feat(ai): add OpenAI image generation

packages/ai/README.md

@@ -1,6 +1,6 @@
 # @opencode-ai/ai
 
-Schema-first LLM core for opencode. One typed request, response, event, and tool language; provider quirks live in adapters, not in calling code.
+Schema-first AI primitives for opencode. Provider quirks live in adapters, not in calling code.
 
 ```ts
 import { Effect } from "effect"
@@ -24,6 +24,45 @@ 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 } 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",
+    count: 2,
+    size: { width: 1024, height: 1024 },
+    providerOptions: { openai: { quality: "high", outputFormat: "webp" } },
+  })
+
+  return response.images // GeneratedImage[] with owned bytes or a provider URL
+})
+```
+
+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.
@@ -32,6 +71,8 @@ Run `LLMClient.stream(request)` instead of `generate` when you want incremental
 - **`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
 
@@ -182,7 +223,7 @@ 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 runtime dispatch and 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 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.
 
 ## See also
 

packages/ai/src/image-client.ts

@@ -0,0 +1,34 @@
+import { Context, Effect, Layer } from "effect"
+import { RequestExecutor } from "./route/executor"
+import type { ImageRequest, ImageResponse } from "./image"
+import type { LLMError } from "./schema"
+
+export type Execute = RequestExecutor.Interface["execute"]
+
+export interface Interface {
+  readonly generate: (request: ImageRequest) => Effect.Effect<ImageResponse, LLMError>
+}
+
+export class Service extends Context.Service<Service, Interface>()("@opencode/ImageClient") {}
+
+export const generate = (request: ImageRequest): 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

packages/ai/src/image.ts

@@ -0,0 +1,107 @@
+import { Effect, Schema } from "effect"
+import { HttpOptions, LLMError, ModelID, ProviderID, ProviderMetadata, Usage } from "./schema"
+import { ImageClient, type Execute as ImageExecute } from "./image-client"
+
+export interface ImageRoute {
+  readonly id: string
+  readonly generate: (request: ImageRequest, execute: ImageExecute) => Effect.Effect<ImageResponse, LLMError>
+}
+
+export class ImageModel {
+  readonly id: ModelID
+  readonly provider: ProviderID
+  readonly route: ImageRoute
+  readonly defaults?: ImageModelDefaults
+
+  constructor(input: ImageModel.Input) {
+    this.id = input.id
+    this.provider = input.provider
+    this.route = input.route
+    this.defaults = input.defaults
+  }
+
+  static make(input: ImageModel.MakeInput) {
+    return new ImageModel({
+      id: ModelID.make(input.id),
+      provider: ProviderID.make(input.provider),
+      route: input.route,
+      defaults: input.defaults,
+    })
+  }
+}
+
+export namespace ImageModel {
+  export interface Input {
+    readonly id: ModelID
+    readonly provider: ProviderID
+    readonly route: ImageRoute
+    readonly defaults?: ImageModelDefaults
+  }
+
+  export interface MakeInput extends Omit<Input, "id" | "provider"> {
+    readonly id: string | ModelID
+    readonly provider: string | ProviderID
+  }
+}
+
+export interface ImageModelDefaults {
+  readonly providerOptions?: Record<string, Record<string, unknown>>
+  readonly http?: HttpOptions
+}
+
+export const ImageModelSchema = Schema.declare((value): value is ImageModel => value instanceof ImageModel, {
+  expected: "Image.Model",
+})
+
+export const ImageSize = Schema.Struct({
+  width: Schema.Number,
+  height: Schema.Number,
+}).annotate({ identifier: "Image.Size" })
+export type ImageSize = Schema.Schema.Type<typeof ImageSize>
+
+export class ImageRequest extends Schema.Class<ImageRequest>("Image.Request")({
+  model: ImageModelSchema,
+  prompt: Schema.String,
+  count: Schema.optional(Schema.Number),
+  size: Schema.optional(ImageSize),
+  aspectRatio: Schema.optional(Schema.String),
+  seed: Schema.optional(Schema.Number),
+  providerOptions: Schema.optional(Schema.Record(Schema.String, Schema.Record(Schema.String, Schema.Unknown))),
+  http: Schema.optional(HttpOptions),
+  metadata: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
+}) {}
+
+export type ImageRequestInput = Omit<ConstructorParameters<typeof ImageRequest>[0], "http"> & {
+  readonly http?: HttpOptions.Input
+}
+
+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 const request = (input: ImageRequest | ImageRequestInput) => {
+  if (input instanceof ImageRequest) return input
+  return new ImageRequest({
+    ...input,
+    http: input.http === undefined ? undefined : HttpOptions.make(input.http),
+  })
+}
+
+export const generate = (input: ImageRequest | ImageRequestInput) => ImageClient.generate(request(input))
+
+export const Image = {
+  request,
+  generate,
+} as const

packages/ai/src/index.ts

@@ -1,4 +1,5 @@
 export { LLMClient } from "./route/client"
+export { ImageClient } from "./image-client"
 export { Auth } from "./route/auth"
 export { Provider } from "./provider"
 export { ProviderPackage } from "./provider-package"
@@ -10,6 +11,9 @@ export type {
   Service as LLMClientService,
 } from "./route/client"
 export * from "./schema"
+export { GeneratedImage, ImageModel, ImageRequest, ImageResponse, ImageSize } from "./image"
+export type { ImageModelDefaults, 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"

packages/ai/src/protocols/index.ts

@@ -2,6 +2,7 @@ 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"

packages/ai/src/protocols/openai-images.ts

@@ -0,0 +1,174 @@
+import { Effect, Encoding, Schema } from "effect"
+import { Headers, HttpClientRequest } from "effect/unstable/http"
+import {
+  ImageModel,
+  GeneratedImage,
+  ImageResponse,
+  type ImageRequest,
+  type ImageModelDefaults,
+  type ImageRoute,
+} from "../image"
+import { Auth, type Definition as AuthDefinition } from "../route/auth"
+import { InvalidProviderOutputReason, LLMError, Usage } from "../schema"
+import { ProviderShared } from "./shared"
+
+const ADAPTER = "openai-images"
+export const DEFAULT_BASE_URL = "https://api.openai.com/v1"
+export const PATH = "/images/generations"
+
+export interface OpenAIImageOptions {
+  readonly quality?: "auto" | "low" | "medium" | "high"
+  readonly background?: "auto" | "opaque" | "transparent"
+  readonly moderation?: "auto" | "low"
+  readonly outputFormat?: "png" | "jpeg" | "webp"
+  readonly outputCompression?: number
+}
+
+const OpenAIImageBody = Schema.Struct({
+  model: Schema.String,
+  prompt: Schema.String,
+  n: Schema.optional(Schema.Number),
+  size: Schema.optional(Schema.String),
+  quality: Schema.optional(Schema.Literals(["auto", "low", "medium", "high"])),
+  background: Schema.optional(Schema.Literals(["auto", "opaque", "transparent"])),
+  moderation: Schema.optional(Schema.Literals(["auto", "low"])),
+  output_format: Schema.optional(Schema.Literals(["png", "jpeg", "webp"])),
+  output_compression: Schema.optional(Schema.Number),
+})
+export type OpenAIImageBody = Schema.Schema.Type<typeof OpenAIImageBody>
+
+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 defaults?: ImageModelDefaults
+}
+
+const providerOptions = (request: ImageRequest): OpenAIImageOptions => ({
+  ...request.model.defaults?.providerOptions?.openai,
+  ...request.providerOptions?.openai,
+})
+
+const body = (request: ImageRequest): OpenAIImageBody => {
+  const options = providerOptions(request)
+  return {
+    model: request.model.id,
+    prompt: request.prompt,
+    n: request.count,
+    size: request.size === undefined ? undefined : `${request.size.width}x${request.size.height}`,
+    quality: options.quality,
+    background: options.background,
+    moderation: options.moderation,
+    output_format: options.outputFormat,
+    output_compression: options.outputCompression,
+  }
+}
+
+const invalidOutput = (message: string) =>
+  new LLMError({
+    module: ADAPTER,
+    method: "generate",
+    reason: new InvalidProviderOutputReason({ message, route: ADAPTER }),
+  })
+
+export const model = (input: ModelInput) => {
+  const route: ImageRoute = {
+    id: ADAPTER,
+    generate: Effect.fn("OpenAIImages.generate")(function* (request: ImageRequest, execute) {
+      if (request.aspectRatio !== undefined)
+        return yield* ProviderShared.invalidRequest("OpenAI Images does not support the common aspectRatio option")
+      if (request.seed !== undefined)
+        return yield* ProviderShared.invalidRequest("OpenAI Images does not support the common seed option")
+
+      const requestBody = yield* ProviderShared.validateWith(Schema.decodeUnknownEffect(OpenAIImageBody))(body(request))
+      const text = Schema.encodeSync(Schema.fromJsonString(OpenAIImageBody))(requestBody)
+      const url = `${(input.baseURL ?? DEFAULT_BASE_URL).replace(/\/$/, "")}${PATH}`
+      const http = request.http ?? request.model.defaults?.http
+      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 OpenAI Images response")),
+      )
+      const decoded = yield* Schema.decodeUnknownEffect(OpenAIImageResponse)(payload).pipe(
+        Effect.mapError(() => invalidOutput("OpenAI Images returned an invalid response")),
+      )
+      const format = decoded.output_format ?? providerOptions(request).outputFormat ?? "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 } },
+      })
+    }),
+  }
+  return ImageModel.make({ id: input.id, provider: "openai", route, defaults: input.defaults })
+}
+
+export const OpenAIImages = {
+  model,
+} as const

packages/ai/src/protocols/openai-responses.ts

@@ -113,11 +113,24 @@ const OpenAIResponsesTool = Schema.Struct({
   parameters: JsonObject,
   strict: Schema.optional(Schema.Boolean),
 })
-type OpenAIResponsesTool = Schema.Schema.Type<typeof OpenAIResponsesTool>
+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.Number),
+  output_format: Schema.optional(Schema.Literals(["png", "jpeg", "webp"])),
+  partial_images: Schema.optional(Schema.Number),
+  quality: Schema.optional(Schema.Literals(["auto", "low", "medium", "high"])),
+  size: Schema.optional(Schema.String),
+})
+const OpenAIResponsesTools = Schema.Union([OpenAIResponsesTool, OpenAIResponsesImageGenerationTool])
+type OpenAIResponsesTool = Schema.Schema.Type<typeof OpenAIResponsesTools>
 
 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`
@@ -128,7 +141,7 @@ const OpenAIResponsesCoreFields = {
   model: Schema.String,
   input: Schema.Array(OpenAIResponsesInputItem),
   instructions: Schema.optional(Schema.String),
-  tools: optionalArray(OpenAIResponsesTool),
+  tools: optionalArray(OpenAIResponsesTools),
   tool_choice: Schema.optional(OpenAIResponsesToolChoice),
   store: Schema.optional(Schema.Boolean),
   service_tier: Schema.optional(OpenAIOptions.OpenAIServiceTier),
@@ -194,6 +207,7 @@ 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),
   error: Schema.optional(Schema.Unknown),
   encrypted_content: optionalNull(Schema.String),
 })
@@ -258,21 +272,30 @@ const invalid = ProviderShared.invalidRequest
 // =============================================================================
 // Request Lowering
 // =============================================================================
-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 nativeImageTool = (tool: ToolDefinition) => {
+  const native = tool.native?.openai
+  return Schema.is(OpenAIResponsesImageGenerationTool)(native) ? native : undefined
+}
 
-const lowerToolChoice = (toolChoice: NonNullable<LLMRequest["toolChoice"]>) =>
+const lowerTool = (tool: ToolDefinition, inputSchema: JsonSchema): OpenAIResponsesTool =>
+  nativeImageTool(tool) ?? {
+    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>) =>
   ProviderShared.matchToolChoice("OpenAI Responses", toolChoice, {
     auto: () => "auto" as const,
     none: () => "none" as const,
     required: () => "required" as const,
-    tool: (name) => ({ type: "function" as const, name }),
+    tool: (name) =>
+      tools.some((tool) => tool.name === name && nativeImageTool(tool) !== undefined)
+        ? ({ type: "image_generation" } as const)
+        : { type: "function" as const, name },
   })
 
 const lowerToolCall = (part: ToolCallPart): OpenAIResponsesInputItem => ({
@@ -488,7 +511,7 @@ const fromRequest = Effect.fn("OpenAIResponses.fromRequest")(function* (request:
         : request.tools.map((tool) =>
             lowerTool(tool, ToolSchemaProjection.modelCompatibility(tool.inputSchema, toolSchemaCompatibility)),
           ),
-    tool_choice: request.toolChoice ? yield* lowerToolChoice(request.toolChoice) : undefined,
+    tool_choice: request.toolChoice ? yield* lowerToolChoice(request.toolChoice, request.tools) : undefined,
     stream: true as const,
     max_output_tokens: generation?.maxTokens,
     temperature: generation?.temperature,
@@ -576,6 +599,11 @@ const isReasoningItem = (
 // outputs / sources / status without re-decoding.
 const hostedToolResult = (item: OpenAIResponsesStreamItem) => {
   const isError = typeof item.error !== "undefined" && item.error !== null
+  if (item.type === "image_generation_call" && item.result)
+    return {
+      type: "content" as const,
+      value: [{ type: "file" as const, uri: `data:image/png;base64,${item.result}`, mime: "image/png" }],
+    }
   return isError ? { type: "error" as const, value: item.error } : { type: "json" as const, value: item }
 }
 

packages/ai/src/providers/openai.ts

@@ -1,12 +1,14 @@
 import { AuthOptions, type ProviderAuthOption } from "../route/auth-options"
 import type { Route, RouteDefaultsInput } from "../route/client"
 import type { ProviderPackage } from "../provider-package"
-import { ProviderID, type ModelID } from "../schema"
+import { HttpOptions, ProviderID, ToolDefinition, 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 OpenAIImageOptions } from "../protocols/openai-images"
 
 export type { OpenAIOptionsInput, OpenAIResponseIncludable } from "./openai-options"
+export type { OpenAIImageOptions } from "../protocols/openai-images"
 
 export const id = ProviderID.make("openai")
 
@@ -22,6 +24,41 @@ export type Config = RouteDefaultsInput &
     readonly providerOptions?: OpenAIProviderOptionsInput
   }
 
+export interface ImageConfig {
+  readonly providerOptions?: OpenAIImageOptions
+}
+
+export interface ImageGenerationOptions {
+  readonly action?: "auto" | "generate" | "edit"
+  readonly background?: "auto" | "opaque" | "transparent"
+  readonly inputFidelity?: "low" | "high"
+  readonly outputCompression?: number
+  readonly outputFormat?: "png" | "jpeg" | "webp"
+  readonly partialImages?: number
+  readonly quality?: "auto" | "low" | "medium" | "high"
+  readonly size?: string
+}
+
+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
@@ -55,6 +92,17 @@ 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, options: ImageConfig = {}) =>
+    OpenAIImages.model({
+      id: modelID,
+      auth: auth(input),
+      baseURL: input.baseURL,
+      headers: input.headers,
+      defaults: {
+        providerOptions: options.providerOptions === undefined ? undefined : { openai: { ...options.providerOptions } },
+        http: input.http === undefined ? undefined : HttpOptions.make(input.http),
+      },
+    })
 
   return {
     id,
@@ -62,6 +110,7 @@ export const configure = (input: Config = {}) => {
     responses,
     responsesWebSocket,
     chat,
+    image,
     configure,
   }
 }
@@ -97,3 +146,4 @@ 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

packages/ai/src/route/auth.ts

@@ -1,6 +1,6 @@
 import { Config, Effect, Redacted } from "effect"
 import { Headers } from "effect/unstable/http"
-import { AuthenticationReason, InvalidRequestReason, LLMError, type LLMRequest } from "../schema"
+import { AuthenticationReason, InvalidRequestReason, LLMError, type HttpOptions } 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: LLMRequest
+  readonly request: { readonly http?: HttpOptions }
   readonly method: "POST" | "GET"
   readonly url: string
   readonly body: string

packages/ai/test/image.test.ts

@@ -0,0 +1,59 @@
+import { describe, expect } from "bun:test"
+import { Effect, Layer } from "effect"
+import { HttpClientRequest } from "effect/unstable/http"
+import { Image, ImageClient } from "../src"
+import { OpenAI } 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" }).image("gpt-image-2"),
+        prompt: "A robot tending a rooftop garden",
+        count: 2,
+        size: { width: 1024, height: 1024 },
+        providerOptions: {
+          openai: { quality: "high", outputFormat: "webp" },
+        },
+      })
+
+      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")
+                expect(request.headers.get("authorization")).toBe("Bearer test")
+                expect(JSON.parse(input.text)).toEqual({
+                  model: "gpt-image-2",
+                  prompt: "A robot tending a rooftop garden",
+                  n: 2,
+                  size: "1024x1024",
+                  quality: "high",
+                  output_format: "webp",
+                })
+                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" } },
+                )
+              }),
+            ),
+          ),
+        ),
+      ),
+    ),
+  )
+})

packages/ai/test/provider/openai-responses.test.ts

@@ -58,6 +58,24 @@ 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("lowers semantic service tier options", () =>
     Effect.gen(function* () {
       const input = LLM.updateRequest(request, { providerOptions: { openai: { serviceTier: "priority" } } })
@@ -1361,6 +1379,37 @@ 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("decodes code_interpreter_call as provider-executed events with code input", () =>
     Effect.gen(function* () {
       const item = {