-s2fT35Sp6SDEsFyvzXb0Vij58s1wW5zkuKgaJiroGgkY86NImuaa3_-wpMK3_9O_wwAbRwV4uBCVzT_rY6rDQHR9-VkM0MbGK8drbdtjXwy3KtAzkux4N4g2nadYU0IIIEUNj_JChUFSHC7VRg7L7LpZMgAFGwHmaUyzQt31LyiVix9WFtcKfBgzehoRV6vstln-oBRd-vFjUW-7WLm7R_lFNHQZ0CKUvKCSpQxdIevczpYT0_lQiDU7Rfp1UBBnicndpq4YQwgRppdZX-QHG5IZxxHNWYdIBtn3eO3ooDXmI-rYryyVcFG6VY5xqssf8GgXqgNy84X_SgYfhGmNiPPQTl5wIBa49f1sxAI-7ri1xLr6FJhQMasOoxm_VuZYNkq6NrAXprtKa70cBRpm1wWBNcPdMul4GNMDQatQZRDENcNLe45iMX-HB1YYSvyc-y5rfSJkP4EgKXC-OqcrzxAlRfB3Bm4jOQL_PSsNBbh26CWAdAYSftM88fbnuqZX2w==\"},{\"type\":\"message\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\",\"text\":\"Hello!\"}],\"status\":\"completed\"},{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Now reply exactly with: Done.\"}]}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"low\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":40,\"stream\":true}"
+        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Think briefly, then reply exactly with: Hello!\"}]},{\"type\":\"reasoning\",\"id\":\"rs_052e7ec551f55289016aa8c8d63eac87d19d6b921611f123e7\",\"summary\":[],\"encrypted_content\":\"gAAAAABqqMjWZ2Eei8_Gf-5FeEFAYp-gSzFL4D4lQBKL_fyyTYXv5iJ-2jql1mOq0wZpqHL8O9MWxebQGW56Ahd-p21qrDD52CyUBqKKlF87eC1d-cTgjXQlFMsPxvwyQeuU6A2l8tanTtJ48sKtzZtHrDuBXZ35u-lONnovjFGMX3Q83xoqG_um_w5rT420TA_SyU4fGt7oiQvOPS1q4PNo97O824oRnI7n_BC1jPYCaJhl2I1rPJg4afuOpjG-u7JcXRD4JPwZdqMa5o2d0uDKHuUYwP25qiPKKDqTTqFka5cDJjZNPF3ZkVHR-cagjZGvMnizXXxgUpPJ9j83gqY4QJLKCkzcaBj9H7mAL-v9yl4I5kn_9_DhpMILs2SZkC8AvIYNgmel3sDV_BG4XZ2JXciZz86ukQ6DwXgQqS4HOB91g-sGHOWMU1ohsZlEvvJBjGkJ_rAdXVMqAbvi2zvE3_NI4sTAGUrIugGJePrQYTe8gqL8f9NsYac6pzHNQL1e_jQNUvp49bu7EsPzCP3KPYVvZCFohDdwe7sMd6wrztrCJwM4CLdAQK61A7sYzU0HyglLtPidmS5QkSmV6U_xgih7JbKnY0oAeCyYw4ADYqdNTi0axmBErh-lbh-XKNG_TnoMa-2IS3X041N8OsDfSdQp3QsAm53fF8seQuLFa27Iaq2etMj3yGeqWVjA-Mae3K34mt2YPGjQ-HbIOMVmYXBLzNr-s2fT35Sp6SDEsFyvzXb0Vij58s1wW5zkuKgaJiroGgkY86NImuaa3_-wpMK3_9O_wwAbRwV4uBCVzT_rY6rDQHR9-VkM0MbGK8drbdtjXwy3KtAzkux4N4g2nadYU0IIIEUNj_JChUFSHC7VRg7L7LpZMgAFGwHmaUyzQt31LyiVix9WFtcKfBgzehoRV6vstln-oBRd-vFjUW-7WLm7R_lFNHQZ0CKUvKCSpQxdIevczpYT0_lQiDU7Rfp1UBBnicndpq4YQwgRppdZX-QHG5IZxxHNWYdIBtn3eO3ooDXmI-rYryyVcFG6VY5xqssf8GgXqgNy84X_SgYfhGmNiPPQTl5wIBa49f1sxAI-7ri1xLr6FJhQMasOoxm_VuZYNkq6NrAXprtKa70cBRpm1wWBNcPdMul4GNMDQatQZRDENcNLe45iMX-HB1YYSvyc-y5rfSJkP4EgKXC-OqcrzxAlRfB3Bm4jOQL_PSsNBbh26CWAdAYSftM88fbnuqZX2w==\"},{\"type\":\"message\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\",\"text\":\"Hello!\"}],\"status\":\"completed\"},{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Now reply exactly with: Done.\"}]}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"low\",\"summary\":\"auto\"},\"max_output_tokens\":40,\"stream\":true}"
      },
      "response": {
        "status": 200,
@@ -24,7 +24,7 @@
        "headers": {
          "content-type": "application/json"
        },
-        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Think briefly, then reply exactly with: Hello!\"}]}],\"instructions\":\"Show concise reasoning when the provider supports visible reasoning summaries.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"low\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":120,\"stream\":true}"
+        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"Think briefly, then reply exactly with: Hello!\"}]}],\"instructions\":\"Show concise reasoning when the provider supports visible reasoning summaries.\",\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"low\",\"summary\":\"auto\"},\"max_output_tokens\":120,\"stream\":true}"
      },
      "response": {
        "status": 200,
@@ -25,7 +25,7 @@
        "headers": {
          "content-type": "application/json"
        },
-        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"What is the weather in Paris?\"}]}],\"instructions\":\"Use the get_weather tool exactly once. After the tool result, reply exactly: Paris is sunny.\",\"tools\":[{\"type\":\"function\",\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":80,\"stream\":true}"
+        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"What is the weather in Paris?\"}]}],\"instructions\":\"Use the get_weather tool exactly once. After the tool result, reply exactly: Paris is sunny.\",\"tools\":[{\"type\":\"function\",\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"max_output_tokens\":80,\"stream\":true}"
      },
      "response": {
        "status": 200,
@@ -43,7 +43,7 @@
        "headers": {
          "content-type": "application/json"
        },
-        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"What is the weather in Paris?\"}]},{\"type\":\"function_call\",\"id\":\"fc_09525c04931d1487016aa8c8d8e12887d193bd327a0f313bd7\",\"call_id\":\"call_p57PJbKe0bX44nj908fpHdRn\",\"name\":\"get_weather\",\"arguments\":\"{\\\"city\\\":\\\"Paris\\\"}\"},{\"type\":\"function_call_output\",\"call_id\":\"call_p57PJbKe0bX44nj908fpHdRn\",\"output\":\"{\\\"temperature\\\":22,\\\"condition\\\":\\\"sunny\\\"}\"}],\"instructions\":\"Use the get_weather tool exactly once. After the tool result, reply exactly: Paris is sunny.\",\"tools\":[{\"type\":\"function\",\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"text\":{\"verbosity\":\"low\"},\"max_output_tokens\":80,\"stream\":true}"
+        "body": "{\"model\":\"gpt-5.5\",\"input\":[{\"type\":\"message\",\"role\":\"user\",\"content\":[{\"type\":\"input_text\",\"text\":\"What is the weather in Paris?\"}]},{\"type\":\"function_call\",\"id\":\"fc_09525c04931d1487016aa8c8d8e12887d193bd327a0f313bd7\",\"call_id\":\"call_p57PJbKe0bX44nj908fpHdRn\",\"name\":\"get_weather\",\"arguments\":\"{\\\"city\\\":\\\"Paris\\\"}\"},{\"type\":\"function_call_output\",\"call_id\":\"call_p57PJbKe0bX44nj908fpHdRn\",\"output\":\"{\\\"temperature\\\":22,\\\"condition\\\":\\\"sunny\\\"}\"}],\"instructions\":\"Use the get_weather tool exactly once. After the tool result, reply exactly: Paris is sunny.\",\"tools\":[{\"type\":\"function\",\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}],\"store\":false,\"include\":[\"reasoning.encrypted_content\"],\"reasoning\":{\"effort\":\"medium\",\"summary\":\"auto\"},\"max_output_tokens\":80,\"stream\":true}"
      },
      "response": {
        "status": 200,
@@ -7,7 +7,6 @@ import {
  type ImageModelOptions,
  type ImageOptions,
  type ImageRequestFor,
-  type ImageRoute,
 } from "../src/index.js"
 import type { Service } from "../src/image-client.js"
 import { Anthropic, BlackForestLabs, Google, OpenAI, Stability, XAI, ZAI } from "../src/providers.js"
@@ -21,8 +20,7 @@ type GoogleLikeOptions = {
  readonly thinkingLevel?: "LOW" | "HIGH"
 } & Record<string, unknown>

-declare const route: ImageRoute<GoogleLikeOptions>
-const google = ImageModel.make<GoogleLikeOptions>({ id: "gemini-image", provider: "google", route })
+declare const google: ImageModel<GoogleLikeOptions>
 // @ts-expect-error Extracted model options retain known provider fields.
 const invalidGoogleOptions: ImageModelOptions<typeof google> = { imageSize: "8K" }
 void invalidGoogleOptions
@@ -152,6 +150,8 @@ Image.generate({ model: zai, prompt: "A lighthouse", providerOptions: { quality:
 Image.generate({ model: zai, prompt: "A lighthouse", providerOptions: { userID: 1 } })

 declare const generic: ImageModel<ImageOptions>
+const widenImage = <Options extends ImageOptions>(model: ImageModel<Options>): ImageModel => model
+void widenImage
 Image.generate({ model: generic, prompt: "A lighthouse", providerOptions: { arbitrary: true } })
 const explicitAsset: Media.Asset = Media.url("https://example.com/image.png")
 void explicitAsset
@@ -6,6 +6,7 @@ import {
  type LanguageModelProviderOptions,
  type ProviderOptions,
 } from "../src/index.js"
+import { ai } from "../src/promise.js"
 import { OpenAIChat } from "../src/protocols.js"

 interface ExampleOptions {
@@ -31,6 +32,10 @@ const generated = LLM.generate(LLM.request({ model, prompt: "Hello" }))
 type GenerateRequirements = Assert<Equal<Requirements<typeof generated>, LLMClientService>>
 const streamed = LLM.stream(LLM.request({ model, prompt: "Hello" }))
 type StreamClientRequirements = Assert<Equal<StreamRequirements<typeof streamed>, LLMClientService>>
+const generatedFromInput = LLM.generate({ model, prompt: "Hello", providerOptions: { mode: "fast" } })
+type InputGenerateRequirements = Assert<Equal<Requirements<typeof generatedFromInput>, LLMClientService>>
+const streamedFromInput = LLM.stream({ model, prompt: "Hello", providerOptions: { mode: "thorough" } })
+type InputStreamRequirements = Assert<Equal<StreamRequirements<typeof streamedFromInput>, LLMClientService>>

 LLM.request({
  model,
@@ -39,6 +44,11 @@ LLM.request({
  providerOptions: { mode: "slow" },
 })

+// @ts-expect-error Direct input keeps the selected model's provider option types.
+LLM.generate({ model, prompt: "Hello", providerOptions: { mode: "slow" } })
+// @ts-expect-error Stream input keeps the selected model's provider option types.
+LLM.stream({ model, prompt: "Hello", providerOptions: { mode: "slow" } })
+
 const generatedObject = LLM.generateObject({
  model,
  prompt: "Hello",
@@ -69,5 +79,16 @@ const options: LanguageModelProviderOptions<typeof model> = { mode: "fast" }
 void (options satisfies LanguageModelProviderOptions<typeof model>)
 void (true satisfies GenerateRequirements)
 void (true satisfies StreamClientRequirements)
+void (true satisfies InputGenerateRequirements)
+void (true satisfies InputStreamRequirements)
 void (true satisfies GenerateObjectRequirements)
 void (true satisfies GenerateDynamicObjectRequirements)
+
+void ai.llm.generate({ model, prompt: "Hello", providerOptions: { mode: "fast" } })
+void ai.llm.stream({ model, prompt: "Hello", providerOptions: { mode: "thorough" } })
+void ai.llm.generate(ai.llm.request({ model, prompt: "Hello" }))
+void ai.llm.stream(ai.llm.request({ model, prompt: "Hello" }))
+// @ts-expect-error Promise direct input keeps the selected model's provider option types.
+void ai.llm.generate({ model, prompt: "Hello", providerOptions: { mode: "slow" } })
+// @ts-expect-error Promise stream input keeps the selected model's provider option types.
+void ai.llm.stream({ model, prompt: "Hello", providerOptions: { mode: "slow" } })
@@ -1,6 +1,7 @@
 import { describe, expect, test } from "bun:test"
-import { Schema } from "effect"
-import { CacheHint, LLM, LLMResponse, ToolEntry, ToolNamespace } from "../src/index.js"
+import { Effect, Schema, Stream } from "effect"
+import { CacheHint, LLM, LLMEvent, LLMResponse, ToolEntry, ToolNamespace } from "../src/index.js"
+import { OpenAI } from "../src/providers.js"
 import * as OpenAIChat from "../src/protocols/openai-chat.js"
 import * as OpenAIResponses from "../src/protocols/openai-responses.js"
 import {
@@ -13,6 +14,8 @@ import {
  ToolDefinition,
  ToolResultPart,
 } from "../src/schema/index.js"
+import { fixedResponse } from "./lib/http.js"
+import { sseEvents } from "./lib/sse.js"

 const chatRoute = OpenAIChat.route
 const responsesRoute = OpenAIResponses.route
@@ -240,6 +243,26 @@ describe("llm constructors", () => {
    expect(request.messages.map((message) => message.role)).toEqual(["user", "system"])
  })

+  test("generates and streams from input or a prebuilt request", async () => {
+    const model = OpenAI.configure({ apiKey: "test", baseURL: "https://openai.test/v1" }).chat("gpt-4o-mini")
+    const layer = fixedResponse(
+      sseEvents({ choices: [{ delta: { content: "Hello" } }] }, { choices: [{ delta: {}, finish_reason: "stop" }] }),
+    )
+    const input = { model, prompt: "Say hello." }
+    const request = LLM.request(input)
+    const generated = await Effect.runPromise(LLM.generate(input).pipe(Effect.provide(layer)))
+    const generatedFromRequest = await Effect.runPromise(LLM.generate(request).pipe(Effect.provide(layer)))
+    expect(generated.text).toBe("Hello")
+    expect(generatedFromRequest.text).toBe(generated.text)
+
+    const streamed = await Effect.runPromise(LLM.stream(input).pipe(Stream.runCollect, Effect.provide(layer)))
+    const streamedFromRequest = await Effect.runPromise(
+      LLM.stream(request).pipe(Stream.runCollect, Effect.provide(layer)),
+    )
+    expect(Array.from(streamed).some(LLMEvent.is.textDelta)).toBe(true)
+    expect(streamedFromRequest).toEqual(streamed)
+  })
+
  test("extracts output text from response events", () => {
    expect(
      LLMResponse.text({
@@ -116,19 +116,32 @@ describe("AI promise client", () => {
    const seen: Array<string> = []
    const ai = AI.make({ layer: executor(seen) })

-    const text = await ai.llm.generate({ model: openai.chat("gpt-4o-mini"), prompt: "Say hello." })
+    const request = ai.llm.request({ model: openai.chat("gpt-4o-mini"), prompt: "Say hello." })
+    const text = await ai.llm.generate(request)
    expect(text.text).toBe("Hello world")
+    expect((await ai.llm.generate({ model: openai.chat("gpt-4o-mini"), prompt: "Say hello." })).text).toBe(
+      "Hello world",
+    )

    const image = await ai.image.generate({ model: openai.image("gpt-image-2"), prompt: "A lighthouse" })
    expect(image.image).toBeInstanceOf(Media.Asset)
    expect(image.image.mediaType).toBe("image/png")
    expect(await ai.run(image.image.bytes())).toEqual(Uint8Array.from([1, 2, 3]))
+    const requested = await ai.image.generate(
+      ai.image.request({ model: openai.image("gpt-image-2"), prompt: "A lighthouse" }),
+    )
+    expect(requested.image.mediaType).toBe("image/png")

    const deltas: Array<string> = []
-    for await (const event of ai.llm.stream({ model: openai.chat("gpt-4o-mini"), prompt: "Say hello." })) {
+    for await (const event of ai.llm.stream(request)) {
      if (LLMEvent.is.textDelta(event)) deltas.push(event.text)
    }
    expect(deltas).toEqual(["Hello", " world"])
+    const directDeltas: Array<string> = []
+    for await (const event of ai.llm.stream({ model: openai.chat("gpt-4o-mini"), prompt: "Say hello." })) {
+      if (LLMEvent.is.textDelta(event)) directDeltas.push(event.text)
+    }
+    expect(directDeltas).toEqual(deltas)

    const imageEvents: Array<string> = []
    for await (const event of ai.image.stream({ model: openai.image("gpt-image-2"), prompt: "A lighthouse" })) {
@@ -137,8 +150,11 @@ describe("AI promise client", () => {
    expect(imageEvents).toEqual(["image-partial", "image", "finish"])

    expect(seen).toEqual([
+      "https://openai.test/v1/chat/completions",
      "https://openai.test/v1/chat/completions",
      "https://openai.test/v1/images/generations",
+      "https://openai.test/v1/images/generations",
+      "https://openai.test/v1/chat/completions",
      "https://openai.test/v1/chat/completions",
      "https://openai.test/v1/images/generations",
    ])
@@ -260,22 +276,27 @@ describe("AI promise client", () => {
    const ai = AI.make({ layer: executor([]) })

    const failure = await ai.llm
-      .generate({ model: openai.responses("gpt-5"), prompt: "Hello" })
+      .generate(ai.llm.request({ model: openai.responses("gpt-5"), prompt: "Hello" }))
      .then(() => undefined)
      .catch((error: unknown) => error)
    expect(failure).toBeInstanceOf(AIError)
    expect(failure instanceof AIError && failure.reason.http?.status).toBe(404)

-    const invalid = await ai.llm
-      // @ts-expect-error Invalid input must reject with AIError, not throw synchronously.
+    const invalidLLM = await ai.llm
+      // @ts-expect-error Invalid input must reject with AIError instead of throwing synchronously.
      .generate({ model: openai.responses("gpt-5"), messages: [{ role: "bogus" }] })
      .catch((error: unknown) => error)
+    expect(invalidLLM instanceof AIError && invalidLLM.reason._tag).toBe("InvalidRequest")
+
+    const invalid = await ai.image
+      .generate({ model: openai.image("gpt-image-2"), prompt: "A lighthouse", n: 1.5 })
+      .catch((error: unknown) => error)
    expect(invalid instanceof AIError && invalid.reason._tag).toBe("InvalidRequest")

    const controller = new AbortController()
    controller.abort()
    const aborted = await ai.llm
-      .generate({ model: openai.chat("gpt-4o-mini"), prompt: "Hello" }, { signal: controller.signal })
+      .generate(ai.llm.request({ model: openai.chat("gpt-4o-mini"), prompt: "Hello" }), { signal: controller.signal })
      .then(() => "completed")
      .catch(() => "aborted")
    expect(aborted).toBe("aborted")
@@ -216,6 +216,33 @@ it.effect("Alibaba keeps native reasoning controls and future efforts on their s
  }),
 )

+it.effect("Alibaba fits explicit thinking budgets to half the output limit", () =>
+  Effect.gen(function* () {
+    const provider = Alibaba.configure({ region: "ap-southeast-1", apiKey: "fixture" })
+    const chat = (maxTokens?: number) =>
+      compileRequest(
+        LLM.request({
+          model: provider.chat("qwen3.7-plus"),
+          prompt: "hi",
+          ...(maxTokens === undefined ? {} : { generation: { maxTokens } }),
+          providerOptions: { enableThinking: true, thinkingBudget: 131_071 },
+        }),
+      ).pipe(Effect.map((prepared) => prepared.body.thinking_budget))
+    const messages = yield* compileRequest(
+      LLM.request({
+        model: provider.messages("qwen3.7-plus"),
+        prompt: "hi",
+        generation: { maxTokens: 32_000 },
+        providerOptions: { thinking: { type: "enabled", budgetTokens: 131_071 } },
+      }),
+    )
+
+    expect(yield* chat(32_000)).toBe(16_000)
+    expect(yield* chat()).toBe(131_071)
+    expect(messages.body.thinking).toEqual({ type: "enabled", budget_tokens: 16_000 })
+  }),
+)
+
 it.effect("Alibaba validates malformed options before execution", () =>
  Effect.gen(function* () {
    const provider = Alibaba.configure({ region: "ap-southeast-1", apiKey: "fixture" })
@@ -148,11 +148,13 @@ describe("Anthropic Messages route", () => {
    Effect.gen(function* () {
      const enabled = yield* compileRequest(
        LLMRequest.update(request, {
+          generation: { maxTokens: 4_096 },
          providerOptions: { thinking: { type: "enabled", budgetTokens: 1_024 } },
        }),
      )
      const legacy = yield* compileRequest(
        LLMRequest.update(request, {
+          generation: { maxTokens: 4_096 },
          providerOptions: { thinking: { type: "enabled", budget_tokens: 2_048 } },
        }),
      )
@@ -168,6 +170,22 @@ describe("Anthropic Messages route", () => {
    }),
  )

+  it.effect("fits the thinking budget to half the output limit", () =>
+    Effect.gen(function* () {
+      const thinking = (maxTokens: number) =>
+        compileRequest(
+          LLMRequest.update(request, {
+            generation: { maxTokens },
+            providerOptions: { thinking: { type: "enabled", budgetTokens: 31_999 } },
+          }),
+        ).pipe(Effect.map((prepared) => prepared.body.thinking))
+
+      expect(yield* thinking(64_000)).toEqual({ type: "enabled", budget_tokens: 31_999 })
+      expect(yield* thinking(20_000)).toEqual({ type: "enabled", budget_tokens: 10_000 })
+      expect(yield* thinking(1_500)).toEqual({ type: "enabled", budget_tokens: 1_024 })
+    }),
+  )
+
  it.effect("rejects enabled thinking without a budget", () =>
    Effect.gen(function* () {
      const error = yield* compileRequest(
@@ -244,6 +244,29 @@ describe("Bedrock Converse route", () => {
    }),
  )

+  it.effect("fits a Claude thinking budget below maxTokens", () =>
+    Effect.gen(function* () {
+      const fields = (maxTokens: number, budgetTokens: number, topK?: number) =>
+        compileRequest(
+          LLMRequest.update(baseRequest, {
+            model: AmazonBedrock.model("us.anthropic.claude-haiku-4-5-20251001-v1:0", {
+              baseURL: "https://bedrock-runtime.test",
+              apiKey: "test-bearer",
+              thinking: { type: "enabled", budgetTokens },
+            }),
+            generation: GenerationOptions.make({ maxTokens, topK }),
+          }),
+        ).pipe(Effect.map((prepared) => prepared.body.additionalModelRequestFields))
+
+      expect(yield* fields(64_000, 31_999)).toEqual({ thinking: { type: "enabled", budget_tokens: 31_999 } })
+      expect(yield* fields(20_000, 31_999, 40)).toEqual({
+        top_k: 40,
+        thinking: { type: "enabled", budget_tokens: 10_000 },
+      })
+      expect(yield* fields(1_500, 31_999)).toEqual({ thinking: { type: "enabled", budget_tokens: 1_024 } })
+    }),
+  )
+
  it.effect("omits additionalModelRequestFields when topK is unset", () =>
    Effect.gen(function* () {
      const prepared = yield* compileRequest(baseRequest)
@@ -22,21 +22,22 @@ testEffect(
      expect(body).toMatchObject({
        model: "fixture",
        stream: true,
-        store: false,
+        store: true,
        instructions: "Keep the context",
-        parallel_tool_calls: true,
+        parallel_tool_calls: false,
        prompt_cache_key: "session-key",
        service_tier: "priority",
        reasoning: { effort: "high", summary: "auto" },
+        context_management: [{ type: "compaction" }],
+        max_tool_calls: 1,
+        tool_choice: "required",
+        text: { verbosity: "high", format: { type: "json_object" } },
        prompt_cache_retention: "24h",
        prompt_cache_options: { mode: "session", ttl: "1h" },
        input: [{ role: "user", content: [{ type: "input_text", text: "hello" }] }, { type: "compaction_trigger" }],
      })
      expect(body.tools).toHaveLength(1)
      expect(body.tools[0].name).toBe("lookup")
-      expect(body.tool_choice).toBeUndefined()
-      expect(body.context_management).toBeUndefined()
-      expect(body.text).toBeUndefined()
      expect(body.max_output_tokens).toBeUndefined()
      expect(body.previous_response_id).toBeUndefined()
      return respond(
@@ -57,7 +58,7 @@ testEffect(
      )
    }),
  ),
-).effect("trigger uses normal request preparation, configured deployment, and supplied subscription headers", () =>
+).effect("trigger keeps request controls, configured deployment, and supplied subscription headers", () =>
  Effect.gen(function* () {
    const calls: string[] = []
    const input = LLM.request({
@@ -67,12 +68,14 @@ testEffect(
      promptCacheKey: "session-key",
      tools: [{ name: "lookup", description: "Lookup", inputSchema: { type: "object", properties: {} } }],
      toolChoice: { type: "tool", name: "lookup" },
-      generation: { maxTokens: 1 },
      providerOptions: {
        store: true,
        reasoningEffort: "high",
        reasoningSummary: "auto",
        contextManagement: [{ type: "compaction" }],
+        parallelToolCalls: false,
+        maxToolCalls: 1,
+        textVerbosity: "low",
      },
      http: {
        headers: { "chatgpt-account-id": "fixture-account", "x-codex-beta-features": "remote_compaction_v2" },
@@ -82,8 +85,7 @@ testEffect(
          prompt_cache_retention: "24h",
          prompt_cache_options: { mode: "session", ttl: "1h" },
          store: true,
-          stream: false,
-          text: { format: { type: "json_object" } },
+          text: { verbosity: "high", format: { type: "json_object" } },
          tool_choice: "required",
        },
      },
@@ -114,6 +116,75 @@ testEffect(
  }),
 )

+testEffect(
+  dynamicResponse(({ text, respond }) =>
+    Effect.sync(() => {
+      expect(JSON.parse(text).text).toEqual({ verbosity: "low", format: { type: "json_object" } })
+      return respond(sseEvents({ type: "response.completed", response: { id: "resp_1", output: [checkpoint] } }), {
+        headers: { "content-type": "text/event-stream" },
+      })
+    }),
+  ),
+).effect("keeps explicit verbosity on a trigger checkpoint for prompt cache reuse", () =>
+  LLMClient.compact(
+    LLM.request({
+      model: OpenAI.configure({ apiKey: "fixture" }).responses("gpt-5.5"),
+      prompt: "Hello.",
+      providerOptions: { textVerbosity: "low" },
+      http: { body: { text: { format: { type: "json_object" } } } },
+    }),
+    trigger,
+  ),
+)
+
+testEffect(
+  dynamicResponse(({ text, respond }) =>
+    Effect.sync(() => {
+      const body = JSON.parse(text)
+      expect(body.text).toEqual({ verbosity: "high", format: { type: "json_object" } })
+      expect(body.max_output_tokens).toBe(20_000)
+      return respond(sseEvents({ type: "response.completed", response: { id: "resp_1", output: [checkpoint] } }), {
+        headers: { "content-type": "text/event-stream" },
+      })
+    }),
+  ),
+).effect("keeps the effective body-overlay verbosity and text formatting", () =>
+  LLMClient.compact(
+    LLM.request({
+      model: OpenAI.configure({ apiKey: "fixture" }).responses("gpt-5.5"),
+      prompt: "Hello.",
+      generation: { maxTokens: 20_000 },
+      providerOptions: { textVerbosity: "low" },
+      http: { body: { text: { verbosity: "high", format: { type: "json_object" } } } },
+    }),
+    trigger,
+  ),
+)
+
+testEffect(
+  dynamicResponse(({ text, respond }) =>
+    Effect.sync(() => {
+      expect(JSON.parse(text).max_output_tokens).toBe(128)
+      return respond(JSON.stringify({ error: { message: "max_output_tokens must be at least 20000" } }), {
+        status: 400,
+        headers: { "content-type": "application/json" },
+      })
+    }),
+  ),
+).effect("passes configured output limits through and leaves rejection to the provider", () =>
+  Effect.gen(function* () {
+    const error = yield* LLMClient.compact(
+      LLM.request({
+        model: OpenAI.configure({ apiKey: "fixture" }).responses("gpt-5.5"),
+        prompt: "Hello.",
+        generation: { maxTokens: 128 },
+      }),
+      trigger,
+    ).pipe(Effect.flip)
+    expect(error.message).toContain("at least 20000")
+  }),
+)
+
 const idless = { type: "compaction", encrypted_content: "opaque" }
 testEffect(
  fixedResponse(
@@ -184,7 +255,7 @@ testEffect(fixedResponse(sseEvents({ type: "response.output_item.done", item: ch
      expect(error.reason._tag).toBe("InvalidProviderOutput")
    }),
 )
-for (const body of [{ input: [] }, { previous_response_id: "stale" }]) {
+for (const body of [{ input: [] }, { previous_response_id: "stale" }, { stream: false }]) {
  testEffect(dynamicResponse(() => Effect.die("Must reject before sending"))).effect(
    `rejects caller-supplied ${Object.keys(body)[0]} before sending trigger`,
    () =>
@@ -110,11 +110,16 @@ for (const model of [
    dynamicResponse(({ request, text, respond }) =>
      Effect.sync(() => {
        expect(new URL(request.url).pathname).toEndWith("/responses/compact")
-        expect(JSON.parse(text)).toEqual({ model: "fixture", input: [item], instructions: "Keep the context" })
+        expect(JSON.parse(text)).toEqual({
+          model: "fixture",
+          input: [item],
+          instructions: "Keep the context",
+          include: ["reasoning.encrypted_content"],
+        })
        return respond(JSON.stringify({ object: "response.compaction", output: [checkpoint] }))
      }),
    ),
-  ).effect(`${model.provider} compacts provider-specific history without lowering generation settings`, () =>
+  ).effect(`${model.provider} validates tools but ignores unrelated unsupported generation settings`, () =>
    Effect.gen(function* () {
      const request = LLM.request({
        model,
@@ -151,6 +156,11 @@ for (const model of [
      ] as const) {
        const error = yield* LLMClient.generate(candidate).pipe(Effect.flip)
        expect(error.reason._tag).toBe(tag)
+        if (candidate.tools.length > 0) {
+          const compactError = yield* LLMClient.compact(candidate).pipe(Effect.flip)
+          expect(compactError.reason._tag).toBe("InvalidRequest")
+          continue
+        }
        const response = yield* LLMClient.compact(candidate)
        expect(response.replacement[0]?.content[0]?.type).toBe("compaction")
      }
@@ -255,6 +265,13 @@ for (const overlay of [undefined, { service_tier: "priority", prompt_cache_key:
          model: "fixture",
          input: [{ type: "message", role: "user", content: [{ type: "input_text", text: "hello" }] }],
          service_tier: overlay?.service_tier ?? "flex",
+          reasoning: { effort: "low" },
+          text: { verbosity: "low", format: { type: "json_object" } },
+          include: ["reasoning.encrypted_content"],
+          parallel_tool_calls: false,
+          tools: [
+            { type: "function", name: "lookup", description: "Lookup", parameters: { type: "object" }, strict: false },
+          ],
          prompt_cache_key: overlay?.prompt_cache_key ?? "affinity",
          prompt_cache_retention: "24h",
          prompt_cache_options: { mode: "explicit", ttl: "30m" },
@@ -268,12 +285,20 @@ for (const overlay of [undefined, { service_tier: "priority", prompt_cache_key:
        model: OpenAI.configure({ apiKey: "test" }).responses("fixture"),
        prompt: "hello",
        promptCacheKey: "affinity",
-        providerOptions: { serviceTier: "flex" },
+        providerOptions: {
+          serviceTier: "flex",
+          reasoningEffort: "low",
+          textVerbosity: "low",
+          include: ["reasoning.encrypted_content"],
+          parallelToolCalls: false,
+        },
        generation: { maxTokens: 100 },
+        tools: [{ name: "lookup", description: "Lookup", inputSchema: {} }],
        http: {
          body: {
            stream: true,
            store: false,
+            text: { format: { type: "json_object" } },
            prompt_cache_retention: "24h",
            prompt_cache_options: { mode: "explicit", ttl: "30m" },
            ...overlay,
@@ -396,6 +421,8 @@ for (const model of [
            model: model.id,
            input: [{ type: "message", role: "user", content: [{ type: "input_text", text: "original" }] }],
            instructions: "system",
+            include: ["reasoning.encrypted_content"],
+            ...(model.id === "gpt-5.3-codex" ? { reasoning: { effort: "medium", summary: "auto" } } : {}),
          })
          return respond(
            JSON.stringify({
@@ -407,7 +434,10 @@ for (const model of [
          )
        }
        expect(new URL(request.url).pathname.endsWith("/responses")).toBe(true)
-        expect(body.input).toEqual([...output, { type: "message", role: "user", content: [{ type: "input_text", text: "continue" }] }])
+        expect(body.input).toEqual([
+          ...output,
+          { type: "message", role: "user", content: [{ type: "input_text", text: "continue" }] },
+        ])
        return respond(sseEvents({ type: "response.completed", response: { id: "resp_1", output: [] } }), {
          headers: { "content-type": "text/event-stream" },
        })
@@ -90,6 +90,23 @@ describe("Gemini route", () => {
    }),
  )

+  it.effect("fits the thinking budget to half the output limit", () =>
+    Effect.gen(function* () {
+      const thinkingBudget = (budget: number, maxTokens = 32_000) =>
+        compileRequest(
+          LLMRequest.update(request, {
+            generation: { maxTokens },
+            providerOptions: { thinkingConfig: { thinkingBudget: budget } },
+          }),
+        ).pipe(Effect.map((prepared) => prepared.body.generationConfig?.thinkingConfig?.thinkingBudget))
+
+      expect(yield* thinkingBudget(32_768)).toBe(16_000)
+      expect(yield* thinkingBudget(8_000)).toBe(8_000)
+      expect(yield* thinkingBudget(-1)).toBe(-1)
+      expect(yield* thinkingBudget(8_192, 1_000)).toBe(512)
+    }),
+  )
+
  it.effect("forwards standard Gemini generation options", () =>
    Effect.gen(function* () {
      const prepared = yield* compileRequest(
@@ -1945,7 +1945,7 @@ describe("OpenAI Responses route", () => {
      expect(prepared.body.prompt_cache_key).toBe("session_123")
      expect(prepared.body.include).toEqual(["reasoning.encrypted_content"])
      expect(prepared.body.reasoning).toEqual({ effort: "high", summary: "auto" })
-      expect(prepared.body.text).toEqual({ verbosity: "low" })
+      expect(prepared.body.text).toBeUndefined()
      expect(prepared.body.metadata).toEqual({ environment: "test", tenant: "acme" })
      expect(prepared.body.safety_identifier).toBe("user_123")
      expect(prepared.body.stream_options).toEqual({ include_obfuscation: false })
@@ -152,6 +152,27 @@ describe("OpenRouter", () => {
    }),
  )

+  it.effect("fits the reasoning budget to half the output limit", () =>
+    Effect.gen(function* () {
+      const reasoning = (maxTokens: number | undefined, value: Record<string, unknown>) =>
+        compileRequest(
+          LLM.request({
+            model: OpenRouter.configure({ apiKey: "test-key" }).model("qwen/qwen3.8-flash"),
+            cache: "none",
+            prompt: "Hello",
+            ...(maxTokens === undefined ? {} : { generation: { maxTokens } }),
+            providerOptions: { reasoning: value },
+          }),
+        ).pipe(Effect.map((prepared) => prepared.body.reasoning))
+
+      expect(yield* reasoning(32_000, { max_tokens: 131_071 })).toEqual({ max_tokens: 16_000 })
+      expect(yield* reasoning(131_072, { max_tokens: 65_536 })).toEqual({ max_tokens: 65_536 })
+      expect(yield* reasoning(1_500, { max_tokens: 65_536 })).toEqual({ max_tokens: 1_024 })
+      expect(yield* reasoning(undefined, { max_tokens: 131_071 })).toEqual({ max_tokens: 131_071 })
+      expect(yield* reasoning(32_000, { effort: "high" })).toEqual({ effort: "high" })
+    }),
+  )
+
  it.effect("applies OpenRouter payload options from the model helper", () =>
    Effect.gen(function* () {
      const prepared = yield* compileRequest(
@@ -1,5 +1,5 @@
 import type { Stream } from "effect"
-import { Speech, type SpeechEvent } from "../src/index.js"
+import { Speech, SpeechModel, type SpeechEvent, type SpeechOptions } from "../src/index.js"
 import { ElevenLabs, OpenAI, Runway } from "../src/providers.js"

 type StreamItem<T> = T extends Stream.Stream<infer A, infer _E, infer _R> ? A : never
@@ -7,6 +7,8 @@ type Equal<A, B> = [A, B] extends [B, A] ? true : false
 type Assert<T extends true> = T

 const elevenlabs = ElevenLabs.configure({ apiKey: "test" }).speech("eleven_flash_v2_5")
+const widenSpeech = <Options extends SpeechOptions>(model: SpeechModel<Options>): SpeechModel => model
+void widenSpeech

 Speech.generate({
  model: elevenlabs,
@@ -1,5 +1,11 @@
 import type { Stream } from "effect"
-import { Media, Transcription, type TranscriptionEvent } from "../src/index.js"
+import {
+  Media,
+  Transcription,
+  TranscriptionModel,
+  type TranscriptionEvent,
+  type TranscriptionOptions,
+} from "../src/index.js"
 import { AssemblyAI, Deepgram, OpenAI } from "../src/providers.js"

 type StreamItem<T> = T extends Stream.Stream<infer A, infer _E, infer _R> ? A : never
@@ -8,6 +14,10 @@ type Assert<T extends true> = T

 const audio = Media.url("https://example.com/call.mp3")
 const deepgram = Deepgram.configure({ apiKey: "test" }).transcription("nova-3")
+const widenTranscription = <Options extends TranscriptionOptions>(
+  model: TranscriptionModel<Options>,
+): TranscriptionModel => model
+void widenTranscription

 Transcription.generate({
  model: deepgram,
@@ -9,7 +9,6 @@ import {
  type VideoModelOptions,
  type VideoOptions,
  type VideoRequestFor,
-  type VideoRoute,
 } from "../src/index.js"
 import type { Service } from "../src/video-client.js"
 import { Anthropic, Fal, Google, OpenAI, Runway, XAI } from "../src/providers.js"
@@ -23,8 +22,7 @@ type VeoLikeOptions = {
  readonly personGeneration?: "allow_all" | "allow_adult"
 } & Record<string, unknown>

-declare const route: VideoRoute<VeoLikeOptions>
-const veo = VideoModel.make<VeoLikeOptions>({ id: "veo", provider: "google", route })
+declare const veo: VideoModel<VeoLikeOptions>
 // @ts-expect-error Extracted model options retain known provider fields.
 const invalidVeoOptions: VideoModelOptions<typeof veo> = { personGeneration: "everyone" }
 void invalidVeoOptions
@@ -99,6 +97,8 @@ Video.generate({ model: google, prompt: "A kitten", durationSeconds: "8s" })
 Video.generate({ model: google, prompt: "A kitten", options: { personGeneration: "allow_all" } })

 declare const generic: VideoModel<VideoOptions>
+const widenVideo = <Options extends VideoOptions>(model: VideoModel<Options>): VideoModel => model
+void widenVideo
 Video.generate({ model: generic, prompt: "A kitten", providerOptions: { arbitrary: true } })

 const request = Video.request({ model: veo, prompt: "A kitten", providerOptions: { personGeneration: "allow_all" } })
@@ -21,7 +21,7 @@
    "build:node": "bun run script/build-node.ts",
    "dev": "bun run src/index.ts",
    "test": "bun test --timeout 30000 --only-failures",
-    "typecheck": "tsgo --noEmit"
+    "typecheck": "tsgo -b"
  },
  "dependencies": {
    "@agentclientprotocol/sdk": "1.2.1",
@@ -7,5 +7,6 @@
    "lib": ["ESNext", "DOM", "DOM.Iterable", "DOM.AsyncIterable"],
    "noUncheckedIndexedAccess": false
  },
-  "exclude": ["dist", "dist-node"]
+  "exclude": ["dist", "dist-node"],
+  "references": [{ "path": "../core" }]
 }
@@ -139,11 +139,24 @@ ultimate source of truth. Upstream test262 files run verbatim from `test/test262
      arrow function.
 - [x] Promise-returning string replacers are coerced synchronously to `"[object Promise]"`, like JavaScript; they are
      not automatically awaited.
- [x] The optional `thisArg` of iteration methods is accepted and ignored: CodeMode functions have no `this`, so
-      ignoring it matches JS arrow-function semantics exactly.
- [ ] `this` in non-arrow CodeMode functions and callbacks.
+- [x] `this` in non-arrow functions is the call's receiver: `obj.m()` and `obj["m"]()` see `obj`, a bare or detached
+      call (`f()`, `const m = obj.m; m()`, `(0, obj.m)()`) sees `undefined`, as in strict JS. Arrows read the enclosing
+      function's `this`. Program code has no receiver, so top-level `this` is `undefined`, as in a module.
+- [x] `arguments` in non-arrow functions: an unmapped ordinary object with the call's arguments as indexed
+      properties and a hidden `length`; iterable, so spread, `for...of`, and `Array.from` work. It is not an Array
+      (`JSON.stringify` gives `{"0":1}`, `String` gives `[object Arguments]`). A parameter named `arguments` shadows
+      it; arrows read the enclosing function's; it is only created for functions whose body mentions it. `callee`
+      and `caller` are absent rather than poisoned.
+- [ ] Array methods on `arguments` and other array-likes (`Array.prototype.slice.call(arguments, 1)`); use
+      `[...arguments]` or a rest parameter meanwhile.
+- [x] `Function.prototype.call`, `apply`, and `bind` on program functions and built-ins:
+      `Array.prototype.push.call(arr, 1)`, `Math.max.apply(null, values)`, `fn.bind(obj, first)`. `apply` accepts an
+      array, an array-like object (its `length` clamped and capped like `Array.from`), or `null`/`undefined`. A
+      bound function is named `bound f`, has its remaining `length`, and is not constructible.
+- [x] `JSON.parse` revivers and `JSON.stringify` function replacers see the holder object as `this`.
+- [ ] The optional `thisArg` of iteration methods (`map`, `forEach`, `Map.prototype.forEach`, `Array.from`, …) is
+      accepted but not yet passed as `this`; callbacks run with `this` undefined.
 - [ ] User-defined constructor calls.
- [ ] `Function.prototype.call`, `apply`, and `bind` for CodeMode functions.
 - [ ] Classes and private fields.
 - [x] Functions are objects: they hold own properties (`fn.count = 1`), enumerate them, and expose read-only `name`
      and `length`. Names follow JavaScript's NamedEvaluation: declarations, named expressions, bindings,
@@ -178,7 +191,7 @@ ultimate source of truth. Upstream test262 files run verbatim from `test/test262
      yielded promises; mixed async request queues; sync and async `yield*` forwarding; malformed methods/results;
      and declaration, expression, and object-method forms with closure and parameter behavior. The adapted suite
      deliberately skips Test262 variants whose observation mechanism requires unsupported getter definitions,
-      proxies, prototype inspection or mutation, non-arrow `this`, classes, or arbitrary symbols. It also skips tests
+      proxies, prototype inspection or mutation, classes, or arbitrary symbols. It also skips tests
      asserting exact promise reaction-turn counts beyond the observable ordering guarantee documented below. These
      are interpreter-surface boundaries, not claims that the corresponding full Test262 families pass unchanged.

@@ -269,7 +282,7 @@ reject }` object.
 - [x] Recursive assimilation of objects with an own callable `then` field across `Promise.resolve`, combinators,
      constructors, reactions, `finally`, `await`, and async returns. Thenable methods run deferred, receive
      first-call-wins resolve/reject functions, and ignore throws after settlement. Inherited/accessor `then` fields
-      and a JavaScript `this` receiver remain outside the supported object/function model.
+      remain outside the supported object model.
 - [x] Dotted tool names are canonicalized into namespace paths; a path can be both callable and a namespace, and the
      last tool supplied for a canonical path wins.
 - [x] Tool path segments may be named `constructor`, `prototype`, or `__proto__` because paths use inert Map keys.
@@ -419,9 +432,9 @@ reject }` object.
 - [x] `JSON.parse` and `JSON.stringify` for supported data objects.
 - [x] Numeric/string indentation for `JSON.stringify`.
 - [x] `JSON.parse` reviver callbacks, including postorder traversal, deletion through `undefined`, and root replacement.
-      Revivers receive `(key, value)` but no `this` holder because CodeMode functions intentionally have no `this`.
+      Revivers receive `(key, value)` with the holder as `this`.
 - [x] `JSON.stringify` function and array replacers. Function replacers receive `(key, value)` in preorder, including
-      the root, but no `this` holder. Array replacers preserve requested property order, deduplicate names, coerce
+      the root, with the holder as `this`. Array replacers preserve requested property order, deduplicate names, coerce
      number primitives, and ignore non-string/non-number entries. Primitive wrapper entries remain unsupported.
 - [x] Captured `console.log`, `console.info`, `console.debug`, `console.warn`, and `console.error`. An Error prints as
      `Error.prototype.toString` would show it (`Error: boom`), wherever it appears in the logged value.
@@ -596,8 +609,8 @@ Nothing is exposed unless a host provides it; extension calls are not tool calls
      non-enumerable: an extension Error carries it, and this JSON form does not. Errors have no `stack`; the diagnostic
      carries a 1-based line and column in the submitted source instead.
 - [x] `instanceof` against any constructor with a `prototype`, including every built-in and `Function`.
- [ ] The derived error constructors inheriting from `Error`: `Object.getPrototypeOf(TypeError)` is
-      `Function.prototype` here, while `TypeError.prototype` does inherit from `Error.prototype`.
+- [x] Derived error constructors extend `Error` itself: `Object.getPrototypeOf(TypeError) === Error`, so
+      `TypeError.isError` is inherited, and `TypeError.prototype` inherits from `Error.prototype`.
 - [x] Catchable user throws, runtime failures raised during interpreted evaluation, awaited tool failures, and awaited
      tool-call-limit failures; parse/compile failures, cooperative timeout, and output bounding remain outside program
      `catch`.
@@ -24,7 +24,7 @@ import { typeofValue } from "./interpreter/references.js"

 export type Json = Schema.Json

-type Replacer<R> = (args: Array<Value>) => Effect.Effect<Value, unknown, R>
+type Replacer<R> = (args: Array<Value>, holder: Obj) => Effect.Effect<Value, unknown, R>

 /**
 * What `JSON.stringify` would serialize for a program value, as host JSON: `toJSON` is honored, functions and
@@ -60,7 +60,7 @@ const walk = <R>(
      const settled = raw instanceof PromiseObj ? yield* ctx.await(raw) : raw
      const toJSON = settled instanceof Obj ? get(settled, "toJSON") : undefined
      const own = toJSON instanceof Callable ? yield* ctx.call(toJSON, settled, [key]) : settled
-      const value = replacer === undefined ? own : yield* replacer([key, own])
+      const value = replacer === undefined ? own : yield* replacer([key, own], holder)
      if (value === undefined || typeofValue(value) === "function") return undefined
      if (typeof value === "number") return Number.isFinite(value) ? value : null
      if (value === null || typeof value === "string" || typeof value === "boolean") return value
@@ -78,7 +78,7 @@ export const applyCollectionCallback = <R>(
  ctx: Interpreter<R>,
  callback: Value,
  name: string,
-): ((args: Array<Value>) => Effect.Effect<Value, unknown, R>) => {
+): ((args: Array<Value>, thisValue?: Value) => Effect.Effect<Value, unknown, R>) => {
  if (!isSupportedCallback(callback)) {
    if (typeofValue(callback) === "function") {
      throw typeError(
@@ -87,5 +87,5 @@ export const applyCollectionCallback = <R>(
    }
    throw typeError(`${name} expects a function callback.`)
  }
-  return (callbackArgs) => ctx.call(callback, undefined, callbackArgs)
+  return (callbackArgs, thisValue) => ctx.call(callback, thisValue, callbackArgs)
 }
@@ -180,6 +180,9 @@ export const errorGlobal = <R>(type: ErrorType, ctx: Interpreter<R>) => {
      ["toString", 0, (thisValue) => errorToString(receiver(Obj, thisValue, "Error.prototype.toString"))],
    ])
    methods(builtins, ctor, [["isError", 1, (_, args) => args[0] instanceof ErrorObj]])
+    return ctor
  }
+  // Derived constructors extend Error itself, so its statics are inherited. The globals table creates Error first.
+  ctor.proto = get(builtins.Error, "constructor") as Obj
  return ctor
 }
@@ -1,5 +1,5 @@
 import { Effect } from "effect"
-import type { Value } from "./objects.js"
+import { Arr, Callable, coerceToInteger, coerceToString, get, Obj, type Value } from "./objects.js"
 import { arrayGlobal } from "../stdlib/array.js"
 import { textDecoderGlobal, textEncoderGlobal, uint8ArrayGlobal } from "../stdlib/bytes.js"
 import { mapGlobal, setGlobal } from "../stdlib/collections.js"
@@ -19,8 +19,9 @@ import { base64Global, cryptoGlobal, structuredCloneGlobal } from "../stdlib/web
 import { ToolReference } from "../tool-runtime.js"
 import { errorGlobal } from "./errors.js"
 import { errorTypes } from "./intrinsics.js"
-import { constants, constructor, native } from "./native.js"
+import { constants, constructor, methods, native, receiver } from "./native.js"
 import { AsyncIteratorSymbol, IteratorSymbol, typeError } from "./model.js"
+import { checkArrayLength } from "./limits.js"
 import { generatorGlobals } from "./generators.js"
 import { promiseGlobal } from "./promises.js"
 import type { Interpreter } from "./interpreter.js"
@@ -31,6 +32,24 @@ const functionGlobal = <R>(ctx: Interpreter<R>) => {
    Effect.sync(() => {
      throw typeError("The Function constructor is not supported; write the function inline.")
    })
+  const target = (thisValue: Value, method: string) => receiver(Callable, thisValue, `Function.prototype.${method}`)
+  methods(ctx.builtins, ctx.builtins.Function, [
+    ["call", 1, (thisValue, args) => ctx.call(target(thisValue, "call"), args[0], args.slice(1))],
+    ["apply", 2, (thisValue, args) => ctx.call(target(thisValue, "apply"), args[0], listFromArrayLike(args[1]))],
+    [
+      "bind",
+      1,
+      (thisValue, args) => {
+        const fn = target(thisValue, "bind")
+        const bound = args.slice(1)
+        return native<R>(ctx.builtins, {
+          name: `bound ${coerceToString(get(fn, "name"))}`,
+          length: Math.max(0, fn.length - bound.length),
+          call: (_, rest) => ctx.call(fn, args[0], [...bound, ...rest]),
+        })
+      },
+    ],
+  ])
  return constructor<R>(ctx.builtins, ctx.builtins.Function, {
    name: "Function",
    length: 1,
@@ -39,6 +58,16 @@ const functionGlobal = <R>(ctx: Interpreter<R>) => {
  })
 }

+// CreateListFromArrayLike: `apply` reads `length` and the indexed properties of any object.
+const listFromArrayLike = (value: Value): Array<Value> => {
+  if (value === undefined || value === null) return []
+  if (value instanceof Arr) return [...value.items]
+  if (!(value instanceof Obj)) throw typeError("Function.prototype.apply expects an array-like argument list.")
+  const length = Math.max(0, coerceToInteger(get(value, "length")))
+  checkArrayLength(length)
+  return Array.from({ length }, (_, index) => get(value, String(index)))
+}
+
 const symbolGlobal = <R>(ctx: Interpreter<R>) => {
  const symbol = native<R>(ctx.builtins, {
    name: "Symbol",
@@ -1,4 +1,5 @@
 import type {
+  AnyNode,
  ArrayExpression,
  ArrayPattern,
  AssignmentPattern,
@@ -81,6 +82,7 @@ import {
  keys,
  Native,
  parseArrayIndex,
+  Arguments,
  Arr,
  Fn,
  GeneratorObj,
@@ -178,6 +180,24 @@ const collectPatternNames = (pattern: Pattern, out: Array<string> = []): Array<s
  return out
 }

+// Whether a function body (or a parameter default) reads `arguments`, looking through arrows but not nested
+// functions, which own theirs. Memoized so the object is only built for calls that can observe it.
+const argumentsUse = new WeakMap<Fn["body"], boolean>()
+const usesArguments = (fn: Fn): boolean => {
+  const cached = argumentsUse.get(fn.body)
+  if (cached !== undefined) return cached
+  const found = [...fn.parameters, fn.body].some(function visit(node: AnyNode | null): boolean {
+    if (node === null || typeof node !== "object") return false
+    if (node.type === "Identifier") return node.name === "arguments"
+    if (node.type === "FunctionDeclaration" || node.type === "FunctionExpression") return false
+    return Object.values(node).some((child) =>
+      Array.isArray(child) ? child.some((item) => visit(item)) : visit(child as AnyNode | null),
+    )
+  })
+  argumentsUse.set(fn.body, found)
+  return found
+}
+
 // `var` names declared anywhere in a function body except inside nested functions, which own theirs.
 // Memoized per body: a function's var names never change, and hoisting runs on every call.
 const varNames = new WeakMap<ReadonlyArray<Statement | ModuleDeclaration>, ReadonlyArray<string>>()
@@ -287,7 +307,8 @@ export class Interpreter<R> {
    this.pending = options.pending
    this.builtins = options.builtins
    this.logs = options.logs ?? []
-    const globalScope = new Map<string, Binding>()
+    // Program code has no receiver: top-level `this` is undefined, as in a module.
+    const globalScope = new Map<string, Binding>([["this", { mutable: false, value: undefined }]])
    // Calling back into the program never reads frame state, so any frame serves; the root is always alive.
    this.root = new Frame(this, new ScopeStack([globalScope]))
    for (const [name, value] of [...globals(this), ...(options.globals?.(this) ?? [])]) {
@@ -468,6 +489,7 @@ class Frame<R> {
      this.scopes.capture(),
      node.async,
      node.generator,
+      node.type === "ArrowFunctionExpression",
    )
    // Each generator function gets its own prototype, so `g() instanceof g` holds as in JS.
    if (node.generator)
@@ -1257,6 +1279,8 @@ class Frame<R> {
      }
      case "Identifier":
        return Effect.sync(() => this.scopes.get(node.name, node))
+      case "ThisExpression":
+        return Effect.sync(() => this.scopes.get("this", node))
      case "BinaryExpression":
        return this.evaluateBinaryExpression(node)
      case "LogicalExpression":
@@ -1622,7 +1646,7 @@ class Frame<R> {
        }
        return yield* self.createToolCallPromise(callable.path, args)
      }
-      if (callable instanceof Fn) return yield* self.invokeFunction(callable, args, node)
+      if (callable instanceof Fn) return yield* self.invokeFunction(callable, thisValue, args, node)
      if (callable instanceof Native) {
        return yield* self.native(() => (callable as Native<R>).call(thisValue, args), node)
      }
@@ -1664,14 +1688,24 @@ class Frame<R> {
  }

  // A callback invoked by a built-in runs below the call that invoked the built-in, so the deeper of the two counts.
-  invokeFunction(fn: Fn, args: Array<Value>, node?: AstNode): Effect.Effect<Value, unknown, R> {
+  invokeFunction(fn: Fn, thisValue: Value, args: Array<Value>, node?: AstNode): Effect.Effect<Value, unknown, R> {
    const self = this
    return Effect.flatMap(CallSite, (site) => {
      const depth = Math.max(self.depth, site.depth) + 1
      if (depth > MAX_CALL_DEPTH) throw rangeError("Maximum call stack size exceeded", node)
      const invocation = new Frame(this.ctx, new ScopeStack([...fn.capturedScopes, new Map()]), depth)
-      // Seed all parameters first so defaults cannot fall through to same-named outer bindings.
      const paramScope = invocation.scopes.current()
+      // `this` and `arguments` are scope bindings so arrows resolve them lexically; a parameter named
+      // `arguments` shadows the object, as in JS.
+      if (!fn.arrow) paramScope.set("this", { mutable: false, value: thisValue, initialized: true })
+      if (!fn.arrow && usesArguments(fn)) {
+        paramScope.set("arguments", {
+          mutable: true,
+          value: new Arguments(self.ctx.builtins.Object, args),
+          initialized: true,
+        })
+      }
+      // Seed all parameters first so defaults cannot fall through to same-named outer bindings.
      for (const parameter of fn.parameters) {
        for (const name of collectPatternNames(parameter)) {
          paramScope.set(name, { mutable: true, value: undefined, initialized: false })
@@ -72,7 +72,7 @@ export const uriError = failure("URIError")

 // Orient the agent rather than enumerate JavaScript; interpreter-support.md is the full matrix.
 export const supportedSyntaxMessage =
-  "This is a restricted JavaScript-like language. Supported: plain and async functions, data literals, destructuring, standard control flow, await and Promise, and built-ins such as Array, Object, Math, JSON, Date, RegExp, Map, Set, and URL. Unsupported: classes, this, getters/setters, BigInt, and custom Symbols. Use plain functions and data objects instead."
+  "This is a restricted JavaScript-like language. Supported: plain and async functions, data literals, destructuring, standard control flow, await and Promise, and built-ins such as Array, Object, Math, JSON, Date, RegExp, Map, Set, and URL. Unsupported: classes, getters/setters, BigInt, and custom Symbols. Use plain functions and data objects instead."

 export const unsupportedSyntax = (kind: string, node: AstNode): PendingThrow =>
  new PendingThrow(
@@ -149,7 +149,11 @@ export abstract class Opaque extends Obj {

 export abstract class Callable extends Opaque {
  override readonly tag = "Function"
-  constructor(proto: Obj, name: string, length: number) {
+  constructor(
+    proto: Obj,
+    name: string,
+    readonly length: number,
+  ) {
    super(proto)
    define(this, "length", length, readonly)
    define(this, "name", name, readonly)
@@ -168,12 +172,29 @@ export class Fn extends Callable {
    readonly capturedScopes: Array<Map<string, Binding>>,
    readonly async: boolean,
    readonly generator: boolean,
+    /** Arrows have no `this` or `arguments` of their own; they read the enclosing function's. */
+    readonly arrow: boolean,
  ) {
    const optional = parameters.findIndex((p) => p.type === "AssignmentPattern" || p.type === "RestElement")
    super(proto, name, optional === -1 ? parameters.length : optional)
  }
 }

+/** The strict `arguments` object: an ordinary object with indexed own properties and a hidden `length`. */
+export class Arguments extends Obj {
+  override readonly tag = "Arguments"
+  constructor(proto: Obj, args: Array<Value>) {
+    super(proto)
+    args.forEach((arg, index) => define(this, String(index), arg))
+    define(this, "length", args.length, hidden)
+  }
+  override iterator() {
+    return keys(this)
+      .map((key) => get(this, key))
+      .values()
+  }
+}
+
 export type NativeCall<R> = (thisValue: Value, args: Array<Value>) => Effect.Effect<Value, unknown, R>
 export type NativeConstruct<R> = (args: Array<Value>, newTarget: Callable) => Effect.Effect<Value, unknown, R>

@@ -40,7 +40,7 @@ const parse = <R>(ctx: Interpreter<R>, args: Array<Value>): Effect.Effect<Value,
          else set(value, name, revived)
        }
      }
-      return yield* apply([key, value])
+      return yield* apply([key, value], holder)
    })
  return visit(record(ctx.builtins.Object, { "": parsed }), "")
 }
@@ -210,11 +210,14 @@ describe("Test262 JSON.stringify replacer adaptations", () => {
 })

 describe("CodeMode JSON callback boundaries", () => {
-  test("this remains unsupported rather than exposing callback holders", async () => {
-    const result = await Effect.runPromise(
-      CodeMode.execute({ code: `return JSON.parse("1", function (key, item) { return this })`, tools: {} }),
-    )
-    expect(result).toMatchObject({ ok: false, error: { kind: "UnsupportedSyntax" } })
+  test("revivers and replacers see the holder as this", async () => {
+    expect(
+      await value(`
+        const revived = JSON.parse('{"a":{"b":1}}', function (key, item) { return key === "b" ? this.b + 1 : item })
+        const text = JSON.stringify({ a: 1, b: 2 }, function (key, item) { return key === "a" ? this.b : item })
+        return [revived, text]
+      `),
+    ).toEqual([{ a: { b: 2 } }, '{"a":2,"b":2}'])
  })

  test("prototype-named keys parse as own data and reach the reviver", async () => {
@@ -77,6 +77,6 @@ describe("new on a non-constructible callee", () => {
    expect(failure.message).toStartWith(
      "SyntaxError: Syntax 'ClassDeclaration' is not supported. This is a restricted JavaScript-like language. Supported: ",
    )
-    expect(failure.message).toContain("Unsupported: classes, this, getters/setters, BigInt, and custom Symbols.")
+    expect(failure.message).toContain("Unsupported: classes, getters/setters, BigInt, and custom Symbols.")
  })
 })
@@ -1461,3 +1461,113 @@ describe("structuredClone", () => {
    expect((await error(`structuredClone()`)).message).toContain("structuredClone requires 1 argument")
  })
 })
+
+describe("error constructor prototype chain", () => {
+  test("derived error constructors extend Error and inherit its statics", async () => {
+    expect(
+      await value(`
+        const derived = [TypeError, RangeError, SyntaxError, ReferenceError, EvalError, URIError, AggregateError]
+        return [
+          derived.every((ctor) => Object.getPrototypeOf(ctor) === Error),
+          Object.getPrototypeOf(Error) === Function.prototype,
+          TypeError.isError(new RangeError("x")),
+          new TypeError("x") instanceof Error,
+        ]
+      `),
+    ).toEqual([true, true, true, true])
+  })
+})
+
+describe("this, arguments, and Function.prototype.call/apply/bind", () => {
+  test("this is the call receiver for non-arrow functions and lexical for arrows", async () => {
+    expect(
+      await value(`
+        const o = { n: 1, m() { return this.n }, a() { return (() => this.n)() }, bare() { return this } }
+        const detached = o.bare
+        function f() { return this }
+        return [o.m(), o["m"](), o?.m(), (o.m)(), o.a(), o.bare() === o, detached(), f(), (0, o.bare)(), this, (() => this)()]
+      `),
+    ).toEqual([1, 1, 1, 1, 1, true, null, null, null, null, null])
+  })
+
+  test("this reaches generator and async methods and plain-function callbacks", async () => {
+    expect(
+      await value(`
+        const o = { n: 2, *g() { yield this.n }, async m() { return this.n }, xs: [1, 2], go() { return this.xs.map(function (x) { return [x, this] }) } }
+        return [[...o.g()], await o.m(), o.go()]
+      `),
+    ).toEqual([
+      [2],
+      2,
+      [
+        [1, null],
+        [2, null],
+      ],
+    ])
+  })
+
+  test("arguments is an unmapped array-like that arrows and parameters interact with as in JS", async () => {
+    expect(
+      await value(`
+        function f(a) { arguments[0] = 9; return [arguments.length, arguments[1], a, [...arguments], Array.isArray(arguments), JSON.stringify(arguments), typeof arguments.map, (() => arguments[1])()] }
+        function shadow(arguments) { return arguments }
+        function hoisted() { var arguments; return arguments.length }
+        let outer
+        try { outer = arguments } catch (error) { outer = error.name }
+        return [f(1, 2), shadow(7), hoisted(1, 2, 3), outer]
+      `),
+    ).toEqual([[2, 2, 1, [9, 2], false, '{"0":9,"1":2}', "undefined", 2], 7, 3, "ReferenceError"])
+  })
+
+  test("call, apply, and bind set this and arguments on program functions and built-ins", async () => {
+    expect(
+      await value(`
+        function f(a, b, c) { return [this, a, b, c] }
+        const g = f.bind({ k: 1 }, "A")
+        const arr = [1]
+        Array.prototype.push.call(arr, 2, 3)
+        return [
+          f.call("t", 1, 2),
+          f.apply({ k: 2 }, [1, 2]),
+          f.apply(null, { length: 2, 0: "x", 1: "y" }),
+          f.apply(null).length,
+          g("B", "C"), g.name, g.length,
+          f.bind(1).bind(2)()[0],
+          arr,
+          Math.max.apply(null, [1, 5, 3]),
+          Math.max.bind(null, 10)(3),
+          [1, 2].map(f.bind(null, 0)).map((r) => r[1]),
+        ]
+      `),
+    ).toEqual([
+      ["t", 1, 2, null],
+      [{ k: 2 }, 1, 2, null],
+      [null, "x", "y", null],
+      4,
+      [{ k: 1 }, "A", "B", "C"],
+      "bound f",
+      2,
+      1,
+      [1, 2, 3],
+      5,
+      10,
+      [0, 0],
+    ])
+  })
+
+  test("call and apply reject non-callable receivers and non-array-like argument lists", async () => {
+    expect((await error(`Function.prototype.call.call(1)`)).message).toContain(
+      "Function.prototype.call called on incompatible receiver",
+    )
+    expect((await error(`(() => 1).apply(null, 5)`)).message).toContain("expects an array-like argument list")
+    expect((await error(`(() => 1).apply(null, { length: 1e9 })`)).message).toContain("Invalid array length")
+    expect(
+      await value(
+        `function f() { return arguments.length } return [f.apply(null, { length: -5 }), f.apply(null, { length: "2" })]`,
+      ),
+    ).toEqual([0, 2])
+    expect((await error(`function f(n) { return f.call(null, n + 1) } f(0)`)).message).toContain(
+      "Maximum call stack size exceeded",
+    )
+  })
+})
@@ -1416,7 +1416,9 @@ describe("built-in iterators", () => {
    const logged = await run(`console.log([1].keys()); return null`)
    expect(logged.logs?.[0]).toBe("[opaque reference]")
    expect((await error(`return [1].keys() + ""`)).message).toContain("Binary operators require data values")
-    expect((await error(`return [1].keys().next.call({})`)).message).toContain("is not a function")
+    expect((await error(`return [1].keys().next.call({})`)).message).toContain(
+      "Iterator.prototype.next called on incompatible receiver a data object",
+    )
    expect((await error(`const it = [1].keys(); const next = it.next; return next()`)).message).toContain(
      "Iterator.prototype.next called on incompatible receiver undefined",
    )
@@ -24,9 +24,9 @@ Without them the runner registers no tests, so CI is unaffected. Licensed under

 `script/sync-test262.ts` skips a file when its frontmatter declares a `flags`, `features`, or `includes` value the
 manifest marks unsupported, or when its code matches one of the manifest's `boundaries` patterns. The sync checks the
-checkout is at the pinned revision, so every machine runs the same 4595 files. Boundaries are
-intentional limits of the interpreter, not compatibility work: classes, `this`, `arguments`, prototype objects,
-property descriptors, accessors, boxed primitives, sloppy mode, `eval`, `Symbol()`, and the `$262` host API. If one
+checkout is at the pinned revision, so every machine runs the same files. Boundaries are
+intentional limits of the interpreter, not compatibility work: classes, prototype objects, property descriptors,
+accessors, boxed primitives, sloppy mode, `eval`, `Symbol()`, and the `$262` host API. If one
 of those decisions changes, delete its entry and re-sync; the tests are upstream, not lost.

 ## Commands
@@ -2,6 +2,9 @@
  "revision": "250f204f23a9249ff204be2baec29600faae7b75",
  "directories": [
    "built-ins/Array/prototype",
+    "built-ins/Function/prototype/apply",
+    "built-ins/Function/prototype/bind",
+    "built-ins/Function/prototype/call",
    "built-ins/Iterator",
    "built-ins/Object/freeze",
    "built-ins/Object/getPrototypeOf",
@@ -11,18 +14,17 @@
    "built-ins/Object/isSealed",
    "built-ins/Object/preventExtensions",
    "built-ins/String/raw",
+    "language/arguments-object",
    "language/expressions/does-not-equals",
    "language/expressions/equals",
    "language/expressions/tagged-template",
+    "language/expressions/this",
    "language/statements"
  ],
  "harness": ["assert.js", "sta.js", "compareArray.js", "doneprintHandle.js"],
  "flags": ["module", "raw", "noStrict"],
  "boundaries": {
    "class": "\\bclass\\s*[A-Za-z_${]",
-    "this": "\\bthis\\b",
-    "arguments": "\\barguments\\b",
-    "call/apply/bind": "\\.(call|apply|bind)\\s*\\(",
    "accessor properties": "\\b(get|set)\\s+[\\w$\\[][^\\n(]*\\(",
    "property descriptors": "Object\\.(defineProperty|defineProperties|getOwnPropertyDescriptors?|getOwnPropertyNames|setPrototypeOf)\\b",
    "boxed primitives": "\\bnew\\s+(String|Number|Boolean)\\s*\\(",
@@ -151,8 +151,12 @@ type Overlay = {
 function options(replacement: string, modelID: string | undefined, settings: Legacy): Overlay {
  const converse = replacement === "@opencode/ai/providers/amazon-bedrock" && modelID !== undefined
  const kept = Struct.omit(settings, ["headers", "extraBody", "useCompletionUrls", ...OPENROUTER_KEYS])
+  const thinking = converse ? bedrockThinking(modelID, settings) : undefined
  return {
-    settings: replacement.startsWith("@opencode/ai/providers/amazon-bedrock") ? bedrockSettings(kept, converse) : kept,
+    settings: {
+      ...(replacement.startsWith("@opencode/ai/providers/amazon-bedrock") ? bedrockSettings(kept, converse) : kept),
+      ...(thinking === undefined ? {} : { thinking }),
+    },
    ...(settings.headers === undefined ? {} : { headers: settings.headers }),
    ...(settings.extraBody === undefined ? {} : { body: settings.extraBody }),
    ...(converse ? bedrockRequest(modelID, settings) : {}),
@@ -196,6 +200,13 @@ function bedrockSettings(settings: Legacy, converse: boolean) {
  }
 }

+// Claude's enabled budget is a typed setting so the protocol can fit it under the output limit.
+function bedrockThinking(modelID: string | undefined, settings: Legacy) {
+  const reasoning = settings.reasoningConfig
+  if (!modelID?.includes("anthropic") || reasoning?.type !== "enabled" || reasoning.budgetTokens === undefined) return
+  return { type: "enabled", budgetTokens: reasoning.budgetTokens }
+}
+
 function bedrockRequest(modelID: string | undefined, settings: Legacy): Pick<Overlay, "body"> {
  const additional = settings.additionalModelRequestFields ?? {}
  const reasoning = settings.reasoningConfig
@@ -210,9 +221,6 @@ function bedrockRequest(modelID: string | undefined, settings: Legacy): Pick<Ove
  const betas = settings.anthropicBeta ?? []
  const fields = Provider.mergeOverlay(additional, {
    ...(betas.length > 0 ? { anthropic_beta: [...(additional.anthropic_beta ?? []), ...betas] } : {}),
-    ...(anthropic && type === "enabled" && budget !== undefined
-      ? { thinking: { type: "enabled", budget_tokens: budget } }
-      : {}),
    ...(anthropic && type === "adaptive"
      ? { thinking: { type: "adaptive", ...(display === undefined ? {} : { display }) } }
      : {}),
@@ -24,7 +24,6 @@ import {
  ProviderMetadata,
  TransportError,
  ToolResultValue,
-  UnknownProviderError,
  type ContentPart,
  type LLMRequest,
  type Media,
@@ -367,6 +366,8 @@ function modelFromLanguage(info: RuntimeInfo, language: LanguageModelV3) {
    provider: ProviderID.make(providerID),
    providerMetadataKey: optionKey,
    protocol: "ai-sdk",
+    // AI SDK providers convert tool schemas themselves, so model-family sanitizers stay off here.
+    sanitizer: "none",
    endpoint: Endpoint.path("/", { baseURL: "https://ai-sdk.local" }),
    auth: Auth.none,
    transport: {
@@ -936,15 +937,18 @@ function llmError(error: unknown, operation: "request" | "read") {
        code: network.code,
      }),
    })
-  return new AIError({
-    reason: new UnknownProviderError({
-      message: unknownErrorMessage(error),
-      body: errorBody(error),
-      cause: error,
-    }),
+  return RequestExecutor.httpFailure({
+    message: unknownErrorMessage(error),
+    data: errorValue(error) ?? error,
+    responseBody: errorBody(error),
+    cause: error,
  })
 }

+// AI SDK stream errors can arrive as plain objects. A gateway's type validation error keeps the provider's error
+// response in `value`, which carries the message and codes.
+const errorValue = (error: unknown) => (ProviderShared.isRecord(error) ? error.value : undefined)
+
 // Runtime-generated network failure shapes. The codes mirror the AI SDK's own
 // Bun network error list in handleFetchError; the messages are undici's fetch
 // TypeError and stream termination strings plus our SSE chunk timeout error.
@@ -1032,7 +1036,15 @@ const decodeProviderError = Schema.decodeUnknownOption(
 )

 function unknownErrorMessage(error: unknown) {
-  const message = error instanceof Error ? error.message : String(error)
+  const message =
+    error instanceof Error
+      ? error.message
+      : typeof error === "string"
+        ? error
+        : ([error, errorValue(error)]
+            .map((value) => Option.getOrUndefined(decodeProviderError(value)))
+            .flatMap((decoded) => [decoded?.error?.message, decoded?.message])
+            .find((value) => value?.trim()) ?? "")
  return message.trim() === "" ? "Provider request failed" : message
 }

@@ -101,6 +101,7 @@ import { VcsHgPlugin } from "./vcs/hg.js"
 import { ToolInputRepairPlugin } from "./tool-input-repair.js"
 import { OptimizePlugin } from "./optimize.js"
 import { VcsGitPlugin } from "./vcs/git.js"
+import { VerbosityPlugin } from "./verbosity.js"
 import { WarmingPlugin } from "./warming.js"
 import { WellKnownPlugin } from "../wellknown/plugin.js"

@@ -230,6 +231,7 @@ const pre = [
  PatchTool.Plugin,
  // Render model prompts after the patch plugin selects the available editing tools.
  ...OptimizePlugin.Plugins,
+  VerbosityPlugin.Plugin,
  IdentityPlugin.Plugin,
  EditTool.Plugin,
  GlobTool.Plugin,
@@ -0,0 +1,54 @@
+export * as VerbosityPlugin from "./verbosity.js"
+
+import { define } from "@opencode/plugin/effect/plugin"
+import type { SessionRequest } from "@opencode/plugin/effect/session"
+import { Effect } from "effect"
+import { Model } from "../model.js"
+import { Provider } from "../provider.js"
+import type { PluginInternal } from "./internal.js"
+
+const direct = new Set([
+  "@opencode/ai/providers/openai",
+  "@opencode/ai/providers/openai/responses",
+  "@opencode/ai/providers/azure",
+  "@opencode/ai/providers/azure/responses",
+])
+const gateways = new Set(["@opencode/ai/providers/cloudflare-ai-gateway", Provider.aisdk("@ai-sdk/gateway")])
+
+export const Plugin = define({
+  id: "opencode.prompt.verbosity",
+  effect: Effect.fn("VerbosityPlugin")(function* (ctx) {
+    const models = yield* Model.Service
+    const hook = (event: SessionRequest) =>
+      Effect.gen(function* () {
+        if (event.options.textVerbosity !== undefined) return
+        const model = yield* models.get(event.model.providerID, event.model.id)
+        if (!model) return
+        const id = openAIModelID(model)
+        if (!id || !supportsVerbosity(id)) return
+        if (model.settings?.textVerbosity !== undefined) return
+        const variant = model.variants.find((item) => item.id === event.model.variant)
+        if (variant?.settings?.textVerbosity !== undefined) return
+        event.options.textVerbosity = "low"
+      })
+    yield* ctx.session.hook("context", hook)
+    yield* ctx.session.hook("compaction", hook)
+    yield* ctx.session.hook("generate", hook)
+    yield* ctx.session.hook("title", hook)
+  }),
+} satisfies PluginInternal.InternalPlugin)
+
+function supportsVerbosity(id: string) {
+  if (id.includes("gpt-6")) return true
+  if (id.includes("-chat") || id.includes("-image")) return false
+  // New GPT-5 minor versions remain unset until their support is known.
+  return /(?:^|[/.])gpt-5\.[1-6](?:[.:-]|$)/.test(id) || /(?:^|[/.])gpt-5(?:-(?:mini|nano)(?:[.:-]|$)|$)/.test(id)
+}
+
+function openAIModelID(model: Model.Info) {
+  const id = model.modelID.toLowerCase()
+  if (direct.has(model.package ?? "")) return id
+  if (model.package === "@opencode/ai/providers/amazon-bedrock/mantle/responses" && id.startsWith("openai."))
+    return id.slice("openai.".length)
+  if (gateways.has(model.package ?? "") && id.startsWith("openai/")) return id.slice("openai/".length)
+}
@@ -37,9 +37,8 @@ import { toLLMMessages } from "./runner/to-llm-message.js"
 import type { AgentNotFoundError } from "./error.js"
 import type { Instructions } from "../instructions/index.js"

-const DEFAULT_BUFFER = 20_000
+const AUTO_THRESHOLD = 0.85
 const DEFAULT_KEEP_TOKENS = 15_000
-const OUTPUT_TOKEN_MAX = 32_000
 const TOOL_OUTPUT_MAX_CHARS = 2_000
 const IMAGE_TOKEN_ESTIMATE = 1_500
 const PDF_TOKEN_ESTIMATE = 2_000
@@ -89,7 +88,7 @@ const LEGACY_HEADING = "## Additional Context"

 export type Settings = {
  auto: boolean
-  buffer: number
+  buffer?: number
  tokens: number
 }

@@ -401,7 +400,7 @@ export const layer = Layer.effect(

    const state = State.create<Settings & { readonly native: NativeStrategy[] }, Editor>({
      name: "session-compaction",
-      initial: () => ({ auto: true, buffer: DEFAULT_BUFFER, tokens: DEFAULT_KEEP_TOKENS, native: [] }),
+      initial: () => ({ auto: true, tokens: DEFAULT_KEEP_TOKENS, native: [] }),
      editor: (editor) => ({
        configure: (settings) => {
          if (settings.auto !== undefined) editor.auto = settings.auto
@@ -754,11 +753,9 @@ export const layer = Layer.effect(
      const limit = input.resolved.limit
      const context = limit.context
      if (context <= 0) return false
-      const output = Math.min(limit.output, OUTPUT_TOKEN_MAX)
-      const promptCeiling = Math.min(
-        limit.input === undefined ? Number.POSITIVE_INFINITY : limit.input - config.buffer,
-        context - Math.max(output, config.buffer),
-      )
+      const usable = Math.min(context, limit.input ?? context)
+      const promptCeiling =
+        config.buffer === undefined ? Math.floor(usable * AUTO_THRESHOLD) : usable - config.buffer
      return estimateTokens(input) >= promptCeiling
    }
    const compactManual = Effect.fn("SessionCompaction.compactManual")(function* (input: ManualInput) {
@@ -43,6 +43,7 @@ type Active = {
  info: Info
  file: string
  size: number
+  newlines: number
  // Resolves with the terminal Info once the command exits, times out, or is killed. A wait
  // started after termination resolves immediately from the already-completed deferred.
  done: Deferred.Deferred<Info, NotFoundError>
@@ -240,10 +241,12 @@ const layer = () =>
          if (page.output.endsWith("\n")) lines.pop()
          const truncated = latest.size > maxBytes || lines.length > maxLines
          const text = lines.length > maxLines ? lines.slice(-maxLines).join("\n") : page.output
+          const total = (yield* require(info.id)).newlines + (page.output.endsWith("\n") ? 0 : 1)
+          const shown = Math.min(lines.length, maxLines)
          const notice = truncated
-            ? `${text ? "\n\n" : ""}[full output saved to ${info.file}]`
+            ? `\n\n[showing lines ${total - shown + 1}-${total} of ${total}; full output saved to ${info.file}]`
            : ""
-          return { output: `${text}${notice}`, truncated }
+          return { output: `${text || "(no output)"}${notice}`, truncated }
        }).pipe(Effect.catchTag("Shell.NotFoundError", () => Effect.succeed(undefined)))
        return { info, capture }
      })
@@ -312,6 +315,7 @@ const layer = () =>
                }),
                file,
                size: 0,
+                newlines: 0,
                done: Deferred.makeUnsafe<Info, NotFoundError>(),
              }
              commands.set(id, command)
@@ -323,6 +327,9 @@ const layer = () =>
                  Effect.sync(() => {
                    stream.write(chunk)
                    command.size += chunk.length
+                    // Count while streaming so truncation notices never rescan the output file.
+                    for (let index = chunk.indexOf(10); index !== -1; index = chunk.indexOf(10, index + 1))
+                      command.newlines++
                  }),
                ),
              )
@@ -75,23 +75,17 @@ const layer = Layer.effect(

      const kept: string[] = []
      let bytes = 0
-      let hitBytes = false
      for (const line of lines.slice(0, limits.maxLines)) {
        const size = Buffer.byteLength(line, "utf-8") + (kept.length > 0 ? 1 : 0)
-        if (bytes + size > limits.maxBytes) {
-          hitBytes = true
-          break
-        }
+        if (bytes + size > limits.maxBytes) break
        kept.push(line)
        bytes += size
      }
-      if (!hitBytes && kept.length === lines.length && totalBytes > bytes) hitBytes = true
-      const removed = hitBytes ? totalBytes - bytes : lines.length - kept.length
-      const unit = hitBytes ? (removed === 1 ? "byte" : "bytes") : removed === 1 ? "line" : "lines"
      const file = path.join(directory, Identifier.ascending("tool"))
      yield* fs.ensureDir(directory).pipe(Effect.orDie)
      yield* fs.writeFileString(file, text).pipe(Effect.orDie)
-      const marker = `... ${removed} ${unit} truncated; full content saved to ${file} ...`
+      const shown = kept.length > 0 ? `lines 1-${kept.length}` : "0 lines"
+      const marker = `[showing ${shown} of ${lines.length}; full output saved to ${file}]`
      const bounded: Tool.Content[] = []
      let remaining = kept.join("\n").length
      let seenText = false
@@ -308,7 +308,7 @@ function registrationError(tool: Tool.Info) {
    if (error) return error
  }
  const name = normalizedName(tool)
-  if (!/^[A-Za-z0-9_-]{1,64}$/.test(name)) return new RegistrationError({ name, message: `Invalid tool name: ${name}` })
+  if (!/^[A-Za-z0-9_-]{1,128}$/.test(name)) return new RegistrationError({ name, message: `Invalid tool name: ${name}` })
  const id = effectiveName(tool)
  if (tool.options?.codemode === false && id === "execute")
    return new RegistrationError({ name: id, message: 'Tool name "execute" is reserved for CodeMode' })
@@ -28,6 +28,8 @@ const EFFORTS = ["low", "medium", "high"]
 const ENCRYPTED_REASONING = ["reasoning.encrypted_content"]
 const ADAPTIVE_THINKING = { type: "adaptive", display: "summarized" }
 const ANTHROPIC_OUTPUT_TOKEN_MAX = 32_000
+// Alibaba thinking budget variants stay under 64k instead of reaching the model's whole output limit.
+const ALIBABA_THINKING_BUDGET_MAX = 64_000

 const variant = (id: string, overlay: Overlay): Variants[number] => ({ id: Model.VariantID.make(id), ...overlay })

@@ -224,9 +226,12 @@ const alibabaChat: Protocol = (model, support) => {
    case "toggle":
      return toggle({ settings: { enableThinking: false } }, { settings: { enableThinking: true } })
    case "budget_tokens":
-      return budgets(model, support, (tokens) => ({
-        settings: { enableThinking: true, thinkingBudget: tokens },
-      }))
+      return budgets(
+        model,
+        support,
+        (tokens) => ({ settings: { enableThinking: true, thinkingBudget: tokens } }),
+        ALIBABA_THINKING_BUDGET_MAX,
+      )
  }
 }

@@ -310,9 +315,12 @@ const alibabaMessages: Protocol = (model, support) => {
    case "toggle":
      return toggle({ settings: { thinking: { type: "disabled" } } }, { settings: { thinking: { type: "enabled" } } })
    case "budget_tokens":
-      return budgets(model, support, (tokens) => ({
-        settings: { thinking: { type: "enabled", budgetTokens: tokens } },
-      }))
+      return budgets(
+        model,
+        support,
+        (tokens) => ({ settings: { thinking: { type: "enabled", budgetTokens: tokens } } }),
+        ALIBABA_THINKING_BUDGET_MAX,
+      )
  }
 }

@@ -390,8 +398,9 @@ const bedrockConverse: Protocol = (model, support) => {
        model,
        support,
        (tokens) =>
+          // Claude's budget is a typed setting so the protocol can fit it under the output limit.
          claude
-            ? fields({ thinking: { type: "enabled", budget_tokens: tokens } })
+            ? { settings: { thinking: { type: "enabled", budgetTokens: tokens } } }
            : fields({ reasoningConfig: { type: "enabled", budgetTokens: tokens } }),
        claude ? ANTHROPIC_OUTPUT_TOKEN_MAX : model.limit.output,
      )
@@ -405,7 +414,12 @@ const alibabaAISDK: Protocol = (model, support) => {
    case "toggle":
      return toggle({ settings: { enableThinking: false } }, { settings: { enableThinking: true } })
    case "budget_tokens":
-      return budgets(model, support, (tokens) => ({ settings: { enableThinking: true, thinkingBudget: tokens } }))
+      return budgets(
+        model,
+        support,
+        (tokens) => ({ settings: { enableThinking: true, thinkingBudget: tokens } }),
+        ALIBABA_THINKING_BUDGET_MAX,
+      )
  }
 }

@@ -247,6 +247,17 @@ describe("AISDKNative", () => {
      },
    })

+    expect(
+      map(
+        "@ai-sdk/amazon-bedrock",
+        { reasoningConfig: { type: "enabled", budgetTokens: 12_000 } },
+        "anthropic.claude-sonnet-4-5-20250929-v1:0",
+      ),
+    ).toEqual({
+      package: "@opencode/ai/providers/amazon-bedrock",
+      settings: { thinking: { type: "enabled", budgetTokens: 12_000 } },
+    })
+
    // gpt-oss (Harmony) keeps the flat chat-completions field.
    expect(
      map("@ai-sdk/amazon-bedrock", { reasoningConfig: { maxReasoningEffort: "high" } }, "openai.gpt-oss-120b-1:0")
@@ -13,6 +13,7 @@ import {
  CompactionPart,
  ProviderID,
  HttpContext,
+  InvalidRequestError,
  LLMEvent,
  Message,
  RateLimitError,
@@ -374,6 +375,18 @@ it.effect("routes AI Gateway model options by upstream prefix", () =>
      bedrock: { reasoningConfig: { type: "enabled" } },
    })

+    const openai = yield* aisdk.model({
+      ...model("@ai-sdk/gateway", { gateway: { order: ["openai"] } }),
+      modelID: Model.ID.make("openai/gpt-5.5"),
+    })
+    const openaiPrepared = yield* compileRequest(
+      LLM.request({ model: openai, prompt: "Hello", providerOptions: { textVerbosity: "low" } }),
+    )
+    expect(openaiPrepared.body.providerOptions).toEqual({
+      gateway: { order: ["openai"] },
+      openai: { textVerbosity: "low" },
+    })
+
    const fallback = yield* aisdk.model({
      ...model("@ai-sdk/gateway", { reasoningEffort: "high" }),
      modelID: Model.ID.make("deepseek/deepseek-v4"),
@@ -883,6 +896,23 @@ Object.values({
  )
 })

+// Shapes the Vercel AI Gateway streams when the upstream rejects a request.
+Object.entries({
+  "type validation": {
+    name: "AI_TypeValidationError",
+    value: { error: { type: "invalid_request_error", message: "Bad max_tokens" } },
+  },
+  invalid_request: { code: "invalid_request", message: "Bad max_tokens" },
+}).forEach(([shape, failure]) =>
+  it.effect(`reads gateway ${shape} stream errors as invalid requests`, () =>
+    Effect.gen(function* () {
+      const error = yield* streamFailure(failure, true)
+      expect(error.message).toBe("Bad max_tokens")
+      expect(error.reason).toBeInstanceOf(InvalidRequestError)
+    }),
+  ),
+)
+
 it.effect("does not copy Error request internals into the provider body", () =>
  Effect.gen(function* () {
    const cause = Object.assign(new Error("Connection failed"), {
@@ -172,5 +172,5 @@ const input = (tokens: number) => {
    },
  }
 }
-const bufferedInput = input(85_000)
+const bufferedInput = input(82_000)
 const nearInput = input(95_000)
@@ -2117,7 +2117,7 @@ testEffect(Layer.empty).live("isolates invalid MCP tools and preserves plugin tr
      }) satisfies Mcp.Tool
    const healthy = [tool("demo", "search"), tool("other", "lookup")]
    const namespace = tool("x".repeat(65), "lookup")
-    const catalog = yield* Ref.make([tool("demo", "x".repeat(65)), ...healthy, namespace])
+    const catalog = yield* Ref.make([tool("demo", "x".repeat(129)), ...healthy, namespace])

    yield* Effect.gen(function* () {
      const registry = yield* Tool.Service
@@ -2146,7 +2146,7 @@ testEffect(Layer.empty).live("isolates invalid MCP tools and preserves plugin tr
        editor.remove("repaired_lookup")
      })

-      yield* Ref.set(catalog, [tool("demo", "y".repeat(65)), ...healthy, tool("demo", "added"), namespace])
+      yield* Ref.set(catalog, [tool("demo", "y".repeat(129)), ...healthy, tool("demo", "added"), namespace])
      yield* bus.publish(McpEvent.ToolsChanged, { server: "demo" })
      yield* waitForTool(registry, "demo_added")
      expect((yield* toolDefinitions(registry)).map((tool) => tool.name)).toEqual([
@@ -0,0 +1,163 @@
+import { expect } from "bun:test"
+import { Effect } from "effect"
+import { Agent } from "@opencode/core/agent"
+import { Model } from "@opencode/core/model"
+import { Plugin } from "@opencode/core/plugin"
+import { PluginHooks } from "@opencode/core/plugin/hooks"
+import { PluginHost } from "@opencode/core/plugin/host"
+import { VerbosityPlugin } from "@opencode/core/plugin/verbosity"
+import { Provider } from "@opencode/core/provider"
+import { Session } from "@opencode/core/session"
+import type { SessionHooks } from "@opencode/plugin/effect/session"
+import { testEffect } from "../lib/effect"
+import { PluginTestLayer } from "./fixture"
+
+const it = testEffect(PluginTestLayer)
+const ref = (providerID: string, id: string, variant?: string) =>
+  Model.Ref.make({
+    providerID: Provider.ID.make(providerID),
+    id: Model.ID.make(id),
+    ...(variant ? { variant: Model.VariantID.make(variant) } : {}),
+  })
+const request = (model: Model.Ref, options: SessionHooks["context"]["options"] = {}): SessionHooks["context"] => ({
+  sessionID: Session.ID.make("ses_verbosity"),
+  agent: Agent.ID.make("build"),
+  model,
+  system: [],
+  messages: [],
+  tools: {},
+  options,
+})
+
+it.effect("sets known OpenAI Responses defaults without overriding configured or unknown models", () =>
+  Effect.gen(function* () {
+    const providers = yield* Provider.Service
+    const plugins = yield* Plugin.Service
+    const hooks = yield* PluginHooks.Service
+    yield* providers.transform((editor) => {
+      editor.add({
+        info: { ...Provider.Info.empty(Provider.ID.make("openai")), package: "@opencode/ai/providers/openai" },
+        models: [
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-5.5")) },
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-5.6-luna-fast")) },
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-5.2-codex")) },
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-5-mini-fast")) },
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-6-astra-pro")) },
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-4o")) },
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-7")) },
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-5.7")) },
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-5.5-chat")) },
+          { ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-5.4-image-2")) },
+          {
+            ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("gpt-6-astra")),
+            variants: [{ id: Model.VariantID.make("quiet"), settings: { textVerbosity: null } }],
+          },
+          {
+            ...Model.Info.default(Provider.ID.make("openai"), Model.ID.make("chat")),
+            modelID: Model.ID.make("gpt-5.5"),
+            package: "@opencode/ai/providers/openai/chat",
+          },
+        ],
+      })
+      editor.add({
+        info: {
+          ...Provider.Info.empty(Provider.ID.make("opencode")),
+          package: "@opencode/ai/providers/openai-compatible",
+        },
+        models: [
+          {
+            ...Model.Info.default(Provider.ID.make("opencode"), Model.ID.make("astra-alias")),
+            modelID: Model.ID.make("gpt-6-astra"),
+            package: "@opencode/ai/providers/openai/responses",
+          },
+          {
+            ...Model.Info.default(Provider.ID.make("opencode"), Model.ID.make("no-default")),
+            modelID: Model.ID.make("gpt-6-astra"),
+            package: "@opencode/ai/providers/openai/responses",
+            settings: { textVerbosity: null },
+          },
+        ],
+      })
+      editor.add({
+        info: { ...Provider.Info.empty(Provider.ID.make("openrouter")), package: "@opencode/ai/providers/openrouter" },
+        models: [Model.Info.default(Provider.ID.make("openrouter"), Model.ID.make("gpt-5.5"))],
+      })
+      editor.add({
+        info: {
+          ...Provider.Info.empty(Provider.ID.make("configured")),
+          package: "@opencode/ai/providers/openai",
+          settings: { textVerbosity: "medium" },
+        },
+        models: [Model.Info.default(Provider.ID.make("configured"), Model.ID.make("gpt-5.5"))],
+      })
+      for (const [providerID, packageName, modelID] of [
+        ["azure", "@opencode/ai/providers/azure/responses", "gpt-5.5"],
+        ["bedrock-mantle", "@opencode/ai/providers/amazon-bedrock/mantle/responses", "openai.gpt-6-sol"],
+        ["cloudflare", "@opencode/ai/providers/cloudflare-ai-gateway", "openai/gpt-5.6-sol"],
+        ["vercel", Provider.aisdk("@ai-sdk/gateway"), "openai/gpt-6-astra-fast"],
+        ["azure-chat", "@opencode/ai/providers/azure/chat", "gpt-5.5"],
+        ["bedrock-converse", "@opencode/ai/providers/amazon-bedrock", "global.openai.gpt-6-sol"],
+        ["cloudflare-chat", "@opencode/ai/providers/cloudflare-ai-gateway", "workers-ai/gpt-5.5"],
+        ["vercel-other", Provider.aisdk("@ai-sdk/gateway"), "anthropic/gpt-5.5"],
+      ] as const) {
+        editor.add({
+          info: { ...Provider.Info.empty(Provider.ID.make(providerID)), package: packageName },
+          models: [
+            {
+              ...Model.Info.default(Provider.ID.make(providerID), Model.ID.make("selected")),
+              modelID: Model.ID.make(modelID),
+            },
+          ],
+        })
+      }
+    })
+    yield* VerbosityPlugin.Plugin.effect(yield* PluginHost.make(plugins))
+
+    for (const kind of ["context", "compaction", "generate", "title"] as const) {
+      const event = request(ref("openai", "gpt-5.5"))
+      yield* hooks.trigger("session", kind, event)
+      expect(event.options.textVerbosity).toBe("low")
+    }
+
+    for (const id of ["gpt-5.6-luna-fast", "gpt-5.2-codex", "gpt-5-mini-fast", "gpt-6-astra-pro"]) {
+      const event = request(ref("openai", id))
+      yield* hooks.trigger("session", "context", event)
+      expect(event.options.textVerbosity).toBe("low")
+    }
+
+    for (const model of [
+      ref("openai", "gpt-4o"),
+      ref("openai", "gpt-7"),
+      ref("openai", "gpt-5.7"),
+      ref("openai", "gpt-5.5-chat"),
+      ref("openai", "gpt-5.4-image-2"),
+      ref("openai", "chat"),
+      ref("openrouter", "gpt-5.5"),
+      ref("configured", "gpt-5.5"),
+      ref("azure-chat", "selected"),
+      ref("bedrock-converse", "selected"),
+      ref("cloudflare-chat", "selected"),
+      ref("vercel-other", "selected"),
+      ref("openai", "gpt-6-astra", "quiet"),
+      ref("opencode", "no-default"),
+    ]) {
+      const event = request(model)
+      yield* hooks.trigger("session", "context", event)
+      expect(event.options.textVerbosity).toBeUndefined()
+    }
+
+    const alias = request(ref("opencode", "astra-alias"))
+    yield* hooks.trigger("session", "context", alias)
+    expect(alias.options.textVerbosity).toBe("low")
+
+    for (const providerID of ["azure", "bedrock-mantle", "cloudflare", "vercel"]) {
+      const event = request(ref(providerID, "selected"))
+      yield* hooks.trigger("session", "context", event)
+      expect(event.options.textVerbosity).toBe("low")
+    }
+
+    const overridden = request(ref("openai", "gpt-5.5"), { textVerbosity: "high" })
+    yield* hooks.trigger("session", "context", overridden)
+    expect(overridden.options.textVerbosity).toBe("high")
+  }),
+)
@@ -153,7 +153,7 @@ test("compaction prompts prohibit task execution", () => {
    expect(SessionCompaction.buildPrompt(update)).toContain("Do not continue the task or call tools")
 })

-it.effect("auto compaction estimates current content against the buffered prompt ceiling", () =>
+it.effect("auto compaction uses 85% by default and a configured buffer instead", () =>
  Effect.gen(function* () {
    const compaction = yield* SessionCompaction.Service
    const session = Session.Info.make({
@@ -205,23 +205,27 @@ it.effect("auto compaction estimates current content against the buffered prompt
    }

    const inputLimited = { context: 400_000, input: 272_000, output: 128_000 }
-    expect(compaction.required(input(251_999, inputLimited))).toBe(false)
-    expect(compaction.required(input(252_000, inputLimited))).toBe(true)
+    expect(compaction.required(input(231_199, inputLimited))).toBe(false)
+    expect(compaction.required(input(231_200, inputLimited))).toBe(true)
    const native = (tokens: number, limit: { context: number; input?: number; output: number } = inputLimited) => {
      const selected = input(tokens, limit)
      return { ...selected, resolved: { ...selected.resolved, compaction: { type: "native" as const } } }
    }
-    expect(compaction.required(native(251_999))).toBe(false)
-    expect(compaction.required(native(252_000))).toBe(true)
+    expect(compaction.required(native(231_199))).toBe(false)
+    expect(compaction.required(native(231_200))).toBe(true)
    expect(compaction.required(native(1_000_000, { context: 0, input: undefined, output: 0 }))).toBe(false)

    const contextLimited = { context: 100_000, output: 10_000 }
-    expect(compaction.required(input(79_999, contextLimited))).toBe(false)
-    expect(compaction.required(input(80_000, contextLimited))).toBe(true)
+    expect(compaction.required(input(84_999, contextLimited))).toBe(false)
+    expect(compaction.required(input(85_000, contextLimited))).toBe(true)

    const outputLimited = { context: 100_000, output: 30_000 }
-    expect(compaction.required(input(69_999, outputLimited))).toBe(false)
-    expect(compaction.required(input(70_000, outputLimited))).toBe(true)
+    expect(compaction.required(input(84_999, outputLimited))).toBe(false)
+    expect(compaction.required(input(85_000, outputLimited))).toBe(true)
+
+    const smallWindow = { context: 32_000, output: 32_000 }
+    expect(compaction.required(input(27_199, smallWindow))).toBe(false)
+    expect(compaction.required(input(27_200, smallWindow))).toBe(true)

    const assistant = input(79_000, contextLimited).messages[0]
    const tool = SessionMessage.AssistantTool.make({
@@ -233,7 +237,9 @@ it.effect("auto compaction estimates current content against the buffered prompt
    })
    const grown = { ...input(79_000, contextLimited), messages: [{ ...assistant, content: [tool] }] }
    expect(SessionCompaction.estimateTokens(grown)).toBe(80_000)
-    expect(compaction.required(grown)).toBe(true)
+    expect(compaction.required(grown)).toBe(false)
+    const near = input(84_000, contextLimited)
+    expect(compaction.required({ ...near, messages: [{ ...near.messages[0], content: [tool] }] })).toBe(true)

    const interrupted = { ...assistant, id: SessionMessage.ID.create(), tokens: undefined }
    expect(SessionCompaction.estimateTokens({ ...grown, messages: [...grown.messages, interrupted] })).toBe(80_001)
@@ -285,6 +291,13 @@ it.effect("auto compaction estimates current content against the buffered prompt
      time: { created: 0, completed: 0 },
    })
    expect(compaction.required({ ...grown, messages: [checkpoint] })).toBe(false)
+
+    yield* compaction.transform((editor) => editor.configure({ buffer: 10_000 }))
+    expect(compaction.required(input(89_999, contextLimited))).toBe(false)
+    expect(compaction.required(input(90_000, contextLimited))).toBe(true)
+    yield* compaction.transform((editor) => editor.configure({ buffer: 0 }))
+    expect(compaction.required(input(99_999, contextLimited))).toBe(false)
+    expect(compaction.required(input(100_000, contextLimited))).toBe(true)
  }),
 )

@@ -362,6 +362,7 @@ it.live("manual and automatic endpoint compaction keep the provider replacement
    expect(JSON.stringify(replacement)).not.toContain("Original user")
    expect(fixture.state.calls).toBe(2)
    expect(fixture.headers[0]?.get("x-http-hook")).toBe("compaction")
+    expect(fixture.bodies[0]).toMatchObject({ tools: [expect.objectContaining({ name: "read" })] })
    expect(fixture.bodies[0]).not.toHaveProperty("context_management")
  }),
 )
@@ -2883,7 +2883,7 @@ describe("SessionRunnerLLM", () => {
        agent.steps = 2
      }),
    )
-    yield* s.llm.push(TestLLM.textWithUsage("Earlier answer", "before-native", 10_000))
+    yield* s.llm.push(TestLLM.textWithUsage("Earlier answer", "before-native", 36_000))
    yield* s.runPrompt("First real request")
    const checkpoint = (encrypted: string) =>
      CompactionCheckpointResponse.make({
@@ -2903,7 +2903,7 @@ describe("SessionRunnerLLM", () => {
    const installed = (yield* s.messages).filter((message) => message.type === "compaction")
    expect(installed).toMatchObject([{ status: "completed", reason: "auto", providerContext: { version: 1 } }])
    // New input without a post-checkpoint usage anchor must not retrigger compaction.
-    yield* s.llm.push(TestLLM.textWithUsage("Measured", "measured", 10_000))
+    yield* s.llm.push(TestLLM.textWithUsage("Measured", "measured", 36_000))
    yield* s.runPrompt("Third real request")
    expect(s.requests).toHaveLength(5)
    yield* s.llm.push(checkpoint("second"), TestLLM.textWithUsage("Continued", "continued", 10_000))
@@ -2929,7 +2929,7 @@ describe("SessionRunnerLLM", () => {
    s.currentModel = LanguageModel.make({ id: "native", provider: "openai", route: OpenAIResponses.route })
    modelLimits.set("native", { context: 42_000, output: 32_000 })
    s.compaction = { type: "native" }
-    yield* s.llm.push(TestLLM.textWithUsage("Earlier answer", "before-native", 10_000))
+    yield* s.llm.push(TestLLM.textWithUsage("Earlier answer", "before-native", 36_000))
    yield* s.runPrompt("Original durable request")
    yield* s.llm.push(
      CompactionCheckpointResponse.make({
@@ -46,14 +46,14 @@ describe("ToolOutput", () => {
          expect(yield* fs.readFileString(outputPath)).toBe("one\ntwo\nthree")
          expect(result.content).toEqual([
            { type: "text", text: "one\ntwo" },
-            { type: "text", text: `... 1 line truncated; full content saved to ${outputPath} ...` },
+            { type: "text", text: `[showing lines 1-2 of 3; full output saved to ${outputPath}]` },
          ])
        }),
      { maxLines: 2, maxBytes: 1_000 },
    ),
  )

-  it.live("reports bytes omitted by the byte limit", () =>
+  it.live("reports lines shown under the byte limit", () =>
    withStore(
      (output) =>
        Effect.gen(function* () {
@@ -62,7 +62,7 @@ describe("ToolOutput", () => {
            { type: "text", text: "one" },
            {
              type: "text",
-              text: expect.stringMatching(/^\.\.\. 4 bytes truncated; full content saved to .+ \.\.\.$/),
+              text: expect.stringMatching(/^\[showing lines 1-1 of 2; full output saved to .+\]$/),
            },
          ])
        }),
@@ -82,7 +82,7 @@ describe("ToolOutput", () => {
            { type: "text", text: "before" },
            file,
            { type: "text", text: "after" },
-            { type: "text", text: expect.stringMatching(/^\.\.\. 1 line truncated; full content saved to /) },
+            { type: "text", text: expect.stringMatching(/^\[showing lines 1-2 of 3; full output saved to /) },
          ])
        }),
      { maxLines: 2, maxBytes: 1_000 },
@@ -130,7 +130,7 @@ describe("ToolOutput", () => {
          const result = yield* output.truncate({ content: [{ type: "text", text: "one\n" }] })
          expect(result.content).toEqual([
            { type: "text", text: "one" },
-            { type: "text", text: expect.stringMatching(/^\.\.\. 1 byte truncated; full content saved to /) },
+            { type: "text", text: expect.stringMatching(/^\[showing lines 1-1 of 1; full output saved to /) },
          ])
        }),
      { maxLines: 2, maxBytes: 3 },
@@ -329,7 +329,7 @@ describe("Tool", () => {
        {
          before: make(),
          "": make(),
-          ["x".repeat(65)]: make(),
+          ["x".repeat(129)]: make(),
          "echo.tool": constant("first"),
          echo_tool: constant("last"),
          execute: make(),
@@ -346,6 +346,27 @@ describe("Tool", () => {
    }),
  )

+  it.effect("registers 128-character MCP tool names in Code Mode", () =>
+    Effect.gen(function* () {
+      const service = yield* Tool.Service
+      const name = "x".repeat(128)
+      yield* transform(service, { [name]: make(), ["x".repeat(129)]: make() }, { namespace: "cloudflare" })
+
+      const snapshot = yield* service.snapshot()
+      expect(codeModeListings(snapshot.codeModeCatalog!).map((tool) => tool.path)).toEqual([`cloudflare.${name}`])
+      const result = yield* snapshot.execute({
+        ...call("execute"),
+        call: {
+          type: "tool-call",
+          id: "call-long-mcp-name",
+          name: "execute",
+          input: { code: `return (await tools.cloudflare[${JSON.stringify(name)}]({ text: "hello" })).text` },
+        },
+      })
+      expect(result.content).toEqual([{ type: "text", text: "hello" }])
+    }),
+  )
+
  it.effect("executes native tools without requiring letter-leading names or namespace segments", () =>
    Effect.gen(function* () {
      const service = yield* Tool.Service
@@ -1555,7 +1555,7 @@ describe("ShellTool", () => {
              {
                id: settled.metadata?.shellID,
                status: "completed",
-                output: "Exited with code 7",
+                output: "(no output)\n\nExited with code 7",
              },
            ])
          }),
@@ -175,8 +175,8 @@ test("spells Chat Completions variants for direct providers", () => {
    ]),
  ).toEqual([
    { id: "none", settings: { enableThinking: false } },
-    { id: "high", settings: { enableThinking: true, thinkingBudget: 131_072 } },
-    { id: "max", settings: { enableThinking: true, thinkingBudget: 262_144 } },
+    { id: "high", settings: { enableThinking: true, thinkingBudget: 32_000 } },
+    { id: "max", settings: { enableThinking: true, thinkingBudget: 63_999 } },
  ])

  expect(
@@ -196,6 +196,17 @@ test("spells Chat Completions variants for direct providers", () => {
  ])
 })

+test("spells Bedrock Converse Claude budgets as a thinking setting", () => {
+  expect(
+    resolve(model("@opencode/ai/providers/amazon-bedrock", "us.anthropic.claude-haiku-4-5-20251001-v1:0", 64_000), [
+      { type: "budget_tokens", min: 1024 },
+    ]),
+  ).toEqual([
+    { id: "high", settings: { thinking: { type: "enabled", budgetTokens: 16_000 } } },
+    { id: "max", settings: { thinking: { type: "enabled", budgetTokens: 31_999 } } },
+  ])
+})
+
 test("spells Bedrock Converse effort for Grok and Nova", () => {
  const supports: Variant.Support[] = [{ type: "effort", values: ["low", "xhigh"] }]
  expect(resolve(model("@opencode/ai/providers/amazon-bedrock", "us.xai.grok-4.6"), supports)).toEqual([
@@ -214,6 +225,25 @@ test("spells Bedrock Converse effort for Grok and Nova", () => {
  ])
 })

+test("caps Alibaba thinking budget variants at 64k", () => {
+  const supports: Variant.Support[] = [{ type: "toggle" }, { type: "budget_tokens" }]
+  expect(resolve(model("@opencode/ai/providers/alibaba/chat", "kimi-k2.6", 262_144), supports)).toEqual([
+    { id: "none", settings: { enableThinking: false } },
+    { id: "high", settings: { enableThinking: true, thinkingBudget: 32_000 } },
+    { id: "max", settings: { enableThinking: true, thinkingBudget: 63_999 } },
+  ])
+  expect(resolve(model("@opencode/ai/providers/alibaba/messages", "kimi-k2.6", 262_144), supports)).toEqual([
+    { id: "none", settings: { thinking: { type: "disabled" } } },
+    { id: "high", settings: { thinking: { type: "enabled", budgetTokens: 32_000 } } },
+    { id: "max", settings: { thinking: { type: "enabled", budgetTokens: 63_999 } } },
+  ])
+  expect(resolve(model("@opencode/ai/providers/alibaba/chat", "kimi-k2.5", 32_768), supports)).toEqual([
+    { id: "none", settings: { enableThinking: false } },
+    { id: "high", settings: { enableThinking: true, thinkingBudget: 16_384 } },
+    { id: "max", settings: { enableThinking: true, thinkingBudget: 32_767 } },
+  ])
+})
+
 test("spells Chat Completions variants for hosting providers", () => {
  expect(
    resolve(model("@opencode/ai/providers/openai-compatible", "deepseek-ai/deepseek-v4-pro", undefined, "nvidia"), [
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Aiden Cline	a9da993e28	fix(core): trigger compaction at 85% of the input window	2026-09-24 17:36:58 -05:00
Aiden Cline	61ecf404b9	fix(core): apply GPT verbosity defaults at request time (#51166 )	2026-09-24 17:13:39 -05:00
Shoubhit Dash	92d2b1700f	refactor(ai): one media client shape and route types erased over options (#51226 )	2026-09-25 03:15:45 +05:30
Aiden Cline	56262121ee	feat(codemode): bind this and arguments in functions, add Function.prototype.call, apply, and bind (#50831 )	2026-09-24 16:34:46 -05:00
Aiden Cline	e3b588e7d2	refactor(ai): apply tool schema rules once per request (#51162 )	2026-09-24 16:27:30 -05:00
opencode-agent[bot]andnexxeln	1de648cb13	feat(ai): restore direct LLM input overloads (#51211 ) Co-authored-by: nexxeln <95541290+nexxeln@users.noreply.github.com>	2026-09-25 02:49:07 +05:30
Aiden Cline	03be7f385b	fix(core): accept 128-character tool names (#51207 )	2026-09-24 14:59:57 -05:00
Aiden Cline	8118690839	fix(core): show line counts when tool output is truncated (#51200 )	2026-09-24 14:50:55 -05:00
Aiden Cline	a16eedfed7	fix(codemode): make derived error constructors inherit from Error (#51045 )	2026-09-24 14:03:10 -05:00
Aiden Cline	e796f2f9a5	fix(core): read provider errors from plain AI SDK stream errors (#51194 )	2026-09-24 13:52:32 -05:00
Aiden Cline	20610e6645	fix(ai): fit Claude thinking budgets on Bedrock Converse (#51190 )	2026-09-24 13:48:39 -05:00
Aiden Cline	7f245b0968	fix(ai): fit OpenRouter reasoning budgets to the output limit (#51189 )	2026-09-24 13:42:10 -05:00
Shoubhit Dash	7013e925f5	refactor(ai): keep LLM calls request-only in the promise client (#51180 )	2026-09-24 23:47:42 +05:30
Aiden Cline	499c2feaa3	fix(core): cap Alibaba thinking budget variants at 64k (#51154 )	2026-09-24 13:14:49 -05:00
Aiden Cline	03af821aa5	fix(core): restore the shell no-output placeholder (#51187 )	2026-09-24 13:12:42 -05:00
Aiden Cline	c903774556	fix(ai): fit thinking budgets to the output limit (#51157 )	2026-09-24 12:58:54 -05:00
James Long	14aaf91e65	fix(tui): mark failed groups with a plain ✗ (#51175 )	2026-09-24 13:52:14 -04:00
James Long	c832432d89	refactor(tui): drop unused yellow alias from opencode theme (#51177 )	2026-09-24 13:17:21 -04:00
opencode-agent[bot]andjlongster	1d431a80df	fix(cli): reuse core declarations during typecheck (#51165 ) Co-authored-by: jlongster <jlongster@users.noreply.github.com>	2026-09-24 13:16:46 -04:00