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 +1,116 @@
+import { Effect, Schema } from "effect"
+import { HttpOptions, InvalidRequestReason, 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.Int.check(Schema.isGreaterThanOrEqualTo(1)),
+  height: Schema.Int.check(Schema.isGreaterThanOrEqualTo(1)),
+}).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.Int.check(Schema.isGreaterThanOrEqualTo(1))),
+  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) =>
+  Effect.try({
+    try: () => request(input),
+    catch: (error) =>
+      new LLMError({
+        module: "Image",
+        method: "generate",
+        reason: new InvalidRequestReason({ message: error instanceof Error ? error.message : String(error) }),
+      }),
+  }).pipe(Effect.flatMap(ImageClient.generate))
+
+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"