feat(tui): add server switcher

2026-07-23 11:16:13 +00:00 · 2026-07-18 19:11:07 +00:00
1678 changed files with 350004 additions and 58018 deletions
@@ -90,18 +90,11 @@ jobs:
          opencode-app-id: ${{ vars.OPENCODE_APP_ID }}
          opencode-app-secret: ${{ secrets.OPENCODE_APP_SECRET }}

-      - name: Build legacy CLI
-        if: github.ref_name != 'v2'
-        run: ./packages/opencode/script/build.ts ${{ (github.ref_name == 'beta' && '--sourcemaps') || '' }}
-        env:
-          OPENCODE_VERSION: ${{ needs.version.outputs.version }}
-          OPENCODE_RELEASE: ${{ needs.version.outputs.release }}
-          GH_REPO: ${{ needs.version.outputs.repo }}
-          GH_TOKEN: ${{ steps.committer.outputs.token }}
-
-      - name: Build preview CLI
+      - name: Build
        id: build
-        run: ./packages/cli/script/build.ts ${{ (github.ref_name == 'beta' && '--sourcemaps') || '' }}
+        run: |
+          ./packages/opencode/script/build.ts ${{ (github.ref_name == 'beta' && '--sourcemaps') || '' }}
+          ./packages/cli/script/build.ts ${{ (github.ref_name == 'beta' && '--sourcemaps') || '' }}
        env:
          OPENCODE_VERSION: ${{ needs.version.outputs.version }}
          OPENCODE_RELEASE: ${{ needs.version.outputs.release }}
@@ -109,7 +102,6 @@ jobs:
          GH_TOKEN: ${{ steps.committer.outputs.token }}

      - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        if: github.ref_name != 'v2'
        with:
          name: opencode-cli
          path: |
@@ -117,7 +109,6 @@ jobs:
            packages/opencode/dist/opencode-linux*

      - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        if: github.ref_name != 'v2'
        with:
          name: opencode-cli-windows
          path: packages/opencode/dist/opencode-windows*
@@ -500,13 +491,11 @@ jobs:
          registry-url: "https://registry.npmjs.org"

      - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
-        if: github.ref_name != 'v2'
        with:
          name: opencode-cli
          path: packages/opencode/dist

      - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
-        if: github.ref_name != 'v2'
        with:
          name: opencode-cli-windows
          path: packages/opencode/dist
@@ -97,11 +97,6 @@ jobs:
        working-directory: packages/client
        run: bun run check:generated

-      - name: Check generated documentation
-        if: runner.os == 'Linux'
-        working-directory: packages/docs
-        run: bun run check:generated
-
  e2e:
    name: e2e (${{ matrix.settings.name }})
    if: github.ref_name != 'v2' && github.head_ref != 'v2'
@@ -1,6 +1,6 @@
 ---
 description: translate English to other languages
-model: opencode/gpt-5.6-sol
+model: opencode/claude-opus-4-8
 ---

 run git diff and translate changed english doc and UI copy files to other international languages. Translate all languages in parallel to save time.
@@ -8,25 +8,6 @@ export const zoneID = "430ba34c138cfb5360826c4909f99be8"
 export const awsStage = $app.stage === "production" ? "production" : "dev"
 export const deployAws = $app.stage === awsStage

-if ($app.stage === "production") {
-  new cloudflare.DnsRecord("TrustCenter", {
-    zoneId: zoneID,
-    name: "trust.opencode.ai",
-    type: "CNAME",
-    content: "3a69a5bb27875189.vercel-dns-016.com",
-    proxied: false,
-    ttl: 60,
-  })
-
-  new cloudflare.DnsRecord("TrustCenterVerification", {
-    zoneId: zoneID,
-    name: "opencode.ai",
-    type: "TXT",
-    content: "compai-domain-verification=org_6993a99c6200a2d642bb115d",
-    ttl: 60,
-  })
-}
-
 new cloudflare.RegionalHostname("RegionalHostname", {
  hostname: domain,
  regionKey: "us",
@@ -8,8 +8,6 @@
  makeWrapper,
  writableTmpDirAsHomeHook,
  autoPatchelfHook,
-  copyDesktopItems,
-  makeDesktopItem,
  opencode,
 }:
 let
@@ -29,12 +27,9 @@ stdenv.mkDerivation (finalAttrs: {
    nodejs
    makeWrapper
    writableTmpDirAsHomeHook
-  ]
-  ++ lib.optionals stdenv.hostPlatform.isLinux [
+  ] ++ lib.optionals stdenv.hostPlatform.isLinux [
    autoPatchelfHook
-    copyDesktopItems
-  ]
-  ++ lib.optionals stdenv.hostPlatform.isDarwin [
+  ] ++ lib.optionals stdenv.hostPlatform.isDarwin [
    # Ad-hoc sign the .app: --config.mac.identity=null below skips signing.
    darwin.autoSignDarwinBinariesHook
  ];
@@ -43,37 +38,20 @@ stdenv.mkDerivation (finalAttrs: {
    (lib.getLib stdenv.cc.cc)
  ];

-  desktopItems = lib.optional stdenv.hostPlatform.isLinux (makeDesktopItem {
-    name = "ai.opencode.desktop";
-    desktopName = "OpenCode";
-    exec = "opencode-desktop %U";
-    icon = "ai.opencode.desktop";
-    # Electron 41 derives X11 WM_CLASS from app.name.
-    startupWMClass = "OpenCode";
-    categories = [ "Development" ];
-  });
-
  env = opencode.env // {
    ELECTRON_SKIP_BINARY_DOWNLOAD = "1";
  };

-  postPatch =
-    # NOTE: Relax Bun version check to be a warning instead of an error
-    ''
-      substituteInPlace packages/script/src/index.ts \
-        --replace-fail 'throw new Error(`This script requires bun@''${expectedBunVersionRange}' \
-                       'console.warn(`Warning: This script requires bun@''${expectedBunVersionRange}'
-    ''
-    # https://github.com/electron/electron/issues/31121
-    # mac builds use a .app bundle which doesnt have this issue
-    + lib.optionalString stdenv.isLinux ''
-      BASE_PATH=packages/desktop
-      FILES=(src/main/windows.ts)
-      for file in "''${FILES[@]}"; do
-        substituteInPlace $BASE_PATH/$file \
-          --replace-fail "process.resourcesPath" "'$out/opt/opencode-desktop/resources'"
-      done
-    '';
+  # https://github.com/electron/electron/issues/31121
+  # mac builds use a .app bundle which doesnt have this issue
+  postPatch = lib.optionalString stdenv.isLinux ''
+    BASE_PATH=packages/desktop
+    FILES=(src/main/windows.ts)
+    for file in "''${FILES[@]}"; do
+      substituteInPlace $BASE_PATH/$file \
+        --replace-fail "process.resourcesPath" "'$out/opt/opencode-desktop/resources'"
+    done
+  '';

  preBuild = ''
    cp -r "${electron.dist}" $HOME/.electron-dist
@@ -98,38 +76,27 @@ stdenv.mkDerivation (finalAttrs: {
    runHook postBuild
  '';

-  installPhase = ''
-    runHook preInstall
-  ''
-  + lib.optionalString stdenv.hostPlatform.isDarwin ''
-    mkdir -p $out/Applications
-    mv dist/mac*/*.app $out/Applications
-    makeWrapper "$out/Applications/OpenCode.app/Contents/MacOS/OpenCode" $out/bin/opencode-desktop
-  ''
-  + lib.optionalString stdenv.hostPlatform.isLinux ''
-    mkdir -p $out/opt/opencode-desktop
-    cp -r dist/linux*-unpacked/{resources,LICENSE*} $out/opt/opencode-desktop
-    install -Dm644 resources/icons/32x32.png \
-      "$out/share/icons/hicolor/32x32/apps/ai.opencode.desktop.png"
-    install -Dm644 resources/icons/64x64.png \
-      "$out/share/icons/hicolor/64x64/apps/ai.opencode.desktop.png"
-    install -Dm644 resources/icons/128x128.png \
-      "$out/share/icons/hicolor/128x128/apps/ai.opencode.desktop.png"
-    install -Dm644 resources/icons/128x128@2x.png \
-      "$out/share/icons/hicolor/256x256/apps/ai.opencode.desktop.png"
-    install -Dm644 resources/icons/icon.png \
-      "$out/share/icons/hicolor/512x512/apps/ai.opencode.desktop.png"
-    install -Dm644 resources/ai.opencode.desktop.metainfo.xml \
-      "$out/share/metainfo/ai.opencode.desktop.metainfo.xml"
-    makeWrapper ${lib.getExe electron} $out/bin/opencode-desktop \
-     --inherit-argv0 \
-     --set ELECTRON_FORCE_IS_PACKAGED 1 \
-     --add-flags $out/opt/opencode-desktop/resources/app.asar \
-     --add-flags "\''${NIXOS_OZONE_WL:+\''${WAYLAND_DISPLAY:+--ozone-platform-hint=auto --enable-features=WaylandWindowDecorations --enable-wayland-ime=true}}"
-  ''
-  + ''
-    runHook postInstall
-  '';
+  installPhase =
+    ''
+      runHook preInstall
+    ''
+    + lib.optionalString stdenv.hostPlatform.isDarwin ''
+      mkdir -p $out/Applications
+      mv dist/mac*/*.app $out/Applications
+      makeWrapper "$out/Applications/OpenCode.app/Contents/MacOS/OpenCode" $out/bin/opencode-desktop
+    ''
+    + lib.optionalString stdenv.hostPlatform.isLinux ''
+      mkdir -p $out/opt/opencode-desktop
+      cp -r dist/linux*-unpacked/{resources,LICENSE*} $out/opt/opencode-desktop
+      makeWrapper ${lib.getExe electron} $out/bin/opencode-desktop \
+        --inherit-argv0 \
+        --set ELECTRON_FORCE_IS_PACKAGED 1 \
+        --add-flags $out/opt/opencode-desktop/resources/app.asar \
+        --add-flags "\''${NIXOS_OZONE_WL:+\''${WAYLAND_DISPLAY:+--ozone-platform-hint=auto --enable-features=WaylandWindowDecorations --enable-wayland-ime=true}}"
+    ''
+    + ''
+      runHook postInstall
+    '';

  autoPatchelfIgnoreMissingDeps = [
    "libc.musl-x86_64.so.1"
@@ -1,8 +1,8 @@
 {
  "nodeModules": {
-    "x86_64-linux": "sha256-qt11SKmOjq0KU542QFbs+u7YyJicn4drCcwCdg325yk=",
-    "aarch64-linux": "sha256-z68doReXTrWS7HeiAjc0btIjAsvzeZZ7hXAlHr0c77Q=",
-    "aarch64-darwin": "sha256-PILYH1Pi8XBvSkuZ+1sNnUTao5kba+m5Z8iJKx6YXPo=",
-    "x86_64-darwin": "sha256-KpcJzP4m0SUavu/WaSffgzOxrHq8ljdy0GOzs9p16lo="
+    "x86_64-linux": "sha256-F1luclnqCPQk9yxfmeSYGaM/nScf28yBu9K3Fv+Xd24=",
+    "aarch64-linux": "sha256-XW0XZnsCRkU3MFJH9TjMRYZHffzVy3cQyiNCkec2gl4=",
+    "aarch64-darwin": "sha256-bf8kvORs3Fs2UYLp3PekF+AJR7NKOcHb+fIQA79RtMk=",
+    "x86_64-darwin": "sha256-sBdQPkzd7JXNW6Lbi9JHiAsfHwdLwTKWY+uPeXAv2Nw="
  }
 }
@@ -15,7 +15,7 @@
    "dev:www": "bun run --cwd packages/www dev",
    "dev:storybook": "bun --cwd packages/storybook storybook",
    "lint": "oxlint",
-    "lint:effect-patterns": "ast-grep scan -c script/ast-grep/sgconfig.yml packages/util/src packages/core/src packages/server/src packages/protocol/src packages/cli/src",
+    "lint:effect-patterns": "ast-grep scan -c script/ast-grep/sgconfig.yml packages/core/src packages/server/src packages/protocol/src packages/cli/src",
    "test:lint-rules": "ast-grep test -c script/ast-grep/sgconfig.yml",
    "typecheck": "bun turbo typecheck --concurrency=3",
    "typecheck:profile": "bun script/profile-typecheck.ts",
@@ -1,6 +1,6 @@
 # @opencode-ai/ai

-Schema-first AI primitives for opencode. Provider quirks live in adapters, not in calling code.
+Schema-first LLM core for opencode. One typed request, response, event, and tool language; provider quirks live in adapters, not in calling code.

 ```ts
 import { Effect } from "effect"
@@ -24,172 +24,6 @@ const program = Effect.gen(function* () {

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

-## Image generation
-
-Use `Image.generate` with an image model for direct asset generation:
-
-```ts
-import { Image, ImageInput } from "@opencode-ai/ai"
-import { OpenAI } from "@opencode-ai/ai/providers"
-
-const program = Effect.gen(function* () {
-  const response = yield* Image.generate({
-    model: OpenAI.configure({ apiKey: process.env.OPENAI_API_KEY }).image("gpt-image-2"),
-    prompt: "A robot tending a rooftop garden",
-    options: {
-      n: 2,
-      size: "1024x1024",
-      quality: "high", // inferred from the OpenAI image model
-      outputFormat: "webp",
-      future_option: true, // unknown native options pass through unchanged
-    },
-  })
-
-  return response.images // GeneratedImage[] with owned bytes or a provider URL
-})
-```
-
-Pass ordered image inputs to the same method for editing, composition, or image-conditioned generation:
-
-```ts
-const response =
-  yield *
-  Image.generate({
-    model,
-    prompt: "Combine these product photos into one studio scene",
-    images: [
-      ImageInput.bytes(firstBytes, "image/png"),
-      ImageInput.url("https://example.com/second.webp"),
-      ImageInput.file("file_123"),
-    ],
-    options,
-    http,
-  })
-```
-
-`ImageInput.fileUri(uri, mediaType)` represents provider file URIs such as Gemini Files. Raw strings are not
-accepted as image inputs, avoiding ambiguity between base64, URLs, and provider IDs. Empty or omitted `images`
-uses text-to-image generation; a non-empty array selects the provider's edit behavior without enforcing provider
-image-count limits locally. `images` is the only common image-editing field. OpenAI uses multipart for byte/data-URL
-edits and its JSON reference body for URL or file-ID edits. Its provider-specific `options.mask` accepts an
-`ImageInput` for inpainting:
-
-```ts
-yield *
-  Image.generate({
-    model: OpenAI.configure({ apiKey }).image("gpt-image-2"),
-    prompt,
-    images: [ImageInput.bytes(sourceBytes, "image/png")],
-    options: { mask: ImageInput.bytes(maskBytes, "image/png") },
-  })
-```
-
-The OpenAI adapter extracts this helper value into the edit request's native `mask` field rather than passing the
-tagged `ImageInput` object through as an ordinary option. On multipart requests, `http.body` can override option
-fields but not structural `model`, `prompt`, `image[]`, or `mask` fields, and the transport owns the multipart
-`Content-Type` boundary. For JSON requests, `http.body` remains the final raw-native overlay. Gemini does not fetch
-public HTTP URLs, and hosted Z.ai image generation does not accept image inputs. These cases fail with
-`InvalidRequest` before network I/O.
-
-Provider-native image options belong to each request. Raw `http.body` fields have final precedence over them:
-
-```ts
-const model = OpenAI.configure({ apiKey }).image("gpt-image-2")
-
-yield *
-  Image.generate({
-    model,
-    prompt,
-    options: { quality: "medium" },
-    http,
-  })
-```
-
-xAI image models use the same request API with xAI-native controls:
-
-```ts
-yield *
-  Image.generate({
-    model: XAI.configure({ apiKey }).image("any-model-id"),
-    prompt,
-    options: {
-      n: 2,
-      aspectRatio: "16:9",
-      resolution: "1k",
-      responseFormat: "b64_json",
-      future_option: true,
-    },
-    http,
-  })
-```
-
-Google's current Gemini image models use the same direct API:
-
-```ts
-import { Google } from "@opencode-ai/ai/providers"
-
-const googleProgram = Effect.gen(function* () {
-  const response = yield* Image.generate({
-    model: Google.configure({ apiKey }).image("any-model-id"),
-    prompt: "A robot tending a rooftop garden",
-    options: {
-      aspectRatio: "16:9",
-      imageSize: "2K",
-      seed: 42,
-      thinkingLevel: "HIGH",
-      includeThoughts: true,
-      futureOption: true,
-    },
-    http,
-  })
-
-  return response.images
-})
-```
-
-Google image options are request-scoped and inferred from the selected model. Known fields autocomplete while
-future string values and arbitrary native Gemini `generationConfig` fields remain available. Native fields override
-their mapped aliases, and `http.body` is the final deep overlay. The selected model ID is sent to Gemini
-`generateContent` without a local allowlist.
-
-Z.ai image models infer open Z.ai-native options from the selected model:
-
-```ts
-yield *
-  Image.generate({
-    model: ZAI.configure({ apiKey }).image("any-model-id"),
-    prompt,
-    options: {
-      quality: "hd",
-      userID: "user-123",
-      future_option: true,
-    },
-    http,
-  })
-```
-
-Z.ai does not include trustworthy MIME metadata for output URLs, so generated images use
-`application/octet-stream`. Output URLs expire after 30 days; download and persist them promptly if they must
-remain available.
-
-Conversational image generation remains part of the LLM interaction. OpenAI Responses exposes it through its hosted image tool:
-
-```ts
-const program = Effect.gen(function* () {
-  const response = yield* LLM.generate(
-    LLM.request({
-      model: OpenAI.configure({ apiKey }).responses("gpt-5"),
-      prompt: "Design a solarpunk rooftop garden, then show me.",
-      tools: [OpenAI.imageGeneration({ quality: "high" })],
-    }),
-  )
-
-  return response.message
-})
-```
-
-The hosted result is represented as a provider-executed tool call and tool result. Its image is a `file` content item with a data URI, so retaining `response.message` preserves the generated image for continuation.
-
 ## Public API

 - **`LLM.request({...})`** — build a provider-neutral `LLMRequest`. Accepts ergonomic inputs (`system: string`, `prompt: string`) that normalize into the canonical Schema classes.
@@ -198,8 +32,6 @@ The hosted result is represented as a provider-executed tool call and tool resul
 - **`Model.make(...)` / `ToolCallPart.make(...)` / `ToolResultPart.make(...)` / `ToolDefinition.make(...)`** — model and tool-related constructors from the canonical schema model.
 - **`LLMClient.prepare(request)`** — compile a request through protocol body construction, validation, and HTTP preparation without sending. Useful for inspection and testing.
 - **`LLMEvent.is.*`** — typed guards (`is.textDelta`, `is.toolCall`, `is.finish`, …) for filtering streams.
- **`Image.generate({...})`** — generate images through a provider-neutral image request and response model.
- **`ImageClient`** — Effect service and layer for image execution, parallel to `LLMClient`.

 ## Caching

@@ -272,7 +104,7 @@ const gateway = CloudflareAIGateway.configure({
 }).model("workers-ai/@cf/meta/llama-3.1-8b-instruct")
 ```

-Included providers: OpenAI, Anthropic, Google (Gemini), Google Vertex Gemini and Anthropic, Amazon Bedrock, Azure OpenAI, Cloudflare AI Gateway, Cloudflare Workers AI, GitHub Copilot, OpenRouter, xAI, Z.ai, plus generic OpenAI-compatible Chat and Responses entrypoints and an Anthropic Messages-compatible entrypoint.
+Included providers: OpenAI, Anthropic, Google (Gemini), Google Vertex Gemini and Anthropic, Amazon Bedrock, Azure OpenAI, Cloudflare AI Gateway, Cloudflare Workers AI, GitHub Copilot, OpenRouter, xAI, plus generic OpenAI-compatible Chat and Responses entrypoints and an Anthropic Messages-compatible entrypoint.

 ### Package-like entrypoints

@@ -350,7 +182,7 @@ Adding a new model or deployment is usually 5-15 lines using `Route.make({ proto

 ## Effect

-This package is built on Effect. Public methods return `Effect` or `Stream`; provide `LLMClient.layer` for LLM dispatch and `ImageClient.layer` for image dispatch, then import the provider/protocol modules for the routes you use. The example at `example/tutorial.ts` is a runnable walkthrough.
+This package is built on Effect. Public methods return `Effect` or `Stream`; provide `LLMClient.layer` for runtime dispatch and import the provider/protocol modules for the routes you use. The example at `example/tutorial.ts` is a runnable walkthrough.

 ## See also

@@ -7,7 +7,7 @@
  "scripts": {
    "setup:recording-env": "bun run script/setup-recording-env.ts",
    "test": "bun test --timeout 30000 --only-failures",
-    "typecheck": "tsgo --noEmit && tsgo --noEmit -p tsconfig.types.json",
+    "typecheck": "tsgo --noEmit",
    "build": "tsc -p tsconfig.build.json"
  },
  "files": [
@@ -1,38 +0,0 @@
-import { Context, Effect, Layer } from "effect"
-import { RequestExecutor } from "./route/executor"
-import type { ImageOptions, ImageRequest, ImageRequestFor, ImageResponse } from "./image"
-import type { LLMError } from "./schema"
-
-export type Execute = RequestExecutor.Interface["execute"]
-
-export interface Interface {
-  readonly generate: <Options extends ImageOptions>(
-    request: ImageRequestFor<Options>,
-  ) => Effect.Effect<ImageResponse, LLMError>
-}
-
-export class Service extends Context.Service<Service, Interface>()("@opencode/ImageClient") {}
-
-export const generate = <Options extends ImageOptions>(
-  request: ImageRequestFor<Options>,
-): Effect.Effect<ImageResponse, LLMError> =>
-  Effect.gen(function* () {
-    const client = yield* Service
-    return yield* client.generate(request)
-  }) as Effect.Effect<ImageResponse, LLMError>
-
-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),
-    })
-  }),
-)
-
-export const ImageClient = {
-  Service,
-  layer,
-  generate,
-} as const
@@ -1,166 +0,0 @@
-import { Effect, Schema } from "effect"
-import { HttpOptions, InvalidRequestReason, LLMError, ModelID, ProviderID, ProviderMetadata, Usage } from "./schema"
-import { ImageClient, Service, type Execute as ImageExecute } from "./image-client"
-
-export interface ImageRoute<Options extends ImageOptions = ImageOptions> {
-  readonly id: string
-  readonly generate: (
-    request: ImageRequestFor<Options>,
-    execute: ImageExecute,
-  ) => Effect.Effect<ImageResponse, LLMError>
-}
-
-export type ImageOptions = Record<string, unknown>
-
-export class ImageModel<Options extends ImageOptions = ImageOptions> {
-  declare protected readonly _Options: (options: Options) => Options
-  readonly id: ModelID
-  readonly provider: ProviderID
-  readonly route: ImageRoute<Options>
-  readonly http?: HttpOptions
-
-  constructor(input: ImageModel.Input<Options>) {
-    this.id = input.id
-    this.provider = input.provider
-    this.route = input.route
-    this.http = input.http
-  }
-
-  static make<Options extends ImageOptions = ImageOptions>(input: ImageModel.MakeInput<Options>) {
-    return new ImageModel<Options>({
-      id: ModelID.make(input.id),
-      provider: ProviderID.make(input.provider),
-      route: input.route,
-      http: input.http,
-    })
-  }
-}
-
-export namespace ImageModel {
-  export interface Input<Options extends ImageOptions = ImageOptions> {
-    readonly id: ModelID
-    readonly provider: ProviderID
-    readonly route: ImageRoute<Options>
-    readonly http?: HttpOptions
-  }
-
-  export interface MakeInput<Options extends ImageOptions = ImageOptions>
-    extends Omit<Input<Options>, "id" | "provider"> {
-    readonly id: string | ModelID
-    readonly provider: string | ProviderID
-  }
-}
-
-export const ImageModelSchema = Schema.declare((value): value is ImageModel => value instanceof ImageModel, {
-  expected: "Image.Model",
-})
-
-const ImageBytesInput = Schema.Struct({
-  type: Schema.Literal("bytes"),
-  data: Schema.Uint8Array,
-  mediaType: Schema.String,
-})
-const ImageUrlInput = Schema.Struct({
-  type: Schema.Literal("url"),
-  url: Schema.String,
-})
-const ImageFileIDInput = Schema.Struct({
-  type: Schema.Literal("file-id"),
-  id: Schema.String,
-})
-const ImageFileURIInput = Schema.Struct({
-  type: Schema.Literal("file-uri"),
-  uri: Schema.String,
-  mediaType: Schema.String,
-})
-
-export const ImageInputSchema = Schema.Union([
-  ImageBytesInput,
-  ImageUrlInput,
-  ImageFileIDInput,
-  ImageFileURIInput,
-]).pipe(Schema.toTaggedUnion("type"))
-export type ImageInput = Schema.Schema.Type<typeof ImageInputSchema>
-
-export const ImageInput = {
-  bytes: (data: Uint8Array, mediaType: string): ImageInput => ({ type: "bytes", data, mediaType }),
-  url: (url: string): ImageInput => ({ type: "url", url }),
-  file: (id: string): ImageInput => ({ type: "file-id", id }),
-  fileUri: (uri: string, mediaType: string): ImageInput => ({ type: "file-uri", uri, mediaType }),
-} as const
-
-export class ImageRequest extends Schema.Class<ImageRequest>("Image.Request")({
-  model: ImageModelSchema,
-  prompt: Schema.String,
-  images: Schema.optional(Schema.Array(ImageInputSchema)),
-  options: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
-  http: Schema.optional(HttpOptions),
-}) {
-  declare protected readonly _ImageRequest: void
-}
-
-export type ImageRequestFor<Options extends ImageOptions = ImageOptions> = Omit<ImageRequest, "model" | "options"> & {
-  readonly model: ImageModel<Options>
-  readonly options?: Options
-}
-
-export type ImageModelOptions<Model> = Model extends ImageModel<infer Options> ? Options : never
-
-export type ImageRequestInput<Model extends object = ImageModel> = Omit<
-  ConstructorParameters<typeof ImageRequest>[0],
-  "model" | "options" | "http"
-> & {
-  readonly model: Model
-  readonly options?: NoInfer<ImageModelOptions<Model>>
-  readonly http?: HttpOptions.Input
-} & (Model extends ImageModel<ImageModelOptions<Model>> ? unknown : never)
-
-export class GeneratedImage extends Schema.Class<GeneratedImage>("Image.Generated")({
-  mediaType: Schema.String,
-  data: Schema.Union([Schema.String, Schema.Uint8Array]),
-  providerMetadata: Schema.optional(ProviderMetadata),
-}) {}
-
-export class ImageResponse extends Schema.Class<ImageResponse>("Image.Response")({
-  images: Schema.Array(GeneratedImage),
-  usage: Schema.optional(Usage),
-  providerMetadata: Schema.optional(ProviderMetadata),
-}) {
-  get image() {
-    return this.images[0]
-  }
-}
-
-export function request<const Model extends object>(
-  input: ImageRequestInput<Model>,
-): ImageRequestFor<ImageModelOptions<Model>>
-export function request(input: ImageRequest): ImageRequest
-export function request(input: ImageRequest | ImageRequestInput) {
-  if (input instanceof ImageRequest) return input
-  return new ImageRequest({
-    ...input,
-    model: input.model as unknown as ImageModel,
-    http: input.http === undefined ? undefined : HttpOptions.make(input.http),
-  })
-}
-
-export function generate<const Model extends object>(
-  input: ImageRequestInput<Model>,
-): Effect.Effect<ImageResponse, LLMError, Service>
-export function generate(input: ImageRequest): Effect.Effect<ImageResponse, LLMError, Service>
-export function generate(input: ImageRequest | ImageRequestInput) {
-  return Effect.try({
-    try: () => (input instanceof ImageRequest ? input : request(input)),
-    catch: (error) =>
-      new LLMError({
-        module: "Image",
-        method: "generate",
-        reason: new InvalidRequestReason({ message: error instanceof Error ? error.message : String(error) }),
-      }),
-  }).pipe(Effect.flatMap((request) => ImageClient.generate(request as unknown as ImageRequestFor<ImageOptions>)))
-}
-
-export const Image = {
-  request,
-  generate,
-} as const
@@ -1,5 +1,4 @@
 export { LLMClient } from "./route/client"
-export { ImageClient } from "./image-client"
 export { Auth } from "./route/auth"
 export { Provider } from "./provider"
 export { ProviderPackage } from "./provider-package"
@@ -11,9 +10,6 @@ export type {
  Service as LLMClientService,
 } from "./route/client"
 export * from "./schema"
-export { GeneratedImage, ImageInput, ImageInputSchema, ImageModel, ImageRequest, ImageResponse } from "./image"
-export type { ImageModelOptions, ImageOptions, ImageRequestFor, ImageRequestInput, ImageRoute } from "./image"
-export { Image } from "./image"
 export { Tool, ToolFailure, toDefinitions } from "./tool"
 export { ToolRuntime } from "./tool-runtime"
 export type { DispatchResult as ToolDispatchResult, ToolSettlement } from "./tool-runtime"
@@ -27,7 +27,6 @@ import { ToolSchemaProjection } from "./utils/tool-schema"
 import { ToolStream } from "./utils/tool-stream"

 const ADAPTER = "anthropic-messages"
-const MEDIA_MIMES = new Set<string>([...ProviderShared.IMAGE_MIMES, ...ProviderShared.PDF_MIMES])
 export const DEFAULT_BASE_URL = "https://api.anthropic.com/v1"
 export const PATH = "/messages"

@@ -57,17 +56,6 @@ const AnthropicImageBlock = Schema.Struct({
 })
 type AnthropicImageBlock = Schema.Schema.Type<typeof AnthropicImageBlock>

-const AnthropicDocumentBlock = Schema.Struct({
-  type: Schema.tag("document"),
-  source: Schema.Struct({
-    type: Schema.tag("base64"),
-    media_type: Schema.Literal("application/pdf"),
-    data: Schema.String,
-  }),
-  cache_control: Schema.optional(AnthropicCacheControl),
-})
-type AnthropicDocumentBlock = Schema.Schema.Type<typeof AnthropicDocumentBlock>
-
 const AnthropicThinkingBlock = Schema.Struct({
  type: Schema.tag("thinking"),
  thinking: Schema.String,
@@ -113,10 +101,13 @@ const AnthropicServerToolResultBlock = Schema.Struct({
 })
 type AnthropicServerToolResultBlock = Schema.Schema.Type<typeof AnthropicServerToolResultBlock>

-// Anthropic accepts either a plain string or an ordered array of text, image, and
-// document blocks inside `tool_result.content`. The array form keeps media as native
-// model input instead of JSON-stringifying base64 into prompt text.
-const AnthropicToolResultContent = Schema.Union([AnthropicTextBlock, AnthropicImageBlock, AnthropicDocumentBlock])
+// Anthropic accepts either a plain string or an ordered array of text/image
+// blocks inside `tool_result.content`. The array form is required when a tool
+// returns image bytes (screenshot, image search, etc.) so they can be passed
+// to the model as proper image inputs instead of being JSON-stringified into
+// the prompt — which silently inflates context by megabytes and can push the
+// conversation over the model's token limit.
+const AnthropicToolResultContent = Schema.Union([AnthropicTextBlock, AnthropicImageBlock])

 const AnthropicToolResultBlock = Schema.Struct({
  type: Schema.tag("tool_result"),
@@ -126,12 +117,7 @@ const AnthropicToolResultBlock = Schema.Struct({
  cache_control: Schema.optional(AnthropicCacheControl),
 })

-const AnthropicUserBlock = Schema.Union([
-  AnthropicTextBlock,
-  AnthropicImageBlock,
-  AnthropicDocumentBlock,
-  AnthropicToolResultBlock,
-])
+const AnthropicUserBlock = Schema.Union([AnthropicTextBlock, AnthropicImageBlock, AnthropicToolResultBlock])
 type AnthropicUserBlock = Schema.Schema.Type<typeof AnthropicUserBlock>
 const AnthropicAssistantBlock = Schema.Union([
  AnthropicTextBlock,
@@ -333,17 +319,12 @@ const lowerServerToolResult = Effect.fn("AnthropicMessages.lowerServerToolResult
  return { type: wireType, tool_use_id: part.id, content: part.result.value } satisfies AnthropicServerToolResultBlock
 })

-const lowerMedia = Effect.fn("AnthropicMessages.lowerMedia")(function* (part: MediaPart) {
-  const media = yield* ProviderShared.validateMedia("Anthropic Messages", part, MEDIA_MIMES)
-  if (media.mime === "application/pdf")
-    return {
-      type: "document" as const,
-      source: {
-        type: "base64" as const,
-        media_type: "application/pdf" as const,
-        data: media.base64,
-      },
-    } satisfies AnthropicDocumentBlock
+const lowerImage = Effect.fn("AnthropicMessages.lowerImage")(function* (part: MediaPart) {
+  const media = yield* ProviderShared.validateMedia(
+    "Anthropic Messages",
+    part,
+    new Set<string>(ProviderShared.IMAGE_MIMES),
+  )
  return {
    type: "image" as const,
    source: {
@@ -354,13 +335,25 @@ const lowerMedia = Effect.fn("AnthropicMessages.lowerMedia")(function* (part: Me
  } satisfies AnthropicImageBlock
 })

-// Tool results may carry structured text, images, and documents. Keep media as provider-native
+// Tool results may carry structured text/images. Keep media as provider-native
 // content instead of JSON-stringifying base64 into a prompt string.
 const lowerToolResultContentItem = Effect.fn("AnthropicMessages.lowerToolResultContentItem")(function* (
  item: ToolContent,
 ) {
  if (item.type === "text") return { type: "text" as const, text: item.text } satisfies AnthropicTextBlock
-  return yield* lowerMedia({ type: "media", mediaType: item.mime, data: item.uri, filename: item.name })
+  const media = yield* ProviderShared.validateToolFile(
+    "Anthropic Messages",
+    item,
+    new Set<string>(ProviderShared.IMAGE_MIMES),
+  )
+  return {
+    type: "image" as const,
+    source: {
+      type: "base64" as const,
+      media_type: media.mime,
+      data: media.base64,
+    },
+  } satisfies AnthropicImageBlock
 })

 const lowerToolResultContent = Effect.fn("AnthropicMessages.lowerToolResultContent")(function* (part: ToolResultPart) {
@@ -452,7 +445,7 @@ const lowerMessages = Effect.fn("AnthropicMessages.lowerMessages")(function* (
          continue
        }
        if (part.type === "media") {
-          content.push(yield* lowerMedia(part))
+          content.push(yield* lowerImage(part))
          continue
        }
        return yield* ProviderShared.unsupportedContent("Anthropic Messages", "user", ["text", "media"])
@@ -710,14 +703,7 @@ const onContentBlockStart = (state: ParserState, event: AnthropicEvent): StepRes
          providerExecuted: block.type === "server_tool_use",
        }),
      },
-      [
-        ...events,
-        LLMEvent.toolInputStart({
-          id: block.id ?? String(event.index),
-          name: block.name ?? "",
-          providerExecuted: block.type === "server_tool_use" ? true : undefined,
-        }),
-      ],
+      [...events, LLMEvent.toolInputStart({ id: block.id ?? String(event.index), name: block.name ?? "" })],
    ]
  }

@@ -52,7 +52,6 @@ const BedrockToolResultContentItem = Schema.Union([
  Schema.Struct({ text: Schema.String }),
  Schema.Struct({ json: Schema.Unknown }),
  BedrockMedia.ImageBlock,
-  BedrockMedia.DocumentBlock,
 ])

 const BedrockToolResultBlock = Schema.Struct({
@@ -284,6 +283,8 @@ const lowerToolResultContent = Effect.fn("BedrockConverse.lowerToolResultContent
      data: item.uri,
      filename: item.name,
    })
+    if (!("image" in media))
+      return yield* ProviderShared.invalidRequest("Bedrock Converse only supports image media in tool results")
    content.push(media)
  }
  return content
@@ -560,9 +561,7 @@ const step = (state: ParserState, event: BedrockEvent) =>
      return [
        {
          ...state,
-          hasToolCalls:
-            resultEvents.some((event) => LLMEvent.is.toolCall(event) || LLMEvent.is.toolInputError(event)) ||
-            state.hasToolCalls,
+          hasToolCalls: resultEvents.some(LLMEvent.is.toolCall) ? true : state.hasToolCalls,
          lifecycle,
          tools: result.tools,
          reasoningSignatures: Object.fromEntries(
@@ -41,11 +41,9 @@ const GeminiInlineDataPart = Schema.Struct({
    data: Schema.String,
  }),
 })
-type GeminiInlineDataPart = Schema.Schema.Type<typeof GeminiInlineDataPart>

 const GeminiFunctionCallPart = Schema.Struct({
  functionCall: Schema.Struct({
-    id: Schema.optional(Schema.String),
    name: Schema.String,
    args: Schema.Unknown,
  }),
@@ -54,10 +52,8 @@ const GeminiFunctionCallPart = Schema.Struct({

 const GeminiFunctionResponsePart = Schema.Struct({
  functionResponse: Schema.Struct({
-    id: Schema.optional(Schema.String),
    name: Schema.String,
    response: Schema.Unknown,
-    parts: Schema.optional(Schema.Array(GeminiInlineDataPart)),
  }),
 })

@@ -201,13 +197,8 @@ const thoughtSignature = (providerMetadata: ProviderMetadata | undefined) => {
    : undefined
 }

-const functionCallId = (providerMetadata: ProviderMetadata | undefined) => {
-  const google = providerMetadata?.google
-  return ProviderShared.isRecord(google) && typeof google.functionCallId === "string" ? google.functionCallId : undefined
-}
-
 const lowerToolCall = (part: ToolCallPart) => ({
-  functionCall: { id: functionCallId(part.providerMetadata), name: part.name, args: part.input },
+  functionCall: { name: part.name, args: part.input },
  thoughtSignature: thoughtSignature(part.providerMetadata),
 })

@@ -264,7 +255,6 @@ const lowerMessages = Effect.fn("Gemini.lowerMessages")(function* (request: LLMR
      if (part.result.type !== "content") {
        parts.push({
          functionResponse: {
-            id: functionCallId(part.providerMetadata),
            name: part.name,
            response: {
              name: part.name,
@@ -276,23 +266,20 @@ const lowerMessages = Effect.fn("Gemini.lowerMessages")(function* (request: LLMR
      }
      const content: ReadonlyArray<ToolContent> = part.result.value
      const text = content.filter((item) => item.type === "text").map((item) => item.text)
-      const media: GeminiInlineDataPart[] = []
-      for (const item of content) {
-        if (item.type === "text") continue
-        const value = yield* ProviderShared.validateToolFile("Gemini", item, MEDIA_MIMES)
-        media.push({ inlineData: { mimeType: value.mime, data: value.base64 } })
-      }
      parts.push({
        functionResponse: {
-          id: functionCallId(part.providerMetadata),
          name: part.name,
          response: {
            name: part.name,
            content: text.join("\n"),
          },
-          parts: media.length > 0 ? media : undefined,
        },
      })
+      for (const item of content) {
+        if (item.type === "text") continue
+        const media = yield* ProviderShared.validateToolFile("Gemini", item, MEDIA_MIMES)
+        parts.push({ inlineData: { mimeType: media.mime, data: media.base64 } })
+      }
    }
    contents.push({ role: "user", parts })
  }
@@ -454,10 +441,6 @@ const step = (state: ParserState, event: GeminiEvent) => {
    if ("functionCall" in part) {
      const input = part.functionCall.args
      const id = `tool_${nextToolCallId++}`
-      const metadata = {
-        ...(part.functionCall.id === undefined ? {} : { functionCallId: part.functionCall.id }),
-        ...(part.thoughtSignature === undefined ? {} : { thoughtSignature: part.thoughtSignature }),
-      }
      lifecycle = Lifecycle.reasoningEnd(
        lifecycle,
        events,
@@ -470,7 +453,9 @@ const step = (state: ParserState, event: GeminiEvent) => {
          id,
          name: part.functionCall.name,
          input,
-          providerMetadata: Object.keys(metadata).length > 0 ? googleMetadata(metadata) : undefined,
+          providerMetadata: part.thoughtSignature
+            ? googleMetadata({ thoughtSignature: part.thoughtSignature })
+            : undefined,
        }),
      )
      hasToolCalls = true
@@ -1,314 +0,0 @@
-import { Effect, Encoding, Schema } from "effect"
-import { Headers, HttpClientRequest } from "effect/unstable/http"
-import {
-  GeneratedImage,
-  ImageModel,
-  ImageResponse,
-  type ImageInput,
-  type ImageRequestFor,
-  type ImageRoute,
-} from "../image"
-import { Auth, type Definition as AuthDefinition } from "../route/auth"
-import {
-  InvalidProviderOutputReason,
-  LLMError,
-  Usage,
-  mergeHttpOptions,
-  mergeJsonRecords,
-  type HttpOptions,
-  type ProviderMetadata,
-} from "../schema"
-import { ProviderShared } from "./shared"
-import { ImageInputs } from "./utils/image-input"
-
-const ADAPTER = "google-images"
-export const DEFAULT_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"
-
-export type GoogleImageString<Known extends string> = Known | (string & {})
-
-export type GoogleImageOptions = {
-  readonly aspectRatio?: GoogleImageString<
-    "1:1" | "2:3" | "3:2" | "3:4" | "4:3" | "4:5" | "5:4" | "9:16" | "16:9" | "21:9"
-  >
-  readonly imageSize?: GoogleImageString<"1K" | "2K" | "4K">
-  readonly seed?: number
-  readonly thinkingLevel?: GoogleImageString<"MINIMAL" | "LOW" | "MEDIUM" | "HIGH">
-  readonly includeThoughts?: boolean
-} & Record<string, unknown>
-
-export type GoogleImageBody = Record<string, unknown> & {
-  readonly contents: ReadonlyArray<{
-    readonly role: "user"
-    readonly parts: ReadonlyArray<Record<string, unknown>>
-  }>
-  readonly generationConfig: Record<string, unknown>
-}
-
-const GoogleUsage = Schema.StructWithRest(
-  Schema.Struct({
-    cachedContentTokenCount: Schema.optional(Schema.Number),
-    thoughtsTokenCount: Schema.optional(Schema.Number),
-    promptTokenCount: Schema.optional(Schema.Number),
-    candidatesTokenCount: Schema.optional(Schema.Number),
-    totalTokenCount: Schema.optional(Schema.Number),
-    promptTokensDetails: Schema.optional(Schema.Unknown),
-    candidatesTokensDetails: Schema.optional(Schema.Unknown),
-  }),
-  [Schema.Record(Schema.String, Schema.Unknown)],
-)
-
-const GoogleImageResponse = Schema.Struct({
-  candidates: Schema.optional(
-    Schema.Array(
-      Schema.Struct({
-        index: Schema.optional(Schema.Number),
-        content: Schema.optional(
-          Schema.Struct({
-            parts: Schema.Array(
-              Schema.Struct({
-                text: Schema.optional(Schema.String),
-                thought: Schema.optional(Schema.Boolean),
-                thoughtSignature: Schema.optional(Schema.String),
-                inlineData: Schema.optional(
-                  Schema.Struct({
-                    mimeType: Schema.String,
-                    data: Schema.String,
-                  }),
-                ),
-              }),
-            ),
-          }),
-        ),
-        finishReason: Schema.optional(Schema.String),
-        finishMessage: Schema.optional(Schema.String),
-        safetyRatings: Schema.optional(Schema.Unknown),
-        citationMetadata: Schema.optional(Schema.Unknown),
-        groundingMetadata: Schema.optional(Schema.Unknown),
-      }),
-    ),
-  ),
-  usageMetadata: Schema.optional(GoogleUsage),
-  modelVersion: Schema.optional(Schema.String),
-  responseId: Schema.optional(Schema.String),
-  promptFeedback: Schema.optional(Schema.Unknown),
-})
-
-export interface ModelInput {
-  readonly id: string
-  readonly auth: AuthDefinition
-  readonly baseURL?: string
-  readonly headers?: Record<string, string>
-  readonly http?: HttpOptions
-}
-
-const nativeOptions = (options: GoogleImageOptions | undefined) => {
-  const { aspectRatio, imageSize, seed, thinkingLevel, includeThoughts, ...native } = options ?? {}
-  const image = {
-    aspectRatio,
-    imageSize,
-  }
-  const thinkingConfig = {
-    thinkingLevel,
-    includeThoughts,
-  }
-  return (
-    mergeJsonRecords(
-      {
-        responseModalities: ["IMAGE"],
-        imageConfig: Object.values(image).some((value) => value !== undefined) ? image : undefined,
-        seed,
-        thinkingConfig: Object.values(thinkingConfig).some((value) => value !== undefined) ? thinkingConfig : undefined,
-      },
-      native,
-    ) ?? { responseModalities: ["IMAGE"] }
-  )
-}
-
-const invalidOutput = (message: string, providerMetadata?: ProviderMetadata) =>
-  new LLMError({
-    module: ADAPTER,
-    method: "generate",
-    reason: new InvalidProviderOutputReason({ message, route: ADAPTER, providerMetadata }),
-  })
-
-const applyQuery = (url: string, query: Record<string, string> | undefined) => {
-  if (!query) return url
-  const next = new URL(url)
-  Object.entries(query).forEach(([key, value]) => next.searchParams.set(key, value))
-  return next.toString()
-}
-
-export const model = (input: ModelInput) => {
-  const route: ImageRoute<GoogleImageOptions> = {
-    id: ADAPTER,
-    generate: Effect.fn("GoogleImages.generate")(function* (request: ImageRequestFor<GoogleImageOptions>, execute) {
-      const imageParts = yield* Effect.forEach(request.images ?? [], googleImagePart)
-      const http = mergeHttpOptions(request.model.http, request.http)
-      const requestBody = mergeJsonRecords(
-        {
-          contents: [{ role: "user", parts: [{ text: request.prompt }, ...imageParts] }],
-          generationConfig: nativeOptions(request.options),
-        },
-        http?.body,
-      ) as GoogleImageBody
-      const text = ProviderShared.encodeJson(requestBody)
-      const url = applyQuery(
-        `${(input.baseURL ?? DEFAULT_BASE_URL).replace(/\/$/, "")}/models/${request.model.id}:generateContent`,
-        http?.query,
-      )
-      const headers = yield* Auth.toEffect(input.auth)({
-        request,
-        method: "POST",
-        url,
-        body: text,
-        headers: Headers.fromInput({ ...input.headers, ...http?.headers }),
-      })
-      const response = yield* execute(
-        HttpClientRequest.post(url).pipe(
-          HttpClientRequest.setHeaders(headers),
-          HttpClientRequest.bodyText(text, "application/json"),
-        ),
-      )
-      const payload = yield* response.json.pipe(
-        Effect.mapError(() => invalidOutput("Failed to read the Google Images response")),
-      )
-      const decoded = yield* Schema.decodeUnknownEffect(GoogleImageResponse)(payload).pipe(
-        Effect.mapError(() => invalidOutput("Google Images returned an invalid response")),
-      )
-      const candidates = decoded.candidates ?? []
-      const candidateMetadata = candidates.map((candidate, candidateIndex) => ({
-        index: candidate.index ?? candidateIndex,
-        finishReason: candidate.finishReason,
-        finishMessage: candidate.finishMessage,
-        safetyRatings: candidate.safetyRatings,
-        citationMetadata: candidate.citationMetadata,
-        groundingMetadata: candidate.groundingMetadata,
-        parts: (candidate.content?.parts ?? []).map((part) =>
-          part.inlineData === undefined
-            ? {
-                type: "text",
-                text: part.text,
-                thought: part.thought,
-                thoughtSignature: part.thoughtSignature,
-              }
-            : {
-                type: "inlineData",
-                mediaType: part.inlineData.mimeType,
-                thought: part.thought,
-                thoughtSignature: part.thoughtSignature,
-              },
-        ),
-      }))
-      const encoded = candidates.flatMap((candidate, candidateIndex) =>
-        (candidate.content?.parts ?? []).flatMap((part, partIndex) =>
-          part.inlineData === undefined || part.thought === true
-            ? []
-            : [{ candidate, candidateIndex, partIndex, inlineData: part.inlineData }],
-        ),
-      )
-      const images = yield* Effect.forEach(encoded, (item) =>
-        Effect.fromResult(Encoding.decodeBase64(item.inlineData.data)).pipe(
-          Effect.mapError(() =>
-            invalidOutput(
-              `Google Images candidate ${item.candidateIndex} part ${item.partIndex} contains invalid base64 data`,
-            ),
-          ),
-          Effect.map(
-            (data) =>
-              new GeneratedImage({
-                mediaType: item.inlineData.mimeType,
-                data,
-                providerMetadata: {
-                  google: {
-                    candidateIndex: item.candidate.index ?? item.candidateIndex,
-                    partIndex: item.partIndex,
-                    finishReason: item.candidate.finishReason,
-                    safetyRatings: item.candidate.safetyRatings,
-                    citationMetadata: item.candidate.citationMetadata,
-                    groundingMetadata: item.candidate.groundingMetadata,
-                    thoughtSignature: item.candidate.content?.parts[item.partIndex]?.thoughtSignature,
-                  },
-                },
-              }),
-          ),
-        ),
-      )
-      if (images.length === 0) {
-        const finishReasons = candidates.flatMap((candidate) =>
-          candidate.finishReason === undefined ? [] : [candidate.finishReason],
-        )
-        return yield* invalidOutput(
-          `Google Images returned no final images${
-            finishReasons.length === 0 ? "" : ` (finish reasons: ${finishReasons.join(", ")})`
-          }; inspect reason.providerMetadata.google for prompt feedback and candidate details`,
-          {
-            google: {
-              promptFeedback: decoded.promptFeedback,
-              candidates: candidateMetadata,
-            },
-          },
-        )
-      }
-      const usage = decoded.usageMetadata
-      const outputTokens =
-        usage?.candidatesTokenCount === undefined
-          ? undefined
-          : usage.candidatesTokenCount + (usage.thoughtsTokenCount ?? 0)
-      return new ImageResponse({
-        images,
-        usage:
-          usage === undefined
-            ? undefined
-            : new Usage({
-                inputTokens: usage.promptTokenCount,
-                outputTokens,
-                nonCachedInputTokens: ProviderShared.subtractTokens(
-                  usage.promptTokenCount,
-                  usage.cachedContentTokenCount,
-                ),
-                cacheReadInputTokens: usage.cachedContentTokenCount,
-                reasoningTokens: usage.thoughtsTokenCount,
-                totalTokens: ProviderShared.totalTokens(usage.promptTokenCount, outputTokens, usage.totalTokenCount),
-                providerMetadata: { google: usage },
-              }),
-        providerMetadata: {
-          google: {
-            modelVersion: decoded.modelVersion,
-            responseId: decoded.responseId,
-            promptFeedback: decoded.promptFeedback,
-            candidates: candidateMetadata,
-          },
-        },
-      })
-    }),
-  }
-  return ImageModel.make<GoogleImageOptions>({ id: input.id, provider: "google", route, http: input.http })
-}
-
-const googleImagePart = (image: ImageInput): Effect.Effect<Record<string, unknown>, LLMError> => {
-  if (image.type === "bytes")
-    return Effect.succeed({ inlineData: { mimeType: image.mediaType, data: Encoding.encodeBase64(image.data) } })
-  if (image.type === "file-uri") return Effect.succeed({ fileData: { mimeType: image.mediaType, fileUri: image.uri } })
-  if (image.type === "url")
-    return ImageInputs.decodeDataUrl(image.url, ADAPTER).pipe(
-      Effect.flatMap((decoded) => {
-        if (decoded === undefined)
-          return Effect.fail(
-            ImageInputs.invalid(
-              ADAPTER,
-              "Google generateContent does not fetch public image URLs; use bytes, a data URL, or a Gemini file URI",
-            ),
-          )
-        return Effect.succeed({
-          inlineData: { mimeType: decoded.mediaType, data: Encoding.encodeBase64(decoded.data) },
-        })
-      }),
-    )
-  return Effect.fail(
-    ImageInputs.invalid(ADAPTER, "Google generateContent requires Gemini file URIs rather than provider file IDs"),
-  )
-}
-
-export const GoogleImages = {
-  model,
-} as const
@@ -2,7 +2,6 @@ export * as AnthropicMessages from "./anthropic-messages"
 export * as BedrockConverse from "./bedrock-converse"
 export * as Gemini from "./gemini"
 export * as OpenAIChat from "./openai-chat"
-export * as OpenAIImages from "./openai-images"
 export * as OpenAICompatibleChat from "./openai-compatible-chat"
 export * as OpenAICompatibleResponses from "./openai-compatible-responses"
 export * as OpenAIResponses from "./openai-responses"
@@ -25,7 +25,6 @@ import { ToolStream } from "./utils/tool-stream"

 const ADAPTER = "openai-chat"
 const IMAGE_MIMES = new Set<string>(ProviderShared.IMAGE_MIMES)
-const RESERVED_REASONING_FIELDS = new Set(["role", "content", "tool_calls"])
 export const DEFAULT_BASE_URL = "https://api.openai.com/v1"
 export const PATH = "/chat/completions"

@@ -71,18 +70,14 @@ const OpenAIChatMessage = Schema.Union([
    role: Schema.Literal("user"),
    content: Schema.Union([Schema.String, Schema.Array(OpenAIChatUserContent)]),
  }),
-  Schema.StructWithRest(
-    Schema.Struct({
-      role: Schema.Literal("assistant"),
-      content: Schema.NullOr(Schema.String),
-      tool_calls: optionalArray(OpenAIChatAssistantToolCall),
-      reasoning_content: Schema.optional(Schema.String),
-      reasoning: Schema.optional(Schema.String),
-      reasoning_text: Schema.optional(Schema.String),
-      reasoning_details: Schema.optional(Schema.Unknown),
-    }),
-    [Schema.Record(Schema.String, Schema.Unknown)],
-  ),
+  Schema.Struct({
+    role: Schema.Literal("assistant"),
+    content: Schema.NullOr(Schema.String),
+    tool_calls: optionalArray(OpenAIChatAssistantToolCall),
+    reasoning_content: Schema.optional(Schema.String),
+    reasoning: Schema.optional(Schema.String),
+    reasoning_text: Schema.optional(Schema.String),
+  }),
  Schema.Struct({ role: Schema.Literal("tool"), tool_call_id: Schema.String, content: Schema.String }),
 ]).pipe(Schema.toTaggedUnion("role"))
 type OpenAIChatMessage = Schema.Schema.Type<typeof OpenAIChatMessage>
@@ -149,17 +144,13 @@ const OpenAIChatToolCallDelta = Schema.Struct({
 })
 type OpenAIChatToolCallDelta = Schema.Schema.Type<typeof OpenAIChatToolCallDelta>

-const OpenAIChatDelta = Schema.StructWithRest(
-  Schema.Struct({
-    content: optionalNull(Schema.String),
-    reasoning_content: optionalNull(Schema.String),
-    reasoning: optionalNull(Schema.String),
-    reasoning_text: optionalNull(Schema.String),
-    reasoning_details: optionalNull(Schema.Unknown),
-    tool_calls: optionalNull(Schema.Array(OpenAIChatToolCallDelta)),
-  }),
-  [Schema.Record(Schema.String, Schema.Unknown)],
-)
+const OpenAIChatDelta = Schema.Struct({
+  content: optionalNull(Schema.String),
+  reasoning_content: optionalNull(Schema.String),
+  reasoning: optionalNull(Schema.String),
+  reasoning_text: optionalNull(Schema.String),
+  tool_calls: optionalNull(Schema.Array(OpenAIChatToolCallDelta)),
+})

 const OpenAIChatChoice = Schema.Struct({
  delta: optionalNull(OpenAIChatDelta),
@@ -173,23 +164,13 @@ export const OpenAIChatEvent = Schema.Struct({
 export type OpenAIChatEvent = Schema.Schema.Type<typeof OpenAIChatEvent>
 type OpenAIChatRequestMessage = LLMRequest["messages"][number]

-interface PendingToolDelta {
-  readonly id?: string
-  readonly name?: string
-  readonly input: string
-}
-
 export interface ParserState {
  readonly tools: ToolStream.State<number>
-  readonly pendingTools: Partial<Record<number, PendingToolDelta>>
  readonly toolCallEvents: ReadonlyArray<LLMEvent>
  readonly usage?: Usage
  readonly finishReason?: FinishReason
  readonly lifecycle: Lifecycle.State
-  readonly reasoningField?: string
-  readonly reasoningDetails: Array<unknown>
-  readonly reasoningDetailsObserved: boolean
-  readonly reasoningEmitted: boolean
+  readonly reasoningField?: "reasoning" | "reasoning_content" | "reasoning_text"
 }

 // =============================================================================
@@ -234,16 +215,8 @@ const openAICompatibleReasoningContent = (native: unknown) =>

 const reasoningField = (part: ReasoningPart) => {
  const field = part.providerMetadata?.openai?.reasoningField
-  return typeof field === "string" ? field : undefined
-}
-
-const reasoningDetails = (parts: ReadonlyArray<ReasoningPart>, native: unknown) => {
-  const observed = parts.flatMap((part) => {
-    const details = part.providerMetadata?.openai?.reasoningDetails
-    return Array.isArray(details) ? details : []
-  })
-  if (parts.some((part) => Array.isArray(part.providerMetadata?.openai?.reasoningDetails))) return observed
-  if (isRecord(native) && Array.isArray(native.reasoning_details)) return native.reasoning_details
+  if (field === "reasoning" || field === "reasoning_content" || field === "reasoning_text") return field
+  return "reasoning_content"
 }

 const lowerUserMessage = Effect.fn("OpenAIChat.lowerUserMessage")(function* (message: OpenAIChatRequestMessage) {
@@ -266,7 +239,6 @@ const lowerUserMessage = Effect.fn("OpenAIChat.lowerUserMessage")(function* (mes

 const lowerAssistantMessage = Effect.fn("OpenAIChat.lowerAssistantMessage")(function* (
  message: OpenAIChatRequestMessage,
-  configuredField?: string,
 ) {
  const content: TextPart[] = []
  const reasoning: ReasoningPart[] = []
@@ -288,30 +260,20 @@ const lowerAssistantMessage = Effect.fn("OpenAIChat.lowerAssistantMessage")(func
    }
  }
  const text = reasoning.map((part) => part.text).join("")
-  const details = reasoningDetails(reasoning, message.native?.openaiCompatible)
-  const observedField = reasoning.map(reasoningField).find((value) => value !== undefined)
-  const nativeReasoning = openAICompatibleReasoningContent(message.native?.openaiCompatible)
-  const fullyStructured = reasoning.every((part) => Array.isArray(part.providerMetadata?.openai?.reasoningDetails))
-  const field = (() => {
-    if (configuredField !== undefined) return configuredField
-    if (reasoning.length === 0) return undefined
-    if (observedField !== undefined) return observedField
-    if (nativeReasoning !== undefined) return "reasoning_content"
-    if (!fullyStructured) return "reasoning_content"
-  })()
-  const reasoningText = (() => {
-    if (configuredField !== undefined) return reasoning.length === 0 ? (nativeReasoning ?? "") : text
-    if (reasoning.length === 0) return nativeReasoning
-    return text
-  })()
-  const result = {
+  const field = reasoning[0] ? reasoningField(reasoning[0]) : "reasoning_content"
+  return {
    role: "assistant" as const,
    content: content.length === 0 ? null : ProviderShared.joinText(content),
    tool_calls: toolCalls.length === 0 ? undefined : toolCalls,
-    reasoning_details: details,
+    reasoning_content:
+      reasoning.length === 0
+        ? openAICompatibleReasoningContent(message.native?.openaiCompatible)
+        : field === "reasoning_content"
+          ? text
+          : undefined,
+    reasoning: reasoning.length > 0 && field === "reasoning" ? text : undefined,
+    reasoning_text: reasoning.length > 0 && field === "reasoning_text" ? text : undefined,
  }
-  if (field === undefined || reasoningText === undefined) return result
-  return { ...result, [field]: reasoningText }
 })

 const lowerToolMessages = Effect.fn("OpenAIChat.lowerToolMessages")(function* (message: OpenAIChatRequestMessage) {
@@ -337,12 +299,9 @@ const lowerToolMessages = Effect.fn("OpenAIChat.lowerToolMessages")(function* (m
  return { messages, images }
 })

-const lowerMessage = Effect.fn("OpenAIChat.lowerMessage")(function* (
-  message: OpenAIChatRequestMessage,
-  reasoningField?: string,
-) {
+const lowerMessage = Effect.fn("OpenAIChat.lowerMessage")(function* (message: OpenAIChatRequestMessage) {
  if (message.role === "user") return [yield* lowerUserMessage(message)]
-  if (message.role === "assistant") return [yield* lowerAssistantMessage(message, reasoningField)]
+  if (message.role === "assistant") return [yield* lowerAssistantMessage(message)]
  return (yield* lowerToolMessages(message)).messages
 })

@@ -380,7 +339,7 @@ const lowerMessages = Effect.fn("OpenAIChat.lowerMessages")(function* (request:
      continue
    }
    flushImages()
-    messages.push(...(yield* lowerMessage(message, request.model.compatibility?.reasoningField)))
+    messages.push(...(yield* lowerMessage(message)))
  }
  flushImages()
  return messages
@@ -398,11 +357,6 @@ const lowerOptions = Effect.fn("OpenAIChat.lowerOptions")(function* (request: LL
 const fromRequest = Effect.fn("OpenAIChat.fromRequest")(function* (request: LLMRequest) {
  // `fromRequest` returns the provider body only. Endpoint, auth, framing,
  // validation, and HTTP execution are composed by `Route.make`.
-  const reasoningField = request.model.compatibility?.reasoningField
-  if (reasoningField && RESERVED_REASONING_FIELDS.has(reasoningField))
-    return yield* ProviderShared.invalidRequest(
-      `OpenAI Chat reasoning field conflicts with reserved field ${reasoningField}`,
-    )
  const generation = request.generation
  const toolSchemaCompatibility = request.model.compatibility?.toolSchema
  return {
@@ -463,73 +417,12 @@ const mapUsage = (usage: OpenAIChatEvent["usage"]): Usage | undefined => {
  })
 }

-const reasoningDelta = (
-  delta: Schema.Schema.Type<typeof OpenAIChatDelta> | null | undefined,
-  configuredField?: string,
-) => {
-  if (!delta) return undefined
-  const fields = new Set([configuredField, "reasoning_content", "reasoning", "reasoning_text"])
-  for (const field of fields) {
-    if (field === undefined) continue
-    const text = delta[field]
-    if (typeof text === "string" && text.length > 0) return { field, text }
-  }
-  return undefined
+const reasoningDelta = (delta: Schema.Schema.Type<typeof OpenAIChatDelta> | null | undefined) => {
+  if (delta?.reasoning_content) return { field: "reasoning_content", text: delta.reasoning_content } as const
+  if (delta?.reasoning) return { field: "reasoning", text: delta.reasoning } as const
+  if (delta?.reasoning_text) return { field: "reasoning_text", text: delta.reasoning_text } as const
 }

-const detailText = (details: ReadonlyArray<unknown>) => {
-  const text = details.flatMap((detail) => {
-    if (!isRecord(detail)) return []
-    if (detail.type === "reasoning.text" && typeof detail.text === "string" && detail.text) return [detail.text]
-    if (detail.type === "reasoning.summary" && typeof detail.summary === "string" && detail.summary)
-      return [detail.summary]
-    return []
-  })
-  if (text.length > 0) return text.join("")
-}
-
-const appendReasoningDetails = (result: Array<unknown>, details: ReadonlyArray<unknown>) => {
-  for (const detail of details) {
-    const previous = result.at(-1)
-    if (
-      !isRecord(previous) ||
-      previous.type !== "reasoning.text" ||
-      !isRecord(detail) ||
-      detail.type !== "reasoning.text" ||
-      conflictingReasoningTextDetails(previous, detail)
-    ) {
-      result.push(detail)
-      continue
-    }
-    result[result.length - 1] = {
-      ...previous,
-      ...Object.fromEntries(Object.entries(detail).filter((entry) => entry[1] !== undefined)),
-      text: `${typeof previous.text === "string" ? previous.text : ""}${typeof detail.text === "string" ? detail.text : ""}`,
-      signature: mergeDetailValue(previous.signature, detail.signature),
-      format: mergeDetailValue(previous.format, detail.format),
-    }
-  }
-}
-
-const mergeDetailValue = (previous: unknown, current: unknown) =>
-  previous || current || (previous !== undefined ? previous : current)
-
-const conflictingReasoningTextDetails = (previous: Record<string, unknown>, current: Record<string, unknown>) =>
-  conflictingDetailValue(previous.id, current.id) ||
-  conflictingDetailValue(previous.index, current.index) ||
-  conflictingDetailValue(previous.format, current.format) ||
-  (Boolean(previous.signature) && Boolean(current.signature) && previous.signature !== current.signature)
-
-const conflictingDetailValue = (previous: unknown, current: unknown) =>
-  previous !== undefined && previous !== null && current !== undefined && current !== null && previous !== current
-
-const reasoningMetadata = (field: ParserState["reasoningField"], details?: ReadonlyArray<unknown>) => ({
-  openai: {
-    ...(field ? { reasoningField: field } : {}),
-    ...(details ? { reasoningDetails: details } : {}),
-  },
-})
-
 const step = (state: ParserState, event: OpenAIChatEvent) =>
  Effect.gen(function* () {
    const events: LLMEvent[] = []
@@ -539,56 +432,29 @@ const step = (state: ParserState, event: OpenAIChatEvent) =>
    const delta = choice?.delta
    const toolDeltas = delta?.tool_calls ?? []
    let tools = state.tools
-    let pendingTools = state.pendingTools

    let lifecycle = state.lifecycle

-    const reasoning = reasoningDelta(delta, state.reasoningField)
-    const reasoningField = state.reasoningField ?? (!state.lifecycle.text.has("text-0") ? reasoning?.field : undefined)
-    const detailDelta = Array.isArray(delta?.reasoning_details) ? delta.reasoning_details : undefined
-    if (detailDelta !== undefined) appendReasoningDetails(state.reasoningDetails, detailDelta)
-    const reasoningDetailsObserved = state.reasoningDetailsObserved || detailDelta !== undefined
-    const deltaMetadata = reasoningMetadata(reasoningField)
-    const text = detailDelta?.length ? (detailText(detailDelta) ?? reasoning?.text) : reasoning?.text
-    if (!state.lifecycle.text.has("text-0") && text !== undefined)
-      lifecycle = Lifecycle.reasoningDelta(lifecycle, events, "reasoning-0", text, deltaMetadata)
-    else if (
-      reasoningDetailsObserved &&
-      !lifecycle.reasoning.has("reasoning-0") &&
-      (Boolean(delta?.content) || toolDeltas.length > 0)
-    )
-      lifecycle = Lifecycle.reasoningStart(lifecycle, events, "reasoning-0", deltaMetadata)
-    const reasoningEmitted = state.reasoningEmitted || lifecycle.reasoning.has("reasoning-0")
+    const reasoning = reasoningDelta(delta)
+    const reasoningField = state.reasoningField ?? reasoning?.field
+    if (reasoning)
+      lifecycle = Lifecycle.reasoningDelta(lifecycle, events, "reasoning-0", reasoning.text, {
+        openai: { reasoningField: reasoningField ?? reasoning.field },
+      })

    if (delta?.content) {
-      lifecycle = Lifecycle.reasoningEnd(
-        lifecycle,
-        events,
-        "reasoning-0",
-        reasoningMetadata(reasoningField, reasoningDetailsObserved ? state.reasoningDetails : undefined),
-      )
+      lifecycle = Lifecycle.reasoningEnd(lifecycle, events, "reasoning-0")
      lifecycle = Lifecycle.textDelta(lifecycle, events, "text-0", delta.content)
    }

+    if (toolDeltas.length) lifecycle = Lifecycle.reasoningEnd(lifecycle, events, "reasoning-0")
+
    for (const tool of toolDeltas) {
-      const current = tools[tool.index]
-      const pending = pendingTools[tool.index]
-      const id = current?.id ?? pending?.id ?? (tool.id || undefined)
-      const name = current?.name ?? pending?.name ?? (tool.function?.name || undefined)
-      const text = `${pending?.input ?? ""}${tool.function?.arguments ?? ""}`
-      if (!current && (!id || !name)) {
-        pendingTools = { ...pendingTools, [tool.index]: { id: id || undefined, name: name || undefined, input: text } }
-        continue
-      }
-      if (pending) {
-        pendingTools = { ...pendingTools }
-        delete pendingTools[tool.index]
-      }
      const result = ToolStream.appendOrStart(
        ADAPTER,
        tools,
        tool.index,
-        { id: id || undefined, name: name || undefined, text },
+        { id: tool.id ?? undefined, name: tool.function?.name ?? undefined, text: tool.function?.arguments ?? "" },
        "OpenAI Chat tool call delta is missing id or name",
      )
      if (ToolStream.isError(result)) return yield* result
@@ -597,11 +463,8 @@ const step = (state: ParserState, event: OpenAIChatEvent) =>
      events.push(...result.events)
    }

-    if (finishReason !== undefined && state.finishReason === undefined && Object.keys(pendingTools).length > 0)
-      return yield* ProviderShared.eventError(ADAPTER, "OpenAI Chat tool call delta is missing id or name")
-
    // Finalize accumulated tool inputs eagerly when finish_reason arrives so
-    // valid calls and malformed local calls settle independently.
+    // JSON parse failures fail the stream at the boundary rather than at halt.
    const finished =
      finishReason !== undefined && state.finishReason === undefined && Object.keys(tools).length > 0
        ? yield* ToolStream.finishAll(ADAPTER, tools)
@@ -610,15 +473,11 @@ const step = (state: ParserState, event: OpenAIChatEvent) =>
    return [
      {
        tools: finished?.tools ?? tools,
-        pendingTools,
        toolCallEvents: finished?.events ?? state.toolCallEvents,
        usage,
        finishReason,
        lifecycle,
        reasoningField,
-        reasoningDetails: state.reasoningDetails,
-        reasoningDetailsObserved,
-        reasoningEmitted,
      },
      events,
    ] as const
@@ -628,16 +487,7 @@ const finishEvents = (state: ParserState): ReadonlyArray<LLMEvent> => {
  const events: LLMEvent[] = []
  const hasToolCalls = state.toolCallEvents.length > 0
  const reason = state.finishReason === "stop" && hasToolCalls ? "tool-calls" : state.finishReason
-  const metadata = reasoningMetadata(
-    state.reasoningField,
-    state.reasoningDetailsObserved ? state.reasoningDetails : undefined,
-  )
-  const started =
-    state.reasoningDetailsObserved && !state.reasoningEmitted
-      ? Lifecycle.reasoningStart(state.lifecycle, events, "reasoning-0", reasoningMetadata(state.reasoningField))
-      : state.lifecycle
-  const ended = Lifecycle.reasoningEnd(started, events, "reasoning-0", metadata)
-  const lifecycle = state.toolCallEvents.length ? Lifecycle.stepStart(ended, events) : ended
+  const lifecycle = state.toolCallEvents.length ? Lifecycle.stepStart(state.lifecycle, events) : state.lifecycle
  events.push(...state.toolCallEvents)
  if (reason) Lifecycle.finish(lifecycle, events, { reason, usage: state.usage })
  return events
@@ -660,15 +510,11 @@ export const protocol = Protocol.make({
  },
  stream: {
    event: Protocol.jsonEvent(OpenAIChatEvent),
-    initial: (request) => ({
+    initial: () => ({
      tools: ToolStream.empty<number>(),
-      pendingTools: {},
      toolCallEvents: [],
      lifecycle: Lifecycle.initial(),
-      reasoningField: request.model.compatibility?.reasoningField,
-      reasoningDetails: [],
-      reasoningDetailsObserved: false,
-      reasoningEmitted: false,
+      reasoningField: undefined,
    }),
    step,
    onHalt: finishEvents,
@@ -1,270 +0,0 @@
-import { Effect, Encoding, Schema } from "effect"
-import { Headers, HttpClientRequest, HttpClientResponse } from "effect/unstable/http"
-import {
-  ImageModel,
-  GeneratedImage,
-  ImageResponse,
-  type ImageInput,
-  type ImageRequestFor,
-  type ImageRoute,
-} from "../image"
-import { Auth, type Definition as AuthDefinition } from "../route/auth"
-import {
-  InvalidProviderOutputReason,
-  LLMError,
-  Usage,
-  mergeHttpOptions,
-  mergeJsonRecords,
-  type HttpOptions,
-} from "../schema"
-import { ProviderShared } from "./shared"
-import { ImageInputs } from "./utils/image-input"
-import { OpenAIImage } from "./utils/openai-image"
-
-const ADAPTER = "openai-images"
-export const DEFAULT_BASE_URL = "https://api.openai.com/v1"
-export const PATH = "/images/generations"
-export const EDIT_PATH = "/images/edits"
-
-export type OpenAIImageString<Known extends string> = Known | (string & {})
-
-export type OpenAIImageOptions = {
-  readonly mask?: ImageInput
-  readonly n?: number
-  readonly size?: OpenAIImageString<
-    "auto" | "256x256" | "512x512" | "1024x1024" | "1536x1024" | "1024x1536" | "1792x1024" | "1024x1792"
-  >
-  readonly quality?: OpenAIImageString<"auto" | "low" | "medium" | "high" | "standard" | "hd">
-  readonly background?: OpenAIImageString<"auto" | "opaque" | "transparent">
-  readonly moderation?: OpenAIImageString<"auto" | "low">
-  readonly outputFormat?: OpenAIImageString<"png" | "jpeg" | "webp">
-  readonly outputCompression?: number
-} & Record<string, unknown>
-
-export type OpenAIImageBody = Record<string, unknown> & {
-  readonly model: string
-  readonly prompt: string
-}
-
-const OpenAIImageResponse = Schema.Struct({
-  data: Schema.Array(
-    Schema.Struct({
-      b64_json: Schema.optional(Schema.String),
-      url: Schema.optional(Schema.String),
-      revised_prompt: Schema.optional(Schema.String),
-    }),
-  ),
-  output_format: Schema.optional(Schema.String),
-  usage: Schema.optional(
-    Schema.Struct({
-      input_tokens: Schema.optional(Schema.Number),
-      output_tokens: Schema.optional(Schema.Number),
-      total_tokens: Schema.optional(Schema.Number),
-      input_tokens_details: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
-      output_tokens_details: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
-    }),
-  ),
-})
-
-export interface ModelInput {
-  readonly id: string
-  readonly auth: AuthDefinition
-  readonly baseURL?: string
-  readonly headers?: Record<string, string>
-  readonly http?: HttpOptions
-}
-
-const nativeOptions = (options: OpenAIImageOptions | undefined) => {
-  if (!options) return undefined
-  const { mask: _, outputFormat, outputCompression, ...native } = options
-  return {
-    output_format: outputFormat,
-    output_compression: outputCompression,
-    ...native,
-  }
-}
-
-const invalidOutput = (message: string) =>
-  new LLMError({
-    module: ADAPTER,
-    method: "generate",
-    reason: new InvalidProviderOutputReason({ message, route: ADAPTER }),
-  })
-
-const applyQuery = (url: string, query: Record<string, string> | undefined) => {
-  if (!query) return url
-  const next = new URL(url)
-  Object.entries(query).forEach(([key, value]) => next.searchParams.set(key, value))
-  return next.toString()
-}
-
-export const model = (input: ModelInput) => {
-  const route: ImageRoute<OpenAIImageOptions> = {
-    id: ADAPTER,
-    generate: Effect.fn("OpenAIImages.generate")(function* (request: ImageRequestFor<OpenAIImageOptions>, execute) {
-      const mask = request.options?.mask
-      if (mask !== undefined && (request.images?.length ?? 0) === 0)
-        return yield* ImageInputs.invalid(ADAPTER, "An OpenAI image mask requires at least one input image")
-      const http = mergeHttpOptions(request.model.http, request.http)
-      const sourceImages = request.images ?? []
-      const multipartImages = yield* Effect.forEach(sourceImages, (image) => {
-        if (image.type === "bytes") return Effect.succeed({ data: image.data, mediaType: image.mediaType })
-        if (image.type === "url") return ImageInputs.decodeDataUrl(image.url, ADAPTER)
-        return Effect.succeed(undefined)
-      })
-      const multipartMask =
-        mask === undefined
-          ? undefined
-          : mask.type === "bytes"
-            ? { data: mask.data, mediaType: mask.mediaType }
-            : mask.type === "url"
-              ? yield* ImageInputs.decodeDataUrl(mask.url, ADAPTER)
-              : undefined
-      const useMultipart =
-        sourceImages.length > 0 &&
-        multipartImages.every((image) => image !== undefined) &&
-        (mask === undefined || multipartMask !== undefined)
-      const path = sourceImages.length === 0 ? PATH : EDIT_PATH
-      const url = applyQuery(`${(input.baseURL ?? DEFAULT_BASE_URL).replace(/\/$/, "")}${path}`, http?.query)
-
-      if (useMultipart) {
-        const form = new FormData()
-        form.append("model", request.model.id)
-        form.append("prompt", request.prompt)
-        Object.entries(mergeJsonRecords(nativeOptions(request.options), http?.body) ?? {}).forEach(([key, value]) => {
-          if (["model", "prompt", "image", "image[]", "images", "mask"].includes(key)) return
-          form.append(key, typeof value === "string" ? value : ProviderShared.encodeJson(value))
-        })
-        multipartImages.forEach((image, index) => {
-          if (image === undefined) return
-          form.append("image[]", imageBlob(image.data, image.mediaType), `image-${index}`)
-        })
-        if (multipartMask !== undefined)
-          form.append("mask", imageBlob(multipartMask.data, multipartMask.mediaType), "mask")
-        const headers = yield* Auth.toEffect(input.auth)({
-          request,
-          method: "POST",
-          url,
-          body: "[multipart/form-data]",
-          headers: Headers.remove(Headers.fromInput({ ...input.headers, ...http?.headers }), "content-type"),
-        })
-        const response = yield* execute(
-          HttpClientRequest.post(url).pipe(HttpClientRequest.setHeaders(headers), HttpClientRequest.bodyFormData(form)),
-        )
-        return yield* parseResponse(response, request.options, http?.body)
-      }
-
-      const references = sourceImages.map((image) => {
-        if (image.type === "bytes") return { image_url: ImageInputs.dataUrl(image) }
-        if (image.type === "url") return { image_url: image.url }
-        if (image.type === "file-id") return { file_id: image.id }
-        return undefined
-      })
-      if (references.some((image) => image === undefined))
-        return yield* ImageInputs.invalid(ADAPTER, "OpenAI Images accepts image URLs, data URLs, bytes, and file IDs")
-      const maskReference =
-        mask === undefined
-          ? undefined
-          : mask.type === "bytes"
-            ? { image_url: ImageInputs.dataUrl(mask) }
-            : mask.type === "url"
-              ? { image_url: mask.url }
-              : mask.type === "file-id"
-                ? { file_id: mask.id }
-                : undefined
-      if (mask !== undefined && maskReference === undefined)
-        return yield* ImageInputs.invalid(ADAPTER, "OpenAI Images accepts masks as URLs, data URLs, bytes, or file IDs")
-      const requestBody = mergeJsonRecords(
-        {
-          model: request.model.id,
-          prompt: request.prompt,
-          images: references.length === 0 ? undefined : references,
-          mask: maskReference,
-        },
-        nativeOptions(request.options),
-        http?.body,
-      ) as OpenAIImageBody
-      const text = ProviderShared.encodeJson(requestBody)
-      const headers = yield* Auth.toEffect(input.auth)({
-        request,
-        method: "POST",
-        url,
-        body: text,
-        headers: Headers.fromInput({ ...input.headers, ...http?.headers }),
-      })
-      const response = yield* execute(
-        HttpClientRequest.post(url).pipe(
-          HttpClientRequest.setHeaders(headers),
-          HttpClientRequest.bodyText(text, "application/json"),
-        ),
-      )
-      return yield* parseResponse(response, request.options, http?.body)
-    }),
-  }
-  return ImageModel.make<OpenAIImageOptions>({ id: input.id, provider: "openai", route, http: input.http })
-}
-
-const parseResponse = Effect.fn("OpenAIImages.parseResponse")(function* (
-  response: HttpClientResponse.HttpClientResponse,
-  options: OpenAIImageOptions | undefined,
-  overlay: Record<string, unknown> | undefined,
-) {
-  const payload = yield* response.json.pipe(
-    Effect.mapError(() => invalidOutput("Failed to read the OpenAI Images response")),
-  )
-  const decoded = yield* Schema.decodeUnknownEffect(OpenAIImageResponse)(payload).pipe(
-    Effect.mapError(() => invalidOutput("OpenAI Images returned an invalid response")),
-  )
-  const requestBody = mergeJsonRecords(nativeOptions(options), overlay)
-  const format =
-    decoded.output_format ?? (typeof requestBody?.output_format === "string" ? requestBody.output_format : "png")
-  const images = yield* Effect.forEach(decoded.data, (item, index) => {
-    if (item.b64_json)
-      return Effect.fromResult(Encoding.decodeBase64(item.b64_json)).pipe(
-        Effect.mapError(() => invalidOutput(`OpenAI Images result ${index} contains invalid base64 data`)),
-        Effect.map(
-          (data) =>
-            new GeneratedImage({
-              mediaType: `image/${format}`,
-              data,
-              providerMetadata:
-                item.revised_prompt === undefined ? undefined : { openai: { revisedPrompt: item.revised_prompt } },
-            }),
-        ),
-      )
-    if (item.url)
-      return Effect.succeed(
-        new GeneratedImage({
-          mediaType: `image/${format}`,
-          data: item.url,
-          providerMetadata:
-            item.revised_prompt === undefined ? undefined : { openai: { revisedPrompt: item.revised_prompt } },
-        }),
-      )
-    return Effect.fail(invalidOutput(`OpenAI Images result ${index} has neither image data nor a URL`))
-  })
-  if (images.length === 0) return yield* invalidOutput("OpenAI Images returned no images")
-  return new ImageResponse({
-    images,
-    usage:
-      decoded.usage === undefined
-        ? undefined
-        : new Usage({
-            inputTokens: decoded.usage.input_tokens,
-            outputTokens: decoded.usage.output_tokens,
-            totalTokens: decoded.usage.total_tokens,
-            providerMetadata: { openai: decoded.usage },
-          }),
-    providerMetadata: { openai: { outputFormat: format } },
-  })
-})
-
-const imageBlob = (data: Uint8Array, mediaType: string) => {
-  const buffer = new ArrayBuffer(data.byteLength)
-  new Uint8Array(buffer).set(data)
-  return new Blob([buffer], { type: mediaType })
-}
-
-export const OpenAIImages = {
-  model,
-} as const
@@ -1,4 +1,4 @@
-import { Effect, Encoding, Schema } from "effect"
+import { Effect, Schema } from "effect"
 import { Route } from "../route/client"
 import { Auth } from "../route/auth"
 import { Endpoint } from "../route/endpoint"
@@ -11,7 +11,6 @@ import {
  type FinishReason,
  type JsonSchema,
  type LLMRequest,
-  type MediaPart,
  type ProviderMetadata,
  type ReasoningPart,
  type TextPart,
@@ -26,10 +25,8 @@ import { OpenAIOptions } from "./utils/openai-options"
 import { Lifecycle } from "./utils/lifecycle"
 import { ToolSchemaProjection } from "./utils/tool-schema"
 import { ToolStream } from "./utils/tool-stream"
-import { OpenAIImage } from "./utils/openai-image"

 const ADAPTER = "openai-responses"
-const MEDIA_MIMES = new Set<string>([...ProviderShared.IMAGE_MIMES, ...ProviderShared.PDF_MIMES])
 export const DEFAULT_BASE_URL = "https://api.openai.com/v1"
 export const PATH = "/responses"

@@ -44,17 +41,7 @@ const OpenAIResponsesInputImage = Schema.Struct({
  type: Schema.tag("input_image"),
  image_url: Schema.String,
 })
-const OpenAIResponsesInputFile = Schema.Struct({
-  type: Schema.tag("input_file"),
-  filename: Schema.String,
-  file_data: Schema.String,
-  mime_type: Schema.optional(Schema.String),
-})
-const OpenAIResponsesInputContent = Schema.Union([
-  OpenAIResponsesInputText,
-  OpenAIResponsesInputImage,
-  OpenAIResponsesInputFile,
-])
+const OpenAIResponsesInputContent = Schema.Union([OpenAIResponsesInputText, OpenAIResponsesInputImage])
 type OpenAIResponsesInputContent = Schema.Schema.Type<typeof OpenAIResponsesInputContent>

 const OpenAIResponsesOutputText = Schema.Struct({
@@ -80,13 +67,9 @@ const OpenAIResponsesItemReference = Schema.Struct({
 })

 // `function_call_output.output` accepts either a plain string or an ordered
-// array of content items so tools can return images and files in addition to text.
+// array of content items so tools can return images in addition to text.
 // https://platform.openai.com/docs/api-reference/responses/object
-const OpenAIResponsesFunctionCallOutputContent = Schema.Union([
-  OpenAIResponsesInputText,
-  OpenAIResponsesInputImage,
-  OpenAIResponsesInputFile,
-])
+const OpenAIResponsesFunctionCallOutputContent = Schema.Union([OpenAIResponsesInputText, OpenAIResponsesInputImage])

 const OpenAIResponsesFunctionCallOutput = Schema.Union([
  Schema.String,
@@ -130,24 +113,11 @@ const OpenAIResponsesTool = Schema.Struct({
  parameters: JsonObject,
  strict: Schema.optional(Schema.Boolean),
 })
-const OpenAIResponsesImageGenerationTool = Schema.Struct({
-  type: Schema.tag("image_generation"),
-  action: Schema.optional(Schema.Literals(["auto", "generate", "edit"])),
-  background: Schema.optional(Schema.Literals(["auto", "opaque", "transparent"])),
-  input_fidelity: Schema.optional(Schema.Literals(["low", "high"])),
-  output_compression: Schema.optional(Schema.Int.check(Schema.isBetween({ minimum: 0, maximum: 100 }))),
-  output_format: Schema.optional(Schema.Literals(["png", "jpeg", "webp"])),
-  partial_images: Schema.optional(Schema.Int.check(Schema.isGreaterThanOrEqualTo(0))),
-  quality: Schema.optional(Schema.Literals(["auto", "low", "medium", "high"])),
-  size: Schema.optional(OpenAIImage.Size),
-})
-const OpenAIResponsesTools = Schema.Union([OpenAIResponsesTool, OpenAIResponsesImageGenerationTool])
-type OpenAIResponsesTool = Schema.Schema.Type<typeof OpenAIResponsesTools>
+type OpenAIResponsesTool = Schema.Schema.Type<typeof OpenAIResponsesTool>

 const OpenAIResponsesToolChoice = Schema.Union([
  Schema.Literals(["auto", "none", "required"]),
  Schema.Struct({ type: Schema.tag("function"), name: Schema.String }),
-  Schema.Struct({ type: Schema.tag("image_generation") }),
 ])

 // Fields shared between the HTTP body and the WebSocket `response.create`
@@ -158,7 +128,7 @@ const OpenAIResponsesCoreFields = {
  model: Schema.String,
  input: Schema.Array(OpenAIResponsesInputItem),
  instructions: Schema.optional(Schema.String),
-  tools: optionalArray(OpenAIResponsesTools),
+  tools: optionalArray(OpenAIResponsesTool),
  tool_choice: Schema.optional(OpenAIResponsesToolChoice),
  store: Schema.optional(Schema.Boolean),
  service_tier: Schema.optional(OpenAIOptions.OpenAIServiceTier),
@@ -224,8 +194,6 @@ const OpenAIResponsesStreamItem = Schema.Struct({
  outputs: Schema.optional(Schema.Unknown),
  server_label: Schema.optional(Schema.String),
  output: Schema.optional(Schema.Unknown),
-  result: Schema.optional(Schema.String),
-  output_format: Schema.optional(Schema.Literals(["png", "jpeg", "webp"])),
  error: Schema.optional(Schema.Unknown),
  encrypted_content: optionalNull(Schema.String),
 })
@@ -290,41 +258,21 @@ const invalid = ProviderShared.invalidRequest
 // =============================================================================
 // Request Lowering
 // =============================================================================
-const nativeImageToolInput = (tool: ToolDefinition) => {
-  const native = tool.native?.openai
-  return ProviderShared.isRecord(native) && native.type === "image_generation" ? native : undefined
-}
-
-const nativeImageTool = (tool: ToolDefinition) => {
-  const native = nativeImageToolInput(tool)
-  return Schema.is(OpenAIResponsesImageGenerationTool)(native) ? native : undefined
-}
-
-const lowerTool = Effect.fn("OpenAIResponses.lowerTool")(function* (tool: ToolDefinition, inputSchema: JsonSchema) {
-  const native = nativeImageToolInput(tool)
-  if (native !== undefined) {
-    if (Schema.is(OpenAIResponsesImageGenerationTool)(native)) return native
-    return yield* invalid("OpenAI Responses image generation tool options are invalid")
-  }
-  return {
-    type: "function" as const,
-    name: tool.name,
-    description: tool.description,
-    parameters: ToolSchemaProjection.openAI(inputSchema),
-    // TODO: Read this from OpenAI-specific tool options so direct LLM callers can opt into strict schemas.
-    strict: false,
-  }
+const lowerTool = (tool: ToolDefinition, inputSchema: JsonSchema): OpenAIResponsesTool => ({
+  type: "function",
+  name: tool.name,
+  description: tool.description,
+  parameters: ToolSchemaProjection.openAI(inputSchema),
+  // TODO: Read this from OpenAI-specific tool options so direct LLM callers can opt into strict schemas.
+  strict: false,
 })

-const lowerToolChoice = (toolChoice: NonNullable<LLMRequest["toolChoice"]>, tools: ReadonlyArray<ToolDefinition>) =>
+const lowerToolChoice = (toolChoice: NonNullable<LLMRequest["toolChoice"]>) =>
  ProviderShared.matchToolChoice("OpenAI Responses", toolChoice, {
    auto: () => "auto" as const,
    none: () => "none" as const,
    required: () => "required" as const,
-    tool: (name) =>
-      tools.some((tool) => tool.name === name && nativeImageTool(tool) !== undefined)
-        ? ({ type: "image_generation" } as const)
-        : { type: "function" as const, name },
+    tool: (name) => ({ type: "function" as const, name }),
  })

 const lowerToolCall = (part: ToolCallPart): OpenAIResponsesInputItem => ({
@@ -359,58 +307,42 @@ const hostedToolItemID = (part: ToolResultPart) => {
    : undefined
 }

-const lowerMedia = Effect.fn("OpenAIResponses.lowerMedia")(function* (part: MediaPart, provider: string) {
-  const media = yield* ProviderShared.validateMedia("OpenAI Responses", part, MEDIA_MIMES)
-  if (media.mime === "application/pdf") {
-    // xAI models inline bytes and MIME separately; OpenAI uses a data URL in file_data.
-    if (provider === "xai")
-      return {
-        type: "input_file" as const,
-        filename: part.filename ?? "document.pdf",
-        file_data: media.base64,
-        mime_type: media.mime,
-      }
-    return {
-      type: "input_file" as const,
-      filename: part.filename ?? "document.pdf",
-      file_data: media.dataUrl,
-    }
-  }
-  return { type: "input_image" as const, image_url: media.dataUrl }
-})
-
 const lowerUserContent = Effect.fn("OpenAIResponses.lowerUserContent")(function* (
  part: LLMRequest["messages"][number]["content"][number],
-  provider: string,
 ) {
  if (part.type === "text") return { type: "input_text" as const, text: part.text }
-  if (part.type === "media") return yield* lowerMedia(part, provider)
+  if (part.type === "media") {
+    const media = yield* ProviderShared.validateMedia(
+      "OpenAI Responses",
+      part,
+      new Set<string>(ProviderShared.IMAGE_MIMES),
+    )
+    return { type: "input_image" as const, image_url: media.dataUrl }
+  }
  return yield* ProviderShared.unsupportedContent("OpenAI Responses", "user", ["text", "media"])
 })

-// Tool results may carry structured text, images, and files. Keep media as provider-native
+// Tool results may carry structured text/images. Keep media as provider-native
 // content instead of JSON-stringifying base64 into a prompt string.
 const lowerToolResultContentItem = Effect.fn("OpenAIResponses.lowerToolResultContentItem")(function* (
  item: ToolContent,
-  provider: string,
 ) {
  if (item.type === "text") return { type: "input_text" as const, text: item.text }
-  return yield* lowerMedia(
-    { type: "media", mediaType: item.mime, data: item.uri, filename: item.name },
-    provider,
+  const media = yield* ProviderShared.validateToolFile(
+    "OpenAI Responses",
+    item,
+    new Set<string>(ProviderShared.IMAGE_MIMES),
  )
+  return { type: "input_image" as const, image_url: media.dataUrl }
 })

-const lowerToolResultOutput = Effect.fn("OpenAIResponses.lowerToolResultOutput")(function* (
-  part: ToolResultPart,
-  provider: string,
-) {
+const lowerToolResultOutput = Effect.fn("OpenAIResponses.lowerToolResultOutput")(function* (part: ToolResultPart) {
  // Text/json/error results are encoded as a plain string for backward
  // compatibility with existing cassettes and provider expectations.
  if (part.result.type !== "content") return ProviderShared.toolResultText(part)
  // Preserve the narrowed array element type when compiled through a consumer package.
  const content: ReadonlyArray<ToolContent> = part.result.value
-  return yield* Effect.forEach(content, (item) => lowerToolResultContentItem(item, provider))
+  return yield* Effect.forEach(content, lowerToolResultContentItem)
 })

 const lowerMessages = Effect.fn("OpenAIResponses.lowerMessages")(function* (request: LLMRequest) {
@@ -433,10 +365,7 @@ const lowerMessages = Effect.fn("OpenAIResponses.lowerMessages")(function* (requ
    }

    if (message.role === "user") {
-      input.push({
-        role: "user",
-        content: yield* Effect.forEach(message.content, (part) => lowerUserContent(part, request.model.provider)),
-      })
+      input.push({ role: "user", content: yield* Effect.forEach(message.content, lowerUserContent) })
      continue
    }

@@ -491,15 +420,6 @@ const lowerMessages = Effect.fn("OpenAIResponses.lowerMessages")(function* (requ
          const itemID = hostedToolItemID(part)
          if (store !== false && itemID && !hostedToolReferences.has(itemID))
            input.push({ type: "item_reference", id: itemID })
-          if (store === false && part.name === "image_generation" && part.result.type === "content") {
-            const content: ReadonlyArray<ToolContent> = part.result.value
-            input.push({
-              role: "user",
-              content: yield* Effect.forEach(content, (item) =>
-                lowerToolResultContentItem(item, request.model.provider),
-              ),
-            })
-          }
          if (itemID) hostedToolReferences.add(itemID)
          continue
        }
@@ -520,7 +440,7 @@ const lowerMessages = Effect.fn("OpenAIResponses.lowerMessages")(function* (requ
      input.push({
        type: "function_call_output",
        call_id: part.id,
-        output: yield* lowerToolResultOutput(part, request.model.provider),
+        output: yield* lowerToolResultOutput(part),
      })
    }
  }
@@ -565,10 +485,10 @@ const fromRequest = Effect.fn("OpenAIResponses.fromRequest")(function* (request:
    tools:
      request.tools.length === 0
        ? undefined
-        : yield* Effect.forEach(request.tools, (tool) =>
+        : request.tools.map((tool) =>
            lowerTool(tool, ToolSchemaProjection.modelCompatibility(tool.inputSchema, toolSchemaCompatibility)),
          ),
-    tool_choice: request.toolChoice ? yield* lowerToolChoice(request.toolChoice, request.tools) : undefined,
+    tool_choice: request.toolChoice ? yield* lowerToolChoice(request.toolChoice) : undefined,
    stream: true as const,
    max_output_tokens: generation?.maxTokens,
    temperature: generation?.temperature,
@@ -654,29 +574,14 @@ const isReasoningItem = (

 // Round-trip the full item as the structured result so consumers can extract
 // outputs / sources / status without re-decoding.
-const hostedToolResult = Effect.fn("OpenAIResponses.hostedToolResult")(function* (item: OpenAIResponsesStreamItem) {
+const hostedToolResult = (item: OpenAIResponsesStreamItem) => {
  const isError = typeof item.error !== "undefined" && item.error !== null
-  if (item.type === "image_generation_call" && item.result) {
-    yield* Effect.fromResult(Encoding.decodeBase64(item.result)).pipe(
-      Effect.mapError(() => ProviderShared.eventError(ADAPTER, "OpenAI Responses returned invalid image base64")),
-    )
-    return {
-      type: "content" as const,
-      value: [
-        {
-          type: "file" as const,
-          uri: `data:image/${item.output_format ?? "png"};base64,${item.result}`,
-          mime: `image/${item.output_format ?? "png"}`,
-        },
-      ],
-    }
-  }
  return isError ? { type: "error" as const, value: item.error } : { type: "json" as const, value: item }
-})
+}

-const hostedToolEvents = Effect.fn("OpenAIResponses.hostedToolEvents")(function* (
+const hostedToolEvents = (
  item: OpenAIResponsesStreamItem & { type: HostedToolType; id: string },
-) {
+): ReadonlyArray<LLMEvent> => {
  const tool = HOSTED_TOOLS[item.type]
  const providerMetadata = openaiMetadata({ itemId: item.id })
  return [
@@ -690,12 +595,12 @@ const hostedToolEvents = Effect.fn("OpenAIResponses.hostedToolEvents")(function*
    LLMEvent.toolResult({
      id: item.id,
      name: tool.name,
-      result: yield* hostedToolResult(item),
+      result: hostedToolResult(item),
      providerExecuted: true,
      providerMetadata,
    }),
  ]
-})
+}

 type StepResult = readonly [ParserState, ReadonlyArray<LLMEvent>]

@@ -930,9 +835,7 @@ const onOutputItemDone = Effect.fn("OpenAIResponses.onOutputItemDone")(function*
      {
        ...state,
        lifecycle,
-        hasFunctionCall:
-          resultEvents.some((event) => LLMEvent.is.toolCall(event) || LLMEvent.is.toolInputError(event)) ||
-          state.hasFunctionCall,
+        hasFunctionCall: resultEvents.some(LLMEvent.is.toolCall) ? true : state.hasFunctionCall,
        tools: result.tools,
      },
      events,
@@ -942,7 +845,7 @@ const onOutputItemDone = Effect.fn("OpenAIResponses.onOutputItemDone")(function*
  if (isHostedToolItem(item)) {
    const events: LLMEvent[] = []
    const lifecycle = Lifecycle.stepStart(state.lifecycle, events)
-    events.push(...(yield* hostedToolEvents(item)))
+    events.push(...hostedToolEvents(item))
    return [{ ...state, lifecycle }, events] satisfies StepResult
  }

@@ -158,8 +158,7 @@ export const parseToolInput = (route: string, name: string, raw: string) =>
 export const IMAGE_MIMES = ["image/png", "image/jpeg", "image/gif", "image/webp"] as const
 export const VIDEO_MIMES = ["video/mp4", "video/webm", "video/quicktime"] as const
 export const AUDIO_MIMES = ["audio/wav", "audio/mp3", "audio/aiff", "audio/aac", "audio/ogg", "audio/flac"] as const
-export const PDF_MIMES = ["application/pdf"] as const
-export const MEDIA_MIMES = [...IMAGE_MIMES, ...VIDEO_MIMES, ...AUDIO_MIMES, ...PDF_MIMES] as const
+export const MEDIA_MIMES = [...IMAGE_MIMES, ...VIDEO_MIMES, ...AUDIO_MIMES] as const
 export const MAX_MEDIA_ENCODED_BYTES = 28 * 1024 * 1024
 export const MAX_MEDIA_DECODED_BYTES = 20 * 1024 * 1024

@@ -49,10 +49,10 @@ const DOCUMENT_FORMATS = {
  "text/markdown": "md",
 } as const satisfies Record<string, DocumentFormat>

-const documentBlock = (name: string, format: DocumentFormat, bytes: string): DocumentBlock => ({
+const documentBlock = (part: MediaPart, format: DocumentFormat, bytes: string): DocumentBlock => ({
  document: {
    format,
-    name,
+    name: part.filename ?? `document.${format}`,
    source: { bytes },
  },
 })
@@ -77,14 +77,12 @@ export const lower = Effect.fn("BedrockMedia.lower")(function* (part: MediaPart)
    return yield* ProviderShared.invalidRequest(`Bedrock Converse does not support image media type ${part.mediaType}`)
  const documentFormat = DOCUMENT_FORMATS[mime as keyof typeof DOCUMENT_FORMATS]
  if (documentFormat) {
-    if (!part.filename)
-      return yield* ProviderShared.invalidRequest("Bedrock Converse document media requires a filename")
    const media = yield* ProviderShared.validateMedia(
      "Bedrock Converse",
      part,
      new Set<string>(Object.keys(DOCUMENT_FORMATS)),
    )
-    return documentBlock(part.filename, documentFormat, media.base64)
+    return documentBlock(part, documentFormat, media.base64)
  }
  return yield* ProviderShared.invalidRequest(`Bedrock Converse does not support media type ${part.mediaType}`)
 })
@@ -1,34 +0,0 @@
-import { Effect, Encoding } from "effect"
-import type { ImageInput } from "../../image"
-import { InvalidRequestReason, LLMError } from "../../schema"
-
-const invalid = (module: string, message: string) =>
-  new LLMError({
-    module,
-    method: "generate",
-    reason: new InvalidRequestReason({ message }),
-  })
-
-export const dataUrl = (input: Extract<ImageInput, { readonly type: "bytes" }>) =>
-  `data:${input.mediaType};base64,${Encoding.encodeBase64(input.data)}`
-
-export const decodeDataUrl = (
-  url: string,
-  module: string,
-): Effect.Effect<{ readonly mediaType: string; readonly data: Uint8Array } | undefined, LLMError> => {
-  if (!url.startsWith("data:")) return Effect.succeed(undefined)
-  const match = /^data:([^;,]+);base64,(.*)$/s.exec(url)
-  if (!match) return Effect.fail(invalid(module, "Image data URLs must contain a MIME type and base64 data"))
-  return Effect.fromResult(Encoding.decodeBase64(match[2])).pipe(
-    Effect.mapError(() => invalid(module, "Image data URL contains invalid base64 data")),
-    Effect.map((data) => ({ mediaType: match[1], data })),
-  )
-}
-
-export const invalidImageInput = invalid
-
-export const ImageInputs = {
-  dataUrl,
-  decodeDataUrl,
-  invalid: invalidImageInput,
-} as const
@@ -44,7 +44,7 @@ export const reasoningDelta = (
  providerMetadata?: ProviderMetadata,
 ): State => {
  const started = reasoningStart(state, events, id, providerMetadata)
-  events.push(LLMEvent.reasoningDelta({ id, text, providerMetadata }))
+  events.push(LLMEvent.reasoningDelta({ id, text }))
  return started
 }

@@ -1,20 +0,0 @@
-import { Schema } from "effect"
-
-const dimensions = (value: string) => {
-  const match = /^(\d+)x(\d+)$/.exec(value)
-  if (!match) return undefined
-  return { width: Number(match[1]), height: Number(match[2]) }
-}
-
-export const Size = Schema.String.check(
-  Schema.makeFilter((value) => {
-    if (value === "auto") return undefined
-    const parsed = dimensions(value)
-    if (!parsed) return "image size must be `auto` or `{width}x{height}`"
-    return parsed.width > 0 && parsed.height > 0 ? undefined : "image dimensions must be positive integers"
-  }),
-)
-
-export const OpenAIImage = {
-  Size,
-} as const
@@ -1,5 +1,5 @@
 import { Effect } from "effect"
-import { LLMError, LLMEvent, type ProviderMetadata, type ToolCall, type ToolInputError } from "../../schema"
+import { LLMError, LLMEvent, type ProviderMetadata, type ToolCall } from "../../schema"
 import { eventError, parseToolInput, type ToolAccumulator } from "../shared"

 type StreamKey = string | number
@@ -53,7 +53,6 @@ const inputStart = (tool: PendingTool) =>
  LLMEvent.toolInputStart({
    id: tool.id,
    name: tool.name,
-    providerExecuted: tool.providerExecuted ? true : undefined,
    providerMetadata: tool.providerMetadata,
  })

@@ -64,36 +63,19 @@ const inputDelta = (tool: PendingTool, text: string) =>
    text,
  })

-const toolCall = (route: string, tool: PendingTool, inputOverride?: string) => {
-  const raw = inputOverride ?? tool.input
-  return parseToolInput(route, tool.name, raw).pipe(
-    Effect.map((input): ToolCall | ToolInputError =>
-      LLMEvent.toolCall({
-        id: tool.id,
-        name: tool.name,
-        input,
-        providerExecuted: tool.providerExecuted ? true : undefined,
-        providerMetadata: tool.providerMetadata,
-      }),
-    ),
-    Effect.catch((error) =>
-      tool.providerExecuted
-        ? Effect.fail(error)
-        : Effect.succeed(
-            LLMEvent.toolInputError({
-              id: tool.id,
-              name: tool.name,
-              raw,
-            }),
-          ),
+const toolCall = (route: string, tool: PendingTool, inputOverride?: string) =>
+  parseToolInput(route, tool.name, inputOverride ?? tool.input).pipe(
+    Effect.map(
+      (input): ToolCall =>
+        LLMEvent.toolCall({
+          id: tool.id,
+          name: tool.name,
+          input,
+          providerExecuted: tool.providerExecuted ? true : undefined,
+          providerMetadata: tool.providerMetadata,
+        }),
    ),
  )
-}
-
-const finishEvents = (tool: PendingTool, event: ToolCall | ToolInputError): ReadonlyArray<LLMEvent> =>
-  event.type === "tool-input-error"
-    ? [event]
-    : [LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }), event]

 /** Store the updated tool and produce the optional public delta event. */
 const appendTool = <K extends StreamKey>(
@@ -140,8 +122,8 @@ export const appendOrStart = <K extends StreamKey>(
  missingToolMessage: string,
 ): AppendOutcome<K> | LLMError => {
  const current = tools[key]
-  const id = current?.id ?? delta.id
-  const name = current?.name ?? delta.name
+  const id = delta.id ?? current?.id
+  const name = delta.name ?? current?.name
  if (!id || !name) return eventError(route, missingToolMessage)

  const tool = {
@@ -176,9 +158,8 @@ export const appendExisting = <K extends StreamKey>(

 /**
 * Finalize one pending tool call: parse the accumulated raw JSON, remove it
- * from state, and return either a call or a non-executable local input error.
- * Missing keys are a no-op because some providers emit stop events for
- * non-tool content blocks.
+ * from state, and return the optional public `tool-call` event. Missing keys are
+ * a no-op because some providers emit stop events for non-tool content blocks.
 */
 export const finish = <K extends StreamKey>(route: string, tools: State<K>, key: K) =>
  Effect.gen(function* () {
@@ -186,7 +167,10 @@ export const finish = <K extends StreamKey>(route: string, tools: State<K>, key:
    if (!tool) return { tools }
    return {
      tools: withoutTool(tools, key),
-      events: finishEvents(tool, yield* toolCall(route, tool)),
+      events: [
+        LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }),
+        yield* toolCall(route, tool),
+      ],
    }
  })

@@ -201,14 +185,17 @@ export const finishWithInput = <K extends StreamKey>(route: string, tools: State
    if (!tool) return { tools }
    return {
      tools: withoutTool(tools, key),
-      events: finishEvents(tool, yield* toolCall(route, tool, input)),
+      events: [
+        LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }),
+        yield* toolCall(route, tool, input),
+      ],
    }
  })

 /**
 * Finalize every pending tool call at once. OpenAI Chat has this shape: it does
- * not emit per-tool stop events, so all accumulated calls finish independently
- * when the choice receives a terminal `finish_reason`.
+ * not emit per-tool stop events, so all accumulated calls finish when the choice
+ * receives a terminal `finish_reason`.
 */
 export const finishAll = <K extends StreamKey>(route: string, tools: State<K>) =>
  Effect.gen(function* () {
@@ -218,7 +205,12 @@ export const finishAll = <K extends StreamKey>(route: string, tools: State<K>) =
    return {
      tools: empty<K>(),
      events: yield* Effect.forEach(pending, (tool) =>
-        toolCall(route, tool).pipe(Effect.map((event) => finishEvents(tool, event))),
+        toolCall(route, tool).pipe(
+          Effect.map((call) => [
+            LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }),
+            call,
+          ]),
+        ),
      ).pipe(Effect.map((events) => events.flat())),
    }
  })
@@ -1,202 +0,0 @@
-import { Effect, Encoding, Schema } from "effect"
-import { Headers, HttpClientRequest } from "effect/unstable/http"
-import { GeneratedImage, ImageModel, ImageResponse, type ImageRequestFor, type ImageRoute } from "../image"
-import { Auth, type Definition as AuthDefinition } from "../route/auth"
-import {
-  InvalidProviderOutputReason,
-  LLMError,
-  Usage,
-  mergeHttpOptions,
-  mergeJsonRecords,
-  type HttpOptions,
-} from "../schema"
-import { ProviderShared, optionalNull } from "./shared"
-import { ImageInputs } from "./utils/image-input"
-
-const ADAPTER = "xai-images"
-export const DEFAULT_BASE_URL = "https://api.x.ai/v1"
-export const PATH = "/images/generations"
-export const EDIT_PATH = "/images/edits"
-
-export type XAIImageString<Known extends string> = Known | (string & {})
-
-export type XAIImageOptions = {
-  readonly n?: number
-  readonly aspectRatio?: XAIImageString<
-    | "1:1"
-    | "3:4"
-    | "4:3"
-    | "9:16"
-    | "16:9"
-    | "2:3"
-    | "3:2"
-    | "9:19.5"
-    | "19.5:9"
-    | "9:20"
-    | "20:9"
-    | "1:2"
-    | "2:1"
-    | "auto"
-  >
-  readonly aspect_ratio?: XAIImageString<
-    | "1:1"
-    | "3:4"
-    | "4:3"
-    | "9:16"
-    | "16:9"
-    | "2:3"
-    | "3:2"
-    | "9:19.5"
-    | "19.5:9"
-    | "9:20"
-    | "20:9"
-    | "1:2"
-    | "2:1"
-    | "auto"
-  >
-  readonly resolution?: XAIImageString<"1k" | "2k">
-  readonly responseFormat?: XAIImageString<"url" | "b64_json">
-  readonly response_format?: XAIImageString<"url" | "b64_json">
-} & Record<string, unknown>
-
-type XAIImageBody = Record<string, unknown> & {
-  readonly model: string
-  readonly prompt: string
-}
-
-const XAIImageResponse = Schema.Struct({
-  data: Schema.Array(
-    Schema.Struct({
-      b64_json: optionalNull(Schema.String),
-      url: optionalNull(Schema.String),
-      revised_prompt: optionalNull(Schema.String),
-      mime_type: optionalNull(Schema.String),
-    }),
-  ),
-  usage: Schema.optional(Schema.Unknown),
-})
-
-export interface ModelInput {
-  readonly id: string
-  readonly auth: AuthDefinition
-  readonly baseURL?: string
-  readonly headers?: Record<string, string>
-  readonly http?: HttpOptions
-}
-
-const nativeOptions = (options: XAIImageOptions | undefined) => {
-  if (!options) return undefined
-  const { aspectRatio, responseFormat, ...native } = options
-  return {
-    aspect_ratio: aspectRatio,
-    response_format: responseFormat,
-    ...native,
-  }
-}
-
-const invalidOutput = (message: string) =>
-  new LLMError({
-    module: ADAPTER,
-    method: "generate",
-    reason: new InvalidProviderOutputReason({ message, route: ADAPTER }),
-  })
-
-const applyQuery = (url: string, query: Record<string, string> | undefined) => {
-  if (!query) return url
-  const next = new URL(url)
-  Object.entries(query).forEach(([key, value]) => next.searchParams.set(key, value))
-  return next.toString()
-}
-
-export const model = (input: ModelInput) => {
-  const route: ImageRoute<XAIImageOptions> = {
-    id: ADAPTER,
-    generate: Effect.fn("XAIImages.generate")(function* (request: ImageRequestFor<XAIImageOptions>, execute) {
-      const http = mergeHttpOptions(request.model.http, request.http)
-      const imageReferences = (request.images ?? []).map((image) => {
-        if (image.type === "bytes") return { url: ImageInputs.dataUrl(image), type: "image_url" as const }
-        if (image.type === "url") return { url: image.url, type: "image_url" as const }
-        if (image.type === "file-id") return { file_id: image.id }
-        return undefined
-      })
-      if (imageReferences.some((image) => image === undefined))
-        return yield* ImageInputs.invalid(ADAPTER, "xAI Images accepts image URLs, data URLs, bytes, and file IDs")
-      const requestBody = mergeJsonRecords(
-        {
-          model: request.model.id,
-          prompt: request.prompt,
-          image: imageReferences.length === 1 ? imageReferences[0] : undefined,
-          images: imageReferences.length > 1 ? imageReferences : undefined,
-        },
-        nativeOptions(request.options),
-        http?.body,
-      ) as XAIImageBody
-      const text = ProviderShared.encodeJson(requestBody)
-      const url = applyQuery(
-        `${(input.baseURL ?? DEFAULT_BASE_URL).replace(/\/$/, "")}${imageReferences.length === 0 ? PATH : EDIT_PATH}`,
-        http?.query,
-      )
-      const headers = yield* Auth.toEffect(input.auth)({
-        request,
-        method: "POST",
-        url,
-        body: text,
-        headers: Headers.fromInput({ ...input.headers, ...http?.headers }),
-      })
-      const response = yield* execute(
-        HttpClientRequest.post(url).pipe(
-          HttpClientRequest.setHeaders(headers),
-          HttpClientRequest.bodyText(text, "application/json"),
-        ),
-      )
-      const payload = yield* response.json.pipe(
-        Effect.mapError(() => invalidOutput("Failed to read the xAI Images response")),
-      )
-      const decoded = yield* Schema.decodeUnknownEffect(XAIImageResponse)(payload).pipe(
-        Effect.mapError(() => invalidOutput("xAI Images returned an invalid response")),
-      )
-      const images = yield* Effect.forEach(decoded.data, (item, index) => {
-        const mediaType = item.mime_type ?? "application/octet-stream"
-        if (item.b64_json)
-          return Effect.fromResult(Encoding.decodeBase64(item.b64_json)).pipe(
-            Effect.mapError(() => invalidOutput(`xAI Images result ${index} contains invalid base64 data`)),
-            Effect.map(
-              (data) =>
-                new GeneratedImage({
-                  mediaType,
-                  data,
-                  providerMetadata:
-                    item.revised_prompt === undefined || item.revised_prompt === null
-                      ? undefined
-                      : { xai: { revisedPrompt: item.revised_prompt } },
-                }),
-            ),
-          )
-        if (item.url)
-          return Effect.succeed(
-            new GeneratedImage({
-              mediaType,
-              data: item.url,
-              providerMetadata:
-                item.revised_prompt === undefined || item.revised_prompt === null
-                  ? undefined
-                  : { xai: { revisedPrompt: item.revised_prompt } },
-            }),
-          )
-        return Effect.fail(invalidOutput(`xAI Images result ${index} has neither image data nor a URL`))
-      })
-      if (images.length === 0) return yield* invalidOutput("xAI Images returned no images")
-      const usage = ProviderShared.isRecord(decoded.usage) ? decoded.usage : undefined
-      return new ImageResponse({
-        images,
-        usage: usage === undefined ? undefined : new Usage({ providerMetadata: { xai: usage } }),
-        providerMetadata: usage === undefined ? undefined : { xai: { usage } },
-      })
-    }),
-  }
-  return ImageModel.make<XAIImageOptions>({ id: input.id, provider: "xai", route, http: input.http })
-}
-
-export const XAIImages = {
-  model,
-} as const
@@ -1,132 +0,0 @@
-import { Effect, Schema } from "effect"
-import { Headers, HttpClientRequest } from "effect/unstable/http"
-import { GeneratedImage, ImageModel, ImageResponse, type ImageRequestFor, type ImageRoute } from "../image"
-import { Auth, type Definition as AuthDefinition } from "../route/auth"
-import { InvalidProviderOutputReason, LLMError, mergeHttpOptions, mergeJsonRecords, type HttpOptions } from "../schema"
-import { ProviderShared } from "./shared"
-import { ImageInputs } from "./utils/image-input"
-
-const ADAPTER = "zai-images"
-export const DEFAULT_BASE_URL = "https://api.z.ai/api/paas/v4"
-export const PATH = "/images/generations"
-
-export type ZAIImageString<Known extends string> = Known | (string & {})
-
-export type ZAIImageOptions = {
-  readonly size?: ZAIImageString<
-    "1024x1024" | "768x1344" | "864x1152" | "1344x768" | "1152x864" | "1440x720" | "720x1440"
-  >
-  readonly quality?: ZAIImageString<"hd" | "standard">
-  readonly userID?: string
-} & Record<string, unknown>
-
-type ZAIImageBody = Record<string, unknown> & {
-  readonly model: string
-  readonly prompt: string
-}
-
-const ZAIImageResponse = Schema.Struct({
-  created: Schema.optional(Schema.Int),
-  id: Schema.optional(Schema.String),
-  request_id: Schema.optional(Schema.String),
-  data: Schema.Array(Schema.Struct({ url: Schema.String })),
-  content_filter: Schema.optional(
-    Schema.Array(
-      Schema.Struct({
-        role: Schema.optional(Schema.String),
-        level: Schema.optional(Schema.Number),
-      }),
-    ),
-  ),
-})
-
-export interface ModelInput {
-  readonly id: string
-  readonly auth: AuthDefinition
-  readonly baseURL?: string
-  readonly headers?: Record<string, string>
-  readonly http?: HttpOptions
-}
-
-const nativeOptions = (options: ZAIImageOptions | undefined) => {
-  if (!options) return undefined
-  const { userID, ...native } = options
-  return {
-    user_id: userID,
-    ...native,
-  }
-}
-
-const invalidOutput = (message: string) =>
-  new LLMError({
-    module: ADAPTER,
-    method: "generate",
-    reason: new InvalidProviderOutputReason({ message, route: ADAPTER }),
-  })
-
-const applyQuery = (url: string, query: Record<string, string> | undefined) => {
-  if (!query) return url
-  const next = new URL(url)
-  Object.entries(query).forEach(([key, value]) => next.searchParams.set(key, value))
-  return next.toString()
-}
-
-export const model = (input: ModelInput) => {
-  const route: ImageRoute<ZAIImageOptions> = {
-    id: ADAPTER,
-    generate: Effect.fn("ZAIImages.generate")(function* (request: ImageRequestFor<ZAIImageOptions>, execute) {
-      if ((request.images?.length ?? 0) > 0)
-        return yield* ImageInputs.invalid(ADAPTER, "Z.ai hosted image generation does not support image inputs")
-      const http = mergeHttpOptions(request.model.http, request.http)
-      const requestBody = mergeJsonRecords(
-        { model: request.model.id, prompt: request.prompt },
-        nativeOptions(request.options),
-        http?.body,
-      ) as ZAIImageBody
-      const text = ProviderShared.encodeJson(requestBody)
-      const url = applyQuery(`${(input.baseURL ?? DEFAULT_BASE_URL).replace(/\/$/, "")}${PATH}`, http?.query)
-      const headers = yield* Auth.toEffect(input.auth)({
-        request,
-        method: "POST",
-        url,
-        body: text,
-        headers: Headers.fromInput({ ...input.headers, ...http?.headers }),
-      })
-      const response = yield* execute(
-        HttpClientRequest.post(url).pipe(
-          HttpClientRequest.setHeaders(headers),
-          HttpClientRequest.bodyText(text, "application/json"),
-        ),
-      )
-      const payload = yield* response.json.pipe(
-        Effect.mapError(() => invalidOutput("Failed to read the Z.ai Images response")),
-      )
-      const decoded = yield* Schema.decodeUnknownEffect(ZAIImageResponse)(payload).pipe(
-        Effect.mapError(() => invalidOutput("Z.ai Images returned an invalid response")),
-      )
-      if (decoded.data.length === 0) return yield* invalidOutput("Z.ai Images returned no images")
-      return new ImageResponse({
-        images: decoded.data.map(
-          (item) =>
-            new GeneratedImage({
-              mediaType: "application/octet-stream",
-              data: item.url,
-            }),
-        ),
-        providerMetadata: {
-          zai: {
-            created: decoded.created,
-            id: decoded.id,
-            requestID: decoded.request_id,
-            contentFilter: decoded.content_filter,
-          },
-        },
-      })
-    }),
-  }
-  return ImageModel.make<ZAIImageOptions>({ id: input.id, provider: "zai", route, http: input.http })
-}
-
-export const ZAIImages = {
-  model,
-} as const
@@ -16,17 +16,13 @@ import {

 const patterns = [
  /prompt is too long/i,
-  /request_too_large/i,
  /input is too long for requested model/i,
  /exceeds the context window/i,
-  /exceeds (?:the )?(?:model'?s )?maximum context length(?: of [\d,]+ tokens?|\s*\([\d,]+\))/i,
  /input token count.*exceeds the maximum/i,
  /tokens in request more than max tokens allowed/i,
  /maximum prompt length is \d+/i,
  /reduce the length of the messages/i,
  /maximum context length is \d+ tokens/i,
-  /exceeds (?:the )?maximum allowed input length of [\d,]+ tokens?/i,
-  /input \(\d+ tokens\) is longer than the model'?s context length \(\d+ tokens\)/i,
  /exceeds the limit of \d+/i,
  /exceeds the available context size/i,
  /greater than the context length/i,
@@ -38,17 +34,11 @@ const patterns = [
  /input length.*exceeds.*context length/i,
  /prompt too long; exceeded (?:max )?context length/i,
  /too large for model with \d+ maximum context length/i,
-  /prompt has [\d,]+ tokens?, but the configured context size is [\d,]+ tokens?/i,
  /model_context_window_exceeded/i,
-  /too many tokens/i,
-  /token limit exceeded/i,
 ]

-const exclusions = [/^(throttling error|service unavailable):/i, /rate limit/i, /too many requests/i]
-
 export const isContextOverflow = (message: string) =>
-  !exclusions.some((pattern) => pattern.test(message)) &&
-  (patterns.some((pattern) => pattern.test(message)) || /^4(00|13)\s*(status code)?\s*\(no body\)/i.test(message))
+  patterns.some((pattern) => pattern.test(message)) || /^4(00|13)\s*(status code)?\s*\(no body\)/i.test(message)

 export const isContextOverflowFailure = (failure: unknown) =>
  failure instanceof LLMError
@@ -2,20 +2,14 @@ import type { RouteDefaultsInput } from "../route/client"
 import { Auth } from "../route/auth"
 import type { ProviderAuthOption } from "../route/auth-options"
 import type { ProviderPackage } from "../provider-package"
-import { HttpOptions, ProviderID, mergeHttpOptions, type ModelID, type ProviderOptions } from "../schema"
-import { Gemini } from "../protocols/gemini"
-import { GoogleImages } from "../protocols/google-images"
-
-export type { GoogleImageOptions } from "../protocols/google-images"
+import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
+import * as Gemini from "../protocols/gemini"

 export const id = ProviderID.make("google")

 export const routes = [Gemini.route]

-export type Config = RouteDefaultsInput &
-  ProviderAuthOption<"optional"> & {
-    readonly baseURL?: string
-  }
+export type Config = RouteDefaultsInput & ProviderAuthOption<"optional"> & { readonly baseURL?: string }

 export interface Settings extends ProviderPackage.Settings {
  readonly apiKey?: string
@@ -37,18 +31,9 @@ const configuredRoute = (input: Config) => {

 export const configure = (input: Config = {}) => {
  const route = configuredRoute(input)
-  const image = (modelID: string | ModelID) =>
-    GoogleImages.model({
-      id: modelID,
-      auth: auth(input),
-      baseURL: input.baseURL,
-      headers: input.headers,
-      http: mergeHttpOptions(input.http === undefined ? undefined : HttpOptions.make(input.http)),
-    })
  return {
    id,
    model: (modelID: string | ModelID) => route.model({ id: modelID }),
-    image,
    configure,
  }
 }
@@ -63,5 +48,3 @@ export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, se
    limits: settings.limits,
    providerOptions: settings.providerOptions,
  }).model(modelID)
-
-export const image = provider.image
@@ -15,4 +15,3 @@ export * as OpenAICompatible from "./openai-compatible"
 export * as OpenAICompatibleResponses from "./openai-compatible-responses"
 export * as OpenRouter from "./openrouter"
 export * as XAI from "./xai"
-export * as ZAI from "./zai"
@@ -1,14 +1,12 @@
 import { AuthOptions, type ProviderAuthOption } from "../route/auth-options"
 import type { Route, RouteDefaultsInput } from "../route/client"
 import type { ProviderPackage } from "../provider-package"
-import { HttpOptions, ProviderID, ToolDefinition, mergeHttpOptions, type ModelID } from "../schema"
+import { ProviderID, type ModelID } from "../schema"
 import * as OpenAIChat from "../protocols/openai-chat"
 import * as OpenAIResponses from "../protocols/openai-responses"
 import { withOpenAIOptions, type OpenAIProviderOptionsInput } from "./openai-options"
-import { OpenAIImages, type OpenAIImageString } from "../protocols/openai-images"

 export type { OpenAIOptionsInput, OpenAIResponseIncludable } from "./openai-options"
-export type { OpenAIImageOptions } from "../protocols/openai-images"

 export const id = ProviderID.make("openai")

@@ -24,39 +22,6 @@ export type Config = RouteDefaultsInput &
    readonly providerOptions?: OpenAIProviderOptionsInput
  }

-export interface ImageGenerationOptions {
-  readonly action?: OpenAIImageString<"auto" | "generate" | "edit">
-  readonly background?: OpenAIImageString<"auto" | "opaque" | "transparent">
-  readonly inputFidelity?: OpenAIImageString<"low" | "high">
-  readonly outputCompression?: number
-  readonly outputFormat?: OpenAIImageString<"png" | "jpeg" | "webp">
-  readonly partialImages?: number
-  readonly quality?: OpenAIImageString<"auto" | "low" | "medium" | "high" | "standard" | "hd">
-  readonly size?: OpenAIImageString<
-    "auto" | "256x256" | "512x512" | "1024x1024" | "1536x1024" | "1024x1536" | "1792x1024" | "1024x1792"
-  >
-}
-
-export const imageGeneration = (options: ImageGenerationOptions = {}) =>
-  ToolDefinition.make({
-    name: "image_generation",
-    description: "Generate or edit an image using OpenAI's hosted image generation tool.",
-    inputSchema: { type: "object", properties: {}, additionalProperties: false },
-    native: {
-      openai: {
-        type: "image_generation",
-        action: options.action,
-        background: options.background,
-        input_fidelity: options.inputFidelity,
-        output_compression: options.outputCompression,
-        output_format: options.outputFormat,
-        partial_images: options.partialImages,
-        quality: options.quality,
-        size: options.size,
-      },
-    },
-  })
-
 export interface Settings extends ProviderPackage.Settings {
  readonly apiKey?: string
  readonly baseURL?: string
@@ -90,17 +55,6 @@ export const configure = (input: Config = {}) => {
  const responsesWebSocket = (id: string | ModelID) =>
    responsesWebSocketRoute.with(withOpenAIOptions(id, modelDefaults, { textVerbosity: true })).model({ id })
  const chat = (id: string | ModelID) => chatRoute.with(withOpenAIOptions(id, modelDefaults)).model({ id })
-  const image = (modelID: string | ModelID) =>
-    OpenAIImages.model({
-      id: modelID,
-      auth: auth(input),
-      baseURL: input.baseURL,
-      headers: input.headers,
-      http: mergeHttpOptions(
-        input.http === undefined ? undefined : HttpOptions.make(input.http),
-        input.queryParams === undefined ? undefined : new HttpOptions({ query: input.queryParams }),
-      ),
-    })

  return {
    id,
@@ -108,7 +62,6 @@ export const configure = (input: Config = {}) => {
    responses,
    responsesWebSocket,
    chat,
-    image,
    configure,
  }
 }
@@ -144,4 +97,3 @@ export const chatModel: ProviderPackage.Definition<Settings>["model"] = (modelID
 export const responses = provider.responses
 export const responsesWebSocket = provider.responsesWebSocket
 export const chat = provider.chat
-export const image = provider.image
@@ -41,31 +41,13 @@ export const protocol = Protocol.make({
    schema: OpenRouterBody,
    from: (request) =>
      OpenAIChat.protocol.body.from(request).pipe(
-        Effect.map((body) => {
-          const sourceAssistants = request.messages.filter((message) => message.role === "assistant")
-          let assistantIndex = 0
-          const messages = body.messages.map((message) => {
-            if (message.role !== "assistant") return message
-            const source = sourceAssistants[assistantIndex++]
-            const reasoning = source?.content
-              .filter((part) => part.type === "reasoning")
-              .map((part) => part.text)
-              .join("")
-            const reasoningDetails = Array.isArray(message.reasoning_details) ? message.reasoning_details : undefined
-            return {
-              ...message,
-              reasoning_content: undefined,
-              reasoning_text: undefined,
-              reasoning: reasoning && reasoningDetails && reasoningDetails.length > 0 ? reasoning : undefined,
-              reasoning_details: reasoningDetails,
-            }
-          })
-          return {
-            ...body,
-            messages,
-            ...bodyOptions(request.providerOptions?.openrouter),
-          } as OpenRouterBody
-        }),
+        Effect.map(
+          (body) =>
+            ({
+              ...body,
+              ...bodyOptions(request.providerOptions?.openrouter),
+            }) as OpenRouterBody,
+        ),
      ),
  },
  stream: OpenAIChat.protocol.stream,
@@ -1,10 +1,9 @@
 import { AuthOptions, type ProviderAuthOption } from "../route/auth-options"
 import type { RouteDefaultsInput } from "../route/client"
-import { HttpOptions, ProviderID, type ModelID } from "../schema"
+import { ProviderID, type ModelID } from "../schema"
 import * as OpenAICompatibleProfiles from "./openai-compatible-profile"
 import * as OpenAICompatibleChat from "../protocols/openai-compatible-chat"
 import * as OpenAIResponses from "../protocols/openai-responses"
-import { XAIImages } from "../protocols/xai-images"

 export const id = ProviderID.make("xai")

@@ -13,8 +12,6 @@ export type ModelOptions = RouteDefaultsInput &
    readonly baseURL?: string
  }

-export type { XAIImageOptions } from "../protocols/xai-images"
-
 export const routes = [OpenAIResponses.route, OpenAICompatibleChat.route]

 const auth = (options: ProviderAuthOption<"optional">) => AuthOptions.bearer(options, "XAI_API_KEY")
@@ -44,20 +41,11 @@ export const configure = (input: ModelOptions = {}) => {
  const chatRoute = configuredChatRoute(input)
  const responses = (modelID: string | ModelID) => responsesRoute.model({ id: modelID })
  const chat = (modelID: string | ModelID) => chatRoute.model({ id: modelID })
-  const image = (modelID: string | ModelID) =>
-    XAIImages.model({
-      id: modelID,
-      auth: auth(input),
-      baseURL: input.baseURL ?? OpenAICompatibleProfiles.profiles.xai.baseURL,
-      headers: input.headers,
-      http: input.http === undefined ? undefined : HttpOptions.make(input.http),
-    })
  return {
    id,
    model: responses,
    responses,
    chat,
-    image,
    configure,
  }
 }
@@ -66,4 +54,3 @@ export const provider = configure()
 export const model = provider.model
 export const responses = provider.responses
 export const chat = provider.chat
-export const image = provider.image
@@ -1,35 +0,0 @@
-import { ZAIImages } from "../protocols/zai-images"
-import { AuthOptions, type ProviderAuthOption } from "../route/auth-options"
-import { HttpOptions, ProviderID, type ModelID } from "../schema"
-
-export const id = ProviderID.make("zai")
-
-export type Config = ProviderAuthOption<"optional"> & {
-  readonly baseURL?: string
-  readonly headers?: Record<string, string>
-  readonly http?: HttpOptions.Input
-}
-
-export type { ZAIImageOptions } from "../protocols/zai-images"
-
-const auth = (options: ProviderAuthOption<"optional">) => AuthOptions.bearer(options, "ZAI_API_KEY")
-
-export const configure = (input: Config = {}) => {
-  const image = (modelID: string | ModelID) =>
-    ZAIImages.model({
-      id: modelID,
-      auth: auth(input),
-      baseURL: input.baseURL,
-      headers: input.headers,
-      http: input.http === undefined ? undefined : HttpOptions.make(input.http),
-    })
-
-  return {
-    id,
-    image,
-    configure,
-  }
-}
-
-export const provider = configure()
-export const image = provider.image
@@ -1,6 +1,6 @@
 import { Config, Effect, Redacted } from "effect"
 import { Headers } from "effect/unstable/http"
-import { AuthenticationReason, InvalidRequestReason, LLMError, type HttpOptions } from "../schema"
+import { AuthenticationReason, InvalidRequestReason, LLMError, type LLMRequest } from "../schema"

 export class MissingCredentialError extends Error {
  readonly _tag = "MissingCredentialError"
@@ -15,7 +15,7 @@ export type AuthError = CredentialError | LLMError
 type Secret = string | Redacted.Redacted | Config.Config<string | Redacted.Redacted>

 export interface AuthInput {
-  readonly request: { readonly http?: HttpOptions }
+  readonly request: LLMRequest
  readonly method: "POST" | "GET"
  readonly url: string
  readonly body: string
@@ -129,7 +129,6 @@ export const ToolInputStart = Schema.Struct({
  type: Schema.tag("tool-input-start"),
  id: ToolCallID,
  name: Schema.String,
-  providerExecuted: Schema.optional(Schema.Boolean),
  providerMetadata: Schema.optional(ProviderMetadata),
 }).annotate({ identifier: "LLM.Event.ToolInputStart" })
 export type ToolInputStart = Schema.Schema.Type<typeof ToolInputStart>
@@ -150,15 +149,6 @@ export const ToolInputEnd = Schema.Struct({
 }).annotate({ identifier: "LLM.Event.ToolInputEnd" })
 export type ToolInputEnd = Schema.Schema.Type<typeof ToolInputEnd>

-/** A local tool call whose final input could not be decoded. */
-export const ToolInputError = Schema.Struct({
-  type: Schema.tag("tool-input-error"),
-  id: ToolCallID,
-  name: Schema.String,
-  raw: Schema.String,
-}).annotate({ identifier: "LLM.Event.ToolInputError" })
-export type ToolInputError = Schema.Schema.Type<typeof ToolInputError>
-
 export const ToolCall = Schema.Struct({
  type: Schema.tag("tool-call"),
  id: ToolCallID,
@@ -226,7 +216,6 @@ const llmEventTagged = Schema.Union([
  ToolInputStart,
  ToolInputDelta,
  ToolInputEnd,
-  ToolInputError,
  ToolCall,
  ToolResult,
  ToolError,
@@ -264,8 +253,6 @@ export const LLMEvent = Object.assign(llmEventTagged, {
  toolInputDelta: (input: WithID<ToolInputDelta, ToolCallID>) =>
    ToolInputDelta.make({ ...input, id: toolCallID(input.id) }),
  toolInputEnd: (input: WithID<ToolInputEnd, ToolCallID>) => ToolInputEnd.make({ ...input, id: toolCallID(input.id) }),
-  toolInputError: (input: WithID<ToolInputError, ToolCallID>) =>
-    ToolInputError.make({ ...input, id: toolCallID(input.id) }),
  toolCall: (input: WithID<ToolCall, ToolCallID>) => ToolCall.make({ ...input, id: toolCallID(input.id) }),
  toolResult: (input: WithID<ToolResult, ToolCallID>) =>
    ToolResult.make({
@@ -296,7 +283,6 @@ export const LLMEvent = Object.assign(llmEventTagged, {
    toolInputStart: llmEventTagged.guards["tool-input-start"],
    toolInputDelta: llmEventTagged.guards["tool-input-delta"],
    toolInputEnd: llmEventTagged.guards["tool-input-end"],
-    toolInputError: llmEventTagged.guards["tool-input-error"],
    toolCall: llmEventTagged.guards["tool-call"],
    toolResult: llmEventTagged.guards["tool-result"],
    toolError: llmEventTagged.guards["tool-error"],
@@ -562,10 +548,6 @@ const reduceResponseState = (state: ResponseState, event: LLMEvent): ResponseSta
      return reduceToolInputDelta(next, event)
    case "tool-input-end":
      return reduceToolInputEnd(next, event)
-    case "tool-input-error": {
-      const { [event.id]: _finished, ...toolInputs } = next.toolInputs
-      return { ...next, toolInputs }
-    }
    case "tool-call":
      return reduceToolCall(next, event)
    case "tool-result":
@@ -168,7 +168,6 @@ export type ModelToolSchemaCompatibility = Schema.Schema.Type<typeof ModelToolSc

 export class ModelCompatibility extends Schema.Class<ModelCompatibility>("LLM.ModelCompatibility")({
  toolSchema: Schema.optional(ModelToolSchemaCompatibility),
-  reasoningField: Schema.optional(Schema.String),
 }) {}

 export namespace ModelCompatibility {
@@ -68,29 +68,10 @@ const result = (call: ToolCallPart, value: ToolResultValueType | ToolSettlement,
    events:
      settlement.result.type === "error"
        ? [
-            LLMEvent.toolError({
-              id: call.id,
-              name: call.name,
-              message: String(settlement.result.value),
-              error,
-              providerMetadata: call.providerMetadata,
-            }),
-            LLMEvent.toolResult({
-              id: call.id,
-              name: call.name,
-              result: settlement.result,
-              providerMetadata: call.providerMetadata,
-            }),
+            LLMEvent.toolError({ id: call.id, name: call.name, message: String(settlement.result.value), error }),
+            LLMEvent.toolResult({ id: call.id, name: call.name, result: settlement.result }),
          ]
-        : [
-            LLMEvent.toolResult({
-              id: call.id,
-              name: call.name,
-              result: settlement.result,
-              output: settlement.output,
-              providerMetadata: call.providerMetadata,
-            }),
-          ],
+        : [LLMEvent.toolResult({ id: call.id, name: call.name, result: settlement.result, output: settlement.output })],
  }
 }

@@ -1,6 +1,7 @@
 import { Config } from "effect"
-import { Auth } from "../src/route"
+import type { Auth } from "../src/route/auth"
 import type { ModelFactory } from "../src/route/auth-options"
+import { Auth as RuntimeAuth } from "../src/route/auth"
 import * as OpenAIChat from "../src/protocols/openai-chat"
 import * as AmazonBedrock from "../src/providers/amazon-bedrock"
 import * as Anthropic from "../src/providers/anthropic"
@@ -27,7 +28,7 @@ type Model = {
  readonly id: string
 }

-declare const auth: Auth.Definition
+declare const auth: Auth
 declare const optionalAuthModel: ModelFactory<BaseOptions, "optional", Model>
 declare const requiredAuthModel: ModelFactory<BaseOptions, "required", Model>
 const configApiKey = Config.redacted("OPENAI_API_KEY")
@@ -75,9 +76,9 @@ OpenAI.responses("gpt-4.1-mini")
 OpenAI.configure({}).responses("gpt-4.1-mini")
 OpenAI.configure({ apiKey: "sk-test" }).responses("gpt-4.1-mini")
 OpenAI.configure({ apiKey: configApiKey }).responses("gpt-4.1-mini")
-OpenAI.configure({ auth: Auth.bearer("oauth-token") }).responses("gpt-4.1-mini")
+OpenAI.configure({ auth: RuntimeAuth.bearer("oauth-token") }).responses("gpt-4.1-mini")
 OpenAI.configure({
-  auth: Auth.headers({ authorization: "Bearer gateway" }),
+  auth: RuntimeAuth.headers({ authorization: "Bearer gateway" }),
  baseURL: "https://gateway.example.com/v1",
 }).responses("gpt-4.1-mini")
 OpenAI.configure({
@@ -101,40 +102,40 @@ OpenAI.configure({ generation: { maxTokens: "many" } })
 OpenAI.configure({ providerOptions: { openai: { store: "false" } } })

 // @ts-expect-error auth is an override, so OpenAI rejects apiKey with auth.
-OpenAI.configure({ apiKey: "sk-test", auth: Auth.bearer("oauth-token") })
+OpenAI.configure({ apiKey: "sk-test", auth: RuntimeAuth.bearer("oauth-token") })

 OpenAI.chat("gpt-4.1-mini")
 OpenAI.configure({ apiKey: "sk-test" }).chat("gpt-4.1-mini")
 OpenAI.configure({ apiKey: configApiKey }).chat("gpt-4.1-mini")
-OpenAI.configure({ auth: Auth.bearer("oauth-token") }).chat("gpt-4.1-mini")
+OpenAI.configure({ auth: RuntimeAuth.bearer("oauth-token") }).chat("gpt-4.1-mini")

 // @ts-expect-error OpenAI chat selectors only accept model ids.
 OpenAI.configure({ apiKey: "sk-test" }).chat("gpt-4.1-mini", {})

 // @ts-expect-error auth is an override, so OpenAI Chat rejects apiKey with auth.
-OpenAI.configure({ apiKey: "sk-test", auth: Auth.bearer("oauth-token") })
+OpenAI.configure({ apiKey: "sk-test", auth: RuntimeAuth.bearer("oauth-token") })

 // @ts-expect-error Azure requires at least one of `resourceName` or `baseURL`.
 Azure.configure()
 Azure.configure({ apiKey: "azure-key", resourceName: "resource" }).responses("deployment")
 Azure.configure({ apiKey: configApiKey, resourceName: "resource" }).responses("deployment")
-Azure.configure({ auth: Auth.header("api-key", "azure-key"), resourceName: "resource" }).responses("deployment")
+Azure.configure({ auth: RuntimeAuth.header("api-key", "azure-key"), resourceName: "resource" }).responses("deployment")

 // @ts-expect-error Azure model selectors only accept deployment ids.
 Azure.configure({ apiKey: "azure-key", resourceName: "resource" }).responses("deployment", {})

 // @ts-expect-error auth is an override, so Azure rejects apiKey with auth.
-Azure.configure({ resourceName: "resource", apiKey: "azure-key", auth: Auth.header("api-key", "override") })
+Azure.configure({ resourceName: "resource", apiKey: "azure-key", auth: RuntimeAuth.header("api-key", "override") })

 Azure.configure({ apiKey: "azure-key", resourceName: "resource" }).chat("deployment")
 Azure.configure({ apiKey: configApiKey, resourceName: "resource" }).chat("deployment")
-Azure.configure({ auth: Auth.header("api-key", "azure-key"), resourceName: "resource" }).chat("deployment")
+Azure.configure({ auth: RuntimeAuth.header("api-key", "azure-key"), resourceName: "resource" }).chat("deployment")

 // @ts-expect-error Azure chat model selectors only accept deployment ids.
 Azure.configure({ apiKey: "azure-key", resourceName: "resource" }).chat("deployment", {})

 // @ts-expect-error auth is an override, so Azure Chat rejects apiKey with auth.
-Azure.configure({ resourceName: "resource", apiKey: "azure-key", auth: Auth.header("api-key", "override") })
+Azure.configure({ resourceName: "resource", apiKey: "azure-key", auth: RuntimeAuth.header("api-key", "override") })

 Anthropic.configure({ apiKey: "anthropic-key" }).model("claude-haiku")
 // @ts-expect-error Anthropic model selectors only accept model ids.
@@ -164,7 +165,7 @@ Google.configure({ apiKey: "google-key" }).model("gemini-2.5-flash", {})

 GoogleVertex.configure({ apiKey: "vertex-key" }).model("gemini-3.5-flash")
 GoogleVertex.configure({ accessToken: "vertex-token", project: "project" }).model("gemini-3.5-flash")
-GoogleVertex.configure({ auth: Auth.bearer("vertex-token"), project: "project" }).model("gemini-3.5-flash")
+GoogleVertex.configure({ auth: RuntimeAuth.bearer("vertex-token"), project: "project" }).model("gemini-3.5-flash")
 // @ts-expect-error Vertex Gemini model selectors only accept model ids.
 GoogleVertex.configure({ apiKey: "vertex-key" }).model("gemini-3.5-flash", {})
 // @ts-expect-error Vertex Gemini config accepts only one auth source.
@@ -173,7 +174,7 @@ GoogleVertex.configure({ accessToken: "vertex-token", apiKey: "vertex-key", proj
 GoogleVertex.model("gemini-3.5-flash", { accessToken: "vertex-token", apiKey: "vertex-key", project: "project" })

 GoogleVertexChat.configure({ accessToken: "vertex-token", project: "project" }).model("deepseek-ai/deepseek-v3.2-maas")
-GoogleVertexChat.configure({ auth: Auth.bearer("vertex-token"), project: "project" }).model(
+GoogleVertexChat.configure({ auth: RuntimeAuth.bearer("vertex-token"), project: "project" }).model(
  "deepseek-ai/deepseek-v3.2-maas",
 )
 // @ts-expect-error Vertex Chat package settings do not accept API keys.
@@ -186,12 +187,12 @@ GoogleVertexChat.configure({ accessToken: "vertex-token", project: "project" }).
 GoogleVertexChat.configure({
  accessToken: "vertex-token",
  // @ts-expect-error Vertex Chat config accepts only one auth source.
-  auth: Auth.bearer("vertex-token"),
+  auth: RuntimeAuth.bearer("vertex-token"),
  project: "project",
 })

 GoogleVertexResponses.configure({ accessToken: "vertex-token", project: "project" }).model("xai/grok-4.20-reasoning")
-GoogleVertexResponses.configure({ auth: Auth.bearer("vertex-token"), project: "project" }).model(
+GoogleVertexResponses.configure({ auth: RuntimeAuth.bearer("vertex-token"), project: "project" }).model(
  "xai/grok-4.20-reasoning",
 )
 // @ts-expect-error Vertex Responses package settings do not accept API keys.
@@ -204,14 +205,16 @@ GoogleVertexResponses.configure({ accessToken: "vertex-token", project: "project
 GoogleVertexResponses.configure({
  accessToken: "vertex-token",
  // @ts-expect-error Vertex Responses config accepts only one auth source.
-  auth: Auth.bearer("vertex-token"),
+  auth: RuntimeAuth.bearer("vertex-token"),
  project: "project",
 })

 GoogleVertexMessages.configure({ accessToken: "vertex-token", project: "project" }).model("claude-sonnet-4-6")
 // @ts-expect-error Vertex Messages package settings do not accept API keys.
 GoogleVertexMessages.model("claude-sonnet-4-6", { apiKey: "vertex-key", project: "project" })
-GoogleVertexMessages.configure({ auth: Auth.bearer("vertex-token"), project: "project" }).model("claude-sonnet-4-6")
+GoogleVertexMessages.configure({ auth: RuntimeAuth.bearer("vertex-token"), project: "project" }).model(
+  "claude-sonnet-4-6",
+)
 GoogleVertexMessages.configure({ accessToken: "vertex-token", project: "project" }).model(
  "claude-sonnet-4-6",
  // @ts-expect-error Vertex Messages model selectors only accept model ids.
@@ -220,7 +223,7 @@ GoogleVertexMessages.configure({ accessToken: "vertex-token", project: "project"
 GoogleVertexMessages.configure({
  accessToken: "vertex-token",
  // @ts-expect-error Vertex Messages config accepts only one auth source.
-  auth: Auth.bearer("vertex-token"),
+  auth: RuntimeAuth.bearer("vertex-token"),
  project: "project",
 })

@@ -1,5 +1,5 @@
 import { describe, expect, test } from "bun:test"
-import { ImageInput, LLM, LLMClient, Provider } from "@opencode-ai/ai"
+import { LLM, LLMClient, Provider } from "@opencode-ai/ai"
 import { Route, Protocol } from "@opencode-ai/ai/route"
 import { Provider as ProviderSubpath } from "@opencode-ai/ai/provider"
 import {
@@ -11,7 +11,12 @@ import {
  XAI,
 } from "@opencode-ai/ai/providers"
 import * as GitHubCopilot from "@opencode-ai/ai/providers/github-copilot"
-import { OpenAIChat, OpenAICompatibleChat, OpenAICompatibleResponses, OpenAIResponses } from "@opencode-ai/ai/protocols"
+import {
+  OpenAIChat,
+  OpenAICompatibleChat,
+  OpenAICompatibleResponses,
+  OpenAIResponses,
+} from "@opencode-ai/ai/protocols"
 import * as AnthropicMessages from "@opencode-ai/ai/protocols/anthropic-messages"

 describe("public exports", () => {
@@ -19,7 +24,6 @@ describe("public exports", () => {
    expect(LLM.request).toBeFunction()
    expect(LLMClient.Service).toBeFunction()
    expect(LLMClient.layer).toBeDefined()
-    expect(ImageInput.bytes).toBeFunction()
    expect(Provider.make).toBeFunction()
    expect(ProviderSubpath.make).toBe(Provider.make)
  })
@@ -1,35 +0,0 @@
-{
-  "version": 1,
-  "metadata": {
-    "tags": [
-      "prefix:pdf",
-      "pdf",
-      "provider:anthropic",
-      "protocol:anthropic-messages",
-      "tool",
-      "tool-result"
-    ],
-    "name": "pdf/anthropic-tool-result",
-    "recordedAt": "2026-07-22T18:15:39.002Z"
-  },
-  "interactions": [
-    {
-      "transport": "http",
-      "request": {
-        "method": "POST",
-        "url": "https://api.anthropic.com/v1/messages",
-        "headers": {
-          "content-type": "application/json"
-        },
-        "body": "{\"model\":\"claude-haiku-4-5-20251001\",\"system\":[{\"type\":\"text\",\"text\":\"Read the PDF returned by the tool and follow the user's response format exactly.\"}],\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"Return only the verification code from the PDF.\"}]},{\"role\":\"assistant\",\"content\":[{\"type\":\"tool_use\",\"id\":\"call_pdf_1\",\"name\":\"read_pdf\",\"input\":{}}]},{\"role\":\"user\",\"content\":[{\"type\":\"tool_result\",\"tool_use_id\":\"call_pdf_1\",\"content\":[{\"type\":\"text\",\"text\":\"PDF read successfully\"},{\"type\":\"document\",\"source\":{\"type\":\"base64\",\"media_type\":\"application/pdf\",\"data\":\"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\"}}]}]}],\"tools\":[{\"name\":\"read_pdf\",\"description\":\"Read the attached PDF.\",\"input_schema\":{\"type\":\"object\",\"properties\":{},\"additionalProperties\":false}}],\"stream\":true,\"max_tokens\":40,\"temperature\":0}"
-      },
-      "response": {
-        "status": 200,
-        "headers": {
-          "content-type": "text/event-stream; charset=utf-8"
-        },
-        "body": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"model\":\"claude-haiku-4-5-20251001\",\"id\":\"msg_011CdHYzxyRpSVFgwTm6ccUr\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"stop_reason\":null,\"stop_sequence\":null,\"stop_details\":null,\"usage\":{\"input_tokens\":2229,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"cache_creation\":{\"ephemeral_5m_input_tokens\":0,\"ephemeral_1h_input_tokens\":0},\"output_tokens\":2,\"service_tier\":\"standard\",\"inference_geo\":\"not_available\"}}        }\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"text\",\"text\":\"\"}              }\n\nevent: ping\ndata: {\"type\": \"ping\"}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"ORCH\"}  }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"ID-7391\"}           }\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0               }\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"end_turn\",\"stop_sequence\":null,\"stop_details\":null},\"usage\":{\"input_tokens\":2229,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"output_tokens\":9}       }\n\nevent: message_stop\ndata: {\"type\":\"message_stop\" }\n\n"
-      }
-    }
-  ]
-}
@@ -1,34 +0,0 @@
-{
-  "version": 1,
-  "metadata": {
-    "tags": [
-      "prefix:pdf",
-      "pdf",
-      "provider:anthropic",
-      "protocol:anthropic-messages",
-      "user-input"
-    ],
-    "name": "pdf/anthropic-user-input",
-    "recordedAt": "2026-07-22T18:15:37.979Z"
-  },
-  "interactions": [
-    {
-      "transport": "http",
-      "request": {
-        "method": "POST",
-        "url": "https://api.anthropic.com/v1/messages",
-        "headers": {
-          "content-type": "application/json"
-        },
-        "body": "{\"model\":\"claude-haiku-4-5-20251001\",\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"document\",\"source\":{\"type\":\"base64\",\"media_type\":\"application/pdf\",\"data\":\"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\"}},{\"type\":\"text\",\"text\":\"Return only the verification code from the PDF.\"}]}],\"stream\":true,\"max_tokens\":40,\"temperature\":0}"
-      },
-      "response": {
-        "status": 200,
-        "headers": {
-          "content-type": "text/event-stream; charset=utf-8"
-        },
-        "body": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"model\":\"claude-haiku-4-5-20251001\",\"id\":\"msg_011CdHYzsayb45rgfamcjFt3\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"stop_reason\":null,\"stop_sequence\":null,\"stop_details\":null,\"usage\":{\"input_tokens\":1602,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"cache_creation\":{\"ephemeral_5m_input_tokens\":0,\"ephemeral_1h_input_tokens\":0},\"output_tokens\":2,\"service_tier\":\"standard\",\"inference_geo\":\"not_available\"}}           }\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"text\",\"text\":\"\"}         }\n\nevent: ping\ndata: {\"type\": \"ping\"}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"ORCH\"}              }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"ID-7391\"}}\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0      }\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"end_turn\",\"stop_sequence\":null,\"stop_details\":null},\"usage\":{\"input_tokens\":1602,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"output_tokens\":9}    }\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"              }\n\n"
-      }
-    }
-  ]
-}
@@ -1,36 +0,0 @@
-{
-  "version": 1,
-  "metadata": {
-    "tags": [
-      "prefix:pdf",
-      "pdf",
-      "provider:amazon-bedrock",
-      "protocol:bedrock-converse",
-      "tool",
-      "tool-result"
-    ],
-    "name": "pdf/bedrock-tool-result",
-    "recordedAt": "2026-07-22T18:15:52.400Z"
-  },
-  "interactions": [
-    {
-      "transport": "http",
-      "request": {
-        "method": "POST",
-        "url": "https://bedrock-runtime.us-east-1.amazonaws.com/model/us.anthropic.claude-haiku-4-5-20251001-v1%3A0/converse-stream",
-        "headers": {
-          "content-type": "application/json"
-        },
-        "body": "{\"modelId\":\"us.anthropic.claude-haiku-4-5-20251001-v1:0\",\"messages\":[{\"role\":\"user\",\"content\":[{\"text\":\"Return only the verification code from the PDF.\"}]},{\"role\":\"assistant\",\"content\":[{\"toolUse\":{\"toolUseId\":\"call_pdf_1\",\"name\":\"read_pdf\",\"input\":{}}}]},{\"role\":\"user\",\"content\":[{\"toolResult\":{\"toolUseId\":\"call_pdf_1\",\"content\":[{\"text\":\"PDF read successfully\"},{\"document\":{\"format\":\"pdf\",\"name\":\"verification\",\"source\":{\"bytes\":\"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\"}}}],\"status\":\"success\"}}]}],\"system\":[{\"text\":\"Read the PDF returned by the tool and follow the user's response format exactly.\"}],\"inferenceConfig\":{\"maxTokens\":40,\"temperature\":0},\"toolConfig\":{\"tools\":[{\"toolSpec\":{\"name\":\"read_pdf\",\"description\":\"Read the attached PDF.\",\"inputSchema\":{\"json\":{\"type\":\"object\",\"properties\":{},\"additionalProperties\":false}}}}]}}"
-      },
-      "response": {
-        "status": 200,
-        "headers": {
-          "content-type": "application/vnd.amazon.eventstream"
-        },
-        "body": "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",
-        "bodyEncoding": "base64"
-      }
-    }
-  ]
-}
@@ -1,35 +0,0 @@
-{
-  "version": 1,
-  "metadata": {
-    "tags": [
-      "prefix:pdf",
-      "pdf",
-      "provider:amazon-bedrock",
-      "protocol:bedrock-converse",
-      "user-input"
-    ],
-    "name": "pdf/bedrock-user-input",
-    "recordedAt": "2026-07-22T18:15:48.408Z"
-  },
-  "interactions": [
-    {
-      "transport": "http",
-      "request": {
-        "method": "POST",
-        "url": "https://bedrock-runtime.us-east-1.amazonaws.com/model/us.anthropic.claude-haiku-4-5-20251001-v1%3A0/converse-stream",
-        "headers": {
-          "content-type": "application/json"
-        },
-        "body": "{\"modelId\":\"us.anthropic.claude-haiku-4-5-20251001-v1:0\",\"messages\":[{\"role\":\"user\",\"content\":[{\"document\":{\"format\":\"pdf\",\"name\":\"verification\",\"source\":{\"bytes\":\"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\"}}},{\"text\":\"Return only the verification code from the PDF.\"}]}],\"inferenceConfig\":{\"maxTokens\":40,\"temperature\":0}}"
-      },
-      "response": {
-        "status": 200,
-        "headers": {
-          "content-type": "application/vnd.amazon.eventstream"
-        },
-        "body": "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",
-        "bodyEncoding": "base64"
-      }
-    }
-  ]
-}
@@ -1,53 +0,0 @@
-{
-  "version": 1,
-  "metadata": {
-    "tags": [
-      "prefix:pdf",
-      "pdf",
-      "provider:google",
-      "protocol:gemini",
-      "tool",
-      "tool-result"
-    ],
-    "name": "pdf/gemini-tool-result",
-    "recordedAt": "2026-07-22T18:21:59.606Z"
-  },
-  "interactions": [
-    {
-      "transport": "http",
-      "request": {
-        "method": "POST",
-        "url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:streamGenerateContent?alt=sse",
-        "headers": {
-          "content-type": "application/json"
-        },
-        "body": "{\"contents\":[{\"role\":\"user\",\"parts\":[{\"text\":\"Use read_pdf with path verification.pdf and return the verification code.\"}]}],\"systemInstruction\":{\"parts\":[{\"text\":\"Call read_pdf exactly once with path verification.pdf, then reply only with the verification code from its PDF.\"}]},\"tools\":[{\"functionDeclarations\":[{\"name\":\"read_pdf\",\"description\":\"Read the attached PDF.\",\"parameters\":{\"required\":[\"path\"],\"type\":\"object\",\"properties\":{\"path\":{\"type\":\"string\"}}}}]}],\"generationConfig\":{\"maxOutputTokens\":256,\"temperature\":0}}"
-      },
-      "response": {
-        "status": 200,
-        "headers": {
-          "content-type": "text/event-stream"
-        },
-        "body": "data: {\"candidates\": [{\"content\": {\"parts\": [{\"functionCall\": {\"name\": \"read_pdf\",\"args\": {\"path\": \"verification.pdf\"},\"id\": \"58shgmez\"},\"thoughtSignature\": \"EqkCCqYCARFNMg/JrCTv5i3zYENFBVpZNFL3pbzJmi5Eu387ncF703xFMB4pwyaP7a1gi49EqBhCI2hWOpesU5nZQOLAhGgExKGa2GM+HzpEB5g62r0NFblm/BGkVZaImTuHR7bytfRC5jHQlHKo4OS27OLUVjvkMkBIYsvjhDErY7niERbXJVpyxTVqUf1GgZMSu8kC9/5WDlMs9xVKNT/6KMW4PhhSR9nXg4KZUa+bC03/ydhsWWgBa5aLCgvTq7WPj217xIsmUkSiRedIffPsUSNjYdMHUvWi8bOlvM1veEEP6GIfv5h9gXXzjnHbEHfQxV8PZuBAyY7iM6nqyfkJNdkZ1HdB7DXMBsMsRN6SgrIrFoXX2WaGrkoEI5tdZx1t/gdwF1jEVT6k\"}],\"role\": \"model\"},\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 81,\"candidatesTokenCount\": 18,\"totalTokenCount\": 151,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 81}],\"thoughtsTokenCount\": 52,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"RAphaui3OaSHz7IPy8Kb4Ak\"}\r\n\r\ndata: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 81,\"candidatesTokenCount\": 18,\"totalTokenCount\": 151,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 81}],\"thoughtsTokenCount\": 52,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"RAphaui3OaSHz7IPy8Kb4Ak\"}\r\n\r\n"
-      }
-    },
-    {
-      "transport": "http",
-      "request": {
-        "method": "POST",
-        "url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:streamGenerateContent?alt=sse",
-        "headers": {
-          "content-type": "application/json"
-        },
-        "body": "{\"contents\":[{\"role\":\"user\",\"parts\":[{\"text\":\"Use read_pdf with path verification.pdf and return the verification code.\"}]},{\"role\":\"model\",\"parts\":[{\"functionCall\":{\"id\":\"58shgmez\",\"name\":\"read_pdf\",\"args\":{\"path\":\"verification.pdf\"}},\"thoughtSignature\":\"EqkCCqYCARFNMg/JrCTv5i3zYENFBVpZNFL3pbzJmi5Eu387ncF703xFMB4pwyaP7a1gi49EqBhCI2hWOpesU5nZQOLAhGgExKGa2GM+HzpEB5g62r0NFblm/BGkVZaImTuHR7bytfRC5jHQlHKo4OS27OLUVjvkMkBIYsvjhDErY7niERbXJVpyxTVqUf1GgZMSu8kC9/5WDlMs9xVKNT/6KMW4PhhSR9nXg4KZUa+bC03/ydhsWWgBa5aLCgvTq7WPj217xIsmUkSiRedIffPsUSNjYdMHUvWi8bOlvM1veEEP6GIfv5h9gXXzjnHbEHfQxV8PZuBAyY7iM6nqyfkJNdkZ1HdB7DXMBsMsRN6SgrIrFoXX2WaGrkoEI5tdZx1t/gdwF1jEVT6k\"}]},{\"role\":\"user\",\"parts\":[{\"functionResponse\":{\"id\":\"58shgmez\",\"name\":\"read_pdf\",\"response\":{\"name\":\"read_pdf\",\"content\":\"PDF read successfully\"},\"parts\":[{\"inlineData\":{\"mimeType\":\"application/pdf\",\"data\":\"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\"}}]}}]}],\"systemInstruction\":{\"parts\":[{\"text\":\"Call read_pdf exactly once with path verification.pdf, then reply only with the verification code from its PDF.\"}]},\"tools\":[{\"functionDeclarations\":[{\"name\":\"read_pdf\",\"description\":\"Read the attached PDF.\",\"parameters\":{\"required\":[\"path\"],\"type\":\"object\",\"properties\":{\"path\":{\"type\":\"string\"}}}}]}],\"generationConfig\":{\"maxOutputTokens\":256,\"temperature\":0}}"
-      },
-      "response": {
-        "status": 200,
-        "headers": {
-          "content-type": "text/event-stream"
-        },
-        "body": "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"ORCHID-7391\"}],\"role\": \"model\"},\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 123,\"candidatesTokenCount\": 8,\"totalTokenCount\": 184,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 123}],\"thoughtsTokenCount\": 53,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"RgphaoL6CMjQz7IPjOnEmQI\"}\r\n\r\ndata: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"\",\"thoughtSignature\": \"EqECCp4CARFNMg9obBl8O6iU9lawUIWiE+1vztZm9NtaT9FuyJz343hd9ruz+xPco4Q1DY1GF81ZiSI2ElBkt8Wfwsqtix9LNGSMvbZhhk/ZnB54t05M/Dft1kujcMvEdZUWUI/jWaJ349tO1bKVH9MacG5+gl0n4y8DwyQZSV3xIcet547drSkcA/TM03RB+yj1/dcLHsvUjmv9EnO897vZgO2Dk4tbZ2NyCtOeQ3JKVhUTLg2pjkGk+POCNiOdESWiUzxdQKw9LiV6nnzi071tXNiMeVimq6d7xAzRVNapI2uXynvn9Uk3eyn85purOFa8cKriK9oD6vcyGMqgd9+gu2m3to0IHqd7o+2YSr1m5qV1xT1R2/WRQEtb1b1AuOAU6w==\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 1277,\"candidatesTokenCount\": 8,\"totalTokenCount\": 1338,\"promptTokensDetails\": [{\"modality\": \"IMAGE\",\"tokenCount\": 1102},{\"modality\": \"TEXT\",\"tokenCount\": 175}],\"thoughtsTokenCount\": 53,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"RgphaoL6CMjQz7IPjOnEmQI\"}\r\n\r\n"
-      }
-    }
-  ]
-}
@@ -1,34 +0,0 @@
-{
-  "version": 1,
-  "metadata": {
-    "tags": [
-      "prefix:pdf",
-      "pdf",
-      "provider:google",
-      "protocol:gemini",
-      "user-input"
-    ],
-    "name": "pdf/gemini-user-input",
-    "recordedAt": "2026-07-22T18:20:55.140Z"
-  },
-  "interactions": [
-    {
-      "transport": "http",
-      "request": {
-        "method": "POST",
-        "url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:streamGenerateContent?alt=sse",
-        "headers": {
-          "content-type": "application/json"
-        },
-        "body": "{\"contents\":[{\"role\":\"user\",\"parts\":[{\"inlineData\":{\"mimeType\":\"application/pdf\",\"data\":\"JVBERi0xLjQKMSAwIG9iago8PCAvVHlwZSAvQ2F0YWxvZyAvUGFnZXMgMiAwIFIgPj4KZW5kb2JqCjIgMCBvYmoKPDwgL1R5cGUgL1BhZ2VzIC9LaWRzIFszIDAgUl0gL0NvdW50IDEgPj4KZW5kb2JqCjMgMCBvYmoKPDwgL1R5cGUgL1BhZ2UgL1BhcmVudCAyIDAgUiAvTWVkaWFCb3ggWzAgMCA2MTIgNzkyXSAvUmVzb3VyY2VzIDw8IC9Gb250IDw8IC9GMSA1IDAgUiA+PiA+PiAvQ29udGVudHMgNCAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0xlbmd0aCA3NSA+PgpzdHJlYW0KQlQKL0YxIDE4IFRmCjcyIDcyMCBUZAooUERGIGNhc3NldHRlIHZlcmlmaWNhdGlvbiBjb2RlOiBPUkNISUQtNzM5MSkgVGoKRVQKZW5kc3RyZWFtCmVuZG9iago1IDAgb2JqCjw8IC9UeXBlIC9Gb250IC9TdWJ0eXBlIC9UeXBlMSAvQmFzZUZvbnQgL0hlbHZldGljYSA+PgplbmRvYmoKeHJlZgowIDYKMDAwMDAwMDAwMCA2NTUzNSBmIAowMDAwMDAwMDA5IDAwMDAwIG4gCjAwMDAwMDAwNTggMDAwMDAgbiAKMDAwMDAwMDExNSAwMDAwMCBuIAowMDAwMDAwMjQxIDAwMDAwIG4gCjAwMDAwMDAzNjUgMDAwMDAgbiAKdHJhaWxlcgo8PCAvU2l6ZSA2IC9Sb290IDEgMCBSID4+CnN0YXJ0eHJlZgo0MzUKJSVFT0YK\"}},{\"text\":\"Return only the verification code from the PDF.\"}]}],\"generationConfig\":{\"maxOutputTokens\":256,\"temperature\":0}}"
-      },
-      "response": {
-        "status": 200,
-        "headers": {
-          "content-type": "text/event-stream"
-        },
-        "body": "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"ORCH\"}],\"role\": \"model\"},\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 10,\"candidatesTokenCount\": 2,\"totalTokenCount\": 127,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 10}],\"thoughtsTokenCount\": 115,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"BQpharW2KaPgz7IP6uSLiAw\"}\r\n\r\ndata: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"ID-7391\"}],\"role\": \"model\"},\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 10,\"candidatesTokenCount\": 8,\"totalTokenCount\": 133,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 10}],\"thoughtsTokenCount\": 115,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"BQpharW2KaPgz7IP6uSLiAw\"}\r\n\r\ndata: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"\",\"thoughtSignature\": \"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\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 530,\"candidatesTokenCount\": 8,\"totalTokenCount\": 653,\"promptTokensDetails\": [{\"modality\": \"IMAGE\",\"tokenCount\": 520},{\"modality\": \"TEXT\",\"tokenCount\": 10}],\"thoughtsTokenCount\": 115,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"BQpharW2KaPgz7IP6uSLiAw\"}\r\n\r\n"
-      }
-    }
-  ]
-}
@@ -1,28 +0,0 @@
-{
-  "version": 1,
-  "metadata": {
-    "tags": ["prefix:zai-images", "provider:zai", "protocol:zai-images"],
-    "name": "zai-images/generates-an-image",
-    "recordedAt": "2026-07-19T16:03:55.761Z"
-  },
-  "interactions": [
-    {
-      "transport": "http",
-      "request": {
-        "method": "POST",
-        "url": "https://api.z.ai/api/paas/v4/images/generations",
-        "headers": {
-          "content-type": "application/json"
-        },
-        "body": "{\"model\":\"cogview-4-250304\",\"prompt\":\"A simple flat red circle centered on a plain white background.\",\"size\":\"1024x1024\",\"quality\":\"standard\",\"user_id\":\"opencode-image-test\"}"
-      },
-      "response": {
-        "status": 200,
-        "headers": {
-          "content-type": "application/json; charset=UTF-8"
-        },
-        "body": "{\"created\":1784477028,\"data\":[{\"url\":\"https://mfile.z.ai/1784477035500-43574eab2b6e402da9063d6ac22dfefb.png?ufileattname=202607200003482062c3bba9b04f7d_watermark.png\"}],\"id\":\"202607200003482062c3bba9b04f7d\",\"request_id\":\"202607200003482062c3bba9b04f7d\"}"
-      }
-    }
-  ]
-}
@@ -1,578 +0,0 @@
-import { describe, expect } from "bun:test"
-import { Effect, Layer } from "effect"
-import { HttpClientRequest } from "effect/unstable/http"
-import { Image, ImageClient, ImageInput } from "../src"
-import { Google, OpenAI, XAI, ZAI } from "../src/providers"
-import { it } from "./lib/effect"
-import { dynamicResponse } from "./lib/http"
-
-describe("Image", () => {
-  it.effect("generates images through the OpenAI Images API", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model: OpenAI.configure({
-          apiKey: "test",
-          baseURL: "https://api.openai.test/v1",
-          queryParams: { "api-version": "v1" },
-          http: { body: { deployment: "test" }, headers: { "x-default": "yes" } },
-        }).image("gpt-image-2"),
-        prompt: "A robot tending a rooftop garden",
-        options: {
-          n: 2,
-          size: "2048x2048",
-          quality: "future-quality",
-          outputFormat: "jpeg",
-          output_format: "avif",
-          outputCompression: 30,
-          output_compression: 40,
-          background: "opaque",
-          native_default: true,
-          future_option: true,
-        },
-        http: {
-          body: { output_format: "webp", output_compression: 50, future_option: "http", request_metadata: "value" },
-          headers: { "x-request": "yes" },
-          query: { trace: "1" },
-        },
-      })
-
-      expect(response.images).toHaveLength(2)
-      expect(response.image?.mediaType).toBe("image/webp")
-      expect(response.image?.data).toEqual(Uint8Array.from([1, 2, 3]))
-      expect(response.image?.providerMetadata).toEqual({ openai: { revisedPrompt: "A precise robot" } })
-      expect(response.usage?.totalTokens).toBe(12)
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) =>
-              Effect.gen(function* () {
-                const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
-                expect(request.url).toBe("https://api.openai.test/v1/images/generations?api-version=v1&trace=1")
-                expect(request.headers.get("authorization")).toBe("Bearer test")
-                expect(request.headers.get("x-default")).toBe("yes")
-                expect(request.headers.get("x-request")).toBe("yes")
-                expect(JSON.parse(input.text)).toEqual({
-                  model: "gpt-image-2",
-                  prompt: "A robot tending a rooftop garden",
-                  n: 2,
-                  size: "2048x2048",
-                  quality: "future-quality",
-                  background: "opaque",
-                  output_format: "webp",
-                  output_compression: 50,
-                  native_default: true,
-                  future_option: "http",
-                  deployment: "test",
-                  request_metadata: "value",
-                })
-                return input.respond(
-                  JSON.stringify({
-                    data: [{ b64_json: "AQID", revised_prompt: "A precise robot" }, { b64_json: "BAUG" }],
-                    output_format: "webp",
-                    usage: { input_tokens: 4, output_tokens: 8, total_tokens: 12 },
-                  }),
-                  { headers: { "content-type": "application/json" } },
-                )
-              }),
-            ),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("preserves native snake_case and unknown request options", () =>
-    Image.generate({
-      model: OpenAI.configure({
-        apiKey: "test",
-        baseURL: "https://api.openai.test/v1",
-      }).image("future-image-model"),
-      prompt: "A lighthouse in fog",
-      options: {
-        outputFormat: "jpeg",
-        output_format: "avif",
-        outputCompression: 30,
-        output_compression: 40,
-        provider_future_option: { enabled: true },
-      },
-    }).pipe(
-      Effect.tap((response) =>
-        Effect.sync(() => {
-          expect(response.image?.mediaType).toBe("image/avif")
-        }),
-      ),
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) => {
-              expect(JSON.parse(input.text)).toEqual({
-                model: "future-image-model",
-                prompt: "A lighthouse in fog",
-                output_format: "avif",
-                output_compression: 40,
-                provider_future_option: { enabled: true },
-              })
-              return Effect.succeed(
-                input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
-                  headers: { "content-type": "application/json" },
-                }),
-              )
-            }),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("routes OpenAI byte inputs and masks through multipart edits", () =>
-    Image.generate({
-      model: OpenAI.configure({ apiKey: "test", baseURL: "https://api.openai.test/v1" }).image("future-model"),
-      prompt: "Combine these images",
-      images: [
-        ImageInput.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
-        ImageInput.url("data:image/jpeg;base64,BAUG"),
-      ],
-      options: {
-        mask: ImageInput.bytes(Uint8Array.from([7, 8, 9]), "image/png"),
-        quality: "high",
-        future_option: true,
-      },
-      http: {
-        body: { quality: "low", model: "corrupt", prompt: "corrupt", image: "corrupt", "image[]": "corrupt" },
-        headers: { "content-type": "application/json" },
-      },
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) =>
-              Effect.gen(function* () {
-                const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
-                expect(request.url).toBe("https://api.openai.test/v1/images/edits")
-                expect(request.headers.get("content-type")).toStartWith("multipart/form-data; boundary=")
-                expect(input.text).toContain('name="model"\r\n\r\nfuture-model')
-                expect(input.text).toContain('name="prompt"\r\n\r\nCombine these images')
-                expect(input.text.match(/name="image\[\]"/g)).toHaveLength(2)
-                expect(input.text).toContain('name="mask"')
-                expect(input.text).toContain('name="quality"\r\n\r\nlow')
-                expect(input.text).not.toContain("corrupt")
-                return input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
-                  headers: { "content-type": "application/json" },
-                })
-              }),
-            ),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("routes OpenAI URL and file inputs through JSON edits", () =>
-    Image.generate({
-      model: OpenAI.configure({ apiKey: "test", baseURL: "https://api.openai.test/v1" }).image("future-model"),
-      prompt: "Combine these images",
-      images: [ImageInput.url("https://example.test/source.png"), ImageInput.file("file_123")],
-      options: { mask: ImageInput.file("file_mask") },
-      http: { body: { future_option: true } },
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) => {
-              expect(JSON.parse(input.text)).toEqual({
-                model: "future-model",
-                prompt: "Combine these images",
-                images: [{ image_url: "https://example.test/source.png" }, { file_id: "file_123" }],
-                mask: { file_id: "file_mask" },
-                future_option: true,
-              })
-              return Effect.succeed(
-                input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
-                  headers: { "content-type": "application/json" },
-                }),
-              )
-            }),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("routes ordered xAI image inputs through JSON edits", () =>
-    Image.generate({
-      model: XAI.configure({ apiKey: "test", baseURL: "https://api.xai.test/v1" }).image("future-model"),
-      prompt: "Combine these images",
-      images: [
-        ImageInput.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
-        ImageInput.url("https://example.test/source.jpg"),
-        ImageInput.file("file_123"),
-      ],
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) => {
-              expect(JSON.parse(input.text)).toEqual({
-                model: "future-model",
-                prompt: "Combine these images",
-                images: [
-                  { url: "data:image/png;base64,AQID", type: "image_url" },
-                  { url: "https://example.test/source.jpg", type: "image_url" },
-                  { file_id: "file_123" },
-                ],
-              })
-              return Effect.succeed(
-                input.respond(JSON.stringify({ data: [{ b64_json: "AQID", mime_type: "image/png" }] }), {
-                  headers: { "content-type": "application/json" },
-                }),
-              )
-            }),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("uses xAI's singular image field for one input", () =>
-    Image.generate({
-      model: XAI.configure({ apiKey: "test", baseURL: "https://api.xai.test/v1" }).image("future-model"),
-      prompt: "Edit this image",
-      images: [ImageInput.file("file_123")],
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) => {
-              expect(JSON.parse(input.text)).toEqual({
-                model: "future-model",
-                prompt: "Edit this image",
-                image: { file_id: "file_123" },
-              })
-              return Effect.succeed(
-                input.respond(JSON.stringify({ data: [{ b64_json: "AQID", mime_type: "image/png" }] }), {
-                  headers: { "content-type": "application/json" },
-                }),
-              )
-            }),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("lowers ordered Google image inputs into generateContent parts", () =>
-    Image.generate({
-      model: Google.configure({ apiKey: "test", baseURL: "https://google.test/v1beta" }).image("future-model"),
-      prompt: "Combine these images",
-      images: [
-        ImageInput.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
-        ImageInput.url("data:image/jpeg;base64,BAUG"),
-        ImageInput.fileUri("https://generativelanguage.googleapis.com/v1beta/files/123", "image/webp"),
-      ],
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) => {
-              expect(JSON.parse(input.text).contents[0].parts).toEqual([
-                { text: "Combine these images" },
-                { inlineData: { mimeType: "image/png", data: "AQID" } },
-                { inlineData: { mimeType: "image/jpeg", data: "BAUG" } },
-                {
-                  fileData: {
-                    mimeType: "image/webp",
-                    fileUri: "https://generativelanguage.googleapis.com/v1beta/files/123",
-                  },
-                },
-              ])
-              return Effect.succeed(
-                input.respond(
-                  JSON.stringify({
-                    candidates: [{ content: { parts: [{ inlineData: { mimeType: "image/png", data: "AQID" } }] } }],
-                  }),
-                  { headers: { "content-type": "application/json" } },
-                ),
-              )
-            }),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("rejects unsupported provider inputs before sending", () =>
-    Effect.gen(function* () {
-      const cases = [
-        Image.generate({
-          model: Google.configure({ apiKey: "test" }).image("model"),
-          prompt: "edit",
-          images: [ImageInput.url("https://example.test/image.png")],
-        }),
-        Image.generate({
-          model: ZAI.configure({ apiKey: "test" }).image("model"),
-          prompt: "edit",
-          images: [ImageInput.bytes(Uint8Array.from([1]), "image/png")],
-        }),
-      ]
-      yield* Effect.forEach(cases, (program) =>
-        program.pipe(
-          Effect.flip,
-          Effect.tap((error) => Effect.sync(() => expect(error.reason._tag).toBe("InvalidRequest"))),
-        ),
-      )
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(dynamicResponse(() => Effect.die("unsupported input reached the network"))),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("generates images through the Google generateContent API", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model: Google.configure({
-          apiKey: "test",
-          baseURL: "https://generativelanguage.test/v1beta/",
-          headers: { "x-default": "yes" },
-          http: { body: { labels: { deployment: "test" } }, query: { api: "v1" } },
-        }).image("any-model-id"),
-        prompt: "A robot tending a rooftop garden",
-        options: {
-          aspectRatio: "16:9",
-          imageSize: "2K",
-          seed: 42,
-          thinkingLevel: "HIGH",
-          includeThoughts: true,
-          futureOption: true,
-          imageConfig: { aspectRatio: "4:3", nativeImageOption: true },
-          thinkingConfig: { thinkingLevel: "LOW", nativeThinkingOption: true },
-        },
-        http: {
-          body: {
-            safetySettings: [],
-            generationConfig: {
-              imageConfig: { aspectRatio: "3:2", httpImageOption: true },
-              thinkingConfig: { includeThoughts: false, httpThinkingOption: true },
-              futureOption: "http",
-              httpOption: true,
-            },
-          },
-          headers: { "x-request": "yes" },
-          query: { trace: "1" },
-        },
-      })
-
-      expect(response.images).toHaveLength(3)
-      expect(response.images.map((image) => image.data)).toEqual([
-        Uint8Array.from([1, 2, 3]),
-        Uint8Array.from([4, 5, 6]),
-        Uint8Array.from([7, 8, 9]),
-      ])
-      expect(response.images.map((image) => image.mediaType)).toEqual(["image/png", "image/jpeg", "image/webp"])
-      expect(response.images[0].providerMetadata).toMatchObject({ google: { thoughtSignature: "signature-1" } })
-      expect(response.images[1].providerMetadata).toMatchObject({
-        google: { candidateIndex: 0, partIndex: 3, finishReason: "STOP" },
-      })
-      expect(response.images[2].providerMetadata).toMatchObject({ google: { candidateIndex: 7, partIndex: 0 } })
-      expect(response.usage?.inputTokens).toBe(5)
-      expect(response.usage?.outputTokens).toBe(10)
-      expect(response.usage?.reasoningTokens).toBe(3)
-      expect(response.usage?.providerMetadata).toMatchObject({ google: { serviceTier: "STANDARD" } })
-      expect(response.providerMetadata).toEqual({
-        google: {
-          modelVersion: "gemini-3.1-flash-image",
-          responseId: "response-1",
-          promptFeedback: undefined,
-          candidates: [
-            {
-              index: 0,
-              finishReason: "STOP",
-              finishMessage: undefined,
-              safetyRatings: [{ category: "safe" }],
-              citationMetadata: undefined,
-              groundingMetadata: undefined,
-              parts: [
-                {
-                  type: "inlineData",
-                  mediaType: "image/png",
-                  thought: undefined,
-                  thoughtSignature: "signature-1",
-                },
-                { type: "text", text: "planning", thought: true, thoughtSignature: "text-signature" },
-                {
-                  type: "inlineData",
-                  mediaType: "image/png",
-                  thought: true,
-                  thoughtSignature: "draft-signature",
-                },
-                {
-                  type: "inlineData",
-                  mediaType: "image/jpeg",
-                  thought: undefined,
-                  thoughtSignature: undefined,
-                },
-              ],
-            },
-            {
-              index: 7,
-              finishReason: undefined,
-              finishMessage: undefined,
-              safetyRatings: undefined,
-              citationMetadata: undefined,
-              groundingMetadata: undefined,
-              parts: [
-                {
-                  type: "inlineData",
-                  mediaType: "image/webp",
-                  thought: undefined,
-                  thoughtSignature: undefined,
-                },
-              ],
-            },
-          ],
-        },
-      })
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) =>
-              Effect.gen(function* () {
-                const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
-                expect(request.url).toBe(
-                  "https://generativelanguage.test/v1beta/models/any-model-id:generateContent?api=v1&trace=1",
-                )
-                expect(request.headers.get("x-goog-api-key")).toBe("test")
-                expect(request.headers.get("x-default")).toBe("yes")
-                expect(request.headers.get("x-request")).toBe("yes")
-                expect(JSON.parse(input.text)).toEqual({
-                  contents: [{ role: "user", parts: [{ text: "A robot tending a rooftop garden" }] }],
-                  generationConfig: {
-                    responseModalities: ["IMAGE"],
-                    imageConfig: {
-                      aspectRatio: "3:2",
-                      imageSize: "2K",
-                      nativeImageOption: true,
-                      httpImageOption: true,
-                    },
-                    seed: 42,
-                    thinkingConfig: {
-                      thinkingLevel: "LOW",
-                      includeThoughts: false,
-                      nativeThinkingOption: true,
-                      httpThinkingOption: true,
-                    },
-                    futureOption: "http",
-                    httpOption: true,
-                  },
-                  labels: { deployment: "test" },
-                  safetySettings: [],
-                })
-                return input.respond(
-                  JSON.stringify({
-                    candidates: [
-                      {
-                        content: {
-                          parts: [
-                            {
-                              inlineData: { mimeType: "image/png", data: "AQID" },
-                              thoughtSignature: "signature-1",
-                            },
-                            { text: "planning", thought: true, thoughtSignature: "text-signature" },
-                            {
-                              inlineData: { mimeType: "image/png", data: "CgsM" },
-                              thought: true,
-                              thoughtSignature: "draft-signature",
-                            },
-                            { inlineData: { mimeType: "image/jpeg", data: "BAUG" } },
-                          ],
-                        },
-                        finishReason: "STOP",
-                        safetyRatings: [{ category: "safe" }],
-                      },
-                      {
-                        index: 7,
-                        content: { parts: [{ inlineData: { mimeType: "image/webp", data: "BwgJ" } }] },
-                      },
-                    ],
-                    usageMetadata: {
-                      promptTokenCount: 5,
-                      candidatesTokenCount: 7,
-                      thoughtsTokenCount: 3,
-                      totalTokenCount: 15,
-                      serviceTier: "STANDARD",
-                    },
-                    modelVersion: "gemini-3.1-flash-image",
-                    responseId: "response-1",
-                  }),
-                  { headers: { "content-type": "application/json" } },
-                )
-              }),
-            ),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("includes Google diagnostics when no final image is returned", () =>
-    Image.generate({
-      model: Google.configure({ apiKey: "test", baseURL: "https://generativelanguage.test/v1beta" }).image(
-        "gemini-3.1-flash-image",
-      ),
-      prompt: "A robot tending a rooftop garden",
-    }).pipe(
-      Effect.flip,
-      Effect.tap((error) =>
-        Effect.sync(() => {
-          expect(error.reason._tag).toBe("InvalidProviderOutput")
-          if (error.reason._tag !== "InvalidProviderOutput") return
-          expect(error.reason.message).toContain("finish reasons: IMAGE_SAFETY")
-          expect(error.reason.providerMetadata).toEqual({
-            google: {
-              promptFeedback: { blockReason: "SAFETY" },
-              candidates: [
-                {
-                  index: 0,
-                  finishReason: "IMAGE_SAFETY",
-                  finishMessage: "The generated image was blocked by safety filters.",
-                  safetyRatings: [{ category: "HARM_CATEGORY_DANGEROUS_CONTENT", blocked: true }],
-                  citationMetadata: undefined,
-                  groundingMetadata: undefined,
-                  parts: [{ type: "text", text: "blocked", thought: false, thoughtSignature: undefined }],
-                },
-              ],
-            },
-          })
-        }),
-      ),
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) =>
-              Effect.succeed(
-                input.respond(
-                  JSON.stringify({
-                    candidates: [
-                      {
-                        content: { parts: [{ text: "blocked", thought: false }] },
-                        finishReason: "IMAGE_SAFETY",
-                        finishMessage: "The generated image was blocked by safety filters.",
-                        safetyRatings: [{ category: "HARM_CATEGORY_DANGEROUS_CONTENT", blocked: true }],
-                      },
-                    ],
-                    promptFeedback: { blockReason: "SAFETY" },
-                  }),
-                  { headers: { "content-type": "application/json" } },
-                ),
-              ),
-            ),
-          ),
-        ),
-      ),
-    ),
-  )
-})
@@ -1,161 +0,0 @@
-import {
-  Image,
-  ImageInput,
-  ImageModel,
-  type ImageModelOptions,
-  type ImageOptions,
-  type ImageRequestFor,
-  type ImageRoute,
-} from "../src"
-import { Google, OpenAI, XAI, ZAI } from "../src/providers"
-
-type GoogleLikeOptions = {
-  readonly aspectRatio?: "1:1" | "16:9"
-  readonly imageSize?: "1K" | "2K"
-} & Record<string, unknown>
-
-declare const route: ImageRoute<GoogleLikeOptions>
-const google = ImageModel.make<GoogleLikeOptions>({ id: "gemini-image", provider: "google", route })
-// @ts-expect-error Extracted model options retain known provider fields.
-const invalidGoogleOptions: ImageModelOptions<typeof google> = { aspectRatio: "wide" }
-void invalidGoogleOptions
-
-Image.generate({
-  model: google,
-  prompt: "A lighthouse",
-  images: [
-    ImageInput.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
-    ImageInput.url("data:image/jpeg;base64,AQID"),
-    ImageInput.fileUri("https://generativelanguage.googleapis.com/v1beta/files/example", "image/webp"),
-  ],
-  options: { aspectRatio: "16:9", imageSize: "2K", futureOption: true },
-})
-
-const googleProvider = Google.configure({ apiKey: "test" }).image("any-model-id")
-Image.generate({
-  model: googleProvider,
-  prompt: "A lighthouse",
-  options: {
-    aspectRatio: "16:9",
-    imageSize: "2K",
-    seed: 42,
-    thinkingLevel: "HIGH",
-    includeThoughts: true,
-    futureOption: true,
-  },
-})
-Image.generate({
-  model: googleProvider,
-  prompt: "A lighthouse",
-  options: { aspectRatio: "future-ratio", imageSize: "8K", thinkingLevel: "FUTURE" },
-})
-// @ts-expect-error Image generation options are request-scoped, not provider configuration.
-Google.configure({ image: { providerOptions: { imageSize: "2K" } } })
-// @ts-expect-error Known Google string options retain their value kind.
-Image.generate({ model: googleProvider, prompt: "A lighthouse", options: { imageSize: 2 } })
-// @ts-expect-error Known Google numeric options retain their value kind.
-Image.generate({ model: googleProvider, prompt: "A lighthouse", options: { seed: "42" } })
-// @ts-expect-error Known Google boolean options retain their value kind.
-Image.generate({ model: googleProvider, prompt: "A lighthouse", options: { includeThoughts: "yes" } })
-
-const openai = OpenAI.image("gpt-image-2")
-// @ts-expect-error Image generation options are request-scoped, not provider configuration.
-OpenAI.configure({ image: { options: { quality: "medium" } } })
-const futureOpenAIOptions: ImageModelOptions<typeof openai> = { quality: "future-quality" }
-void futureOpenAIOptions
-Image.generate({
-  model: openai,
-  prompt: "A lighthouse",
-  images: [ImageInput.url("https://example.com/source.png"), ImageInput.file("file_123")],
-  options: {
-    mask: ImageInput.bytes(Uint8Array.from([1]), "image/png"),
-    quality: "hd",
-    outputFormat: "webp",
-    size: "2048x2048",
-    future_option: true,
-  },
-})
-Image.generate({ model: openai, prompt: "A lighthouse", options: { quality: "future-quality", size: "256x256" } })
-Image.generate({ model: openai, prompt: "A lighthouse", options: { size: "1792x1024" } })
-Image.generate({ model: openai, prompt: "A lighthouse", options: { native_future_option: true } })
-// @ts-expect-error Known OpenAI string options retain their value kind.
-Image.generate({ model: openai, prompt: "A lighthouse", options: { quality: 1 } })
-// @ts-expect-error Known OpenAI numeric options retain their value kind.
-Image.generate({ model: openai, prompt: "A lighthouse", options: { outputCompression: "80" } })
-OpenAI.imageGeneration({ action: "future-action", quality: "future-quality", size: "2048x2048" })
-// @ts-expect-error Hosted image generation numeric options retain their value kind.
-OpenAI.imageGeneration({ partialImages: "2" })
-// @ts-expect-error Known Google-like options are inferred from the selected model.
-Image.generate({ model: google, prompt: "A lighthouse", options: { aspectRatio: "wide" } })
-
-const xai = XAI.configure({ apiKey: "test" }).image("any-model-id")
-// @ts-expect-error Image generation options are request-scoped, not provider configuration.
-XAI.configure({ image: { options: { resolution: "1k" } } })
-Image.generate({
-  model: xai,
-  prompt: "A lighthouse",
-  images: [ImageInput.url("data:image/png;base64,AQID"), ImageInput.file("file_123")],
-  options: {
-    n: 2,
-    aspectRatio: "future-ratio",
-    resolution: "future-resolution",
-    responseFormat: "future-format",
-    future_option: true,
-  },
-})
-Image.generate({
-  model: xai,
-  prompt: "A lighthouse",
-  options: { aspect_ratio: "16:9", response_format: "b64_json", native_future_option: true },
-})
-// @ts-expect-error Known xAI numeric options retain their value kind.
-Image.generate({ model: xai, prompt: "A lighthouse", options: { n: "2" } })
-// @ts-expect-error Known xAI string options retain their value kind.
-Image.generate({ model: xai, prompt: "A lighthouse", options: { resolution: 2 } })
-
-const zai = ZAI.configure({ apiKey: "test" }).image("any-model-id")
-// @ts-expect-error Image generation options are request-scoped, not provider configuration.
-ZAI.configure({ image: { options: { quality: "hd" } } })
-Image.generate({
-  model: zai,
-  prompt: "A lighthouse",
-  options: { quality: "future-quality", userID: "user-123", future_option: true },
-})
-Image.generate({ model: zai, prompt: "A lighthouse", options: { user_id: "raw-user" } })
-// @ts-expect-error Known Z.ai string options retain their value kind.
-Image.generate({ model: zai, prompt: "A lighthouse", options: { quality: 1 } })
-// @ts-expect-error Known Z.ai user IDs retain their value kind.
-Image.generate({ model: zai, prompt: "A lighthouse", options: { userID: 1 } })
-
-declare const generic: ImageModel<ImageOptions>
-Image.generate({ model: generic, prompt: "A lighthouse", options: { arbitrary: true } })
-const explicitImageInput: ImageInput = ImageInput.url("https://example.com/image.png")
-void explicitImageInput
-
-// @ts-expect-error Raw strings are ambiguous and are not image inputs.
-Image.generate({ model: openai, prompt: "A lighthouse", images: ["AQID"] })
-// @ts-expect-error Byte image inputs require an explicit MIME type.
-Image.generate({ model: openai, prompt: "A lighthouse", images: [{ type: "bytes", data: new Uint8Array() }] })
-// @ts-expect-error File URIs require an explicit MIME type for Gemini fileData.
-Image.generate({ model: google, prompt: "A lighthouse", images: [{ type: "file-uri", uri: "files/123" }] })
-
-const request = Image.request({
-  model: google,
-  prompt: "A lighthouse",
-  options: { aspectRatio: "1:1", futureOption: true },
-})
-const typedRequest: ImageRequestFor<GoogleLikeOptions> = request
-void typedRequest
-
-// @ts-expect-error Image requests no longer expose a common count option.
-Image.generate({ model: openai, prompt: "A lighthouse", count: 2 })
-// @ts-expect-error Image requests no longer expose a common size option.
-Image.generate({ model: openai, prompt: "A lighthouse", size: { width: 1024, height: 1024 } })
-// @ts-expect-error Image requests no longer expose a common aspectRatio option.
-Image.generate({ model: openai, prompt: "A lighthouse", aspectRatio: "16:9" })
-// @ts-expect-error Image requests no longer expose a common seed option.
-Image.generate({ model: openai, prompt: "A lighthouse", seed: 1 })
-// @ts-expect-error Image requests do not expose metadata.
-Image.generate({ model: openai, prompt: "A lighthouse", metadata: { trace: true } })
-// @ts-expect-error Masks are provider options, not a common image request field.
-Image.generate({ model: openai, prompt: "A lighthouse", mask: ImageInput.url("https://example.com/mask.png") })
@@ -1,29 +0,0 @@
-export const dimensions = (data: Uint8Array) => {
-  if (data[0] === 0x89 && data[1] === 0x50 && data[2] === 0x4e && data[3] === 0x47)
-    return {
-      width: readUint32(data, 16),
-      height: readUint32(data, 20),
-    }
-  if (data[0] === 0xff && data[1] === 0xd8) {
-    for (let offset = 2; offset + 8 < data.length; ) {
-      if (data[offset] !== 0xff) {
-        offset++
-        continue
-      }
-      const marker = data[offset + 1]
-      if (
-        marker !== undefined &&
-        [0xc0, 0xc1, 0xc2, 0xc3, 0xc5, 0xc6, 0xc7, 0xc9, 0xca, 0xcb, 0xcd, 0xce, 0xcf].includes(marker)
-      )
-        return {
-          width: (data[offset + 7] << 8) | data[offset + 8],
-          height: (data[offset + 5] << 8) | data[offset + 6],
-        }
-      offset += 2 + ((data[offset + 2] << 8) | data[offset + 3])
-    }
-  }
-  throw new Error("Unsupported image fixture format")
-}
-
-const readUint32 = (data: Uint8Array, offset: number) =>
-  ((data[offset] << 24) | (data[offset + 1] << 16) | (data[offset + 2] << 8) | data[offset + 3]) >>> 0
@@ -59,12 +59,7 @@ export const runTools = <T extends Tools>(options: RunOptions<T>) =>
            ...request.messages,
            Message.assistant(state.assistantContent),
            ...dispatched.map(([call, dispatched]) =>
-              Message.tool({
-                id: call.id,
-                name: call.name,
-                result: dispatched.result,
-                providerMetadata: call.providerMetadata,
-              }),
+              Message.tool({ id: call.id, name: call.name, result: dispatched.result }),
            ),
          ],
        })
@@ -3,30 +3,8 @@ import { isContextOverflow } from "../src"
 import { classifyProviderFailure } from "../src/provider-error"

 describe("provider error classification", () => {
-  test("classifies provider token limit messages as context overflow", () => {
-    const messages = [
-      "tokens in request more than max tokens allowed",
-      '{"error":{"type":"request_too_large","message":"Request exceeds the maximum size"}}',
-      "Requested token count exceeds the model's maximum context length of 131072 tokens.",
-      "Input length (265330) exceeds model's maximum context length (262144).",
-      "Input length 131393 exceeds the maximum allowed input length of 131040 tokens.",
-      "The input (516368 tokens) is longer than the model's context length (262144 tokens).",
-      "Prompt has 5,958,968 tokens, but the configured context size is 256,000 tokens",
-      "Too many tokens",
-      "Token limit exceeded",
-    ]
-
-    expect(messages.every(isContextOverflow)).toBe(true)
-  })
-
-  test("does not classify rate limits as context overflow", () => {
-    const messages = [
-      "Throttling error: Too many tokens, please wait before trying again.",
-      "Rate limit exceeded, please retry after 30 seconds.",
-      "Too many requests. Please slow down.",
-    ]
-
-    expect(messages.some(isContextOverflow)).toBe(false)
+  test("classifies Z.AI GLM token limit messages as context overflow", () => {
+    expect(isContextOverflow("tokens in request more than max tokens allowed")).toBe(true)
  })

  test("classifies V1 plain-text rate limit fallbacks", () => {
@@ -235,9 +235,9 @@ describe("Anthropic Messages route", () => {
    }),
  )

-  // Regression: read tool results must stay structured so base64 media data is
-  // not JSON-stringified into `tool_result.content`.
-  it.effect("lowers media tool-result content as structured blocks", () =>
+  // Regression: screenshot/read tool results must stay structured so base64
+  // image data is not JSON-stringified into `tool_result.content`.
+  it.effect("lowers image tool-result content as structured image blocks", () =>
    Effect.gen(function* () {
      const prepared = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
        LLM.request({
@@ -253,7 +253,6 @@ describe("Anthropic Messages route", () => {
              result: [
                { type: "text", text: "Image read successfully" },
                { type: "file", uri: "data:image/png;base64,AAECAw==", mime: "image/png" },
-                { type: "file", uri: "data:application/pdf;base64,JVBERi0xLjQ=", mime: "application/pdf" },
              ],
            }),
          ],
@@ -264,7 +263,6 @@ describe("Anthropic Messages route", () => {
      expect(expectToolResult(prepared.body).content).toEqual([
        { type: "text", text: "Image read successfully" },
        { type: "image", source: { type: "base64", media_type: "image/png", data: "AAECAw==" } },
-        { type: "document", source: { type: "base64", media_type: "application/pdf", data: "JVBERi0xLjQ=" } },
      ])
    }),
  )
@@ -294,7 +292,7 @@ describe("Anthropic Messages route", () => {
    }),
  )

-  it.effect("rejects unsupported media in tool-result content with a clear error", () =>
+  it.effect("rejects non-image media in tool-result content with a clear error", () =>
    Effect.gen(function* () {
      const error = yield* LLMClient.prepare(
        LLM.request({
@@ -486,30 +484,6 @@ describe("Anthropic Messages route", () => {
    }),
  )

-  it.effect("keeps malformed server tool input terminal", () =>
-    Effect.gen(function* () {
-      const body = sseEvents(
-        { type: "message_start", message: { usage: { input_tokens: 5 } } },
-        {
-          type: "content_block_start",
-          index: 0,
-          content_block: { type: "server_tool_use", id: "call_1", name: "web_search" },
-        },
-        {
-          type: "content_block_delta",
-          index: 0,
-          delta: { type: "input_json_delta", partial_json: '{"query":"partial' },
-        },
-        { type: "content_block_stop", index: 0 },
-      )
-
-      const error = yield* LLMClient.generate(request).pipe(Effect.provide(fixedResponse(body)), Effect.flip)
-
-      expect(error).toBeInstanceOf(LLMError)
-      expect(error.message).toContain("Invalid JSON input for anthropic-messages tool call web_search")
-    }),
-  )
-
  it.effect("fails with a typed provider error for stream error frames", () =>
    Effect.gen(function* () {
      const error = yield* LLMClient.generate(request).pipe(
@@ -758,7 +732,7 @@ describe("Anthropic Messages route", () => {
    }),
  )

-  it.effect("continues a conversation with user media content", () =>
+  it.effect("continues a conversation with user image content", () =>
    Effect.gen(function* () {
      const response = yield* LLMClient.generate(
        LLM.request({
@@ -768,7 +742,6 @@ describe("Anthropic Messages route", () => {
            Message.user([
              { type: "text", text: "What is in this image?" },
              { type: "media", mediaType: "image/png", data: "AAECAw==" },
-              { type: "media", mediaType: "application/pdf", data: "JVBERi0xLjQ=", filename: "report.pdf" },
            ]),
          ],
        }),
@@ -784,7 +757,6 @@ describe("Anthropic Messages route", () => {
                    content: [
                      { type: "text", text: "What is in this image?" },
                      { type: "image", source: { type: "base64", media_type: "image/png", data: "AAECAw==" } },
-                      { type: "document", source: { type: "base64", media_type: "application/pdf", data: "JVBERi0xLjQ=" } },
                    ],
                  },
                ],
@@ -303,32 +303,6 @@ describe("Bedrock Converse route", () => {
    }),
  )

-  it.effect("emits malformed tool input as an unexecuted tool error", () =>
-    Effect.gen(function* () {
-      const body = eventStreamBody(
-        ["messageStart", { role: "assistant" }],
-        [
-          "contentBlockStart",
-          {
-            contentBlockIndex: 0,
-            start: { toolUse: { toolUseId: "tool_1", name: "lookup" } },
-          },
-        ],
-        ["contentBlockDelta", { contentBlockIndex: 0, delta: { toolUse: { input: '{"query":"partial' } } }],
-        ["contentBlockStop", { contentBlockIndex: 0 }],
-        ["messageStop", { stopReason: "end_turn" }],
-      )
-      const response = yield* LLMClient.generate(baseRequest).pipe(Effect.provide(fixedBytes(body)))
-
-      expect(response.events.find((event) => event.type === "tool-input-error")).toMatchObject({
-        id: "tool_1",
-        name: "lookup",
-        raw: '{"query":"partial',
-      })
-      expect(response.finishReason).toBe("tool-calls")
-    }),
-  )
-
  it.effect("decodes reasoning deltas", () =>
    Effect.gen(function* () {
      const body = eventStreamBody(
@@ -549,12 +523,10 @@ describe("Bedrock Converse route", () => {
        LLM.request({
          id: "req_doc",
          model,
-          cache: "none",
          messages: [
            Message.user([
-              { type: "text", text: "Summarize these documents." },
              { type: "media", mediaType: "application/pdf", data: "UERGREFUQQ==", filename: "report.pdf" },
-              { type: "media", mediaType: "text/csv", data: "Q1NWREFUQQ==", filename: "data.csv" },
+              { type: "media", mediaType: "text/csv", data: "Q1NWREFUQQ==" },
            ]),
          ],
        }),
@@ -565,9 +537,10 @@ describe("Bedrock Converse route", () => {
          {
            role: "user",
            content: [
-              { text: "Summarize these documents." },
+              // Filename round-trips when supplied.
              { document: { format: "pdf", name: "report.pdf", source: { bytes: "UERGREFUQQ==" } } },
-              { document: { format: "csv", name: "data.csv", source: { bytes: "Q1NWREFUQQ==" } } },
+              // Falls back to a stable placeholder when filename is missing.
+              { document: { format: "csv", name: "document.csv", source: { bytes: "Q1NWREFUQQ==" } } },
            ],
          },
        ],
@@ -575,96 +548,6 @@ describe("Bedrock Converse route", () => {
    }),
  )

-  it.effect("requires names for document media", () =>
-    Effect.gen(function* () {
-      const error = yield* LLMClient.prepare(
-        LLM.request({
-          model,
-          messages: [Message.user({ type: "media", mediaType: "application/pdf", data: "UERGREFUQQ==" })],
-        }),
-      ).pipe(Effect.flip)
-
-      expect(error.message).toContain("document media requires a filename")
-    }),
-  )
-
-  it.effect("passes named document-only messages through for provider validation", () =>
-    Effect.gen(function* () {
-      const prepared = yield* LLMClient.prepare<BedrockConverse.BedrockConverseBody>(
-        LLM.request({
-          model,
-          cache: "none",
-          messages: [
-            Message.user({
-              type: "media",
-              mediaType: "application/pdf",
-              data: "UERGREFUQQ==",
-              filename: "report.pdf",
-            }),
-          ],
-        }),
-      )
-
-      expect(prepared.body.messages).toEqual([
-        {
-          role: "user",
-          content: [{ document: { format: "pdf", name: "report.pdf", source: { bytes: "UERGREFUQQ==" } } }],
-        },
-      ])
-    }),
-  )
-
-  it.effect("lowers document media in tool results", () =>
-    Effect.gen(function* () {
-      const prepared = yield* LLMClient.prepare<BedrockConverse.BedrockConverseBody>(
-        LLM.request({
-          model,
-          messages: [
-            Message.assistant([ToolCallPart.make({ id: "call_1", name: "read", input: { path: "report.pdf" } })]),
-            Message.tool({
-              id: "call_1",
-              name: "read",
-              result: {
-                type: "content",
-                value: [
-                  { type: "text", text: "Read successfully" },
-                  {
-                    type: "file",
-                    uri: "data:application/pdf;base64,UERGREFUQQ==",
-                    mime: "application/pdf",
-                    name: "report",
-                  },
-                ],
-              },
-            }),
-          ],
-        }),
-      )
-
-      expect(prepared.body.messages).toEqual([
-        {
-          role: "assistant",
-          content: [{ toolUse: { toolUseId: "call_1", name: "read", input: { path: "report.pdf" } } }],
-        },
-        {
-          role: "user",
-          content: [
-            {
-              toolResult: {
-                toolUseId: "call_1",
-                status: "success",
-                content: [
-                  { text: "Read successfully" },
-                  { document: { format: "pdf", name: "report", source: { bytes: "UERGREFUQQ==" } } },
-                ],
-              },
-            },
-          ],
-        },
-      ])
-    }),
-  )
-
  it.effect("rejects unsupported image media types", () =>
    Effect.gen(function* () {
      const error = yield* LLMClient.prepare(
@@ -1,7 +1,7 @@
 import { describe, expect } from "bun:test"
 import { ConfigProvider, Effect, Schema } from "effect"
 import { HttpClientRequest } from "effect/unstable/http"
-import { LLM, LLMEvent } from "../../src"
+import { LLM } from "../../src"
 import { CloudflareAIGateway, CloudflareWorkersAI } from "../../src/providers/cloudflare"
 import { LLMClient } from "../../src/route"
 import { it } from "../lib/effect"
@@ -83,59 +83,6 @@ describe("Cloudflare", () => {
    }),
  )

-  it.effect("preserves reasoning details for AI Gateway continuation", () =>
-    Effect.gen(function* () {
-      const model = CloudflareAIGateway.configure({
-        accountId: "test-account",
-        gatewayId: "test-gateway",
-        apiKey: "test-token",
-      }).model("anthropic/claude-sonnet-4.6")
-      const details = [
-        { type: "reasoning.text", text: "Think", format: "anthropic-claude-v1", index: 0 },
-        { type: "reasoning.text", text: "ing", format: "anthropic-claude-v1", index: 0 },
-        { type: "reasoning.text", signature: "signed", format: "anthropic-claude-v1", index: 0 },
-      ]
-      const merged = [
-        {
-          type: "reasoning.text",
-          text: "Thinking",
-          signature: "signed",
-          format: "anthropic-claude-v1",
-          index: 0,
-        },
-      ]
-      const response = yield* LLM.generate(LLM.request({ model, prompt: "Say hello." })).pipe(
-        Effect.provide(
-          dynamicResponse((input) =>
-            Effect.succeed(
-              input.respond(
-                sseEvents(
-                  deltaChunk({ reasoning: "Think", reasoning_details: [details[0]] }),
-                  deltaChunk({ reasoning: "ing", reasoning_details: [details[1]] }),
-                  deltaChunk({ reasoning_details: [details[2]] }),
-                  deltaChunk({ content: "Hello" }),
-                  deltaChunk({}, "stop"),
-                ),
-                { headers: { "content-type": "text/event-stream" } },
-              ),
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("Thinking")
-      expect(response.events.filter(LLMEvent.is.reasoningDelta)).toHaveLength(2)
-      expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-        openai: { reasoningField: "reasoning", reasoningDetails: merged },
-      })
-
-      const replay = yield* LLMClient.prepare(LLM.request({ model, messages: [response.message] }))
-      expect(replay.body.messages).toEqual([
-        { role: "assistant", content: "Hello", reasoning: "Thinking", reasoning_details: merged },
-      ])
-    }),
-  )
-
  it.effect("defaults AI Gateway id to default when omitted or blank", () =>
    Effect.gen(function* () {
      expect(
@@ -70,7 +70,6 @@ describe("Gemini route", () => {
            Message.user([
              { type: "text", text: "What is in this image?" },
              { type: "media", mediaType: "image/png", data: "AAECAw==" },
-              { type: "media", mediaType: "application/pdf", data: "JVBERi0xLjQ=" },
            ]),
            Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
            Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
@@ -82,11 +81,7 @@ describe("Gemini route", () => {
        contents: [
          {
            role: "user",
-            parts: [
-              { text: "What is in this image?" },
-              { inlineData: { mimeType: "image/png", data: "AAECAw==" } },
-              { inlineData: { mimeType: "application/pdf", data: "JVBERi0xLjQ=" } },
-            ],
+            parts: [{ text: "What is in this image?" }, { inlineData: { mimeType: "image/png", data: "AAECAw==" } }],
          },
          {
            role: "model",
@@ -95,12 +90,7 @@ describe("Gemini route", () => {
          {
            role: "user",
            parts: [
-              {
-                functionResponse: {
-                  name: "lookup",
-                  response: { name: "lookup", content: '{"forecast":"sunny"}' },
-                },
-              },
+              { functionResponse: { name: "lookup", response: { name: "lookup", content: '{"forecast":"sunny"}' } } },
            ],
          },
        ],
@@ -120,7 +110,7 @@ describe("Gemini route", () => {
    }),
  )

-  it.effect("continues media tool results as inline model input without base64 text", () =>
+  it.effect("continues image tool results as inline vision input without base64 text", () =>
    Effect.gen(function* () {
      const prepared = yield* LLMClient.prepare<Gemini.GeminiBody>(
        LLM.request({
@@ -135,7 +125,6 @@ describe("Gemini route", () => {
                value: [
                  { type: "text", text: "Image read successfully" },
                  { type: "file", uri: "data:image/png;base64,AAECAw==", mime: "image/png", name: "pixel.png" },
-                  { type: "file", uri: "data:application/pdf;base64,JVBERi0xLjQ=", mime: "application/pdf" },
                ],
              },
            }),
@@ -152,12 +141,9 @@ describe("Gemini route", () => {
              functionResponse: {
                name: "read",
                response: { name: "read", content: "Image read successfully" },
-                parts: [
-                  { inlineData: { mimeType: "image/png", data: "AAECAw==" } },
-                  { inlineData: { mimeType: "application/pdf", data: "JVBERi0xLjQ=" } },
-                ],
              },
            },
+            { inlineData: { mimeType: "image/png", data: "AAECAw==" } },
          ],
        },
      ])
@@ -188,13 +174,8 @@ describe("Gemini route", () => {
        {
          role: "user",
          parts: [
-            {
-              functionResponse: {
-                name: "read",
-                response: { name: "read", content: "" },
-                parts: [{ inlineData: { mimeType: "image/jpeg", data: "/9j/" } }],
-              },
-            },
+            { functionResponse: { name: "read", response: { name: "read", content: "" } } },
+            { inlineData: { mimeType: "image/jpeg", data: "/9j/" } },
          ],
        },
      ])
@@ -391,10 +372,7 @@ describe("Gemini route", () => {
              parts: [
                { text: "thinking", thought: true },
                { text: "", thought: true, thoughtSignature: "thought_sig" },
-                {
-                  functionCall: { id: "provider_call", name: "lookup", args: { query: "weather" } },
-                  thoughtSignature: "tool_sig",
-                },
+                { functionCall: { name: "lookup", args: { query: "weather" } }, thoughtSignature: "tool_sig" },
              ],
            },
            finishReason: "STOP",
@@ -420,10 +398,7 @@ describe("Gemini route", () => {
        id: "reasoning-0",
        providerMetadata: { google: { thoughtSignature: "thought_sig" } },
      })
-      expect(toolCall).toMatchObject({
-        id: "tool_0",
-        providerMetadata: { google: { functionCallId: "provider_call", thoughtSignature: "tool_sig" } },
-      })
+      expect(toolCall).toMatchObject({ providerMetadata: { google: { thoughtSignature: "tool_sig" } } })
      expect(response.events.findIndex((event) => event.type === "reasoning-end")).toBeLessThan(
        response.events.findIndex((event) => event.type === "tool-call"),
      )
@@ -441,13 +416,6 @@ describe("Gemini route", () => {
                providerMetadata: toolCall?.providerMetadata,
              }),
            ]),
-            Message.tool({
-              id: "tool_0",
-              name: "lookup",
-              result: "done",
-              resultType: "text",
-              providerMetadata: toolCall?.providerMetadata,
-            }),
          ],
        }),
      )
@@ -456,22 +424,7 @@ describe("Gemini route", () => {
          role: "model",
          parts: [
            { text: "thinking", thought: true, thoughtSignature: "thought_sig" },
-            {
-              functionCall: { id: "provider_call", name: "lookup", args: { query: "weather" } },
-              thoughtSignature: "tool_sig",
-            },
-          ],
-        },
-        {
-          role: "user",
-          parts: [
-            {
-              functionResponse: {
-                id: "provider_call",
-                name: "lookup",
-                response: { name: "lookup", content: "done" },
-              },
-            },
+            { functionCall: { name: "lookup", args: { query: "weather" } }, thoughtSignature: "tool_sig" },
          ],
        },
      ])
@@ -545,7 +498,7 @@ describe("Gemini route", () => {
            content: {
              role: "model",
              parts: [
-                { functionCall: { id: "tool_0", name: "lookup", args: { query: "weather" } } },
+                { functionCall: { name: "lookup", args: { query: "weather" } } },
                { functionCall: { name: "lookup", args: { query: "news" } } },
              ],
            },
@@ -560,13 +513,7 @@ describe("Gemini route", () => {
      ).pipe(Effect.provide(fixedResponse(body)))

      expect(response.toolCalls).toEqual([
-        {
-          type: "tool-call",
-          id: "tool_0",
-          name: "lookup",
-          input: { query: "weather" },
-          providerMetadata: { google: { functionCallId: "tool_0" } },
-        },
+        { type: "tool-call", id: "tool_0", name: "lookup", input: { query: "weather" } },
        { type: "tool-call", id: "tool_1", name: "lookup", input: { query: "news" } },
      ])
      expect(response.events.at(-1)).toMatchObject({ type: "finish", reason: "tool-calls" })
@@ -1,56 +0,0 @@
-import { describe, expect } from "bun:test"
-import { Effect } from "effect"
-import { Image, ImageInput } from "../../src"
-import { Google } from "../../src/providers"
-import { dimensions } from "../lib/image"
-import { recordedTests } from "../recorded-test"
-
-const model = Google.configure({
-  apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY ?? "fixture",
-}).image("gemini-3.1-flash-image")
-
-const recorded = recordedTests({
-  prefix: "google-images",
-  provider: "google",
-  protocol: "google-images",
-  requires: ["GOOGLE_GENERATIVE_AI_API_KEY"],
-})
-
-describe("Google Images recorded", () => {
-  recorded.effect("generates an image", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model,
-        prompt: "A simple flat blue circle centered on a plain white background.",
-        options: { aspectRatio: "1:1" },
-      })
-
-      expect(response.images).toHaveLength(1)
-      expect(response.image?.mediaType).toMatch(/^image\//)
-      expect(response.image?.data).toBeInstanceOf(Uint8Array)
-      expect(response.image?.data.length).toBeGreaterThan(0)
-    }),
-  )
-
-  recorded.effect("edits an image", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model,
-        prompt:
-          "Transform this minimal source into a bright orange sun icon with eight rounded rays on a pale blue background.",
-        images: [
-          ImageInput.bytes(
-            yield* Effect.promise(() => Bun.file("test/fixtures/images/edit-source.jpg").bytes()),
-            "image/jpeg",
-          ),
-        ],
-        options: { aspectRatio: "1:1" },
-      })
-
-      expect(response.image?.mediaType).toBe("image/jpeg")
-      expect(response.image?.data).toBeInstanceOf(Uint8Array)
-      if (!(response.image?.data instanceof Uint8Array)) throw new Error("Expected owned Google image bytes")
-      expect(dimensions(response.image.data)).toEqual({ width: 1024, height: 1024 })
-    }),
-  )
-})
@@ -1,41 +1,31 @@
 import { describe, expect } from "bun:test"
 import { Effect } from "effect"
-import { LLM, LLMEvent, LLMResponse, Model } from "../../src"
-import { OpenAIChat } from "../../src/protocols/openai-chat"
+import { LLM, LLMEvent } from "../../src"
 import * as OpenAICompatible from "../../src/providers/openai-compatible"
 import * as OpenRouter from "../../src/providers/openrouter"
 import { LLMClient } from "../../src/route"
 import { recordedTests } from "../recorded-test"
-import { expectWeatherToolLoop, goldenWeatherToolLoopRequest, runWeatherToolLoop } from "../recorded-scenarios"

 const cases = [
  {
    name: "OpenRouter",
-    model: Model.update(
-      OpenRouter.configure({
-        apiKey: process.env.OPENROUTER_API_KEY ?? "fixture",
-        providerOptions: { openrouter: { reasoning: { max_tokens: 1024 } } },
-      }).model("anthropic/claude-sonnet-4.6"),
-      { compatibility: { reasoningField: "reasoning" } },
-    ),
+    model: OpenRouter.configure({
+      apiKey: process.env.OPENROUTER_API_KEY ?? "fixture",
+      providerOptions: { openrouter: { reasoning: { max_tokens: 1024 } } },
+    }).model("anthropic/claude-sonnet-4.6"),
    requires: ["OPENROUTER_API_KEY"],
    cassette: "openrouter-reasoning",
-    structured: true,
  },
  {
    name: "Vercel AI Gateway",
-    model: Model.update(
-      OpenAICompatible.configure({
-        provider: "vercel-ai-gateway",
-        baseURL: "https://ai-gateway.vercel.sh/v1",
-        apiKey: process.env.AI_GATEWAY_API_KEY ?? "fixture",
-        http: { body: { reasoning: { enabled: true, max_tokens: 1024 } } },
-      }).model("anthropic/claude-sonnet-4.6"),
-      { compatibility: { reasoningField: "reasoning" } },
-    ),
+    model: OpenAICompatible.configure({
+      provider: "vercel-ai-gateway",
+      baseURL: "https://ai-gateway.vercel.sh/v1",
+      apiKey: process.env.AI_GATEWAY_API_KEY ?? "fixture",
+      http: { body: { reasoning: { enabled: true, max_tokens: 1024 } } },
+    }).model("anthropic/claude-sonnet-4.6"),
    requires: ["AI_GATEWAY_API_KEY"],
    cassette: "vercel-ai-gateway-reasoning",
-    structured: true,
  },
 ] as const

@@ -67,82 +57,11 @@ for (const item of cases) {
          expect(response.text.replaceAll(",", "").trim()).toBe("37887")
          expect(response.reasoning.length).toBeGreaterThan(0)
          expect(response.events.some(LLMEvent.is.reasoningDelta)).toBe(true)
-          const metadata = response.message.content.find((part) => part.type === "reasoning")?.providerMetadata
-          expect(metadata?.openai?.reasoningField).toBe(item.structured ? "reasoning" : "reasoning_content")
-          expect(Array.isArray(metadata?.openai?.reasoningDetails)).toBe(item.structured)
-          if (!item.structured) return
-          const details = metadata?.openai?.reasoningDetails
-          if (!Array.isArray(details)) return
-          expect(
-            details.some(
-              (detail) =>
-                typeof detail === "object" &&
-                detail !== null &&
-                "signature" in detail &&
-                typeof detail.signature === "string" &&
-                detail.signature.length > 0,
-            ),
-          ).toBe(true)
-
-          const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-            LLM.request({ model: item.model, messages: [response.message] }),
-          )
-          expect(replay.body.messages).toMatchObject([
-            { role: "assistant", content: response.text, reasoning: response.reasoning },
-          ])
-          const replayDetails =
-            replay.body.messages[0]?.role === "assistant" ? replay.body.messages[0].reasoning_details : undefined
-          expect(Array.isArray(replayDetails)).toBe(true)
-          if (!Array.isArray(replayDetails)) return
-          expect(replayDetails).toEqual(details)
-          expect(replayDetails).toHaveLength(1)
-          expect(replayDetails[0]).toMatchObject({
-            type: "reasoning.text",
-            text: response.reasoning,
-            signature: expect.any(String),
+          expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
+            openai: { reasoningField: "reasoning" },
          })
        }),
      30_000,
    )
-
-    recorded.effect.with(
-      "continues signed reasoning through a tool loop",
-      { cassette: `${item.cassette}-tool-loop`, tags: ["continuation", "tool", "tool-loop"] },
-      () =>
-        Effect.gen(function* () {
-          const events = yield* runWeatherToolLoop(
-            goldenWeatherToolLoopRequest({
-              id: `${item.cassette}-tool-loop`,
-              model: item.model,
-              maxTokens: 1536,
-              temperature: false,
-            }),
-          )
-
-          expectWeatherToolLoop(events)
-          expect(
-            LLMResponse.text({
-              events: events.slice(events.findIndex(LLMEvent.is.stepFinish) + 1),
-            }).trim(),
-          ).toMatch(/^Paris is sunny\.?$/)
-          const details = events
-            .filter(LLMEvent.is.reasoningEnd)
-            .map((event) => event.providerMetadata?.openai?.reasoningDetails)
-            .find(Array.isArray)
-          expect(Array.isArray(details)).toBe(item.structured)
-          if (!item.structured || !Array.isArray(details)) return
-          expect(
-            details.some(
-              (detail) =>
-                typeof detail === "object" &&
-                detail !== null &&
-                "signature" in detail &&
-                typeof detail.signature === "string" &&
-                detail.signature.length > 0,
-            ),
-          ).toBe(true)
-        }),
-      60_000,
-    )
  })
 }
@@ -92,45 +92,6 @@ describe("OpenAI Chat route", () => {
    }),
  )

-  it.effect("writes reasoning to a configured custom field on every assistant message", () =>
-    Effect.gen(function* () {
-      const prepared = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({
-          model: Model.update(model, { compatibility: { reasoningField: "vendor_reasoning" } }),
-          messages: [
-            Message.assistant([
-              {
-                type: "reasoning",
-                text: "thinking",
-                providerMetadata: { openai: { reasoningField: "reasoning" } },
-              },
-              { type: "text", text: "Hello" },
-            ]),
-            Message.assistant("Done"),
-          ],
-        }),
-      )
-
-      expect(prepared.body.messages).toEqual([
-        { role: "assistant", content: "Hello", vendor_reasoning: "thinking" },
-        { role: "assistant", content: "Done", vendor_reasoning: "" },
-      ])
-    }),
-  )
-
-  it.effect("rejects reasoning fields that conflict with assistant message fields", () =>
-    Effect.gen(function* () {
-      const error = yield* LLMClient.prepare(
-        LLM.request({
-          model: Model.update(model, { compatibility: { reasoningField: "content" } }),
-          messages: [Message.assistant([{ type: "reasoning", text: "thinking" }])],
-        }),
-      ).pipe(Effect.flip)
-
-      expect(error.message).toContain("reserved field content")
-    }),
-  )
-
  it.effect("maps OpenAI provider options to Chat options", () =>
    Effect.gen(function* () {
      const prepared = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
@@ -609,404 +570,6 @@ describe("OpenAI Chat route", () => {
    }),
  )

-  it.effect("parses and replays a configured custom reasoning field", () =>
-    Effect.gen(function* () {
-      const custom = Model.update(model, { compatibility: { reasoningField: "vendor_reasoning" } })
-      const response = yield* LLMClient.generate(LLM.updateRequest(request, { model: custom })).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { vendor_reasoning: "thinking" } }] },
-              { choices: [{ delta: { content: "Hello" } }] },
-              { choices: [{ delta: {}, finish_reason: "stop" }] },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("thinking")
-      expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-        openai: { reasoningField: "vendor_reasoning" },
-      })
-
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({ model: custom, messages: [response.message] }),
-      )
-      expect(replay.body.messages).toEqual([
-        { role: "assistant", content: "Hello", vendor_reasoning: "thinking" },
-      ])
-    }),
-  )
-
-  it.effect("preserves and replays reasoning details alongside scalar reasoning", () =>
-    Effect.gen(function* () {
-      const details = [
-        { type: "reasoning.text", text: "thinking", format: "anthropic-claude-v1", index: 0 },
-        { type: "reasoning.encrypted", data: "opaque", format: "anthropic-claude-v1", index: 1 },
-      ]
-      const response = yield* LLMClient.generate(
-        LLM.updateRequest(request, {
-          tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
-        }),
-      ).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { reasoning: "thinking", reasoning_details: [details[0]] } }] },
-              { choices: [{ delta: { reasoning_details: [details[1]] } }] },
-              {
-                choices: [
-                  {
-                    delta: {
-                      tool_calls: [
-                        { index: 0, id: "call_1", function: { name: "lookup", arguments: '{"query":"weather"}' } },
-                      ],
-                    },
-                    finish_reason: "tool_calls",
-                  },
-                ],
-              },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("thinking")
-      expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-        openai: { reasoningField: "reasoning", reasoningDetails: details },
-      })
-
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({ model, messages: [response.message] }),
-      )
-      expect(replay.body.messages).toEqual([
-        {
-          role: "assistant",
-          content: null,
-          reasoning: "thinking",
-          reasoning_details: details,
-          tool_calls: [
-            {
-              id: "call_1",
-              type: "function",
-              function: { name: "lookup", arguments: '{"query":"weather"}' },
-            },
-          ],
-        },
-      ])
-    }),
-  )
-
-  it.effect("uses reasoning details as display fallback without inventing a scalar replay field", () =>
-    Effect.gen(function* () {
-      const details = [
-        { type: "reasoning.summary", summary: "thinking", format: "openai-responses-v1", index: 0 },
-        { type: "reasoning.encrypted", data: "opaque", format: "openai-responses-v1", index: 1 },
-      ]
-      const response = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { reasoning_details: [details[0]] } }] },
-              { choices: [{ delta: { reasoning_details: [details[1]] } }] },
-              { choices: [{ delta: { content: "Hello" } }] },
-              { choices: [{ delta: {}, finish_reason: "stop" }] },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("thinking")
-      expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-        openai: { reasoningDetails: details },
-      })
-
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({ model, messages: [response.message] }),
-      )
-      expect(replay.body.messages).toEqual([{ role: "assistant", content: "Hello", reasoning_details: details }])
-    }),
-  )
-
-  it.effect("preserves unknown reasoning details while using scalar display text", () =>
-    Effect.gen(function* () {
-      const details = [{ type: "reasoning.future", format: "provider-v2", state: { opaque: true } }]
-      const response = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { reasoning: "thinking", reasoning_details: details } }] },
-              { choices: [{ delta: { content: "Hello" } }] },
-              { choices: [{ delta: {}, finish_reason: "stop" }] },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("thinking")
-      expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-        openai: { reasoningField: "reasoning", reasoningDetails: details },
-      })
-
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({ model, messages: [response.message] }),
-      )
-      expect(replay.body.messages).toEqual([
-        { role: "assistant", content: "Hello", reasoning: "thinking", reasoning_details: details },
-      ])
-    }),
-  )
-
-  it.effect("uses scalar display text for signature-only reasoning details", () =>
-    Effect.gen(function* () {
-      const details = [{ type: "reasoning.text", signature: "signed", format: "provider-v2", index: 0 }]
-      const response = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { reasoning: "thinking", reasoning_details: details } }] },
-              { choices: [{ delta: { content: "Hello" } }] },
-              { choices: [{ delta: {}, finish_reason: "stop" }] },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("thinking")
-      expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-        openai: { reasoningField: "reasoning", reasoningDetails: details },
-      })
-    }),
-  )
-
-  it.effect("ignores scalar reasoning after content starts", () =>
-    Effect.gen(function* () {
-      const details = [{ type: "reasoning.text", text: "detail", format: "unknown", index: 0 }]
-      const response = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { reasoning_details: details } }] },
-              { choices: [{ delta: { content: "Hello" } }] },
-              { choices: [{ delta: { reasoning: "scalar" } }] },
-              { choices: [{ delta: {}, finish_reason: "stop" }] },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("detail")
-      expect(response.events.filter(LLMEvent.is.reasoningStart)).toHaveLength(1)
-      expect(response.events.filter(LLMEvent.is.reasoningEnd)).toHaveLength(1)
-      expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-        openai: { reasoningDetails: details },
-      })
-    }),
-  )
-
-  it.effect("preserves an explicitly empty reasoning details array", () =>
-    Effect.gen(function* () {
-      const response = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { reasoning_details: [] } }] },
-              { choices: [{ delta: { content: "Hello" } }] },
-              { choices: [{ delta: {}, finish_reason: "stop" }] },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("")
-      expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-        openai: { reasoningDetails: [] },
-      })
-
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({ model, messages: [response.message] }),
-      )
-      expect(replay.body.messages).toEqual([{ role: "assistant", content: "Hello", reasoning_details: [] }])
-    }),
-  )
-
-  it.effect("attaches signature-only details that arrive after content", () =>
-    Effect.gen(function* () {
-      const details = [
-        { type: "reasoning.text", text: "thinking", format: "anthropic-claude-v1", index: 0 },
-        { type: "reasoning.text", signature: "signed", format: "anthropic-claude-v1", index: 0 },
-      ]
-      const merged = [
-        {
-          type: "reasoning.text",
-          text: "thinking",
-          signature: "signed",
-          format: "anthropic-claude-v1",
-          index: 0,
-        },
-      ]
-      const response = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { reasoning: "thinking", reasoning_details: [details[0]] } }] },
-              { choices: [{ delta: { content: "Hello" } }] },
-              { choices: [{ delta: { reasoning_details: [details[1]] } }] },
-              { choices: [{ delta: {}, finish_reason: "stop" }] },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("thinking")
-      expect(response.message.content.filter((part) => part.type === "reasoning")).toHaveLength(1)
-      expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-        openai: { reasoningField: "reasoning", reasoningDetails: merged },
-      })
-      expect(response.events.filter(LLMEvent.is.reasoningStart)).toHaveLength(1)
-      expect(response.events.filter(LLMEvent.is.reasoningDelta)).toHaveLength(1)
-      expect(response.events.filter(LLMEvent.is.reasoningEnd)).toHaveLength(1)
-      expect(response.events.filter(LLMEvent.is.reasoningEnd).at(-1)?.providerMetadata).toEqual({
-        openai: { reasoningField: "reasoning", reasoningDetails: merged },
-      })
-      expect(response.events.findIndex(LLMEvent.is.reasoningEnd)).toBeLessThan(
-        response.events.findIndex(LLMEvent.is.textStart),
-      )
-
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({ model, messages: [response.message] }),
-      )
-      expect(replay.body.messages).toEqual([
-        { role: "assistant", content: "Hello", reasoning: "thinking", reasoning_details: merged },
-      ])
-    }),
-  )
-
-  it.effect("preserves metadata-only reasoning when the stream ends", () =>
-    Effect.gen(function* () {
-      const details = [{ type: "reasoning.encrypted", data: "opaque", format: "openai-responses-v1", index: 0 }]
-      const response = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { reasoning_details: details } }] },
-              { choices: [{ delta: {}, finish_reason: "stop" }] },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.message.content).toEqual([
-        { type: "reasoning", text: "", providerMetadata: { openai: { reasoningDetails: details } } },
-      ])
-      expect(response.events.filter(LLMEvent.is.reasoningStart)).toHaveLength(1)
-      expect(response.events.filter(LLMEvent.is.reasoningEnd)).toHaveLength(1)
-
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({ model, messages: [response.message] }),
-      )
-      expect(replay.body.messages).toEqual([{ role: "assistant", content: null, reasoning_details: details }])
-    }),
-  )
-
-  it.effect("flushes details-only display reasoning when the stream ends", () =>
-    Effect.gen(function* () {
-      const details = [{ type: "reasoning.summary", summary: "summary", format: "openai-responses-v1", index: 0 }]
-      const response = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { choices: [{ delta: { reasoning_details: details } }] },
-              { choices: [{ delta: {}, finish_reason: "stop" }] },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.reasoning).toBe("summary")
-      expect(response.message.content).toEqual([
-        { type: "reasoning", text: "summary", providerMetadata: { openai: { reasoningDetails: details } } },
-      ])
-    }),
-  )
-
-  it.effect("replays details from multiple reasoning parts in order", () =>
-    Effect.gen(function* () {
-      const first = { type: "reasoning.text", text: "first", signature: "signed-0", index: 0 }
-      const second = { type: "reasoning.text", text: "second", signature: "signed-1", index: 1 }
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({
-          model,
-          messages: [
-            Message.assistant([
-              {
-                type: "reasoning",
-                text: "first",
-                providerMetadata: { openai: { reasoningDetails: [first] } },
-              },
-              {
-                type: "reasoning",
-                text: "second",
-                providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: [second] } },
-              },
-            ]),
-          ],
-        }),
-      )
-
-      expect(replay.body.messages).toEqual([
-        { role: "assistant", content: null, reasoning: "firstsecond", reasoning_details: [first, second] },
-      ])
-    }),
-  )
-
-  it.effect("retains scalar replay for mixed structured reasoning parts", () =>
-    Effect.gen(function* () {
-      const detail = { type: "reasoning.encrypted", data: "opaque", index: 0 }
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({
-          model,
-          messages: [
-            Message.assistant([
-              {
-                type: "reasoning",
-                text: "A",
-                providerMetadata: { openai: { reasoningDetails: [detail] } },
-              },
-              { type: "reasoning", text: "B" },
-            ]),
-          ],
-        }),
-      )
-
-      expect(replay.body.messages).toEqual([
-        { role: "assistant", content: null, reasoning_content: "AB", reasoning_details: [detail] },
-      ])
-    }),
-  )
-
-  it.effect("replays native scalar reasoning alongside native details", () =>
-    Effect.gen(function* () {
-      const details = [{ type: "reasoning.encrypted", data: "opaque", index: 0 }]
-      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        LLM.request({
-          model,
-          messages: [
-            Message.make({
-              role: "assistant",
-              content: [{ type: "reasoning", text: "thinking" }],
-              native: { openaiCompatible: { reasoning_content: "thinking", reasoning_details: details } },
-            }),
-          ],
-        }),
-      )
-
-      expect(replay.body.messages).toEqual([
-        { role: "assistant", content: null, reasoning_content: "thinking", reasoning_details: details },
-      ])
-    }),
-  )
-
  it.effect("assembles streamed tool call input", () =>
    Effect.gen(function* () {
      const body = sseEvents(
@@ -1043,67 +606,6 @@ describe("OpenAI Chat route", () => {
    }),
  )

-  it.effect("ignores empty identity fields on later tool call deltas", () =>
-    Effect.gen(function* () {
-      const body = sseEvents(
-        deltaChunk({
-          tool_calls: [{ index: 0, id: "call_1", function: { name: "lookup", arguments: "{" } }],
-        }),
-        deltaChunk({
-          tool_calls: [{ index: 0, id: "", function: { name: "", arguments: '\"query\":\"weather\"}' } }],
-        }),
-        deltaChunk({}, "tool_calls"),
-      )
-      const response = yield* LLMClient.generate(
-        LLM.updateRequest(request, {
-          tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
-        }),
-      ).pipe(Effect.provide(fixedResponse(body)))
-
-      expect(response.toolCalls).toMatchObject([{ id: "call_1", name: "lookup", input: { query: "weather" } }])
-    }),
-  )
-
-  it.effect("buffers tool call deltas until the function name arrives", () =>
-    Effect.gen(function* () {
-      const body = sseEvents(
-        deltaChunk({
-          tool_calls: [{ index: 0, id: "call_1", function: { arguments: "{" } }],
-        }),
-        deltaChunk({
-          tool_calls: [{ index: 0, function: { name: "lookup", arguments: '\"query\":' } }],
-        }),
-        deltaChunk({ tool_calls: [{ index: 0, function: { arguments: '\"weather\"}' } }] }),
-        deltaChunk({}, "tool_calls"),
-      )
-      const response = yield* LLMClient.generate(
-        LLM.updateRequest(request, {
-          tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
-        }),
-      ).pipe(Effect.provide(fixedResponse(body)))
-
-      expect(response.toolCalls).toMatchObject([{ id: "call_1", name: "lookup", input: { query: "weather" } }])
-    }),
-  )
-
-  it.effect("fails when a buffered tool call never receives a function name", () =>
-    Effect.gen(function* () {
-      const body = sseEvents(
-        deltaChunk({
-          tool_calls: [{ index: 0, id: "call_1", function: { arguments: "{}" } }],
-        }),
-        deltaChunk({}, "tool_calls"),
-      )
-      const error = yield* LLMClient.generate(
-        LLM.updateRequest(request, {
-          tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
-        }),
-      ).pipe(Effect.provide(fixedResponse(body)), Effect.flip)
-
-      expect(error.message).toContain("OpenAI Chat tool call delta is missing id or name")
-    }),
-  )
-
  it.effect("fails a streamed tool call when the provider ends without a finish reason", () =>
    Effect.gen(function* () {
      const body = sseEvents(
@@ -1,62 +0,0 @@
-import { describe, expect } from "bun:test"
-import { Effect } from "effect"
-import { Image, ImageInput } from "../../src"
-import { OpenAI } from "../../src/providers"
-import { dimensions } from "../lib/image"
-import { recordedTests } from "../recorded-test"
-
-const model = OpenAI.configure({
-  apiKey: process.env.OPENAI_API_KEY ?? "fixture",
-}).image("gpt-image-1-mini")
-
-const recorded = recordedTests({
-  prefix: "openai-images",
-  provider: "openai",
-  protocol: "openai-images",
-  requires: ["OPENAI_API_KEY"],
-})
-
-describe("OpenAI Images recorded", () => {
-  recorded.effect("generates an image", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model,
-        prompt: "A simple flat black circle centered on a plain white background.",
-        options: { quality: "low", outputFormat: "jpeg", outputCompression: 10, size: "1024x1024" },
-      })
-
-      expect(response.images).toHaveLength(1)
-      expect(response.image?.mediaType).toBe("image/jpeg")
-      expect(response.image?.data).toBeInstanceOf(Uint8Array)
-      expect(response.image?.data.length).toBeGreaterThan(0)
-    }),
-  )
-
-  recorded.effect.with(
-    "edits an image",
-    {
-      options: {
-        match: (incoming, recorded) => incoming.method === recorded.method && incoming.url === recorded.url,
-      },
-    },
-    () =>
-      Effect.gen(function* () {
-        const response = yield* Image.generate({
-          model,
-          prompt: "Keep the simple shape and change it from black to bright green.",
-          images: [
-            ImageInput.bytes(
-              yield* Effect.promise(() => Bun.file("test/fixtures/images/edit-source.jpg").bytes()),
-              "image/jpeg",
-            ),
-          ],
-          options: { quality: "low", outputFormat: "jpeg", outputCompression: 10, size: "1024x1024" },
-        })
-
-        expect(response.image?.mediaType).toBe("image/jpeg")
-        expect(response.image?.data).toBeInstanceOf(Uint8Array)
-        if (!(response.image?.data instanceof Uint8Array)) throw new Error("Expected owned OpenAI image bytes")
-        expect(dimensions(response.image.data)).toEqual({ width: 1024, height: 1024 })
-      }),
-  )
-})
@@ -1,66 +0,0 @@
-import { describe, expect } from "bun:test"
-import { Effect } from "effect"
-import { LLM, LLMEvent, Message } from "../../src"
-import { OpenAI } from "../../src/providers"
-import { recordedTests } from "../recorded-test"
-
-const openai = OpenAI.configure({
-  apiKey: process.env.OPENAI_API_KEY ?? "fixture",
-})
-
-const recorded = recordedTests({
-  prefix: "openai-responses-images",
-  provider: "openai",
-  protocol: "openai-responses",
-  requires: ["OPENAI_API_KEY"],
-})
-
-describe("OpenAI Responses image generation recorded", () => {
-  recorded.effect("generates and edits an image with the hosted tool", () =>
-    Effect.gen(function* () {
-      const initial = Message.user("Generate a simple flat black triangle centered on a plain white background.")
-      const tools = [
-        OpenAI.imageGeneration({
-          action: "auto",
-          quality: "low",
-          size: "1024x1024",
-          outputFormat: "jpeg",
-          outputCompression: 10,
-          partialImages: 0,
-        }),
-      ]
-      const response = yield* LLM.generate(
-        LLM.request({
-          model: openai.responses("gpt-5-mini"),
-          messages: [initial],
-          tools,
-          toolChoice: "image_generation",
-        }),
-      )
-
-      const result = response.events.find(LLMEvent.is.toolResult)
-      expect(result).toBeDefined()
-      expect(result?.providerExecuted).toBe(true)
-      expect(result?.result.type).toBe("content")
-      if (result?.result.type !== "content") return
-      expect(result.result.value).toHaveLength(1)
-      expect(result.result.value[0]?.type).toBe("file")
-      if (result.result.value[0]?.type !== "file") return
-      expect(result.result.value[0].mime).toBe("image/jpeg")
-      expect(result.result.value[0].uri.startsWith("data:image/jpeg;base64,")).toBe(true)
-
-      const edited = yield* LLM.generate(
-        LLM.request({
-          model: openai.responses("gpt-5-mini"),
-          messages: [initial, response.message, Message.user("Now make the triangle blue.")],
-          tools,
-          toolChoice: "image_generation",
-        }),
-      )
-      const editedResult = edited.events.find(LLMEvent.is.toolResult)
-      expect(editedResult?.result.type).toBe("content")
-      if (editedResult?.result.type !== "content") return
-      expect(editedResult.result.value[0]?.type).toBe("file")
-    }),
-  )
-})
@@ -1,11 +1,10 @@
 import { describe, expect } from "bun:test"
 import { ConfigProvider, Effect, Layer, Stream } from "effect"
 import { Headers, HttpClientRequest } from "effect/unstable/http"
-import { LLM, LLMError, LLMEvent, Message, Model, ToolCallPart, ToolResultPart, Usage } from "../../src"
+import { LLM, LLMError, Message, Model, ToolCallPart, Usage } from "../../src"
 import { Auth, LLMClient, RequestExecutor, WebSocketExecutor } from "../../src/route"
 import * as Azure from "../../src/providers/azure"
 import * as OpenAI from "../../src/providers/openai"
-import * as XAI from "../../src/providers/xai"
 import * as OpenAIResponses from "../../src/protocols/openai-responses"
 import * as ProviderShared from "../../src/protocols/shared"
 import { continuationRequest, nativeOpenAIResponsesContinuation } from "../continuation-scenarios"
@@ -17,8 +16,6 @@ const model = OpenAIResponses.route
  .with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") })
  .model({ id: "gpt-4.1-mini" })

-const xaiModel = XAI.configure({ apiKey: "test", baseURL: "https://api.x.ai/v1" }).responses("grok-4.5")
-
 const request = LLM.request({
  id: "req_1",
  model,
@@ -61,39 +58,6 @@ describe("OpenAI Responses route", () => {
    }),
  )

-  it.effect("lowers the hosted OpenAI image generation tool", () =>
-    Effect.gen(function* () {
-      const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
-        LLM.request({
-          model,
-          prompt: "Show me a rooftop garden.",
-          tools: [OpenAI.imageGeneration({ action: "generate", quality: "high", size: "1024x1024" })],
-          toolChoice: "image_generation",
-        }),
-      )
-
-      expect(prepared.body.tools).toEqual([
-        { type: "image_generation", action: "generate", quality: "high", size: "1024x1024" },
-      ])
-      expect(prepared.body.tool_choice).toEqual({ type: "image_generation" })
-    }),
-  )
-
-  it.effect("rejects invalid hosted image generation options locally", () =>
-    Effect.gen(function* () {
-      const error = yield* LLMClient.prepare(
-        LLM.request({
-          model,
-          prompt: "Show me a rooftop garden.",
-          tools: [OpenAI.imageGeneration({ outputCompression: -1, partialImages: 4, size: "bogus" })],
-        }),
-      ).pipe(Effect.flip)
-
-      expect(error.reason._tag).toBe("InvalidRequest")
-      expect(error.message).toContain("image generation tool options are invalid")
-    }),
-  )
-
  it.effect("lowers semantic service tier options", () =>
    Effect.gen(function* () {
      const input = LLM.updateRequest(request, { providerOptions: { openai: { serviceTier: "priority" } } })
@@ -527,77 +491,7 @@ describe("OpenAI Responses route", () => {
    }),
  )

-  it.effect("lowers PDF tool-result content as structured input_file array", () =>
-    Effect.gen(function* () {
-      const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
-        LLM.request({
-          id: "req_tool_result_pdf",
-          model,
-          messages: [
-            Message.assistant([ToolCallPart.make({ id: "call_1", name: "read", input: {} })]),
-            Message.tool({
-              id: "call_1",
-              name: "read",
-              resultType: "content",
-              result: [
-                {
-                  type: "file",
-                  uri: "data:application/pdf;base64,JVBERi0xLjQ=",
-                  mime: "application/pdf",
-                  name: "report.pdf",
-                },
-              ],
-            }),
-          ],
-        }),
-      )
-
-      expect(expectToolOutput(prepared.body).output).toEqual([
-        {
-          type: "input_file",
-          filename: "report.pdf",
-          file_data: "data:application/pdf;base64,JVBERi0xLjQ=",
-        },
-      ])
-    }),
-  )
-
-  it.effect("uses xAI inline file encoding for PDF tool results", () =>
-    Effect.gen(function* () {
-      const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
-        LLM.request({
-          model: xaiModel,
-          messages: [
-            Message.assistant([ToolCallPart.make({ id: "call_1", name: "read", input: {} })]),
-            Message.tool({
-              id: "call_1",
-              name: "read",
-              resultType: "content",
-              result: [
-                {
-                  type: "file",
-                  uri: "data:application/pdf;base64,JVBERi0xLjQ=",
-                  mime: "application/pdf",
-                  name: "report.pdf",
-                },
-              ],
-            }),
-          ],
-        }),
-      )
-
-      expect(expectToolOutput(prepared.body).output).toEqual([
-        {
-          type: "input_file",
-          filename: "report.pdf",
-          file_data: "JVBERi0xLjQ=",
-          mime_type: "application/pdf",
-        },
-      ])
-    }),
-  )
-
-  it.effect("rejects unsupported media in tool-result content with a clear error", () =>
+  it.effect("rejects non-image media in tool-result content with a clear error", () =>
    Effect.gen(function* () {
      const error = yield* LLMClient.prepare(
        LLM.request({
@@ -1209,48 +1103,6 @@ describe("OpenAI Responses route", () => {
    }),
  )

-  it.effect("continues stateless hosted image generation with the generated image", () =>
-    Effect.gen(function* () {
-      const imageTool = OpenAI.imageGeneration({ action: "edit" })
-      const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
-        LLM.request({
-          model,
-          messages: [
-            Message.user("Generate a black triangle."),
-            Message.assistant([
-              ToolCallPart.make({
-                id: "ig_1",
-                name: "image_generation",
-                input: {},
-                providerExecuted: true,
-                providerMetadata: { openai: { itemId: "ig_1" } },
-              }),
-              ToolResultPart.make({
-                id: "ig_1",
-                name: "image_generation",
-                result: {
-                  type: "content",
-                  value: [{ type: "file", uri: "data:image/png;base64,AQID", mime: "image/png" }],
-                },
-                providerExecuted: true,
-                providerMetadata: { openai: { itemId: "ig_1" } },
-              }),
-            ]),
-            Message.user("Make it blue."),
-          ],
-          tools: [imageTool],
-        }),
-      )
-
-      expect(prepared.body.store).toBe(false)
-      expect(prepared.body.input).toEqual([
-        { role: "user", content: [{ type: "input_text", text: "Generate a black triangle." }] },
-        { role: "user", content: [{ type: "input_image", image_url: "data:image/png;base64,AQID" }] },
-        { role: "user", content: [{ type: "input_text", text: "Make it blue." }] },
-      ])
-    }),
-  )
-
  it.effect("joins streamed summary blocks into one continuation reasoning item", () =>
    Effect.gen(function* () {
      const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
@@ -1407,69 +1259,6 @@ describe("OpenAI Responses route", () => {
    }),
  )

-  it.effect("emits malformed final function arguments as an unexecuted tool error", () =>
-    Effect.gen(function* () {
-      const body = sseEvents(
-        {
-          type: "response.output_item.added",
-          item: { type: "function_call", id: "item_1", call_id: "call_1", name: "lookup", arguments: "" },
-        },
-        { type: "response.function_call_arguments.delta", item_id: "item_1", delta: '{"query":"streamed"}' },
-        {
-          type: "response.output_item.done",
-          item: {
-            type: "function_call",
-            id: "item_1",
-            call_id: "call_1",
-            name: "lookup",
-            arguments: '{"query":"partial',
-          },
-        },
-        { type: "response.completed", response: { usage: { input_tokens: 5, output_tokens: 1 } } },
-      )
-      const response = yield* LLMClient.generate(
-        LLM.updateRequest(request, {
-          tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
-        }),
-      ).pipe(Effect.provide(fixedResponse(body)))
-
-      expect(response.events.find(LLMEvent.is.toolInputError)).toEqual({
-        type: "tool-input-error",
-        id: "call_1",
-        name: "lookup",
-        raw: '{"query":"partial',
-      })
-      expect(response.finishReason).toBe("tool-calls")
-      expect(response.events.some(LLMEvent.is.toolCall)).toBeFalse()
-    }),
-  )
-
-  it.effect("settles malformed function arguments when output_item.added is absent", () =>
-    Effect.gen(function* () {
-      const body = sseEvents(
-        {
-          type: "response.output_item.done",
-          item: {
-            type: "function_call",
-            id: "item_1",
-            call_id: "call_1",
-            name: "lookup",
-            arguments: '{"query":"partial',
-          },
-        },
-        { type: "response.completed", response: { usage: { input_tokens: 5, output_tokens: 1 } } },
-      )
-      const response = yield* LLMClient.generate(request).pipe(Effect.provide(fixedResponse(body)))
-
-      expect(response.events.find(LLMEvent.is.toolInputError)).toMatchObject({
-        id: "call_1",
-        name: "lookup",
-        raw: '{"query":"partial',
-      })
-      expect(response.finishReason).toBe("tool-calls")
-    }),
-  )
-
  it.effect("decodes web_search_call as provider-executed tool-call + tool-result", () =>
    Effect.gen(function* () {
      const item = {
@@ -1509,59 +1298,6 @@ describe("OpenAI Responses route", () => {
    }),
  )

-  it.effect("decodes image generation output as image content", () =>
-    Effect.gen(function* () {
-      const item = {
-        type: "image_generation_call",
-        id: "ig_1",
-        status: "completed",
-        result: "AQID",
-      }
-      const response = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              { type: "response.output_item.done", item },
-              { type: "response.completed", response: { usage: { input_tokens: 5, output_tokens: 1 } } },
-            ),
-          ),
-        ),
-      )
-
-      expect(response.events.find(LLMEvent.is.toolResult)).toMatchObject({
-        id: "ig_1",
-        name: "image_generation",
-        providerExecuted: true,
-        result: {
-          type: "content",
-          value: [{ type: "file", uri: "data:image/png;base64,AQID", mime: "image/png" }],
-        },
-      })
-    }),
-  )
-
-  it.effect("rejects malformed image generation base64", () =>
-    Effect.gen(function* () {
-      const error = yield* LLMClient.generate(request).pipe(
-        Effect.provide(
-          fixedResponse(
-            sseEvents(
-              {
-                type: "response.output_item.done",
-                item: { type: "image_generation_call", id: "ig_bad", status: "completed", result: "%%%" },
-              },
-              { type: "response.completed", response: {} },
-            ),
-          ),
-        ),
-        Effect.flip,
-      )
-
-      expect(error.reason._tag).toBe("InvalidProviderOutput")
-      expect(error.message).toContain("invalid image base64")
-    }),
-  )
-
  it.effect("decodes code_interpreter_call as provider-executed events with code input", () =>
    Effect.gen(function* () {
      const item = {
@@ -1599,64 +1335,20 @@ describe("OpenAI Responses route", () => {
    }),
  )

-  it.effect("lowers user image and PDF content", () =>
+  it.effect("lowers user image content", () =>
    Effect.gen(function* () {
      const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
        LLM.request({
          id: "req_media",
          model,
-          messages: [
-            Message.user([
-              { type: "media", mediaType: "image/png", data: "AAECAw==" },
-              { type: "media", mediaType: "application/pdf", data: "JVBERi0xLjQ=", filename: "report.pdf" },
-            ]),
-          ],
+          messages: [Message.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
        }),
      )

      expect(prepared.body.input).toEqual([
        {
          role: "user",
-          content: [
-            { type: "input_image", image_url: "data:image/png;base64,AAECAw==" },
-            {
-              type: "input_file",
-              filename: "report.pdf",
-              file_data: "data:application/pdf;base64,JVBERi0xLjQ=",
-            },
-          ],
-        },
-      ])
-    }),
-  )
-
-  it.effect("uses xAI inline file encoding for user PDFs", () =>
-    Effect.gen(function* () {
-      const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
-        LLM.request({
-          model: xaiModel,
-          messages: [
-            Message.user({
-              type: "media",
-              mediaType: "application/pdf",
-              data: "data:application/pdf;base64,JVBERi0xLjQ=",
-              filename: "report.pdf",
-            }),
-          ],
-        }),
-      )
-
-      expect(prepared.body.input).toEqual([
-        {
-          role: "user",
-          content: [
-            {
-              type: "input_file",
-              filename: "report.pdf",
-              file_data: "JVBERi0xLjQ=",
-              mime_type: "application/pdf",
-            },
-          ],
+          content: [{ type: "input_image", image_url: "data:image/png;base64,AAECAw==" }],
        },
      ])
    }),
@@ -1668,11 +1360,11 @@ describe("OpenAI Responses route", () => {
        LLM.request({
          id: "req_media",
          model,
-          messages: [Message.user({ type: "media", mediaType: "application/x-tar", data: "AAECAw==" })],
+          messages: [Message.user({ type: "media", mediaType: "application/pdf", data: "AAECAw==" })],
        }),
      ).pipe(Effect.flip)

-      expect(error.message).toContain("OpenAI Responses does not support media type application/x-tar")
+      expect(error.message).toContain("OpenAI Responses does not support media type application/pdf")
    }),
  )

@@ -1,6 +1,6 @@
 import { describe, expect } from "bun:test"
 import { Effect } from "effect"
-import { LLM, Message } from "../../src"
+import { LLM } from "../../src"
 import { LLMClient } from "../../src/route"
 import * as OpenRouter from "../../src/providers/openrouter"
 import { it } from "../lib/effect"
@@ -53,102 +53,4 @@ describe("OpenRouter", () => {
      })
    }),
  )
-
-  it.effect("preserves manually supplied reasoning details", () =>
-    Effect.gen(function* () {
-      const details = [
-        { type: "reasoning.text", text: "Think", format: "anthropic-claude-v1", index: 0 },
-        { type: "reasoning.text", text: "ing", format: "anthropic-claude-v1", index: 0 },
-        { type: "reasoning.text", signature: "signed", format: "anthropic-claude-v1", index: 0 },
-        { type: "reasoning.encrypted", data: "opaque", format: "openai-responses-v1", index: 1 },
-      ]
-      const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
-        LLM.request({
-          model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
-          messages: [
-            Message.assistant([
-              {
-                type: "reasoning",
-                text: "Thinking",
-                providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: details } },
-              },
-            ]),
-          ],
-        }),
-      )
-
-      expect(prepared.body.messages).toEqual([
-        {
-          role: "assistant",
-          content: null,
-          reasoning: "Thinking",
-          reasoning_details: details,
-        },
-      ])
-    }),
-  )
-
-  it.effect("preserves opaque and duplicate continuation details", () =>
-    Effect.gen(function* () {
-      const details = [
-        { type: "reasoning.future", format: "provider-v2", state: { opaque: true } },
-        { type: "reasoning.encrypted", id: "state", data: "opaque" },
-        { type: "reasoning.encrypted", id: "state", data: "opaque" },
-      ]
-      const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
-        LLM.request({
-          model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
-          messages: [
-            Message.assistant({
-              type: "reasoning",
-              text: "Thinking",
-              providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: details } },
-            }),
-          ],
-        }),
-      )
-
-      expect(prepared.body.messages).toEqual([
-        { role: "assistant", content: null, reasoning: "Thinking", reasoning_details: details },
-      ])
-    }),
-  )
-
-  it.effect("does not merge distinct adjacent reasoning text blocks", () =>
-    Effect.gen(function* () {
-      const details = [
-        { type: "reasoning.text", id: "first", index: 0, text: "A", opaque: "first" },
-        { type: "reasoning.text", id: "second", index: 1, text: "B", opaque: "second" },
-      ]
-      const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
-        LLM.request({
-          model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
-          messages: [
-            Message.assistant({
-              type: "reasoning",
-              text: "AB",
-              providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: details } },
-            }),
-          ],
-        }),
-      )
-
-      expect(prepared.body.messages).toEqual([
-        { role: "assistant", content: null, reasoning: "AB", reasoning_details: details },
-      ])
-    }),
-  )
-
-  it.effect("omits scalar reasoning without continuation details", () =>
-    Effect.gen(function* () {
-      const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
-        LLM.request({
-          model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
-          messages: [Message.assistant({ type: "reasoning", text: "Thinking" })],
-        }),
-      )
-
-      expect(prepared.body.messages).toEqual([{ role: "assistant", content: null }])
-    }),
-  )
 })
@@ -1,207 +0,0 @@
-import { describe, expect } from "bun:test"
-import { Effect, Schema, Stream } from "effect"
-import { LLM, LLMResponse, Message, ToolDefinition, type Model } from "../../src"
-import { AmazonBedrock, Anthropic, Google, OpenAI, XAI } from "../../src/providers"
-import { LLMClient } from "../../src/route"
-import { Tool } from "../../src/tool"
-import { runTools } from "../lib/tool-runtime"
-import { recordedTests } from "../recorded-test"
-
-const CODE = "ORCHID-7391"
-const PDF =
-  "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"
-
-const openai = OpenAI.configure({ apiKey: process.env.OPENAI_API_KEY ?? "fixture" })
-const anthropic = Anthropic.configure({ apiKey: process.env.ANTHROPIC_API_KEY ?? "fixture" })
-const google = Google.configure({ apiKey: process.env.GOOGLE_API_KEY ?? "fixture" })
-const xai = XAI.configure({ apiKey: process.env.XAI_API_KEY ?? "fixture" })
-const bedrock = AmazonBedrock.configure({
-  apiKey: process.env.AWS_BEDROCK_API_KEY ?? "fixture",
-  region: process.env.AWS_REGION ?? "us-east-1",
-})
-
-const targets: ReadonlyArray<{
-  readonly id: string
-  readonly name: string
-  readonly provider: string
-  readonly protocol: string
-  readonly requires: string
-  readonly filename: string
-  readonly maxTokens: number
-  readonly model: Model
-}> = [
-  {
-    id: "openai",
-    name: "OpenAI Responses gpt-4o-mini",
-    provider: "openai",
-    protocol: "openai-responses",
-    requires: "OPENAI_API_KEY",
-    filename: "verification.pdf",
-    maxTokens: 40,
-    model: openai.responses("gpt-4o-mini"),
-  },
-  {
-    id: "anthropic",
-    name: "Anthropic Haiku 4.5",
-    provider: "anthropic",
-    protocol: "anthropic-messages",
-    requires: "ANTHROPIC_API_KEY",
-    filename: "verification.pdf",
-    maxTokens: 40,
-    model: anthropic.model("claude-haiku-4-5-20251001"),
-  },
-  {
-    id: "gemini",
-    name: "Gemini 3.5 Flash",
-    provider: "google",
-    protocol: "gemini",
-    requires: "GOOGLE_API_KEY",
-    filename: "verification.pdf",
-    maxTokens: 256,
-    model: google.model("gemini-3.5-flash"),
-  },
-  {
-    id: "xai",
-    name: "xAI Grok 4.5",
-    provider: "xai",
-    protocol: "openai-responses",
-    requires: "XAI_API_KEY",
-    filename: "verification.pdf",
-    maxTokens: 40,
-    model: xai.responses("grok-4.5"),
-  },
-  {
-    id: "bedrock",
-    name: "Bedrock Claude Haiku 4.5",
-    provider: "amazon-bedrock",
-    protocol: "bedrock-converse",
-    requires: "AWS_BEDROCK_API_KEY",
-    filename: "verification",
-    maxTokens: 40,
-    model: bedrock.model("us.anthropic.claude-haiku-4-5-20251001-v1:0"),
-  },
-]
-
-const recorded = recordedTests({ prefix: "pdf", tags: ["pdf"] })
-const prompt = "Return only the verification code from the PDF."
-const readPdf = ToolDefinition.make({
-  name: "read_pdf",
-  description: "Read the attached PDF.",
-  inputSchema: { type: "object", properties: {}, additionalProperties: false },
-})
-const readPdfRuntime = Tool.make({
-  description: readPdf.description,
-  parameters: Schema.Struct({ path: Schema.String }),
-  success: Schema.String,
-  execute: () => Effect.succeed("PDF read successfully"),
-  toModelOutput: () => [
-    { type: "text", text: "PDF read successfully" },
-    {
-      type: "file",
-      uri: `data:application/pdf;base64,${PDF}`,
-      mime: "application/pdf",
-      name: "verification.pdf",
-    },
-  ],
-})
-
-const expectCode = (response: LLMResponse) => {
-  expect(response.finishReason).toBe("stop")
-  expect(response.text.toUpperCase()).toContain(CODE)
-}
-
-describe("PDF recorded", () => {
-  for (const target of targets) {
-    recorded.effect.with(
-      `reads a user PDF with ${target.name}`,
-      {
-        id: `${target.id}-user-input`,
-        provider: target.provider,
-        protocol: target.protocol,
-        requires: [target.requires],
-        tags: ["user-input"],
-      },
-      Effect.gen(function* () {
-        expectCode(
-          yield* LLMClient.generate(
-            LLM.request({
-              id: `recorded_pdf_${target.id}_user_input`,
-              model: target.model,
-              cache: "none",
-              generation: { maxTokens: target.maxTokens, temperature: 0 },
-              messages: [
-                Message.user([
-                  { type: "media", mediaType: "application/pdf", data: PDF, filename: target.filename },
-                  { type: "text", text: prompt },
-                ]),
-              ],
-            }),
-          ),
-        )
-      }),
-    )
-
-    recorded.effect.with(
-      `reads a PDF tool result with ${target.name}`,
-      {
-        id: `${target.id}-tool-result`,
-        provider: target.provider,
-        protocol: target.protocol,
-        requires: [target.requires],
-        tags: ["tool", "tool-result"],
-      },
-      Effect.gen(function* () {
-        if (target.id === "gemini") {
-          const events = Array.from(
-            yield* runTools({
-              request: LLM.request({
-                id: "recorded_pdf_gemini_tool_result",
-                model: target.model,
-                system:
-                  "Call read_pdf exactly once with path verification.pdf, then reply only with the verification code from its PDF.",
-                prompt: "Use read_pdf with path verification.pdf and return the verification code.",
-                cache: "none",
-                generation: { maxTokens: target.maxTokens, temperature: 0 },
-              }),
-              tools: { read_pdf: readPdfRuntime },
-            }).pipe(Stream.runCollect),
-          )
-          expect(events.at(-1)).toMatchObject({ type: "finish", reason: "stop" })
-          expect(LLMResponse.text({ events }).toUpperCase()).toContain(CODE)
-          return
-        }
-
-        expectCode(
-          yield* LLMClient.generate(
-            LLM.request({
-              id: `recorded_pdf_${target.id}_tool_result`,
-              model: target.model,
-              system: "Read the PDF returned by the tool and follow the user's response format exactly.",
-              cache: "none",
-              generation: { maxTokens: target.maxTokens, temperature: 0 },
-              messages: [
-                Message.user(prompt),
-                Message.assistant([{ type: "tool-call", id: "call_pdf_1", name: readPdf.name, input: {} }]),
-                Message.tool({
-                  id: "call_pdf_1",
-                  name: readPdf.name,
-                  resultType: "content",
-                  result: [
-                    { type: "text", text: "PDF read successfully" },
-                    {
-                      type: "file",
-                      uri: `data:application/pdf;base64,${PDF}`,
-                      mime: "application/pdf",
-                      name: target.filename,
-                    },
-                  ],
-                }),
-              ],
-              tools: [readPdf],
-            }),
-          ),
-        )
-      }),
-    )
-  }
-})
@@ -1,55 +0,0 @@
-import { describe, expect } from "bun:test"
-import { Effect } from "effect"
-import { Image, ImageInput } from "../../src"
-import { XAI } from "../../src/providers"
-import { dimensions } from "../lib/image"
-import { recordedTests } from "../recorded-test"
-
-const model = XAI.configure({
-  apiKey: process.env.XAI_API_KEY ?? "fixture",
-}).image("grok-imagine-image")
-
-const recorded = recordedTests({
-  prefix: "xai-images",
-  provider: "xai",
-  protocol: "xai-images",
-  requires: ["XAI_API_KEY"],
-})
-
-describe("xAI Images recorded", () => {
-  recorded.effect("generates an image", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model,
-        prompt: "A simple flat black diamond centered on a plain white background.",
-        options: { aspectRatio: "1:1", resolution: "1k", responseFormat: "b64_json" },
-      })
-
-      expect(response.images).toHaveLength(1)
-      expect(response.image?.mediaType.startsWith("image/")).toBe(true)
-      expect(response.image?.data).toBeInstanceOf(Uint8Array)
-      expect(response.image?.data.length).toBeGreaterThan(0)
-    }),
-  )
-
-  recorded.effect("edits an image", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model,
-        prompt: "Keep the simple shape and change it from black to bright purple.",
-        images: [
-          ImageInput.bytes(
-            yield* Effect.promise(() => Bun.file("test/fixtures/images/edit-source.jpg").bytes()),
-            "image/jpeg",
-          ),
-        ],
-        options: { aspectRatio: "1:1", resolution: "1k", responseFormat: "b64_json" },
-      })
-
-      expect(response.image?.mediaType).toMatch(/^image\/(jpeg|png)$/)
-      expect(response.image?.data).toBeInstanceOf(Uint8Array)
-      if (!(response.image?.data instanceof Uint8Array)) throw new Error("Expected owned xAI image bytes")
-      expect(dimensions(response.image.data)).toEqual({ width: 1024, height: 1024 })
-    }),
-  )
-})
@@ -1,109 +0,0 @@
-import { describe, expect } from "bun:test"
-import { Effect, Layer } from "effect"
-import { Headers, HttpClientRequest } from "effect/unstable/http"
-import { Image, ImageClient } from "../../src"
-import { XAI } from "../../src/providers"
-import { Auth } from "../../src/route"
-import { it } from "../lib/effect"
-import { dynamicResponse } from "../lib/http"
-
-describe("xAI Images", () => {
-  it.effect("generates through the OpenAI-compatible Images API", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model: XAI.configure({
-          apiKey: "test",
-          baseURL: "https://api.xai.test/v1",
-          http: { body: { configured: true }, headers: { "x-default": "yes" } },
-        }).image("grok-imagine-image"),
-        prompt: "A robot tending a rooftop garden",
-        options: {
-          n: 2,
-          aspectRatio: "16:9",
-          aspect_ratio: "4:3",
-          resolution: "1k",
-          responseFormat: "url",
-          response_format: "b64_json",
-          future_option: true,
-        },
-        http: {
-          body: { resolution: "2k", future_option: "http" },
-          headers: { "x-request": "yes" },
-          query: { trace: "1" },
-        },
-      })
-
-      expect(response.images).toHaveLength(2)
-      expect(response.image?.mediaType).toBe("image/jpeg")
-      expect(response.image?.data).toEqual(Uint8Array.from([1, 2, 3]))
-      expect(response.images[1]?.mediaType).toBe("application/octet-stream")
-      expect(response.images[1]?.data).toBe("https://api.xai.test/image.jpg")
-      expect(response.usage?.providerMetadata).toEqual({ xai: { num_images: 2 } })
-      expect(response.providerMetadata).toEqual({ xai: { usage: { num_images: 2 } } })
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) =>
-              Effect.gen(function* () {
-                const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
-                expect(request.url).toBe("https://api.xai.test/v1/images/generations?trace=1")
-                expect(request.headers.get("authorization")).toBe("Bearer test")
-                expect(request.headers.get("x-default")).toBe("yes")
-                expect(request.headers.get("x-request")).toBe("yes")
-                expect(JSON.parse(input.text)).toEqual({
-                  model: "grok-imagine-image",
-                  prompt: "A robot tending a rooftop garden",
-                  n: 2,
-                  aspect_ratio: "4:3",
-                  resolution: "2k",
-                  response_format: "b64_json",
-                  future_option: "http",
-                  configured: true,
-                })
-                return input.respond(
-                  JSON.stringify({
-                    data: [
-                      { b64_json: "AQID", url: null, mime_type: "image/jpeg" },
-                      { b64_json: null, url: "https://api.xai.test/image.jpg", mime_type: null },
-                    ],
-                    usage: { num_images: 2 },
-                  }),
-                  { headers: { "content-type": "application/json" } },
-                )
-              }),
-            ),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("supports request-level custom auth", () =>
-    Image.generate({
-      model: XAI.configure({
-        baseURL: "https://api.xai.test/v1",
-        auth: Auth.custom((input) =>
-          Effect.succeed(Headers.set(input.headers, "x-custom-auth", new URL(input.url).hostname)),
-        ),
-      }).image("grok-imagine-image"),
-      prompt: "A robot tending a rooftop garden",
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) =>
-              Effect.gen(function* () {
-                const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
-                expect(request.headers.get("x-custom-auth")).toBe("api.xai.test")
-                return input.respond(JSON.stringify({ data: [{ b64_json: "AQID", mime_type: "image/png" }] }), {
-                  headers: { "content-type": "application/json" },
-                })
-              }),
-            ),
-          ),
-        ),
-      ),
-    ),
-  )
-})
@@ -1,32 +0,0 @@
-import { describe, expect } from "bun:test"
-import { Effect } from "effect"
-import { Image } from "../../src"
-import { ZAI } from "../../src/providers"
-import { recordedTests } from "../recorded-test"
-
-const model = ZAI.configure({ apiKey: process.env.ZAI_API_KEY ?? "fixture" }).image("cogview-4-250304")
-
-const recorded = recordedTests({
-  prefix: "zai-images",
-  provider: "zai",
-  protocol: "zai-images",
-  requires: ["ZAI_API_KEY"],
-})
-
-describe("Z.ai Images recorded", () => {
-  recorded.effect("generates an image", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model,
-        prompt: "A simple flat red circle centered on a plain white background.",
-        options: { size: "1024x1024", quality: "standard", userID: "opencode-image-test" },
-      })
-
-      expect(response.images).toHaveLength(1)
-      expect(response.image?.mediaType).toBe("application/octet-stream")
-      expect(response.image?.data).toBeString()
-      expect(response.image?.data).toStartWith("https://")
-      expect(response.providerMetadata?.zai).toBeDefined()
-    }),
-  )
-})
@@ -1,130 +0,0 @@
-import { describe, expect } from "bun:test"
-import { Effect, Layer } from "effect"
-import { HttpClientRequest } from "effect/unstable/http"
-import { Image, ImageClient } from "../../src"
-import { ZAI } from "../../src/providers"
-import { it } from "../lib/effect"
-import { dynamicResponse, fixedResponse } from "../lib/http"
-
-describe("Z.ai Images", () => {
-  it.effect("generates through the Z.ai Images API", () =>
-    Effect.gen(function* () {
-      const response = yield* Image.generate({
-        model: ZAI.configure({
-          apiKey: "test",
-          baseURL: "https://api.z.ai.test/api/paas/v4",
-          headers: { "x-default": "yes" },
-          http: { body: { configured: true, quality: "configured" }, query: { trace: "default" } },
-        }).image("glm-image"),
-        prompt: "A red circle on a white background",
-        options: {
-          quality: "hd",
-          userID: "alias-user",
-          user_id: "raw-user",
-          future_option: true,
-        },
-        http: {
-          headers: { "x-request": "yes" },
-          query: { trace: "request" },
-          body: { quality: "final", user_id: "final-user" },
-        },
-      })
-
-      expect(response.images).toHaveLength(1)
-      expect(response.image?.mediaType).toBe("application/octet-stream")
-      expect(response.image?.data).toBe("https://cdn.z.ai/generated.png")
-      expect(response.providerMetadata).toEqual({
-        zai: {
-          created: 1_760_335_349,
-          id: "generation-1",
-          requestID: "request-1",
-          contentFilter: [{ role: "future-role", level: 4.5 }],
-        },
-      })
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) =>
-              Effect.gen(function* () {
-                const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
-                expect(request.url).toBe("https://api.z.ai.test/api/paas/v4/images/generations?trace=request")
-                expect(request.headers.get("authorization")).toBe("Bearer test")
-                expect(request.headers.get("x-default")).toBe("yes")
-                expect(request.headers.get("x-request")).toBe("yes")
-                expect(JSON.parse(input.text)).toEqual({
-                  model: "glm-image",
-                  prompt: "A red circle on a white background",
-                  quality: "final",
-                  user_id: "final-user",
-                  future_option: true,
-                  configured: true,
-                })
-                return input.respond(
-                  JSON.stringify({
-                    created: 1_760_335_349,
-                    id: "generation-1",
-                    request_id: "request-1",
-                    data: [{ url: "https://cdn.z.ai/generated.png" }],
-                    content_filter: [{ role: "future-role", level: 4.5 }],
-                  }),
-                  { headers: { "content-type": "application/json" } },
-                )
-              }),
-            ),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("lets raw native options override aliases", () =>
-    Image.generate({
-      model: ZAI.configure({ apiKey: "test" }).image("model"),
-      prompt: "test",
-      options: { quality: "future-quality", userID: "x", user_id: "raw-user" },
-    }).pipe(
-      Effect.provide(
-        ImageClient.layer.pipe(
-          Layer.provide(
-            dynamicResponse((input) => {
-              expect(JSON.parse(input.text)).toMatchObject({ quality: "future-quality", user_id: "raw-user" })
-              return Effect.succeed(
-                input.respond(JSON.stringify({ data: [{ url: "https://example.test/image.jpg" }] }), {
-                  headers: { "content-type": "application/json" },
-                }),
-              )
-            }),
-          ),
-        ),
-      ),
-    ),
-  )
-
-  it.effect("rejects invalid response structures", () =>
-    Effect.gen(function* () {
-      const model = ZAI.configure({ apiKey: "test" }).image("model")
-      const payloads = [
-        {},
-        { data: [] },
-        { data: [{ b64_json: "image" }] },
-        { data: [{ url: 1 }] },
-        { data: [{ url: "https://example.test/image.jpg" }], content_filter: [{ role: 1, level: "high" }] },
-      ]
-
-      yield* Effect.forEach(payloads, (payload) =>
-        Image.generate({ model, prompt: "test" }).pipe(
-          Effect.provide(
-            ImageClient.layer.pipe(
-              Layer.provide(
-                fixedResponse(JSON.stringify(payload), { headers: { "content-type": "application/json" } }),
-              ),
-            ),
-          ),
-          Effect.flip,
-          Effect.tap((error) => Effect.sync(() => expect(error.reason._tag).toBe("InvalidProviderOutput"))),
-        ),
-      )
-    }),
-  )
-})
@@ -120,8 +120,29 @@ export const runWeatherToolLoop = (request: LLMRequest) =>
    throw new Error("Weather tool loop exceeded 10 steps")
  })

-const assistantContent = (events: ReadonlyArray<LLMEvent>) =>
-  events.reduce(LLMResponse.reduce, LLMResponse.empty()).message.content
+const assistantContent = (events: ReadonlyArray<LLMEvent>) => {
+  const content: ContentPart[] = []
+  for (const event of events) {
+    if (event.type === "text-delta" || event.type === "reasoning-delta") {
+      const type = event.type === "text-delta" ? "text" : "reasoning"
+      const last = content.at(-1)
+      if (last?.type === type) {
+        content[content.length - 1] = { ...last, text: `${last.text}${event.text}` }
+      } else {
+        content.push({ type, text: event.text })
+      }
+      continue
+    }
+    if (event.type === "text-end" || event.type === "reasoning-end") {
+      const type = event.type === "text-end" ? "text" : "reasoning"
+      const last = content.at(-1)
+      if (last?.type === type) content[content.length - 1] = { ...last, providerMetadata: event.providerMetadata }
+      continue
+    }
+    if (event.type === "tool-call") content.push(event)
+  }
+  return content
+}

 export const expectFinish = (
  events: ReadonlyArray<LLMEvent>,
@@ -3,8 +3,6 @@ import { Layer } from "effect"
 import * as path from "node:path"
 import { fileURLToPath } from "node:url"
 import { LLMClient, RequestExecutor, WebSocketExecutor } from "../src/route"
-import { ImageClient } from "../src/image-client"
-import type { Service as ImageClientService } from "../src/image-client"
 import type { Service as LLMClientService } from "../src/route/client"
 import type { Service as RequestExecutorService } from "../src/route/executor"
 import type { Service as WebSocketExecutorService } from "../src/route/transport/websocket"
@@ -17,7 +15,7 @@ import {
 const __dirname = path.dirname(fileURLToPath(import.meta.url))
 const FIXTURES_DIR = path.resolve(__dirname, "fixtures", "recordings")

-type RecordedEnv = RequestExecutorService | WebSocketExecutorService | LLMClientService | ImageClientService
+type RecordedEnv = RequestExecutorService | WebSocketExecutorService | LLMClientService

 type RecordedTestsOptions = RecordedGroupOptions & {
  readonly options?: HttpRecorder.RecorderOptions
@@ -83,10 +81,6 @@ export const recordedTests = (options: RecordedTestsOptions) =>
        ),
      )
      const deps = Layer.mergeAll(requestExecutor, WebSocketExecutor.layer)
-      return Layer.mergeAll(
-        deps,
-        LLMClient.layer.pipe(Layer.provide(deps)),
-        ImageClient.layer.pipe(Layer.provide(deps)),
-      )
+      return Layer.mergeAll(deps, LLMClient.layer.pipe(Layer.provide(deps)))
    },
  })
@@ -95,19 +95,4 @@ describe("LLMResponse reducer", () => {
      { type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" } },
    ])
  })
-
-  test("clears malformed tool input without appending an executable call", () => {
-    const state = reduce([
-      LLMEvent.toolInputStart({ id: "call_1", name: "lookup" }),
-      LLMEvent.toolInputDelta({ id: "call_1", name: "lookup", text: '{"query":"partial' }),
-      LLMEvent.toolInputError({
-        id: "call_1",
-        name: "lookup",
-        raw: '{"query":"partial',
-      }),
-    ])
-
-    expect(state.toolInputs).toEqual({})
-    expect(state.message.content).toEqual([])
-  })
 })
@@ -183,51 +183,6 @@ describe("LLMClient tools", () => {
    }),
  )

-  it.effect("preserves provider metadata on dispatched tool results", () =>
-    Effect.gen(function* () {
-      const tool = Tool.make({
-        description: "Return text.",
-        parameters: Schema.Struct({}),
-        success: Schema.String,
-        execute: () => Effect.succeed("hello"),
-      })
-      const providerMetadata = { google: { functionCallId: "provider_call" } }
-      const dispatched = yield* ToolRuntime.dispatch(
-        { tool },
-        LLMEvent.toolCall({ id: "call_1", name: "tool", input: {}, providerMetadata }),
-      )
-
-      expect(dispatched.events).toEqual([
-        LLMEvent.toolResult({
-          id: "call_1",
-          name: "tool",
-          result: { type: "text", value: "hello" },
-          output: { structured: "hello", content: [{ type: "text", text: "hello" }] },
-          providerMetadata,
-        }),
-      ])
-
-      const failed = yield* ToolRuntime.dispatch(
-        {},
-        LLMEvent.toolCall({ id: "call_2", name: "missing", input: {}, providerMetadata }),
-      )
-      expect(failed.events).toEqual([
-        LLMEvent.toolError({
-          id: "call_2",
-          name: "missing",
-          message: "Unknown tool: missing",
-          providerMetadata,
-        }),
-        LLMEvent.toolResult({
-          id: "call_2",
-          name: "missing",
-          result: { type: "error", value: "Unknown tool: missing" },
-          providerMetadata,
-        }),
-      ])
-    }),
-  )
-
  it.effect("uses the narrow default projection for encoded typed success", () =>
    Effect.gen(function* () {
      const text = Tool.make({
@@ -36,33 +36,6 @@ describe("ToolStream", () => {
    }),
  )

-  it.effect("keeps accumulated identity when later deltas contain empty strings", () =>
-    Effect.gen(function* () {
-      const first = ToolStream.appendOrStart(
-        ADAPTER,
-        ToolStream.empty<number>(),
-        0,
-        { id: "call_1", name: "lookup", text: '{"query"' },
-        "missing tool",
-      )
-      if (ToolStream.isError(first)) return yield* first
-      const second = ToolStream.appendOrStart(
-        ADAPTER,
-        first.tools,
-        0,
-        { id: "", name: "", text: ':"weather"}' },
-        "missing tool",
-      )
-      if (ToolStream.isError(second)) return yield* second
-      const finished = yield* ToolStream.finish(ADAPTER, second.tools, 0)
-
-      expect(finished.events).toEqual([
-        { type: "tool-input-end", id: "call_1", name: "lookup" },
-        { type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" } },
-      ])
-    }),
-  )
-
  it.effect("fails appendExisting when the provider skipped the tool start", () =>
    Effect.gen(function* () {
      const error = ToolStream.appendExisting(ADAPTER, ToolStream.empty<number>(), 0, "{}", "missing tool")
@@ -91,73 +64,6 @@ describe("ToolStream", () => {
    }),
  )

-  it.effect("finalizes malformed local input as a non-executable tool error", () =>
-    Effect.gen(function* () {
-      const tools = ToolStream.start(ToolStream.empty<string>(), "item_1", {
-        id: "call_1",
-        name: "lookup",
-        input: '{"query":"partial',
-      })
-      const finished = yield* ToolStream.finish(ADAPTER, tools, "item_1")
-
-      expect(finished).toEqual({
-        tools: {},
-        events: [
-          {
-            type: "tool-input-error",
-            id: "call_1",
-            name: "lookup",
-            raw: '{"query":"partial',
-          },
-        ],
-      })
-    }),
-  )
-
-  it.effect("preserves valid siblings when one parallel input is malformed", () =>
-    Effect.gen(function* () {
-      const valid = ToolStream.start(ToolStream.empty<number>(), 0, {
-        id: "call_valid",
-        name: "lookup",
-        input: '{"query":"weather"}',
-      })
-      const tools = ToolStream.start(valid, 1, {
-        id: "call_invalid",
-        name: "lookup",
-        input: '{"query":"partial',
-      })
-      const finished = yield* ToolStream.finishAll(ADAPTER, tools)
-
-      expect(finished).toEqual({
-        tools: {},
-        events: [
-          { type: "tool-input-end", id: "call_valid", name: "lookup" },
-          { type: "tool-call", id: "call_valid", name: "lookup", input: { query: "weather" } },
-          {
-            type: "tool-input-error",
-            id: "call_invalid",
-            name: "lookup",
-            raw: '{"query":"partial',
-          },
-        ],
-      })
-    }),
-  )
-
-  it.effect("keeps malformed provider-executed input terminal", () =>
-    Effect.gen(function* () {
-      const tools = ToolStream.start(ToolStream.empty<string>(), "item_1", {
-        id: "call_1",
-        name: "web_search",
-        input: '{"query":"partial',
-        providerExecuted: true,
-      })
-      const result = yield* Effect.exit(ToolStream.finish(ADAPTER, tools, "item_1"))
-
-      expect(result._tag).toBe("Failure")
-    }),
-  )
-
  it.effect("preserves providerExecuted and clears all tools", () =>
    Effect.gen(function* () {
      const first: ToolStream.State<number> = ToolStream.start(ToolStream.empty<number>(), 0, {
@@ -1,9 +0,0 @@
-{
-  "$schema": "https://json.schemastore.org/tsconfig",
-  "extends": "./tsconfig.json",
-  "compilerOptions": {
-    "noEmit": true,
-    "rootDir": "."
-  },
-  "include": ["test/**/*.types.ts"]
-}
@@ -1,50 +0,0 @@
-import { expect, test } from "@playwright/test"
-import { base64Encode } from "@opencode-ai/core/util/encode"
-import { mockOpenCodeServer } from "../utils/mock-server"
-import { expectAppVisible } from "../utils/waits"
-
-const directory = "C:/OpenCode/PromptInputV2Editing"
-const projectID = "proj_prompt_input_v2_editing"
-const sessionID = "ses_prompt_input_v2_editing"
-
-test("preserves the draft when a populated command menu triggers a built-in", async ({ page }) => {
-  await mockOpenCodeServer(page, {
-    directory,
-    project: {
-      id: projectID,
-      worktree: directory,
-      vcs: "git",
-      name: "prompt-input-v2-editing",
-      time: { created: 1700000000000, updated: 1700000000000 },
-      sandboxes: [],
-    },
-    provider: { all: [], connected: [], default: {} },
-    sessions: [
-      {
-        id: sessionID,
-        slug: "prompt-input-v2-editing",
-        projectID,
-        directory,
-        title: "Prompt input V2 editing",
-        version: "dev",
-        time: { created: 1700000000000, updated: 1700000000000 },
-      },
-    ],
-    pageMessages: () => ({ items: [] }),
-  })
-  await page.addInitScript(() => {
-    localStorage.setItem("settings.v3", JSON.stringify({ general: { newLayoutDesigns: true } }))
-  })
-
-  await page.goto(`/${base64Encode(directory)}/session/${sessionID}`)
-  const composer = page.locator('[data-component="prompt-input-v2"]')
-  const input = composer.locator('[data-component="prompt-input"]')
-  await expectAppVisible(composer)
-
-  await input.fill("keep me")
-  await composer.getByRole("button", { name: "Add images and files" }).click()
-  await page.getByRole("menuitem", { name: "Commands" }).click()
-  await page.locator('[data-suggestion-id="model.choose"]').click()
-
-  await expect(input).toHaveText("keep me")
-})
@@ -54,15 +54,18 @@ test("shows the V2 thinking level control while relevant", async ({ page }) => {
  })

  await page.goto(`/${base64Encode(directory)}/session/${sessionID}`)
-  const composer = page.locator('[data-component="prompt-input-v2"]')
+  const composer = page.locator('[data-component="session-composer"]')
  const input = composer.locator('[data-component="prompt-input"]')
-  const control = composer.getByRole("button", { name: "Choose model variant" })
+  const control = composer.locator('[data-component="prompt-variant-control"]')
  await expectAppVisible(composer)

  await idleComposer(page)
+  await expect(control).toBeHidden()
+
+  await composer.hover()
  await expect(control).toBeVisible()

-  await control.click()
+  await control.locator('[data-action="prompt-model-variant"]').click()
  const high = page.getByRole("menuitemradio", { name: "high" })
  await expect(high).toBeVisible()
  await page.mouse.move(0, 0)
@@ -32,23 +32,6 @@ for (const expanded of [false, true]) {
  })
 }

-test("shows and expands a running shell command without shimmering it", async ({ page }) => {
-  const id = "prt_shell_running_command"
-  const command = "sleep 10 && echo done"
-  await setupTimeline(page, {
-    messages: [userMessage(), assistantMessage([shell(id, "running", "still running", command)], { completed: false })],
-    settings: { shellToolPartsExpanded: false },
-  })
-
-  const tool = page.locator(`[data-timeline-part-id="${id}"]`)
-  await expect(tool.locator('[data-component="text-shimmer"]')).toHaveAttribute("data-active", "true")
-  await expect(tool.locator('[data-component="shell-submessage"]')).toHaveText(command)
-  await expect(tool.locator('[data-component="shell-submessage"] [data-component="text-shimmer"]')).toHaveCount(0)
-  await tool.locator('[data-slot="collapsible-trigger"]').click()
-  await expect(tool.locator('[data-slot="collapsible-trigger"]')).toHaveAttribute("aria-expanded", "true")
-  await expect(tool.locator('[data-slot="bash-pre"]')).toContainText("still running")
-})
-
 test("transitions thinking and hidden reasoning through busy to idle", async ({ page }) => {
  const reasoningID = "prt_reasoning_hidden"
  const assistant = assistantMessage([reasoningPart(reasoningID, "## Inspecting stability")], { completed: false })
@@ -736,5 +736,5 @@ async function switchTitlebarSession(page: Page, sessionID: string, title: strin
 }

 async function expectSessionReady(page: Page) {
-  await expectAppVisible(page.getByRole("textbox", { name: "Prompt" }))
+  await expectAppVisible(page.getByRole("textbox", { name: /Ask anything/i }))
 }
@@ -1,6 +1,6 @@
 {
  "name": "@opencode-ai/app",
-  "version": "1.18.4",
+  "version": "1.18.3",
  "description": "",
  "type": "module",
  "exports": {
@@ -81,7 +81,7 @@
    "diff": "catalog:",
    "effect": "catalog:",
    "fuzzysort": "catalog:",
-    "ghostty-web": "github:anomalyco/ghostty-web#83c0a07b8628b748aed073b232cb4b52a6ca11c1",
+    "ghostty-web": "github:anomalyco/ghostty-web#513463a6f1190253057e8a3f0dac8f6ee8393553",
    "luxon": "catalog:",
    "marked": "catalog:",
    "marked-shiki": "catalog:",
@@ -37,14 +37,6 @@ function writeAndWait(term: Terminal, data: string): Promise<void> {
 }

 describe("SerializeAddon", () => {
-  test("preserves color scheme reporting mode", async () => {
-    const { term, addon } = createTerminal()
-    await writeAndWait(term, "\x1b[?2031h")
-
-    expect(addon.serialize().startsWith("\x1b[?2031h")).toBe(true)
-    expect(addon.serialize({ excludeModes: true }).startsWith("\x1b[?2031h")).toBe(false)
-  })
-
  describe("ANSI color preservation", () => {
    test("should preserve text attributes (bold, italic, underline)", async () => {
      const { term, addon } = createTerminal()
@@ -89,13 +89,6 @@ const getTerminalBuffers = (value: ITerminalCore): TerminalBuffers | undefined =
  return { active, normal, alternate }
 }

-const getTerminalMode = (value: ITerminalCore, mode: number) => {
-  if (!isRecord(value)) return false
-  const terminal = value.wasmTerm
-  if (!isRecord(terminal) || typeof terminal.getMode !== "function") return false
-  return terminal.getMode(mode) === true
-}
-
 // ============================================================================
 // Types
 // ============================================================================
@@ -551,8 +544,7 @@ export class SerializeAddon implements ITerminalAddon {
      return ""
    }

-    let content = !options?.excludeModes && getTerminalMode(this._terminal, 2031) ? "\u001b[?2031h" : ""
-    content += options?.range
+    let content = options?.range
      ? this._serializeBufferByRange(normalBuffer, options.range, true)
      : this._serializeBufferByScrollback(normalBuffer, options?.scrollback)

@@ -9,16 +9,7 @@ import { Font } from "@opencode-ai/ui/font"
 import { Splash } from "@opencode-ai/ui/logo"
 import { ThemeProvider } from "@opencode-ai/ui/theme/context"
 import { MetaProvider } from "@solidjs/meta"
-import {
-  type BaseRouterProps,
-  Navigate,
-  Route,
-  Router,
-  useLocation,
-  useNavigate,
-  useParams,
-  useSearchParams,
-} from "@solidjs/router"
+import { type BaseRouterProps, Navigate, Route, Router, useNavigate, useParams, useSearchParams } from "@solidjs/router"
 import { QueryClient, QueryClientProvider } from "@tanstack/solid-query"
 import { Effect } from "effect"
 import { base64Encode } from "@opencode-ai/core/util/encode"
@@ -38,7 +29,6 @@ import {
  Show,
 } from "solid-js"
 import { Dynamic } from "solid-js/web"
-import { makeEventListener } from "@solid-primitives/event-listener"
 import { CommandProvider, useCommand, type CommandOption } from "@/context/command"
 import { CommentsProvider } from "@/context/comments"
 import { FileProvider } from "@/context/file"
@@ -5,7 +5,6 @@ import { makeEventListener } from "@solid-primitives/event-listener"
 import { Tooltip } from "@opencode-ai/ui/tooltip"
 import { TooltipV2 } from "@opencode-ai/ui/v2/tooltip-v2"
 import { useLanguage } from "@/context/language"
-import { usePlatform } from "@/context/platform"

 type Mem = Performance & {
  memory?: {
@@ -108,45 +107,8 @@ function Cell(props: {
  )
 }

-function FocusCell(props: { active: boolean; inline?: boolean; onClick: () => void }) {
-  const content = () => (
-    <button
-      type="button"
-      aria-label="Force focus styles on all interactive elements"
-      aria-pressed={props.active}
-      classList={{
-        "flex min-w-0 items-center font-mono uppercase hover:bg-surface-raised-base focus-visible:outline focus-visible:outline-2 focus-visible:outline-offset-[-2px] focus-visible:outline-border-focus": true,
-        "min-h-[20px] w-fit flex-row justify-start gap-1.5 rounded px-1.5 py-0.5 text-left": !!props.inline,
-        "min-h-[42px] w-full flex-col justify-center rounded-[8px] px-0.5 py-1 text-center": !props.inline,
-        "bg-surface-raised-base text-text-strong": props.active,
-      }}
-      onClick={props.onClick}
-    >
-      <span class="text-[10px] leading-none font-black tracking-[0.04em] opacity-70">FOCUS</span>
-      <span classList={{ "leading-none font-bold": true, "text-[11px]": !!props.inline, "text-[13px]": !props.inline }}>
-        {props.active ? "ON" : "OFF"}
-      </span>
-    </button>
-  )
-
-  if (props.inline) {
-    return (
-      <TooltipV2 value="Force focus styles on all interactive elements" placement="top">
-        {content()}
-      </TooltipV2>
-    )
-  }
-
-  return (
-    <Tooltip value="Force focus styles on all interactive elements" placement="top">
-      {content()}
-    </Tooltip>
-  )
-}
-
 export function DebugBar(props: { inline?: boolean } = {}) {
  const language = useLanguage()
-  const platform = usePlatform()
  const location = useLocation()
  const routing = useIsRouting()
  const [state, setState] = createStore({
@@ -154,7 +116,6 @@ export function DebugBar(props: { inline?: boolean } = {}) {
    delay: undefined as number | undefined,
    fps: undefined as number | undefined,
    gap: undefined as number | undefined,
-    focus: false,
    heap: {
      limit: undefined as number | undefined,
      used: undefined as number | undefined,
@@ -181,16 +142,6 @@ export function DebugBar(props: { inline?: boolean } = {}) {
  }
  const longv = () => (state.long.count === undefined ? na() : `${time(state.long.block) ?? na()}/${state.long.count}`)
  const navv = () => (state.nav.pending ? "..." : (time(state.nav.dur) ?? na()))
-  const toggleFocus = async () => {
-    if (!platform.setForceFocus) return
-    const enabled = !state.focus
-    await platform.setForceFocus(enabled)
-    setState("focus", enabled)
-  }
-
-  onCleanup(() => {
-    if (state.focus) void platform.setForceFocus?.(false).catch(() => undefined)
-  })

  let prev = ""
  let start = 0
@@ -539,11 +490,8 @@ export function DebugBar(props: { inline?: boolean } = {}) {
          bad={bad(heap(), 0.8)}
          dim={state.heap.used === undefined}
          inline={props.inline}
-          wide={!platform.setForceFocus}
+          wide
        />
-        {platform.setForceFocus && (
-          <FocusCell active={state.focus} inline={props.inline} onClick={() => void toggleFocus()} />
-        )}
      </div>
    </aside>
  )
@@ -1,73 +0,0 @@
-// @ts-nocheck
-import { Button } from "@opencode-ai/ui/button"
-import { useDialog } from "@opencode-ai/ui/context/dialog"
-import { QueryClient, QueryClientProvider } from "@tanstack/solid-query"
-import { mockProviderAuth } from "@/context/server-sync"
-import { onCleanup, onMount } from "solid-js"
-import { DialogConnectProvider, useProviderConnectController } from "./dialog-connect-provider"
-
-function ConnectProviderDialogStory() {
-  const dialog = useDialog()
-  const open = () => dialog.show(() => <DialogConnectProvider />)
-
-  onMount(open)
-
-  return (
-    <Button variant="secondary" onClick={open}>
-      Open connect provider dialog
-    </Button>
-  )
-}
-
-function ProviderConnectionDialogStory(props) {
-  onCleanup(mockProviderAuth(props.provider, props.methods))
-  const dialog = useDialog()
-  const controller = useProviderConnectController()
-  controller.select(props.provider)
-  const open = () => dialog.show(() => <DialogConnectProvider controller={controller} />)
-
-  onMount(open)
-
-  return (
-    <Button variant="secondary" onClick={open}>
-      Open {props.provider} connection dialog
-    </Button>
-  )
-}
-
-function renderConnection(provider, methods) {
-  return () => (
-    <QueryClientProvider client={new QueryClient()}>
-      <ProviderConnectionDialogStory provider={provider} methods={methods} />
-    </QueryClientProvider>
-  )
-}
-
-export default {
-  title: "App/Dialogs/Connect Provider",
-  id: "app-dialog-connect-provider",
-}
-
-export const V2 = {
-  render: () => (
-    <QueryClientProvider client={new QueryClient()}>
-      <ConnectProviderDialogStory />
-    </QueryClientProvider>
-  ),
-}
-
-export const ApiKey = {
-  render: renderConnection("openrouter", [{ type: "api", label: "API key" }]),
-}
-
-export const OpenCodeZen = {
-  render: renderConnection("opencode", [{ type: "api", label: "API key" }]),
-}
-
-export const LoginMethods = {
-  render: renderConnection("openai", [
-    { type: "oauth", label: "ChatGPT Pro/Plus (browser)" },
-    { type: "oauth", label: "ChatGPT Pro/Plus (headless)" },
-    { type: "api", label: "API key" },
-  ]),
-}
--- a/Show More
+++ b/Show More