get codex working in copilot (#4914)

Co-authored-by: OpeOginni <[email protected]>
Co-authored-by: GitHub Action <[email protected]>

bun.lock

@@ -224,6 +224,8 @@
         "@ai-sdk/mcp": "0.0.8",
         "@ai-sdk/openai": "2.0.71",
         "@ai-sdk/openai-compatible": "1.0.27",
+        "@ai-sdk/provider": "2.0.0",
+        "@ai-sdk/provider-utils": "3.0.18",
         "@clack/prompts": "1.0.0-alpha.1",
         "@hono/standard-validator": "0.1.5",
         "@hono/zod-validator": "catalog:",
@@ -503,7 +505,7 @@
 
     "@ai-sdk/provider": ["@ai-sdk/[email protected]", "", { "dependencies": { "json-schema": "^0.4.0" } }, "sha512-6o7Y2SeO9vFKB8lArHXehNuusnpddKPk7xqL7T2/b+OvXMRIXUO1rR4wcv1hAFUAT9avGZshty3Wlua/XA7TvA=="],
 
-    "@ai-sdk/provider-utils": ["@ai-sdk/[email protected].0", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.3", "zod-to-json-schema": "^3.24.1" }, "peerDependencies": { "zod": "^3.25.76 || ^4" } }, "sha512-BoQZtGcBxkeSH1zK+SRYNDtJPIPpacTeiMZqnG4Rv6xXjEwM0FH4MGs9c+PlhyEWmQCzjRM2HAotEydFhD4dYw=="],
+    "@ai-sdk/provider-utils": ["@ai-sdk/[email protected].18", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-ypv1xXMsgGcNKUP+hglKqtdDuMg68nWHucPPAhIENrbFAI+xCHiqPVN8Zllxyv1TNZwGWUghPxJXU+Mqps0YRQ=="],
 
     "@alloc/quick-lru": ["@alloc/[email protected]", "", {}, "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw=="],
 
@@ -3737,20 +3739,22 @@
 
     "@ai-sdk/amazon-bedrock/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-TR3Gs4I3Tym4Ll+EPdzRdvo/rc8Js6c4nVhFLuvGLX/Y4V9ZcQMa/HTiYsHEgmYrf1zVi6Q145UEZUfleOwOjw=="],
 
+    "@ai-sdk/anthropic/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.3", "zod-to-json-schema": "^3.24.1" }, "peerDependencies": { "zod": "^3.25.76 || ^4" } }, "sha512-BoQZtGcBxkeSH1zK+SRYNDtJPIPpacTeiMZqnG4Rv6xXjEwM0FH4MGs9c+PlhyEWmQCzjRM2HAotEydFhD4dYw=="],
+
     "@ai-sdk/azure/@ai-sdk/openai": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@ai-sdk/provider-utils": "3.0.17" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-tg+gj+R0z/On9P4V7hy7/7o04cQPjKGayMCL3gzWD/aNGjAKkhEnaocuNDidSnghizt8g2zJn16cAuAolnW+qQ=="],
 
     "@ai-sdk/azure/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-TR3Gs4I3Tym4Ll+EPdzRdvo/rc8Js6c4nVhFLuvGLX/Y4V9ZcQMa/HTiYsHEgmYrf1zVi6Q145UEZUfleOwOjw=="],
 
     "@ai-sdk/gateway/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-TR3Gs4I3Tym4Ll+EPdzRdvo/rc8Js6c4nVhFLuvGLX/Y4V9ZcQMa/HTiYsHEgmYrf1zVi6Q145UEZUfleOwOjw=="],
 
-    "@ai-sdk/google/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-ypv1xXMsgGcNKUP+hglKqtdDuMg68nWHucPPAhIENrbFAI+xCHiqPVN8Zllxyv1TNZwGWUghPxJXU+Mqps0YRQ=="],
-
     "@ai-sdk/google-vertex/@ai-sdk/anthropic": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@ai-sdk/provider-utils": "3.0.18" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-21PaHfoLmouOXXNINTsZJsMw+wE5oLR2He/1kq/sKokTVKyq7ObGT1LDk6ahwxaz/GoaNaGankMh+EgVcdv2Cw=="],
 
-    "@ai-sdk/google-vertex/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-ypv1xXMsgGcNKUP+hglKqtdDuMg68nWHucPPAhIENrbFAI+xCHiqPVN8Zllxyv1TNZwGWUghPxJXU+Mqps0YRQ=="],
-
     "@ai-sdk/mcp/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-TR3Gs4I3Tym4Ll+EPdzRdvo/rc8Js6c4nVhFLuvGLX/Y4V9ZcQMa/HTiYsHEgmYrf1zVi6Q145UEZUfleOwOjw=="],
 
+    "@ai-sdk/openai/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.3", "zod-to-json-schema": "^3.24.1" }, "peerDependencies": { "zod": "^3.25.76 || ^4" } }, "sha512-BoQZtGcBxkeSH1zK+SRYNDtJPIPpacTeiMZqnG4Rv6xXjEwM0FH4MGs9c+PlhyEWmQCzjRM2HAotEydFhD4dYw=="],
+
+    "@ai-sdk/openai-compatible/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.3", "zod-to-json-schema": "^3.24.1" }, "peerDependencies": { "zod": "^3.25.76 || ^4" } }, "sha512-BoQZtGcBxkeSH1zK+SRYNDtJPIPpacTeiMZqnG4Rv6xXjEwM0FH4MGs9c+PlhyEWmQCzjRM2HAotEydFhD4dYw=="],
+
     "@astrojs/cloudflare/vite": ["[email protected]", "", { "dependencies": { "esbuild": "^0.25.0", "fdir": "^6.4.4", "picomatch": "^4.0.2", "postcss": "^8.5.3", "rollup": "^4.34.9", "tinyglobby": "^0.2.13" }, "optionalDependencies": { "fsevents": "~2.3.3" }, "peerDependencies": { "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0", "jiti": ">=1.21.0", "less": "*", "lightningcss": "^1.21.0", "sass": "*", "sass-embedded": "*", "stylus": "*", "sugarss": "*", "terser": "^5.16.0", "tsx": "^4.8.1", "yaml": "^2.4.2" }, "optionalPeers": ["@types/node", "jiti", "less", "lightningcss", "sass", "sass-embedded", "stylus", "sugarss", "terser", "tsx", "yaml"], "bin": { "vite": "bin/vite.js" } }, "sha512-+Oxm7q9hDoLMyJOYfUYBuHQo+dkAloi33apOPP56pzj+vsdJDzr+j1NISE5pyaAuKL4A3UD34qd0lx5+kfKp2g=="],
 
     "@astrojs/markdown-remark/@astrojs/internal-helpers": ["@astrojs/[email protected]", "", {}, "sha512-l5Pqf6uZu31aG+3Lv8nl/3s4DbUzdlxTWDof4pEpto6GUJNhhCbelVi9dEyurOVyqaelwmS9oSyOWOENSfgo9A=="],
@@ -4619,8 +4623,6 @@
 
     "jsonwebtoken/jws/jwa": ["[email protected]", "", { "dependencies": { "buffer-equal-constant-time": "^1.0.1", "ecdsa-sig-formatter": "1.0.11", "safe-buffer": "^5.0.1" } }, "sha512-eeH5JO+21J78qMvTIDdBXidBd6nG2kZjg5Ohz/1fpa28Z4CcsWUzJ1ZZyFq/3z3N17aZy+ZuBoHljASbL1WfOw=="],
 
-    "opencode/@ai-sdk/anthropic/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-ypv1xXMsgGcNKUP+hglKqtdDuMg68nWHucPPAhIENrbFAI+xCHiqPVN8Zllxyv1TNZwGWUghPxJXU+Mqps0YRQ=="],
-
     "opencode/@ai-sdk/openai/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-TR3Gs4I3Tym4Ll+EPdzRdvo/rc8Js6c4nVhFLuvGLX/Y4V9ZcQMa/HTiYsHEgmYrf1zVi6Q145UEZUfleOwOjw=="],
 
     "opencode/@ai-sdk/openai-compatible/@ai-sdk/provider-utils": ["@ai-sdk/[email protected]", "", { "dependencies": { "@ai-sdk/provider": "2.0.0", "@standard-schema/spec": "^1.0.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-TR3Gs4I3Tym4Ll+EPdzRdvo/rc8Js6c4nVhFLuvGLX/Y4V9ZcQMa/HTiYsHEgmYrf1zVi6Q145UEZUfleOwOjw=="],

flake.lock

@@ -2,11 +2,11 @@
   "nodes": {
     "nixpkgs": {
       "locked": {
-        "lastModified": 1764384123,
-        "narHash": "sha256-UoliURDJFaOolycBZYrjzd9Cc66zULEyHqGFH3QHEq0=",
+        "lastModified": 1764445028,
+        "narHash": "sha256-ik6H/0Zl+qHYDKTXFPpzuVHSZE+uvVz2XQuQd1IVXzo=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "59b6c96beacc898566c9be1052ae806f3835f87d",
+        "rev": "a09378c0108815dbf3961a0e085936f4146ec415",
         "type": "github"
       },
       "original": {

nix/hashes.json

@@ -1,3 +1,3 @@
 {
-  "nodeModules": "sha256-+PJZG5jNxBGkxblpnNa4lvfBi9YEvHaGQRE0+avNwHY="
+  "nodeModules": "sha256-jLrT8GVq0Fh34tN1MPgJpPKd9SGhOauaBl8f1oZ/XgI="
 }

packages/opencode/package.json

@@ -50,6 +50,8 @@
     "@ai-sdk/mcp": "0.0.8",
     "@ai-sdk/openai": "2.0.71",
     "@ai-sdk/openai-compatible": "1.0.27",
+    "@ai-sdk/provider": "2.0.0",
+    "@ai-sdk/provider-utils": "3.0.18",
     "@clack/prompts": "1.0.0-alpha.1",
     "@hono/standard-validator": "0.1.5",
     "@hono/zod-validator": "catalog:",

packages/opencode/src/cli/cmd/tui/component/prompt/index.tsx

@@ -843,6 +843,7 @@ export function Prompt(props: PromptProps) {
               justifyContent={status().type === "retry" ? "space-between" : "flex-start"}
             >
               <box flexShrink={0} flexDirection="row" gap={1}>
+                {/* @ts-ignore // SpinnerOptions doesn't support marginLeft */}
                 <spinner marginLeft={1} color={spinnerDef().color} frames={spinnerDef().frames} interval={40} />
                 <box flexDirection="row" gap={1} flexShrink={0}>
                   {(() => {

packages/opencode/src/provider/provider.ts

@@ -23,6 +23,7 @@ import { createVertexAnthropic } from "@ai-sdk/google-vertex/anthropic"
 import { createOpenAI } from "@ai-sdk/openai"
 import { createOpenAICompatible } from "@ai-sdk/openai-compatible"
 import { createOpenRouter } from "@openrouter/ai-sdk-provider"
+import { createOpenaiCompatible as createGitHubCopilotOpenAICompatible } from "./sdk/openai-compatible/src"
 
 export namespace Provider {
   const log = Log.create({ service: "provider" })
@@ -37,6 +38,8 @@ export namespace Provider {
     "@ai-sdk/openai": createOpenAI,
     "@ai-sdk/openai-compatible": createOpenAICompatible,
     "@openrouter/ai-sdk-provider": createOpenRouter,
+    // @ts-ignore (TODO: kill this code so we dont have to maintain it)
+    "@ai-sdk/github-copilot": createGitHubCopilotOpenAICompatible,
   }
 
   type CustomLoader = (provider: ModelsDev.Provider) => Promise<{
@@ -87,6 +90,30 @@ export namespace Provider {
         options: {},
       }
     },
+    "github-copilot": async () => {
+      return {
+        autoload: false,
+        async getModel(sdk: any, modelID: string, _options?: Record<string, any>) {
+          if (modelID.includes("gpt-5")) {
+            return sdk.responses(modelID)
+          }
+          return sdk.chat(modelID)
+        },
+        options: {},
+      }
+    },
+    "github-copilot-enterprise": async () => {
+      return {
+        autoload: false,
+        async getModel(sdk: any, modelID: string, _options?: Record<string, any>) {
+          if (modelID.includes("gpt-5")) {
+            return sdk.responses(modelID)
+          }
+          return sdk.chat(modelID)
+        },
+        options: {},
+      }
+    },
     azure: async () => {
       return {
         autoload: false,
@@ -428,15 +455,6 @@ export namespace Provider {
       }
     }
 
-    // load custom
-    for (const [providerID, fn] of Object.entries(CUSTOM_LOADERS)) {
-      if (disabled.has(providerID)) continue
-      const result = await fn(database[providerID])
-      if (result && (result.autoload || providers[providerID])) {
-        mergeProvider(providerID, result.options ?? {}, "custom", result.getModel)
-      }
-    }
-
     for (const plugin of await Plugin.list()) {
       if (!plugin.auth) continue
       const providerID = plugin.auth.provider
@@ -478,6 +496,14 @@ export namespace Provider {
       }
     }
 
+    for (const [providerID, fn] of Object.entries(CUSTOM_LOADERS)) {
+      if (disabled.has(providerID)) continue
+      const result = await fn(database[providerID])
+      if (result && (result.autoload || providers[providerID])) {
+        mergeProvider(providerID, result.options ?? {}, "custom", result.getModel)
+      }
+    }
+
     // load config
     for (const [providerID, provider] of configProviders) {
       mergeProvider(providerID, provider.options ?? {}, "config")
@@ -489,6 +515,10 @@ export namespace Provider {
         continue
       }
 
+      if (providerID === "github-copilot") {
+        provider.info.npm = "@ai-sdk/github-copilot"
+      }
+
       const configProvider = config.provider?.[providerID]
       const filteredModels = Object.fromEntries(
         Object.entries(provider.info.models)

packages/opencode/src/provider/sdk/openai-compatible/src/README.md

@@ -0,0 +1,5 @@
+This is a temporary package used primarily for github copilot compatibility.
+
+Avoid making changes to these files unless you want to only affect Copilot provider.
+
+Also this should ONLY be used for Copilot provider.

packages/opencode/src/provider/sdk/openai-compatible/src/index.ts

@@ -0,0 +1,2 @@
+export { createOpenaiCompatible, openaiCompatible } from "./openai-compatible-provider"
+export type { OpenaiCompatibleProvider, OpenaiCompatibleProviderSettings } from "./openai-compatible-provider"

packages/opencode/src/provider/sdk/openai-compatible/src/openai-compatible-provider.ts

@@ -0,0 +1,100 @@
+import type { LanguageModelV2 } from "@ai-sdk/provider"
+import { OpenAICompatibleChatLanguageModel } from "@ai-sdk/openai-compatible"
+import { type FetchFunction, withoutTrailingSlash, withUserAgentSuffix } from "@ai-sdk/provider-utils"
+import { OpenAIResponsesLanguageModel } from "./responses/openai-responses-language-model"
+
+// Import the version or define it
+const VERSION = "0.1.0"
+
+export type OpenaiCompatibleModelId = string
+
+export interface OpenaiCompatibleProviderSettings {
+  /**
+   * API key for authenticating requests.
+   */
+  apiKey?: string
+
+  /**
+   * Base URL for the OpenAI Compatible API calls.
+   */
+  baseURL?: string
+
+  /**
+   * Name of the provider.
+   */
+  name?: string
+
+  /**
+   * Custom headers to include in the requests.
+   */
+  headers?: Record<string, string>
+
+  /**
+   * Custom fetch implementation.
+   */
+  fetch?: FetchFunction
+}
+
+export interface OpenaiCompatibleProvider {
+  (modelId: OpenaiCompatibleModelId): LanguageModelV2
+  chat(modelId: OpenaiCompatibleModelId): LanguageModelV2
+  responses(modelId: OpenaiCompatibleModelId): LanguageModelV2
+  languageModel(modelId: OpenaiCompatibleModelId): LanguageModelV2
+
+  // embeddingModel(modelId: any): EmbeddingModelV2
+
+  // imageModel(modelId: any): ImageModelV2
+}
+
+/**
+ * Create an OpenAI Compatible provider instance.
+ */
+export function createOpenaiCompatible(options: OpenaiCompatibleProviderSettings = {}): OpenaiCompatibleProvider {
+  const baseURL = withoutTrailingSlash(options.baseURL ?? "https://api.openai.com/v1")
+
+  if (!baseURL) {
+    throw new Error("baseURL is required")
+  }
+
+  // Merge headers: defaults first, then user overrides
+  const headers = {
+    // Default OpenAI Compatible headers (can be overridden by user)
+    ...(options.apiKey && { Authorization: `Bearer ${options.apiKey}` }),
+    ...options.headers,
+  }
+
+  const getHeaders = () => withUserAgentSuffix(headers, `ai-sdk/openai-compatible/${VERSION}`)
+
+  const createChatModel = (modelId: OpenaiCompatibleModelId) => {
+    return new OpenAICompatibleChatLanguageModel(modelId, {
+      provider: `${options.name ?? "openai-compatible"}.chat`,
+      headers: getHeaders,
+      url: ({ path }) => `${baseURL}${path}`,
+      fetch: options.fetch,
+    })
+  }
+
+  const createResponsesModel = (modelId: OpenaiCompatibleModelId) => {
+    return new OpenAIResponsesLanguageModel(modelId, {
+      provider: `${options.name ?? "openai-compatible"}.responses`,
+      headers: getHeaders,
+      url: ({ path }) => `${baseURL}${path}`,
+      fetch: options.fetch,
+    })
+  }
+
+  const createLanguageModel = (modelId: OpenaiCompatibleModelId) => createChatModel(modelId)
+
+  const provider = function (modelId: OpenaiCompatibleModelId) {
+    return createChatModel(modelId)
+  }
+
+  provider.languageModel = createLanguageModel
+  provider.chat = createChatModel
+  provider.responses = createResponsesModel
+
+  return provider as OpenaiCompatibleProvider
+}
+
+// Default OpenAI Compatible provider instance
+export const openaiCompatible = createOpenaiCompatible()

packages/opencode/src/provider/sdk/openai-compatible/src/responses/convert-to-openai-responses-input.ts

@@ -0,0 +1,303 @@
+import {
+  type LanguageModelV2CallWarning,
+  type LanguageModelV2Prompt,
+  type LanguageModelV2ToolCallPart,
+  UnsupportedFunctionalityError,
+} from "@ai-sdk/provider"
+import { convertToBase64, parseProviderOptions } from "@ai-sdk/provider-utils"
+import { z } from "zod/v4"
+import type { OpenAIResponsesInput, OpenAIResponsesReasoning } from "./openai-responses-api-types"
+import { localShellInputSchema, localShellOutputSchema } from "./tool/local-shell"
+
+/**
+ * Check if a string is a file ID based on the given prefixes
+ * Returns false if prefixes is undefined (disables file ID detection)
+ */
+function isFileId(data: string, prefixes?: readonly string[]): boolean {
+  if (!prefixes) return false
+  return prefixes.some((prefix) => data.startsWith(prefix))
+}
+
+export async function convertToOpenAIResponsesInput({
+  prompt,
+  systemMessageMode,
+  fileIdPrefixes,
+  store,
+  hasLocalShellTool = false,
+}: {
+  prompt: LanguageModelV2Prompt
+  systemMessageMode: "system" | "developer" | "remove"
+  fileIdPrefixes?: readonly string[]
+  store: boolean
+  hasLocalShellTool?: boolean
+}): Promise<{
+  input: OpenAIResponsesInput
+  warnings: Array<LanguageModelV2CallWarning>
+}> {
+  const input: OpenAIResponsesInput = []
+  const warnings: Array<LanguageModelV2CallWarning> = []
+
+  for (const { role, content } of prompt) {
+    switch (role) {
+      case "system": {
+        switch (systemMessageMode) {
+          case "system": {
+            input.push({ role: "system", content })
+            break
+          }
+          case "developer": {
+            input.push({ role: "developer", content })
+            break
+          }
+          case "remove": {
+            warnings.push({
+              type: "other",
+              message: "system messages are removed for this model",
+            })
+            break
+          }
+          default: {
+            const _exhaustiveCheck: never = systemMessageMode
+            throw new Error(`Unsupported system message mode: ${_exhaustiveCheck}`)
+          }
+        }
+        break
+      }
+
+      case "user": {
+        input.push({
+          role: "user",
+          content: content.map((part, index) => {
+            switch (part.type) {
+              case "text": {
+                return { type: "input_text", text: part.text }
+              }
+              case "file": {
+                if (part.mediaType.startsWith("image/")) {
+                  const mediaType = part.mediaType === "image/*" ? "image/jpeg" : part.mediaType
+
+                  return {
+                    type: "input_image",
+                    ...(part.data instanceof URL
+                      ? { image_url: part.data.toString() }
+                      : typeof part.data === "string" && isFileId(part.data, fileIdPrefixes)
+                        ? { file_id: part.data }
+                        : {
+                            image_url: `data:${mediaType};base64,${convertToBase64(part.data)}`,
+                          }),
+                    detail: part.providerOptions?.openai?.imageDetail,
+                  }
+                } else if (part.mediaType === "application/pdf") {
+                  if (part.data instanceof URL) {
+                    return {
+                      type: "input_file",
+                      file_url: part.data.toString(),
+                    }
+                  }
+                  return {
+                    type: "input_file",
+                    ...(typeof part.data === "string" && isFileId(part.data, fileIdPrefixes)
+                      ? { file_id: part.data }
+                      : {
+                          filename: part.filename ?? `part-${index}.pdf`,
+                          file_data: `data:application/pdf;base64,${convertToBase64(part.data)}`,
+                        }),
+                  }
+                } else {
+                  throw new UnsupportedFunctionalityError({
+                    functionality: `file part media type ${part.mediaType}`,
+                  })
+                }
+              }
+            }
+          }),
+        })
+
+        break
+      }
+
+      case "assistant": {
+        const reasoningMessages: Record<string, OpenAIResponsesReasoning> = {}
+        const toolCallParts: Record<string, LanguageModelV2ToolCallPart> = {}
+
+        for (const part of content) {
+          switch (part.type) {
+            case "text": {
+              input.push({
+                role: "assistant",
+                content: [{ type: "output_text", text: part.text }],
+                id: (part.providerOptions?.openai?.itemId as string) ?? undefined,
+              })
+              break
+            }
+            case "tool-call": {
+              toolCallParts[part.toolCallId] = part
+
+              if (part.providerExecuted) {
+                break
+              }
+
+              if (hasLocalShellTool && part.toolName === "local_shell") {
+                const parsedInput = localShellInputSchema.parse(part.input)
+                input.push({
+                  type: "local_shell_call",
+                  call_id: part.toolCallId,
+                  id: (part.providerOptions?.openai?.itemId as string) ?? undefined,
+                  action: {
+                    type: "exec",
+                    command: parsedInput.action.command,
+                    timeout_ms: parsedInput.action.timeoutMs,
+                    user: parsedInput.action.user,
+                    working_directory: parsedInput.action.workingDirectory,
+                    env: parsedInput.action.env,
+                  },
+                })
+
+                break
+              }
+
+              input.push({
+                type: "function_call",
+                call_id: part.toolCallId,
+                name: part.toolName,
+                arguments: JSON.stringify(part.input),
+                id: (part.providerOptions?.openai?.itemId as string) ?? undefined,
+              })
+              break
+            }
+
+            // assistant tool result parts are from provider-executed tools:
+            case "tool-result": {
+              if (store) {
+                // use item references to refer to tool results from built-in tools
+                input.push({ type: "item_reference", id: part.toolCallId })
+              } else {
+                warnings.push({
+                  type: "other",
+                  message: `Results for OpenAI tool ${part.toolName} are not sent to the API when store is false`,
+                })
+              }
+
+              break
+            }
+
+            case "reasoning": {
+              const providerOptions = await parseProviderOptions({
+                provider: "openai",
+                providerOptions: part.providerOptions,
+                schema: openaiResponsesReasoningProviderOptionsSchema,
+              })
+
+              const reasoningId = providerOptions?.itemId
+
+              if (reasoningId != null) {
+                const reasoningMessage = reasoningMessages[reasoningId]
+
+                if (store) {
+                  if (reasoningMessage === undefined) {
+                    // use item references to refer to reasoning (single reference)
+                    input.push({ type: "item_reference", id: reasoningId })
+
+                    // store unused reasoning message to mark id as used
+                    reasoningMessages[reasoningId] = {
+                      type: "reasoning",
+                      id: reasoningId,
+                      summary: [],
+                    }
+                  }
+                } else {
+                  const summaryParts: Array<{
+                    type: "summary_text"
+                    text: string
+                  }> = []
+
+                  if (part.text.length > 0) {
+                    summaryParts.push({
+                      type: "summary_text",
+                      text: part.text,
+                    })
+                  } else if (reasoningMessage !== undefined) {
+                    warnings.push({
+                      type: "other",
+                      message: `Cannot append empty reasoning part to existing reasoning sequence. Skipping reasoning part: ${JSON.stringify(part)}.`,
+                    })
+                  }
+
+                  if (reasoningMessage === undefined) {
+                    reasoningMessages[reasoningId] = {
+                      type: "reasoning",
+                      id: reasoningId,
+                      encrypted_content: providerOptions?.reasoningEncryptedContent,
+                      summary: summaryParts,
+                    }
+                    input.push(reasoningMessages[reasoningId])
+                  } else {
+                    reasoningMessage.summary.push(...summaryParts)
+                  }
+                }
+              } else {
+                warnings.push({
+                  type: "other",
+                  message: `Non-OpenAI reasoning parts are not supported. Skipping reasoning part: ${JSON.stringify(part)}.`,
+                })
+              }
+              break
+            }
+          }
+        }
+
+        break
+      }
+
+      case "tool": {
+        for (const part of content) {
+          const output = part.output
+
+          if (hasLocalShellTool && part.toolName === "local_shell" && output.type === "json") {
+            input.push({
+              type: "local_shell_call_output",
+              call_id: part.toolCallId,
+              output: localShellOutputSchema.parse(output.value).output,
+            })
+            break
+          }
+
+          let contentValue: string
+          switch (output.type) {
+            case "text":
+            case "error-text":
+              contentValue = output.value
+              break
+            case "content":
+            case "json":
+            case "error-json":
+              contentValue = JSON.stringify(output.value)
+              break
+          }
+
+          input.push({
+            type: "function_call_output",
+            call_id: part.toolCallId,
+            output: contentValue,
+          })
+        }
+
+        break
+      }
+
+      default: {
+        const _exhaustiveCheck: never = role
+        throw new Error(`Unsupported role: ${_exhaustiveCheck}`)
+      }
+    }
+  }
+
+  return { input, warnings }
+}
+
+const openaiResponsesReasoningProviderOptionsSchema = z.object({
+  itemId: z.string().nullish(),
+  reasoningEncryptedContent: z.string().nullish(),
+})
+
+export type OpenAIResponsesReasoningProviderOptions = z.infer<typeof openaiResponsesReasoningProviderOptionsSchema>

packages/opencode/src/provider/sdk/openai-compatible/src/responses/map-openai-responses-finish-reason.ts

@@ -0,0 +1,22 @@
+import type { LanguageModelV2FinishReason } from "@ai-sdk/provider"
+
+export function mapOpenAIResponseFinishReason({
+  finishReason,
+  hasFunctionCall,
+}: {
+  finishReason: string | null | undefined
+  // flag that checks if there have been client-side tool calls (not executed by openai)
+  hasFunctionCall: boolean
+}): LanguageModelV2FinishReason {
+  switch (finishReason) {
+    case undefined:
+    case null:
+      return hasFunctionCall ? "tool-calls" : "stop"
+    case "max_output_tokens":
+      return "length"
+    case "content_filter":
+      return "content-filter"
+    default:
+      return hasFunctionCall ? "tool-calls" : "unknown"
+  }
+}

packages/opencode/src/provider/sdk/openai-compatible/src/responses/openai-config.ts

@@ -0,0 +1,18 @@
+import type { FetchFunction } from "@ai-sdk/provider-utils"
+
+export type OpenAIConfig = {
+  provider: string
+  url: (options: { modelId: string; path: string }) => string
+  headers: () => Record<string, string | undefined>
+  fetch?: FetchFunction
+  generateId?: () => string
+  /**
+   * File ID prefixes used to identify file IDs in Responses API.
+   * When undefined, all file data is treated as base64 content.
+   *
+   * Examples:
+   * - OpenAI: ['file-'] for IDs like 'file-abc123'
+   * - Azure OpenAI: ['assistant-'] for IDs like 'assistant-abc123'
+   */
+  fileIdPrefixes?: readonly string[]
+}

packages/opencode/src/provider/sdk/openai-compatible/src/responses/openai-error.ts

@@ -0,0 +1,22 @@
+import { z } from "zod/v4"
+import { createJsonErrorResponseHandler } from "@ai-sdk/provider-utils"
+
+export const openaiErrorDataSchema = z.object({
+  error: z.object({
+    message: z.string(),
+
+    // The additional information below is handled loosely to support
+    // OpenAI-compatible providers that have slightly different error
+    // responses:
+    type: z.string().nullish(),
+    param: z.any().nullish(),
+    code: z.union([z.string(), z.number()]).nullish(),
+  }),
+})
+
+export type OpenAIErrorData = z.infer<typeof openaiErrorDataSchema>
+
+export const openaiFailedResponseHandler: any = createJsonErrorResponseHandler({
+  errorSchema: openaiErrorDataSchema,
+  errorToMessage: (data) => data.error.message,
+})

packages/opencode/src/provider/sdk/openai-compatible/src/responses/openai-responses-api-types.ts

@@ -0,0 +1,207 @@
+import type { JSONSchema7 } from "@ai-sdk/provider"
+
+export type OpenAIResponsesInput = Array<OpenAIResponsesInputItem>
+
+export type OpenAIResponsesInputItem =
+  | OpenAIResponsesSystemMessage
+  | OpenAIResponsesUserMessage
+  | OpenAIResponsesAssistantMessage
+  | OpenAIResponsesFunctionCall
+  | OpenAIResponsesFunctionCallOutput
+  | OpenAIResponsesComputerCall
+  | OpenAIResponsesLocalShellCall
+  | OpenAIResponsesLocalShellCallOutput
+  | OpenAIResponsesReasoning
+  | OpenAIResponsesItemReference
+
+export type OpenAIResponsesIncludeValue =
+  | "web_search_call.action.sources"
+  | "code_interpreter_call.outputs"
+  | "computer_call_output.output.image_url"
+  | "file_search_call.results"
+  | "message.input_image.image_url"
+  | "message.output_text.logprobs"
+  | "reasoning.encrypted_content"
+
+export type OpenAIResponsesIncludeOptions = Array<OpenAIResponsesIncludeValue> | undefined | null
+
+export type OpenAIResponsesSystemMessage = {
+  role: "system" | "developer"
+  content: string
+}
+
+export type OpenAIResponsesUserMessage = {
+  role: "user"
+  content: Array<
+    | { type: "input_text"; text: string }
+    | { type: "input_image"; image_url: string }
+    | { type: "input_image"; file_id: string }
+    | { type: "input_file"; file_url: string }
+    | { type: "input_file"; filename: string; file_data: string }
+    | { type: "input_file"; file_id: string }
+  >
+}
+
+export type OpenAIResponsesAssistantMessage = {
+  role: "assistant"
+  content: Array<{ type: "output_text"; text: string }>
+  id?: string
+}
+
+export type OpenAIResponsesFunctionCall = {
+  type: "function_call"
+  call_id: string
+  name: string
+  arguments: string
+  id?: string
+}
+
+export type OpenAIResponsesFunctionCallOutput = {
+  type: "function_call_output"
+  call_id: string
+  output: string
+}
+
+export type OpenAIResponsesComputerCall = {
+  type: "computer_call"
+  id: string
+  status?: string
+}
+
+export type OpenAIResponsesLocalShellCall = {
+  type: "local_shell_call"
+  id: string
+  call_id: string
+  action: {
+    type: "exec"
+    command: string[]
+    timeout_ms?: number
+    user?: string
+    working_directory?: string
+    env?: Record<string, string>
+  }
+}
+
+export type OpenAIResponsesLocalShellCallOutput = {
+  type: "local_shell_call_output"
+  call_id: string
+  output: string
+}
+
+export type OpenAIResponsesItemReference = {
+  type: "item_reference"
+  id: string
+}
+
+/**
+ * A filter used to compare a specified attribute key to a given value using a defined comparison operation.
+ */
+export type OpenAIResponsesFileSearchToolComparisonFilter = {
+  /**
+   * The key to compare against the value.
+   */
+  key: string
+
+  /**
+   * Specifies the comparison operator: eq, ne, gt, gte, lt, lte.
+   */
+  type: "eq" | "ne" | "gt" | "gte" | "lt" | "lte"
+
+  /**
+   * The value to compare against the attribute key; supports string, number, or boolean types.
+   */
+  value: string | number | boolean
+}
+
+/**
+ * Combine multiple filters using and or or.
+ */
+export type OpenAIResponsesFileSearchToolCompoundFilter = {
+  /**
+   * Type of operation: and or or.
+   */
+  type: "and" | "or"
+
+  /**
+   * Array of filters to combine. Items can be ComparisonFilter or CompoundFilter.
+   */
+  filters: Array<OpenAIResponsesFileSearchToolComparisonFilter | OpenAIResponsesFileSearchToolCompoundFilter>
+}
+
+export type OpenAIResponsesTool =
+  | {
+      type: "function"
+      name: string
+      description: string | undefined
+      parameters: JSONSchema7
+      strict: boolean | undefined
+    }
+  | {
+      type: "web_search"
+      filters: { allowed_domains: string[] | undefined } | undefined
+      search_context_size: "low" | "medium" | "high" | undefined
+      user_location:
+        | {
+            type: "approximate"
+            city?: string
+            country?: string
+            region?: string
+            timezone?: string
+          }
+        | undefined
+    }
+  | {
+      type: "web_search_preview"
+      search_context_size: "low" | "medium" | "high" | undefined
+      user_location:
+        | {
+            type: "approximate"
+            city?: string
+            country?: string
+            region?: string
+            timezone?: string
+          }
+        | undefined
+    }
+  | {
+      type: "code_interpreter"
+      container: string | { type: "auto"; file_ids: string[] | undefined }
+    }
+  | {
+      type: "file_search"
+      vector_store_ids: string[]
+      max_num_results: number | undefined
+      ranking_options: { ranker?: string; score_threshold?: number } | undefined
+      filters: OpenAIResponsesFileSearchToolComparisonFilter | OpenAIResponsesFileSearchToolCompoundFilter | undefined
+    }
+  | {
+      type: "image_generation"
+      background: "auto" | "opaque" | "transparent" | undefined
+      input_fidelity: "low" | "high" | undefined
+      input_image_mask:
+        | {
+            file_id: string | undefined
+            image_url: string | undefined
+          }
+        | undefined
+      model: string | undefined
+      moderation: "auto" | undefined
+      output_compression: number | undefined
+      output_format: "png" | "jpeg" | "webp" | undefined
+      partial_images: number | undefined
+      quality: "auto" | "low" | "medium" | "high" | undefined
+      size: "auto" | "1024x1024" | "1024x1536" | "1536x1024" | undefined
+    }
+  | {
+      type: "local_shell"
+    }
+
+export type OpenAIResponsesReasoning = {
+  type: "reasoning"
+  id: string
+  encrypted_content?: string | null
+  summary: Array<{
+    type: "summary_text"
+    text: string
+  }>
+}

packages/opencode/src/provider/sdk/openai-compatible/src/responses/openai-responses-language-model.ts

@@ -0,0 +1,1713 @@
+import {
+  APICallError,
+  type LanguageModelV2,
+  type LanguageModelV2CallWarning,
+  type LanguageModelV2Content,
+  type LanguageModelV2FinishReason,
+  type LanguageModelV2ProviderDefinedTool,
+  type LanguageModelV2StreamPart,
+  type LanguageModelV2Usage,
+  type SharedV2ProviderMetadata,
+} from "@ai-sdk/provider"
+import {
+  combineHeaders,
+  createEventSourceResponseHandler,
+  createJsonResponseHandler,
+  generateId,
+  parseProviderOptions,
+  type ParseResult,
+  postJsonToApi,
+} from "@ai-sdk/provider-utils"
+import { z } from "zod/v4"
+import type { OpenAIConfig } from "./openai-config"
+import { openaiFailedResponseHandler } from "./openai-error"
+import { codeInterpreterInputSchema, codeInterpreterOutputSchema } from "./tool/code-interpreter"
+import { fileSearchOutputSchema } from "./tool/file-search"
+import { imageGenerationOutputSchema } from "./tool/image-generation"
+import { convertToOpenAIResponsesInput } from "./convert-to-openai-responses-input"
+import { mapOpenAIResponseFinishReason } from "./map-openai-responses-finish-reason"
+import type { OpenAIResponsesIncludeOptions, OpenAIResponsesIncludeValue } from "./openai-responses-api-types"
+import { prepareResponsesTools } from "./openai-responses-prepare-tools"
+import type { OpenAIResponsesModelId } from "./openai-responses-settings"
+import { localShellInputSchema } from "./tool/local-shell"
+
+const webSearchCallItem = z.object({
+  type: z.literal("web_search_call"),
+  id: z.string(),
+  status: z.string(),
+  action: z
+    .discriminatedUnion("type", [
+      z.object({
+        type: z.literal("search"),
+        query: z.string().nullish(),
+      }),
+      z.object({
+        type: z.literal("open_page"),
+        url: z.string(),
+      }),
+      z.object({
+        type: z.literal("find"),
+        url: z.string(),
+        pattern: z.string(),
+      }),
+    ])
+    .nullish(),
+})
+
+const fileSearchCallItem = z.object({
+  type: z.literal("file_search_call"),
+  id: z.string(),
+  queries: z.array(z.string()),
+  results: z
+    .array(
+      z.object({
+        attributes: z.record(z.string(), z.unknown()),
+        file_id: z.string(),
+        filename: z.string(),
+        score: z.number(),
+        text: z.string(),
+      }),
+    )
+    .nullish(),
+})
+
+const codeInterpreterCallItem = z.object({
+  type: z.literal("code_interpreter_call"),
+  id: z.string(),
+  code: z.string().nullable(),
+  container_id: z.string(),
+  outputs: z
+    .array(
+      z.discriminatedUnion("type", [
+        z.object({ type: z.literal("logs"), logs: z.string() }),
+        z.object({ type: z.literal("image"), url: z.string() }),
+      ]),
+    )
+    .nullable(),
+})
+
+const localShellCallItem = z.object({
+  type: z.literal("local_shell_call"),
+  id: z.string(),
+  call_id: z.string(),
+  action: z.object({
+    type: z.literal("exec"),
+    command: z.array(z.string()),
+    timeout_ms: z.number().optional(),
+    user: z.string().optional(),
+    working_directory: z.string().optional(),
+    env: z.record(z.string(), z.string()).optional(),
+  }),
+})
+
+const imageGenerationCallItem = z.object({
+  type: z.literal("image_generation_call"),
+  id: z.string(),
+  result: z.string(),
+})
+
+/**
+ * `top_logprobs` request body argument can be set to an integer between
+ * 0 and 20 specifying the number of most likely tokens to return at each
+ * token position, each with an associated log probability.
+ *
+ * @see https://platform.openai.com/docs/api-reference/responses/create#responses_create-top_logprobs
+ */
+const TOP_LOGPROBS_MAX = 20
+
+const LOGPROBS_SCHEMA = z.array(
+  z.object({
+    token: z.string(),
+    logprob: z.number(),
+    top_logprobs: z.array(
+      z.object({
+        token: z.string(),
+        logprob: z.number(),
+      }),
+    ),
+  }),
+)
+
+export class OpenAIResponsesLanguageModel implements LanguageModelV2 {
+  readonly specificationVersion = "v2"
+
+  readonly modelId: OpenAIResponsesModelId
+
+  private readonly config: OpenAIConfig
+
+  constructor(modelId: OpenAIResponsesModelId, config: OpenAIConfig) {
+    this.modelId = modelId
+    this.config = config
+  }
+
+  readonly supportedUrls: Record<string, RegExp[]> = {
+    "image/*": [/^https?:\/\/.*$/],
+    "application/pdf": [/^https?:\/\/.*$/],
+  }
+
+  get provider(): string {
+    return this.config.provider
+  }
+
+  private async getArgs({
+    maxOutputTokens,
+    temperature,
+    stopSequences,
+    topP,
+    topK,
+    presencePenalty,
+    frequencyPenalty,
+    seed,
+    prompt,
+    providerOptions,
+    tools,
+    toolChoice,
+    responseFormat,
+  }: Parameters<LanguageModelV2["doGenerate"]>[0]) {
+    const warnings: LanguageModelV2CallWarning[] = []
+    const modelConfig = getResponsesModelConfig(this.modelId)
+
+    if (topK != null) {
+      warnings.push({ type: "unsupported-setting", setting: "topK" })
+    }
+
+    if (seed != null) {
+      warnings.push({ type: "unsupported-setting", setting: "seed" })
+    }
+
+    if (presencePenalty != null) {
+      warnings.push({
+        type: "unsupported-setting",
+        setting: "presencePenalty",
+      })
+    }
+
+    if (frequencyPenalty != null) {
+      warnings.push({
+        type: "unsupported-setting",
+        setting: "frequencyPenalty",
+      })
+    }
+
+    if (stopSequences != null) {
+      warnings.push({ type: "unsupported-setting", setting: "stopSequences" })
+    }
+
+    const openaiOptions = await parseProviderOptions({
+      provider: "openai",
+      providerOptions,
+      schema: openaiResponsesProviderOptionsSchema,
+    })
+
+    const { input, warnings: inputWarnings } = await convertToOpenAIResponsesInput({
+      prompt,
+      systemMessageMode: modelConfig.systemMessageMode,
+      fileIdPrefixes: this.config.fileIdPrefixes,
+      store: openaiOptions?.store ?? true,
+      hasLocalShellTool: hasOpenAITool("openai.local_shell"),
+    })
+
+    warnings.push(...inputWarnings)
+
+    const strictJsonSchema = openaiOptions?.strictJsonSchema ?? false
+
+    let include: OpenAIResponsesIncludeOptions = openaiOptions?.include
+
+    function addInclude(key: OpenAIResponsesIncludeValue) {
+      include = include != null ? [...include, key] : [key]
+    }
+
+    function hasOpenAITool(id: string) {
+      return tools?.find((tool) => tool.type === "provider-defined" && tool.id === id) != null
+    }
+
+    // when logprobs are requested, automatically include them:
+    const topLogprobs =
+      typeof openaiOptions?.logprobs === "number"
+        ? openaiOptions?.logprobs
+        : openaiOptions?.logprobs === true
+          ? TOP_LOGPROBS_MAX
+          : undefined
+
+    if (topLogprobs) {
+      addInclude("message.output_text.logprobs")
+    }
+
+    // when a web search tool is present, automatically include the sources:
+    const webSearchToolName = (
+      tools?.find(
+        (tool) =>
+          tool.type === "provider-defined" &&
+          (tool.id === "openai.web_search" || tool.id === "openai.web_search_preview"),
+      ) as LanguageModelV2ProviderDefinedTool | undefined
+    )?.name
+
+    if (webSearchToolName) {
+      addInclude("web_search_call.action.sources")
+    }
+
+    // when a code interpreter tool is present, automatically include the outputs:
+    if (hasOpenAITool("openai.code_interpreter")) {
+      addInclude("code_interpreter_call.outputs")
+    }
+
+    const baseArgs = {
+      model: this.modelId,
+      input,
+      temperature,
+      top_p: topP,
+      max_output_tokens: maxOutputTokens,
+
+      ...((responseFormat?.type === "json" || openaiOptions?.textVerbosity) && {
+        text: {
+          ...(responseFormat?.type === "json" && {
+            format:
+              responseFormat.schema != null
+                ? {
+                    type: "json_schema",
+                    strict: strictJsonSchema,
+                    name: responseFormat.name ?? "response",
+                    description: responseFormat.description,
+                    schema: responseFormat.schema,
+                  }
+                : { type: "json_object" },
+          }),
+          ...(openaiOptions?.textVerbosity && {
+            verbosity: openaiOptions.textVerbosity,
+          }),
+        },
+      }),
+
+      // provider options:
+      max_tool_calls: openaiOptions?.maxToolCalls,
+      metadata: openaiOptions?.metadata,
+      parallel_tool_calls: openaiOptions?.parallelToolCalls,
+      previous_response_id: openaiOptions?.previousResponseId,
+      store: openaiOptions?.store,
+      user: openaiOptions?.user,
+      instructions: openaiOptions?.instructions,
+      service_tier: openaiOptions?.serviceTier,
+      include,
+      prompt_cache_key: openaiOptions?.promptCacheKey,
+      safety_identifier: openaiOptions?.safetyIdentifier,
+      top_logprobs: topLogprobs,
+
+      // model-specific settings:
+      ...(modelConfig.isReasoningModel &&
+        (openaiOptions?.reasoningEffort != null || openaiOptions?.reasoningSummary != null) && {
+          reasoning: {
+            ...(openaiOptions?.reasoningEffort != null && {
+              effort: openaiOptions.reasoningEffort,
+            }),
+            ...(openaiOptions?.reasoningSummary != null && {
+              summary: openaiOptions.reasoningSummary,
+            }),
+          },
+        }),
+      ...(modelConfig.requiredAutoTruncation && {
+        truncation: "auto",
+      }),
+    }
+
+    if (modelConfig.isReasoningModel) {
+      // remove unsupported settings for reasoning models
+      // see https://platform.openai.com/docs/guides/reasoning#limitations
+      if (baseArgs.temperature != null) {
+        baseArgs.temperature = undefined
+        warnings.push({
+          type: "unsupported-setting",
+          setting: "temperature",
+          details: "temperature is not supported for reasoning models",
+        })
+      }
+
+      if (baseArgs.top_p != null) {
+        baseArgs.top_p = undefined
+        warnings.push({
+          type: "unsupported-setting",
+          setting: "topP",
+          details: "topP is not supported for reasoning models",
+        })
+      }
+    } else {
+      if (openaiOptions?.reasoningEffort != null) {
+        warnings.push({
+          type: "unsupported-setting",
+          setting: "reasoningEffort",
+          details: "reasoningEffort is not supported for non-reasoning models",
+        })
+      }
+
+      if (openaiOptions?.reasoningSummary != null) {
+        warnings.push({
+          type: "unsupported-setting",
+          setting: "reasoningSummary",
+          details: "reasoningSummary is not supported for non-reasoning models",
+        })
+      }
+    }
+
+    // Validate flex processing support
+    if (openaiOptions?.serviceTier === "flex" && !modelConfig.supportsFlexProcessing) {
+      warnings.push({
+        type: "unsupported-setting",
+        setting: "serviceTier",
+        details: "flex processing is only available for o3, o4-mini, and gpt-5 models",
+      })
+      // Remove from args if not supported
+      delete (baseArgs as any).service_tier
+    }
+
+    // Validate priority processing support
+    if (openaiOptions?.serviceTier === "priority" && !modelConfig.supportsPriorityProcessing) {
+      warnings.push({
+        type: "unsupported-setting",
+        setting: "serviceTier",
+        details:
+          "priority processing is only available for supported models (gpt-4, gpt-5, gpt-5-mini, o3, o4-mini) and requires Enterprise access. gpt-5-nano is not supported",
+      })
+      // Remove from args if not supported
+      delete (baseArgs as any).service_tier
+    }
+
+    const {
+      tools: openaiTools,
+      toolChoice: openaiToolChoice,
+      toolWarnings,
+    } = prepareResponsesTools({
+      tools,
+      toolChoice,
+      strictJsonSchema,
+    })
+
+    return {
+      webSearchToolName,
+      args: {
+        ...baseArgs,
+        tools: openaiTools,
+        tool_choice: openaiToolChoice,
+      },
+      warnings: [...warnings, ...toolWarnings],
+    }
+  }
+
+  async doGenerate(
+    options: Parameters<LanguageModelV2["doGenerate"]>[0],
+  ): Promise<Awaited<ReturnType<LanguageModelV2["doGenerate"]>>> {
+    const { args: body, warnings, webSearchToolName } = await this.getArgs(options)
+    const url = this.config.url({
+      path: "/responses",
+      modelId: this.modelId,
+    })
+
+    const {
+      responseHeaders,
+      value: response,
+      rawValue: rawResponse,
+    } = await postJsonToApi({
+      url,
+      headers: combineHeaders(this.config.headers(), options.headers),
+      body,
+      failedResponseHandler: openaiFailedResponseHandler,
+      successfulResponseHandler: createJsonResponseHandler(
+        z.object({
+          id: z.string(),
+          created_at: z.number(),
+          error: z
+            .object({
+              code: z.string(),
+              message: z.string(),
+            })
+            .nullish(),
+          model: z.string(),
+          output: z.array(
+            z.discriminatedUnion("type", [
+              z.object({
+                type: z.literal("message"),
+                role: z.literal("assistant"),
+                id: z.string(),
+                content: z.array(
+                  z.object({
+                    type: z.literal("output_text"),
+                    text: z.string(),
+                    logprobs: LOGPROBS_SCHEMA.nullish(),
+                    annotations: z.array(
+                      z.discriminatedUnion("type", [
+                        z.object({
+                          type: z.literal("url_citation"),
+                          start_index: z.number(),
+                          end_index: z.number(),
+                          url: z.string(),
+                          title: z.string(),
+                        }),
+                        z.object({
+                          type: z.literal("file_citation"),
+                          file_id: z.string(),
+                          filename: z.string().nullish(),
+                          index: z.number().nullish(),
+                          start_index: z.number().nullish(),
+                          end_index: z.number().nullish(),
+                          quote: z.string().nullish(),
+                        }),
+                        z.object({
+                          type: z.literal("container_file_citation"),
+                        }),
+                      ]),
+                    ),
+                  }),
+                ),
+              }),
+              webSearchCallItem,
+              fileSearchCallItem,
+              codeInterpreterCallItem,
+              imageGenerationCallItem,
+              localShellCallItem,
+              z.object({
+                type: z.literal("function_call"),
+                call_id: z.string(),
+                name: z.string(),
+                arguments: z.string(),
+                id: z.string(),
+              }),
+              z.object({
+                type: z.literal("computer_call"),
+                id: z.string(),
+                status: z.string().optional(),
+              }),
+              z.object({
+                type: z.literal("reasoning"),
+                id: z.string(),
+                encrypted_content: z.string().nullish(),
+                summary: z.array(
+                  z.object({
+                    type: z.literal("summary_text"),
+                    text: z.string(),
+                  }),
+                ),
+              }),
+            ]),
+          ),
+          service_tier: z.string().nullish(),
+          incomplete_details: z.object({ reason: z.string() }).nullish(),
+          usage: usageSchema,
+        }),
+      ),
+      abortSignal: options.abortSignal,
+      fetch: this.config.fetch,
+    })
+
+    if (response.error) {
+      throw new APICallError({
+        message: response.error.message,
+        url,
+        requestBodyValues: body,
+        statusCode: 400,
+        responseHeaders,
+        responseBody: rawResponse as string,
+        isRetryable: false,
+      })
+    }
+
+    const content: Array<LanguageModelV2Content> = []
+    const logprobs: Array<z.infer<typeof LOGPROBS_SCHEMA>> = []
+
+    // flag that checks if there have been client-side tool calls (not executed by openai)
+    let hasFunctionCall = false
+
+    // map response content to content array
+    for (const part of response.output) {
+      switch (part.type) {
+        case "reasoning": {
+          // when there are no summary parts, we need to add an empty reasoning part:
+          if (part.summary.length === 0) {
+            part.summary.push({ type: "summary_text", text: "" })
+          }
+
+          for (const summary of part.summary) {
+            content.push({
+              type: "reasoning" as const,
+              text: summary.text,
+              providerMetadata: {
+                openai: {
+                  itemId: part.id,
+                  reasoningEncryptedContent: part.encrypted_content ?? null,
+                },
+              },
+            })
+          }
+          break
+        }
+
+        case "image_generation_call": {
+          content.push({
+            type: "tool-call",
+            toolCallId: part.id,
+            toolName: "image_generation",
+            input: "{}",
+            providerExecuted: true,
+          })
+
+          content.push({
+            type: "tool-result",
+            toolCallId: part.id,
+            toolName: "image_generation",
+            result: {
+              result: part.result,
+            } satisfies z.infer<typeof imageGenerationOutputSchema>,
+            providerExecuted: true,
+          })
+
+          break
+        }
+
+        case "local_shell_call": {
+          content.push({
+            type: "tool-call",
+            toolCallId: part.call_id,
+            toolName: "local_shell",
+            input: JSON.stringify({ action: part.action } satisfies z.infer<typeof localShellInputSchema>),
+            providerMetadata: {
+              openai: {
+                itemId: part.id,
+              },
+            },
+          })
+
+          break
+        }
+
+        case "message": {
+          for (const contentPart of part.content) {
+            if (options.providerOptions?.openai?.logprobs && contentPart.logprobs) {
+              logprobs.push(contentPart.logprobs)
+            }
+
+            content.push({
+              type: "text",
+              text: contentPart.text,
+              providerMetadata: {
+                openai: {
+                  itemId: part.id,
+                },
+              },
+            })
+
+            for (const annotation of contentPart.annotations) {
+              if (annotation.type === "url_citation") {
+                content.push({
+                  type: "source",
+                  sourceType: "url",
+                  id: this.config.generateId?.() ?? generateId(),
+                  url: annotation.url,
+                  title: annotation.title,
+                })
+              } else if (annotation.type === "file_citation") {
+                content.push({
+                  type: "source",
+                  sourceType: "document",
+                  id: this.config.generateId?.() ?? generateId(),
+                  mediaType: "text/plain",
+                  title: annotation.quote ?? annotation.filename ?? "Document",
+                  filename: annotation.filename ?? annotation.file_id,
+                })
+              }
+            }
+          }
+
+          break
+        }
+
+        case "function_call": {
+          hasFunctionCall = true
+
+          content.push({
+            type: "tool-call",
+            toolCallId: part.call_id,
+            toolName: part.name,
+            input: part.arguments,
+            providerMetadata: {
+              openai: {
+                itemId: part.id,
+              },
+            },
+          })
+          break
+        }
+
+        case "web_search_call": {
+          content.push({
+            type: "tool-call",
+            toolCallId: part.id,
+            toolName: webSearchToolName ?? "web_search",
+            input: JSON.stringify({ action: part.action }),
+            providerExecuted: true,
+          })
+
+          content.push({
+            type: "tool-result",
+            toolCallId: part.id,
+            toolName: webSearchToolName ?? "web_search",
+            result: { status: part.status },
+            providerExecuted: true,
+          })
+
+          break
+        }
+
+        case "computer_call": {
+          content.push({
+            type: "tool-call",
+            toolCallId: part.id,
+            toolName: "computer_use",
+            input: "",
+            providerExecuted: true,
+          })
+
+          content.push({
+            type: "tool-result",
+            toolCallId: part.id,
+            toolName: "computer_use",
+            result: {
+              type: "computer_use_tool_result",
+              status: part.status || "completed",
+            },
+            providerExecuted: true,
+          })
+          break
+        }
+
+        case "file_search_call": {
+          content.push({
+            type: "tool-call",
+            toolCallId: part.id,
+            toolName: "file_search",
+            input: "{}",
+            providerExecuted: true,
+          })
+
+          content.push({
+            type: "tool-result",
+            toolCallId: part.id,
+            toolName: "file_search",
+            result: {
+              queries: part.queries,
+              results:
+                part.results?.map((result) => ({
+                  attributes: result.attributes,
+                  fileId: result.file_id,
+                  filename: result.filename,
+                  score: result.score,
+                  text: result.text,
+                })) ?? null,
+            } satisfies z.infer<typeof fileSearchOutputSchema>,
+            providerExecuted: true,
+          })
+          break
+        }
+
+        case "code_interpreter_call": {
+          content.push({
+            type: "tool-call",
+            toolCallId: part.id,
+            toolName: "code_interpreter",
+            input: JSON.stringify({
+              code: part.code,
+              containerId: part.container_id,
+            } satisfies z.infer<typeof codeInterpreterInputSchema>),
+            providerExecuted: true,
+          })
+
+          content.push({
+            type: "tool-result",
+            toolCallId: part.id,
+            toolName: "code_interpreter",
+            result: {
+              outputs: part.outputs,
+            } satisfies z.infer<typeof codeInterpreterOutputSchema>,
+            providerExecuted: true,
+          })
+          break
+        }
+      }
+    }
+
+    const providerMetadata: SharedV2ProviderMetadata = {
+      openai: { responseId: response.id },
+    }
+
+    if (logprobs.length > 0) {
+      providerMetadata.openai.logprobs = logprobs
+    }
+
+    if (typeof response.service_tier === "string") {
+      providerMetadata.openai.serviceTier = response.service_tier
+    }
+
+    return {
+      content,
+      finishReason: mapOpenAIResponseFinishReason({
+        finishReason: response.incomplete_details?.reason,
+        hasFunctionCall,
+      }),
+      usage: {
+        inputTokens: response.usage.input_tokens,
+        outputTokens: response.usage.output_tokens,
+        totalTokens: response.usage.input_tokens + response.usage.output_tokens,
+        reasoningTokens: response.usage.output_tokens_details?.reasoning_tokens ?? undefined,
+        cachedInputTokens: response.usage.input_tokens_details?.cached_tokens ?? undefined,
+      },
+      request: { body },
+      response: {
+        id: response.id,
+        timestamp: new Date(response.created_at * 1000),
+        modelId: response.model,
+        headers: responseHeaders,
+        body: rawResponse,
+      },
+      providerMetadata,
+      warnings,
+    }
+  }
+
+  async doStream(
+    options: Parameters<LanguageModelV2["doStream"]>[0],
+  ): Promise<Awaited<ReturnType<LanguageModelV2["doStream"]>>> {
+    const { args: body, warnings, webSearchToolName } = await this.getArgs(options)
+
+    const { responseHeaders, value: response } = await postJsonToApi({
+      url: this.config.url({
+        path: "/responses",
+        modelId: this.modelId,
+      }),
+      headers: combineHeaders(this.config.headers(), options.headers),
+      body: {
+        ...body,
+        stream: true,
+      },
+      failedResponseHandler: openaiFailedResponseHandler,
+      successfulResponseHandler: createEventSourceResponseHandler(openaiResponsesChunkSchema),
+      abortSignal: options.abortSignal,
+      fetch: this.config.fetch,
+    })
+
+    const self = this
+
+    let finishReason: LanguageModelV2FinishReason = "unknown"
+    const usage: LanguageModelV2Usage = {
+      inputTokens: undefined,
+      outputTokens: undefined,
+      totalTokens: undefined,
+    }
+    const logprobs: Array<z.infer<typeof LOGPROBS_SCHEMA>> = []
+    let responseId: string | null = null
+    const ongoingToolCalls: Record<
+      number,
+      | {
+          toolName: string
+          toolCallId: string
+          codeInterpreter?: {
+            containerId: string
+          }
+        }
+      | undefined
+    > = {}
+
+    // flag that checks if there have been client-side tool calls (not executed by openai)
+    let hasFunctionCall = false
+
+    const activeReasoning: Record<
+      string,
+      {
+        encryptedContent?: string | null
+        summaryParts: number[]
+      }
+    > = {}
+
+    // Track a stable text part id for the current assistant message.
+    // Copilot may change item_id across text deltas; normalize to one id.
+    let currentTextId: string | null = null
+
+    let serviceTier: string | undefined
+
+    return {
+      stream: response.pipeThrough(
+        new TransformStream<ParseResult<z.infer<typeof openaiResponsesChunkSchema>>, LanguageModelV2StreamPart>({
+          start(controller) {
+            controller.enqueue({ type: "stream-start", warnings })
+          },
+
+          transform(chunk, controller) {
+            if (options.includeRawChunks) {
+              controller.enqueue({ type: "raw", rawValue: chunk.rawValue })
+            }
+
+            // handle failed chunk parsing / validation:
+            if (!chunk.success) {
+              finishReason = "error"
+              controller.enqueue({ type: "error", error: chunk.error })
+              return
+            }
+
+            const value = chunk.value
+
+            if (isResponseOutputItemAddedChunk(value)) {
+              if (value.item.type === "function_call") {
+                ongoingToolCalls[value.output_index] = {
+                  toolName: value.item.name,
+                  toolCallId: value.item.call_id,
+                }
+
+                controller.enqueue({
+                  type: "tool-input-start",
+                  id: value.item.call_id,
+                  toolName: value.item.name,
+                })
+              } else if (value.item.type === "web_search_call") {
+                ongoingToolCalls[value.output_index] = {
+                  toolName: webSearchToolName ?? "web_search",
+                  toolCallId: value.item.id,
+                }
+
+                controller.enqueue({
+                  type: "tool-input-start",
+                  id: value.item.id,
+                  toolName: webSearchToolName ?? "web_search",
+                })
+              } else if (value.item.type === "computer_call") {
+                ongoingToolCalls[value.output_index] = {
+                  toolName: "computer_use",
+                  toolCallId: value.item.id,
+                }
+
+                controller.enqueue({
+                  type: "tool-input-start",
+                  id: value.item.id,
+                  toolName: "computer_use",
+                })
+              } else if (value.item.type === "code_interpreter_call") {
+                ongoingToolCalls[value.output_index] = {
+                  toolName: "code_interpreter",
+                  toolCallId: value.item.id,
+                  codeInterpreter: {
+                    containerId: value.item.container_id,
+                  },
+                }
+
+                controller.enqueue({
+                  type: "tool-input-start",
+                  id: value.item.id,
+                  toolName: "code_interpreter",
+                })
+
+                controller.enqueue({
+                  type: "tool-input-delta",
+                  id: value.item.id,
+                  delta: `{"containerId":"${value.item.container_id}","code":"`,
+                })
+              } else if (value.item.type === "file_search_call") {
+                controller.enqueue({
+                  type: "tool-call",
+                  toolCallId: value.item.id,
+                  toolName: "file_search",
+                  input: "{}",
+                  providerExecuted: true,
+                })
+              } else if (value.item.type === "image_generation_call") {
+                controller.enqueue({
+                  type: "tool-call",
+                  toolCallId: value.item.id,
+                  toolName: "image_generation",
+                  input: "{}",
+                  providerExecuted: true,
+                })
+              } else if (value.item.type === "message") {
+                // Start a stable text part for this assistant message
+                currentTextId = value.item.id
+                controller.enqueue({
+                  type: "text-start",
+                  id: value.item.id,
+                  providerMetadata: {
+                    openai: {
+                      itemId: value.item.id,
+                    },
+                  },
+                })
+              } else if (isResponseOutputItemAddedReasoningChunk(value)) {
+                activeReasoning[value.item.id] = {
+                  encryptedContent: value.item.encrypted_content,
+                  summaryParts: [0],
+                }
+
+                controller.enqueue({
+                  type: "reasoning-start",
+                  id: `${value.item.id}:0`,
+                  providerMetadata: {
+                    openai: {
+                      itemId: value.item.id,
+                      reasoningEncryptedContent: value.item.encrypted_content ?? null,
+                    },
+                  },
+                })
+              }
+            } else if (isResponseOutputItemDoneChunk(value)) {
+              if (value.item.type === "function_call") {
+                ongoingToolCalls[value.output_index] = undefined
+                hasFunctionCall = true
+
+                controller.enqueue({
+                  type: "tool-input-end",
+                  id: value.item.call_id,
+                })
+
+                controller.enqueue({
+                  type: "tool-call",
+                  toolCallId: value.item.call_id,
+                  toolName: value.item.name,
+                  input: value.item.arguments,
+                  providerMetadata: {
+                    openai: {
+                      itemId: value.item.id,
+                    },
+                  },
+                })
+              } else if (value.item.type === "web_search_call") {
+                ongoingToolCalls[value.output_index] = undefined
+
+                controller.enqueue({
+                  type: "tool-input-end",
+                  id: value.item.id,
+                })
+
+                controller.enqueue({
+                  type: "tool-call",
+                  toolCallId: value.item.id,
+                  toolName: "web_search",
+                  input: JSON.stringify({ action: value.item.action }),
+                  providerExecuted: true,
+                })
+
+                controller.enqueue({
+                  type: "tool-result",
+                  toolCallId: value.item.id,
+                  toolName: "web_search",
+                  result: { status: value.item.status },
+                  providerExecuted: true,
+                })
+              } else if (value.item.type === "computer_call") {
+                ongoingToolCalls[value.output_index] = undefined
+
+                controller.enqueue({
+                  type: "tool-input-end",
+                  id: value.item.id,
+                })
+
+                controller.enqueue({
+                  type: "tool-call",
+                  toolCallId: value.item.id,
+                  toolName: "computer_use",
+                  input: "",
+                  providerExecuted: true,
+                })
+
+                controller.enqueue({
+                  type: "tool-result",
+                  toolCallId: value.item.id,
+                  toolName: "computer_use",
+                  result: {
+                    type: "computer_use_tool_result",
+                    status: value.item.status || "completed",
+                  },
+                  providerExecuted: true,
+                })
+              } else if (value.item.type === "file_search_call") {
+                ongoingToolCalls[value.output_index] = undefined
+
+                controller.enqueue({
+                  type: "tool-result",
+                  toolCallId: value.item.id,
+                  toolName: "file_search",
+                  result: {
+                    queries: value.item.queries,
+                    results:
+                      value.item.results?.map((result) => ({
+                        attributes: result.attributes,
+                        fileId: result.file_id,
+                        filename: result.filename,
+                        score: result.score,
+                        text: result.text,
+                      })) ?? null,
+                  } satisfies z.infer<typeof fileSearchOutputSchema>,
+                  providerExecuted: true,
+                })
+              } else if (value.item.type === "code_interpreter_call") {
+                ongoingToolCalls[value.output_index] = undefined
+
+                controller.enqueue({
+                  type: "tool-result",
+                  toolCallId: value.item.id,
+                  toolName: "code_interpreter",
+                  result: {
+                    outputs: value.item.outputs,
+                  } satisfies z.infer<typeof codeInterpreterOutputSchema>,
+                  providerExecuted: true,
+                })
+              } else if (value.item.type === "image_generation_call") {
+                controller.enqueue({
+                  type: "tool-result",
+                  toolCallId: value.item.id,
+                  toolName: "image_generation",
+                  result: {
+                    result: value.item.result,
+                  } satisfies z.infer<typeof imageGenerationOutputSchema>,
+                  providerExecuted: true,
+                })
+              } else if (value.item.type === "local_shell_call") {
+                ongoingToolCalls[value.output_index] = undefined
+
+                controller.enqueue({
+                  type: "tool-call",
+                  toolCallId: value.item.call_id,
+                  toolName: "local_shell",
+                  input: JSON.stringify({
+                    action: {
+                      type: "exec",
+                      command: value.item.action.command,
+                      timeoutMs: value.item.action.timeout_ms,
+                      user: value.item.action.user,
+                      workingDirectory: value.item.action.working_directory,
+                      env: value.item.action.env,
+                    },
+                  } satisfies z.infer<typeof localShellInputSchema>),
+                  providerMetadata: {
+                    openai: { itemId: value.item.id },
+                  },
+                })
+              } else if (value.item.type === "message") {
+                if (currentTextId) {
+                  controller.enqueue({
+                    type: "text-end",
+                    id: currentTextId,
+                  })
+                  currentTextId = null
+                }
+              } else if (isResponseOutputItemDoneReasoningChunk(value)) {
+                const activeReasoningPart = activeReasoning[value.item.id]
+                if (activeReasoningPart) {
+                  for (const summaryIndex of activeReasoningPart.summaryParts) {
+                    controller.enqueue({
+                      type: "reasoning-end",
+                      id: `${value.item.id}:${summaryIndex}`,
+                      providerMetadata: {
+                        openai: {
+                          itemId: value.item.id,
+                          reasoningEncryptedContent: value.item.encrypted_content ?? null,
+                        },
+                      },
+                    })
+                  }
+                }
+                delete activeReasoning[value.item.id]
+              }
+            } else if (isResponseFunctionCallArgumentsDeltaChunk(value)) {
+              const toolCall = ongoingToolCalls[value.output_index]
+
+              if (toolCall != null) {
+                controller.enqueue({
+                  type: "tool-input-delta",
+                  id: toolCall.toolCallId,
+                  delta: value.delta,
+                })
+              }
+            } else if (isResponseImageGenerationCallPartialImageChunk(value)) {
+              controller.enqueue({
+                type: "tool-result",
+                toolCallId: value.item_id,
+                toolName: "image_generation",
+                result: {
+                  result: value.partial_image_b64,
+                } satisfies z.infer<typeof imageGenerationOutputSchema>,
+                providerExecuted: true,
+              })
+            } else if (isResponseCodeInterpreterCallCodeDeltaChunk(value)) {
+              const toolCall = ongoingToolCalls[value.output_index]
+
+              if (toolCall != null) {
+                controller.enqueue({
+                  type: "tool-input-delta",
+                  id: toolCall.toolCallId,
+                  // The delta is code, which is embedding in a JSON string.
+                  // To escape it, we use JSON.stringify and slice to remove the outer quotes.
+                  delta: JSON.stringify(value.delta).slice(1, -1),
+                })
+              }
+            } else if (isResponseCodeInterpreterCallCodeDoneChunk(value)) {
+              const toolCall = ongoingToolCalls[value.output_index]
+
+              if (toolCall != null) {
+                controller.enqueue({
+                  type: "tool-input-delta",
+                  id: toolCall.toolCallId,
+                  delta: '"}',
+                })
+
+                controller.enqueue({
+                  type: "tool-input-end",
+                  id: toolCall.toolCallId,
+                })
+
+                // immediately send the tool call after the input end:
+                controller.enqueue({
+                  type: "tool-call",
+                  toolCallId: toolCall.toolCallId,
+                  toolName: "code_interpreter",
+                  input: JSON.stringify({
+                    code: value.code,
+                    containerId: toolCall.codeInterpreter!.containerId,
+                  } satisfies z.infer<typeof codeInterpreterInputSchema>),
+                  providerExecuted: true,
+                })
+              }
+            } else if (isResponseCreatedChunk(value)) {
+              responseId = value.response.id
+              controller.enqueue({
+                type: "response-metadata",
+                id: value.response.id,
+                timestamp: new Date(value.response.created_at * 1000),
+                modelId: value.response.model,
+              })
+            } else if (isTextDeltaChunk(value)) {
+              // Ensure a text-start exists, and normalize deltas to a stable id
+              if (!currentTextId) {
+                currentTextId = value.item_id
+                controller.enqueue({
+                  type: "text-start",
+                  id: currentTextId,
+                  providerMetadata: {
+                    openai: { itemId: value.item_id },
+                  },
+                })
+              }
+
+              controller.enqueue({
+                type: "text-delta",
+                id: currentTextId,
+                delta: value.delta,
+              })
+
+              if (options.providerOptions?.openai?.logprobs && value.logprobs) {
+                logprobs.push(value.logprobs)
+              }
+            } else if (isResponseReasoningSummaryPartAddedChunk(value)) {
+              // the first reasoning start is pushed in isResponseOutputItemAddedReasoningChunk.
+              if (value.summary_index > 0) {
+                activeReasoning[value.item_id]?.summaryParts.push(value.summary_index)
+
+                controller.enqueue({
+                  type: "reasoning-start",
+                  id: `${value.item_id}:${value.summary_index}`,
+                  providerMetadata: {
+                    openai: {
+                      itemId: value.item_id,
+                      reasoningEncryptedContent: activeReasoning[value.item_id]?.encryptedContent ?? null,
+                    },
+                  },
+                })
+              }
+            } else if (isResponseReasoningSummaryTextDeltaChunk(value)) {
+              controller.enqueue({
+                type: "reasoning-delta",
+                id: `${value.item_id}:${value.summary_index}`,
+                delta: value.delta,
+                providerMetadata: {
+                  openai: {
+                    itemId: value.item_id,
+                  },
+                },
+              })
+            } else if (isResponseFinishedChunk(value)) {
+              finishReason = mapOpenAIResponseFinishReason({
+                finishReason: value.response.incomplete_details?.reason,
+                hasFunctionCall,
+              })
+              usage.inputTokens = value.response.usage.input_tokens
+              usage.outputTokens = value.response.usage.output_tokens
+              usage.totalTokens = value.response.usage.input_tokens + value.response.usage.output_tokens
+              usage.reasoningTokens = value.response.usage.output_tokens_details?.reasoning_tokens ?? undefined
+              usage.cachedInputTokens = value.response.usage.input_tokens_details?.cached_tokens ?? undefined
+              if (typeof value.response.service_tier === "string") {
+                serviceTier = value.response.service_tier
+              }
+            } else if (isResponseAnnotationAddedChunk(value)) {
+              if (value.annotation.type === "url_citation") {
+                controller.enqueue({
+                  type: "source",
+                  sourceType: "url",
+                  id: self.config.generateId?.() ?? generateId(),
+                  url: value.annotation.url,
+                  title: value.annotation.title,
+                })
+              } else if (value.annotation.type === "file_citation") {
+                controller.enqueue({
+                  type: "source",
+                  sourceType: "document",
+                  id: self.config.generateId?.() ?? generateId(),
+                  mediaType: "text/plain",
+                  title: value.annotation.quote ?? value.annotation.filename ?? "Document",
+                  filename: value.annotation.filename ?? value.annotation.file_id,
+                })
+              }
+            } else if (isErrorChunk(value)) {
+              controller.enqueue({ type: "error", error: value })
+            }
+          },
+
+          flush(controller) {
+            // Close any dangling text part
+            if (currentTextId) {
+              controller.enqueue({ type: "text-end", id: currentTextId })
+              currentTextId = null
+            }
+
+            const providerMetadata: SharedV2ProviderMetadata = {
+              openai: {
+                responseId,
+              },
+            }
+
+            if (logprobs.length > 0) {
+              providerMetadata.openai.logprobs = logprobs
+            }
+
+            if (serviceTier !== undefined) {
+              providerMetadata.openai.serviceTier = serviceTier
+            }
+
+            controller.enqueue({
+              type: "finish",
+              finishReason,
+              usage,
+              providerMetadata,
+            })
+          },
+        }),
+      ),
+      request: { body },
+      response: { headers: responseHeaders },
+    }
+  }
+}
+
+const usageSchema = z.object({
+  input_tokens: z.number(),
+  input_tokens_details: z.object({ cached_tokens: z.number().nullish() }).nullish(),
+  output_tokens: z.number(),
+  output_tokens_details: z.object({ reasoning_tokens: z.number().nullish() }).nullish(),
+})
+
+const textDeltaChunkSchema = z.object({
+  type: z.literal("response.output_text.delta"),
+  item_id: z.string(),
+  delta: z.string(),
+  logprobs: LOGPROBS_SCHEMA.nullish(),
+})
+
+const errorChunkSchema = z.object({
+  type: z.literal("error"),
+  code: z.string(),
+  message: z.string(),
+  param: z.string().nullish(),
+  sequence_number: z.number(),
+})
+
+const responseFinishedChunkSchema = z.object({
+  type: z.enum(["response.completed", "response.incomplete"]),
+  response: z.object({
+    incomplete_details: z.object({ reason: z.string() }).nullish(),
+    usage: usageSchema,
+    service_tier: z.string().nullish(),
+  }),
+})
+
+const responseCreatedChunkSchema = z.object({
+  type: z.literal("response.created"),
+  response: z.object({
+    id: z.string(),
+    created_at: z.number(),
+    model: z.string(),
+    service_tier: z.string().nullish(),
+  }),
+})
+
+const responseOutputItemAddedSchema = z.object({
+  type: z.literal("response.output_item.added"),
+  output_index: z.number(),
+  item: z.discriminatedUnion("type", [
+    z.object({
+      type: z.literal("message"),
+      id: z.string(),
+    }),
+    z.object({
+      type: z.literal("reasoning"),
+      id: z.string(),
+      encrypted_content: z.string().nullish(),
+    }),
+    z.object({
+      type: z.literal("function_call"),
+      id: z.string(),
+      call_id: z.string(),
+      name: z.string(),
+      arguments: z.string(),
+    }),
+    z.object({
+      type: z.literal("web_search_call"),
+      id: z.string(),
+      status: z.string(),
+      action: z
+        .object({
+          type: z.literal("search"),
+          query: z.string().optional(),
+        })
+        .nullish(),
+    }),
+    z.object({
+      type: z.literal("computer_call"),
+      id: z.string(),
+      status: z.string(),
+    }),
+    z.object({
+      type: z.literal("file_search_call"),
+      id: z.string(),
+    }),
+    z.object({
+      type: z.literal("image_generation_call"),
+      id: z.string(),
+    }),
+    z.object({
+      type: z.literal("code_interpreter_call"),
+      id: z.string(),
+      container_id: z.string(),
+      code: z.string().nullable(),
+      outputs: z
+        .array(
+          z.discriminatedUnion("type", [
+            z.object({ type: z.literal("logs"), logs: z.string() }),
+            z.object({ type: z.literal("image"), url: z.string() }),
+          ]),
+        )
+        .nullable(),
+      status: z.string(),
+    }),
+  ]),
+})
+
+const responseOutputItemDoneSchema = z.object({
+  type: z.literal("response.output_item.done"),
+  output_index: z.number(),
+  item: z.discriminatedUnion("type", [
+    z.object({
+      type: z.literal("message"),
+      id: z.string(),
+    }),
+    z.object({
+      type: z.literal("reasoning"),
+      id: z.string(),
+      encrypted_content: z.string().nullish(),
+    }),
+    z.object({
+      type: z.literal("function_call"),
+      id: z.string(),
+      call_id: z.string(),
+      name: z.string(),
+      arguments: z.string(),
+      status: z.literal("completed"),
+    }),
+    codeInterpreterCallItem,
+    imageGenerationCallItem,
+    webSearchCallItem,
+    fileSearchCallItem,
+    localShellCallItem,
+    z.object({
+      type: z.literal("computer_call"),
+      id: z.string(),
+      status: z.literal("completed"),
+    }),
+  ]),
+})
+
+const responseFunctionCallArgumentsDeltaSchema = z.object({
+  type: z.literal("response.function_call_arguments.delta"),
+  item_id: z.string(),
+  output_index: z.number(),
+  delta: z.string(),
+})
+
+const responseImageGenerationCallPartialImageSchema = z.object({
+  type: z.literal("response.image_generation_call.partial_image"),
+  item_id: z.string(),
+  output_index: z.number(),
+  partial_image_b64: z.string(),
+})
+
+const responseCodeInterpreterCallCodeDeltaSchema = z.object({
+  type: z.literal("response.code_interpreter_call_code.delta"),
+  item_id: z.string(),
+  output_index: z.number(),
+  delta: z.string(),
+})
+
+const responseCodeInterpreterCallCodeDoneSchema = z.object({
+  type: z.literal("response.code_interpreter_call_code.done"),
+  item_id: z.string(),
+  output_index: z.number(),
+  code: z.string(),
+})
+
+const responseAnnotationAddedSchema = z.object({
+  type: z.literal("response.output_text.annotation.added"),
+  annotation: z.discriminatedUnion("type", [
+    z.object({
+      type: z.literal("url_citation"),
+      url: z.string(),
+      title: z.string(),
+    }),
+    z.object({
+      type: z.literal("file_citation"),
+      file_id: z.string(),
+      filename: z.string().nullish(),
+      index: z.number().nullish(),
+      start_index: z.number().nullish(),
+      end_index: z.number().nullish(),
+      quote: z.string().nullish(),
+    }),
+  ]),
+})
+
+const responseReasoningSummaryPartAddedSchema = z.object({
+  type: z.literal("response.reasoning_summary_part.added"),
+  item_id: z.string(),
+  summary_index: z.number(),
+})
+
+const responseReasoningSummaryTextDeltaSchema = z.object({
+  type: z.literal("response.reasoning_summary_text.delta"),
+  item_id: z.string(),
+  summary_index: z.number(),
+  delta: z.string(),
+})
+
+const openaiResponsesChunkSchema = z.union([
+  textDeltaChunkSchema,
+  responseFinishedChunkSchema,
+  responseCreatedChunkSchema,
+  responseOutputItemAddedSchema,
+  responseOutputItemDoneSchema,
+  responseFunctionCallArgumentsDeltaSchema,
+  responseImageGenerationCallPartialImageSchema,
+  responseCodeInterpreterCallCodeDeltaSchema,
+  responseCodeInterpreterCallCodeDoneSchema,
+  responseAnnotationAddedSchema,
+  responseReasoningSummaryPartAddedSchema,
+  responseReasoningSummaryTextDeltaSchema,
+  errorChunkSchema,
+  z.object({ type: z.string() }).loose(), // fallback for unknown chunks
+])
+
+type ExtractByType<T, K extends T extends { type: infer U } ? U : never> = T extends { type: K } ? T : never
+
+function isTextDeltaChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof textDeltaChunkSchema> {
+  return chunk.type === "response.output_text.delta"
+}
+
+function isResponseOutputItemDoneChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseOutputItemDoneSchema> {
+  return chunk.type === "response.output_item.done"
+}
+
+function isResponseOutputItemDoneReasoningChunk(chunk: z.infer<typeof openaiResponsesChunkSchema>): chunk is z.infer<
+  typeof responseOutputItemDoneSchema
+> & {
+  item: ExtractByType<z.infer<typeof responseOutputItemDoneSchema>["item"], "reasoning">
+} {
+  return isResponseOutputItemDoneChunk(chunk) && chunk.item.type === "reasoning"
+}
+
+function isResponseFinishedChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseFinishedChunkSchema> {
+  return chunk.type === "response.completed" || chunk.type === "response.incomplete"
+}
+
+function isResponseCreatedChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseCreatedChunkSchema> {
+  return chunk.type === "response.created"
+}
+
+function isResponseFunctionCallArgumentsDeltaChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseFunctionCallArgumentsDeltaSchema> {
+  return chunk.type === "response.function_call_arguments.delta"
+}
+function isResponseImageGenerationCallPartialImageChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseImageGenerationCallPartialImageSchema> {
+  return chunk.type === "response.image_generation_call.partial_image"
+}
+
+function isResponseCodeInterpreterCallCodeDeltaChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseCodeInterpreterCallCodeDeltaSchema> {
+  return chunk.type === "response.code_interpreter_call_code.delta"
+}
+
+function isResponseCodeInterpreterCallCodeDoneChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseCodeInterpreterCallCodeDoneSchema> {
+  return chunk.type === "response.code_interpreter_call_code.done"
+}
+
+function isResponseOutputItemAddedChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseOutputItemAddedSchema> {
+  return chunk.type === "response.output_item.added"
+}
+
+function isResponseOutputItemAddedReasoningChunk(chunk: z.infer<typeof openaiResponsesChunkSchema>): chunk is z.infer<
+  typeof responseOutputItemAddedSchema
+> & {
+  item: ExtractByType<z.infer<typeof responseOutputItemAddedSchema>["item"], "reasoning">
+} {
+  return isResponseOutputItemAddedChunk(chunk) && chunk.item.type === "reasoning"
+}
+
+function isResponseAnnotationAddedChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseAnnotationAddedSchema> {
+  return chunk.type === "response.output_text.annotation.added"
+}
+
+function isResponseReasoningSummaryPartAddedChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseReasoningSummaryPartAddedSchema> {
+  return chunk.type === "response.reasoning_summary_part.added"
+}
+
+function isResponseReasoningSummaryTextDeltaChunk(
+  chunk: z.infer<typeof openaiResponsesChunkSchema>,
+): chunk is z.infer<typeof responseReasoningSummaryTextDeltaSchema> {
+  return chunk.type === "response.reasoning_summary_text.delta"
+}
+
+function isErrorChunk(chunk: z.infer<typeof openaiResponsesChunkSchema>): chunk is z.infer<typeof errorChunkSchema> {
+  return chunk.type === "error"
+}
+
+type ResponsesModelConfig = {
+  isReasoningModel: boolean
+  systemMessageMode: "remove" | "system" | "developer"
+  requiredAutoTruncation: boolean
+  supportsFlexProcessing: boolean
+  supportsPriorityProcessing: boolean
+}
+
+function getResponsesModelConfig(modelId: string): ResponsesModelConfig {
+  const supportsFlexProcessing =
+    modelId.startsWith("o3") ||
+    modelId.startsWith("o4-mini") ||
+    (modelId.startsWith("gpt-5") && !modelId.startsWith("gpt-5-chat"))
+  const supportsPriorityProcessing =
+    modelId.startsWith("gpt-4") ||
+    modelId.startsWith("gpt-5-mini") ||
+    (modelId.startsWith("gpt-5") && !modelId.startsWith("gpt-5-nano") && !modelId.startsWith("gpt-5-chat")) ||
+    modelId.startsWith("o3") ||
+    modelId.startsWith("o4-mini")
+  const defaults = {
+    requiredAutoTruncation: false,
+    systemMessageMode: "system" as const,
+    supportsFlexProcessing,
+    supportsPriorityProcessing,
+  }
+
+  // gpt-5-chat models are non-reasoning
+  if (modelId.startsWith("gpt-5-chat")) {
+    return {
+      ...defaults,
+      isReasoningModel: false,
+    }
+  }
+
+  // o series reasoning models:
+  if (
+    modelId.startsWith("o") ||
+    modelId.startsWith("gpt-5") ||
+    modelId.startsWith("codex-") ||
+    modelId.startsWith("computer-use")
+  ) {
+    if (modelId.startsWith("o1-mini") || modelId.startsWith("o1-preview")) {
+      return {
+        ...defaults,
+        isReasoningModel: true,
+        systemMessageMode: "remove",
+      }
+    }
+
+    return {
+      ...defaults,
+      isReasoningModel: true,
+      systemMessageMode: "developer",
+    }
+  }
+
+  // gpt models:
+  return {
+    ...defaults,
+    isReasoningModel: false,
+  }
+}
+
+// TODO AI SDK 6: use optional here instead of nullish
+const openaiResponsesProviderOptionsSchema = z.object({
+  include: z
+    .array(z.enum(["reasoning.encrypted_content", "file_search_call.results", "message.output_text.logprobs"]))
+    .nullish(),
+  instructions: z.string().nullish(),
+
+  /**
+   * Return the log probabilities of the tokens.
+   *
+   * Setting to true will return the log probabilities of the tokens that
+   * were generated.
+   *
+   * Setting to a number will return the log probabilities of the top n
+   * tokens that were generated.
+   *
+   * @see https://platform.openai.com/docs/api-reference/responses/create
+   * @see https://cookbook.openai.com/examples/using_logprobs
+   */
+  logprobs: z.union([z.boolean(), z.number().min(1).max(TOP_LOGPROBS_MAX)]).optional(),
+
+  /**
+   * The maximum number of total calls to built-in tools that can be processed in a response.
+   * This maximum number applies across all built-in tool calls, not per individual tool.
+   * Any further attempts to call a tool by the model will be ignored.
+   */
+  maxToolCalls: z.number().nullish(),
+
+  metadata: z.any().nullish(),
+  parallelToolCalls: z.boolean().nullish(),
+  previousResponseId: z.string().nullish(),
+  promptCacheKey: z.string().nullish(),
+  reasoningEffort: z.string().nullish(),
+  reasoningSummary: z.string().nullish(),
+  safetyIdentifier: z.string().nullish(),
+  serviceTier: z.enum(["auto", "flex", "priority"]).nullish(),
+  store: z.boolean().nullish(),
+  strictJsonSchema: z.boolean().nullish(),
+  textVerbosity: z.enum(["low", "medium", "high"]).nullish(),
+  user: z.string().nullish(),
+})
+
+export type OpenAIResponsesProviderOptions = z.infer<typeof openaiResponsesProviderOptionsSchema>

packages/opencode/src/provider/sdk/openai-compatible/src/responses/openai-responses-prepare-tools.ts

@@ -0,0 +1,177 @@
+import {
+  type LanguageModelV2CallOptions,
+  type LanguageModelV2CallWarning,
+  UnsupportedFunctionalityError,
+} from "@ai-sdk/provider"
+import { codeInterpreterArgsSchema } from "./tool/code-interpreter"
+import { fileSearchArgsSchema } from "./tool/file-search"
+import { webSearchArgsSchema } from "./tool/web-search"
+import { webSearchPreviewArgsSchema } from "./tool/web-search-preview"
+import { imageGenerationArgsSchema } from "./tool/image-generation"
+import type { OpenAIResponsesTool } from "./openai-responses-api-types"
+
+export function prepareResponsesTools({
+  tools,
+  toolChoice,
+  strictJsonSchema,
+}: {
+  tools: LanguageModelV2CallOptions["tools"]
+  toolChoice?: LanguageModelV2CallOptions["toolChoice"]
+  strictJsonSchema: boolean
+}): {
+  tools?: Array<OpenAIResponsesTool>
+  toolChoice?:
+    | "auto"
+    | "none"
+    | "required"
+    | { type: "file_search" }
+    | { type: "web_search_preview" }
+    | { type: "web_search" }
+    | { type: "function"; name: string }
+    | { type: "code_interpreter" }
+    | { type: "image_generation" }
+  toolWarnings: LanguageModelV2CallWarning[]
+} {
+  // when the tools array is empty, change it to undefined to prevent errors:
+  tools = tools?.length ? tools : undefined
+
+  const toolWarnings: LanguageModelV2CallWarning[] = []
+
+  if (tools == null) {
+    return { tools: undefined, toolChoice: undefined, toolWarnings }
+  }
+
+  const openaiTools: Array<OpenAIResponsesTool> = []
+
+  for (const tool of tools) {
+    switch (tool.type) {
+      case "function":
+        openaiTools.push({
+          type: "function",
+          name: tool.name,
+          description: tool.description,
+          parameters: tool.inputSchema,
+          strict: strictJsonSchema,
+        })
+        break
+      case "provider-defined": {
+        switch (tool.id) {
+          case "openai.file_search": {
+            const args = fileSearchArgsSchema.parse(tool.args)
+
+            openaiTools.push({
+              type: "file_search",
+              vector_store_ids: args.vectorStoreIds,
+              max_num_results: args.maxNumResults,
+              ranking_options: args.ranking
+                ? {
+                    ranker: args.ranking.ranker,
+                    score_threshold: args.ranking.scoreThreshold,
+                  }
+                : undefined,
+              filters: args.filters,
+            })
+
+            break
+          }
+          case "openai.local_shell": {
+            openaiTools.push({
+              type: "local_shell",
+            })
+            break
+          }
+          case "openai.web_search_preview": {
+            const args = webSearchPreviewArgsSchema.parse(tool.args)
+            openaiTools.push({
+              type: "web_search_preview",
+              search_context_size: args.searchContextSize,
+              user_location: args.userLocation,
+            })
+            break
+          }
+          case "openai.web_search": {
+            const args = webSearchArgsSchema.parse(tool.args)
+            openaiTools.push({
+              type: "web_search",
+              filters: args.filters != null ? { allowed_domains: args.filters.allowedDomains } : undefined,
+              search_context_size: args.searchContextSize,
+              user_location: args.userLocation,
+            })
+            break
+          }
+          case "openai.code_interpreter": {
+            const args = codeInterpreterArgsSchema.parse(tool.args)
+            openaiTools.push({
+              type: "code_interpreter",
+              container:
+                args.container == null
+                  ? { type: "auto", file_ids: undefined }
+                  : typeof args.container === "string"
+                    ? args.container
+                    : { type: "auto", file_ids: args.container.fileIds },
+            })
+            break
+          }
+          case "openai.image_generation": {
+            const args = imageGenerationArgsSchema.parse(tool.args)
+            openaiTools.push({
+              type: "image_generation",
+              background: args.background,
+              input_fidelity: args.inputFidelity,
+              input_image_mask: args.inputImageMask
+                ? {
+                    file_id: args.inputImageMask.fileId,
+                    image_url: args.inputImageMask.imageUrl,
+                  }
+                : undefined,
+              model: args.model,
+              moderation: args.moderation,
+              partial_images: args.partialImages,
+              quality: args.quality,
+              output_compression: args.outputCompression,
+              output_format: args.outputFormat,
+              size: args.size,
+            })
+            break
+          }
+        }
+        break
+      }
+      default:
+        toolWarnings.push({ type: "unsupported-tool", tool })
+        break
+    }
+  }
+
+  if (toolChoice == null) {
+    return { tools: openaiTools, toolChoice: undefined, toolWarnings }
+  }
+
+  const type = toolChoice.type
+
+  switch (type) {
+    case "auto":
+    case "none":
+    case "required":
+      return { tools: openaiTools, toolChoice: type, toolWarnings }
+    case "tool":
+      return {
+        tools: openaiTools,
+        toolChoice:
+          toolChoice.toolName === "code_interpreter" ||
+          toolChoice.toolName === "file_search" ||
+          toolChoice.toolName === "image_generation" ||
+          toolChoice.toolName === "web_search_preview" ||
+          toolChoice.toolName === "web_search"
+            ? { type: toolChoice.toolName }
+            : { type: "function", name: toolChoice.toolName },
+        toolWarnings,
+      }
+    default: {
+      const _exhaustiveCheck: never = type
+      throw new UnsupportedFunctionalityError({
+        functionality: `tool choice type: ${_exhaustiveCheck}`,
+      })
+    }
+  }
+}

packages/opencode/src/provider/sdk/openai-compatible/src/responses/openai-responses-settings.ts

@@ -0,0 +1 @@
+export type OpenAIResponsesModelId = string

packages/opencode/src/provider/sdk/openai-compatible/src/responses/tool/code-interpreter.ts

@@ -0,0 +1,88 @@
+import { createProviderDefinedToolFactoryWithOutputSchema } from "@ai-sdk/provider-utils"
+import { z } from "zod/v4"
+
+export const codeInterpreterInputSchema = z.object({
+  code: z.string().nullish(),
+  containerId: z.string(),
+})
+
+export const codeInterpreterOutputSchema = z.object({
+  outputs: z
+    .array(
+      z.discriminatedUnion("type", [
+        z.object({ type: z.literal("logs"), logs: z.string() }),
+        z.object({ type: z.literal("image"), url: z.string() }),
+      ]),
+    )
+    .nullish(),
+})
+
+export const codeInterpreterArgsSchema = z.object({
+  container: z
+    .union([
+      z.string(),
+      z.object({
+        fileIds: z.array(z.string()).optional(),
+      }),
+    ])
+    .optional(),
+})
+
+type CodeInterpreterArgs = {
+  /**
+   * The code interpreter container.
+   * Can be a container ID
+   * or an object that specifies uploaded file IDs to make available to your code.
+   */
+  container?: string | { fileIds?: string[] }
+}
+
+export const codeInterpreterToolFactory = createProviderDefinedToolFactoryWithOutputSchema<
+  {
+    /**
+     * The code to run, or null if not available.
+     */
+    code?: string | null
+
+    /**
+     * The ID of the container used to run the code.
+     */
+    containerId: string
+  },
+  {
+    /**
+     * The outputs generated by the code interpreter, such as logs or images.
+     * Can be null if no outputs are available.
+     */
+    outputs?: Array<
+      | {
+          type: "logs"
+
+          /**
+           * The logs output from the code interpreter.
+           */
+          logs: string
+        }
+      | {
+          type: "image"
+
+          /**
+           * The URL of the image output from the code interpreter.
+           */
+          url: string
+        }
+    > | null
+  },
+  CodeInterpreterArgs
+>({
+  id: "openai.code_interpreter",
+  name: "code_interpreter",
+  inputSchema: codeInterpreterInputSchema,
+  outputSchema: codeInterpreterOutputSchema,
+})
+
+export const codeInterpreter = (
+  args: CodeInterpreterArgs = {}, // default
+) => {
+  return codeInterpreterToolFactory(args)
+}

packages/opencode/src/provider/sdk/openai-compatible/src/responses/tool/file-search.ts

@@ -0,0 +1,128 @@
+import { createProviderDefinedToolFactoryWithOutputSchema } from "@ai-sdk/provider-utils"
+import type {
+  OpenAIResponsesFileSearchToolComparisonFilter,
+  OpenAIResponsesFileSearchToolCompoundFilter,
+} from "../openai-responses-api-types"
+import { z } from "zod/v4"
+
+const comparisonFilterSchema = z.object({
+  key: z.string(),
+  type: z.enum(["eq", "ne", "gt", "gte", "lt", "lte"]),
+  value: z.union([z.string(), z.number(), z.boolean()]),
+})
+
+const compoundFilterSchema: z.ZodType<any> = z.object({
+  type: z.enum(["and", "or"]),
+  filters: z.array(z.union([comparisonFilterSchema, z.lazy(() => compoundFilterSchema)])),
+})
+
+export const fileSearchArgsSchema = z.object({
+  vectorStoreIds: z.array(z.string()),
+  maxNumResults: z.number().optional(),
+  ranking: z
+    .object({
+      ranker: z.string().optional(),
+      scoreThreshold: z.number().optional(),
+    })
+    .optional(),
+  filters: z.union([comparisonFilterSchema, compoundFilterSchema]).optional(),
+})
+
+export const fileSearchOutputSchema = z.object({
+  queries: z.array(z.string()),
+  results: z
+    .array(
+      z.object({
+        attributes: z.record(z.string(), z.unknown()),
+        fileId: z.string(),
+        filename: z.string(),
+        score: z.number(),
+        text: z.string(),
+      }),
+    )
+    .nullable(),
+})
+
+export const fileSearch = createProviderDefinedToolFactoryWithOutputSchema<
+  {},
+  {
+    /**
+     * The search query to execute.
+     */
+    queries: string[]
+
+    /**
+     * The results of the file search tool call.
+     */
+    results:
+      | null
+      | {
+          /**
+           * Set of 16 key-value pairs that can be attached to an object.
+           * This can be useful for storing additional information about the object
+           * in a structured format, and querying for objects via API or the dashboard.
+           * Keys are strings with a maximum length of 64 characters.
+           * Values are strings with a maximum length of 512 characters, booleans, or numbers.
+           */
+          attributes: Record<string, unknown>
+
+          /**
+           * The unique ID of the file.
+           */
+          fileId: string
+
+          /**
+           * The name of the file.
+           */
+          filename: string
+
+          /**
+           * The relevance score of the file - a value between 0 and 1.
+           */
+          score: number
+
+          /**
+           * The text that was retrieved from the file.
+           */
+          text: string
+        }[]
+  },
+  {
+    /**
+     * List of vector store IDs to search through.
+     */
+    vectorStoreIds: string[]
+
+    /**
+     * Maximum number of search results to return. Defaults to 10.
+     */
+    maxNumResults?: number
+
+    /**
+     * Ranking options for the search.
+     */
+    ranking?: {
+      /**
+       * The ranker to use for the file search.
+       */
+      ranker?: string
+
+      /**
+       * The score threshold for the file search, a number between 0 and 1.
+       * Numbers closer to 1 will attempt to return only the most relevant results,
+       * but may return fewer results.
+       */
+      scoreThreshold?: number
+    }
+
+    /**
+     * A filter to apply.
+     */
+    filters?: OpenAIResponsesFileSearchToolComparisonFilter | OpenAIResponsesFileSearchToolCompoundFilter
+  }
+>({
+  id: "openai.file_search",
+  name: "file_search",
+  inputSchema: z.object({}),
+  outputSchema: fileSearchOutputSchema,
+})

packages/opencode/src/provider/sdk/openai-compatible/src/responses/tool/image-generation.ts

@@ -0,0 +1,115 @@
+import { createProviderDefinedToolFactoryWithOutputSchema } from "@ai-sdk/provider-utils"
+import { z } from "zod/v4"
+
+export const imageGenerationArgsSchema = z
+  .object({
+    background: z.enum(["auto", "opaque", "transparent"]).optional(),
+    inputFidelity: z.enum(["low", "high"]).optional(),
+    inputImageMask: z
+      .object({
+        fileId: z.string().optional(),
+        imageUrl: z.string().optional(),
+      })
+      .optional(),
+    model: z.string().optional(),
+    moderation: z.enum(["auto"]).optional(),
+    outputCompression: z.number().int().min(0).max(100).optional(),
+    outputFormat: z.enum(["png", "jpeg", "webp"]).optional(),
+    partialImages: z.number().int().min(0).max(3).optional(),
+    quality: z.enum(["auto", "low", "medium", "high"]).optional(),
+    size: z.enum(["1024x1024", "1024x1536", "1536x1024", "auto"]).optional(),
+  })
+  .strict()
+
+export const imageGenerationOutputSchema = z.object({
+  result: z.string(),
+})
+
+type ImageGenerationArgs = {
+  /**
+   * Background type for the generated image. Default is 'auto'.
+   */
+  background?: "auto" | "opaque" | "transparent"
+
+  /**
+   * Input fidelity for the generated image. Default is 'low'.
+   */
+  inputFidelity?: "low" | "high"
+
+  /**
+   * Optional mask for inpainting.
+   * Contains image_url (string, optional) and file_id (string, optional).
+   */
+  inputImageMask?: {
+    /**
+     * File ID for the mask image.
+     */
+    fileId?: string
+
+    /**
+     * Base64-encoded mask image.
+     */
+    imageUrl?: string
+  }
+
+  /**
+   * The image generation model to use. Default: gpt-image-1.
+   */
+  model?: string
+
+  /**
+   * Moderation level for the generated image. Default: auto.
+   */
+  moderation?: "auto"
+
+  /**
+   * Compression level for the output image. Default: 100.
+   */
+  outputCompression?: number
+
+  /**
+   * The output format of the generated image. One of png, webp, or jpeg.
+   * Default: png
+   */
+  outputFormat?: "png" | "jpeg" | "webp"
+
+  /**
+   * Number of partial images to generate in streaming mode, from 0 (default value) to 3.
+   */
+  partialImages?: number
+
+  /**
+   * The quality of the generated image.
+   * One of low, medium, high, or auto. Default: auto.
+   */
+  quality?: "auto" | "low" | "medium" | "high"
+
+  /**
+   * The size of the generated image.
+   * One of 1024x1024, 1024x1536, 1536x1024, or auto.
+   * Default: auto.
+   */
+  size?: "auto" | "1024x1024" | "1024x1536" | "1536x1024"
+}
+
+const imageGenerationToolFactory = createProviderDefinedToolFactoryWithOutputSchema<
+  {},
+  {
+    /**
+     * The generated image encoded in base64.
+     */
+    result: string
+  },
+  ImageGenerationArgs
+>({
+  id: "openai.image_generation",
+  name: "image_generation",
+  inputSchema: z.object({}),
+  outputSchema: imageGenerationOutputSchema,
+})
+
+export const imageGeneration = (
+  args: ImageGenerationArgs = {}, // default
+) => {
+  return imageGenerationToolFactory(args)
+}

packages/opencode/src/provider/sdk/openai-compatible/src/responses/tool/local-shell.ts

@@ -0,0 +1,65 @@
+import { createProviderDefinedToolFactoryWithOutputSchema } from "@ai-sdk/provider-utils"
+import { z } from "zod/v4"
+
+export const localShellInputSchema = z.object({
+  action: z.object({
+    type: z.literal("exec"),
+    command: z.array(z.string()),
+    timeoutMs: z.number().optional(),
+    user: z.string().optional(),
+    workingDirectory: z.string().optional(),
+    env: z.record(z.string(), z.string()).optional(),
+  }),
+})
+
+export const localShellOutputSchema = z.object({
+  output: z.string(),
+})
+
+export const localShell = createProviderDefinedToolFactoryWithOutputSchema<
+  {
+    /**
+     * Execute a shell command on the server.
+     */
+    action: {
+      type: "exec"
+
+      /**
+       * The command to run.
+       */
+      command: string[]
+
+      /**
+       * Optional timeout in milliseconds for the command.
+       */
+      timeoutMs?: number
+
+      /**
+       * Optional user to run the command as.
+       */
+      user?: string
+
+      /**
+       * Optional working directory to run the command in.
+       */
+      workingDirectory?: string
+
+      /**
+       * Environment variables to set for the command.
+       */
+      env?: Record<string, string>
+    }
+  },
+  {
+    /**
+     * The output of local shell tool call.
+     */
+    output: string
+  },
+  {}
+>({
+  id: "openai.local_shell",
+  name: "local_shell",
+  inputSchema: localShellInputSchema,
+  outputSchema: localShellOutputSchema,
+})

packages/opencode/src/provider/sdk/openai-compatible/src/responses/tool/web-search-preview.ts

@@ -0,0 +1,104 @@
+import { createProviderDefinedToolFactory } from "@ai-sdk/provider-utils"
+import { z } from "zod/v4"
+
+// Args validation schema
+export const webSearchPreviewArgsSchema = z.object({
+  /**
+   * Search context size to use for the web search.
+   * - high: Most comprehensive context, highest cost, slower response
+   * - medium: Balanced context, cost, and latency (default)
+   * - low: Least context, lowest cost, fastest response
+   */
+  searchContextSize: z.enum(["low", "medium", "high"]).optional(),
+
+  /**
+   * User location information to provide geographically relevant search results.
+   */
+  userLocation: z
+    .object({
+      /**
+       * Type of location (always 'approximate')
+       */
+      type: z.literal("approximate"),
+      /**
+       * Two-letter ISO country code (e.g., 'US', 'GB')
+       */
+      country: z.string().optional(),
+      /**
+       * City name (free text, e.g., 'Minneapolis')
+       */
+      city: z.string().optional(),
+      /**
+       * Region name (free text, e.g., 'Minnesota')
+       */
+      region: z.string().optional(),
+      /**
+       * IANA timezone (e.g., 'America/Chicago')
+       */
+      timezone: z.string().optional(),
+    })
+    .optional(),
+})
+
+export const webSearchPreview = createProviderDefinedToolFactory<
+  {
+    // Web search doesn't take input parameters - it's controlled by the prompt
+  },
+  {
+    /**
+     * Search context size to use for the web search.
+     * - high: Most comprehensive context, highest cost, slower response
+     * - medium: Balanced context, cost, and latency (default)
+     * - low: Least context, lowest cost, fastest response
+     */
+    searchContextSize?: "low" | "medium" | "high"
+
+    /**
+     * User location information to provide geographically relevant search results.
+     */
+    userLocation?: {
+      /**
+       * Type of location (always 'approximate')
+       */
+      type: "approximate"
+      /**
+       * Two-letter ISO country code (e.g., 'US', 'GB')
+       */
+      country?: string
+      /**
+       * City name (free text, e.g., 'Minneapolis')
+       */
+      city?: string
+      /**
+       * Region name (free text, e.g., 'Minnesota')
+       */
+      region?: string
+      /**
+       * IANA timezone (e.g., 'America/Chicago')
+       */
+      timezone?: string
+    }
+  }
+>({
+  id: "openai.web_search_preview",
+  name: "web_search_preview",
+  inputSchema: z.object({
+    action: z
+      .discriminatedUnion("type", [
+        z.object({
+          type: z.literal("search"),
+          query: z.string().nullish(),
+        }),
+        z.object({
+          type: z.literal("open_page"),
+          url: z.string(),
+        }),
+        z.object({
+          type: z.literal("find"),
+          url: z.string(),
+          pattern: z.string(),
+        }),
+      ])
+      .nullish(),
+  }),
+})

packages/opencode/src/provider/sdk/openai-compatible/src/responses/tool/web-search.ts

@@ -0,0 +1,103 @@
+import { createProviderDefinedToolFactory } from "@ai-sdk/provider-utils"
+import { z } from "zod/v4"
+
+export const webSearchArgsSchema = z.object({
+  filters: z
+    .object({
+      allowedDomains: z.array(z.string()).optional(),
+    })
+    .optional(),
+
+  searchContextSize: z.enum(["low", "medium", "high"]).optional(),
+
+  userLocation: z
+    .object({
+      type: z.literal("approximate"),
+      country: z.string().optional(),
+      city: z.string().optional(),
+      region: z.string().optional(),
+      timezone: z.string().optional(),
+    })
+    .optional(),
+})
+
+export const webSearchToolFactory = createProviderDefinedToolFactory<
+  {
+    // Web search doesn't take input parameters - it's controlled by the prompt
+  },
+  {
+    /**
+     * Filters for the search.
+     */
+    filters?: {
+      /**
+       * Allowed domains for the search.
+       * If not provided, all domains are allowed.
+       * Subdomains of the provided domains are allowed as well.
+       */
+      allowedDomains?: string[]
+    }
+
+    /**
+     * Search context size to use for the web search.
+     * - high: Most comprehensive context, highest cost, slower response
+     * - medium: Balanced context, cost, and latency (default)
+     * - low: Least context, lowest cost, fastest response
+     */
+    searchContextSize?: "low" | "medium" | "high"
+
+    /**
+     * User location information to provide geographically relevant search results.
+     */
+    userLocation?: {
+      /**
+       * Type of location (always 'approximate')
+       */
+      type: "approximate"
+      /**
+       * Two-letter ISO country code (e.g., 'US', 'GB')
+       */
+      country?: string
+      /**
+       * City name (free text, e.g., 'Minneapolis')
+       */
+      city?: string
+      /**
+       * Region name (free text, e.g., 'Minnesota')
+       */
+      region?: string
+      /**
+       * IANA timezone (e.g., 'America/Chicago')
+       */
+      timezone?: string
+    }
+  }
+>({
+  id: "openai.web_search",
+  name: "web_search",
+  inputSchema: z.object({
+    action: z
+      .discriminatedUnion("type", [
+        z.object({
+          type: z.literal("search"),
+          query: z.string().nullish(),
+        }),
+        z.object({
+          type: z.literal("open_page"),
+          url: z.string(),
+        }),
+        z.object({
+          type: z.literal("find"),
+          url: z.string(),
+          pattern: z.string(),
+        }),
+      ])
+      .nullish(),
+  }),
+})
+
+export const webSearch = (
+  args: Parameters<typeof webSearchToolFactory>[0] = {}, // default
+) => {
+  return webSearchToolFactory(args)
+}

packages/plugin/package.json

@@ -24,4 +24,4 @@
     "typescript": "catalog:",
     "@typescript/native-preview": "catalog:"
   }
-}
+}

packages/sdk/js/package.json

@@ -26,4 +26,4 @@
   "publishConfig": {
     "directory": "dist"
   }
-}
+}