Vendor OpenClaw source as Adolf fork baseline

Adolf is a fork/vendored clone of github.com/openclaw/openclaw (v2026.6.11),
free to diverge. Tree copied sans upstream .git; upstream remote added for
future syncs. Node pinned to 24 (.nvmrc); engines already require >=22.19.
Preserves docs/ARCHITECTURE.md.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LeqyaxJF2nbRXJtae2kNB2

packages/ai/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 OpenClaw Foundation
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

packages/ai/README.md

@@ -0,0 +1,28 @@
+# `@openclaw/ai`
+
+Reusable model API contracts, provider adapters, and streaming primitives from
+OpenClaw. The package supports isolated runtime instances; importing it does not
+register providers globally.
+
+```ts
+import { createLlmRuntime } from "@openclaw/ai";
+import { registerBuiltInApiProviders } from "@openclaw/ai/providers";
+
+const runtime = createLlmRuntime();
+registerBuiltInApiProviders(runtime.registry);
+```
+
+Provider-neutral contracts, validation, diagnostics, and event streams are
+available from the package root and focused subpaths such as
+`@openclaw/ai/event-stream` and `@openclaw/ai/validation`. No second OpenClaw
+runtime package is required.
+
+Provider ids, credentials, model catalogs, retries, and failover remain
+application concerns. OpenClaw supplies those policies around this package.
+Host policy (request fetch guarding, secret redaction, strict-tool defaults,
+diagnostics logging) can be injected with `configureAiTransportHost`; the
+defaults are inert.
+
+`@openclaw/ai/internal/*` subpaths exist for the OpenClaw application itself.
+They carry no semver guarantee and can change or disappear in any release; do
+not depend on them outside OpenClaw.

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+          "url": "https://feross.org/support"
+        }
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+      "license": "MIT",
+      "funding": {
+        "url": "https://github.com/sponsors/colinhacks"
+      }
+    },
+    "node_modules/zod-to-json-schema": {
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+      "resolved": "https://registry.npmjs.org/zod-to-json-schema/-/zod-to-json-schema-3.25.2.tgz",
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+      "license": "ISC",
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+        "zod": "^3.25.28 || ^4"
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+}

packages/ai/package.json

@@ -0,0 +1,89 @@
+{
+  "name": "@openclaw/ai",
+  "version": "2026.6.11",
+  "description": "Reusable model provider adapters and streaming runtime from OpenClaw",
+  "keywords": [
+    "ai",
+    "anthropic",
+    "google",
+    "llm",
+    "mistral",
+    "openai",
+    "streaming"
+  ],
+  "homepage": "https://github.com/openclaw/openclaw#readme",
+  "bugs": {
+    "url": "https://github.com/openclaw/openclaw/issues"
+  },
+  "license": "MIT",
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/openclaw/openclaw.git",
+    "directory": "packages/ai"
+  },
+  "files": [
+    "dist",
+    "npm-shrinkwrap.json",
+    "LICENSE",
+    "README.md"
+  ],
+  "type": "module",
+  "main": "./dist/index.mjs",
+  "types": "./dist/index.d.mts",
+  "exports": {
+    ".": {
+      "types": "./dist/index.d.mts",
+      "import": "./dist/index.mjs",
+      "default": "./dist/index.mjs"
+    },
+    "./providers": {
+      "types": "./dist/providers.d.mts",
+      "import": "./dist/providers.mjs",
+      "default": "./dist/providers.mjs"
+    },
+    "./diagnostics": {
+      "types": "./dist/diagnostics.d.mts",
+      "import": "./dist/diagnostics.mjs",
+      "default": "./dist/diagnostics.mjs"
+    },
+    "./event-stream": {
+      "types": "./dist/event-stream.d.mts",
+      "import": "./dist/event-stream.mjs",
+      "default": "./dist/event-stream.mjs"
+    },
+    "./types": {
+      "types": "./dist/types.d.mts",
+      "import": "./dist/types.mjs",
+      "default": "./dist/types.mjs"
+    },
+    "./validation": {
+      "types": "./dist/validation.d.mts",
+      "import": "./dist/validation.mjs",
+      "default": "./dist/validation.mjs"
+    },
+    "./internal/*": {
+      "types": "./dist/internal/*.d.mts",
+      "import": "./dist/internal/*.mjs",
+      "default": "./dist/internal/*.mjs"
+    }
+  },
+  "dependencies": {
+    "@anthropic-ai/sdk": "0.109.1",
+    "@google/genai": "2.10.0",
+    "@mistralai/mistralai": "2.4.0",
+    "openai": "6.45.0",
+    "partial-json": "0.1.7",
+    "typebox": "1.3.3"
+  },
+  "engines": {
+    "node": ">=22.19.0"
+  },
+  "publishConfig": {
+    "access": "public"
+  },
+  "openclaw": {
+    "release": {
+      "publishToNpm": true
+    }
+  }
+}

packages/ai/src/api-registry.test.ts

@@ -0,0 +1,76 @@
+// LLM Runtime tests cover api registry behavior.
+import { describe, expect, it, vi } from "vitest";
+import {
+  createApiRegistry,
+  createAssistantMessageEventStream,
+  createLlmRuntime,
+  type Model,
+} from "./index.js";
+
+const TEST_SOURCE_ID = "test:llm-runtime-api-registry";
+const emptyStream = () => createAssistantMessageEventStream();
+
+const model = {
+  id: "test-model",
+  name: "Test Model",
+  api: "test-api",
+  provider: "test-provider",
+  baseUrl: "https://example.invalid",
+  input: ["text"],
+  reasoning: false,
+  contextWindow: 1000,
+  maxTokens: 100,
+  cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+} satisfies Model;
+
+describe("LLM API registry", () => {
+  it("rejects mismatched model API calls", () => {
+    const registry = createApiRegistry();
+    registry.registerApiProvider(
+      {
+        api: "test-api",
+        stream: emptyStream,
+        streamSimple: emptyStream,
+      },
+      TEST_SOURCE_ID,
+    );
+
+    const provider = registry.getApiProvider("test-api");
+    expect(provider).toBeDefined();
+    expect(() => provider?.streamSimple({ ...model, api: "other-api" }, { messages: [] })).toThrow(
+      "Mismatched api: other-api expected test-api",
+    );
+  });
+
+  it("isolates providers between runtime instances", () => {
+    const first = createLlmRuntime();
+    const second = createLlmRuntime();
+    const streamSimple = vi.fn(() => createAssistantMessageEventStream());
+    first.registry.registerApiProvider({ api: "test-api", stream: streamSimple, streamSimple });
+
+    first.streamSimple(model, { messages: [] });
+
+    expect(streamSimple).toHaveBeenCalledOnce();
+    expect(() => second.streamSimple(model, { messages: [] })).toThrow(
+      "No API provider registered for api: test-api",
+    );
+  });
+
+  it("unregisters every provider owned by one source", () => {
+    const registry = createApiRegistry();
+    for (const api of ["test-api", "test-api-2"] as const) {
+      registry.registerApiProvider(
+        {
+          api,
+          stream: emptyStream,
+          streamSimple: emptyStream,
+        },
+        TEST_SOURCE_ID,
+      );
+    }
+
+    registry.unregisterApiProviders(TEST_SOURCE_ID);
+
+    expect(registry.getApiProviders()).toEqual([]);
+  });
+});

packages/ai/src/api-registry.ts

@@ -0,0 +1,119 @@
+// LLM Runtime module implements api registry behavior.
+import type {
+  Api,
+  AssistantMessageEventStreamContract,
+  Context,
+  Model,
+  SimpleStreamOptions,
+  StreamFunction,
+  StreamOptions,
+} from "@openclaw/llm-core";
+
+/** Runtime stream adapter signature stored in the API provider registry. */
+export type ApiStreamFunction = (
+  model: Model,
+  context: Context,
+  options?: StreamOptions,
+) => AssistantMessageEventStreamContract;
+
+/** Runtime simple-stream adapter signature stored in the API provider registry. */
+export type ApiStreamSimpleFunction = (
+  model: Model,
+  context: Context,
+  options?: SimpleStreamOptions,
+) => AssistantMessageEventStreamContract;
+
+/** Provider implementation registered by core or plugins for a specific model API. */
+export interface ApiProvider<
+  TApi extends Api = Api,
+  TOptions extends StreamOptions = StreamOptions,
+> {
+  /** Model API id this provider handles. */
+  api: TApi;
+  /** Full streaming adapter for callers that already own structured options. */
+  stream: StreamFunction<TApi, TOptions>;
+  /** Simple streaming adapter used by agent and plugin runtime defaults. */
+  streamSimple: StreamFunction<TApi, SimpleStreamOptions>;
+}
+
+/** Type-erased provider returned by a registry after API guards are installed. */
+export interface RegisteredApiProvider {
+  api: Api;
+  stream: ApiStreamFunction;
+  streamSimple: ApiStreamSimpleFunction;
+}
+
+type RegisteredApiProviderEntry = {
+  provider: RegisteredApiProvider;
+  sourceId?: string;
+};
+
+function wrapStream<TApi extends Api, TOptions extends StreamOptions>(
+  api: TApi,
+  stream: StreamFunction<TApi, TOptions>,
+): ApiStreamFunction {
+  return (model, context, options) => {
+    if (model.api !== api) {
+      throw new Error(`Mismatched api: ${model.api} expected ${api}`);
+    }
+    return stream(model as Model<TApi>, context, options as TOptions);
+  };
+}
+
+function wrapStreamSimple<TApi extends Api>(
+  api: TApi,
+  streamSimple: StreamFunction<TApi, SimpleStreamOptions>,
+): ApiStreamSimpleFunction {
+  return (model, context, options) => {
+    if (model.api !== api) {
+      throw new Error(`Mismatched api: ${model.api} expected ${api}`);
+    }
+    return streamSimple(model as Model<TApi>, context, options);
+  };
+}
+
+/** Creates an isolated provider registry for one runtime or tenant. */
+export function createApiRegistry() {
+  const providers = new Map<string, RegisteredApiProviderEntry>();
+
+  function registerApiProvider<TApi extends Api, TOptions extends StreamOptions>(
+    provider: ApiProvider<TApi, TOptions>,
+    /** Optional source id used to unregister all providers owned by one plugin/runtime. */
+    sourceId?: string,
+  ): void {
+    providers.set(provider.api, {
+      provider: {
+        api: provider.api,
+        stream: wrapStream(provider.api, provider.stream),
+        streamSimple: wrapStreamSimple(provider.api, provider.streamSimple),
+      },
+      sourceId,
+    });
+  }
+
+  function getApiProvider(api: Api): RegisteredApiProvider | undefined {
+    return providers.get(api)?.provider;
+  }
+
+  function getApiProviders(): RegisteredApiProvider[] {
+    return Array.from(providers.values(), (entry) => entry.provider);
+  }
+
+  function unregisterApiProviders(sourceId: string): void {
+    for (const [api, entry] of providers.entries()) {
+      if (entry.sourceId === sourceId) {
+        providers.delete(api);
+      }
+    }
+  }
+
+  return {
+    registerApiProvider,
+    getApiProvider,
+    getApiProviders,
+    unregisterApiProviders,
+    clearApiProviders: () => providers.clear(),
+  };
+}
+
+export type ApiRegistry = ReturnType<typeof createApiRegistry>;

packages/ai/src/env-api-keys.ts

@@ -0,0 +1,261 @@
+// NEVER convert to top-level imports - breaks browser/Vite builds
+let existsSync: typeof import("node:fs").existsSync | null = null;
+let homedir: typeof import("node:os").homedir | null = null;
+let join: typeof import("node:path").join | null = null;
+
+type DynamicImport = (specifier: string) => Promise<unknown>;
+type NodeBuiltinModule =
+  | typeof import("node:fs")
+  | typeof import("node:os")
+  | typeof import("node:path");
+
+const dynamicImport: DynamicImport = (specifier) => import(specifier);
+const NODE_FS_SPECIFIER = "node:fs";
+const NODE_OS_SPECIFIER = "node:os";
+const NODE_PATH_SPECIFIER = "node:path";
+
+function loadNodeBuiltinModule(specifier: string): NodeBuiltinModule | null {
+  const getBuiltinModule = (typeof process !== "undefined" ? process : undefined) as
+    | (NodeJS.Process & { getBuiltinModule?: (id: string) => unknown })
+    | undefined;
+  if (typeof getBuiltinModule?.getBuiltinModule === "function") {
+    return getBuiltinModule.getBuiltinModule(specifier) as NodeBuiltinModule;
+  }
+  if (typeof require === "function") {
+    return require(specifier) as NodeBuiltinModule;
+  }
+  return null;
+}
+
+function loadNodeHelpersSync(): boolean {
+  try {
+    const fsModule = loadNodeBuiltinModule(NODE_FS_SPECIFIER) as typeof import("node:fs") | null;
+    const osModule = loadNodeBuiltinModule(NODE_OS_SPECIFIER) as typeof import("node:os") | null;
+    const pathModule = loadNodeBuiltinModule(NODE_PATH_SPECIFIER) as
+      | typeof import("node:path")
+      | null;
+    existsSync ??= fsModule?.existsSync ?? null;
+    homedir ??= osModule?.homedir ?? null;
+    join ??= pathModule?.join ?? null;
+    if (!existsSync || !homedir || !join) {
+      return false;
+    }
+    return true;
+  } catch {
+    return false;
+  }
+}
+
+// Eagerly load in Node.js/Bun environment only
+if (typeof process !== "undefined" && (process.versions?.node || process.versions?.bun)) {
+  if (!loadNodeHelpersSync()) {
+    void dynamicImport(NODE_FS_SPECIFIER).then((m) => {
+      existsSync = (m as typeof import("node:fs")).existsSync;
+    });
+    void dynamicImport(NODE_OS_SPECIFIER).then((m) => {
+      homedir = (m as typeof import("node:os")).homedir;
+    });
+    void dynamicImport(NODE_PATH_SPECIFIER).then((m) => {
+      join = (m as typeof import("node:path")).join;
+    });
+  }
+}
+
+let procEnvCache: Map<string, string> | null = null;
+
+function getProcessEnv(): NodeJS.ProcessEnv | undefined {
+  return typeof process === "undefined" ? undefined : process.env;
+}
+
+/**
+ * Fallback for https://github.com/oven-sh/bun/issues/27802
+ * Bun compiled binaries have an empty `process.env` inside sandbox
+ * environments on Linux. We can recover the env from `/proc/self/environ`.
+ */
+function getProcEnv(key: string): string | undefined {
+  if (typeof process === "undefined" || !process.versions?.bun) {
+    return undefined;
+  }
+  const env = getProcessEnv();
+  if (!env) {
+    return undefined;
+  }
+
+  // If process.env already has entries, the bug is not triggered.
+  if (Object.keys(env).length > 0) {
+    return undefined;
+  }
+
+  if (procEnvCache === null) {
+    procEnvCache = new Map();
+    try {
+      const { readFileSync } = require("node:fs") as typeof import("node:fs");
+      const data = readFileSync("/proc/self/environ", "utf-8");
+      for (const entry of data.split("\0")) {
+        const idx = entry.indexOf("=");
+        if (idx > 0) {
+          procEnvCache.set(entry.slice(0, idx), entry.slice(idx + 1));
+        }
+      }
+    } catch {
+      // /proc/self/environ may not be readable.
+    }
+  }
+
+  return procEnvCache.get(key);
+}
+
+function getEnvValue(key: string): string | undefined {
+  return getProcessEnv()?.[key] || getProcEnv(key);
+}
+
+let cachedVertexAdcCredentialsExists: true | null = null;
+
+function hasVertexAdcCredentials(): boolean {
+  if (cachedVertexAdcCredentialsExists === null) {
+    if (!existsSync || !homedir || !join) {
+      const isNode =
+        typeof process !== "undefined" && (process.versions?.node || process.versions?.bun);
+      if (!isNode || !loadNodeHelpersSync()) {
+        return false;
+      }
+    }
+    const nodeExistsSync = existsSync;
+    const nodeHomedir = homedir;
+    const nodeJoin = join;
+    if (!nodeExistsSync || !nodeHomedir || !nodeJoin) {
+      return false;
+    }
+
+    // Check GOOGLE_APPLICATION_CREDENTIALS env var first (standard way)
+    const gacPath = getEnvValue("GOOGLE_APPLICATION_CREDENTIALS");
+    if (gacPath) {
+      cachedVertexAdcCredentialsExists = nodeExistsSync(gacPath) ? true : null;
+    } else {
+      // Fall back to default ADC path (lazy evaluation)
+      cachedVertexAdcCredentialsExists = nodeExistsSync(
+        nodeJoin(nodeHomedir(), ".config", "gcloud", "application_default_credentials.json"),
+      )
+        ? true
+        : null;
+    }
+  }
+  return cachedVertexAdcCredentialsExists === true;
+}
+
+function getApiKeyEnvVars(provider: string): readonly string[] | undefined {
+  if (provider === "github-copilot") {
+    return ["COPILOT_GITHUB_TOKEN"];
+  }
+
+  // ANTHROPIC_OAUTH_TOKEN takes precedence over ANTHROPIC_API_KEY
+  if (provider === "anthropic") {
+    return ["ANTHROPIC_OAUTH_TOKEN", "ANTHROPIC_API_KEY"];
+  }
+
+  if (provider === "moonshot") {
+    return ["MOONSHOT_API_KEY", "KIMI_API_KEY"];
+  }
+
+  if (provider === "kimi" || provider === "kimi-coding") {
+    return ["KIMI_API_KEY", "KIMICODE_API_KEY"];
+  }
+
+  const envMap: Record<string, string> = {
+    openai: "OPENAI_API_KEY",
+    "azure-openai-responses": "AZURE_OPENAI_API_KEY",
+    deepseek: "DEEPSEEK_API_KEY",
+    google: "GEMINI_API_KEY",
+    "google-vertex": "GOOGLE_CLOUD_API_KEY",
+    groq: "GROQ_API_KEY",
+    cerebras: "CEREBRAS_API_KEY",
+    xai: "XAI_API_KEY",
+    openrouter: "OPENROUTER_API_KEY",
+    "vercel-ai-gateway": "AI_GATEWAY_API_KEY",
+    zai: "ZAI_API_KEY",
+    mistral: "MISTRAL_API_KEY",
+    minimax: "MINIMAX_API_KEY",
+    "minimax-cn": "MINIMAX_CN_API_KEY",
+    moonshotai: "MOONSHOT_API_KEY",
+    "moonshotai-cn": "MOONSHOT_API_KEY",
+    huggingface: "HF_TOKEN",
+    fireworks: "FIREWORKS_API_KEY",
+    together: "TOGETHER_API_KEY",
+    opencode: "OPENCODE_API_KEY",
+    "opencode-go": "OPENCODE_API_KEY",
+    "cloudflare-workers-ai": "CLOUDFLARE_API_KEY",
+    "cloudflare-ai-gateway": "CLOUDFLARE_API_KEY",
+    xiaomi: "XIAOMI_API_KEY",
+    "xiaomi-token-plan-cn": "XIAOMI_TOKEN_PLAN_CN_API_KEY",
+    "xiaomi-token-plan-ams": "XIAOMI_TOKEN_PLAN_AMS_API_KEY",
+    "xiaomi-token-plan-sgp": "XIAOMI_TOKEN_PLAN_SGP_API_KEY",
+  };
+
+  const envVar = envMap[provider];
+  return envVar ? [envVar] : undefined;
+}
+
+/**
+ * Find configured environment variables that can provide an API key for a provider.
+ *
+ * This only reports actual API key variables. It intentionally excludes ambient
+ * credential sources such as AWS profiles, AWS IAM credentials, and Google
+ * Application Default Credentials.
+ */
+export function findEnvKeys(provider: string): string[] | undefined {
+  const envVars = getApiKeyEnvVars(provider);
+  if (!envVars) {
+    return undefined;
+  }
+
+  const found = envVars.filter((envVar) => Boolean(getEnvValue(envVar)));
+  return found.length > 0 ? found : undefined;
+}
+
+/**
+ * Get API key for provider from known environment variables, e.g. OPENAI_API_KEY.
+ *
+ * Will not return API keys for providers that require OAuth tokens.
+ */
+export function getEnvApiKey(provider: string): string | undefined {
+  const envKeys = findEnvKeys(provider);
+  if (envKeys?.[0]) {
+    return getEnvValue(envKeys[0]);
+  }
+
+  // Vertex AI supports either an explicit API key or Application Default Credentials.
+  // Auth is configured via `gcloud auth application-default login`.
+  if (provider === "google-vertex") {
+    const hasCredentials = hasVertexAdcCredentials();
+    const hasProject = Boolean(
+      getEnvValue("GOOGLE_CLOUD_PROJECT") || getEnvValue("GCLOUD_PROJECT"),
+    );
+    const hasLocation = Boolean(getEnvValue("GOOGLE_CLOUD_LOCATION"));
+
+    if (hasCredentials && hasProject && hasLocation) {
+      return "<authenticated>";
+    }
+  }
+
+  if (provider === "amazon-bedrock") {
+    // Amazon Bedrock supports multiple credential sources:
+    // 1. AWS_PROFILE - named profile from ~/.aws/credentials
+    // 2. AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY - standard IAM keys
+    // 3. AWS_BEARER_TOKEN_BEDROCK - Bedrock bearer token
+    // 4. AWS_CONTAINER_CREDENTIALS_RELATIVE_URI - ECS task roles
+    // 5. AWS_CONTAINER_CREDENTIALS_FULL_URI - ECS task roles (full URI)
+    // 6. AWS_WEB_IDENTITY_TOKEN_FILE - IRSA (IAM Roles for Service Accounts)
+    if (
+      getEnvValue("AWS_PROFILE") ||
+      (getEnvValue("AWS_ACCESS_KEY_ID") && getEnvValue("AWS_SECRET_ACCESS_KEY")) ||
+      getEnvValue("AWS_BEARER_TOKEN_BEDROCK") ||
+      getEnvValue("AWS_CONTAINER_CREDENTIALS_RELATIVE_URI") ||
+      getEnvValue("AWS_CONTAINER_CREDENTIALS_FULL_URI") ||
+      getEnvValue("AWS_WEB_IDENTITY_TOKEN_FILE")
+    ) {
+      return "<authenticated>";
+    }
+  }
+
+  return undefined;
+}

packages/ai/src/host.ts

@@ -0,0 +1,66 @@
+// Host policy ports for the reusable transport package. Fetch guarding,
+// secret redaction, strict-tool policy, and diagnostics logging are owned by
+// the embedding application (OpenClaw core installs its implementations via
+// configureAiTransportHost); the library defaults below are inert so external
+// consumers get safe, dependency-free behavior without wiring anything.
+import type { Model } from "@openclaw/llm-core";
+
+/** Strict-tool policy inputs for OpenAI-compatible routes. */
+export interface OpenAIStrictToolSettingOptions {
+  transport?: "stream" | "websocket";
+  supportsStrictMode?: boolean;
+}
+
+/** Narrow host ports consumed by the built-in provider adapters. */
+export interface AiTransportHost {
+  /**
+   * Builds a policy-guarded fetch for one model request.
+   * Returning undefined keeps the provider SDK's default fetch.
+   */
+  buildModelFetch(
+    model: Model,
+    timeoutMs?: number,
+    options?: { sanitizeSse?: boolean },
+  ): typeof fetch | undefined;
+  /** Redacts secrets inside structured tool-result payloads. */
+  redactSecrets<T>(value: T): T;
+  /** Redacts secret-bearing text in tool payload strings. */
+  redactToolPayloadText(text: string): string;
+  /**
+   * Resolves the host strict-tool default for OpenAI-compatible routes.
+   * undefined lets the request omit the strict flag entirely.
+   */
+  resolveOpenAIStrictToolSetting(
+    model: Pick<Model, "provider" | "api" | "baseUrl" | "id"> & { compat?: unknown },
+    options?: OpenAIStrictToolSettingOptions,
+  ): boolean | undefined;
+  /**
+   * Emits one transport diagnostic; build runs only when the host logs it and
+   * may return null to suppress the entry (e.g. de-duplication).
+   */
+  logDebug(
+    subsystem: string,
+    build: () => { message: string; data?: Record<string, unknown> } | null,
+  ): void;
+}
+
+const inertAiTransportHost: AiTransportHost = {
+  buildModelFetch: () => undefined,
+  redactSecrets: (value) => value,
+  redactToolPayloadText: (text) => text,
+  resolveOpenAIStrictToolSetting: (_model, options) =>
+    options?.supportsStrictMode ? false : undefined,
+  logDebug: () => {},
+};
+
+let activeAiTransportHost = inertAiTransportHost;
+
+/** Installs host implementations for the transport policy ports. */
+export function configureAiTransportHost(host: Partial<AiTransportHost>): void {
+  activeAiTransportHost = { ...inertAiTransportHost, ...host };
+}
+
+/** Returns the active transport host (inert defaults unless configured). */
+export function getAiTransportHost(): AiTransportHost {
+  return activeAiTransportHost;
+}

packages/ai/src/index.ts

@@ -0,0 +1,5 @@
+/** Reusable model API contracts, provider adapters, and streaming runtime. */
+export * from "@openclaw/llm-core";
+export * from "./api-registry.js";
+export * from "./host.js";
+export * from "./stream.js";

packages/ai/src/internal/anthropic.ts

@@ -0,0 +1,8 @@
+export * from "../providers/anthropic.js";
+export * from "../providers/anthropic-auth-headers.js";
+export * from "../providers/anthropic-model-contract.js";
+export * from "../providers/anthropic-refusal.js";
+export * from "../providers/anthropic-server-fallback.js";
+export * from "../providers/anthropic-thinking-replay.js";
+export * from "../providers/anthropic-tool-projection.js";
+export * from "../providers/anthropic-usage.js";

packages/ai/src/internal/default-runtime.ts

@@ -0,0 +1,19 @@
+// Process-default registry/runtime retained for the OpenClaw compatibility
+// facade (src/llm). Deliberately not part of the public package API: external
+// consumers create isolated runtimes via createLlmRuntime(); exporting these
+// from the root barrel would reintroduce the mutable process-global registry.
+import { createApiRegistry } from "../api-registry.js";
+import { createLlmRuntime } from "../stream.js";
+
+export const defaultApiRegistry = createApiRegistry();
+export const defaultLlmRuntime = createLlmRuntime(defaultApiRegistry);
+
+export const {
+  registerApiProvider,
+  getApiProvider,
+  getApiProviders,
+  unregisterApiProviders,
+  clearApiProviders,
+} = defaultApiRegistry;
+
+export const { stream, complete, streamSimple, completeSimple } = defaultLlmRuntime;

packages/ai/src/internal/openai.ts

@@ -0,0 +1,14 @@
+export * from "../providers/agent-tools-parameter-schema.js";
+export * from "../providers/azure-deployment-map.js";
+export * from "../providers/azure-openai-responses-client-compat.js";
+export * from "../providers/clean-for-gemini.js";
+export * from "../providers/openai-completions.js";
+export * from "../providers/openai-prompt-cache.js";
+export * from "../providers/openai-reasoning-effort.js";
+export * from "../providers/openai-responses.js";
+export * from "../providers/openai-responses-stream-compat.js";
+export * from "../providers/openai-stop-reason.js";
+export * from "../providers/openai-tool-projection.js";
+export * from "../providers/openai-tool-schema.js";
+export * from "../providers/schema-keyword-strip.js";
+export * from "../providers/tool-schema-json-projection.js";

packages/ai/src/internal/runtime.ts

@@ -0,0 +1,15 @@
+export * from "./default-runtime.js";
+export * from "../env-api-keys.js";
+export * from "../model-utils.js";
+export * from "../session-resources.js";
+export * from "../utils/deferred-event-buffer.js";
+export * from "../utils/hash.js";
+export * from "../utils/headers.js";
+export * from "../utils/json-parse.js";
+export * from "../utils/llm-request-activity.js";
+export * from "../utils/oauth/openai-chatgpt-jwt.js";
+export * from "../utils/overflow.js";
+export * from "../utils/reasoning-tag-text-partitioner.js";
+export * from "../utils/sanitize-unicode.js";
+export * from "../utils/stream-first-event-timeout.js";
+export * from "../utils/streaming-byte-guard.js";

packages/ai/src/internal/shared.ts

@@ -0,0 +1,5 @@
+export * from "../providers/simple-options.js";
+export * from "../providers/tool-result-text.js";
+export * from "../providers/transform-messages.js";
+export * from "../utils/prompt-cache-stability.js";
+export * from "../utils/system-prompt-cache-boundary.js";

packages/ai/src/model-utils.ts

@@ -0,0 +1,110 @@
+// Provides model selection, usage, and thinking-level utility helpers.
+import {
+  resolveClaudeFable5ModelIdentity,
+  resolveClaudeNativeThinkingLevelMap,
+} from "@openclaw/llm-core";
+import type { Api, Model, ModelThinkingLevel, Usage } from "./types.js";
+
+/** Calculates and stores model cost fields from token usage and per-million pricing. */
+export function calculateCost<TApi extends Api>(model: Model<TApi>, usage: Usage): Usage["cost"] {
+  usage.cost.input = (model.cost.input / 1000000) * usage.input;
+  usage.cost.output = (model.cost.output / 1000000) * usage.output;
+  usage.cost.cacheRead = (model.cost.cacheRead / 1000000) * usage.cacheRead;
+  usage.cost.cacheWrite = (model.cost.cacheWrite / 1000000) * usage.cacheWrite;
+  usage.cost.total =
+    usage.cost.input + usage.cost.output + usage.cost.cacheRead + usage.cost.cacheWrite;
+  return usage.cost;
+}
+
+const EXTENDED_THINKING_LEVELS: ModelThinkingLevel[] = [
+  "off",
+  "minimal",
+  "low",
+  "medium",
+  "high",
+  "xhigh",
+  "max",
+];
+
+function resolveThinkingLevelMap<TApi extends Api>(model: Model<TApi>) {
+  return model.api === "anthropic-messages"
+    ? (resolveClaudeNativeThinkingLevelMap(model) ?? model.thinkingLevelMap)
+    : model.thinkingLevelMap;
+}
+
+/** Returns thinking levels exposed by a reasoning-capable model. */
+export function getSupportedThinkingLevels<TApi extends Api>(
+  model: Model<TApi>,
+): ModelThinkingLevel[] {
+  const fableContract =
+    model.api === "anthropic-messages" && resolveClaudeFable5ModelIdentity(model) !== undefined;
+  if (!model.reasoning && !fableContract) {
+    return ["off"];
+  }
+  const thinkingLevelMap = resolveThinkingLevelMap(model);
+
+  return EXTENDED_THINKING_LEVELS.filter((level) => {
+    const mapped = thinkingLevelMap?.[level];
+    if (mapped === null) {
+      return false;
+    }
+    if (level === "xhigh" || level === "max") {
+      return mapped !== undefined;
+    }
+    return true;
+  });
+}
+
+/** Clamps a requested thinking level to the closest supported level for a model. */
+export function clampThinkingLevel<TApi extends Api>(
+  model: Model<TApi>,
+  level: ModelThinkingLevel,
+): ModelThinkingLevel {
+  const availableLevels = getSupportedThinkingLevels(model);
+  if (availableLevels.includes(level)) {
+    return level;
+  }
+
+  const requestedIndex = EXTENDED_THINKING_LEVELS.indexOf(level);
+  if (requestedIndex === -1) {
+    return availableLevels[0] ?? "off";
+  }
+
+  // Explicit provider opt-outs are hard caps. Downgrade them before considering
+  // stronger levels so unsupported xhigh/max requests cannot increase cost.
+  const thinkingLevelMap = resolveThinkingLevelMap(model);
+  if ((level === "xhigh" || level === "max") && thinkingLevelMap?.[level] === null) {
+    for (let i = requestedIndex - 1; i >= 0; i--) {
+      const candidate = EXTENDED_THINKING_LEVELS[i];
+      if (availableLevels.includes(candidate)) {
+        return candidate;
+      }
+    }
+  }
+
+  // Prefer the next stronger available level, then walk down if the request was above the model cap.
+  for (let i = requestedIndex; i < EXTENDED_THINKING_LEVELS.length; i++) {
+    const candidate = EXTENDED_THINKING_LEVELS[i];
+    if (availableLevels.includes(candidate)) {
+      return candidate;
+    }
+  }
+  for (let i = requestedIndex - 1; i >= 0; i--) {
+    const candidate = EXTENDED_THINKING_LEVELS[i];
+    if (availableLevels.includes(candidate)) {
+      return candidate;
+    }
+  }
+  return availableLevels[0] ?? "off";
+}
+
+/** Compares model identity by provider and id. */
+export function modelsAreEqual<TApi extends Api>(
+  a: Model<TApi> | null | undefined,
+  b: Model<TApi> | null | undefined,
+): boolean {
+  if (!a || !b) {
+    return false;
+  }
+  return a.id === b.id && a.provider === b.provider;
+}

packages/ai/src/providers.ts

@@ -0,0 +1,6 @@
+/** Lazy built-in protocol adapter registration. */
+export {
+  BUILT_IN_API_PROVIDER_SOURCE_ID,
+  registerBuiltInApiProviders,
+  resetApiProviders,
+} from "./providers/register-builtins.js";

packages/ai/src/providers/agent-tools-parameter-schema.ts

@@ -0,0 +1,971 @@
+/**
+ * Normalizes model-facing tool parameter schemas across provider quirks.
+ * Handles local JSON Schema refs, OpenAPI nullable syntax, top-level unions,
+ * and provider-specific unsupported keyword stripping.
+ */
+import { isRecord as isSchemaRecord } from "@openclaw/normalization-core/record-coerce";
+import { normalizeLowercaseStringOrEmpty } from "@openclaw/normalization-core/string-coerce";
+import {
+  normalizeStringEntries,
+  uniqueValues,
+} from "@openclaw/normalization-core/string-normalization";
+import type { TSchema } from "typebox";
+import { cleanSchemaForGemini } from "./clean-for-gemini.js";
+import { stripUnsupportedSchemaKeywords } from "./schema-keyword-strip.js";
+
+/**
+ * Narrow structural view of the host's model compat config. packages/ai must stay
+ * config-agnostic, so only tool-schema-relevant fields are modeled here; the host's
+ * ModelCompatConfig remains structurally assignable.
+ */
+export type ToolSchemaModelCompat = {
+  toolSchemaProfile?: string;
+  unsupportedToolSchemaKeywords?: string[];
+  omitEmptyArrayItems?: boolean;
+};
+
+/** Extracts the compat record whether callers pass a model (`{ compat }`) or the compat itself. */
+export function extractToolSchemaModelCompat(
+  modelOrCompat: { compat?: unknown } | ToolSchemaModelCompat | undefined,
+): ToolSchemaModelCompat | undefined {
+  if (!modelOrCompat || typeof modelOrCompat !== "object") {
+    return undefined;
+  }
+  if ("compat" in modelOrCompat) {
+    const compat = (modelOrCompat as { compat?: unknown }).compat;
+    return compat && typeof compat === "object" ? (compat as ToolSchemaModelCompat) : undefined;
+  }
+  return modelOrCompat as ToolSchemaModelCompat;
+}
+
+/** JSON Schema keywords this model/provider rejects in tool schemas. */
+export function resolveUnsupportedToolSchemaKeywords(
+  modelOrCompat: { compat?: unknown } | ToolSchemaModelCompat | undefined,
+): ReadonlySet<string> {
+  const keywords = extractToolSchemaModelCompat(modelOrCompat)?.unsupportedToolSchemaKeywords ?? [];
+  return new Set(
+    normalizeStringEntries(
+      keywords.filter((keyword): keyword is string => typeof keyword === "string"),
+    ),
+  );
+}
+
+/** Whether empty `items: {}` on array schemas must be omitted for this model/provider. */
+export function shouldOmitEmptyArrayItems(
+  modelOrCompat: { compat?: unknown } | ToolSchemaModelCompat | undefined,
+): boolean {
+  return extractToolSchemaModelCompat(modelOrCompat)?.omitEmptyArrayItems === true;
+}
+
+export type ToolParameterSchemaOptions = {
+  modelProvider?: string;
+  modelId?: string;
+  modelCompat?: ToolSchemaModelCompat;
+};
+
+const MAX_TOOL_PARAMETER_SCHEMA_CACHE_ENTRIES_PER_SCHEMA = 8;
+const toolParameterSchemaCache = new WeakMap<object, Array<{ key: string; value: TSchema }>>();
+
+function resolveToolParameterSchemaCacheKey(
+  options: ToolParameterSchemaOptions | undefined,
+): string {
+  const normalizedProvider = normalizeLowercaseStringOrEmpty(options?.modelProvider);
+  const normalizedModelId = normalizeLowercaseStringOrEmpty(options?.modelId);
+  const toolSchemaProfile = normalizeLowercaseStringOrEmpty(
+    options?.modelCompat?.toolSchemaProfile,
+  );
+  const unsupportedKeywords = Array.from(
+    resolveUnsupportedToolSchemaKeywords(options?.modelCompat),
+  ).toSorted();
+  const omitEmptyArrayItems = shouldOmitEmptyArrayItems(options?.modelCompat);
+  return JSON.stringify([
+    normalizedProvider,
+    normalizedModelId,
+    toolSchemaProfile,
+    unsupportedKeywords,
+    omitEmptyArrayItems,
+  ]);
+}
+
+function getCachedToolParameterSchema(schema: object, key: string): TSchema | undefined {
+  return toolParameterSchemaCache.get(schema)?.find((entry) => entry.key === key)?.value;
+}
+
+function rememberCachedToolParameterSchema(schema: object, key: string, value: TSchema): TSchema {
+  const entries = toolParameterSchemaCache.get(schema) ?? [];
+  toolParameterSchemaCache.set(
+    schema,
+    [{ key, value }, ...entries.filter((entry) => entry.key !== key)].slice(
+      0,
+      MAX_TOOL_PARAMETER_SCHEMA_CACHE_ENTRIES_PER_SCHEMA,
+    ),
+  );
+  return value;
+}
+
+function isGeminiModelId(modelId: string): boolean {
+  return /(?:^|[/:])gemini(?:$|[-/:.])/.test(modelId);
+}
+
+function extractEnumValues(schema: unknown): unknown[] | undefined {
+  if (!schema || typeof schema !== "object") {
+    return undefined;
+  }
+  const record = schema as Record<string, unknown>;
+  if (Array.isArray(record.enum)) {
+    return record.enum;
+  }
+  if ("const" in record) {
+    return [record.const];
+  }
+  const variants = Array.isArray(record.anyOf)
+    ? record.anyOf
+    : Array.isArray(record.oneOf)
+      ? record.oneOf
+      : null;
+  if (variants) {
+    const values = variants.flatMap((variant) => {
+      const extracted = extractEnumValues(variant);
+      return extracted ?? [];
+    });
+    return values.length > 0 ? values : undefined;
+  }
+  return undefined;
+}
+
+function mergePropertySchemas(existing: unknown, incoming: unknown): unknown {
+  if (!existing) {
+    return incoming;
+  }
+  if (!incoming) {
+    return existing;
+  }
+
+  const existingEnum = extractEnumValues(existing);
+  const incomingEnum = extractEnumValues(incoming);
+  if (existingEnum || incomingEnum) {
+    const values = uniqueValues([...(existingEnum ?? []), ...(incomingEnum ?? [])]);
+    const merged: Record<string, unknown> = {};
+    for (const source of [existing, incoming]) {
+      if (!source || typeof source !== "object") {
+        continue;
+      }
+      const record = source as Record<string, unknown>;
+      for (const key of ["title", "description", "default"]) {
+        if (!(key in merged) && key in record) {
+          merged[key] = record[key];
+        }
+      }
+    }
+    const types = new Set(values.map((value) => typeof value));
+    if (types.size === 1) {
+      merged.type = Array.from(types)[0];
+    }
+    merged.enum = values;
+    return merged;
+  }
+
+  return existing;
+}
+
+type FlattenableVariantKey = "anyOf" | "oneOf";
+type TopLevelConditionalKey = FlattenableVariantKey | "allOf";
+
+function setOwnSchemaProperty(target: Record<string, unknown>, key: string, value: unknown): void {
+  Object.defineProperty(target, key, {
+    value,
+    enumerable: true,
+    configurable: true,
+    writable: true,
+  });
+}
+
+function hasTopLevelArrayKeyword(
+  schemaRecord: Record<string, unknown>,
+  key: TopLevelConditionalKey,
+): boolean {
+  return Array.isArray(schemaRecord[key]);
+}
+
+function getFlattenableVariantKey(
+  schemaRecord: Record<string, unknown>,
+): FlattenableVariantKey | null {
+  if (hasTopLevelArrayKeyword(schemaRecord, "anyOf")) {
+    return "anyOf";
+  }
+  if (hasTopLevelArrayKeyword(schemaRecord, "oneOf")) {
+    return "oneOf";
+  }
+  return null;
+}
+
+function getTopLevelConditionalKey(
+  schemaRecord: Record<string, unknown>,
+): TopLevelConditionalKey | null {
+  return (
+    getFlattenableVariantKey(schemaRecord) ??
+    (hasTopLevelArrayKeyword(schemaRecord, "allOf") ? "allOf" : null)
+  );
+}
+
+function hasTopLevelObjectSchema(
+  schemaRecord: Record<string, unknown>,
+  conditionalKey: TopLevelConditionalKey | null,
+): boolean {
+  return (
+    schemaRecord.type === "object" &&
+    isSchemaRecord(schemaRecord.properties) &&
+    conditionalKey === null
+  );
+}
+
+function isObjectLikeSchemaMissingType(
+  schemaRecord: Record<string, unknown>,
+  conditionalKey: TopLevelConditionalKey | null,
+): boolean {
+  return (
+    !("type" in schemaRecord) &&
+    (isSchemaRecord(schemaRecord.properties) || Array.isArray(schemaRecord.required)) &&
+    conditionalKey === null
+  );
+}
+
+function isTypedObjectSchemaMissingValidProperties(
+  schemaRecord: Record<string, unknown>,
+  conditionalKey: TopLevelConditionalKey | null,
+): boolean {
+  return (
+    schemaRecord.type === "object" &&
+    !isSchemaRecord(schemaRecord.properties) &&
+    conditionalKey === null
+  );
+}
+
+function isTrulyEmptySchema(schemaRecord: Record<string, unknown>): boolean {
+  return Object.keys(schemaRecord).length === 0;
+}
+
+function normalizeArraySchemasMissingItems(schema: unknown): unknown {
+  if (!isSchemaRecord(schema)) {
+    return schema;
+  }
+
+  let changed = false;
+  const nextSchema: Record<string, unknown> = { ...schema };
+  if (nextSchema.type === "array" && nextSchema.items === undefined) {
+    nextSchema.items = {};
+    changed = true;
+  }
+
+  const normalizeSchemaValue = (key: string): void => {
+    if (!(key in nextSchema)) {
+      return;
+    }
+    const value = nextSchema[key];
+    if (Array.isArray(value)) {
+      const normalized = value.map(normalizeArraySchemasMissingItems);
+      if (normalized.some((entry, index) => entry !== value[index])) {
+        nextSchema[key] = normalized;
+        changed = true;
+      }
+      return;
+    }
+
+    const normalized = normalizeArraySchemasMissingItems(value);
+    if (normalized !== value) {
+      nextSchema[key] = normalized;
+      changed = true;
+    }
+  };
+
+  for (const key of [
+    "items",
+    "contains",
+    "additionalProperties",
+    "propertyNames",
+    "not",
+    "if",
+    "then",
+    "else",
+  ]) {
+    normalizeSchemaValue(key);
+  }
+
+  for (const key of ["anyOf", "oneOf", "allOf", "prefixItems"]) {
+    normalizeSchemaValue(key);
+  }
+
+  for (const key of [
+    "properties",
+    "patternProperties",
+    "dependentSchemas",
+    "$defs",
+    "definitions",
+  ]) {
+    const value = nextSchema[key];
+    if (!isSchemaRecord(value)) {
+      continue;
+    }
+    let entriesChanged = false;
+    const normalizedEntries: Array<[string, unknown]> = Object.entries(value).map(
+      ([entryKey, entryValue]) => {
+        const normalizedEntryValue = normalizeArraySchemasMissingItems(entryValue);
+        if (normalizedEntryValue !== entryValue) {
+          entriesChanged = true;
+        }
+        return [entryKey, normalizedEntryValue];
+      },
+    );
+    if (entriesChanged) {
+      nextSchema[key] = Object.fromEntries(normalizedEntries);
+      changed = true;
+    }
+  }
+
+  return changed ? nextSchema : schema;
+}
+
+function schemaAllowsArrayType(schema: Record<string, unknown>): boolean {
+  const type = schema.type;
+  return type === "array" || (Array.isArray(type) && type.includes("array"));
+}
+
+const ARRAY_ITEMS_SCHEMA_OBJECT_KEYS = new Set([
+  "additionalProperties",
+  "contains",
+  "else",
+  "if",
+  "items",
+  "not",
+  "propertyNames",
+  "then",
+]);
+
+const ARRAY_ITEMS_SCHEMA_ARRAY_KEYS = new Set(["allOf", "anyOf", "oneOf", "prefixItems"]);
+
+const ARRAY_ITEMS_SCHEMA_MAP_KEYS = new Set([
+  "$defs",
+  "definitions",
+  "dependentSchemas",
+  "patternProperties",
+  "properties",
+]);
+
+function stripEmptyArrayItemsFromArraySchemas(schema: unknown): unknown {
+  if (Array.isArray(schema)) {
+    let changed = false;
+    const entries = schema.map((entry) => {
+      const next = stripEmptyArrayItemsFromArraySchemas(entry);
+      changed ||= next !== entry;
+      return next;
+    });
+    return changed ? entries : schema;
+  }
+  if (!isSchemaRecord(schema)) {
+    return schema;
+  }
+
+  let changed = false;
+  const entries = Object.entries(schema).flatMap(([key, value]) => {
+    if (
+      key === "items" &&
+      schemaAllowsArrayType(schema) &&
+      isSchemaRecord(value) &&
+      isTrulyEmptySchema(value)
+    ) {
+      changed = true;
+      return [];
+    }
+
+    if (ARRAY_ITEMS_SCHEMA_OBJECT_KEYS.has(key)) {
+      const next = stripEmptyArrayItemsFromArraySchemas(value);
+      changed ||= next !== value;
+      return [[key, next] as const];
+    }
+
+    if (ARRAY_ITEMS_SCHEMA_ARRAY_KEYS.has(key) && Array.isArray(value)) {
+      const next = stripEmptyArrayItemsFromArraySchemas(value);
+      changed ||= next !== value;
+      return [[key, next] as const];
+    }
+
+    if (ARRAY_ITEMS_SCHEMA_MAP_KEYS.has(key) && isSchemaRecord(value)) {
+      let mapChanged = false;
+      const next = Object.fromEntries(
+        Object.entries(value).map(([entryKey, entryValue]) => {
+          const entryNext = stripEmptyArrayItemsFromArraySchemas(entryValue);
+          mapChanged ||= entryNext !== entryValue;
+          return [entryKey, entryNext] as const;
+        }),
+      );
+      changed ||= mapChanged;
+      return [[key, mapChanged ? next : value] as const];
+    }
+
+    return [[key, value] as const];
+  });
+  return changed ? Object.fromEntries(entries) : schema;
+}
+
+type SchemaDefs = {
+  $defs: Map<string, unknown>;
+  definitions: Map<string, unknown>;
+};
+
+function copySchemaMeta(from: Record<string, unknown>, to: Record<string, unknown>): void {
+  for (const key of ["title", "description", "default"] as const) {
+    if (key in from && from[key] !== undefined) {
+      to[key] = from[key];
+    }
+  }
+}
+
+function extendSchemaDefs(
+  defs: SchemaDefs | undefined,
+  schema: Record<string, unknown>,
+): SchemaDefs | undefined {
+  const defsEntry =
+    schema.$defs && typeof schema.$defs === "object" && !Array.isArray(schema.$defs)
+      ? (schema.$defs as Record<string, unknown>)
+      : undefined;
+  const legacyDefsEntry =
+    schema.definitions &&
+    typeof schema.definitions === "object" &&
+    !Array.isArray(schema.definitions)
+      ? (schema.definitions as Record<string, unknown>)
+      : undefined;
+
+  if (!defsEntry && !legacyDefsEntry) {
+    return defs;
+  }
+
+  const next: SchemaDefs = defs
+    ? {
+        $defs: new Map(defs.$defs),
+        definitions: new Map(defs.definitions),
+      }
+    : {
+        $defs: new Map<string, unknown>(),
+        definitions: new Map<string, unknown>(),
+      };
+  if (defsEntry) {
+    for (const [key, value] of Object.entries(defsEntry)) {
+      next.$defs.set(key, value);
+    }
+  }
+  if (legacyDefsEntry) {
+    for (const [key, value] of Object.entries(legacyDefsEntry)) {
+      next.definitions.set(key, value);
+    }
+  }
+  return next;
+}
+
+function decodeJsonPointerSegment(segment: string): string {
+  return segment.replaceAll("~1", "/").replaceAll("~0", "~");
+}
+
+function resolveJsonPointerPath(value: unknown, segments: string[]): unknown {
+  let current = value;
+  for (const segment of segments) {
+    if (!current || typeof current !== "object") {
+      return undefined;
+    }
+    const key = decodeJsonPointerSegment(segment);
+    if (Array.isArray(current)) {
+      const index = Number(key);
+      if (!Number.isInteger(index) || index < 0 || index >= current.length) {
+        return undefined;
+      }
+      current = current[index];
+      continue;
+    }
+    const record = current as Record<string, unknown>;
+    if (!Object.hasOwn(record, key)) {
+      return undefined;
+    }
+    current = record[key];
+  }
+  return current;
+}
+
+function resolveLocalJsonPointer(rootDocument: unknown, ref: string): unknown {
+  if (!ref.startsWith("#/")) {
+    return undefined;
+  }
+  return resolveJsonPointerPath(rootDocument, ref.slice(2).split("/"));
+}
+
+const SCHEMA_MAP_KEYS = new Set([
+  "$defs",
+  "definitions",
+  "dependentSchemas",
+  "patternProperties",
+  "properties",
+]);
+
+const SCHEMA_OBJECT_KEYS = new Set([
+  "additionalProperties",
+  "contains",
+  "else",
+  "if",
+  "items",
+  "not",
+  "propertyNames",
+  "then",
+]);
+
+const SCHEMA_ARRAY_KEYS = new Set(["allOf", "anyOf", "items", "oneOf", "prefixItems"]);
+
+const SCHEMA_LITERAL_KEYS = new Set(["const", "default", "enum", "examples"]);
+
+function tryResolveLocalRef(
+  ref: string,
+  defs: SchemaDefs | undefined,
+  rootDocument: unknown,
+): unknown {
+  const match = ref.match(/^#\/(\$defs|definitions)\/([^/]+)(?:\/(.*))?$/);
+  if (match && defs) {
+    const namespace = match[1] === "$defs" ? defs.$defs : defs.definitions;
+    const name = decodeJsonPointerSegment(match[2] ?? "");
+    const resolved = name ? namespace.get(name) : undefined;
+    if (resolved !== undefined) {
+      const remainingPath = match[3] ? match[3].split("/") : [];
+      return resolveJsonPointerPath(resolved, remainingPath);
+    }
+  }
+  return resolveLocalJsonPointer(rootDocument, ref);
+}
+
+function inlineLocalSchemaRefsWithDefs(
+  schema: unknown,
+  defs: SchemaDefs | undefined,
+  refStack: Set<string> | undefined,
+  state: { unresolvedLocalRefs: boolean },
+  rootDocument: unknown,
+): unknown {
+  if (!schema || typeof schema !== "object") {
+    return schema;
+  }
+  if (Array.isArray(schema)) {
+    return schema.map((entry) =>
+      inlineLocalSchemaRefsWithDefs(entry, defs, refStack, state, rootDocument),
+    );
+  }
+
+  const obj = schema as Record<string, unknown>;
+  const nextDefs = extendSchemaDefs(defs, obj);
+  const refValue = typeof obj.$ref === "string" ? obj.$ref : undefined;
+
+  if (refValue) {
+    if (refStack?.has(refValue)) {
+      return {};
+    }
+    const resolved = tryResolveLocalRef(refValue, nextDefs, rootDocument);
+    if (resolved === undefined) {
+      if (refValue.startsWith("#/")) {
+        state.unresolvedLocalRefs = true;
+      }
+      return { ...obj };
+    }
+    const nextRefStack = refStack ? new Set(refStack) : new Set<string>();
+    nextRefStack.add(refValue);
+    const inlined = inlineLocalSchemaRefsWithDefs(
+      resolved,
+      nextDefs,
+      nextRefStack,
+      state,
+      rootDocument,
+    );
+    if (!inlined || typeof inlined !== "object" || Array.isArray(inlined)) {
+      return inlined;
+    }
+    const result: Record<string, unknown> = { ...(inlined as Record<string, unknown>) };
+    copySchemaMeta(obj, result);
+    if (obj.nullable === true) {
+      result.nullable = true;
+    }
+    return result;
+  }
+
+  const result: Record<string, unknown> = {};
+  for (const [key, value] of Object.entries(obj)) {
+    if (key === "$defs" || key === "definitions" || key === "components") {
+      continue;
+    }
+    if (SCHEMA_LITERAL_KEYS.has(key)) {
+      setOwnSchemaProperty(result, key, value);
+      continue;
+    }
+    if (SCHEMA_MAP_KEYS.has(key) && isSchemaRecord(value)) {
+      setOwnSchemaProperty(
+        result,
+        key,
+        Object.fromEntries(
+          Object.entries(value).map(([entryKey, entryValue]) => [
+            entryKey,
+            inlineLocalSchemaRefsWithDefs(entryValue, nextDefs, refStack, state, rootDocument),
+          ]),
+        ),
+      );
+      continue;
+    }
+    if (SCHEMA_OBJECT_KEYS.has(key) && isSchemaRecord(value)) {
+      setOwnSchemaProperty(
+        result,
+        key,
+        inlineLocalSchemaRefsWithDefs(value, nextDefs, refStack, state, rootDocument),
+      );
+      continue;
+    }
+    if (SCHEMA_ARRAY_KEYS.has(key) && Array.isArray(value)) {
+      setOwnSchemaProperty(
+        result,
+        key,
+        value.map((entry) =>
+          inlineLocalSchemaRefsWithDefs(entry, nextDefs, refStack, state, rootDocument),
+        ),
+      );
+      continue;
+    }
+    setOwnSchemaProperty(result, key, value);
+  }
+  if (state.unresolvedLocalRefs) {
+    if ("$defs" in obj) {
+      result.$defs = obj.$defs;
+    }
+    if ("definitions" in obj) {
+      result.definitions = obj.definitions;
+    }
+    if ("components" in obj) {
+      result.components = obj.components;
+    }
+  }
+  return result;
+}
+
+/** Inline local $ref pointers so providers receive self-contained tool schemas. */
+function inlineLocalToolSchemaRefs(schema: unknown): TSchema {
+  if (!schema || typeof schema !== "object") {
+    return schema as TSchema;
+  }
+  const defs = extendSchemaDefs(undefined, schema as Record<string, unknown>);
+  return inlineLocalSchemaRefsWithDefs(
+    schema,
+    defs,
+    undefined,
+    {
+      unresolvedLocalRefs: false,
+    },
+    schema,
+  ) as TSchema;
+}
+
+const OPENAPI_SCHEMA_ANNOTATION_KEYS = new Set([
+  "discriminator",
+  "externalDocs",
+  "readOnly",
+  "writeOnly",
+  "xml",
+  "example",
+]);
+
+function appendNullSchemaType(type: unknown): unknown {
+  if (type === "null") {
+    return type;
+  }
+  if (typeof type === "string") {
+    return [type, "null"];
+  }
+  if (Array.isArray(type)) {
+    return type.includes("null") ? type : [...type, "null"];
+  }
+  return type;
+}
+
+function isNullSchemaLike(schema: unknown): boolean {
+  if (!isSchemaRecord(schema)) {
+    return false;
+  }
+  if (schema.type === "null") {
+    return true;
+  }
+  if (Array.isArray(schema.type) && schema.type.includes("null")) {
+    return true;
+  }
+  if ("const" in schema && schema.const === null) {
+    return true;
+  }
+  return Array.isArray(schema.enum) && schema.enum.includes(null);
+}
+
+function hasOpenApiComposition(schema: Record<string, unknown>): boolean {
+  return ["allOf", "anyOf", "oneOf"].some((key) => Array.isArray(schema[key]));
+}
+
+function schemaCompositionAlreadyAllowsNull(schema: Record<string, unknown>): boolean {
+  return (
+    (Array.isArray(schema.anyOf) && schema.anyOf.some(isNullSchemaLike)) ||
+    (Array.isArray(schema.oneOf) && schema.oneOf.some(isNullSchemaLike))
+  );
+}
+
+function wrapNullableComposedSchema(schema: Record<string, unknown>): Record<string, unknown> {
+  if (schemaCompositionAlreadyAllowsNull(schema)) {
+    return schema;
+  }
+  const wrapped: Record<string, unknown> = {
+    anyOf: [schema, { type: "null" }],
+  };
+  copySchemaMeta(schema, wrapped);
+  return wrapped;
+}
+
+function normalizeOpenApiSchemaKeywords(schema: unknown): unknown {
+  if (Array.isArray(schema)) {
+    let changed = false;
+    const normalized = schema.map((entry) => {
+      const next = normalizeOpenApiSchemaKeywords(entry);
+      changed ||= next !== entry;
+      return next;
+    });
+    return changed ? normalized : schema;
+  }
+  if (!isSchemaRecord(schema)) {
+    return schema;
+  }
+
+  let changed = false;
+  const nullable = schema.nullable === true;
+  const normalized: Record<string, unknown> = {};
+  for (const [key, value] of Object.entries(schema)) {
+    if (key === "nullable" || OPENAPI_SCHEMA_ANNOTATION_KEYS.has(key)) {
+      changed = true;
+      continue;
+    }
+    if (SCHEMA_LITERAL_KEYS.has(key)) {
+      normalized[key] = value;
+      continue;
+    }
+    if (SCHEMA_MAP_KEYS.has(key) && isSchemaRecord(value)) {
+      let mapChanged = false;
+      const next = Object.fromEntries(
+        Object.entries(value).map(([entryKey, entryValue]) => {
+          const nextEntry = normalizeOpenApiSchemaKeywords(entryValue);
+          mapChanged ||= nextEntry !== entryValue;
+          return [entryKey, nextEntry];
+        }),
+      );
+      normalized[key] = mapChanged ? next : value;
+      changed ||= mapChanged;
+      continue;
+    }
+    if (key === "components") {
+      normalized[key] = value;
+      continue;
+    }
+    if (SCHEMA_OBJECT_KEYS.has(key) && isSchemaRecord(value)) {
+      const next = normalizeOpenApiSchemaKeywords(value);
+      normalized[key] = next;
+      changed ||= next !== value;
+      continue;
+    }
+    if (SCHEMA_ARRAY_KEYS.has(key) && Array.isArray(value)) {
+      const next = value.map(normalizeOpenApiSchemaKeywords);
+      normalized[key] = next;
+      changed ||= next.some((entry, index) => entry !== value[index]);
+      continue;
+    }
+    normalized[key] = value;
+  }
+
+  if (nullable) {
+    if (hasOpenApiComposition(normalized)) {
+      return wrapNullableComposedSchema(normalized);
+    }
+    if ("type" in normalized) {
+      const nextType = appendNullSchemaType(normalized.type);
+      if (nextType !== normalized.type) {
+        normalized.type = nextType;
+      }
+    }
+    if (Array.isArray(normalized.enum) && !normalized.enum.includes(null)) {
+      normalized.enum = [...normalized.enum, null];
+    }
+  }
+
+  return changed || nullable ? normalized : schema;
+}
+
+function normalizeToolParameterSchemaUncached(
+  schema: unknown,
+  options?: ToolParameterSchemaOptions,
+): TSchema {
+  const inlinedSchema = normalizeOpenApiSchemaKeywords(inlineLocalToolSchemaRefs(schema));
+  const schemaRecord =
+    inlinedSchema && typeof inlinedSchema === "object"
+      ? (inlinedSchema as Record<string, unknown>)
+      : undefined;
+  if (!schemaRecord) {
+    return inlinedSchema as TSchema;
+  }
+
+  // Provider quirks:
+  // - Gemini rejects several JSON Schema keywords, so we scrub those.
+  // - OpenAI rejects function tool schemas unless the *top-level* is `type: "object"`.
+  //   (TypeBox root unions compile to `{ anyOf: [...] }` without `type`).
+  // - Anthropic expects full JSON Schema draft 2020-12 compliance.
+  // - xAI rejects validation-constraint keywords (minLength, maxLength, etc.) outright.
+  //
+  // Normalize once here so callers can always pass `tools` through unchanged.
+  const normalizedProvider = normalizeLowercaseStringOrEmpty(options?.modelProvider);
+  const normalizedModelId = normalizeLowercaseStringOrEmpty(options?.modelId);
+  const normalizedToolSchemaProfile = normalizeLowercaseStringOrEmpty(
+    options?.modelCompat?.toolSchemaProfile,
+  );
+  const isGeminiProvider =
+    normalizedProvider.includes("google") ||
+    normalizedProvider.includes("gemini") ||
+    isGeminiModelId(normalizedModelId) ||
+    normalizedToolSchemaProfile === "gemini";
+  const isAnthropicProvider = normalizedProvider.includes("anthropic");
+  const unsupportedToolSchemaKeywords = resolveUnsupportedToolSchemaKeywords(options?.modelCompat);
+  const omitEmptyArrayItems = shouldOmitEmptyArrayItems(options?.modelCompat);
+
+  function applyProviderCleaning(s: unknown): TSchema {
+    const normalizedSchema = normalizeArraySchemasMissingItems(s);
+    const arrayItemsCompatibleSchema = omitEmptyArrayItems
+      ? stripEmptyArrayItemsFromArraySchemas(normalizedSchema)
+      : normalizedSchema;
+    if (isGeminiProvider && !isAnthropicProvider) {
+      const geminiCompatibleSchema = cleanSchemaForGemini(arrayItemsCompatibleSchema);
+      return unsupportedToolSchemaKeywords.size > 0
+        ? (stripUnsupportedSchemaKeywords(
+            geminiCompatibleSchema,
+            unsupportedToolSchemaKeywords,
+          ) as TSchema)
+        : geminiCompatibleSchema;
+    }
+    if (unsupportedToolSchemaKeywords.size > 0) {
+      return stripUnsupportedSchemaKeywords(
+        arrayItemsCompatibleSchema,
+        unsupportedToolSchemaKeywords,
+      ) as TSchema;
+    }
+    return arrayItemsCompatibleSchema as TSchema;
+  }
+
+  const conditionalKey = getTopLevelConditionalKey(schemaRecord);
+  const flattenableVariantKey = getFlattenableVariantKey(schemaRecord);
+
+  if (hasTopLevelObjectSchema(schemaRecord, conditionalKey)) {
+    return applyProviderCleaning(schemaRecord);
+  }
+
+  if (isObjectLikeSchemaMissingType(schemaRecord, conditionalKey)) {
+    return applyProviderCleaning({
+      ...schemaRecord,
+      type: "object",
+      properties: isSchemaRecord(schemaRecord.properties) ? schemaRecord.properties : {},
+    });
+  }
+
+  if (isTypedObjectSchemaMissingValidProperties(schemaRecord, conditionalKey)) {
+    return applyProviderCleaning({ ...schemaRecord, properties: {} });
+  }
+
+  if (!flattenableVariantKey) {
+    if (isTrulyEmptySchema(schemaRecord)) {
+      // Handle the proven MCP no-parameter case: a truly empty schema object.
+      return applyProviderCleaning({ type: "object", properties: {} });
+    }
+    if (conditionalKey === "allOf") {
+      // Top-level `allOf` is not safely flattenable with the same heuristics we
+      // use for unions. Keep it explicit rather than silently rewriting it.
+      return applyProviderCleaning(inlinedSchema);
+    }
+    return applyProviderCleaning(inlinedSchema);
+  }
+  const variants = schemaRecord[flattenableVariantKey] as unknown[];
+  const mergedProperties: Record<string, unknown> = {};
+  const requiredCounts = new Map<string, number>();
+  let objectVariants = 0;
+
+  for (const entry of variants) {
+    if (!entry || typeof entry !== "object") {
+      continue;
+    }
+    const props = (entry as { properties?: unknown }).properties;
+    if (!props || typeof props !== "object") {
+      continue;
+    }
+    objectVariants += 1;
+    for (const [key, value] of Object.entries(props as Record<string, unknown>)) {
+      if (!(key in mergedProperties)) {
+        mergedProperties[key] = value;
+        continue;
+      }
+      mergedProperties[key] = mergePropertySchemas(mergedProperties[key], value);
+    }
+    const required = Array.isArray((entry as { required?: unknown }).required)
+      ? (entry as { required: unknown[] }).required
+      : [];
+    for (const key of required) {
+      if (typeof key !== "string") {
+        continue;
+      }
+      requiredCounts.set(key, (requiredCounts.get(key) ?? 0) + 1);
+    }
+  }
+
+  const baseRequired = Array.isArray(schemaRecord.required)
+    ? schemaRecord.required.filter((key) => typeof key === "string")
+    : undefined;
+  const mergedRequired =
+    baseRequired && baseRequired.length > 0
+      ? baseRequired
+      : objectVariants > 0
+        ? Array.from(requiredCounts.entries())
+            .filter(([, count]) => count === objectVariants)
+            .map(([key]) => key)
+        : undefined;
+
+  const nextSchema: Record<string, unknown> = { ...schemaRecord };
+  const flattenedSchema = {
+    type: "object",
+    ...(typeof nextSchema.title === "string" ? { title: nextSchema.title } : {}),
+    ...(typeof nextSchema.description === "string" ? { description: nextSchema.description } : {}),
+    properties:
+      Object.keys(mergedProperties).length > 0 ? mergedProperties : (schemaRecord.properties ?? {}),
+    ...(mergedRequired && mergedRequired.length > 0 ? { required: mergedRequired } : {}),
+    additionalProperties:
+      "additionalProperties" in schemaRecord ? schemaRecord.additionalProperties : true,
+  };
+
+  // Flatten union schemas into a single object schema:
+  // - Gemini doesn't allow top-level `type` together with `anyOf`.
+  // - OpenAI rejects schemas without top-level `type: "object"`.
+  // - Anthropic accepts proper JSON Schema with constraints.
+  // Merging properties preserves useful enums like `action` while keeping schemas portable.
+  return applyProviderCleaning(flattenedSchema);
+}
+
+/** Return a provider-compatible JSON schema for a model-facing tool. */
+export function normalizeToolParameterSchema(
+  schema: unknown,
+  options?: ToolParameterSchemaOptions,
+): TSchema {
+  if (!schema || typeof schema !== "object") {
+    return normalizeToolParameterSchemaUncached(schema, options);
+  }
+  const cacheKey = resolveToolParameterSchemaCacheKey(options);
+  const cached = getCachedToolParameterSchema(schema, cacheKey);
+  if (cached) {
+    return cached;
+  }
+  return rememberCachedToolParameterSchema(
+    schema,
+    cacheKey,
+    normalizeToolParameterSchemaUncached(schema, options),
+  );
+}

packages/ai/src/providers/anthropic-auth-headers.ts

@@ -0,0 +1,38 @@
+type AnthropicAuthModel = {
+  provider?: string;
+  authHeader?: boolean;
+  headers?: Record<string, string>;
+};
+
+export function usesFoundryBearerAuth(model: AnthropicAuthModel): boolean {
+  return (
+    model.provider === "microsoft-foundry" &&
+    (model.authHeader === true || hasBearerAuthorizationHeader(model.headers))
+  );
+}
+
+function hasBearerAuthorizationHeader(headers?: Record<string, string>): boolean {
+  if (!headers) {
+    return false;
+  }
+  return Object.entries(headers).some(
+    ([key, value]) => key.toLowerCase() === "authorization" && /^bearer\s+\S+/i.test(value.trim()),
+  );
+}
+
+export function omitFoundryBearerCredentialHeaders(
+  headers?: Record<string, string>,
+): Record<string, string> | undefined {
+  if (!headers) {
+    return undefined;
+  }
+  const next: Record<string, string> = {};
+  for (const [key, value] of Object.entries(headers)) {
+    const lower = key.toLowerCase();
+    if (lower === "authorization" || lower === "x-api-key" || lower === "api-key") {
+      continue;
+    }
+    next[key] = value;
+  }
+  return Object.keys(next).length > 0 ? next : undefined;
+}

packages/ai/src/providers/anthropic-model-contract.ts

@@ -0,0 +1,98 @@
+// Model-bound thinking cannot be exposed or replayed after a model switch.
+import { resolveClaudeFable5ModelIdentity, resolveClaudeModelIdentity } from "@openclaw/llm-core";
+import { normalizeLowercaseStringOrEmpty } from "@openclaw/normalization-core/string-coerce";
+export {
+  resolveClaudeFable5ModelIdentity,
+  resolveClaudeModelIdentity,
+  resolveClaudeNativeThinkingLevelMap,
+  supportsClaudeAdaptiveThinking,
+  supportsClaudeNativeMaxEffort,
+  supportsClaudeNativeXhighEffort,
+} from "@openclaw/llm-core";
+
+type ReplayModelRef = {
+  provider?: string;
+  api?: string;
+  modelId?: string;
+  responseModelId?: string;
+  modelParams?: Record<string, unknown>;
+};
+
+function normalizeModelId(modelId?: string): string {
+  const normalized = normalizeLowercaseStringOrEmpty(modelId);
+  const unprefixed = normalized.startsWith("anthropic/")
+    ? normalized.slice("anthropic/".length)
+    : normalized;
+  return unprefixed.replace(/[._\s]+/g, "-");
+}
+
+function normalizeApi(api?: string): string {
+  const normalized = normalizeLowercaseStringOrEmpty(api);
+  return normalized === "openclaw-anthropic-messages-transport" ? "anthropic-messages" : normalized;
+}
+
+function hasConcreteResponseModel(ref: ReplayModelRef): boolean {
+  const responseModelId = normalizeModelId(ref.responseModelId);
+  // Deployment APIs may echo the requested alias. Only a different response
+  // model proves the backing identity and overrides configured metadata.
+  return responseModelId.length > 0 && responseModelId !== normalizeModelId(ref.modelId);
+}
+
+export function usesClaudeFable5MessagesContract(model: {
+  id?: string;
+  params?: Record<string, unknown>;
+  api?: string;
+}): boolean {
+  return (
+    normalizeApi(model.api) === "anthropic-messages" &&
+    resolveClaudeFable5ModelIdentity(model) !== undefined
+  );
+}
+
+export function requiresClaudeAdaptiveThinking(model: {
+  id?: string;
+  params?: Record<string, unknown>;
+  api?: string;
+}): boolean {
+  if (normalizeApi(model.api) !== "anthropic-messages") {
+    return false;
+  }
+  const modelId = resolveClaudeModelIdentity(model);
+  return (
+    resolveClaudeFable5ModelIdentity(model) !== undefined ||
+    /(?:^|-)claude-mythos-preview(?=$|[^a-z0-9])/.test(modelId)
+  );
+}
+
+function resolveReplayFableIdentity(ref: ReplayModelRef): string | undefined {
+  if (normalizeApi(ref.api) !== "anthropic-messages") {
+    return undefined;
+  }
+  if (hasConcreteResponseModel(ref)) {
+    return resolveClaudeFable5ModelIdentity({ id: ref.responseModelId });
+  }
+  return resolveClaudeFable5ModelIdentity({ id: ref.modelId, params: ref.modelParams });
+}
+
+export function resolveModelBoundThinkingReplayMode(params: {
+  source: ReplayModelRef;
+  target: ReplayModelRef;
+}): "default" | "preserve" | "drop" {
+  const sourceApi = normalizeApi(params.source.api);
+  const targetApi = normalizeApi(params.target.api);
+  const sourceIdentity = resolveReplayFableIdentity(params.source);
+  const targetIdentity = resolveReplayFableIdentity(params.target);
+  const sameRoute =
+    normalizeLowercaseStringOrEmpty(params.source.provider) ===
+      normalizeLowercaseStringOrEmpty(params.target.provider) &&
+    sourceApi === targetApi &&
+    normalizeModelId(params.source.modelId) === normalizeModelId(params.target.modelId);
+  if (!sourceIdentity && !targetIdentity) {
+    return "default";
+  }
+  if (!sourceIdentity && !hasConcreteResponseModel(params.source) && targetIdentity && sameRoute) {
+    return "preserve";
+  }
+  const sameModel = sourceApi === targetApi && sourceIdentity === targetIdentity;
+  return sameModel ? "preserve" : "drop";
+}

packages/ai/src/providers/anthropic-refusal.ts

@@ -0,0 +1,55 @@
+import type { AssistantMessageDiagnostic } from "../types.js";
+
+type AnthropicRefusalOutput = {
+  stopReason: string;
+  errorMessage?: string;
+  diagnostics?: AssistantMessageDiagnostic[];
+};
+
+type AnthropicRefusalDetails = {
+  category: string | null;
+  explanation: string | null;
+};
+
+function readNullableString(value: unknown): string | null {
+  return typeof value === "string" && value.trim() ? value.trim() : null;
+}
+
+function readAnthropicRefusalDetails(value: unknown): AnthropicRefusalDetails {
+  if (!value || typeof value !== "object") {
+    return { category: null, explanation: null };
+  }
+  const details = value as Record<string, unknown>;
+  return {
+    category: readNullableString(details.category),
+    explanation: readNullableString(details.explanation),
+  };
+}
+
+function formatAnthropicRefusalMessage(details: AnthropicRefusalDetails): string {
+  const category = details.category ? ` (category: ${details.category})` : "";
+  const explanation = details.explanation ? `: ${details.explanation}` : ".";
+  return `Anthropic refusal${category}${explanation}`;
+}
+
+export function applyAnthropicRefusal(
+  output: AnthropicRefusalOutput,
+  stopDetails: unknown,
+  provider: string,
+): void {
+  const details = readAnthropicRefusalDetails(stopDetails);
+  output.stopReason = "error";
+  output.errorMessage = formatAnthropicRefusalMessage(details);
+  output.diagnostics = [
+    ...(output.diagnostics ?? []),
+    {
+      type: "provider_refusal",
+      timestamp: Date.now(),
+      details: {
+        provider,
+        category: details.category,
+        explanation: details.explanation,
+      },
+    },
+  ];
+}

packages/ai/src/providers/anthropic-server-fallback.ts

@@ -0,0 +1,83 @@
+import type { AssistantMessageDiagnostic } from "../types.js";
+
+/** Anthropic beta that re-serves safety refusals on an allowed fallback model. */
+export const ANTHROPIC_SERVER_SIDE_FALLBACK_BETA = "server-side-fallback-2026-06-01";
+
+// Anthropic documents claude-opus-4-8 as the allowed fallback for claude-fable-5.
+export const CLAUDE_FABLE_5_FALLBACK_MODEL = "claude-opus-4-8";
+
+// Fallback-served turns bill at the serving model's rates.
+export const CLAUDE_FABLE_5_FALLBACK_MODEL_COST = {
+  input: 5,
+  output: 25,
+  cacheRead: 0.5,
+  cacheWrite: 6.25,
+} as const;
+
+export function buildAnthropicServerSideFallbacks(): Array<{ model: string }> {
+  return [{ model: CLAUDE_FABLE_5_FALLBACK_MODEL }];
+}
+
+export type AnthropicFallbackBoundary = {
+  fromModel: string | null;
+  toModel: string | null;
+};
+
+function readBoundaryModel(value: unknown): string | null {
+  if (!value || typeof value !== "object") {
+    return null;
+  }
+  const model = (value as { model?: unknown }).model;
+  return typeof model === "string" && model.trim() ? model : null;
+}
+
+/** Reads a `fallback` content block marking where one model's output gives way to the next. */
+export function readAnthropicFallbackBoundary(block: unknown): AnthropicFallbackBoundary | null {
+  if (!block || typeof block !== "object") {
+    return null;
+  }
+  const record = block as { type?: unknown; from?: unknown; to?: unknown };
+  if (record.type !== "fallback") {
+    return null;
+  }
+  return {
+    fromModel: readBoundaryModel(record.from),
+    toModel: readBoundaryModel(record.to),
+  };
+}
+
+/**
+ * Drops pre-fallback thinking/tool calls while preserving the text prefix that
+ * the serving model continued. Dropped tool calls must never execute or replay.
+ */
+export function applyAnthropicFallbackBoundary(params: {
+  output: {
+    content: Array<{ type: string }>;
+    responseModel?: string;
+    diagnostics?: AssistantMessageDiagnostic[];
+  };
+  boundary: AnthropicFallbackBoundary;
+  provider: string;
+}): void {
+  const { output, boundary } = params;
+  const survivors = output.content.filter((block) => block.type === "text");
+  for (const survivor of survivors) {
+    delete (survivor as { textSignature?: string }).textSignature;
+  }
+  output.content.splice(0, output.content.length, ...survivors);
+  if (boundary.toModel) {
+    output.responseModel = boundary.toModel;
+  }
+  output.diagnostics = [
+    ...(output.diagnostics ?? []),
+    {
+      type: "provider_fallback",
+      timestamp: Date.now(),
+      details: {
+        provider: params.provider,
+        fromModel: boundary.fromModel,
+        toModel: boundary.toModel,
+      },
+    },
+  ];
+}

packages/ai/src/providers/anthropic-thinking-replay.ts

@@ -0,0 +1,58 @@
+type ReplayMessage = {
+  role?: unknown;
+  content?: unknown;
+  toolCallId?: unknown;
+};
+
+export const ANTHROPIC_OMITTED_REASONING_TEXT = "[assistant reasoning omitted]";
+
+function asReplayMessage(value: unknown): ReplayMessage | undefined {
+  return value && typeof value === "object" ? (value as ReplayMessage) : undefined;
+}
+
+/**
+ * Anthropic tool results continue the preceding assistant turn. Preserve that
+ * turn's signed thinking even when the next request disables new thinking.
+ */
+export function findActiveAnthropicToolTurnAssistantIndex(messages: readonly unknown[]): number {
+  const toolResultIds = new Set<string>();
+  let index = messages.length - 1;
+
+  while (index >= 0) {
+    const message = asReplayMessage(messages[index]);
+    if (message?.role !== "toolResult") {
+      break;
+    }
+    if (typeof message.toolCallId === "string") {
+      toolResultIds.add(message.toolCallId);
+    }
+    index -= 1;
+  }
+
+  if (toolResultIds.size === 0) {
+    return -1;
+  }
+
+  const assistant = asReplayMessage(messages[index]);
+  if (assistant?.role !== "assistant" || !Array.isArray(assistant.content)) {
+    return -1;
+  }
+
+  const toolCallIds = new Set<string>();
+  for (const block of assistant.content) {
+    if (!block || typeof block !== "object") {
+      continue;
+    }
+    const record = block as { type?: unknown; id?: unknown };
+    if (
+      (record.type === "toolCall" ||
+        record.type === "tool_use" ||
+        record.type === "function_call") &&
+      typeof record.id === "string"
+    ) {
+      toolCallIds.add(record.id);
+    }
+  }
+
+  return [...toolResultIds].every((toolCallId) => toolCallIds.has(toolCallId)) ? index : -1;
+}

packages/ai/src/providers/anthropic-tool-projection.ts

@@ -0,0 +1,163 @@
+import { isRecord } from "@openclaw/normalization-core/record-coerce";
+import { projectRuntimeToolInputSchema } from "./tool-schema-json-projection.js";
+
+type AnthropicToolDescriptor = {
+  readonly name: string;
+  readonly description: string;
+  readonly parameters: unknown;
+};
+
+type AnthropicProjectedTool = {
+  readonly originalName: string;
+  readonly wireName: string;
+  readonly description?: string;
+  readonly inputSchema: {
+    readonly type: "object";
+    readonly properties: Record<string, unknown>;
+    readonly required: string[];
+  };
+};
+
+export type AnthropicToolProjection = {
+  readonly inputToolCount: number;
+  readonly unavailableOriginalNames: ReadonlySet<string>;
+  readonly tools: readonly AnthropicProjectedTool[];
+};
+
+type AnthropicParallelToolChoice = {
+  readonly disable_parallel_tool_use?: boolean;
+};
+
+export type AnthropicProjectedToolChoice =
+  | ({ readonly type: "auto" } & AnthropicParallelToolChoice)
+  | ({ readonly type: "any" } & AnthropicParallelToolChoice)
+  | { readonly type: "none" }
+  | ({ readonly type: "tool"; readonly name: string } & AnthropicParallelToolChoice);
+
+function isProviderSupportedViolation(violation: string): boolean {
+  return violation.endsWith(".$dynamicRef") || violation.endsWith(".$dynamicAnchor");
+}
+
+/** Snapshots direct/custom tool descriptors before Anthropic payload construction. */
+export function projectAnthropicTools(
+  tools: readonly AnthropicToolDescriptor[],
+  toWireName: (name: string) => string,
+): AnthropicToolProjection {
+  const projectedTools: AnthropicProjectedTool[] = [];
+  const unavailableOriginalNames = new Set<string>();
+  for (const tool of tools) {
+    let projectedTool: AnthropicProjectedTool;
+    let originalName: string | undefined;
+    try {
+      const name = tool.name;
+      originalName = name;
+      if (!name) {
+        continue;
+      }
+      const schemaProjection = projectRuntimeToolInputSchema(tool.parameters, `${name}.parameters`);
+      if (
+        !isRecord(schemaProjection.schema) ||
+        schemaProjection.violations.some((violation) => !isProviderSupportedViolation(violation))
+      ) {
+        unavailableOriginalNames.add(name);
+        continue;
+      }
+      const properties = schemaProjection.schema.properties;
+      const required = schemaProjection.schema.required;
+      if (
+        (properties !== undefined && properties !== null && !isRecord(properties)) ||
+        (required !== undefined &&
+          required !== null &&
+          (!Array.isArray(required) || required.some((entry) => typeof entry !== "string")))
+      ) {
+        unavailableOriginalNames.add(name);
+        continue;
+      }
+      let description: string | undefined;
+      try {
+        description = typeof tool.description === "string" ? tool.description : undefined;
+      } catch {
+        // Description is optional; keep the usable tool schema.
+      }
+      const wireName = toWireName(name);
+      projectedTool = {
+        originalName: name,
+        wireName,
+        ...(description ? { description } : {}),
+        inputSchema: {
+          type: "object",
+          properties: (properties ?? {}) as Record<string, unknown>,
+          required: (required ?? []) as string[],
+        },
+      };
+    } catch {
+      // Direct/custom tool arrays can bypass the runtime quarantine.
+      if (originalName) {
+        unavailableOriginalNames.add(originalName);
+      }
+      continue;
+    }
+    const conflictingTool = projectedTools.find(
+      (entry) => entry.wireName === projectedTool.wireName,
+    );
+    if (conflictingTool && conflictingTool.originalName !== projectedTool.originalName) {
+      throw new Error(
+        `Anthropic tool names "${conflictingTool.originalName}" and "${projectedTool.originalName}" both map to "${projectedTool.wireName}"`,
+      );
+    }
+    projectedTools.push(projectedTool);
+  }
+  return {
+    inputToolCount: tools.length,
+    unavailableOriginalNames,
+    tools: projectedTools,
+  };
+}
+
+/** Keeps forced Anthropic tool choices aligned with the projected wire names. */
+export function reconcileAnthropicToolChoice(
+  choice: AnthropicProjectedToolChoice,
+  projection: AnthropicToolProjection,
+): AnthropicProjectedToolChoice | undefined {
+  if (projection.inputToolCount === 0) {
+    return choice;
+  }
+  if (choice.type === "tool") {
+    const requestedName = choice.name;
+    const originalMatch = projection.tools.find((tool) => tool.originalName === requestedName);
+    if (originalMatch) {
+      return { ...choice, name: originalMatch.wireName };
+    }
+    if (projection.unavailableOriginalNames.has(requestedName)) {
+      throw new Error(
+        `Anthropic tool_choice requested unavailable tool "${requestedName}" after schema conversion`,
+      );
+    }
+    const matchedTool = projection.tools.find((tool) => tool.wireName === requestedName);
+    if (!matchedTool) {
+      throw new Error(
+        `Anthropic tool_choice requested unavailable tool "${requestedName}" after schema conversion`,
+      );
+    }
+    return { ...choice, name: matchedTool.wireName };
+  }
+  if (projection.tools.length === 0) {
+    if (choice.type === "auto") {
+      return undefined;
+    }
+    if (choice.type === "any") {
+      throw new Error(
+        "Anthropic tool_choice requires a tool, but no tools survived schema conversion",
+      );
+    }
+  }
+  return choice;
+}
+
+/** Maps Claude Code wire names without trusting every direct/custom descriptor. */
+export function resolveOriginalAnthropicToolName(
+  name: string,
+  projection: AnthropicToolProjection | undefined,
+): string {
+  return projection?.tools.find((tool) => tool.wireName === name)?.originalName ?? name;
+}

packages/ai/src/providers/anthropic-usage.test.ts

@@ -0,0 +1,77 @@
+import { describe, expect, it } from "vitest";
+import { readLastAnthropicIterationUsage } from "./anthropic-usage.js";
+
+describe("readLastAnthropicIterationUsage", () => {
+  it.each(["message", "compaction", "advisor_message"])(
+    "reads the final %s iteration as the context snapshot",
+    (type) => {
+      expect(
+        readLastAnthropicIterationUsage({
+          iterations: [
+            {
+              type: "message",
+              input_tokens: 1,
+              output_tokens: 2,
+              cache_read_input_tokens: 3,
+              cache_creation_input_tokens: 4,
+            },
+            {
+              type,
+              input_tokens: 12,
+              output_tokens: 15_104,
+              cache_read_input_tokens: 148_862,
+              cache_creation_input_tokens: 0,
+            },
+          ],
+        }),
+      ).toEqual({
+        state: "valid",
+        usage: {
+          contextPromptTokens: 148_874,
+          totalTokens: 163_978,
+        },
+      });
+    },
+  );
+
+  it("reports absent iterations separately from malformed iterations", () => {
+    expect(readLastAnthropicIterationUsage({ input_tokens: 1 })).toEqual({ state: "absent" });
+  });
+
+  it("does not reuse an earlier iteration when the final iteration is malformed", () => {
+    expect(
+      readLastAnthropicIterationUsage({
+        iterations: [
+          {
+            type: "message",
+            input_tokens: 12,
+            output_tokens: 15_104,
+            cache_read_input_tokens: 148_862,
+            cache_creation_input_tokens: 0,
+          },
+          {
+            type: "message",
+            input_tokens: "malformed",
+            output_tokens: 1,
+            cache_read_input_tokens: 0,
+            cache_creation_input_tokens: 0,
+          },
+        ],
+      }),
+    ).toEqual({ state: "invalid" });
+  });
+
+  it("rejects a final iteration with incomplete cache usage", () => {
+    expect(
+      readLastAnthropicIterationUsage({
+        iterations: [
+          {
+            type: "message",
+            input_tokens: 12,
+            output_tokens: 15_104,
+          },
+        ],
+      }),
+    ).toEqual({ state: "invalid" });
+  });
+});

packages/ai/src/providers/anthropic-usage.ts

@@ -0,0 +1,83 @@
+type AnthropicUsagePayload = {
+  input_tokens?: unknown;
+  output_tokens?: unknown;
+  cache_read_input_tokens?: unknown;
+  cache_creation_input_tokens?: unknown;
+  iterations?: unknown;
+};
+
+export type AnthropicPromptUsageSnapshot = {
+  input: number;
+  cacheRead: number;
+  cacheWrite: number;
+};
+
+export type AnthropicIterationUsageSnapshot = {
+  contextPromptTokens: number;
+  totalTokens: number;
+};
+
+export type AnthropicIterationUsageResult =
+  | { state: "absent" }
+  | { state: "invalid" }
+  | { state: "valid"; usage: AnthropicIterationUsageSnapshot };
+
+export function readAnthropicUsageTokenCount(value: unknown): number | undefined {
+  return typeof value === "number" && Number.isFinite(value) && value >= 0 ? value : undefined;
+}
+
+export function readAnthropicPromptUsageSnapshot(
+  usage: AnthropicUsagePayload,
+): AnthropicPromptUsageSnapshot | undefined {
+  const input = readAnthropicUsageTokenCount(usage.input_tokens);
+  const cacheRead =
+    usage.cache_read_input_tokens == null
+      ? 0
+      : readAnthropicUsageTokenCount(usage.cache_read_input_tokens);
+  const cacheWrite =
+    usage.cache_creation_input_tokens == null
+      ? 0
+      : readAnthropicUsageTokenCount(usage.cache_creation_input_tokens);
+  if (input === undefined || cacheRead === undefined || cacheWrite === undefined) {
+    return undefined;
+  }
+  return { input, cacheRead, cacheWrite };
+}
+
+export function readLastAnthropicIterationUsage(
+  usage: AnthropicUsagePayload,
+): AnthropicIterationUsageResult {
+  if (usage.iterations == null) {
+    return { state: "absent" };
+  }
+  if (!Array.isArray(usage.iterations) || usage.iterations.length === 0) {
+    return { state: "invalid" };
+  }
+  // Anthropic documents the final iteration as the true context window.
+  // Top-level cache fields remain cumulative billing totals across iterations.
+  const iteration = usage.iterations.at(-1);
+  if (!iteration || typeof iteration !== "object" || Array.isArray(iteration)) {
+    return { state: "invalid" };
+  }
+  const record = iteration as AnthropicUsagePayload;
+  const input = readAnthropicUsageTokenCount(record.input_tokens);
+  const cacheRead = readAnthropicUsageTokenCount(record.cache_read_input_tokens);
+  const cacheWrite = readAnthropicUsageTokenCount(record.cache_creation_input_tokens);
+  const outputTokens = readAnthropicUsageTokenCount(record.output_tokens);
+  if (
+    input === undefined ||
+    cacheRead === undefined ||
+    cacheWrite === undefined ||
+    outputTokens === undefined
+  ) {
+    return { state: "invalid" };
+  }
+  const contextPromptTokens = input + cacheRead + cacheWrite;
+  return {
+    state: "valid",
+    usage: {
+      contextPromptTokens,
+      totalTokens: contextPromptTokens + outputTokens,
+    },
+  };
+}

packages/ai/src/providers/azure-deployment-map.test.ts

@@ -0,0 +1,30 @@
+// Azure deployment map tests cover model-to-deployment resolution.
+import { describe, expect, it } from "vitest";
+import {
+  parseAzureDeploymentNameMap,
+  resolveAzureDeploymentNameFromMap,
+} from "./azure-deployment-map.js";
+
+describe("Azure deployment name map", () => {
+  it("preserves equals signs inside deployment names", () => {
+    const map = parseAzureDeploymentNameMap("gpt-5=deployment=blue, ignored, gpt-4 = prod = east ");
+
+    expect(map.get("gpt-5")).toBe("deployment=blue");
+    expect(map.get("gpt-4")).toBe("prod = east");
+    expect(
+      resolveAzureDeploymentNameFromMap({
+        modelId: "gpt-5",
+        deploymentMap: "gpt-5=deployment=blue",
+      }),
+    ).toBe("deployment=blue");
+  });
+
+  it("falls back to the model id when the map has no usable entry", () => {
+    expect(
+      resolveAzureDeploymentNameFromMap({
+        modelId: "gpt-5",
+        deploymentMap: "other=deployment,missing-value=",
+      }),
+    ).toBe("gpt-5");
+  });
+});

packages/ai/src/providers/azure-deployment-map.ts

@@ -0,0 +1,32 @@
+/** Parses AZURE_OPENAI_DEPLOYMENT_MAP-style model=deployment entries. */
+export function parseAzureDeploymentNameMap(value: string | undefined): Map<string, string> {
+  const map = new Map<string, string>();
+  if (!value) {
+    return map;
+  }
+  for (const entry of value.split(",")) {
+    const trimmed = entry.trim();
+    if (!trimmed) {
+      continue;
+    }
+    const separator = trimmed.indexOf("=");
+    if (separator <= 0) {
+      continue;
+    }
+    const modelId = trimmed.slice(0, separator).trim();
+    const deploymentName = trimmed.slice(separator + 1).trim();
+    if (!modelId || !deploymentName) {
+      continue;
+    }
+    map.set(modelId, deploymentName);
+  }
+  return map;
+}
+
+/** Resolves the Azure deployment name for a model id, falling back to the model id. */
+export function resolveAzureDeploymentNameFromMap(params: {
+  modelId: string;
+  deploymentMap?: string;
+}): string {
+  return parseAzureDeploymentNameMap(params.deploymentMap).get(params.modelId) || params.modelId;
+}

packages/ai/src/providers/azure-openai-responses-client-compat.ts

@@ -0,0 +1,29 @@
+export function isTraditionalAzureOpenAIHost(hostname: string): boolean {
+  return (
+    hostname.endsWith(".openai.azure.com") || hostname.endsWith(".cognitiveservices.azure.com")
+  );
+}
+
+export function isOpenAICompatibleAzureResponsesBaseUrl(baseUrl: string): boolean {
+  let url: URL;
+  try {
+    url = new URL(baseUrl);
+  } catch {
+    return false;
+  }
+
+  if (isTraditionalAzureOpenAIHost(url.hostname)) {
+    return false;
+  }
+
+  const hostname = url.hostname.toLowerCase();
+  const isFoundryHost =
+    hostname.endsWith(".services.ai.azure.com") ||
+    hostname.endsWith(".api.cognitive.microsoft.com");
+  if (!isFoundryHost) {
+    return false;
+  }
+
+  const normalizedPath = url.pathname.replace(/\/+$/, "");
+  return normalizedPath === "/openai/v1" || normalizedPath.endsWith("/openai/v1");
+}

packages/ai/src/providers/azure-openai-responses.test.ts

@@ -0,0 +1,73 @@
+import { describe, expect, it } from "vitest";
+import type { Model } from "../types.js";
+import { testing } from "./azure-openai-responses.js";
+
+const azureResponsesModel = {
+  id: "gpt-5.5",
+  name: "GPT-5.5",
+  api: "azure-openai-responses",
+  provider: "azure",
+  baseUrl: "https://example.openai.azure.com/openai/v1",
+  reasoning: true,
+  input: ["text"],
+  cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+  contextWindow: 200000,
+  maxTokens: 8192,
+} satisfies Model<"azure-openai-responses">;
+
+describe("azure-openai-responses", () => {
+  it("keeps traditional Azure OpenAI hosts on the AzureOpenAI client path", () => {
+    const config = testing.resolveAzureConfig(azureResponsesModel, {
+      azureResourceName: "example",
+      azureApiVersion: "v1",
+    });
+
+    expect(config).toEqual({
+      baseUrl: "https://example.openai.azure.com/openai/v1",
+      apiVersion: "v1",
+    });
+    expect(testing.isOpenAICompatibleAzureResponsesBaseUrl(config.baseUrl)).toBe(false);
+    expect(
+      testing.isOpenAICompatibleAzureResponsesBaseUrl(
+        "https://example.cognitiveservices.azure.com/openai/v1",
+      ),
+    ).toBe(false);
+  });
+
+  it("uses the OpenAI-compatible client path for Foundry /openai/v1 endpoints", () => {
+    expect(
+      testing.isOpenAICompatibleAzureResponsesBaseUrl(
+        "https://project.services.ai.azure.com/api/projects/demo/openai/v1",
+      ),
+    ).toBe(true);
+    expect(
+      testing.isOpenAICompatibleAzureResponsesBaseUrl(
+        "https://project.services.ai.azure.com/openai/v1",
+      ),
+    ).toBe(true);
+    expect(
+      testing.isOpenAICompatibleAzureResponsesBaseUrl(
+        "https://eastus.api.cognitive.microsoft.com/openai/v1",
+      ),
+    ).toBe(true);
+  });
+
+  it("does not treat non-v1 custom endpoints as OpenAI-compatible Responses bases", () => {
+    expect(
+      testing.isOpenAICompatibleAzureResponsesBaseUrl(
+        "https://project.services.ai.azure.com/api/projects/demo",
+      ),
+    ).toBe(false);
+  });
+
+  it("keeps private or APIM Azure OpenAI-compatible paths on the AzureOpenAI client path", () => {
+    expect(testing.isOpenAICompatibleAzureResponsesBaseUrl("https://aoai.internal/openai/v1")).toBe(
+      false,
+    );
+    expect(
+      testing.isOpenAICompatibleAzureResponsesBaseUrl(
+        "https://gateway.example.com/proxy/openai/v1",
+      ),
+    ).toBe(false);
+  });
+});

packages/ai/src/providers/azure-openai-responses.ts

@@ -0,0 +1,252 @@
+// Azure OpenAI Responses provider adapts Azure deployments to Responses API streams.
+import OpenAI, { AzureOpenAI } from "openai";
+import type { ResponseCreateParamsStreaming } from "openai/resources/responses/responses.js";
+import { getEnvApiKey } from "../env-api-keys.js";
+import { getAiTransportHost } from "../host.js";
+import type {
+  Context,
+  Model,
+  SimpleStreamOptions,
+  StreamFunction,
+  StreamOptions,
+} from "../types.js";
+import { AssistantMessageEventStream } from "../utils/event-stream.js";
+import { resolveAzureDeploymentNameFromMap } from "./azure-deployment-map.js";
+import { isOpenAICompatibleAzureResponsesBaseUrl } from "./azure-openai-responses-client-compat.js";
+import { clampOpenAIPromptCacheKey } from "./openai-prompt-cache.js";
+import {
+  applyCommonResponsesParams,
+  convertResponsesMessages,
+  createResponsesAssistantOutput,
+  resolveResponsesReasoningEffort,
+  runResponsesStreamLifecycle,
+} from "./openai-responses-shared.js";
+import { buildBaseOptions } from "./simple-options.js";
+
+const DEFAULT_AZURE_API_VERSION = "v1";
+const AZURE_TOOL_CALL_PROVIDERS = new Set(["openai", "opencode", "azure-openai-responses"]);
+
+function resolveDeploymentName(
+  model: Model<"azure-openai-responses">,
+  options?: AzureOpenAIResponsesOptions,
+): string {
+  if (options?.azureDeploymentName) {
+    return options.azureDeploymentName;
+  }
+  return resolveAzureDeploymentNameFromMap({
+    modelId: model.id,
+    deploymentMap: process.env.AZURE_OPENAI_DEPLOYMENT_NAME_MAP,
+  });
+}
+
+function formatAzureOpenAIError(error: unknown): string {
+  if (error instanceof Error) {
+    const status = (error as Error & { status?: unknown }).status;
+    const statusCode = typeof status === "number" ? status : undefined;
+    if (statusCode !== undefined) {
+      return `Azure OpenAI API error (${statusCode}): ${error.message}`;
+    }
+    return error.message;
+  }
+  try {
+    return JSON.stringify(error);
+  } catch {
+    return String(error);
+  }
+}
+
+// Azure OpenAI Responses-specific options
+export interface AzureOpenAIResponsesOptions extends StreamOptions {
+  reasoningEffort?: "minimal" | "low" | "medium" | "high" | "xhigh";
+  reasoningSummary?: "auto" | "detailed" | "concise" | null;
+  azureApiVersion?: string;
+  azureResourceName?: string;
+  azureBaseUrl?: string;
+  azureDeploymentName?: string;
+}
+
+/**
+ * Generate function for Azure OpenAI Responses API
+ */
+export const streamAzureOpenAIResponses: StreamFunction<
+  "azure-openai-responses",
+  AzureOpenAIResponsesOptions
+> = (
+  model: Model<"azure-openai-responses">,
+  context: Context,
+  options?: AzureOpenAIResponsesOptions,
+) => {
+  const stream = new AssistantMessageEventStream();
+  const output = createResponsesAssistantOutput(model, "azure-openai-responses");
+
+  // Start async processing
+  void runResponsesStreamLifecycle({
+    stream,
+    model,
+    output,
+    options,
+    createClient: () => {
+      const apiKey = options?.apiKey || getEnvApiKey(model.provider) || "";
+      return createClient(model, apiKey, options);
+    },
+    buildParams: () => buildParams(model, context, options, resolveDeploymentName(model, options)),
+    formatError: formatAzureOpenAIError,
+  });
+
+  return stream;
+};
+
+export const streamSimpleAzureOpenAIResponses: StreamFunction<
+  "azure-openai-responses",
+  SimpleStreamOptions
+> = (model: Model<"azure-openai-responses">, context: Context, options?: SimpleStreamOptions) => {
+  const apiKey = options?.apiKey || getEnvApiKey(model.provider);
+  if (!apiKey) {
+    throw new Error(`No API key for provider: ${model.provider}`);
+  }
+
+  const base = buildBaseOptions(model, options, apiKey);
+  const reasoningEffort = resolveResponsesReasoningEffort(model, options?.reasoning);
+
+  return streamAzureOpenAIResponses(model, context, {
+    ...base,
+    reasoningEffort: reasoningEffort === "max" ? "xhigh" : reasoningEffort,
+  } satisfies AzureOpenAIResponsesOptions);
+};
+
+function normalizeAzureBaseUrl(baseUrl: string): string {
+  const trimmed = baseUrl.trim().replace(/\/+$/, "");
+  let url: URL;
+  try {
+    url = new URL(trimmed);
+  } catch {
+    throw new Error(`Invalid Azure OpenAI base URL: ${baseUrl}`);
+  }
+
+  const isAzureHost =
+    url.hostname.endsWith(".openai.azure.com") ||
+    url.hostname.endsWith(".cognitiveservices.azure.com");
+  const normalizedPath = url.pathname.replace(/\/+$/, "");
+
+  // Ensure Azure hosts have /openai/v1 as base path so the AzureOpenAI SDK
+  // can append /deployments/<model>/... and ?api-version=v1 correctly.
+  if (
+    isAzureHost &&
+    (normalizedPath === "" || normalizedPath === "/" || normalizedPath === "/openai")
+  ) {
+    url.pathname = "/openai/v1";
+    url.search = "";
+  }
+
+  return url.toString().replace(/\/+$/, "");
+}
+
+function buildDefaultBaseUrl(resourceName: string): string {
+  return `https://${resourceName}.openai.azure.com/openai/v1`;
+}
+
+function resolveAzureConfig(
+  model: Model<"azure-openai-responses">,
+  options?: AzureOpenAIResponsesOptions,
+): { baseUrl: string; apiVersion: string } {
+  const apiVersion =
+    options?.azureApiVersion || process.env.AZURE_OPENAI_API_VERSION || DEFAULT_AZURE_API_VERSION;
+
+  const baseUrl =
+    options?.azureBaseUrl?.trim() || process.env.AZURE_OPENAI_BASE_URL?.trim() || undefined;
+  const resourceName = options?.azureResourceName || process.env.AZURE_OPENAI_RESOURCE_NAME;
+
+  let resolvedBaseUrl = baseUrl;
+
+  if (!resolvedBaseUrl && resourceName) {
+    resolvedBaseUrl = buildDefaultBaseUrl(resourceName);
+  }
+
+  if (!resolvedBaseUrl && model.baseUrl) {
+    resolvedBaseUrl = model.baseUrl;
+  }
+
+  if (!resolvedBaseUrl) {
+    throw new Error(
+      "Azure OpenAI base URL is required. Set AZURE_OPENAI_BASE_URL or AZURE_OPENAI_RESOURCE_NAME, or pass azureBaseUrl, azureResourceName, or model.baseUrl.",
+    );
+  }
+
+  return {
+    baseUrl: normalizeAzureBaseUrl(resolvedBaseUrl),
+    apiVersion,
+  };
+}
+
+function createClient(
+  model: Model<"azure-openai-responses">,
+  apiKeyInput: string,
+  options?: AzureOpenAIResponsesOptions,
+) {
+  let apiKey = apiKeyInput;
+  if (!apiKey) {
+    if (!process.env.AZURE_OPENAI_API_KEY) {
+      throw new Error(
+        "Azure OpenAI API key is required. Set AZURE_OPENAI_API_KEY environment variable or pass it as an argument.",
+      );
+    }
+    apiKey = process.env.AZURE_OPENAI_API_KEY;
+  }
+
+  const headers = { ...model.headers };
+
+  if (options?.headers) {
+    Object.assign(headers, options.headers);
+  }
+
+  const { baseUrl, apiVersion } = resolveAzureConfig(model, options);
+  const guardedFetch = getAiTransportHost().buildModelFetch({ ...model, baseUrl });
+
+  if (isOpenAICompatibleAzureResponsesBaseUrl(baseUrl)) {
+    return new OpenAI({
+      apiKey,
+      dangerouslyAllowBrowser: true,
+      defaultHeaders: headers,
+      baseURL: baseUrl,
+      fetch: guardedFetch,
+    });
+  }
+
+  return new AzureOpenAI({
+    apiKey,
+    apiVersion,
+    dangerouslyAllowBrowser: true,
+    defaultHeaders: headers,
+    baseURL: baseUrl,
+    fetch: guardedFetch,
+  });
+}
+
+function buildParams(
+  model: Model<"azure-openai-responses">,
+  context: Context,
+  options: AzureOpenAIResponsesOptions | undefined,
+  deploymentName: string,
+) {
+  const messages = convertResponsesMessages(model, context, AZURE_TOOL_CALL_PROVIDERS);
+
+  const params: ResponseCreateParamsStreaming = {
+    model: deploymentName,
+    input: messages,
+    stream: true,
+    prompt_cache_key:
+      options?.cacheRetention === "none"
+        ? undefined
+        : clampOpenAIPromptCacheKey(options?.promptCacheKey ?? options?.sessionId),
+  };
+
+  applyCommonResponsesParams(params, model, context, options);
+
+  return params;
+}
+
+export const testing = {
+  isOpenAICompatibleAzureResponsesBaseUrl,
+  normalizeAzureBaseUrl,
+  resolveAzureConfig,
+};

packages/ai/src/providers/cache-retention.ts

@@ -0,0 +1,15 @@
+import type { CacheRetention } from "../types.js";
+
+/**
+ * Resolve cache retention preference.
+ * Defaults to "short" and uses OPENCLAW_CACHE_RETENTION for backward compatibility.
+ */
+export function resolveCacheRetention(cacheRetention?: CacheRetention): CacheRetention {
+  if (cacheRetention) {
+    return cacheRetention;
+  }
+  if (typeof process !== "undefined" && process.env.OPENCLAW_CACHE_RETENTION === "long") {
+    return "long";
+  }
+  return "short";
+}

packages/ai/src/providers/clean-for-gemini.test.ts

@@ -0,0 +1,228 @@
+// Gemini schema cleaner tests cover OpenAPI-compatible tool schema cleanup for
+// Gemini-backed providers before schemas are sent upstream.
+import { describe, expect, it } from "vitest";
+import { cleanSchemaForGemini } from "./clean-for-gemini.js";
+
+describe("cleanSchemaForGemini", () => {
+  it("coerces null properties to an empty object", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: null,
+    }) as { type?: unknown; properties?: unknown };
+
+    expect(cleaned.type).toBe("object");
+    expect(cleaned.properties).toStrictEqual({});
+  });
+
+  it("coerces non-object properties to an empty object", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: "invalid",
+    }) as { properties?: unknown };
+
+    expect(cleaned.properties).toStrictEqual({});
+  });
+
+  it("coerces array properties to an empty object", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: [],
+    }) as { properties?: unknown };
+
+    expect(cleaned.properties).toStrictEqual({});
+  });
+
+  it("filters required fields that are not in properties", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        action: { type: "string" },
+        amount: { type: "number" },
+      },
+      required: ["action", "amount", "token"],
+    }) as { required?: string[] };
+
+    expect(cleaned.required).toEqual(["action", "amount"]);
+  });
+
+  it("preserves required when all fields exist in properties", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        action: { type: "string" },
+        amount: { type: "number" },
+      },
+      required: ["action", "amount"],
+    }) as { required?: string[] };
+
+    expect(cleaned.required).toEqual(["action", "amount"]);
+  });
+
+  it("removes required entirely when no fields match properties", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        action: { type: "string" },
+      },
+      required: ["missing_a", "missing_b"],
+    }) as { required?: string[] };
+
+    expect(cleaned.required).toBeUndefined();
+  });
+
+  it("removes required from object schemas when properties is absent", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      required: ["a", "b"],
+    }) as { required?: string[] };
+
+    expect(cleaned.required).toBeUndefined();
+  });
+
+  it("leaves required as-is for non-object schemas when properties is absent", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "array",
+      required: ["a", "b"],
+    }) as { required?: string[] };
+
+    expect(cleaned.required).toEqual(["a", "b"]);
+  });
+
+  it("filters required in nested object properties", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        config: {
+          type: "object",
+          properties: {
+            name: { type: "string" },
+          },
+          required: ["name", "ghost"],
+        },
+      },
+    }) as { properties?: { config?: { required?: string[] } } };
+
+    expect(cleaned.properties?.config?.required).toEqual(["name"]);
+  });
+
+  it("does not treat inherited keys as declared properties", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        name: { type: "string" },
+      },
+      required: ["toString", "name"],
+    }) as { required?: string[] };
+
+    expect(cleaned.required).toEqual(["name"]);
+  });
+
+  it("coerces nested null properties while preserving valid siblings", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        bad: {
+          type: "object",
+          properties: null,
+        },
+        good: {
+          type: "string",
+        },
+      },
+    }) as {
+      properties?: {
+        bad?: { properties?: unknown };
+        good?: { type?: unknown };
+      };
+    };
+
+    expect(cleaned.properties?.bad?.properties).toStrictEqual({});
+    expect(cleaned.properties?.good?.type).toBe("string");
+  });
+
+  it("strips empty required arrays", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        name: { type: "string" },
+      },
+      required: [],
+    }) as Record<string, unknown>;
+
+    expect(cleaned).not.toHaveProperty("required");
+    expect(cleaned.type).toBe("object");
+  });
+
+  it("preserves non-empty required arrays", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        name: { type: "string" },
+      },
+      required: ["name"],
+    }) as Record<string, unknown>;
+
+    expect(cleaned.required).toEqual(["name"]);
+  });
+
+  it("strips empty required arrays in nested schemas", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        nested: {
+          type: "object",
+          properties: {
+            optional: { type: "string" },
+          },
+          required: [],
+        },
+      },
+      required: ["nested"],
+    }) as { properties?: { nested?: Record<string, unknown> }; required?: string[] };
+
+    expect(cleaned.required).toEqual(["nested"]);
+    expect(cleaned.properties?.nested).not.toHaveProperty("required");
+  });
+
+  it("strips the not keyword from schemas", () => {
+    // `not` is outside the OpenAPI 3.0 subset accepted by Gemini-backed
+    // providers and triggers upstream HTTP 400s if left in tool schemas.
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      not: { const: true },
+      properties: {
+        name: { type: "string" },
+      },
+    }) as Record<string, unknown>;
+
+    expect(cleaned).not.toHaveProperty("not");
+    expect(cleaned.type).toBe("object");
+    expect(cleaned.properties).toEqual({ name: { type: "string" } });
+  });
+
+  it("collapses type arrays by stripping null entries", () => {
+    // Type arrays like ["string", "null"] must collapse to a scalar OpenAPI
+    // type for Gemini compatibility.
+    const cleaned = cleanSchemaForGemini({
+      type: ["string", "null"],
+      description: "nullable field",
+    }) as Record<string, unknown>;
+
+    expect(cleaned.type).toBe("string");
+    expect(cleaned.description).toBe("nullable field");
+  });
+
+  it("collapses type arrays in nested property schemas", () => {
+    const cleaned = cleanSchemaForGemini({
+      type: "object",
+      properties: {
+        agentId: {
+          type: ["string", "null"],
+          description: "Agent id",
+        },
+      },
+    }) as { properties?: { agentId?: Record<string, unknown> } };
+
+    expect(cleaned.properties?.agentId?.type).toBe("string");
+  });
+});

packages/ai/src/providers/clean-for-gemini.ts

@@ -0,0 +1,458 @@
+// Cloud Code Assist API rejects a subset of JSON Schema keywords.
+// This module scrubs/normalizes tool schemas to keep Gemini happy.
+
+import type { TSchema } from "typebox";
+
+// Keywords that Cloud Code Assist API rejects (not compliant with their JSON Schema subset)
+export const GEMINI_UNSUPPORTED_SCHEMA_KEYWORDS = new Set([
+  "patternProperties",
+  "additionalProperties",
+  "$schema",
+  "$id",
+  "$ref",
+  "$defs",
+  "definitions",
+  // Non-standard (OpenAPI) keyword; Claude validators reject it.
+  "examples",
+
+  // Cloud Code Assist appears to validate tool schemas more strictly/quirkily than
+  // draft 2020-12 in practice; these constraints frequently trigger 400s.
+  "minLength",
+  "maxLength",
+  "minimum",
+  "maximum",
+  "multipleOf",
+  "pattern",
+  "format",
+  "minItems",
+  "maxItems",
+  "uniqueItems",
+  "minProperties",
+  "maxProperties",
+
+  // JSON Schema composition keywords not supported by OpenAPI 3.0 subset.
+  // `const` is handled separately (converted to enum) in the cleaning loop,
+  // but `not` has no safe equivalent and must be stripped.
+  "not",
+]);
+
+const SCHEMA_META_KEYS = ["description", "title", "default"] as const;
+
+function copySchemaMeta(from: Record<string, unknown>, to: Record<string, unknown>): void {
+  for (const key of SCHEMA_META_KEYS) {
+    if (key in from && from[key] !== undefined) {
+      to[key] = from[key];
+    }
+  }
+}
+
+// Check if an anyOf/oneOf array contains only literal values that can be flattened.
+// TypeBox Type.Literal generates { const: "value", type: "string" }.
+// Some schemas may use { enum: ["value"], type: "string" }.
+// Both patterns are flattened to { type: "string", enum: ["a", "b", ...] }.
+function tryFlattenLiteralAnyOf(variants: unknown[]): { type: string; enum: unknown[] } | null {
+  if (variants.length === 0) {
+    return null;
+  }
+
+  const allValues: unknown[] = [];
+  let commonType: string | null = null;
+
+  for (const variant of variants) {
+    if (!variant || typeof variant !== "object") {
+      return null;
+    }
+    const v = variant as Record<string, unknown>;
+
+    let literalValue: unknown;
+    if ("const" in v) {
+      literalValue = v.const;
+    } else if (Array.isArray(v.enum) && v.enum.length === 1) {
+      literalValue = v.enum[0];
+    } else {
+      return null;
+    }
+
+    const variantType = typeof v.type === "string" ? v.type : null;
+    if (!variantType) {
+      return null;
+    }
+    if (commonType === null) {
+      commonType = variantType;
+    } else if (commonType !== variantType) {
+      return null;
+    }
+
+    allValues.push(literalValue);
+  }
+
+  if (commonType && allValues.length > 0) {
+    return { type: commonType, enum: allValues };
+  }
+  return null;
+}
+
+function isNullSchema(variant: unknown): boolean {
+  if (!variant || typeof variant !== "object" || Array.isArray(variant)) {
+    return false;
+  }
+  const record = variant as Record<string, unknown>;
+  if ("const" in record && record.const === null) {
+    return true;
+  }
+  if (Array.isArray(record.enum) && record.enum.length === 1) {
+    return record.enum[0] === null;
+  }
+  const typeValue = record.type;
+  if (typeValue === "null") {
+    return true;
+  }
+  if (Array.isArray(typeValue) && typeValue.length === 1 && typeValue[0] === "null") {
+    return true;
+  }
+  return false;
+}
+
+function stripNullVariants(variants: unknown[]): {
+  variants: unknown[];
+  stripped: boolean;
+} {
+  if (variants.length === 0) {
+    return { variants, stripped: false };
+  }
+  const nonNull = variants.filter((variant) => !isNullSchema(variant));
+  return {
+    variants: nonNull,
+    stripped: nonNull.length !== variants.length,
+  };
+}
+
+type SchemaDefs = Map<string, unknown>;
+
+function extendSchemaDefs(
+  defs: SchemaDefs | undefined,
+  schema: Record<string, unknown>,
+): SchemaDefs | undefined {
+  const defsEntry =
+    schema.$defs && typeof schema.$defs === "object" && !Array.isArray(schema.$defs)
+      ? (schema.$defs as Record<string, unknown>)
+      : undefined;
+  const legacyDefsEntry =
+    schema.definitions &&
+    typeof schema.definitions === "object" &&
+    !Array.isArray(schema.definitions)
+      ? (schema.definitions as Record<string, unknown>)
+      : undefined;
+
+  if (!defsEntry && !legacyDefsEntry) {
+    return defs;
+  }
+
+  const next = defs ? new Map(defs) : new Map<string, unknown>();
+  if (defsEntry) {
+    for (const [key, value] of Object.entries(defsEntry)) {
+      next.set(key, value);
+    }
+  }
+  if (legacyDefsEntry) {
+    for (const [key, value] of Object.entries(legacyDefsEntry)) {
+      next.set(key, value);
+    }
+  }
+  return next;
+}
+
+function decodeJsonPointerSegment(segment: string): string {
+  return segment.replaceAll("~1", "/").replaceAll("~0", "~");
+}
+
+function tryResolveLocalRef(ref: string, defs: SchemaDefs | undefined): unknown {
+  if (!defs) {
+    return undefined;
+  }
+  const match = ref.match(/^#\/(?:\$defs|definitions)\/(.+)$/);
+  if (!match) {
+    return undefined;
+  }
+  const name = decodeJsonPointerSegment(match[1] ?? "");
+  if (!name) {
+    return undefined;
+  }
+  return defs.get(name);
+}
+
+function simplifyUnionVariants(params: { obj: Record<string, unknown>; variants: unknown[] }): {
+  variants: unknown[];
+  simplified?: unknown;
+} {
+  const { obj, variants } = params;
+
+  const { variants: nonNullVariants, stripped } = stripNullVariants(variants);
+
+  const flattened = tryFlattenLiteralAnyOf(nonNullVariants);
+  if (flattened) {
+    const result: Record<string, unknown> = {
+      type: flattened.type,
+      enum: flattened.enum,
+    };
+    copySchemaMeta(obj, result);
+    return { variants: nonNullVariants, simplified: result };
+  }
+
+  if (stripped && nonNullVariants.length === 1) {
+    const lone = nonNullVariants[0];
+    if (lone && typeof lone === "object" && !Array.isArray(lone)) {
+      const result: Record<string, unknown> = {
+        ...(lone as Record<string, unknown>),
+      };
+      copySchemaMeta(obj, result);
+      return { variants: nonNullVariants, simplified: result };
+    }
+    return { variants: nonNullVariants, simplified: lone };
+  }
+
+  return { variants: stripped ? nonNullVariants : variants };
+}
+
+// Gemini rejects object schemas whose `required` entries do not exist in `properties`.
+function sanitizeRequiredFields(schema: Record<string, unknown>): Record<string, unknown> {
+  if (!Array.isArray(schema.required)) {
+    return schema;
+  }
+
+  if (
+    !schema.properties ||
+    typeof schema.properties !== "object" ||
+    Array.isArray(schema.properties)
+  ) {
+    if (schema.type === "object") {
+      delete schema.required;
+    }
+    return schema;
+  }
+
+  const properties = schema.properties as Record<string, unknown>;
+  const required = schema.required.filter(
+    (key): key is string => typeof key === "string" && Object.hasOwn(properties, key),
+  );
+
+  if (required.length > 0) {
+    schema.required = required;
+  } else {
+    delete schema.required;
+  }
+
+  return schema;
+}
+
+function cleanSchemaForGeminiWithDefs(
+  schema: unknown,
+  defs: SchemaDefs | undefined,
+  refStack: Set<string> | undefined,
+): unknown {
+  if (!schema || typeof schema !== "object") {
+    return schema;
+  }
+  if (Array.isArray(schema)) {
+    return schema.map((item) => cleanSchemaForGeminiWithDefs(item, defs, refStack));
+  }
+
+  const obj = schema as Record<string, unknown>;
+  const nextDefs = extendSchemaDefs(defs, obj);
+
+  const refValue = typeof obj.$ref === "string" ? obj.$ref : undefined;
+  if (refValue) {
+    if (refStack?.has(refValue)) {
+      return {};
+    }
+
+    const resolved = tryResolveLocalRef(refValue, nextDefs);
+    if (resolved) {
+      const nextRefStack = refStack ? new Set(refStack) : new Set<string>();
+      nextRefStack.add(refValue);
+
+      const cleaned = cleanSchemaForGeminiWithDefs(resolved, nextDefs, nextRefStack);
+      if (!cleaned || typeof cleaned !== "object" || Array.isArray(cleaned)) {
+        return cleaned;
+      }
+
+      const result: Record<string, unknown> = {
+        ...(cleaned as Record<string, unknown>),
+      };
+      copySchemaMeta(obj, result);
+      return result;
+    }
+
+    const result: Record<string, unknown> = {};
+    copySchemaMeta(obj, result);
+    return result;
+  }
+
+  const hasAnyOf = "anyOf" in obj && Array.isArray(obj.anyOf);
+  const hasOneOf = "oneOf" in obj && Array.isArray(obj.oneOf);
+  let cleanedAnyOf = hasAnyOf
+    ? (obj.anyOf as unknown[]).map((variant) =>
+        cleanSchemaForGeminiWithDefs(variant, nextDefs, refStack),
+      )
+    : undefined;
+  let cleanedOneOf = hasOneOf
+    ? (obj.oneOf as unknown[]).map((variant) =>
+        cleanSchemaForGeminiWithDefs(variant, nextDefs, refStack),
+      )
+    : undefined;
+
+  if (hasAnyOf) {
+    const simplified = simplifyUnionVariants({ obj, variants: cleanedAnyOf ?? [] });
+    cleanedAnyOf = simplified.variants;
+    if ("simplified" in simplified) {
+      return simplified.simplified;
+    }
+  }
+
+  if (hasOneOf) {
+    const simplified = simplifyUnionVariants({ obj, variants: cleanedOneOf ?? [] });
+    cleanedOneOf = simplified.variants;
+    if ("simplified" in simplified) {
+      return simplified.simplified;
+    }
+  }
+
+  const cleaned: Record<string, unknown> = {};
+
+  for (const [key, value] of Object.entries(obj)) {
+    if (GEMINI_UNSUPPORTED_SCHEMA_KEYWORDS.has(key)) {
+      continue;
+    }
+
+    if (key === "const") {
+      cleaned.enum = [value];
+      continue;
+    }
+
+    // Google's schema validator rejects `"required": []` — omit empty arrays.
+    if (key === "required" && Array.isArray(value) && value.length === 0) {
+      continue;
+    }
+
+    if (key === "type" && (hasAnyOf || hasOneOf)) {
+      continue;
+    }
+    if (
+      key === "type" &&
+      Array.isArray(value) &&
+      value.every((entry) => typeof entry === "string")
+    ) {
+      const types = value.filter((entry) => entry !== "null");
+      cleaned.type = types.length === 1 ? types[0] : types;
+      continue;
+    }
+
+    if (key === "properties") {
+      if (value && typeof value === "object" && !Array.isArray(value)) {
+        const props = value as Record<string, unknown>;
+        cleaned[key] = Object.fromEntries(
+          Object.entries(props).map(([k, v]) => [
+            k,
+            cleanSchemaForGeminiWithDefs(v, nextDefs, refStack),
+          ]),
+        );
+      } else {
+        // Guard malformed schemas (e.g. properties: null) that can trigger
+        // downstream Object.* crashes in strict provider validators.
+        cleaned[key] = {};
+      }
+    } else if (key === "items" && value) {
+      if (Array.isArray(value)) {
+        cleaned[key] = value.map((entry) =>
+          cleanSchemaForGeminiWithDefs(entry, nextDefs, refStack),
+        );
+      } else if (typeof value === "object") {
+        cleaned[key] = cleanSchemaForGeminiWithDefs(value, nextDefs, refStack);
+      } else {
+        cleaned[key] = value;
+      }
+    } else if (key === "anyOf" && Array.isArray(value)) {
+      cleaned[key] =
+        cleanedAnyOf ??
+        value.map((variant) => cleanSchemaForGeminiWithDefs(variant, nextDefs, refStack));
+    } else if (key === "oneOf" && Array.isArray(value)) {
+      cleaned[key] =
+        cleanedOneOf ??
+        value.map((variant) => cleanSchemaForGeminiWithDefs(variant, nextDefs, refStack));
+    } else if (key === "allOf" && Array.isArray(value)) {
+      cleaned[key] = value.map((variant) =>
+        cleanSchemaForGeminiWithDefs(variant, nextDefs, refStack),
+      );
+    } else {
+      cleaned[key] = value;
+    }
+  }
+
+  // Cloud Code Assist API rejects anyOf/oneOf in nested schemas even after
+  // simplifyUnionVariants runs above. Flatten remaining unions as a fallback:
+  // pick the common type or use the first variant's type so the tool
+  // declaration is accepted by Google's validation layer.
+  if (cleaned.anyOf && Array.isArray(cleaned.anyOf)) {
+    const flattened = flattenUnionFallback(cleaned, cleaned.anyOf);
+    if (flattened) {
+      return sanitizeRequiredFields(flattened);
+    }
+  }
+  if (cleaned.oneOf && Array.isArray(cleaned.oneOf)) {
+    const flattened = flattenUnionFallback(cleaned, cleaned.oneOf);
+    if (flattened) {
+      return sanitizeRequiredFields(flattened);
+    }
+  }
+
+  return sanitizeRequiredFields(cleaned);
+}
+
+/**
+ * Last-resort flattening for anyOf/oneOf arrays that could not be simplified
+ * by `simplifyUnionVariants`. Picks a representative type so the schema is
+ * accepted by Google's restricted JSON Schema validation.
+ */
+function flattenUnionFallback(
+  obj: Record<string, unknown>,
+  variants: unknown[],
+): Record<string, unknown> | undefined {
+  const objects = variants.filter(
+    (v): v is Record<string, unknown> => Boolean(v) && typeof v === "object",
+  );
+  if (objects.length === 0) {
+    return undefined;
+  }
+  const types = new Set(objects.map((v) => v.type).filter(Boolean));
+  if (objects.length === 1) {
+    const merged: Record<string, unknown> = { ...objects[0] };
+    copySchemaMeta(obj, merged);
+    return merged;
+  }
+  if (types.size === 1) {
+    const merged: Record<string, unknown> = { type: Array.from(types)[0] };
+    copySchemaMeta(obj, merged);
+    return merged;
+  }
+  const first = objects[0];
+  if (first?.type) {
+    const merged: Record<string, unknown> = { type: first.type };
+    copySchemaMeta(obj, merged);
+    return merged;
+  }
+  const merged: Record<string, unknown> = {};
+  copySchemaMeta(obj, merged);
+  return merged;
+}
+
+export function cleanSchemaForGemini(schema: unknown): TSchema {
+  if (!schema || typeof schema !== "object") {
+    return schema as TSchema;
+  }
+  if (Array.isArray(schema)) {
+    return schema.map(cleanSchemaForGemini) as TSchema;
+  }
+
+  const defs = extendSchemaDefs(undefined, schema as Record<string, unknown>);
+  return cleanSchemaForGeminiWithDefs(schema, defs, undefined) as TSchema;
+}

packages/ai/src/providers/cloudflare.ts

@@ -0,0 +1,22 @@
+// Cloudflare provider metadata describes Cloudflare-hosted model capabilities.
+import type { Model } from "../types.js";
+
+export function isCloudflareProvider(provider: string): boolean {
+  return provider === "cloudflare-workers-ai" || provider === "cloudflare-ai-gateway";
+}
+
+/** Substitute `{VAR}` placeholders in a Cloudflare baseUrl from process.env. */
+export function resolveCloudflareBaseUrl(model: Model): string {
+  const url = model.baseUrl;
+  if (!url.includes("{")) {
+    return url;
+  }
+  const baseUrl = url.replace(/\{([A-Z_][A-Z0-9_]*)\}/g, (_match, name: string) => {
+    const value = process.env[name];
+    if (!value) {
+      throw new Error(`${name} is required for provider ${model.provider} but is not set.`);
+    }
+    return value;
+  });
+  return baseUrl;
+}

packages/ai/src/providers/github-copilot-headers.ts

@@ -0,0 +1,38 @@
+// GitHub Copilot header helpers build request headers for Copilot-backed providers.
+import type { Message } from "../types.js";
+
+// Copilot expects X-Initiator to indicate whether the request is user-initiated
+// or agent-initiated (e.g. follow-up after assistant/tool messages).
+export function inferCopilotInitiator(messages: Message[]): "user" | "agent" {
+  const last = messages[messages.length - 1];
+  return last && last.role !== "user" ? "agent" : "user";
+}
+
+// Copilot requires Copilot-Vision-Request header when sending images
+export function hasCopilotVisionInput(messages: Message[]): boolean {
+  return messages.some((msg) => {
+    if (msg.role === "user" && Array.isArray(msg.content)) {
+      return msg.content.some((c) => c.type === "image");
+    }
+    if (msg.role === "toolResult" && Array.isArray(msg.content)) {
+      return msg.content.some((c) => c.type === "image");
+    }
+    return false;
+  });
+}
+
+export function buildCopilotDynamicHeaders(params: {
+  messages: Message[];
+  hasImages: boolean;
+}): Record<string, string> {
+  const headers: Record<string, string> = {
+    "X-Initiator": inferCopilotInitiator(params.messages),
+    "Openai-Intent": "conversation-edits",
+  };
+
+  if (params.hasImages) {
+    headers["Copilot-Vision-Request"] = "true";
+  }
+
+  return headers;
+}

packages/ai/src/providers/google-shared.convert.test.ts

@@ -0,0 +1,403 @@
+// Google shared conversion tests cover runtime-to-Google payload conversion.
+import { describe, expect, it } from "vitest";
+import type { Context, Tool } from "../types.js";
+import { convertMessages, convertTools } from "./google-shared.js";
+import {
+  asRecord,
+  expectConvertedRoles,
+  getFirstToolParameters,
+  makeGeminiCliAssistantMessage,
+  makeGeminiCliModel,
+  makeGoogleAssistantMessage,
+  makeModel,
+} from "./google-shared.test-helpers.js";
+
+type GoogleSharedTestModel = ReturnType<typeof makeModel> | ReturnType<typeof makeGeminiCliModel>;
+const convertMessagesForTest = convertMessages as unknown as (
+  model: GoogleSharedTestModel,
+  context: Context,
+) => ReturnType<typeof convertMessages>;
+
+function requireRecordProperty(
+  record: Record<string, unknown>,
+  key: string,
+): Record<string, unknown> {
+  const value = record[key];
+  if (!value || typeof value !== "object" || Array.isArray(value)) {
+    throw new Error(`expected object property ${key}`);
+  }
+  return value as Record<string, unknown>;
+}
+
+describe("google-shared convertTools", () => {
+  it("preserves parameters when type is missing", () => {
+    const tools = [
+      {
+        name: "noType",
+        description: "Tool with properties but no type",
+        parameters: {
+          properties: {
+            action: { type: "string" },
+          },
+          required: ["action"],
+        },
+      },
+    ] as unknown as Tool[];
+
+    const converted = convertTools(tools);
+    const params = getFirstToolParameters(
+      converted as Parameters<typeof getFirstToolParameters>[0],
+    );
+
+    expect(params.type).toBeUndefined();
+    expect(params.properties).toEqual({
+      action: { type: "string" },
+    });
+    expect(params.required).toEqual(["action"]);
+  });
+
+  it("keeps unsupported JSON Schema keywords intact", () => {
+    const tools = [
+      {
+        name: "example",
+        description: "Example tool",
+        parameters: {
+          type: "object",
+          patternProperties: {
+            "^x-": { type: "string" },
+          },
+          additionalProperties: false,
+          properties: {
+            mode: {
+              type: "string",
+              const: "fast",
+            },
+            options: {
+              anyOf: [{ type: "string" }, { type: "number" }],
+            },
+            list: {
+              type: "array",
+              items: {
+                type: "string",
+                const: "item",
+              },
+            },
+          },
+          required: ["mode"],
+        },
+      },
+    ] as unknown as Tool[];
+
+    const converted = convertTools(tools);
+    const params = getFirstToolParameters(
+      converted as Parameters<typeof getFirstToolParameters>[0],
+    );
+    const properties = asRecord(params.properties);
+    const mode = asRecord(properties.mode);
+    const options = asRecord(properties.options);
+    const list = asRecord(properties.list);
+    const items = asRecord(list.items);
+
+    expect(params.patternProperties).toEqual({ "^x-": { type: "string" } });
+    expect(params.additionalProperties).toBe(false);
+    expect(mode.const).toBe("fast");
+    expect(options.anyOf).toEqual([{ type: "string" }, { type: "number" }]);
+    expect(items.const).toBe("item");
+    expect(params.required).toEqual(["mode"]);
+  });
+
+  it("keeps supported schema fields", () => {
+    const tools = [
+      {
+        name: "settings",
+        description: "Settings tool",
+        parameters: {
+          type: "object",
+          properties: {
+            config: {
+              type: "object",
+              properties: {
+                retries: { type: "number", minimum: 1 },
+                tags: {
+                  type: "array",
+                  items: { type: "string" },
+                },
+              },
+              required: ["retries"],
+            },
+          },
+          required: ["config"],
+        },
+      },
+    ] as unknown as Tool[];
+
+    const converted = convertTools(tools);
+    const params = getFirstToolParameters(
+      converted as Parameters<typeof getFirstToolParameters>[0],
+    );
+    const config = asRecord(asRecord(params.properties).config);
+    const configProps = asRecord(config.properties);
+    const retries = asRecord(configProps.retries);
+    const tags = asRecord(configProps.tags);
+    const items = asRecord(tags.items);
+
+    expect(params.type).toBe("object");
+    expect(config.type).toBe("object");
+    expect(retries.minimum).toBe(1);
+    expect(tags.type).toBe("array");
+    expect(items.type).toBe("string");
+    expect(config.required).toEqual(["retries"]);
+    expect(params.required).toEqual(["config"]);
+  });
+});
+
+describe("google-shared convertMessages", () => {
+  function expectConsecutiveMessagesNotMerged(params: {
+    modelId: string;
+    first: string;
+    second: string;
+  }) {
+    const model = makeModel(params.modelId);
+    const context = {
+      messages: [
+        {
+          role: "user",
+          content: params.first,
+        },
+        {
+          role: "user",
+          content: params.second,
+        },
+      ],
+    } as unknown as Context;
+
+    const contents = convertMessagesForTest(model, context);
+    expect(contents).toHaveLength(2);
+    expect(contents[0].role).toBe("user");
+    expect(contents[1].role).toBe("user");
+    expect(contents[0].parts).toHaveLength(1);
+    expect(contents[1].parts).toHaveLength(1);
+  }
+
+  it("keeps thinking blocks when provider/model match", () => {
+    const model = makeModel("gemini-1.5-pro");
+    const context = {
+      messages: [
+        makeGoogleAssistantMessage(model.id, [
+          {
+            type: "thinking",
+            thinking: "hidden",
+            thinkingSignature: "c2ln",
+          },
+        ]),
+      ],
+    } as unknown as Context;
+
+    const contents = convertMessagesForTest(model, context);
+    expect(contents).toHaveLength(1);
+    expect(contents[0].role).toBe("model");
+    const part = asRecord(contents[0].parts?.[0]);
+    expect(part.thought).toBe(true);
+    expect(part.thoughtSignature).toBe("c2ln");
+  });
+
+  it("keeps thought signatures for Claude models", () => {
+    const model = makeModel("claude-3-opus");
+    const context = {
+      messages: [
+        makeGoogleAssistantMessage(model.id, [
+          {
+            type: "thinking",
+            thinking: "structured",
+            thinkingSignature: "c2ln",
+          },
+        ]),
+      ],
+    } as unknown as Context;
+
+    const contents = convertMessagesForTest(model, context);
+    const parts = contents?.[0]?.parts ?? [];
+    expect(parts).toHaveLength(1);
+    const part = asRecord(parts[0]);
+    expect(part.thought).toBe(true);
+    expect(part.thoughtSignature).toBe("c2ln");
+  });
+
+  it("does not merge consecutive user messages for Gemini", () => {
+    expectConsecutiveMessagesNotMerged({
+      modelId: "gemini-1.5-pro",
+      first: "Hello",
+      second: "How are you?",
+    });
+  });
+
+  it("does not merge consecutive user messages for non-Gemini Google models", () => {
+    expectConsecutiveMessagesNotMerged({
+      modelId: "claude-3-opus",
+      first: "First",
+      second: "Second",
+    });
+  });
+
+  it("does not merge consecutive model messages for Gemini", () => {
+    const model = makeModel("gemini-1.5-pro");
+    const context = {
+      messages: [
+        {
+          role: "user",
+          content: "Hello",
+        },
+        makeGoogleAssistantMessage(model.id, [{ type: "text", text: "Hi there!" }]),
+        makeGoogleAssistantMessage(model.id, [{ type: "text", text: "How can I help?" }]),
+      ],
+    } as unknown as Context;
+
+    const contents = convertMessagesForTest(model, context);
+    expectConvertedRoles(contents, ["user", "model", "model"]);
+    expect(contents[1].parts).toHaveLength(1);
+    expect(contents[2].parts).toHaveLength(1);
+  });
+
+  it("handles user message after tool result without model response in between", () => {
+    const model = makeModel("gemini-1.5-pro");
+    const context = {
+      messages: [
+        {
+          role: "user",
+          content: "Use a tool",
+        },
+        makeGoogleAssistantMessage(model.id, [
+          {
+            type: "toolCall",
+            id: "call_1",
+            name: "myTool",
+            arguments: { arg: "value" },
+          },
+        ]),
+        {
+          role: "toolResult",
+          toolCallId: "call_1",
+          toolName: "myTool",
+          content: [{ type: "text", text: "Tool result" }],
+          isError: false,
+          timestamp: 0,
+        },
+        {
+          role: "user",
+          content: "Now do something else",
+        },
+      ],
+    } as unknown as Context;
+
+    const contents = convertMessagesForTest(model, context);
+    expect(contents).toHaveLength(4);
+    expect(contents[0].role).toBe("user");
+    expect(contents[1].role).toBe("model");
+    expect(contents[2].role).toBe("user");
+    expect(contents[3].role).toBe("user");
+    const toolResponsePart = contents[2].parts?.find(
+      (part) => typeof part === "object" && part !== null && "functionResponse" in part,
+    );
+    const toolResponse = asRecord(toolResponsePart);
+    expect(requireRecordProperty(toolResponse, "functionResponse").name).toBe("myTool");
+    expect(contents[3].role).toBe("user");
+  });
+
+  it("ensures function call comes after user turn, not after model turn", () => {
+    const model = makeModel("gemini-1.5-pro");
+    const context = {
+      messages: [
+        {
+          role: "user",
+          content: "Hello",
+        },
+        makeGoogleAssistantMessage(model.id, [{ type: "text", text: "Hi!" }]),
+        makeGoogleAssistantMessage(model.id, [
+          {
+            type: "toolCall",
+            id: "call_1",
+            name: "myTool",
+            arguments: {},
+          },
+        ]),
+      ],
+    } as unknown as Context;
+
+    const contents = convertMessagesForTest(model, context);
+    expectConvertedRoles(contents, ["user", "model", "model", "user"]);
+    const toolCallPart = contents[2].parts?.find(
+      (part) => typeof part === "object" && part !== null && "functionCall" in part,
+    );
+    const toolCall = asRecord(toolCallPart);
+    expect(requireRecordProperty(toolCall, "functionCall").name).toBe("myTool");
+  });
+
+  it("strips tool call and response ids for google-gemini-cli", () => {
+    const model = makeGeminiCliModel("gemini-3-flash");
+    const context = {
+      messages: [
+        {
+          role: "user",
+          content: "Use a tool",
+        },
+        makeGeminiCliAssistantMessage(model.id, [
+          {
+            type: "toolCall",
+            id: "call_1",
+            name: "myTool",
+            arguments: { arg: "value" },
+            thoughtSignature: "dGVzdA==",
+          },
+        ]),
+        {
+          role: "toolResult",
+          toolCallId: "call_1",
+          toolName: "myTool",
+          content: [{ type: "text", text: "Tool result" }],
+          isError: false,
+          timestamp: 0,
+        },
+      ],
+    } as unknown as Context;
+
+    const contents = convertMessagesForTest(model, context);
+    const parts = contents.flatMap((content) => content.parts ?? []);
+    const toolCallPart = parts.find(
+      (part) => typeof part === "object" && part !== null && "functionCall" in part,
+    );
+    const toolResponsePart = parts.find(
+      (part) => typeof part === "object" && part !== null && "functionResponse" in part,
+    );
+
+    const toolCall = asRecord(toolCallPart);
+    const toolResponse = asRecord(toolResponsePart);
+
+    expect(asRecord(toolCall.functionCall).id).toBeUndefined();
+    expect(asRecord(toolResponse.functionResponse).id).toBeUndefined();
+  });
+
+  it("serializes structured tool results into function responses", () => {
+    const model = makeModel("gemini-1.5-pro");
+    const context = {
+      messages: [
+        {
+          role: "toolResult",
+          toolCallId: "call_1",
+          toolName: "session_status",
+          content: [{ type: "json", payload: { sessionKey: "current", status: "ok" } }],
+          isError: false,
+          timestamp: 0,
+        },
+      ],
+    } as unknown as Context;
+    const contents = convertMessagesForTest(model, context);
+    const toolResponsePart = contents[0]?.parts?.find(
+      (part) => typeof part === "object" && part !== null && "functionResponse" in part,
+    );
+    expect(toolResponsePart).toBeDefined();
+    const toolResponse = requireRecordProperty(asRecord(toolResponsePart), "functionResponse");
+    expect(asRecord(toolResponse.response).output).toBe(
+      '{"type":"json","payload":{"sessionKey":"current","status":"ok"}}',
+    );
+  });
+});

packages/ai/src/providers/google-shared.parallel-image-repro.test.ts

@@ -0,0 +1,87 @@
+import type { Part } from "@google/genai";
+import { describe, expect, it } from "vitest";
+import type { Context, Model } from "../types.js";
+import { convertMessages } from "./google-shared.js";
+import { makeGoogleAssistantMessage } from "./google-shared.test-helpers.js";
+
+const convertMessagesForTest = convertMessages as unknown as (
+  model: Model<"google-generative-ai">,
+  context: Context,
+) => ReturnType<typeof convertMessages>;
+
+const makeVisionModel = (id: string): Model<"google-generative-ai"> =>
+  ({
+    id,
+    name: id,
+    api: "google-generative-ai",
+    provider: "google",
+    baseUrl: "https://example.invalid",
+    reasoning: false,
+    input: ["text", "image"],
+    cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+    contextWindow: 1,
+    maxTokens: 1,
+  }) as Model<"google-generative-ai">;
+
+function countFunctionResponses(parts: readonly Part[] | undefined): number {
+  return (parts ?? []).filter((p) => p.functionResponse != null).length;
+}
+
+function functionResponseNames(parts: readonly Part[] | undefined): string[] {
+  return (parts ?? []).flatMap((part) =>
+    part.functionResponse?.name ? [part.functionResponse.name] : [],
+  );
+}
+
+describe("google-shared convertMessages — parallel tool results with an image (Gemini < 3)", () => {
+  it.each([
+    ["image first", ["screenshot", "weather"]],
+    ["image last", ["weather", "screenshot"]],
+  ] as const)(
+    "keeps the response run immediate and retains a deferred %s result",
+    (_label, resultOrder) => {
+      const model = makeVisionModel("gemini-2.5-flash");
+      const toolResults = {
+        screenshot: {
+          role: "toolResult",
+          toolCallId: "call_1",
+          toolName: "screenshot",
+          content: [{ type: "image", mimeType: "image/png", data: "AAAA" }],
+          isError: false,
+          timestamp: 0,
+        },
+        weather: {
+          role: "toolResult",
+          toolCallId: "call_2",
+          toolName: "weather",
+          content: [{ type: "text", text: "Sunny, 21C" }],
+          isError: false,
+          timestamp: 0,
+        },
+      } as const;
+      const context = {
+        messages: [
+          { role: "user", content: "Screenshot the page and check the weather." },
+          makeGoogleAssistantMessage(model.id, [
+            { type: "toolCall", id: "call_1", name: "screenshot", arguments: {} },
+            { type: "toolCall", id: "call_2", name: "weather", arguments: {} },
+          ]),
+          ...resultOrder.map((name) => toolResults[name]),
+        ],
+      } as unknown as Context;
+
+      const contents = convertMessagesForTest(model, context);
+
+      expect(contents.map((content) => content.role)).toEqual(["user", "model", "user", "user"]);
+      expect(functionResponseNames(contents[2].parts)).toEqual(resultOrder);
+      expect(contents[3]).toEqual({
+        role: "user",
+        parts: [
+          { text: "Tool result image:" },
+          { inlineData: { mimeType: "image/png", data: "AAAA" } },
+        ],
+      });
+      expect(contents.slice(3).some((c) => countFunctionResponses(c.parts) > 0)).toBe(false);
+    },
+  );
+});

packages/ai/src/providers/google-shared.test-helpers.ts

@@ -0,0 +1,100 @@
+// Google provider test helpers assert converted message and stream payloads.
+import { expect } from "vitest";
+import type { Model } from "../types.js";
+
+function makeZeroUsageSnapshot() {
+  return {
+    inputTokens: 0,
+    outputTokens: 0,
+    cacheReadInputTokens: 0,
+    cacheCreationInputTokens: 0,
+    reasoningTokens: 0,
+    cost: {
+      input: 0,
+      output: 0,
+      cacheRead: 0,
+      cacheWrite: 0,
+      total: 0,
+    },
+  };
+}
+
+export const asRecord = (value: unknown): Record<string, unknown> => {
+  if (!value || typeof value !== "object" || Array.isArray(value)) {
+    throw new Error("expected record");
+  }
+  return value as Record<string, unknown>;
+};
+
+type ConvertedTools = ReadonlyArray<{
+  functionDeclarations?: ReadonlyArray<{
+    parametersJsonSchema?: unknown;
+    parameters?: unknown;
+  }>;
+}>;
+
+export const getFirstToolParameters = (converted: ConvertedTools): Record<string, unknown> => {
+  const functionDeclaration = asRecord(converted?.[0]?.functionDeclarations?.[0]);
+  return asRecord(functionDeclaration.parametersJsonSchema ?? functionDeclaration.parameters);
+};
+
+export const makeModel = (id: string): Model<"google-generative-ai"> =>
+  ({
+    id,
+    name: id,
+    api: "google-generative-ai",
+    provider: "google",
+    baseUrl: "https://example.invalid",
+    reasoning: false,
+    input: ["text"],
+    cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+    contextWindow: 1,
+    maxTokens: 1,
+  }) as Model<"google-generative-ai">;
+
+export const makeGeminiCliModel = (id: string): Model<"google-gemini-cli"> =>
+  ({
+    id,
+    name: id,
+    api: "google-gemini-cli",
+    provider: "google-gemini-cli",
+    baseUrl: "https://example.invalid",
+    reasoning: false,
+    input: ["text"],
+    cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+    contextWindow: 1,
+    maxTokens: 1,
+  }) as Model<"google-gemini-cli">;
+
+export function makeGoogleAssistantMessage(model: string, content: unknown) {
+  return {
+    role: "assistant",
+    content,
+    api: "google-generative-ai",
+    provider: "google",
+    model,
+    usage: makeZeroUsageSnapshot(),
+    stopReason: "stop",
+    timestamp: 0,
+  };
+}
+
+export function makeGeminiCliAssistantMessage(model: string, content: unknown) {
+  return {
+    role: "assistant",
+    content,
+    api: "google-gemini-cli",
+    provider: "google-gemini-cli",
+    model,
+    usage: makeZeroUsageSnapshot(),
+    stopReason: "stop",
+    timestamp: 0,
+  };
+}
+
+export function expectConvertedRoles(contents: Array<{ role?: string }>, expectedRoles: string[]) {
+  expect(contents).toHaveLength(expectedRoles.length);
+  for (const [index, role] of expectedRoles.entries()) {
+    expect(contents[index]?.role).toBe(role);
+  }
+}

packages/ai/src/providers/google-shared.test.ts

@@ -0,0 +1,252 @@
+// Google shared provider tests cover response conversion and finish reasons.
+import { FinishReason, type GenerateContentResponse } from "@google/genai";
+import { describe, expect, it } from "vitest";
+import type { AssistantMessage, Model } from "../types.js";
+import { AssistantMessageEventStream } from "../utils/event-stream.js";
+import { SYSTEM_PROMPT_CACHE_BOUNDARY } from "../utils/system-prompt-cache-boundary.js";
+import {
+  buildGoogleGenerateContentParams,
+  consumeGoogleGenerateContentStream,
+} from "./google-shared.js";
+
+const model: Model<"google-generative-ai"> = {
+  id: "gemini-test",
+  name: "Gemini Test",
+  api: "google-generative-ai",
+  provider: "google",
+  baseUrl: "",
+  reasoning: true,
+  input: ["text"],
+  cost: {
+    input: 1,
+    output: 2,
+    cacheRead: 0.25,
+    cacheWrite: 0,
+  },
+  contextWindow: 128_000,
+  maxTokens: 8_192,
+};
+
+function createOutput(): AssistantMessage {
+  return {
+    role: "assistant",
+    content: [],
+    api: model.api,
+    provider: model.provider,
+    model: model.id,
+    usage: {
+      input: 0,
+      output: 0,
+      cacheRead: 0,
+      cacheWrite: 0,
+      totalTokens: 0,
+      cost: {
+        input: 0,
+        output: 0,
+        cacheRead: 0,
+        cacheWrite: 0,
+        total: 0,
+      },
+    },
+    stopReason: "stop",
+    timestamp: 0,
+  };
+}
+
+async function* chunks(items: GenerateContentResponse[]) {
+  yield* items;
+}
+
+describe("consumeGoogleGenerateContentStream", () => {
+  it("projects text, thinking, tool calls, response id, and usage into one stream", async () => {
+    const output = createOutput();
+    const stream = new AssistantMessageEventStream();
+    const events: string[] = [];
+    const collect = (async () => {
+      for await (const event of stream) {
+        events.push(event.type);
+      }
+    })();
+
+    await consumeGoogleGenerateContentStream({
+      chunks: chunks([
+        {
+          responseId: "response-1",
+          candidates: [
+            {
+              content: {
+                parts: [
+                  { text: "thinking", thought: true, thoughtSignature: "dGhpbms=" },
+                  { text: "hello" },
+                  { functionCall: { name: "lookup", args: { query: "cats" } } },
+                ],
+              },
+            },
+          ],
+        } as GenerateContentResponse,
+        {
+          candidates: [{ finishReason: FinishReason.STOP }],
+          usageMetadata: {
+            promptTokenCount: 10,
+            cachedContentTokenCount: 2,
+            candidatesTokenCount: 3,
+            thoughtsTokenCount: 4,
+            totalTokenCount: 17,
+          },
+        } as GenerateContentResponse,
+      ]),
+      model,
+      output,
+      stream,
+      nextToolCallId: (name) => `generated-${name}`,
+    });
+    await collect;
+
+    expect(events).toEqual([
+      "start",
+      "thinking_start",
+      "thinking_delta",
+      "thinking_end",
+      "text_start",
+      "text_delta",
+      "text_end",
+      "toolcall_start",
+      "toolcall_delta",
+      "toolcall_end",
+      "done",
+    ]);
+    expect(output.responseId).toBe("response-1");
+    expect(output.stopReason).toBe("toolUse");
+    expect(output.content).toEqual([
+      { type: "thinking", thinking: "thinking", thinkingSignature: "dGhpbms=" },
+      { type: "text", text: "hello" },
+      {
+        type: "toolCall",
+        id: "generated-lookup",
+        name: "lookup",
+        arguments: { query: "cats" },
+      },
+    ]);
+    expect(output.usage).toMatchObject({
+      input: 8,
+      output: 7,
+      cacheRead: 2,
+      totalTokens: 17,
+    });
+    expect(output.usage.cost.total).toBeGreaterThan(0);
+  });
+
+  it("preserves MAX_TOKENS when the partial response contains a function call", async () => {
+    const output = createOutput();
+    const stream = new AssistantMessageEventStream();
+    const terminalReason = (async () => {
+      for await (const event of stream) {
+        if (event.type === "done") {
+          return event.reason;
+        }
+      }
+      return undefined;
+    })();
+
+    await consumeGoogleGenerateContentStream({
+      chunks: chunks([
+        {
+          candidates: [
+            {
+              content: {
+                parts: [{ functionCall: { name: "lookup", args: { query: "cats" } } }],
+              },
+              finishReason: FinishReason.MAX_TOKENS,
+            },
+          ],
+        } as unknown as GenerateContentResponse,
+      ]),
+      model,
+      output,
+      stream,
+      nextToolCallId: (name) => `generated-${name}`,
+    });
+
+    expect(await terminalReason).toBe("length");
+    expect(output.stopReason).toBe("length");
+    expect(output.content).toEqual([expect.objectContaining({ type: "toolCall", name: "lookup" })]);
+  });
+
+  it("generates a new id when Google repeats a streamed tool-call id", async () => {
+    const output = createOutput();
+    const stream = new AssistantMessageEventStream();
+    const events: string[] = [];
+    const collect = (async () => {
+      for await (const event of stream) {
+        events.push(event.type);
+      }
+    })();
+
+    await consumeGoogleGenerateContentStream({
+      chunks: chunks([
+        {
+          candidates: [
+            {
+              content: {
+                parts: [{ functionCall: { id: "call_1", name: "lookup", args: {} } }],
+              },
+            },
+          ],
+        } as GenerateContentResponse,
+        {
+          candidates: [
+            {
+              content: {
+                parts: [{ functionCall: { id: "call_1", name: "lookup", args: {} } }],
+              },
+              finishReason: FinishReason.STOP,
+            },
+          ],
+        } as GenerateContentResponse,
+      ]),
+      model,
+      output,
+      stream,
+      nextToolCallId: (name) => `generated-${name}`,
+    });
+    await collect;
+
+    expect(events.at(-1)).toBe("done");
+    expect(output.content).toEqual([
+      {
+        type: "toolCall",
+        id: "call_1",
+        name: "lookup",
+        arguments: {},
+      },
+      {
+        type: "toolCall",
+        id: "generated-lookup",
+        name: "lookup",
+        arguments: {},
+      },
+    ]);
+  });
+});
+
+describe("buildGoogleGenerateContentParams", () => {
+  it("forwards stop sequences to Google generation config", () => {
+    const params = buildGoogleGenerateContentParams(
+      model,
+      { messages: [{ role: "user", content: "hello", timestamp: 0 }] },
+      { stop: ["STOP"] },
+    );
+
+    expect(params.config?.stopSequences).toEqual(["STOP"]);
+  });
+
+  it("strips the internal cache boundary marker from systemInstruction", () => {
+    const params = buildGoogleGenerateContentParams(model, {
+      systemPrompt: `Stable${SYSTEM_PROMPT_CACHE_BOUNDARY}Dynamic`,
+      messages: [{ role: "user", content: "hello", timestamp: 0 }],
+    });
+
+    expect(params.config?.systemInstruction).toBe("Stable\nDynamic");
+    expect(JSON.stringify(params)).not.toContain("OPENCLAW_CACHE_BOUNDARY");
+  });
+});

packages/ai/src/providers/google-shared.ts

@@ -0,0 +1,924 @@
+/**
+ * Shared utilities for Google Generative AI and Google Vertex providers.
+ */
+
+import {
+  type Content,
+  FinishReason,
+  FunctionCallingConfigMode,
+  type GenerateContentConfig,
+  type GenerateContentParameters,
+  type GenerateContentResponse,
+  type Part,
+  type ThinkingConfig,
+} from "@google/genai";
+import { calculateCost, clampThinkingLevel } from "../model-utils.js";
+import type {
+  Api,
+  AssistantMessage,
+  Context,
+  ImageContent,
+  Model,
+  SimpleStreamOptions,
+  StopReason,
+  TextContent,
+  ThinkingBudgets,
+  ThinkingContent,
+  ThinkingLevel as AgentThinkingLevel,
+  Tool,
+  ToolCall,
+  StreamOptions,
+} from "../types.js";
+import type { AssistantMessageEventStream } from "../utils/event-stream.js";
+import { sanitizeSurrogates } from "../utils/sanitize-unicode.js";
+import { stripSystemPromptCacheBoundary } from "../utils/system-prompt-cache-boundary.js";
+import { describeToolResultMediaPlaceholder, extractToolResultText } from "./tool-result-text.js";
+import { transformMessages } from "./transform-messages.js";
+
+export type GoogleApiType = "google-generative-ai" | "google-vertex";
+
+/**
+ * Thinking level for Gemini 3 models.
+ * Mirrors Google's ThinkingLevel enum values.
+ */
+export type GoogleThinkingLevel =
+  | "THINKING_LEVEL_UNSPECIFIED"
+  | "MINIMAL"
+  | "LOW"
+  | "MEDIUM"
+  | "HIGH";
+
+export type GoogleToolChoice = "auto" | "none" | "any";
+
+export type GoogleThinkingOptions = {
+  enabled: boolean;
+  budgetTokens?: number;
+  level?: GoogleThinkingLevel;
+};
+
+export type GoogleProviderOptions = StreamOptions & {
+  toolChoice?: GoogleToolChoice;
+  thinking?: GoogleThinkingOptions;
+};
+
+type GoogleGenerateContentClient = {
+  models: {
+    generateContentStream(
+      params: GenerateContentParameters,
+    ): Promise<AsyncIterable<GenerateContentResponse>> | AsyncIterable<GenerateContentResponse>;
+  };
+};
+
+type ClampedGoogleThinkingLevel = Exclude<AgentThinkingLevel, "xhigh" | "max">;
+
+/**
+ * Determines whether a streamed Gemini `Part` should be treated as "thinking".
+ *
+ * Protocol note (Gemini / Vertex AI thought signatures):
+ * - `thought: true` is the definitive marker for thinking content (thought summaries).
+ * - `thoughtSignature` is an encrypted representation of the model's internal thought process
+ *   used to preserve reasoning context across multi-turn interactions.
+ * - `thoughtSignature` can appear on ANY part type (text, functionCall, etc.) - it does NOT
+ *   indicate the part itself is thinking content.
+ * - For non-functionCall responses, the signature appears on the last part for context replay.
+ * - When persisting/replaying model outputs, signature-bearing parts must be preserved as-is;
+ *   do not merge/move signatures across parts.
+ *
+ * See: https://ai.google.dev/gemini-api/docs/thought-signatures
+ */
+export function isThinkingPart(part: Pick<Part, "thought" | "thoughtSignature">): boolean {
+  return part.thought === true;
+}
+
+/**
+ * Retain thought signatures during streaming.
+ *
+ * Some backends only send `thoughtSignature` on the first delta for a given part/block; later deltas may omit it.
+ * This helper preserves the last non-empty signature for the current block.
+ *
+ * Note: this does NOT merge or move signatures across distinct response parts. It only prevents
+ * a signature from being overwritten with `undefined` within the same streamed block.
+ */
+export function retainThoughtSignature(
+  existing: string | undefined,
+  incoming: string | undefined,
+): string | undefined {
+  if (typeof incoming === "string" && incoming.length > 0) {
+    return incoming;
+  }
+  return existing;
+}
+
+// Thought signatures must be base64 for Google APIs (TYPE_BYTES).
+const base64SignaturePattern = /^[A-Za-z0-9+/]+={0,2}$/;
+
+function isValidThoughtSignature(signature: string | undefined): boolean {
+  if (!signature) {
+    return false;
+  }
+  if (signature.length % 4 !== 0) {
+    return false;
+  }
+  return base64SignaturePattern.test(signature);
+}
+
+/**
+ * Only keep signatures from the same provider/model and with valid base64.
+ */
+function resolveThoughtSignature(
+  isSameProviderAndModel: boolean,
+  signature: string | undefined,
+): string | undefined {
+  return isSameProviderAndModel && isValidThoughtSignature(signature) ? signature : undefined;
+}
+
+/**
+ * Models via Google APIs that require explicit tool call IDs in function calls/responses.
+ */
+export function requiresToolCallId(modelId: string): boolean {
+  return modelId.startsWith("claude-") || modelId.startsWith("gpt-oss-");
+}
+
+function getGeminiMajorVersion(modelId: string): number | undefined {
+  const match = modelId.toLowerCase().match(/^gemini(?:-live)?-(\d+)/);
+  if (!match) {
+    return undefined;
+  }
+  return Number.parseInt(match[1], 10);
+}
+
+function supportsMultimodalFunctionResponse(modelId: string): boolean {
+  const geminiMajorVersion = getGeminiMajorVersion(modelId);
+  if (geminiMajorVersion !== undefined) {
+    return geminiMajorVersion >= 3;
+  }
+  return true;
+}
+
+/**
+ * Convert internal messages to Gemini Content[] format.
+ */
+export function convertMessages<T extends GoogleApiType>(
+  model: Model<T>,
+  context: Context,
+): Content[] {
+  const contents: Content[] = [];
+  const normalizeToolCallId = (id: string): string => {
+    if (!requiresToolCallId(model.id)) {
+      return id;
+    }
+    return id.replace(/[^a-zA-Z0-9_-]/g, "_").slice(0, 64);
+  };
+
+  const transformedMessages = transformMessages(context.messages, model, normalizeToolCallId);
+
+  // Parallel calls need one immediate function-response turn. Gemini < 3 images cannot
+  // live inside functionResponse, so hold them until the consecutive result run ends.
+  const pendingToolResultImageTurns: Content[] = [];
+  let activeToolResultParts: Part[] | undefined;
+  const flushToolResultRun = (): void => {
+    contents.push(...pendingToolResultImageTurns);
+    pendingToolResultImageTurns.length = 0;
+    activeToolResultParts = undefined;
+  };
+
+  for (const msg of transformedMessages) {
+    if (msg.role !== "toolResult") {
+      flushToolResultRun();
+    }
+    if (msg.role === "user") {
+      if (typeof msg.content === "string") {
+        contents.push({
+          role: "user",
+          parts: [{ text: sanitizeSurrogates(msg.content) }],
+        });
+      } else {
+        const parts: Part[] = msg.content.map((item) => {
+          if (item.type === "text") {
+            return { text: sanitizeSurrogates(item.text) };
+          }
+          return {
+            inlineData: {
+              mimeType: item.mimeType,
+              data: item.data,
+            },
+          };
+        });
+        if (parts.length === 0) {
+          continue;
+        }
+        contents.push({
+          role: "user",
+          parts,
+        });
+      }
+    } else if (msg.role === "assistant") {
+      const parts: Part[] = [];
+      // Check if message is from same provider and model - only then keep thinking blocks
+      const isSameProviderAndModel = msg.provider === model.provider && msg.model === model.id;
+
+      for (const block of msg.content) {
+        if (block.type === "text") {
+          // Skip empty text blocks
+          if (!block.text || block.text.trim() === "") {
+            continue;
+          }
+          const thoughtSignature = resolveThoughtSignature(
+            isSameProviderAndModel,
+            block.textSignature,
+          );
+          parts.push({
+            text: sanitizeSurrogates(block.text),
+            ...(thoughtSignature && { thoughtSignature }),
+          });
+        } else if (block.type === "thinking") {
+          // Skip empty thinking blocks
+          if (!block.thinking || block.thinking.trim() === "") {
+            continue;
+          }
+          // Only keep as thinking block if same provider AND same model
+          // Otherwise convert to plain text (no tags to avoid model mimicking them)
+          if (isSameProviderAndModel) {
+            const thoughtSignature = resolveThoughtSignature(
+              isSameProviderAndModel,
+              block.thinkingSignature,
+            );
+            parts.push({
+              thought: true,
+              text: sanitizeSurrogates(block.thinking),
+              ...(thoughtSignature && { thoughtSignature }),
+            });
+          } else {
+            parts.push({
+              text: sanitizeSurrogates(block.thinking),
+            });
+          }
+        } else if (block.type === "toolCall") {
+          const thoughtSignature = resolveThoughtSignature(
+            isSameProviderAndModel,
+            block.thoughtSignature,
+          );
+          const part: Part = {
+            functionCall: {
+              name: block.name,
+              args: block.arguments ?? {},
+              ...(requiresToolCallId(model.id) ? { id: block.id } : {}),
+            },
+            ...(thoughtSignature && { thoughtSignature }),
+          };
+          parts.push(part);
+        }
+      }
+
+      if (parts.length === 0) {
+        continue;
+      }
+      contents.push({
+        role: "model",
+        parts,
+      });
+    } else if (msg.role === "toolResult") {
+      // Extract text and image content
+      const textResult = extractToolResultText(msg.content);
+      const imageContent = model.input.includes("image")
+        ? msg.content.filter((c): c is ImageContent => c.type === "image")
+        : [];
+
+      const hasText = textResult.length > 0;
+      const hasImages = imageContent.length > 0;
+      const mediaPlaceholder = describeToolResultMediaPlaceholder(msg.content);
+
+      // Gemini 3+ models support multimodal function responses with images nested inside
+      // functionResponse.parts. Claude and other non-Gemini models behind Cloud Code Assist /
+      // Gemini < 3 still needs a separate user image turn.
+      const modelSupportsMultimodalFunctionResponse = supportsMultimodalFunctionResponse(model.id);
+
+      // Use "output" key for success, "error" key for errors as per SDK documentation
+      const responseValue = hasText ? sanitizeSurrogates(textResult) : (mediaPlaceholder ?? "");
+
+      const imageParts: Part[] = imageContent.map((imageBlock) => ({
+        inlineData: {
+          mimeType: imageBlock.mimeType,
+          data: imageBlock.data,
+        },
+      }));
+
+      const includeId = requiresToolCallId(model.id);
+      const functionResponsePart: Part = {
+        functionResponse: {
+          name: msg.toolName,
+          response: msg.isError ? { error: responseValue } : { output: responseValue },
+          ...(hasImages && modelSupportsMultimodalFunctionResponse && { parts: imageParts }),
+          ...(includeId ? { id: msg.toolCallId } : {}),
+        },
+      };
+
+      // Cloud Code Assist API requires all function responses to be in a single user turn.
+      if (activeToolResultParts) {
+        activeToolResultParts.push(functionResponsePart);
+      } else {
+        activeToolResultParts = [functionResponsePart];
+        contents.push({
+          role: "user",
+          parts: activeToolResultParts,
+        });
+      }
+
+      // For Gemini < 3, add images in a separate user message
+      if (hasImages && !modelSupportsMultimodalFunctionResponse) {
+        pendingToolResultImageTurns.push({
+          role: "user",
+          parts: [{ text: "Tool result image:" }, ...imageParts],
+        });
+      }
+    }
+  }
+
+  flushToolResultRun();
+  return contents;
+}
+
+const JSON_SCHEMA_META_DECLARATIONS = new Set([
+  "$schema",
+  "$id",
+  "$anchor",
+  "$dynamicAnchor",
+  "$vocabulary",
+  "$comment",
+  "$defs",
+  "definitions", // pre-draft-2019-09 equivalent of $defs
+]);
+
+/**
+ * Strip meta-declarations from a schema obj
+ */
+function sanitizeForOpenApi(schema: unknown): unknown {
+  if (typeof schema !== "object" || schema === null || Array.isArray(schema)) {
+    return schema;
+  }
+
+  const result: Record<string, unknown> = {};
+  for (const [key, value] of Object.entries(schema)) {
+    if (JSON_SCHEMA_META_DECLARATIONS.has(key)) {
+      continue;
+    }
+    result[key] = sanitizeForOpenApi(value);
+  }
+  return result;
+}
+
+/**
+ * Convert tools to Gemini function declarations format.
+ *
+ * By default uses `parametersJsonSchema` which supports full JSON Schema (including
+ * anyOf, oneOf, const, etc.). Set `useParameters` to true to use the legacy `parameters`
+ * field instead (OpenAPI 3.03 Schema). This is needed for Cloud Code Assist with Claude
+ * models, where the API translates `parameters` into Anthropic's `input_schema`.
+ */
+export function convertTools(
+  tools: Tool[],
+  useParameters = false,
+): { functionDeclarations: Record<string, unknown>[] }[] | undefined {
+  if (tools.length === 0) {
+    return undefined;
+  }
+  return [
+    {
+      functionDeclarations: tools.map((tool) => ({
+        name: tool.name,
+        description: tool.description,
+        ...(useParameters
+          ? { parameters: sanitizeForOpenApi(tool.parameters as unknown) }
+          : { parametersJsonSchema: tool.parameters }),
+      })),
+    },
+  ];
+}
+
+/**
+ * Map tool choice string to Gemini FunctionCallingConfigMode.
+ */
+export function mapToolChoice(choice: string): FunctionCallingConfigMode {
+  switch (choice) {
+    case "auto":
+      return FunctionCallingConfigMode.AUTO;
+    case "none":
+      return FunctionCallingConfigMode.NONE;
+    case "any":
+      return FunctionCallingConfigMode.ANY;
+    default:
+      return FunctionCallingConfigMode.AUTO;
+  }
+}
+
+export function createGoogleAssistantOutput<T extends GoogleApiType>(
+  model: Model<T>,
+  api: Api = model.api,
+): AssistantMessage {
+  return {
+    role: "assistant",
+    content: [],
+    api,
+    provider: model.provider,
+    model: model.id,
+    usage: {
+      input: 0,
+      output: 0,
+      cacheRead: 0,
+      cacheWrite: 0,
+      totalTokens: 0,
+      cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
+    },
+    stopReason: "stop",
+    timestamp: Date.now(),
+  };
+}
+
+export async function runGoogleGenerateContentLifecycle<T extends GoogleApiType>(params: {
+  stream: AssistantMessageEventStream;
+  model: Model<T>;
+  output: AssistantMessage;
+  options?: Pick<StreamOptions, "signal" | "onPayload">;
+  createClient: () => GoogleGenerateContentClient;
+  buildParams: () => GenerateContentParameters;
+  nextToolCallId: (name: string | undefined) => string;
+}): Promise<void> {
+  const { stream, model, output, options } = params;
+
+  try {
+    const client = params.createClient();
+    let requestParams = params.buildParams();
+    const nextParams = await options?.onPayload?.(requestParams, model);
+    if (nextParams !== undefined) {
+      requestParams = nextParams as GenerateContentParameters;
+    }
+    const googleStream = await client.models.generateContentStream(requestParams);
+    await consumeGoogleGenerateContentStream({
+      chunks: googleStream,
+      model,
+      output,
+      stream,
+      signal: options?.signal,
+      nextToolCallId: params.nextToolCallId,
+    });
+  } catch (error) {
+    for (const block of output.content) {
+      if ("index" in block) {
+        delete (block as { index?: number }).index;
+      }
+    }
+    output.stopReason = options?.signal?.aborted ? "aborted" : "error";
+    output.errorMessage = error instanceof Error ? error.message : JSON.stringify(error);
+    stream.push({ type: "error", reason: output.stopReason, error: output });
+    stream.end();
+  }
+}
+
+export function buildGoogleGenerateContentParams<T extends GoogleApiType>(
+  model: Model<T>,
+  context: Context,
+  options: GoogleProviderOptions = {},
+  configHooks?: {
+    mapThinkingLevel?: (level: GoogleThinkingLevel) => ThinkingConfig["thinkingLevel"];
+    getDisabledThinkingConfig?: (model: Model<T>) => ThinkingConfig;
+  },
+): GenerateContentParameters {
+  const contents = convertMessages(model, context);
+
+  const generationConfig: GenerateContentConfig = {};
+  if (options.temperature !== undefined) {
+    generationConfig.temperature = options.temperature;
+  }
+  if (options.maxTokens !== undefined) {
+    generationConfig.maxOutputTokens = options.maxTokens;
+  }
+  if (options.stop !== undefined && options.stop.length > 0) {
+    generationConfig.stopSequences = options.stop;
+  }
+
+  const config: GenerateContentConfig = {
+    ...(Object.keys(generationConfig).length > 0 && generationConfig),
+    ...(context.systemPrompt && {
+      systemInstruction: sanitizeSurrogates(stripSystemPromptCacheBoundary(context.systemPrompt)),
+    }),
+    ...(context.tools && context.tools.length > 0 && { tools: convertTools(context.tools) }),
+  };
+
+  if (context.tools && context.tools.length > 0 && options.toolChoice) {
+    config.toolConfig = {
+      functionCallingConfig: {
+        mode: mapToolChoice(options.toolChoice),
+      },
+    };
+  } else {
+    config.toolConfig = undefined;
+  }
+
+  if (options.thinking?.enabled && model.reasoning) {
+    const thinkingConfig: ThinkingConfig = { includeThoughts: true };
+    if (options.thinking.level !== undefined) {
+      thinkingConfig.thinkingLevel = configHooks?.mapThinkingLevel
+        ? configHooks.mapThinkingLevel(options.thinking.level)
+        : (options.thinking.level as ThinkingConfig["thinkingLevel"]);
+    } else if (options.thinking.budgetTokens !== undefined) {
+      thinkingConfig.thinkingBudget = options.thinking.budgetTokens;
+    }
+    config.thinkingConfig = thinkingConfig;
+  } else if (model.reasoning && options.thinking && !options.thinking.enabled) {
+    config.thinkingConfig = configHooks?.getDisabledThinkingConfig
+      ? configHooks.getDisabledThinkingConfig(model)
+      : getDisabledGoogleThinkingConfig(model);
+  }
+
+  if (options.signal) {
+    if (options.signal.aborted) {
+      throw new Error("Request aborted");
+    }
+    config.abortSignal = options.signal;
+  }
+
+  return {
+    model: model.id,
+    contents,
+    config,
+  };
+}
+
+export function buildGoogleSimpleThinking<T extends GoogleApiType>(
+  model: Model<T>,
+  options: SimpleStreamOptions | undefined,
+  config?: {
+    includeGemma4ThinkingLevel?: boolean;
+    useFlashLiteBudgets?: boolean;
+  },
+): GoogleThinkingOptions {
+  if (!options?.reasoning) {
+    return { enabled: false };
+  }
+
+  const clampedReasoning = clampThinkingLevel(model, options.reasoning);
+  const effort = (
+    clampedReasoning === "off" || clampedReasoning === "max" ? "high" : clampedReasoning
+  ) as ClampedGoogleThinkingLevel;
+
+  if (
+    isGemini3ProModel(model) ||
+    isGemini3FlashModel(model) ||
+    (config?.includeGemma4ThinkingLevel && isGemma4Model(model))
+  ) {
+    return {
+      enabled: true,
+      level: getGoogleThinkingLevel(effort, model, {
+        includeGemma4: config?.includeGemma4ThinkingLevel,
+      }),
+    };
+  }
+
+  return {
+    enabled: true,
+    budgetTokens: getGoogleBudget(model, effort, options.thinkingBudgets, {
+      useFlashLiteBudgets: config?.useFlashLiteBudgets,
+    }),
+  };
+}
+
+export function getDisabledGoogleThinkingConfig<T extends GoogleApiType>(
+  model: Model<T>,
+  config?: {
+    includeGemma4?: boolean;
+    mapThinkingLevel?: (level: GoogleThinkingLevel) => ThinkingConfig["thinkingLevel"];
+  },
+): ThinkingConfig {
+  const mapThinkingLevel = (level: GoogleThinkingLevel): ThinkingConfig["thinkingLevel"] =>
+    config?.mapThinkingLevel
+      ? config.mapThinkingLevel(level)
+      : (level as ThinkingConfig["thinkingLevel"]);
+
+  // Google docs: Gemini 3.1 Pro cannot disable thinking, and Gemini 3 Flash / Flash-Lite
+  // do not support full thinking-off either. For Gemini 3 models, use the lowest supported
+  // thinkingLevel without includeThoughts so hidden thinking remains invisible to OpenClaw.
+  if (isGemini3ProModel(model)) {
+    return { thinkingLevel: mapThinkingLevel("LOW") };
+  }
+  if (isGemini3FlashModel(model)) {
+    return { thinkingLevel: mapThinkingLevel("MINIMAL") };
+  }
+  if (config?.includeGemma4 && isGemma4Model(model)) {
+    return { thinkingLevel: mapThinkingLevel("MINIMAL") };
+  }
+
+  // Gemini 2.x supports disabling via thinkingBudget = 0.
+  return { thinkingBudget: 0 };
+}
+
+export function isGemma4Model<T extends GoogleApiType>(model: Model<T>): boolean {
+  return /gemma-?4/.test(model.id.toLowerCase());
+}
+
+export function isGemini3ProModel<T extends GoogleApiType>(model: Model<T>): boolean {
+  return /gemini-3(?:\.\d+)?-pro/.test(model.id.toLowerCase());
+}
+
+export function isGemini3FlashModel<T extends GoogleApiType>(model: Model<T>): boolean {
+  return /gemini-3(?:\.\d+)?-flash/.test(model.id.toLowerCase());
+}
+
+function getGoogleThinkingLevel<T extends GoogleApiType>(
+  effort: ClampedGoogleThinkingLevel,
+  model: Model<T>,
+  config?: { includeGemma4?: boolean },
+): GoogleThinkingLevel {
+  if (isGemini3ProModel(model)) {
+    switch (effort) {
+      case "minimal":
+      case "low":
+        return "LOW";
+      case "medium":
+      case "high":
+        return "HIGH";
+    }
+  }
+  if (config?.includeGemma4 && isGemma4Model(model)) {
+    switch (effort) {
+      case "minimal":
+      case "low":
+        return "MINIMAL";
+      case "medium":
+      case "high":
+        return "HIGH";
+    }
+  }
+  switch (effort) {
+    case "minimal":
+      return "MINIMAL";
+    case "low":
+      return "LOW";
+    case "medium":
+      return "MEDIUM";
+    case "high":
+      return "HIGH";
+  }
+  return "HIGH";
+}
+
+function getGoogleBudget<T extends GoogleApiType>(
+  model: Model<T>,
+  effort: ClampedGoogleThinkingLevel,
+  customBudgets?: ThinkingBudgets,
+  config?: { useFlashLiteBudgets?: boolean },
+): number {
+  if (customBudgets?.[effort] !== undefined) {
+    return customBudgets[effort];
+  }
+
+  if (model.id.includes("2.5-pro")) {
+    const budgets: Record<ClampedGoogleThinkingLevel, number> = {
+      minimal: 128,
+      low: 2048,
+      medium: 8192,
+      high: 32768,
+    };
+    return budgets[effort];
+  }
+
+  if (config?.useFlashLiteBudgets && model.id.includes("2.5-flash-lite")) {
+    const budgets: Record<ClampedGoogleThinkingLevel, number> = {
+      minimal: 512,
+      low: 2048,
+      medium: 8192,
+      high: 24576,
+    };
+    return budgets[effort];
+  }
+
+  if (model.id.includes("2.5-flash")) {
+    const budgets: Record<ClampedGoogleThinkingLevel, number> = {
+      minimal: 128,
+      low: 2048,
+      medium: 8192,
+      high: 24576,
+    };
+    return budgets[effort];
+  }
+
+  return -1;
+}
+
+/**
+ * Map Gemini FinishReason to our StopReason.
+ */
+export function mapStopReason(reason: FinishReason): StopReason {
+  switch (reason) {
+    case FinishReason.STOP:
+      return "stop";
+    case FinishReason.MAX_TOKENS:
+      return "length";
+    case FinishReason.BLOCKLIST:
+    case FinishReason.PROHIBITED_CONTENT:
+    case FinishReason.SPII:
+    case FinishReason.SAFETY:
+    case FinishReason.IMAGE_SAFETY:
+    case FinishReason.IMAGE_PROHIBITED_CONTENT:
+    case FinishReason.IMAGE_RECITATION:
+    case FinishReason.IMAGE_OTHER:
+    case FinishReason.RECITATION:
+    case FinishReason.FINISH_REASON_UNSPECIFIED:
+    case FinishReason.OTHER:
+    case FinishReason.LANGUAGE:
+    case FinishReason.MALFORMED_FUNCTION_CALL:
+    case FinishReason.UNEXPECTED_TOOL_CALL:
+    case FinishReason.NO_IMAGE:
+      return "error";
+    default: {
+      const exhaustive: never = reason;
+      throw new Error(`Unhandled stop reason: ${String(exhaustive)}`);
+    }
+  }
+}
+
+export async function consumeGoogleGenerateContentStream<T extends GoogleApiType>(params: {
+  chunks: AsyncIterable<GenerateContentResponse>;
+  model: Model<T>;
+  output: AssistantMessage;
+  stream: AssistantMessageEventStream;
+  signal?: AbortSignal;
+  nextToolCallId: (name: string | undefined) => string;
+}): Promise<void> {
+  params.stream.push({ type: "start", partial: params.output });
+  let currentBlock: TextContent | ThinkingContent | null = null;
+  const blocks = params.output.content;
+  const toolCallIds = new Set<string>();
+  for (const block of blocks) {
+    if (block.type === "toolCall") {
+      toolCallIds.add(block.id);
+    }
+  }
+  const blockIndex = () => blocks.length - 1;
+
+  const endCurrentBlock = () => {
+    if (!currentBlock) {
+      return;
+    }
+    if (currentBlock.type === "text") {
+      params.stream.push({
+        type: "text_end",
+        contentIndex: blockIndex(),
+        content: currentBlock.text,
+        partial: params.output,
+      });
+    } else {
+      params.stream.push({
+        type: "thinking_end",
+        contentIndex: blockIndex(),
+        content: currentBlock.thinking,
+        partial: params.output,
+      });
+    }
+    currentBlock = null;
+  };
+
+  for await (const chunk of params.chunks) {
+    params.output.responseId ||= chunk.responseId;
+    const candidate = chunk.candidates?.[0];
+    if (candidate?.content?.parts) {
+      for (const part of candidate.content.parts) {
+        if (part.text !== undefined) {
+          const isThinking = isThinkingPart(part);
+          if (
+            !currentBlock ||
+            (isThinking && currentBlock.type !== "thinking") ||
+            (!isThinking && currentBlock.type !== "text")
+          ) {
+            endCurrentBlock();
+            if (isThinking) {
+              currentBlock = { type: "thinking", thinking: "", thinkingSignature: undefined };
+              params.output.content.push(currentBlock);
+              params.stream.push({
+                type: "thinking_start",
+                contentIndex: blockIndex(),
+                partial: params.output,
+              });
+            } else {
+              currentBlock = { type: "text", text: "" };
+              params.output.content.push(currentBlock);
+              params.stream.push({
+                type: "text_start",
+                contentIndex: blockIndex(),
+                partial: params.output,
+              });
+            }
+          }
+          if (currentBlock.type === "thinking") {
+            currentBlock.thinking += part.text;
+            currentBlock.thinkingSignature = retainThoughtSignature(
+              currentBlock.thinkingSignature,
+              part.thoughtSignature,
+            );
+            params.stream.push({
+              type: "thinking_delta",
+              contentIndex: blockIndex(),
+              delta: part.text,
+              partial: params.output,
+            });
+          } else {
+            currentBlock.text += part.text;
+            currentBlock.textSignature = retainThoughtSignature(
+              currentBlock.textSignature,
+              part.thoughtSignature,
+            );
+            params.stream.push({
+              type: "text_delta",
+              contentIndex: blockIndex(),
+              delta: part.text,
+              partial: params.output,
+            });
+          }
+        }
+
+        if (part.functionCall) {
+          endCurrentBlock();
+          const providedId = part.functionCall.id;
+          const needsNewId = !providedId || toolCallIds.has(providedId);
+          const toolCall: ToolCall = {
+            type: "toolCall",
+            id: needsNewId ? params.nextToolCallId(part.functionCall.name) : providedId,
+            name: part.functionCall.name || "",
+            arguments: (part.functionCall.args as Record<string, unknown>) ?? {},
+            ...(part.thoughtSignature && { thoughtSignature: part.thoughtSignature }),
+          };
+
+          params.output.content.push(toolCall);
+          toolCallIds.add(toolCall.id);
+          params.stream.push({
+            type: "toolcall_start",
+            contentIndex: blockIndex(),
+            partial: params.output,
+          });
+          params.stream.push({
+            type: "toolcall_delta",
+            contentIndex: blockIndex(),
+            delta: JSON.stringify(toolCall.arguments),
+            partial: params.output,
+          });
+          params.stream.push({
+            type: "toolcall_end",
+            contentIndex: blockIndex(),
+            toolCall,
+            partial: params.output,
+          });
+        }
+      }
+    }
+
+    if (candidate?.finishReason) {
+      params.output.stopReason = mapStopReason(candidate.finishReason);
+      // MAX_TOKENS can leave a complete-looking partial call. Only a normal
+      // Google stop may promote parsed calls into an executable tool-use turn.
+      if (
+        params.output.stopReason === "stop" &&
+        params.output.content.some((block) => block.type === "toolCall")
+      ) {
+        params.output.stopReason = "toolUse";
+      }
+    }
+
+    if (chunk.usageMetadata) {
+      params.output.usage = {
+        input:
+          (chunk.usageMetadata.promptTokenCount || 0) -
+          (chunk.usageMetadata.cachedContentTokenCount || 0),
+        output:
+          (chunk.usageMetadata.candidatesTokenCount || 0) +
+          (chunk.usageMetadata.thoughtsTokenCount || 0),
+        cacheRead: chunk.usageMetadata.cachedContentTokenCount || 0,
+        cacheWrite: 0,
+        totalTokens: chunk.usageMetadata.totalTokenCount || 0,
+        cost: {
+          input: 0,
+          output: 0,
+          cacheRead: 0,
+          cacheWrite: 0,
+          total: 0,
+        },
+      };
+      calculateCost(params.model, params.output.usage);
+    }
+  }
+
+  endCurrentBlock();
+
+  if (params.signal?.aborted) {
+    throw new Error("Request was aborted");
+  }
+
+  if (params.output.stopReason === "aborted" || params.output.stopReason === "error") {
+    throw new Error("An unknown error occurred");
+  }
+
+  params.stream.push({
+    type: "done",
+    reason: params.output.stopReason,
+    message: params.output,
+  });
+  params.stream.end();
+}

packages/ai/src/providers/google-vertex.ts

@@ -0,0 +1,193 @@
+// Google Vertex provider wires Google shared streaming through Vertex credentials.
+import {
+  type GenerateContentParameters,
+  GoogleGenAI,
+  type HttpOptions,
+  ResourceScope,
+  ThinkingLevel as VertexThinkingLevel,
+} from "@google/genai";
+import type { Context, Model, SimpleStreamOptions, StreamFunction } from "../types.js";
+import { AssistantMessageEventStream } from "../utils/event-stream.js";
+import type { GoogleThinkingLevel } from "./google-shared.js";
+import {
+  buildGoogleGenerateContentParams,
+  buildGoogleSimpleThinking,
+  createGoogleAssistantOutput,
+  getDisabledGoogleThinkingConfig,
+  type GoogleProviderOptions,
+  runGoogleGenerateContentLifecycle,
+} from "./google-shared.js";
+import { buildBaseOptions } from "./simple-options.js";
+
+export interface GoogleVertexOptions extends GoogleProviderOptions {
+  project?: string;
+  location?: string;
+}
+
+const API_VERSION = "v1";
+const GCP_VERTEX_CREDENTIALS_MARKER = "gcp-vertex-credentials";
+
+const THINKING_LEVEL_MAP: Record<GoogleThinkingLevel, VertexThinkingLevel> = {
+  THINKING_LEVEL_UNSPECIFIED: VertexThinkingLevel.THINKING_LEVEL_UNSPECIFIED,
+  MINIMAL: VertexThinkingLevel.MINIMAL,
+  LOW: VertexThinkingLevel.LOW,
+  MEDIUM: VertexThinkingLevel.MEDIUM,
+  HIGH: VertexThinkingLevel.HIGH,
+};
+
+// Counter for generating unique tool call IDs
+let toolCallCounter = 0;
+
+export const streamGoogleVertex: StreamFunction<"google-vertex", GoogleVertexOptions> = (
+  model: Model<"google-vertex">,
+  context: Context,
+  options?: GoogleVertexOptions,
+) => {
+  const stream = new AssistantMessageEventStream();
+  const output = createGoogleAssistantOutput(model, "google-vertex");
+
+  void runGoogleGenerateContentLifecycle({
+    stream,
+    model,
+    output,
+    options,
+    createClient: () => {
+      const apiKey = resolveApiKey(options);
+      // Create the client using either a Vertex API key, if provided, or ADC with project and location
+      return apiKey
+        ? createClientWithApiKey(model, apiKey, options?.headers)
+        : createClient(model, resolveProject(options), resolveLocation(options), options?.headers);
+    },
+    buildParams: () => buildParams(model, context, options),
+    nextToolCallId: (name) => `${name}_${Date.now()}_${++toolCallCounter}`,
+  });
+
+  return stream;
+};
+
+export const streamSimpleGoogleVertex: StreamFunction<"google-vertex", SimpleStreamOptions> = (
+  model: Model<"google-vertex">,
+  context: Context,
+  options?: SimpleStreamOptions,
+) => {
+  const base = buildBaseOptions(model, options, undefined);
+  return streamGoogleVertex(model, context, {
+    ...base,
+    thinking: buildGoogleSimpleThinking(model, options),
+  } satisfies GoogleVertexOptions);
+};
+
+function createClient(
+  model: Model<"google-vertex">,
+  project: string,
+  location: string,
+  optionsHeaders?: Record<string, string>,
+): GoogleGenAI {
+  return new GoogleGenAI({
+    vertexai: true,
+    project,
+    location,
+    apiVersion: API_VERSION,
+    httpOptions: buildHttpOptions(model, optionsHeaders),
+  });
+}
+
+function createClientWithApiKey(
+  model: Model<"google-vertex">,
+  apiKey: string,
+  optionsHeaders?: Record<string, string>,
+): GoogleGenAI {
+  return new GoogleGenAI({
+    vertexai: true,
+    apiKey,
+    apiVersion: API_VERSION,
+    httpOptions: buildHttpOptions(model, optionsHeaders),
+  });
+}
+
+function buildHttpOptions(
+  model: Model<"google-vertex">,
+  optionsHeaders?: Record<string, string>,
+): HttpOptions | undefined {
+  const httpOptions: HttpOptions = {};
+  const baseUrl = resolveCustomBaseUrl(model.baseUrl);
+  if (baseUrl) {
+    httpOptions.baseUrl = baseUrl;
+    httpOptions.baseUrlResourceScope = ResourceScope.COLLECTION;
+    if (baseUrlIncludesApiVersion(baseUrl)) {
+      httpOptions.apiVersion = "";
+    }
+  }
+
+  if (model.headers || optionsHeaders) {
+    httpOptions.headers = { ...model.headers, ...optionsHeaders };
+  }
+
+  return Object.keys(httpOptions).length > 0 ? httpOptions : undefined;
+}
+
+function resolveCustomBaseUrl(baseUrl: string): string | undefined {
+  const trimmed = baseUrl.trim();
+  if (!trimmed || trimmed.includes("{location}")) {
+    return undefined;
+  }
+  return trimmed;
+}
+
+function baseUrlIncludesApiVersion(baseUrl: string): boolean {
+  try {
+    const url = new URL(baseUrl);
+    return url.pathname.split("/").some((part) => /^v\d+(?:beta\d*)?$/.test(part));
+  } catch {
+    return /(?:^|\/)v\d+(?:beta\d*)?(?:\/|$)/.test(baseUrl);
+  }
+}
+
+function resolveApiKey(options?: GoogleVertexOptions): string | undefined {
+  const apiKey = options?.apiKey?.trim() || process.env.GOOGLE_CLOUD_API_KEY?.trim();
+  if (!apiKey || apiKey === GCP_VERTEX_CREDENTIALS_MARKER || isPlaceholderApiKey(apiKey)) {
+    return undefined;
+  }
+  return apiKey;
+}
+
+function isPlaceholderApiKey(apiKey: string): boolean {
+  return /^<[^>]+>$/.test(apiKey);
+}
+
+function resolveProject(options?: GoogleVertexOptions): string {
+  const project =
+    options?.project || process.env.GOOGLE_CLOUD_PROJECT || process.env.GCLOUD_PROJECT;
+  if (!project) {
+    throw new Error(
+      "Vertex AI requires a project ID. Set GOOGLE_CLOUD_PROJECT/GCLOUD_PROJECT or pass project in options.",
+    );
+  }
+  return project;
+}
+
+function resolveLocation(options?: GoogleVertexOptions): string {
+  const location = options?.location || process.env.GOOGLE_CLOUD_LOCATION;
+  if (!location) {
+    throw new Error(
+      "Vertex AI requires a location. Set GOOGLE_CLOUD_LOCATION or pass location in options.",
+    );
+  }
+  return location;
+}
+
+function buildParams(
+  model: Model<"google-vertex">,
+  context: Context,
+  options: GoogleVertexOptions = {},
+): GenerateContentParameters {
+  return buildGoogleGenerateContentParams(model, context, options, {
+    mapThinkingLevel: mapVertexThinkingLevel,
+    getDisabledThinkingConfig: (modelLocal) =>
+      getDisabledGoogleThinkingConfig(modelLocal, { mapThinkingLevel: mapVertexThinkingLevel }),
+  });
+}
+
+function mapVertexThinkingLevel(level: GoogleThinkingLevel): VertexThinkingLevel {
+  return THINKING_LEVEL_MAP[level];
+}

packages/ai/src/providers/google.ts

@@ -0,0 +1,95 @@
+// Google provider adapts Gemini streams and tools to the agent runtime.
+import { type GenerateContentParameters, GoogleGenAI } from "@google/genai";
+import { getEnvApiKey } from "../env-api-keys.js";
+import type { Context, Model, SimpleStreamOptions, StreamFunction } from "../types.js";
+import { AssistantMessageEventStream } from "../utils/event-stream.js";
+import {
+  buildGoogleGenerateContentParams,
+  buildGoogleSimpleThinking,
+  createGoogleAssistantOutput,
+  getDisabledGoogleThinkingConfig,
+  type GoogleProviderOptions,
+  runGoogleGenerateContentLifecycle,
+} from "./google-shared.js";
+import { buildBaseOptions } from "./simple-options.js";
+
+export type GoogleOptions = GoogleProviderOptions;
+
+// Counter for generating unique tool call IDs
+let toolCallCounter = 0;
+
+export const streamGoogle: StreamFunction<"google-generative-ai", GoogleOptions> = (
+  model: Model<"google-generative-ai">,
+  context: Context,
+  options?: GoogleOptions,
+) => {
+  const stream = new AssistantMessageEventStream();
+  const output = createGoogleAssistantOutput(model, "google-generative-ai");
+
+  void runGoogleGenerateContentLifecycle({
+    stream,
+    model,
+    output,
+    options,
+    createClient: () => {
+      const apiKey = options?.apiKey || getEnvApiKey(model.provider) || "";
+      return createClient(model, apiKey, options?.headers);
+    },
+    buildParams: () => buildParams(model, context, options),
+    nextToolCallId: (name) => `${name}_${Date.now()}_${++toolCallCounter}`,
+  });
+
+  return stream;
+};
+
+export const streamSimpleGoogle: StreamFunction<"google-generative-ai", SimpleStreamOptions> = (
+  model: Model<"google-generative-ai">,
+  context: Context,
+  options?: SimpleStreamOptions,
+) => {
+  const apiKey = options?.apiKey || getEnvApiKey(model.provider);
+  if (!apiKey) {
+    throw new Error(`No API key for provider: ${model.provider}`);
+  }
+
+  const base = buildBaseOptions(model, options, apiKey);
+  return streamGoogle(model, context, {
+    ...base,
+    thinking: buildGoogleSimpleThinking(model, options, {
+      includeGemma4ThinkingLevel: true,
+      useFlashLiteBudgets: true,
+    }),
+  } satisfies GoogleOptions);
+};
+
+function createClient(
+  model: Model<"google-generative-ai">,
+  apiKey?: string,
+  optionsHeaders?: Record<string, string>,
+): GoogleGenAI {
+  const httpOptions: { baseUrl?: string; apiVersion?: string; headers?: Record<string, string> } =
+    {};
+  if (model.baseUrl) {
+    httpOptions.baseUrl = model.baseUrl;
+    httpOptions.apiVersion = ""; // baseUrl already includes version path, don't append
+  }
+  if (model.headers || optionsHeaders) {
+    httpOptions.headers = { ...model.headers, ...optionsHeaders };
+  }
+
+  return new GoogleGenAI({
+    apiKey,
+    httpOptions: Object.keys(httpOptions).length > 0 ? httpOptions : undefined,
+  });
+}
+
+function buildParams(
+  model: Model<"google-generative-ai">,
+  context: Context,
+  options: GoogleOptions = {},
+): GenerateContentParameters {
+  return buildGoogleGenerateContentParams(model, context, options, {
+    getDisabledThinkingConfig: (modelLocal) =>
+      getDisabledGoogleThinkingConfig(modelLocal, { includeGemma4: true }),
+  });
+}

packages/ai/src/providers/mistral.bounded-stream.test.ts

@@ -0,0 +1,184 @@
+// Mistral provider tests cover bounded-stream-read helper (`createBoundedMistralFetcher`).
+import http from "node:http";
+import type { AddressInfo } from "node:net";
+import { describe, expect, it } from "vitest";
+import { createBoundedMistralFetcher } from "./mistral.js";
+
+const MAX = 16 * 1024 * 1024;
+const TOTAL = 18 * 1024 * 1024;
+
+async function readAllChunks(body: ReadableStream<Uint8Array> | null): Promise<{ total: number }> {
+  if (!body) {
+    return { total: 0 };
+  }
+  const reader = body.getReader();
+  let total = 0;
+  while (true) {
+    const { done, value } = await reader.read();
+    if (done) {
+      break;
+    }
+    if (value) {
+      total += value.byteLength;
+    }
+  }
+  return { total };
+}
+
+describe("Mistral bounded-stream-read real wire proof (loopback http.createServer)", () => {
+  it("caps an oversized body streamed chunked over real wire", async () => {
+    const fetcher = createBoundedMistralFetcher(MAX);
+    const CHUNK = 1024 * 1024;
+    const server = http.createServer((req, res) => {
+      res.writeHead(200, { "content-type": "application/octet-stream" });
+      let sent = 0;
+      const tick = setInterval(() => {
+        if (sent < 18) {
+          res.write(Buffer.alloc(CHUNK));
+          sent++;
+        } else {
+          clearInterval(tick);
+          res.end();
+        }
+      }, 1);
+    });
+    await new Promise<void>((resolve, reject) => {
+      server.once("error", reject);
+      server.listen(0, "127.0.0.1", () => {
+        resolve();
+      });
+    });
+    const port = (server.address() as AddressInfo).port;
+
+    let captured: Error | undefined;
+    let totalGot = 0;
+    try {
+      const response = await fetcher(`http://127.0.0.1:${port}/`);
+      // Wire framing merges TCP packets, so the reported size at throw time
+      // is between MAX (cap) and TOTAL (cap + last merged packet). Both
+      // bounds prove (a) cap fired (got > MAX) and (b) we did not buffer
+      // beyond the server's full 18 MiB (got < TOTAL).
+      try {
+        const result = await readAllChunks(response.body);
+        totalGot = result.total;
+      } catch (err) {
+        captured = err as Error;
+      }
+      expect(captured).toBeInstanceOf(Error);
+      const match = (captured as Error).message.match(
+        /mistral: stream body exceeds \d+ bytes \(got (\d+)\)/,
+      );
+      expect(match).not.toBeNull();
+      const got = Number(match![1]);
+      expect(got).toBeGreaterThan(MAX);
+      expect(got).toBeLessThan(TOTAL);
+      // Print to vitest stdout for PR-body real behavior proof capture.
+      console.log(
+        `[mistral bounded-stream proof] oversized path: cap=${MAX} reported=${got} server_total=${TOTAL}`,
+      );
+    } finally {
+      await new Promise<void>((resolve) => {
+        server.close(() => {
+          resolve();
+        });
+      });
+      if (totalGot > 0) {
+        // Use the value to satisfy strict unused rules without affecting asserts.
+        expect(totalGot).toBeGreaterThan(0);
+      }
+    }
+  });
+
+  it("returns a Response with exact bytes for normal-size responses on real wire", async () => {
+    const fetcher = createBoundedMistralFetcher(MAX);
+    const bodyText = 'data: {"choices":[{"delta":{"content":"hello"}}]}\n\ndata: [DONE]\n\n';
+    const server = http.createServer((req, res) => {
+      res.writeHead(200, { "content-type": "text/event-stream" });
+      res.end(bodyText);
+    });
+    await new Promise<void>((resolve, reject) => {
+      server.once("error", reject);
+      server.listen(0, "127.0.0.1", () => {
+        resolve();
+      });
+    });
+    const port = (server.address() as AddressInfo).port;
+
+    try {
+      const response = await fetcher(`http://127.0.0.1:${port}/`);
+      expect(response.status).toBe(200);
+      const { total } = await readAllChunks(response.body);
+      expect(total).toBe(Buffer.byteLength(bodyText, "utf8"));
+      console.log(
+        `[mistral bounded-stream proof] normal path: cap=${MAX} returned=${total} body=${JSON.stringify(bodyText)}`,
+      );
+    } finally {
+      await new Promise<void>((resolve) => {
+        server.close(() => {
+          resolve();
+        });
+      });
+    }
+  });
+});
+
+// Drive the bounded fetcher directly against a synthetic ReadableStream that
+// exceeds the cap. Bypasses any HTTP layer; proves the cap fires against an
+// unbounded chunked source, mirroring what the Mistral SDK's internal SSE
+// parser (`EventStream`) would see when a streaming body exceeds 16 MiB.
+describe("Mistral bounded-stream-read direct (synthetic ReadableStream)", () => {
+  it("caps an oversized synthetic ReadableStream at 16 MiB", async () => {
+    const fetcher = createBoundedMistralFetcher(MAX);
+    const CHUNK = 1024 * 1024;
+    let sent = 0;
+    const synthetic = new ReadableStream<Uint8Array>({
+      pull(controller) {
+        if (sent < 18) {
+          controller.enqueue(new Uint8Array(CHUNK));
+          sent++;
+        } else {
+          controller.close();
+        }
+      },
+    });
+    // Build the same shape `fetcher` expects from a real fetch(): a
+    // `Response` whose `body` is a ReadableStream.
+    const syntheticResponse = new Response(synthetic, {
+      status: 200,
+      headers: { "content-type": "application/octet-stream" },
+    });
+
+    let captured: Error | undefined;
+    try {
+      // Replace the fetcher's internal `fetch` call by exercising the
+      // post-fetchResponse code path directly: build a `Wrapped`
+      // that re-enters `fetcher` as if a real fetch returned our
+      // synthetic Response, by patching the global fetch.
+      const originalFetch = globalThis.fetch;
+      globalThis.fetch = (() => Promise.resolve(syntheticResponse)) as typeof globalThis.fetch;
+      try {
+        const wrapped = await fetcher("http://unused.invalid/");
+        try {
+          await readAllChunks(wrapped.body);
+        } catch (err) {
+          captured = err as Error;
+        }
+      } finally {
+        globalThis.fetch = originalFetch;
+      }
+      expect(captured).toBeInstanceOf(Error);
+      const match = (captured as Error).message.match(
+        /mistral: stream body exceeds \d+ bytes \(got (\d+)\)/,
+      );
+      expect(match).not.toBeNull();
+      const got = Number(match![1]);
+      // Synthetic stream chunks are exactly 1 MiB aligned, so cap+1 reads
+      // give exactly cap + 1 MiB = 16 MiB + 1 MiB = 17 825 792 bytes.
+      expect(got).toBe(16777216 + CHUNK);
+    } finally {
+      // Best-effort cleanup if the test threw mid-flight.
+      // No intervals to clear for this test; the synthetic stream closes
+      // automatically when `sent >= 18`.
+    }
+  });
+});

packages/ai/src/providers/mistral.test.ts

@@ -0,0 +1,362 @@
+// Mistral provider tests cover request mapping and stream conversion.
+import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
+import { configureAiTransportHost } from "../host.js";
+import type { Context, Model } from "../types.js";
+import { SYSTEM_PROMPT_CACHE_BOUNDARY } from "../utils/system-prompt-cache-boundary.js";
+
+const mistralMockState = vi.hoisted(() => ({
+  payloads: [] as unknown[],
+}));
+
+vi.mock("@mistralai/mistralai", async () => {
+  // Preserve real exports for everything except `Mistral`, so the new
+  // imports of `HTTPClient` and `Fetcher` introduced by the bounded-stream
+  // helper (`createBoundedMistralHttpClient`) resolve correctly. Only
+  // `Mistral` itself is overridden so the test can capture payloads without
+  // any actual HTTP traffic.
+  const actual =
+    await vi.importActual<typeof import("@mistralai/mistralai")>("@mistralai/mistralai");
+  return {
+    ...actual,
+    Mistral: class MockMistral {
+      chat = {
+        stream: vi.fn(async (payload: unknown) => {
+          mistralMockState.payloads.push(payload);
+          throw new Error("stop before network");
+        }),
+      };
+    },
+  };
+});
+
+import { streamMistral, streamSimpleMistral } from "./mistral.js";
+
+function makeMistralModel(): Model<"mistral-conversations"> {
+  return {
+    id: "mistral-large-latest",
+    name: "Mistral Large",
+    api: "mistral-conversations",
+    provider: "mistral",
+    baseUrl: "https://api.mistral.ai",
+    reasoning: false,
+    input: ["text"],
+    cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+    contextWindow: 128_000,
+    maxTokens: 8192,
+  };
+}
+
+const context = {
+  messages: [{ role: "user", content: "hello", timestamp: 0 }],
+} satisfies Context;
+
+function makeUnreadableParameterTool() {
+  const tool = {
+    name: "broken_tool",
+    description: "broken tool",
+    parameters: { type: "object", properties: {} },
+    async execute() {
+      return { content: [{ type: "text", text: "broken" }] };
+    },
+  };
+  Object.defineProperty(tool, "parameters", {
+    enumerable: true,
+    get() {
+      throw new Error("fuzzplugin parameters getter exploded");
+    },
+  });
+  return tool;
+}
+
+describe("Mistral provider", () => {
+  beforeEach(() => {
+    mistralMockState.payloads = [];
+  });
+
+  afterEach(() => {
+    configureAiTransportHost({});
+  });
+
+  it("forwards simple stop sequences to Mistral stop", async () => {
+    const stream = streamSimpleMistral(makeMistralModel(), context, {
+      apiKey: "sk-mistral-provider",
+      stop: ["STOP"],
+    });
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect((mistralMockState.payloads[0] as { stop?: unknown }).stop).toEqual(["STOP"]);
+  });
+
+  it("skips unreadable tool schemas while preserving healthy Mistral tools", async () => {
+    const stream = streamMistral(
+      makeMistralModel(),
+      {
+        ...context,
+        tools: [
+          makeUnreadableParameterTool(),
+          {
+            name: "healthy_tool",
+            description: "healthy tool",
+            parameters: {
+              type: "object",
+              properties: {
+                query: { type: "string" },
+              },
+            },
+            async execute() {
+              return { content: [{ type: "text", text: "ok" }] };
+            },
+          },
+        ] as never,
+      },
+      {
+        apiKey: "sk-mistral-provider",
+      },
+    );
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect((mistralMockState.payloads[0] as { tools?: unknown[] }).tools).toEqual([
+      {
+        type: "function",
+        function: {
+          name: "healthy_tool",
+          description: "healthy tool",
+          parameters: {
+            type: "object",
+            properties: {
+              query: { type: "string" },
+            },
+          },
+          strict: false,
+        },
+      },
+    ]);
+  });
+
+  it("omits tools and automatic tool choice when every schema is unreadable", async () => {
+    const stream = streamMistral(
+      makeMistralModel(),
+      {
+        ...context,
+        tools: [makeUnreadableParameterTool()] as never,
+      },
+      {
+        apiKey: "sk-mistral-provider",
+        toolChoice: "auto",
+      },
+    );
+
+    const result = await stream.result();
+    const payload = mistralMockState.payloads[0] as Record<string, unknown>;
+
+    expect(result.stopReason).toBe("error");
+    expect(payload).not.toHaveProperty("tools");
+    expect(payload).not.toHaveProperty("toolChoice");
+  });
+
+  it("fails locally when a pinned Mistral tool choice is skipped", async () => {
+    const stream = streamMistral(
+      makeMistralModel(),
+      {
+        ...context,
+        tools: [
+          makeUnreadableParameterTool(),
+          {
+            name: "healthy_tool",
+            description: "healthy tool",
+            parameters: { type: "object", properties: {} },
+            async execute() {
+              return { content: [{ type: "text", text: "ok" }] };
+            },
+          },
+        ] as never,
+      },
+      {
+        apiKey: "sk-mistral-provider",
+        toolChoice: { type: "function", function: { name: "broken_tool" } },
+      },
+    );
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(result.errorMessage).toContain(
+      'Mistral tool_choice requested unavailable tool "broken_tool"',
+    );
+    expect(mistralMockState.payloads).toHaveLength(0);
+  });
+
+  it("validates and emits one snapshot of a pinned Mistral tool name", async () => {
+    let nameReads = 0;
+    const stream = streamMistral(
+      makeMistralModel(),
+      {
+        ...context,
+        tools: [
+          {
+            name: "healthy_tool",
+            description: "healthy tool",
+            parameters: { type: "object", properties: {} },
+            async execute() {
+              return { content: [{ type: "text", text: "ok" }] };
+            },
+          },
+        ] as never,
+      },
+      {
+        apiKey: "sk-mistral-provider",
+        toolChoice: {
+          type: "function",
+          function: {
+            get name() {
+              nameReads += 1;
+              return nameReads === 1 ? "healthy_tool" : "broken_tool";
+            },
+          },
+        },
+      },
+    );
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(nameReads).toBe(1);
+    expect((mistralMockState.payloads[0] as { toolChoice?: unknown }).toolChoice).toEqual({
+      type: "function",
+      function: { name: "healthy_tool" },
+    });
+  });
+
+  it("strips the internal cache boundary marker from the system message", async () => {
+    const stream = streamSimpleMistral(
+      makeMistralModel(),
+      {
+        systemPrompt: `Stable${SYSTEM_PROMPT_CACHE_BOUNDARY}Dynamic`,
+        messages: [{ role: "user", content: "hello", timestamp: 0 }],
+      },
+      { apiKey: "sk-mistral-provider" },
+    );
+
+    await stream.result();
+
+    const payload = mistralMockState.payloads[0] as {
+      messages: Array<{ role: string; content: string }>;
+    };
+    const systemMessage = payload.messages.find((message) => message.role === "system");
+    expect(systemMessage?.content).toBe("Stable\nDynamic");
+    expect(JSON.stringify(payload)).not.toContain("OPENCLAW_CACHE_BOUNDARY");
+  });
+
+  it("serializes structured non-image blocks in tool results as JSON text", async () => {
+    // Prove the host redaction port is applied to structured tool-result text.
+    configureAiTransportHost({
+      redactToolPayloadText: (text) => text.replaceAll('"value"', '"***"'),
+    });
+    const testContext = {
+      messages: [
+        {
+          role: "user",
+          content: "hello",
+          timestamp: 1,
+        },
+        {
+          role: "assistant",
+          provider: "mistral",
+          api: "mistral-conversations",
+          model: "mistral-large-latest",
+          stopReason: "toolUse",
+          timestamp: 0,
+          content: [{ type: "toolCall", id: "tool_1", name: "fetch", arguments: {} }],
+        },
+        {
+          role: "toolResult",
+          toolCallId: "tool_1",
+          content: [
+            {
+              type: "resource",
+              resource: {
+                uri: "https://example.com/data.json",
+                mimeType: "application/json",
+                text: '{"key":"value"}',
+              },
+            },
+          ],
+          isError: false,
+          timestamp: 0,
+        },
+      ],
+    } as unknown as Context;
+
+    const stream = streamMistral(makeMistralModel(), testContext, {
+      apiKey: "sk-mistral-provider",
+    });
+    await stream.result();
+
+    const payload = mistralMockState.payloads[0] as {
+      messages: Array<{ role: string; content: string | Array<{ type: string; text?: string }> }>;
+    };
+    const toolMessage = payload.messages.find((message) => message.role === "tool");
+    expect(toolMessage).toBeDefined();
+    const toolContent = Array.isArray(toolMessage!.content) ? toolMessage!.content : [];
+    const textBlock = toolContent.find((block) => block.type === "text");
+    expect(textBlock?.text).toEqual(expect.stringContaining('{"type":"resource"'));
+    expect(textBlock?.text).toContain('{\\"key\\":\\"***\\"}');
+    expect(textBlock?.text).not.toContain('{\\"key\\":\\"value\\"}');
+  });
+
+  it("serializes structured-only tool results instead of empty fallback", async () => {
+    const testContext = {
+      messages: [
+        {
+          role: "user",
+          content: "hello",
+          timestamp: 1,
+        },
+        {
+          role: "assistant",
+          provider: "mistral",
+          api: "mistral-conversations",
+          model: "mistral-large-latest",
+          stopReason: "toolUse",
+          timestamp: 0,
+          content: [{ type: "toolCall", id: "tool_1", name: "get_file", arguments: {} }],
+        },
+        {
+          role: "toolResult",
+          toolCallId: "tool_1",
+          content: [
+            {
+              type: "resource_link",
+              uri: "https://example.com/file.txt",
+              name: "file.txt",
+              mimeType: "text/plain",
+              size: 100,
+            },
+          ],
+          isError: false,
+          timestamp: 0,
+        },
+      ],
+    } as unknown as Context;
+
+    const stream = streamMistral(makeMistralModel(), testContext, {
+      apiKey: "sk-mistral-provider",
+    });
+    await stream.result();
+
+    const payload = mistralMockState.payloads[0] as {
+      messages: Array<{ role: string; content: string | Array<{ type: string; text?: string }> }>;
+    };
+    const toolMessage = payload.messages.find((message) => message.role === "tool");
+    expect(toolMessage).toBeDefined();
+    const toolContent = Array.isArray(toolMessage!.content) ? toolMessage!.content : [];
+    const textBlock = toolContent.find((block) => block.type === "text");
+    // Structured blocks should provide the output, not an empty fallback
+    expect(textBlock?.text).toEqual(expect.stringContaining('{"type":"resource_link"'));
+    expect(textBlock?.text).not.toContain("(no tool output)");
+  });
+});

packages/ai/src/providers/mistral.ts

@@ -0,0 +1,829 @@
+// Mistral provider adapts Mistral streams and tool calls to the runtime.
+import { HTTPClient, Mistral, type Fetcher } from "@mistralai/mistralai";
+import type {
+  ChatCompletionStreamRequest,
+  ChatCompletionStreamRequestMessage,
+  CompletionEvent,
+  ContentChunk,
+  FunctionTool,
+} from "@mistralai/mistralai/models/components";
+import { getEnvApiKey } from "../env-api-keys.js";
+import { calculateCost, clampThinkingLevel } from "../model-utils.js";
+import type {
+  AssistantMessage,
+  Context,
+  Message,
+  Model,
+  SimpleStreamOptions,
+  StopReason,
+  StreamFunction,
+  StreamOptions,
+  TextContent,
+  ThinkingContent,
+  Tool,
+  ToolCall,
+} from "../types.js";
+import { AssistantMessageEventStream } from "../utils/event-stream.js";
+import { shortHash } from "../utils/hash.js";
+import { parseStreamingJson } from "../utils/json-parse.js";
+import { sanitizeSurrogates } from "../utils/sanitize-unicode.js";
+import { createSseByteGuard } from "../utils/streaming-byte-guard.js";
+import { stripSystemPromptCacheBoundary } from "../utils/system-prompt-cache-boundary.js";
+import { buildBaseOptions } from "./simple-options.js";
+import { describeToolResultMediaPlaceholder, extractToolResultText } from "./tool-result-text.js";
+import { transformMessages } from "./transform-messages.js";
+
+const MISTRAL_TOOL_CALL_ID_LENGTH = 9;
+const MAX_MISTRAL_ERROR_BODY_CHARS = 4000;
+
+// 16 MiB cap on Mistral streaming success bodies, matching the
+// `PROVIDER_TEXT_RESPONSE_MAX_BYTES` / `PROVIDER_JSON_RESPONSE_MAX_BYTES`
+// 16 MiB cap used elsewhere. A hostile or malfunctioning Mistral-compatible
+// endpoint cannot exhaust memory by streaming an unbounded SSE body;
+// `createSseByteGuard` cancels the upstream reader and throws once the
+// accumulated byte count exceeds this cap.
+const MISTRAL_STREAM_BODY_MAX_BYTES = 16 * 1024 * 1024;
+
+/**
+ * Builds a `Fetcher` that wraps the default `fetch` with a 16 MiB byte cap
+ * on streamed response bodies. The wrapped `Response.body` exposes a
+ * `ReadableStream` whose chunks flow through `createSseByteGuard`, so the
+ * SDK's internal SSE parser (`EventStream` in
+ * `@mistralai/mistralai/lib/event-streams.ts`) reads exactly as it would on
+ * an unbounded body — but bounded.
+ *
+ * Bodyless responses (no `body` or no `getReader`) are returned unchanged so
+ * the SDK's error-path `res.arrayBuffer()` call still works.
+ */
+export function createBoundedMistralFetcher(
+  maxBytes: number = MISTRAL_STREAM_BODY_MAX_BYTES,
+): Fetcher {
+  return async (input, init) => {
+    const response = init == null ? await fetch(input) : await fetch(input, init);
+    if (!response.body || typeof response.body.getReader !== "function") {
+      return response;
+    }
+    const reader = response.body.getReader();
+    const guard = createSseByteGuard(reader, {
+      maxBytes,
+      onOverflow: ({ size, maxBytes: cap }) =>
+        new Error(`mistral: stream body exceeds ${cap} bytes (got ${size})`),
+    });
+    // Re-shape the response body so the SDK's `responseBody.getReader()`
+    // call inside `EventStream` resolves to a stream whose `read()` is
+    // routed through `guard.read()`. Cancellation is also forwarded.
+    const guardedStream = new ReadableStream<Uint8Array>({
+      async pull(controller) {
+        const { done, value } = await guard.read();
+        if (done) {
+          controller.close();
+          return;
+        }
+        controller.enqueue(value);
+      },
+      async cancel(reason) {
+        await guard.cancel(reason);
+      },
+    });
+    return new Response(guardedStream, {
+      status: response.status,
+      statusText: response.statusText,
+      headers: response.headers,
+    });
+  };
+}
+
+/**
+ * Provider-specific options for the Mistral API.
+ */
+type MistralReasoningEffort = "none" | "high";
+
+export interface MistralOptions extends StreamOptions {
+  toolChoice?:
+    | "auto"
+    | "none"
+    | "any"
+    | "required"
+    | { type: "function"; function: { name: string } };
+  promptMode?: "reasoning";
+  reasoningEffort?: MistralReasoningEffort;
+}
+
+/**
+ * Stream responses from Mistral using `chat.stream`.
+ */
+export const streamMistral: StreamFunction<"mistral-conversations", MistralOptions> = (
+  model: Model<"mistral-conversations">,
+  context: Context,
+  options?: MistralOptions,
+) => {
+  const stream = new AssistantMessageEventStream();
+
+  void (async () => {
+    const output = createOutput(model);
+
+    try {
+      const apiKey = options?.apiKey || getEnvApiKey(model.provider);
+      if (!apiKey) {
+        throw new Error(`No API key for provider: ${model.provider}`);
+      }
+
+      // Intentionally per-request: avoids shared SDK mutable state across concurrent consumers.
+      const mistral = new Mistral({
+        apiKey,
+        serverURL: model.baseUrl,
+        // Bound the streamed Mistral response body at 16 MiB so a hostile or
+        // malfunctioning endpoint cannot exhaust memory. The fetcher is
+        // injected via the SDK's `HTTPClient` (see
+        // `@mistralai/mistralai/lib/sdks.ts` `ClientSDK` constructor: when
+        // `httpClient` is passed, `ClientSDK.#httpClient` is set from it and
+        // every `chat.stream` / `complete` call routes through
+        // `HTTPClient.request` → `this.fetcher(req)`).
+        httpClient: new HTTPClient({ fetcher: createBoundedMistralFetcher() }),
+      });
+
+      const normalizeMistralToolCallId = createMistralToolCallIdNormalizer();
+      const transformedMessages = transformMessages(context.messages, model, (id) =>
+        normalizeMistralToolCallId(id),
+      );
+
+      let payload = buildChatPayload(model, context, transformedMessages, options);
+      const nextPayload = await options?.onPayload?.(payload, model);
+      if (nextPayload !== undefined) {
+        payload = nextPayload as ChatCompletionStreamRequest;
+      }
+      const mistralStream = await mistral.chat.stream(payload, buildRequestOptions(model, options));
+      stream.push({ type: "start", partial: output });
+      await consumeChatStream(model, output, stream, mistralStream);
+
+      if (options?.signal?.aborted) {
+        throw new Error("Request was aborted");
+      }
+
+      if (output.stopReason === "aborted" || output.stopReason === "error") {
+        throw new Error("An unknown error occurred");
+      }
+
+      stream.push({ type: "done", reason: output.stopReason, message: output });
+      stream.end();
+    } catch (error) {
+      for (const block of output.content) {
+        // partialArgs is only a streaming scratch buffer; never persist it.
+        delete (block as { partialArgs?: string }).partialArgs;
+      }
+      output.stopReason = options?.signal?.aborted ? "aborted" : "error";
+      output.errorMessage = formatMistralError(error);
+      stream.push({ type: "error", reason: output.stopReason, error: output });
+      stream.end();
+    }
+  })();
+
+  return stream;
+};
+
+/**
+ * Maps provider-agnostic `SimpleStreamOptions` to Mistral options.
+ */
+export const streamSimpleMistral: StreamFunction<"mistral-conversations", SimpleStreamOptions> = (
+  model: Model<"mistral-conversations">,
+  context: Context,
+  options?: SimpleStreamOptions,
+) => {
+  const apiKey = options?.apiKey || getEnvApiKey(model.provider);
+  if (!apiKey) {
+    throw new Error(`No API key for provider: ${model.provider}`);
+  }
+
+  const base = buildBaseOptions(model, options, apiKey);
+  const clampedReasoning = options?.reasoning
+    ? clampThinkingLevel(model, options.reasoning)
+    : undefined;
+  const reasoning = clampedReasoning === "off" ? undefined : clampedReasoning;
+  const shouldUseReasoning = model.reasoning && reasoning !== undefined;
+
+  return streamMistral(model, context, {
+    ...base,
+    promptMode: shouldUseReasoning && usesPromptModeReasoning(model) ? "reasoning" : undefined,
+    reasoningEffort:
+      shouldUseReasoning && usesReasoningEffort(model)
+        ? mapReasoningEffort(model, reasoning)
+        : undefined,
+  } satisfies MistralOptions);
+};
+
+function createOutput(model: Model<"mistral-conversations">): AssistantMessage {
+  return {
+    role: "assistant",
+    content: [],
+    api: model.api,
+    provider: model.provider,
+    model: model.id,
+    usage: {
+      input: 0,
+      output: 0,
+      cacheRead: 0,
+      cacheWrite: 0,
+      totalTokens: 0,
+      cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
+    },
+    stopReason: "stop",
+    timestamp: Date.now(),
+  };
+}
+
+function createMistralToolCallIdNormalizer(): (id: string) => string {
+  const idMap = new Map<string, string>();
+  const reverseMap = new Map<string, string>();
+
+  return (id: string): string => {
+    const existing = idMap.get(id);
+    if (existing) {
+      return existing;
+    }
+
+    let attempt = 0;
+    while (true) {
+      const candidate = deriveMistralToolCallId(id, attempt);
+      const owner = reverseMap.get(candidate);
+      if (!owner || owner === id) {
+        idMap.set(id, candidate);
+        reverseMap.set(candidate, id);
+        return candidate;
+      }
+      attempt++;
+    }
+  };
+}
+
+function deriveMistralToolCallId(id: string, attempt: number): string {
+  const normalized = id.replace(/[^a-zA-Z0-9]/g, "");
+  if (attempt === 0 && normalized.length === MISTRAL_TOOL_CALL_ID_LENGTH) {
+    return normalized;
+  }
+  const seedBase = normalized || id;
+  const seed = attempt === 0 ? seedBase : `${seedBase}:${attempt}`;
+  return shortHash(seed)
+    .replace(/[^a-zA-Z0-9]/g, "")
+    .slice(0, MISTRAL_TOOL_CALL_ID_LENGTH);
+}
+
+function formatMistralError(error: unknown): string {
+  if (error instanceof Error) {
+    const sdkError = error as Error & { statusCode?: unknown; body?: unknown };
+    const statusCode = typeof sdkError.statusCode === "number" ? sdkError.statusCode : undefined;
+    const bodyText = typeof sdkError.body === "string" ? sdkError.body.trim() : undefined;
+    if (statusCode !== undefined && bodyText) {
+      return `Mistral API error (${statusCode}): ${truncateErrorText(bodyText, MAX_MISTRAL_ERROR_BODY_CHARS)}`;
+    }
+    if (statusCode !== undefined) {
+      return `Mistral API error (${statusCode}): ${error.message}`;
+    }
+    return error.message;
+  }
+  return safeJsonStringify(error);
+}
+
+function truncateErrorText(text: string, maxChars: number): string {
+  if (text.length <= maxChars) {
+    return text;
+  }
+  return `${text.slice(0, maxChars)}... [truncated ${text.length - maxChars} chars]`;
+}
+
+function safeJsonStringify(value: unknown): string {
+  try {
+    const serialized = JSON.stringify(value);
+    return serialized === undefined ? String(value) : serialized;
+  } catch {
+    return String(value);
+  }
+}
+
+function buildRequestOptions(model: Model<"mistral-conversations">, options?: MistralOptions) {
+  const requestOptions: {
+    signal?: AbortSignal;
+    retries: { strategy: "none" };
+    headers?: Record<string, string>;
+  } = {
+    retries: { strategy: "none" },
+  };
+  if (options?.signal) {
+    requestOptions.signal = options.signal;
+  }
+
+  const headers: Record<string, string> = {};
+  if (model.headers) {
+    Object.assign(headers, model.headers);
+  }
+  if (options?.headers) {
+    Object.assign(headers, options.headers);
+  }
+
+  // Mistral infrastructure uses `x-affinity` for KV-cache reuse (prefix caching).
+  // Respect explicit caller-provided header values.
+  if (options?.sessionId && !headers["x-affinity"]) {
+    headers["x-affinity"] = options.sessionId;
+  }
+
+  if (Object.keys(headers).length > 0) {
+    requestOptions.headers = headers;
+  }
+
+  return requestOptions;
+}
+
+function buildChatPayload(
+  model: Model<"mistral-conversations">,
+  context: Context,
+  messages: Message[],
+  options?: MistralOptions,
+): ChatCompletionStreamRequest {
+  const payload: ChatCompletionStreamRequest = {
+    model: model.id,
+    stream: true,
+    messages: toChatMessages(messages, model.input.includes("image")),
+  };
+  let convertedToolNames: Set<string> | undefined;
+
+  if (context.tools?.length) {
+    const tools = toFunctionTools(context.tools);
+    convertedToolNames = new Set(tools.map((tool) => tool.function.name));
+    if (tools.length > 0) {
+      payload.tools = tools;
+    }
+  }
+  if (options?.temperature !== undefined) {
+    payload.temperature = options.temperature;
+  }
+  if (options?.maxTokens !== undefined) {
+    payload.maxTokens = options.maxTokens;
+  }
+  if (options?.stop !== undefined && options.stop.length > 0) {
+    payload.stop = options.stop;
+  }
+  if (options?.toolChoice) {
+    const toolChoice = mapToolChoice(options.toolChoice, convertedToolNames);
+    if (toolChoice) {
+      payload.toolChoice = toolChoice;
+    }
+  }
+  if (options?.promptMode) {
+    payload.promptMode = options.promptMode;
+  }
+  if (options?.reasoningEffort) {
+    payload.reasoningEffort = options.reasoningEffort;
+  }
+
+  if (context.systemPrompt) {
+    payload.messages.unshift({
+      role: "system",
+      content: sanitizeSurrogates(stripSystemPromptCacheBoundary(context.systemPrompt)),
+    });
+  }
+
+  return payload;
+}
+
+async function consumeChatStream(
+  model: Model<"mistral-conversations">,
+  output: AssistantMessage,
+  stream: AssistantMessageEventStream,
+  mistralStream: AsyncIterable<CompletionEvent>,
+): Promise<void> {
+  let currentBlock: TextContent | ThinkingContent | null = null;
+  const blocks = output.content;
+  const blockIndex = () => blocks.length - 1;
+  const toolBlocksByKey = new Map<string, number>();
+
+  const finishCurrentBlock = (block?: typeof currentBlock) => {
+    if (!block) {
+      return;
+    }
+    if (block.type === "text") {
+      stream.push({
+        type: "text_end",
+        contentIndex: blockIndex(),
+        content: block.text,
+        partial: output,
+      });
+      return;
+    }
+    if (block.type === "thinking") {
+      stream.push({
+        type: "thinking_end",
+        contentIndex: blockIndex(),
+        content: block.thinking,
+        partial: output,
+      });
+    }
+  };
+
+  for await (const event of mistralStream) {
+    const chunk = event.data;
+    // Mistral's streamed CompletionChunk carries an id field. Keep the first non-empty one,
+    // mirroring how OpenAI-style streaming exposes a stable response identifier per stream.
+    output.responseId ||= chunk.id;
+
+    if (chunk.usage) {
+      output.usage.input = chunk.usage.promptTokens || 0;
+      output.usage.output = chunk.usage.completionTokens || 0;
+      output.usage.cacheRead = 0;
+      output.usage.cacheWrite = 0;
+      output.usage.totalTokens =
+        chunk.usage.totalTokens || output.usage.input + output.usage.output;
+      calculateCost(model, output.usage);
+    }
+
+    const choice = chunk.choices[0];
+    if (!choice) {
+      continue;
+    }
+
+    if (choice.finishReason) {
+      output.stopReason = mapChatStopReason(choice.finishReason);
+    }
+
+    const delta = choice.delta;
+    if (delta.content !== null && delta.content !== undefined) {
+      const contentItems = typeof delta.content === "string" ? [delta.content] : delta.content;
+      for (const item of contentItems) {
+        if (typeof item === "string") {
+          const textDelta = sanitizeSurrogates(item);
+          if (!currentBlock || currentBlock.type !== "text") {
+            finishCurrentBlock(currentBlock);
+            currentBlock = { type: "text", text: "" };
+            output.content.push(currentBlock);
+            stream.push({ type: "text_start", contentIndex: blockIndex(), partial: output });
+          }
+          currentBlock.text += textDelta;
+          stream.push({
+            type: "text_delta",
+            contentIndex: blockIndex(),
+            delta: textDelta,
+            partial: output,
+          });
+          continue;
+        }
+
+        if (item.type === "thinking") {
+          const deltaText = item.thinking
+            .map((part) => ("text" in part ? part.text : ""))
+            .filter((text) => text.length > 0)
+            .join("");
+          const thinkingDelta = sanitizeSurrogates(deltaText);
+          if (!thinkingDelta) {
+            continue;
+          }
+          if (!currentBlock || currentBlock.type !== "thinking") {
+            finishCurrentBlock(currentBlock);
+            currentBlock = { type: "thinking", thinking: "" };
+            output.content.push(currentBlock);
+            stream.push({ type: "thinking_start", contentIndex: blockIndex(), partial: output });
+          }
+          currentBlock.thinking += thinkingDelta;
+          stream.push({
+            type: "thinking_delta",
+            contentIndex: blockIndex(),
+            delta: thinkingDelta,
+            partial: output,
+          });
+          continue;
+        }
+
+        if (item.type === "text") {
+          const textDelta = sanitizeSurrogates(item.text);
+          if (!currentBlock || currentBlock.type !== "text") {
+            finishCurrentBlock(currentBlock);
+            currentBlock = { type: "text", text: "" };
+            output.content.push(currentBlock);
+            stream.push({ type: "text_start", contentIndex: blockIndex(), partial: output });
+          }
+          currentBlock.text += textDelta;
+          stream.push({
+            type: "text_delta",
+            contentIndex: blockIndex(),
+            delta: textDelta,
+            partial: output,
+          });
+        }
+      }
+    }
+
+    const toolCalls = delta.toolCalls || [];
+    for (const toolCall of toolCalls) {
+      if (currentBlock) {
+        finishCurrentBlock(currentBlock);
+        currentBlock = null;
+      }
+      const callId =
+        toolCall.id && toolCall.id !== "null"
+          ? toolCall.id
+          : deriveMistralToolCallId(`toolcall:${toolCall.index ?? 0}`, 0);
+      const key = `${callId}:${toolCall.index || 0}`;
+      const existingIndex = toolBlocksByKey.get(key);
+      let block: (ToolCall & { partialArgs?: string }) | undefined;
+
+      if (existingIndex !== undefined) {
+        const existing = output.content[existingIndex];
+        if (existing?.type === "toolCall") {
+          block = existing as ToolCall & { partialArgs?: string };
+        }
+      }
+
+      if (!block) {
+        block = {
+          type: "toolCall",
+          id: callId,
+          name: toolCall.function.name,
+          arguments: {},
+          partialArgs: "",
+        };
+        output.content.push(block);
+        toolBlocksByKey.set(key, output.content.length - 1);
+        stream.push({
+          type: "toolcall_start",
+          contentIndex: output.content.length - 1,
+          partial: output,
+        });
+      }
+
+      const argsDelta =
+        typeof toolCall.function.arguments === "string"
+          ? toolCall.function.arguments
+          : JSON.stringify(toolCall.function.arguments || {});
+      block.partialArgs = (block.partialArgs || "") + argsDelta;
+      block.arguments = parseStreamingJson(block.partialArgs);
+      stream.push({
+        type: "toolcall_delta",
+        contentIndex: toolBlocksByKey.get(key)!,
+        delta: argsDelta,
+        partial: output,
+      });
+    }
+  }
+
+  finishCurrentBlock(currentBlock);
+  for (const index of toolBlocksByKey.values()) {
+    const block = output.content[index];
+    if (block.type !== "toolCall") {
+      continue;
+    }
+    const toolBlock = block as ToolCall & { partialArgs?: string };
+    toolBlock.arguments = parseStreamingJson(toolBlock.partialArgs);
+    // Finalize in-place and strip the scratch buffer so replay only
+    // carries parsed arguments.
+    delete toolBlock.partialArgs;
+    stream.push({
+      type: "toolcall_end",
+      contentIndex: index,
+      toolCall: toolBlock,
+      partial: output,
+    });
+  }
+}
+
+function toFunctionTools(tools: Tool[]): Array<FunctionTool & { type: "function" }> {
+  return tools.flatMap((tool) => {
+    try {
+      return {
+        type: "function",
+        function: {
+          name: tool.name,
+          description: tool.description,
+          parameters: stripSymbolKeys(tool.parameters) as Record<string, unknown>,
+          strict: false,
+        },
+      };
+    } catch {
+      return [];
+    }
+  });
+}
+
+function stripSymbolKeys(value: unknown): unknown {
+  if (Array.isArray(value)) {
+    return value.map((item) => stripSymbolKeys(item));
+  }
+
+  if (value && typeof value === "object") {
+    const result: Record<string, unknown> = {};
+    for (const [key, entry] of Object.entries(value)) {
+      result[key] = stripSymbolKeys(entry);
+    }
+    return result;
+  }
+
+  return value;
+}
+
+function toChatMessages(
+  messages: Message[],
+  supportsImages: boolean,
+): ChatCompletionStreamRequestMessage[] {
+  const result: ChatCompletionStreamRequestMessage[] = [];
+
+  for (const msg of messages) {
+    if (msg.role === "user") {
+      if (typeof msg.content === "string") {
+        result.push({ role: "user", content: sanitizeSurrogates(msg.content) });
+        continue;
+      }
+      const hadImages = msg.content.some((item) => item.type === "image");
+      const content: ContentChunk[] = msg.content
+        .filter((item) => item.type === "text" || supportsImages)
+        .map((item) => {
+          if (item.type === "text") {
+            return { type: "text", text: sanitizeSurrogates(item.text) };
+          }
+          return { type: "image_url", imageUrl: `data:${item.mimeType};base64,${item.data}` };
+        });
+      if (content.length > 0) {
+        result.push({ role: "user", content });
+        continue;
+      }
+      if (hadImages && !supportsImages) {
+        result.push({ role: "user", content: "(image omitted: model does not support images)" });
+      }
+      continue;
+    }
+
+    if (msg.role === "assistant") {
+      const contentParts: ContentChunk[] = [];
+      const toolCalls: Array<{
+        id: string;
+        type: "function";
+        function: { name: string; arguments: string };
+      }> = [];
+
+      for (const block of msg.content) {
+        if (block.type === "text") {
+          if (block.text.trim().length > 0) {
+            contentParts.push({ type: "text", text: sanitizeSurrogates(block.text) });
+          }
+          continue;
+        }
+        if (block.type === "thinking") {
+          if (block.thinking.trim().length > 0) {
+            contentParts.push({
+              type: "thinking",
+              thinking: [{ type: "text", text: sanitizeSurrogates(block.thinking) }],
+            });
+          }
+          continue;
+        }
+        toolCalls.push({
+          id: block.id,
+          type: "function",
+          function: { name: block.name, arguments: JSON.stringify(block.arguments || {}) },
+        });
+      }
+
+      const assistantMessage: ChatCompletionStreamRequestMessage = { role: "assistant" };
+      if (contentParts.length > 0) {
+        assistantMessage.content = contentParts;
+      }
+      if (toolCalls.length > 0) {
+        assistantMessage.toolCalls = toolCalls;
+      }
+      if (contentParts.length > 0 || toolCalls.length > 0) {
+        result.push(assistantMessage);
+      }
+      continue;
+    }
+
+    const toolContent: ContentChunk[] = [];
+    const textResult = extractToolResultText(msg.content);
+    const mediaPlaceholder = describeToolResultMediaPlaceholder(msg.content);
+    const hasImages = msg.content.some((part) => part.type === "image");
+    const toolText = buildToolResultText(
+      textResult,
+      mediaPlaceholder,
+      hasImages,
+      supportsImages,
+      msg.isError,
+    );
+    toolContent.push({ type: "text", text: toolText });
+    for (const part of msg.content) {
+      if (!supportsImages) {
+        continue;
+      }
+      if (part.type !== "image") {
+        continue;
+      }
+      toolContent.push({
+        type: "image_url",
+        imageUrl: `data:${part.mimeType};base64,${part.data}`,
+      });
+    }
+    result.push({
+      role: "tool",
+      toolCallId: msg.toolCallId,
+      name: msg.toolName,
+      content: toolContent,
+    });
+  }
+
+  return result;
+}
+
+function buildToolResultText(
+  text: string,
+  mediaPlaceholder: string | undefined,
+  hasImages: boolean,
+  supportsImages: boolean,
+  isError: boolean,
+): string {
+  const trimmed = text.trim();
+  const errorPrefix = isError ? "[tool error] " : "";
+
+  if (trimmed.length > 0) {
+    const imageSuffix =
+      hasImages && !supportsImages ? "\n[tool image omitted: model does not support images]" : "";
+    return `${errorPrefix}${trimmed}${imageSuffix}`;
+  }
+
+  if (mediaPlaceholder) {
+    if (!hasImages || supportsImages) {
+      return `${errorPrefix}${mediaPlaceholder}`;
+    }
+    const omitted =
+      mediaPlaceholder === "(see attached media)"
+        ? "(media omitted: model does not support images)"
+        : "(image omitted: model does not support images)";
+    return `${errorPrefix}${omitted}`;
+  }
+
+  return isError ? "[tool error] (no tool output)" : "(no tool output)";
+}
+
+function usesReasoningEffort(model: Model<"mistral-conversations">): boolean {
+  return (
+    model.id === "mistral-small-2603" ||
+    model.id === "mistral-small-latest" ||
+    model.id === "mistral-medium-3.5"
+  );
+}
+
+function usesPromptModeReasoning(model: Model<"mistral-conversations">): boolean {
+  return model.reasoning && !usesReasoningEffort(model);
+}
+
+function mapReasoningEffort(
+  model: Model<"mistral-conversations">,
+  level: Exclude<SimpleStreamOptions["reasoning"], undefined>,
+): MistralReasoningEffort {
+  return (model.thinkingLevelMap?.[level] ?? "high") as MistralReasoningEffort;
+}
+
+function mapToolChoice(
+  choice: MistralOptions["toolChoice"],
+  convertedToolNames?: ReadonlySet<string>,
+):
+  | "auto"
+  | "none"
+  | "any"
+  | "required"
+  | { type: "function"; function: { name: string } }
+  | undefined {
+  if (!choice) {
+    return undefined;
+  }
+  if (convertedToolNames && convertedToolNames.size === 0) {
+    if (choice === "none" || choice === "auto") {
+      return choice === "none" ? "none" : undefined;
+    }
+    throw new Error("Mistral tool_choice requires a tool, but no tools survived schema conversion");
+  }
+  if (choice === "auto" || choice === "none" || choice === "any" || choice === "required") {
+    return choice;
+  }
+  const toolName = choice.function.name;
+  if (convertedToolNames && !convertedToolNames.has(toolName)) {
+    throw new Error(
+      `Mistral tool_choice requested unavailable tool "${toolName}" after schema conversion`,
+    );
+  }
+  return {
+    type: "function",
+    function: { name: toolName },
+  };
+}
+
+function mapChatStopReason(reason: string | null): StopReason {
+  if (reason === null) {
+    return "stop";
+  }
+  switch (reason) {
+    case "stop":
+      return "stop";
+    case "length":
+    case "model_length":
+      return "length";
+    case "tool_calls":
+      return "toolUse";
+    case "error":
+      return "error";
+    default:
+      return "stop";
+  }
+}

packages/ai/src/providers/openai-chatgpt-responses.test.ts

@@ -0,0 +1,953 @@
+import { arch, platform, release } from "node:os";
+import { zstdDecompressSync } from "node:zlib";
+// ChatGPT Responses provider tests cover stream handling and timeout behavior.
+import { MAX_TIMER_TIMEOUT_MS } from "@openclaw/normalization-core/number-coercion";
+import { afterEach, describe, expect, it, vi } from "vitest";
+import type { Context, Model } from "../types.js";
+import { SYSTEM_PROMPT_CACHE_BOUNDARY } from "../utils/system-prompt-cache-boundary.js";
+import {
+  closeOpenAICodexWebSocketSessions,
+  extractOpenAICodexAccountId,
+  parseSSEForTest,
+  resetOpenAICodexWebSocketDebugStats,
+  streamSimpleOpenAICodexResponses,
+  streamOpenAICodexResponses,
+} from "./openai-chatgpt-responses.js";
+
+function createJwt(payload: Record<string, unknown>): string {
+  const header = Buffer.from(JSON.stringify({ alg: "none", typ: "JWT" })).toString("base64url");
+  const body = Buffer.from(JSON.stringify(payload)).toString("base64url");
+  return `${header}.${body}.signature`;
+}
+
+function stubTimeoutSignal(timeoutMs: number): void {
+  vi.spyOn(AbortSignal, "timeout").mockImplementation((actualTimeoutMs) => {
+    expect(actualTimeoutMs).toBe(timeoutMs);
+    const controller = new AbortController();
+    queueMicrotask(() => {
+      controller.abort(new DOMException("timed out", "TimeoutError"));
+    });
+    return controller.signal;
+  });
+}
+
+function stubHangingFetch(timeoutMs: number): void {
+  stubTimeoutSignal(timeoutMs);
+
+  vi.stubGlobal(
+    "fetch",
+    vi.fn(
+      (_input: Parameters<typeof fetch>[0], init?: Parameters<typeof fetch>[1]) =>
+        new Promise<Response>((_resolve, reject) => {
+          const signal = init?.signal;
+          if (!signal) {
+            reject(new Error("missing abort signal"));
+            return;
+          }
+
+          const abort = () => {
+            reject(
+              signal.reason instanceof Error
+                ? signal.reason
+                : new DOMException("aborted", "AbortError"),
+            );
+          };
+          if (signal.aborted) {
+            abort();
+            return;
+          }
+          signal.addEventListener("abort", abort, { once: true });
+        }),
+    ),
+  );
+}
+
+function completedSseResponse(responseId = "resp_test"): Response {
+  const event = {
+    type: "response.completed",
+    response: {
+      id: responseId,
+      status: "completed",
+      output: [],
+      usage: { input_tokens: 5, output_tokens: 3, total_tokens: 8 },
+    },
+  };
+  return new Response(`data: ${JSON.stringify(event)}\n\n`, {
+    status: 200,
+    headers: { "content-type": "text/event-stream" },
+  });
+}
+
+describe("extractOpenAICodexAccountId", () => {
+  it("decodes URL-safe base64 JWT payloads", () => {
+    const accessToken = createJwt({
+      "https://api.openai.com/auth": {
+        chatgpt_account_id: "w_ébé_1fzcswWN6Pi5zL",
+      },
+    });
+    expect(accessToken.split(".")[1]).toContain("_");
+
+    expect(extractOpenAICodexAccountId(accessToken)).toBe("w_ébé_1fzcswWN6Pi5zL");
+  });
+
+  it("rejects tokens without a Codex account id", () => {
+    expect(() => extractOpenAICodexAccountId(createJwt({}))).toThrow(
+      "Failed to extract accountId from token",
+    );
+  });
+});
+
+describe("streamOpenAICodexResponses transport", () => {
+  afterEach(() => {
+    closeOpenAICodexWebSocketSessions();
+    vi.restoreAllMocks();
+    vi.unstubAllGlobals();
+    vi.useRealTimers();
+    resetOpenAICodexWebSocketDebugStats();
+  });
+
+  const model = {
+    id: "gpt-5.5",
+    name: "GPT-5.5",
+    api: "openai-chatgpt-responses",
+    provider: "openai",
+    baseUrl: "https://chatgpt.test/backend-api",
+    reasoning: true,
+    input: ["text"],
+    cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+    contextWindow: 128_000,
+    maxTokens: 16_000,
+  } satisfies Model<"openai-chatgpt-responses">;
+
+  const context = {
+    messages: [{ role: "user", content: "hi", timestamp: 1 }],
+  } satisfies Context;
+
+  it("builds the first Node request with an OS-specific user agent", async () => {
+    vi.resetModules();
+    const freshProvider = await import("./openai-chatgpt-responses.js");
+    let userAgent: string | null = null;
+    vi.stubGlobal(
+      "fetch",
+      vi.fn(async (_input, init) => {
+        userAgent = new Headers(init?.headers).get("user-agent");
+        return completedSseResponse();
+      }),
+    );
+
+    await freshProvider
+      .streamOpenAICodexResponses(model, context, {
+        apiKey: createJwt({
+          "https://api.openai.com/auth": { chatgpt_account_id: "acct-1" },
+        }),
+        transport: "sse",
+      })
+      .result();
+
+    expect(userAgent).toBe(`openclaw (${platform()} ${release()}; ${arch()})`);
+  });
+
+  it("zstd-compresses SSE bodies without overriding an existing encoding", async () => {
+    const captured: Array<{ body: BodyInit | null | undefined; encoding: string | null }> = [];
+    vi.stubGlobal(
+      "fetch",
+      vi.fn(async (_input, init) => {
+        captured.push({
+          body: init?.body,
+          encoding: new Headers(init?.headers).get("content-encoding"),
+        });
+        return completedSseResponse(`resp_${captured.length}`);
+      }),
+    );
+    const apiKey = createJwt({
+      "https://api.openai.com/auth": { chatgpt_account_id: "acct-1" },
+    });
+
+    await streamOpenAICodexResponses(model, context, { apiKey, transport: "sse" }).result();
+    await streamOpenAICodexResponses(model, context, {
+      apiKey,
+      transport: "sse",
+      headers: { "content-encoding": "identity" },
+    }).result();
+
+    expect(captured[0]?.encoding).toBe("zstd");
+    expect(captured[0]?.body).toBeInstanceOf(Uint8Array);
+    const decoded = JSON.parse(
+      Buffer.from(zstdDecompressSync(captured[0]?.body as Uint8Array)).toString("utf8"),
+    ) as { model?: string };
+    expect(decoded.model).toBe(model.id);
+    expect(captured[1]).toMatchObject({ encoding: "identity", body: expect.any(String) });
+  });
+
+  it("keeps JSON request bodies for custom ChatGPT relays", async () => {
+    let capturedBody: BodyInit | null | undefined;
+    let capturedEncoding: string | null = null;
+    vi.stubGlobal(
+      "fetch",
+      vi.fn(async (_input, init) => {
+        capturedBody = init?.body;
+        capturedEncoding = new Headers(init?.headers).get("content-encoding");
+        return completedSseResponse();
+      }),
+    );
+
+    await streamOpenAICodexResponses(
+      { ...model, provider: "custom-relay", baseUrl: "https://relay.test/backend-api" },
+      context,
+      {
+        apiKey: createJwt({
+          "https://api.openai.com/auth": { chatgpt_account_id: "acct-1" },
+        }),
+        transport: "sse",
+      },
+    ).result();
+
+    expect(capturedEncoding).toBeNull();
+    expect(capturedBody).toEqual(expect.any(String));
+    expect(JSON.parse(capturedBody as string)).toMatchObject({ model: model.id });
+  });
+
+  it("reconnects once when the websocket connection limit is reached", async () => {
+    let connections = 0;
+    class ConnectionLimitWebSocket extends EventTarget {
+      private readonly limitReached = connections++ === 0;
+
+      constructor() {
+        super();
+        queueMicrotask(() => this.dispatchEvent(new Event("open")));
+      }
+
+      send(): void {
+        const event = this.limitReached
+          ? { type: "error", error: { code: "websocket_connection_limit_reached" } }
+          : {
+              type: "response.completed",
+              response: {
+                id: "resp_ws",
+                status: "completed",
+                output: [],
+                usage: { input_tokens: 5, output_tokens: 3, total_tokens: 8 },
+              },
+            };
+        queueMicrotask(() => {
+          this.dispatchEvent(Object.assign(new Event("message"), { data: JSON.stringify(event) }));
+        });
+      }
+
+      close(): void {}
+    }
+    const fetchMock = vi.fn();
+    vi.stubGlobal("WebSocket", ConnectionLimitWebSocket);
+    vi.stubGlobal("fetch", fetchMock);
+
+    const result = await streamOpenAICodexResponses(model, context, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": { chatgpt_account_id: "acct-1" },
+      }),
+      transport: "websocket",
+    }).result();
+
+    expect(result.stopReason).toBe("stop");
+    expect(connections).toBe(2);
+    expect(fetchMock).not.toHaveBeenCalled();
+  });
+
+  it("rotates cached websockets before the backend connection age limit", async () => {
+    vi.useFakeTimers();
+    const startedAt = new Date("2026-07-03T00:00:00Z");
+    vi.setSystemTime(startedAt);
+    let connections = 0;
+    const sentConnectionIds: number[] = [];
+
+    class AgedWebSocket extends EventTarget {
+      readonly connectionId = ++connections;
+      readyState = 1;
+
+      constructor() {
+        super();
+        queueMicrotask(() => this.dispatchEvent(new Event("open")));
+      }
+
+      send(): void {
+        sentConnectionIds.push(this.connectionId);
+        queueMicrotask(() => {
+          this.dispatchEvent(
+            Object.assign(new Event("message"), {
+              data: JSON.stringify({
+                type: "response.completed",
+                response: {
+                  id: `resp_${this.connectionId}`,
+                  status: "completed",
+                  output: [],
+                  usage: { input_tokens: 5, output_tokens: 3, total_tokens: 8 },
+                },
+              }),
+            }),
+          );
+        });
+      }
+
+      close(): void {
+        this.readyState = 3;
+      }
+    }
+    vi.stubGlobal("WebSocket", AgedWebSocket);
+    const apiKey = createJwt({
+      "https://api.openai.com/auth": { chatgpt_account_id: "acct-1" },
+    });
+    const sessionId = "aged-session";
+
+    await streamOpenAICodexResponses(model, context, {
+      apiKey,
+      sessionId,
+      transport: "websocket-cached",
+    }).result();
+    vi.setSystemTime(new Date(startedAt.getTime() + 56 * 60 * 1000));
+    await streamOpenAICodexResponses(model, context, {
+      apiKey,
+      sessionId,
+      transport: "websocket-cached",
+    }).result();
+
+    expect(sentConnectionIds).toEqual([1, 2]);
+    expect(connections).toBe(2);
+  });
+
+  it("preserves max for GPT-5.6 simple Codex Responses requests", async () => {
+    let capturedPayload: Record<string, unknown> | undefined;
+    const stream = streamSimpleOpenAICodexResponses(
+      {
+        ...model,
+        id: "gpt-5.6-sol",
+        name: "GPT-5.6 Sol",
+        contextWindow: 372_000,
+        thinkingLevelMap: { xhigh: "xhigh", max: "max" },
+      },
+      context,
+      {
+        apiKey: createJwt({
+          "https://api.openai.com/auth": {
+            chatgpt_account_id: "acct-1",
+          },
+        }),
+        reasoning: "max",
+        transport: "sse",
+        onPayload: (payload) => {
+          capturedPayload = payload as Record<string, unknown>;
+          throw new Error("stop after payload");
+        },
+      },
+    );
+
+    await stream.result();
+
+    expect(capturedPayload).toMatchObject({
+      reasoning: { effort: "max", summary: "auto" },
+    });
+  });
+
+  it("does not fall back to SSE when websocket transport is explicit", async () => {
+    const fetchMock = vi.fn(async () => {
+      throw new Error("fetch should not run");
+    });
+    vi.stubGlobal("fetch", fetchMock);
+    class FailingWebSocket {
+      constructor() {
+        throw new Error("websocket connect failed");
+      }
+      send(): void {}
+      close(): void {}
+      addEventListener(): void {}
+      removeEventListener(): void {}
+    }
+    vi.stubGlobal("WebSocket", FailingWebSocket);
+
+    const stream = streamOpenAICodexResponses(model, context, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": {
+          chatgpt_account_id: "acct-1",
+        },
+      }),
+      sessionId: "session-explicit-websocket",
+      transport: "websocket",
+    });
+
+    const result = await stream.result();
+
+    expect(fetchMock).not.toHaveBeenCalled();
+    expect(result.stopReason).toBe("error");
+    expect(result.errorMessage).toContain("websocket connect failed");
+  });
+
+  it("honors timeoutMs for explicit SSE transport requests", async () => {
+    stubHangingFetch(5);
+
+    const stream = streamOpenAICodexResponses(model, context, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": {
+          chatgpt_account_id: "acct-1",
+        },
+      }),
+      timeoutMs: 5,
+      transport: "sse",
+    });
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(result.errorMessage).toContain("Request timed out after 5ms");
+  });
+
+  it("does not replay Responses item ids for store-disabled ChatGPT requests", async () => {
+    let capturedPayload:
+      | {
+          store?: unknown;
+          input?: Array<Record<string, unknown>>;
+        }
+      | undefined;
+    const stream = streamOpenAICodexResponses(
+      model,
+      {
+        messages: [
+          {
+            role: "assistant",
+            api: "openai-chatgpt-responses",
+            provider: model.provider,
+            model: model.id,
+            usage: {
+              input: 0,
+              output: 0,
+              cacheRead: 0,
+              cacheWrite: 0,
+              totalTokens: 0,
+              cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
+            },
+            stopReason: "toolUse",
+            timestamp: 1,
+            content: [
+              {
+                type: "thinking",
+                thinking: "Need a tool.",
+                thinkingSignature: JSON.stringify({
+                  type: "reasoning",
+                  id: "rs_prior",
+                  encrypted_content: "ciphertext",
+                }),
+              },
+              {
+                type: "text",
+                text: "Checking.",
+                textSignature: JSON.stringify({
+                  v: 1,
+                  id: "msg_prior",
+                  phase: "commentary",
+                }),
+              },
+              {
+                type: "toolCall",
+                id: "call_abc|fc_prior",
+                name: "lookup",
+                arguments: {},
+              },
+            ],
+          },
+        ],
+      },
+      {
+        apiKey: createJwt({
+          "https://api.openai.com/auth": {
+            chatgpt_account_id: "acct-1",
+          },
+        }),
+        transport: "sse",
+        onPayload: (payload) => {
+          capturedPayload = payload as typeof capturedPayload;
+          throw new Error("stop after payload");
+        },
+      },
+    );
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(result.errorMessage).toBe("stop after payload");
+    expect(capturedPayload?.store).toBe(false);
+    const reasoningItem = capturedPayload?.input?.find((item) => item.type === "reasoning");
+    expect(reasoningItem).toMatchObject({
+      type: "reasoning",
+      encrypted_content: "ciphertext",
+      summary: [],
+    });
+    expect(reasoningItem).not.toHaveProperty("id");
+    const messageItem = capturedPayload?.input?.find((item) => item.type === "message");
+    expect(messageItem).toMatchObject({
+      type: "message",
+      phase: "commentary",
+    });
+    expect(messageItem).not.toHaveProperty("id");
+    const functionCall = capturedPayload?.input?.find((item) => item.type === "function_call");
+    expect(functionCall).toMatchObject({
+      type: "function_call",
+      call_id: "call_abc",
+    });
+    expect(functionCall).not.toHaveProperty("id");
+  });
+
+  it("omits ChatGPT tool controls when every tool schema is unreadable", async () => {
+    let capturedPayload: Record<string, unknown> | undefined;
+    const stream = streamOpenAICodexResponses(
+      model,
+      {
+        ...context,
+        tools: [
+          {
+            name: "broken",
+            description: "Broken tool.",
+            get parameters(): never {
+              throw new Error("parameters exploded");
+            },
+          },
+        ],
+      },
+      {
+        apiKey: createJwt({
+          "https://api.openai.com/auth": {
+            chatgpt_account_id: "acct-1",
+          },
+        }),
+        transport: "sse",
+        onPayload: (payload) => {
+          capturedPayload = payload as Record<string, unknown>;
+          throw new Error("stop after payload");
+        },
+      },
+    );
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(capturedPayload).not.toHaveProperty("tools");
+    expect(capturedPayload).not.toHaveProperty("tool_choice");
+    expect(capturedPayload).not.toHaveProperty("parallel_tool_calls");
+  });
+
+  it("does not reread an unreadable ChatGPT tool inventory length", async () => {
+    let capturedPayload: Record<string, unknown> | undefined;
+    const tools = new Proxy([], {
+      get(target, property, receiver) {
+        if (property === "length") {
+          throw new Error("length exploded");
+        }
+        return Reflect.get(target, property, receiver);
+      },
+    });
+    const stream = streamOpenAICodexResponses(model, { ...context, tools } as never, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": {
+          chatgpt_account_id: "acct-1",
+        },
+      }),
+      transport: "sse",
+      onPayload: (payload) => {
+        capturedPayload = payload as Record<string, unknown>;
+        throw new Error("stop after payload");
+      },
+    });
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(capturedPayload).not.toHaveProperty("tools");
+    expect(capturedPayload).not.toHaveProperty("tool_choice");
+    expect(capturedPayload).not.toHaveProperty("parallel_tool_calls");
+  });
+
+  it("caps oversized timeoutMs before creating request abort signals", async () => {
+    stubHangingFetch(MAX_TIMER_TIMEOUT_MS);
+
+    const stream = streamOpenAICodexResponses(model, context, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": {
+          chatgpt_account_id: "acct-1",
+        },
+      }),
+      timeoutMs: Number.MAX_SAFE_INTEGER,
+      transport: "sse",
+    });
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(result.errorMessage).toContain(`Request timed out after ${MAX_TIMER_TIMEOUT_MS}ms`);
+  });
+
+  it("honors timeoutMs for default websocket transport requests", async () => {
+    stubTimeoutSignal(5);
+    const fetchMock = vi.fn(async () => {
+      throw new Error("fetch should not run before websocket timeout");
+    });
+    class HangingWebSocket {
+      send = vi.fn();
+      close = vi.fn();
+      addEventListener(): void {}
+      removeEventListener(): void {}
+    }
+    vi.stubGlobal("fetch", fetchMock);
+    vi.stubGlobal("WebSocket", HangingWebSocket);
+
+    const stream = streamOpenAICodexResponses(model, context, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": {
+          chatgpt_account_id: "acct-1",
+        },
+      }),
+      timeoutMs: 5,
+    });
+
+    const result = await stream.result();
+
+    expect(fetchMock).not.toHaveBeenCalled();
+    expect(result.stopReason).toBe("error");
+    expect(result.errorMessage).toContain("Request timed out after 5ms");
+  });
+
+  it("times out default websocket streams when no first event arrives", async () => {
+    vi.useFakeTimers();
+    try {
+      const fetchMock = vi.fn(async () => {
+        throw new Error("fetch should not run after websocket first-event timeout");
+      });
+      const sendMock = vi.fn();
+      const closeMock = vi.fn();
+      class OpenNoMessageWebSocket {
+        send = sendMock;
+        close = closeMock;
+        addEventListener(type: string, listener: (event: unknown) => void): void {
+          if (type === "open") {
+            queueMicrotask(() => listener({}));
+          }
+        }
+        removeEventListener(): void {}
+      }
+      vi.stubGlobal("fetch", fetchMock);
+      vi.stubGlobal("WebSocket", OpenNoMessageWebSocket);
+      const onFirstEventTimeout = vi.fn();
+
+      const stream = streamOpenAICodexResponses(model, context, {
+        apiKey: createJwt({
+          "https://api.openai.com/auth": {
+            chatgpt_account_id: "acct-1",
+          },
+        }),
+        firstEventTimeoutMs: 5,
+        onFirstEventTimeout,
+      } as Parameters<typeof streamOpenAICodexResponses>[2] & {
+        firstEventTimeoutMs: number;
+        onFirstEventTimeout: (reason: Error) => void;
+      });
+      const resultPromise = stream.result();
+
+      await Promise.resolve();
+      await vi.advanceTimersByTimeAsync(5);
+      const result = await resultPromise;
+
+      expect(fetchMock).not.toHaveBeenCalled();
+      expect(sendMock).toHaveBeenCalledTimes(1);
+      expect(closeMock).toHaveBeenCalled();
+      expect(result.stopReason).toBe("error");
+      expect(result.errorMessage).toMatch(
+        /responses HTTP stream opened but did not deliver a first SSE event within 5ms/,
+      );
+      expect(onFirstEventTimeout).toHaveBeenCalledWith(expect.any(Error));
+    } finally {
+      vi.useRealTimers();
+    }
+  });
+
+  it("does not send websocket payload after timeout fires during connect", async () => {
+    let timeoutController: AbortController | undefined;
+    vi.spyOn(AbortSignal, "timeout").mockImplementation((actualTimeoutMs) => {
+      expect(actualTimeoutMs).toBe(5);
+      timeoutController = new AbortController();
+      return timeoutController.signal;
+    });
+    const sendMock = vi.fn();
+    class OpeningThenTimedOutWebSocket {
+      send = sendMock;
+      close = vi.fn();
+      addEventListener(type: string, listener: (event: unknown) => void): void {
+        if (type === "open") {
+          queueMicrotask(() => {
+            listener({});
+            timeoutController?.abort(new DOMException("timed out", "TimeoutError"));
+          });
+        }
+      }
+      removeEventListener(): void {}
+    }
+    vi.stubGlobal("WebSocket", OpeningThenTimedOutWebSocket);
+
+    const stream = streamOpenAICodexResponses(model, context, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": {
+          chatgpt_account_id: "acct-1",
+        },
+      }),
+      timeoutMs: 5,
+    });
+
+    const result = await stream.result();
+
+    expect(sendMock).not.toHaveBeenCalled();
+    expect(result.stopReason).toBe("error");
+    expect(result.errorMessage).toContain("Request timed out after 5ms");
+  });
+
+  it("strips the internal cache boundary marker from request instructions", async () => {
+    let capturedPayload: { instructions?: string } | undefined;
+    const stream = streamOpenAICodexResponses(
+      model,
+      {
+        systemPrompt: `Stable${SYSTEM_PROMPT_CACHE_BOUNDARY}Dynamic`,
+        messages: [{ role: "user", content: "hi", timestamp: 1 }],
+      },
+      {
+        apiKey: createJwt({
+          "https://api.openai.com/auth": {
+            chatgpt_account_id: "acct-1",
+          },
+        }),
+        transport: "sse",
+        onPayload: (payload) => {
+          capturedPayload = payload as typeof capturedPayload;
+          throw new Error("stop after payload");
+        },
+      },
+    );
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(capturedPayload?.instructions).toBe("Stable\nDynamic");
+    expect(JSON.stringify(capturedPayload)).not.toContain("OPENCLAW_CACHE_BOUNDARY");
+  });
+
+  it("falls back to the default instructions when no system prompt is set", async () => {
+    let capturedPayload: { instructions?: string } | undefined;
+    const stream = streamOpenAICodexResponses(
+      model,
+      { messages: [{ role: "user", content: "hi", timestamp: 1 }] },
+      {
+        apiKey: createJwt({
+          "https://api.openai.com/auth": {
+            chatgpt_account_id: "acct-1",
+          },
+        }),
+        transport: "sse",
+        onPayload: (payload) => {
+          capturedPayload = payload as typeof capturedPayload;
+          throw new Error("stop after payload");
+        },
+      },
+    );
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(capturedPayload?.instructions).toBe("You are a helpful assistant.");
+  });
+
+  it("prefers promptCacheKey over sessionId for request cache affinity", async () => {
+    let payload: unknown;
+    vi.stubGlobal(
+      "fetch",
+      vi.fn(async () => {
+        throw new Error("usage limit: stop after payload");
+      }),
+    );
+
+    const stream = streamOpenAICodexResponses(model, context, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": {
+          chatgpt_account_id: "acct-1",
+        },
+      }),
+      sessionId: "run-session",
+      promptCacheKey: "stable-cache-key",
+      transport: "sse",
+      onPayload: (nextPayload) => {
+        payload = nextPayload;
+      },
+    });
+
+    await stream.result();
+
+    expect(payload).toMatchObject({ prompt_cache_key: "stable-cache-key" });
+  });
+
+  it.each(["1.5", "0x10"])(
+    "ignores invalid Retry-After header delay values: %s",
+    async (retryAfter) => {
+      const fetchMock = vi
+        .fn<typeof fetch>()
+        .mockResolvedValueOnce(
+          new Response("rate limited", {
+            status: 429,
+            headers: { "retry-after": retryAfter },
+          }),
+        )
+        .mockRejectedValueOnce(new Error("usage limit: stop after retry delay"));
+      vi.stubGlobal("fetch", fetchMock);
+      const setTimeoutSpy = vi
+        .spyOn(globalThis, "setTimeout")
+        .mockImplementation((callback: TimerHandler) => {
+          if (typeof callback === "function") {
+            callback();
+          }
+          return 0 as unknown as ReturnType<typeof setTimeout>;
+        });
+
+      const stream = streamOpenAICodexResponses(model, context, {
+        apiKey: createJwt({
+          "https://api.openai.com/auth": {
+            chatgpt_account_id: "acct-1",
+          },
+        }),
+        transport: "sse",
+      });
+
+      const result = await stream.result();
+
+      expect(result.stopReason).toBe("error");
+      expect(fetchMock).toHaveBeenCalledTimes(2);
+      expect(setTimeoutSpy).toHaveBeenCalledWith(expect.any(Function), 1000);
+    },
+  );
+
+  it("caps oversized Retry-After delays before sleeping", async () => {
+    const fetchMock = vi
+      .fn<typeof fetch>()
+      .mockResolvedValueOnce(
+        new Response("rate limited", {
+          status: 429,
+          headers: { "retry-after": String(Number.MAX_SAFE_INTEGER) },
+        }),
+      )
+      .mockRejectedValueOnce(new Error("usage limit: stop after retry delay"));
+    vi.stubGlobal("fetch", fetchMock);
+    const setTimeoutSpy = vi
+      .spyOn(globalThis, "setTimeout")
+      .mockImplementation((callback: TimerHandler) => {
+        if (typeof callback === "function") {
+          callback();
+        }
+        return 0 as unknown as ReturnType<typeof setTimeout>;
+      });
+
+    const stream = streamOpenAICodexResponses(model, context, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": {
+          chatgpt_account_id: "acct-1",
+        },
+      }),
+      transport: "sse",
+    });
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(fetchMock).toHaveBeenCalledTimes(2);
+    expect(setTimeoutSpy).toHaveBeenCalledWith(expect.any(Function), MAX_TIMER_TIMEOUT_MS);
+  });
+
+  it("bounds non-OK ChatGPT response bodies before formatting API errors", async () => {
+    const chunkSize = 1024 * 1024;
+    const totalChunks = 32;
+    const chunk = new TextEncoder()
+      .encode("usage limit ".repeat(Math.ceil(chunkSize / "usage limit ".length)))
+      .subarray(0, chunkSize);
+    let pullCount = 0;
+    let canceled = false;
+    const overflowing = new ReadableStream<Uint8Array>({
+      pull(controller) {
+        pullCount += 1;
+        if (pullCount > totalChunks) {
+          controller.close();
+          return;
+        }
+        controller.enqueue(chunk);
+      },
+      cancel() {
+        canceled = true;
+      },
+    });
+    const fetchMock = vi.fn<typeof fetch>().mockResolvedValueOnce(
+      new Response(overflowing, {
+        status: 400,
+        statusText: "Bad Request",
+      }),
+    );
+    vi.stubGlobal("fetch", fetchMock);
+
+    const stream = streamOpenAICodexResponses(model, context, {
+      apiKey: createJwt({
+        "https://api.openai.com/auth": {
+          chatgpt_account_id: "acct-1",
+        },
+      }),
+      transport: "sse",
+    });
+
+    const result = await stream.result();
+
+    expect(result.stopReason).toBe("error");
+    expect(result.errorMessage).toContain("usage limit");
+    expect(result.errorMessage?.length).toBeLessThanOrEqual(16 * 1024);
+    expect(canceled).toBe(true);
+    expect(pullCount).toBeGreaterThanOrEqual(1);
+    expect(pullCount).toBeLessThanOrEqual(3);
+    expect(fetchMock).toHaveBeenCalledTimes(1);
+  });
+});
+
+describe("parseSSEForTest", () => {
+  it("bounds streamed OpenAI ChatGPT Responses success bodies without content-length", async () => {
+    // 1 MiB chunks; cap is 16 MiB so the bounded reader cancels well before
+    // draining the full 32 MiB advertised body.
+    const CHUNK = 1024 * 1024;
+    const TOTAL = 32;
+    let pullCount = 0;
+    let cancelReason: unknown;
+    const overflowing = new ReadableStream<Uint8Array>({
+      pull(controller) {
+        pullCount += 1;
+        if (pullCount > TOTAL) {
+          controller.close();
+          return;
+        }
+        controller.enqueue(new Uint8Array(CHUNK));
+      },
+      cancel(reason) {
+        cancelReason = reason;
+      },
+    });
+    let caught: Error | null = null;
+    try {
+      // parseSSE expects a Response-like; pass the streaming body directly
+      // through a minimal Response shim that only exposes .body.
+      const response = { body: overflowing } as unknown as Response;
+      for await (const event of parseSSEForTest(response)) {
+        expect(event).toBeDefined();
+      }
+    } catch (err) {
+      caught = err as Error;
+    }
+    expect(caught?.message).toMatch(
+      /OpenAI ChatGPT Responses success body exceeded 16777216 bytes/,
+    );
+    expect(cancelReason).toBeInstanceOf(Error);
+    // 16 MiB + a couple of overshoot pulls, well under 32.
+    expect(pullCount).toBeGreaterThanOrEqual(17);
+    expect(pullCount).toBeLessThanOrEqual(20);
+  });
+});

packages/ai/src/providers/openai-prompt-cache.ts

@@ -0,0 +1,14 @@
+/** Maximum prompt cache key length accepted by OpenAI-compatible request metadata. */
+export const OPENAI_PROMPT_CACHE_KEY_MAX_LENGTH = 64;
+
+/** Truncates a prompt cache key by Unicode code point count. */
+export function clampOpenAIPromptCacheKey(key: string | undefined): string | undefined {
+  if (key === undefined) {
+    return undefined;
+  }
+  const chars = Array.from(key);
+  if (chars.length <= OPENAI_PROMPT_CACHE_KEY_MAX_LENGTH) {
+    return key;
+  }
+  return chars.slice(0, OPENAI_PROMPT_CACHE_KEY_MAX_LENGTH).join("");
+}

packages/ai/src/providers/openai-reasoning-effort.test.ts

@@ -0,0 +1,114 @@
+// Verifies model-specific OpenAI reasoning-effort normalization and disablement.
+import { describe, expect, it } from "vitest";
+import {
+  resolveOpenAIReasoningEffortForModel,
+  resolveOpenAISupportedReasoningEfforts,
+} from "./openai-reasoning-effort.js";
+
+describe("OpenAI reasoning effort support", () => {
+  it("preserves max for the GPT-5.6 series", () => {
+    const sol = { provider: "openai", id: "gpt-5.6-sol" };
+    const terra = { provider: "openai", id: "gpt-5.6-terra" };
+    const luna = { provider: "openai", id: "gpt-5.6-luna" };
+
+    expect(resolveOpenAIReasoningEffortForModel({ model: sol, effort: "max" })).toBe("max");
+    expect(resolveOpenAIReasoningEffortForModel({ model: terra, effort: "max" })).toBe("max");
+    expect(resolveOpenAIReasoningEffortForModel({ model: luna, effort: "max" })).toBe("max");
+  });
+
+  it.each([
+    { provider: "openai", id: "gpt-5.5" },
+    { provider: "openai", id: "gpt-5.5" },
+  ])("preserves xhigh for $provider/$id", (model) => {
+    expect(resolveOpenAISupportedReasoningEfforts(model)).toContain("xhigh");
+    expect(resolveOpenAIReasoningEffortForModel({ model, effort: "xhigh" })).toBe("xhigh");
+  });
+
+  it("preserves reasoning_effort metadata for gpt-5.4-mini in Chat Completions", () => {
+    const model = { provider: "openai", id: "gpt-5.4-mini", api: "openai-completions" };
+    expect(resolveOpenAISupportedReasoningEfforts(model)).toContain("medium");
+    expect(resolveOpenAIReasoningEffortForModel({ model, effort: "medium" })).toBe("medium");
+  });
+
+  it("preserves reasoning_effort for gpt-5.4-mini in Responses", () => {
+    const model = { provider: "openai", id: "gpt-5.4-mini", api: "openai-responses" };
+    expect(resolveOpenAISupportedReasoningEfforts(model)).toContain("medium");
+    expect(resolveOpenAIReasoningEffortForModel({ model, effort: "medium" })).toBe("medium");
+  });
+
+  it("does not downgrade xhigh when model compat metadata declares it explicitly", () => {
+    const model = {
+      provider: "openai",
+      id: "gpt-5.5",
+      compat: {
+        supportedReasoningEfforts: ["low", "medium", "high", "xhigh"],
+      },
+    };
+
+    expect(resolveOpenAIReasoningEffortForModel({ model, effort: "xhigh" })).toBe("xhigh");
+  });
+
+  it("allows provider-native compat values when explicitly declared", () => {
+    // Some OpenAI-compatible providers expose their own reasoning effort labels.
+    const model = {
+      provider: "groq",
+      id: "qwen/qwen3-32b",
+      compat: {
+        supportedReasoningEfforts: ["none", "default"],
+        reasoningEffortMap: {
+          off: "none",
+          low: "default",
+          medium: "default",
+          high: "default",
+        },
+      },
+    };
+
+    expect(resolveOpenAISupportedReasoningEfforts(model)).toEqual(["none", "default"]);
+    expect(
+      resolveOpenAIReasoningEffortForModel({
+        model,
+        effort: "medium",
+        fallbackMap: model.compat.reasoningEffortMap,
+      }),
+    ).toBe("default");
+    expect(
+      resolveOpenAIReasoningEffortForModel({
+        model,
+        effort: "off",
+        fallbackMap: model.compat.reasoningEffortMap,
+      }),
+    ).toBe("none");
+  });
+
+  it("omits unsupported disabled reasoning instead of falling back to enabled effort", () => {
+    expect(
+      resolveOpenAIReasoningEffortForModel({
+        model: { provider: "groq", id: "openai/gpt-oss-120b" },
+        effort: "off",
+      }),
+    ).toBeUndefined();
+  });
+
+  it("honors compat metadata that disables reasoning effort payloads", () => {
+    const model = {
+      provider: "xai",
+      id: "grok-4.20-beta-latest-reasoning",
+      compat: { supportsReasoningEffort: false },
+    };
+
+    expect(resolveOpenAISupportedReasoningEfforts(model)).toEqual([]);
+    expect(resolveOpenAIReasoningEffortForModel({ model, effort: "high" })).toBeUndefined();
+  });
+
+  it("does not turn disabled reasoning into a fallback effort when compat omits none", () => {
+    const model = {
+      provider: "xai",
+      id: "grok-4.3",
+      compat: { supportedReasoningEfforts: ["low", "medium", "high"] },
+    };
+
+    expect(resolveOpenAIReasoningEffortForModel({ model, effort: "none" })).toBeUndefined();
+    expect(resolveOpenAIReasoningEffortForModel({ model, effort: "high" })).toBe("high");
+  });
+});

packages/ai/src/providers/openai-reasoning-effort.ts

@@ -0,0 +1,165 @@
+/**
+ * OpenAI-compatible reasoning-effort normalization. Different GPT families
+ * expose different accepted effort enums, so callers map requested values here
+ * before constructing provider payloads.
+ */
+import { normalizeLowercaseStringOrEmpty } from "@openclaw/normalization-core/string-coerce";
+import {
+  normalizeStringEntries,
+  uniqueStrings,
+} from "@openclaw/normalization-core/string-normalization";
+
+export type OpenAIReasoningEffort =
+  | "none"
+  | "minimal"
+  | "low"
+  | "medium"
+  | "high"
+  | "xhigh"
+  | "max";
+
+export type OpenAIApiReasoningEffort = OpenAIReasoningEffort | (string & {});
+
+type OpenAIReasoningModel = {
+  provider?: unknown;
+  id?: unknown;
+  name?: unknown;
+  api?: unknown;
+  baseUrl?: unknown;
+  compat?: unknown;
+};
+
+const GPT_5_REASONING_EFFORTS = ["minimal", "low", "medium", "high"] as const;
+const GPT_51_REASONING_EFFORTS = ["none", "low", "medium", "high"] as const;
+const GPT_52_REASONING_EFFORTS = ["none", "low", "medium", "high", "xhigh"] as const;
+const GPT_56_REASONING_EFFORTS = ["low", "medium", "high", "xhigh", "max"] as const;
+const GPT_CODEX_REASONING_EFFORTS = ["low", "medium", "high", "xhigh"] as const;
+const GPT_PRO_REASONING_EFFORTS = ["medium", "high", "xhigh"] as const;
+const GPT_5_PRO_REASONING_EFFORTS = ["high"] as const;
+const GPT_51_CODEX_MAX_REASONING_EFFORTS = ["none", "medium", "high", "xhigh"] as const;
+const GPT_51_CODEX_MINI_REASONING_EFFORTS = ["medium"] as const;
+const GENERIC_REASONING_EFFORTS = ["low", "medium", "high"] as const;
+
+function normalizeModelId(id: string | null | undefined): string {
+  return normalizeLowercaseStringOrEmpty(id ?? "").replace(/-\d{4}-\d{2}-\d{2}$/u, "");
+}
+
+/** Return whether a model is the GPT-5.4 mini family. */
+export function isOpenAIGpt54MiniModel(model: OpenAIReasoningModel): boolean {
+  const id = normalizeModelId(typeof model.id === "string" ? model.id : undefined);
+  return /^gpt-5\.4-mini(?:-|$)/u.test(id);
+}
+
+/** Return whether a model is the GPT-5.5 family. */
+export function isOpenAIGpt55Model(model: OpenAIReasoningModel): boolean {
+  const id = normalizeModelId(typeof model.id === "string" ? model.id : undefined);
+  const name = normalizeModelId(typeof model.name === "string" ? model.name : undefined);
+  return /^gpt-5\.5(?:-|$)/u.test(id) || /^gpt-5\.5(?:\s|\(|-|$)/u.test(name);
+}
+
+/** Normalize user-facing reasoning effort names to API effort names. */
+export function normalizeOpenAIReasoningEffort(effort: string): string {
+  return effort === "minimal" ? "minimal" : effort;
+}
+
+function readCompatReasoningEfforts(compat: unknown): OpenAIApiReasoningEffort[] | undefined {
+  if (!compat || typeof compat !== "object") {
+    return undefined;
+  }
+  if ((compat as { supportsReasoningEffort?: unknown }).supportsReasoningEffort === false) {
+    return [];
+  }
+  const raw = (compat as { supportedReasoningEfforts?: unknown }).supportedReasoningEfforts;
+  if (!Array.isArray(raw)) {
+    return undefined;
+  }
+  const supported = uniqueStrings(
+    normalizeStringEntries(raw.filter((value) => typeof value === "string")),
+  );
+  return supported.length > 0 ? supported : undefined;
+}
+
+function isDisabledReasoningEffort(effort: string): boolean {
+  return effort === "none" || effort === "off";
+}
+
+/** Resolve the reasoning efforts accepted by a specific OpenAI-compatible model. */
+export function resolveOpenAISupportedReasoningEfforts(
+  model: OpenAIReasoningModel,
+): readonly OpenAIApiReasoningEffort[] {
+  const compatEfforts = readCompatReasoningEfforts(model.compat);
+  if (compatEfforts) {
+    return compatEfforts;
+  }
+
+  const id = normalizeModelId(typeof model.id === "string" ? model.id : undefined);
+  if (/^gpt-5\.6(?:-|$)/u.test(id)) {
+    return GPT_56_REASONING_EFFORTS;
+  }
+  if (id === "gpt-5.1-codex-mini") {
+    return GPT_51_CODEX_MINI_REASONING_EFFORTS;
+  }
+  if (id === "gpt-5.1-codex-max") {
+    return GPT_51_CODEX_MAX_REASONING_EFFORTS;
+  }
+  if (/^gpt-5(?:\.\d+)?-codex(?:-|$)/u.test(id)) {
+    return GPT_CODEX_REASONING_EFFORTS;
+  }
+  if (id === "gpt-5-pro") {
+    return GPT_5_PRO_REASONING_EFFORTS;
+  }
+  if (/^gpt-5\.[2-9](?:\.\d+)?-pro(?:-|$)/u.test(id)) {
+    return GPT_PRO_REASONING_EFFORTS;
+  }
+  if (/^gpt-5\.[2-9](?:\.\d+)?(?:-|$)/u.test(id)) {
+    return GPT_52_REASONING_EFFORTS;
+  }
+  if (/^gpt-5\.1(?:-|$)/u.test(id)) {
+    return GPT_51_REASONING_EFFORTS;
+  }
+  if (/^gpt-5(?:-|$)/u.test(id)) {
+    return GPT_5_REASONING_EFFORTS;
+  }
+  return GENERIC_REASONING_EFFORTS;
+}
+
+/** Return whether a model accepts a requested reasoning effort. */
+export function supportsOpenAIReasoningEffort(
+  model: OpenAIReasoningModel,
+  effort: string,
+): boolean {
+  return resolveOpenAISupportedReasoningEfforts(model).includes(
+    normalizeOpenAIReasoningEffort(effort) as OpenAIApiReasoningEffort,
+  );
+}
+
+/** Resolve a requested reasoning effort to the closest value supported by the model. */
+export function resolveOpenAIReasoningEffortForModel(params: {
+  model: OpenAIReasoningModel;
+  effort: string;
+  fallbackMap?: Record<string, string>;
+}): OpenAIApiReasoningEffort | undefined {
+  const requested = normalizeOpenAIReasoningEffort(params.effort);
+  const mapped = params.fallbackMap?.[requested] ?? requested;
+  const normalized = normalizeOpenAIReasoningEffort(mapped);
+  const supported = resolveOpenAISupportedReasoningEfforts(params.model);
+  if (supported.includes(normalized as OpenAIApiReasoningEffort)) {
+    return normalized as OpenAIApiReasoningEffort;
+  }
+  if (isDisabledReasoningEffort(requested) || isDisabledReasoningEffort(normalized)) {
+    return undefined;
+  }
+  if (requested === "minimal" && supported.includes("low")) {
+    return "low";
+  }
+  if ((requested === "minimal" || requested === "low") && supported.includes("medium")) {
+    return "medium";
+  }
+  if (requested === "xhigh" && supported.includes("high")) {
+    return "high";
+  }
+  if (requested === "max" && supported.includes("xhigh")) {
+    return "xhigh";
+  }
+  return supported.find((effort) => effort !== "none");
+}

packages/ai/src/providers/openai-responses-stream-compat.ts

@@ -0,0 +1,78 @@
+export const OPENAI_RESPONSES_OUTPUT_TEXT_CONTENT_PART_TYPE = "output_text";
+export const AZURE_RESPONSES_TEXT_CONTENT_PART_TYPE = "text";
+export const OPENAI_RESPONSES_OUTPUT_TEXT_DELTA_EVENT_TYPE = "response.output_text.delta";
+export const AZURE_RESPONSES_TEXT_DELTA_EVENT_TYPE = "response.text.delta";
+
+export type ResponsesTextContentPartType =
+  | typeof OPENAI_RESPONSES_OUTPUT_TEXT_CONTENT_PART_TYPE
+  | typeof AZURE_RESPONSES_TEXT_CONTENT_PART_TYPE;
+
+export type ResponsesTextDeltaEventType =
+  | typeof OPENAI_RESPONSES_OUTPUT_TEXT_DELTA_EVENT_TYPE
+  | typeof AZURE_RESPONSES_TEXT_DELTA_EVENT_TYPE;
+
+export type AzureResponsesTextContentPart = {
+  type: typeof AZURE_RESPONSES_TEXT_CONTENT_PART_TYPE;
+  text: string;
+};
+
+export type AzureResponsesTextDeltaEvent = {
+  type: typeof AZURE_RESPONSES_TEXT_DELTA_EVENT_TYPE;
+  delta: string;
+};
+
+export function isResponsesTextContentPartType(
+  type: unknown,
+): type is ResponsesTextContentPartType {
+  return (
+    type === OPENAI_RESPONSES_OUTPUT_TEXT_CONTENT_PART_TYPE ||
+    type === AZURE_RESPONSES_TEXT_CONTENT_PART_TYPE
+  );
+}
+
+export function isResponsesTextDeltaEventType(type: unknown): type is ResponsesTextDeltaEventType {
+  return (
+    type === OPENAI_RESPONSES_OUTPUT_TEXT_DELTA_EVENT_TYPE ||
+    type === AZURE_RESPONSES_TEXT_DELTA_EVENT_TYPE
+  );
+}
+
+export function isAzureResponsesTextDeltaEventType(
+  type: unknown,
+): type is typeof AZURE_RESPONSES_TEXT_DELTA_EVENT_TYPE {
+  return type === AZURE_RESPONSES_TEXT_DELTA_EVENT_TYPE;
+}
+
+export function isAzureResponsesTextDeltaEvent(event: {
+  type?: unknown;
+  delta?: unknown;
+}): event is AzureResponsesTextDeltaEvent {
+  return isAzureResponsesTextDeltaEventType(event.type) && typeof event.delta === "string";
+}
+
+export type ResponsesMessageSnapshotCollapse = { kind: "extend"; text: string } | { kind: "keep" };
+
+// Some openai-responses providers re-emit the assistant message as cumulative
+// snapshot items — each a strict prefix-superset of the previous one — instead
+// of one final message item. A same-phase strict extension replaces the prior
+// text block, or the visible reply repeats once per snapshot (#91959).
+// Extension-only on purpose: equal or shrinking adjacent items stay distinct
+// (the Responses protocol allows multiple message items per response), so a
+// false positive can only merge rendering — it can never lose text.
+// `prior` must be the immediately preceding output item: collapsing across
+// reasoning/function_call boundaries would drop real post-tool messages and
+// orphan reasoning items, which OpenAI replay rejects.
+export function resolveResponsesMessageSnapshotCollapse(params: {
+  prior: { text: string; phase: string | undefined } | null;
+  nextText: string;
+  nextPhase: string | undefined;
+}): ResponsesMessageSnapshotCollapse {
+  const { prior, nextText } = params;
+  if (!prior?.text || !nextText || prior.phase !== params.nextPhase) {
+    return { kind: "keep" };
+  }
+  if (nextText.length > prior.text.length && nextText.startsWith(prior.text)) {
+    return { kind: "extend", text: nextText };
+  }
+  return { kind: "keep" };
+}

packages/ai/src/providers/openai-responses-tools.ts

@@ -0,0 +1,171 @@
+// OpenAI Responses tool helpers convert runtime tools to Responses API schemas.
+import { createHash } from "node:crypto";
+import type { Tool as OpenAITool } from "openai/resources/responses/responses.js";
+import { getAiTransportHost } from "../host.js";
+import type { Model, Tool } from "../types.js";
+import { projectOpenAITools, type OpenAIToolProjection } from "./openai-tool-projection.js";
+import {
+  findOpenAIStrictToolProjectionDiagnostics,
+  normalizeOpenAIStrictToolParameters,
+  resolveOpenAIProjectedToolsStrictToolFlag,
+} from "./openai-tool-schema.js";
+
+/** Options for converting internal tool schemas to OpenAI Responses function tools. */
+export interface ConvertResponsesToolsOptions {
+  strict?: boolean | null;
+  model?: Model;
+  supportsStrictMode?: boolean;
+}
+
+type OpenAIToolSchemaCompat = Parameters<typeof normalizeOpenAIStrictToolParameters>[2];
+type ResponsesFunctionTool = {
+  type: "function";
+  name: string;
+  description?: string;
+  parameters: Record<string, unknown>;
+  strict?: boolean | null;
+};
+
+export type ConvertedResponsesTools = {
+  projection: OpenAIToolProjection;
+  tools: OpenAITool[];
+};
+
+// Converts OpenClaw tool schemas to OpenAI Responses tools, including strict-mode compatibility.
+const LOG_SUBSYSTEM = "llm/openai-responses";
+const MAX_STRICT_TOOL_DOWNGRADE_DIAGNOSTIC_KEYS = 64;
+const loggedStrictToolDowngradeDiagnosticKeys = new Set<string>();
+
+/** Converts tools to deterministic OpenAI Responses function tool definitions. */
+export function convertResponsesTools(
+  tools: Tool[],
+  options?: ConvertResponsesToolsOptions,
+): OpenAITool[] {
+  return convertResponsesToolPayload(tools, options).tools;
+}
+
+/** Converts and returns the projection used to reconcile tool choices. */
+export function convertResponsesToolPayload(
+  tools: Tool[],
+  options?: ConvertResponsesToolsOptions,
+): ConvertedResponsesTools {
+  const projection = projectOpenAITools(tools);
+  const strictSetting = resolveResponsesStrictToolSetting(options);
+  const strict = resolveResponsesStrictToolFlag(projection, strictSetting, options?.model);
+  // Sort tools before request construction so prompt-cache bytes stay deterministic.
+  const convertedTools = sortResponsesToolsByName(projection.tools).map((tool) => {
+    const result: ResponsesFunctionTool = {
+      type: "function",
+      name: tool.name,
+      description: tool.description,
+      parameters: normalizeOpenAIStrictToolParameters(
+        tool.parameters,
+        strict === true,
+        options?.model?.compat as OpenAIToolSchemaCompat,
+      ),
+    };
+    if (strict !== undefined) {
+      result.strict = strict;
+    }
+    return result as OpenAITool;
+  });
+  return { projection, tools: convertedTools };
+}
+
+function resolveResponsesStrictToolSetting(
+  options: ConvertResponsesToolsOptions | undefined,
+): boolean | null | undefined {
+  if (options?.strict !== undefined) {
+    return options.strict;
+  }
+  if (options?.model) {
+    return getAiTransportHost().resolveOpenAIStrictToolSetting(options.model, {
+      transport: "stream",
+      supportsStrictMode: options.supportsStrictMode,
+    });
+  }
+  return false;
+}
+
+function resolveResponsesStrictToolFlag(
+  projection: OpenAIToolProjection,
+  strictSetting: boolean | null | undefined,
+  model: Model | undefined,
+): boolean | undefined {
+  const strict = resolveOpenAIProjectedToolsStrictToolFlag(projection, strictSetting);
+  if (strictSetting === true && strict === false && model) {
+    getAiTransportHost().logDebug(LOG_SUBSYSTEM, () => {
+      const diagnostics = findOpenAIStrictToolProjectionDiagnostics(projection);
+      if (!shouldLogStrictToolDowngradeDiagnostic(diagnostics, model)) {
+        return null;
+      }
+      const sample = diagnostics.slice(0, 5).map((entry) => ({
+        tool: entry.toolName ?? `tool[${entry.toolIndex}]`,
+        violations: entry.violations.slice(0, 8),
+      }));
+      return {
+        message:
+          `OpenAI responses tool schema strict mode downgraded to strict=false for ` +
+          `${model.provider ?? "unknown"}/${model.id ?? "unknown"} because ` +
+          `${diagnostics.length} tool schema(s) are not strict-compatible`,
+        data: {
+          provider: model.provider,
+          model: model.id,
+          incompatibleToolCount: diagnostics.length,
+          sample,
+        },
+      };
+    });
+  }
+  return strict;
+}
+
+function shouldLogStrictToolDowngradeDiagnostic(
+  diagnostics: ReturnType<typeof findOpenAIStrictToolProjectionDiagnostics>,
+  model: Model,
+): boolean {
+  // Strict downgrade diagnostics can repeat per turn; hash details and cap memory.
+  const key = createHash("sha256")
+    .update(
+      JSON.stringify({
+        provider: model.provider,
+        model: model.id,
+        diagnostics: diagnostics.map((entry) => ({
+          toolIndex: entry.toolIndex,
+          toolName: entry.toolName ?? null,
+          violations: entry.violations,
+        })),
+      }),
+    )
+    .digest("hex");
+  if (loggedStrictToolDowngradeDiagnosticKeys.has(key)) {
+    return false;
+  }
+  if (loggedStrictToolDowngradeDiagnosticKeys.size >= MAX_STRICT_TOOL_DOWNGRADE_DIAGNOSTIC_KEYS) {
+    loggedStrictToolDowngradeDiagnosticKeys.clear();
+  }
+  loggedStrictToolDowngradeDiagnosticKeys.add(key);
+  return true;
+}
+
+function compareToolText(left: string | undefined, right: string | undefined): number {
+  const leftText = left ?? "";
+  const rightText = right ?? "";
+  if (leftText < rightText) {
+    return -1;
+  }
+  if (leftText > rightText) {
+    return 1;
+  }
+  return 0;
+}
+
+function sortResponsesToolsByName<T extends { name?: string; description?: string }>(
+  tools: readonly T[],
+): T[] {
+  return tools.toSorted(
+    (left, right) =>
+      compareToolText(left.name, right.name) ||
+      compareToolText(left.description, right.description),
+  );
+}

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

@@ -0,0 +1,250 @@
+// OpenAI Responses provider adapts OpenAI response streams to the agent runtime.
+import OpenAI from "openai";
+import type { ResponseCreateParamsStreaming } from "openai/resources/responses/responses.js";
+import { getEnvApiKey } from "../env-api-keys.js";
+import type {
+  CacheRetention,
+  Context,
+  Model,
+  OpenAIResponsesCompat,
+  SimpleStreamOptions,
+  StreamFunction,
+  StreamOptions,
+  Usage,
+} from "../types.js";
+import { AssistantMessageEventStream } from "../utils/event-stream.js";
+import { resolveCacheRetention } from "./cache-retention.js";
+import { isCloudflareProvider, resolveCloudflareBaseUrl } from "./cloudflare.js";
+import { buildCopilotDynamicHeaders, hasCopilotVisionInput } from "./github-copilot-headers.js";
+import { clampOpenAIPromptCacheKey } from "./openai-prompt-cache.js";
+import {
+  applyCommonResponsesParams,
+  convertResponsesMessages,
+  createResponsesAssistantOutput,
+  resolveResponsesReasoningEffort,
+  runResponsesStreamLifecycle,
+} from "./openai-responses-shared.js";
+import { buildBaseOptions } from "./simple-options.js";
+
+const OPENAI_TOOL_CALL_PROVIDERS = new Set(["openai", "opencode"]);
+
+function getCompat(model: Model<"openai-responses">): Required<OpenAIResponsesCompat> {
+  return {
+    sendSessionIdHeader: model.compat?.sendSessionIdHeader ?? true,
+    supportsLongCacheRetention: model.compat?.supportsLongCacheRetention ?? true,
+  };
+}
+
+function getPromptCacheRetention(
+  compat: Required<OpenAIResponsesCompat>,
+  cacheRetention: CacheRetention,
+): "24h" | undefined {
+  return cacheRetention === "long" && compat.supportsLongCacheRetention ? "24h" : undefined;
+}
+
+function formatOpenAIResponsesError(error: unknown): string {
+  if (error instanceof Error) {
+    const status = (error as Error & { status?: unknown }).status;
+    const statusCode = typeof status === "number" ? status : undefined;
+    if (statusCode !== undefined) {
+      return `OpenAI API error (${statusCode}): ${error.message}`;
+    }
+    return error.message;
+  }
+  try {
+    return JSON.stringify(error);
+  } catch {
+    return String(error);
+  }
+}
+
+// OpenAI Responses-specific options
+export interface OpenAIResponsesOptions extends StreamOptions {
+  reasoningEffort?: "minimal" | "low" | "medium" | "high" | "xhigh" | "max";
+  reasoningSummary?: "auto" | "detailed" | "concise" | null;
+  replayResponsesItemIds?: boolean;
+  serviceTier?: ResponseCreateParamsStreaming["service_tier"];
+}
+
+type OpenAIResponsesReplayOptions = SimpleStreamOptions & {
+  replayResponsesItemIds?: boolean;
+};
+
+/**
+ * Generate function for OpenAI Responses API
+ */
+export const streamOpenAIResponses: StreamFunction<"openai-responses", OpenAIResponsesOptions> = (
+  model: Model<"openai-responses">,
+  context: Context,
+  options?: OpenAIResponsesOptions,
+) => {
+  const stream = new AssistantMessageEventStream();
+  const output = createResponsesAssistantOutput(model);
+
+  // Start async processing
+  void runResponsesStreamLifecycle({
+    stream,
+    model,
+    output,
+    options,
+    createClient: () => {
+      const apiKey = options?.apiKey || getEnvApiKey(model.provider) || "";
+      const cacheRetention = resolveCacheRetention(options?.cacheRetention);
+      const cacheSessionId = cacheRetention === "none" ? undefined : options?.sessionId;
+      return createClient(model, context, apiKey, options?.headers, cacheSessionId);
+    },
+    buildParams: () => buildParams(model, context, options),
+    processStreamOptions: {
+      serviceTier: options?.serviceTier,
+      applyServiceTierPricing: (usage, serviceTier) =>
+        applyServiceTierPricing(usage, serviceTier, model),
+    },
+    formatError: formatOpenAIResponsesError,
+  });
+
+  return stream;
+};
+
+export const streamSimpleOpenAIResponses: StreamFunction<
+  "openai-responses",
+  SimpleStreamOptions
+> = (model: Model<"openai-responses">, context: Context, options?: SimpleStreamOptions) => {
+  const apiKey = options?.apiKey || getEnvApiKey(model.provider);
+  if (!apiKey) {
+    throw new Error(`No API key for provider: ${model.provider}`);
+  }
+
+  const base = buildBaseOptions(model, options, apiKey);
+
+  return streamOpenAIResponses(model, context, {
+    ...base,
+    reasoningEffort: resolveResponsesReasoningEffort(model, options?.reasoning),
+    replayResponsesItemIds: (options as OpenAIResponsesReplayOptions | undefined)
+      ?.replayResponsesItemIds,
+  } satisfies OpenAIResponsesOptions);
+};
+
+function createClient(
+  model: Model<"openai-responses">,
+  context: Context,
+  apiKey?: string,
+  optionsHeaders?: Record<string, string>,
+  sessionId?: string,
+) {
+  if (!apiKey) {
+    throw new Error(`No API key for provider: ${model.provider}`);
+  }
+
+  const compat = getCompat(model);
+  const headers = { ...model.headers };
+  if (model.provider === "github-copilot") {
+    const hasImages = hasCopilotVisionInput(context.messages);
+    const copilotHeaders = buildCopilotDynamicHeaders({
+      messages: context.messages,
+      hasImages,
+    });
+    Object.assign(headers, copilotHeaders);
+  }
+
+  if (sessionId) {
+    if (compat.sendSessionIdHeader) {
+      headers.session_id = sessionId;
+    }
+    headers["x-client-request-id"] = sessionId;
+  }
+
+  // Merge options headers last so they can override defaults
+  if (optionsHeaders) {
+    Object.assign(headers, optionsHeaders);
+  }
+
+  const defaultHeaders =
+    model.provider === "cloudflare-ai-gateway"
+      ? {
+          ...headers,
+          Authorization: headers.Authorization ?? null,
+          "cf-aig-authorization": `Bearer ${apiKey}`,
+        }
+      : headers;
+
+  return new OpenAI({
+    apiKey,
+    baseURL: isCloudflareProvider(model.provider) ? resolveCloudflareBaseUrl(model) : model.baseUrl,
+    dangerouslyAllowBrowser: true,
+    defaultHeaders,
+  });
+}
+
+function buildParams(
+  model: Model<"openai-responses">,
+  context: Context,
+  options?: OpenAIResponsesOptions,
+) {
+  const messages = convertResponsesMessages(model, context, OPENAI_TOOL_CALL_PROVIDERS, {
+    replayResponsesItemIds: options?.replayResponsesItemIds ?? false,
+  });
+
+  const cacheRetention = resolveCacheRetention(options?.cacheRetention);
+  const compat = getCompat(model);
+  const params: ResponseCreateParamsStreaming = {
+    model: model.id,
+    input: messages,
+    stream: true,
+    prompt_cache_key:
+      cacheRetention === "none"
+        ? undefined
+        : clampOpenAIPromptCacheKey(options?.promptCacheKey ?? options?.sessionId),
+    prompt_cache_retention: getPromptCacheRetention(compat, cacheRetention),
+    store: false,
+  };
+
+  if (options?.maxTokens) {
+    params.max_output_tokens = options?.maxTokens;
+  }
+
+  if (options?.temperature !== undefined) {
+    params.temperature = options?.temperature;
+  }
+
+  if (options?.serviceTier !== undefined) {
+    params.service_tier = options.serviceTier;
+  }
+
+  applyCommonResponsesParams(params, model, context, options, {
+    setDefaultReasoningOff: model.provider !== "github-copilot",
+  });
+
+  return params;
+}
+
+function getServiceTierCostMultiplier(
+  model: Pick<Model<"openai-responses">, "id">,
+  serviceTier: ResponseCreateParamsStreaming["service_tier"] | undefined,
+): number {
+  switch (serviceTier) {
+    case "flex":
+      return 0.5;
+    case "priority":
+      return model.id === "gpt-5.5" ? 2.5 : 2;
+    default:
+      return 1;
+  }
+}
+
+function applyServiceTierPricing(
+  usage: Usage,
+  serviceTier: ResponseCreateParamsStreaming["service_tier"] | undefined,
+  model: Pick<Model<"openai-responses">, "id">,
+) {
+  const multiplier = getServiceTierCostMultiplier(model, serviceTier);
+  if (multiplier === 1) {
+    return;
+  }
+
+  usage.cost.input *= multiplier;
+  usage.cost.output *= multiplier;
+  usage.cost.cacheRead *= multiplier;
+  usage.cost.cacheWrite *= multiplier;
+  usage.cost.total =
+    usage.cost.input + usage.cost.output + usage.cost.cacheRead + usage.cost.cacheWrite;
+}

packages/ai/src/providers/openai-stop-reason.test.ts

@@ -0,0 +1,36 @@
+import { describe, expect, it } from "vitest";
+import { mapOpenAIStopReason } from "./openai-stop-reason.js";
+
+describe("mapOpenAIStopReason", () => {
+  it.each([
+    ["stop", { stopReason: "stop" }],
+    ["end", { stopReason: "stop" }],
+    ["length", { stopReason: "length" }],
+    ["function_call", { stopReason: "toolUse" }],
+    ["tool_calls", { stopReason: "toolUse" }],
+    [null, { stopReason: "stop" }],
+  ] as const)("maps %s", (reason, expected) => {
+    expect(mapOpenAIStopReason(reason)).toEqual(expected);
+  });
+
+  it("keeps singular tool_call opt-in", () => {
+    expect(mapOpenAIStopReason("tool_call")).toEqual({
+      stopReason: "error",
+      errorMessage: "Provider finish_reason: tool_call",
+    });
+    expect(mapOpenAIStopReason("tool_call", { allowSingularToolCall: true })).toEqual({
+      stopReason: "toolUse",
+    });
+  });
+
+  it("surfaces provider errors and unknown reasons", () => {
+    expect(mapOpenAIStopReason("content_filter")).toEqual({
+      stopReason: "error",
+      errorMessage: "Provider finish_reason: content_filter",
+    });
+    expect(mapOpenAIStopReason("unexpected")).toEqual({
+      stopReason: "error",
+      errorMessage: "Provider finish_reason: unexpected",
+    });
+  });
+});

packages/ai/src/providers/openai-stop-reason.ts

@@ -0,0 +1,40 @@
+import type { StopReason } from "../types.js";
+
+export type OpenAIStopReasonResult = {
+  stopReason: StopReason;
+  errorMessage?: string;
+};
+
+export function mapOpenAIStopReason(
+  reason: string | null,
+  options?: { allowSingularToolCall?: boolean },
+): OpenAIStopReasonResult {
+  if (reason === null) {
+    return { stopReason: "stop" };
+  }
+
+  switch (reason) {
+    case "stop":
+    case "end":
+      return { stopReason: "stop" };
+    case "length":
+      return { stopReason: "length" };
+    case "function_call":
+    case "tool_calls":
+      return { stopReason: "toolUse" };
+    case "tool_call":
+      if (options?.allowSingularToolCall) {
+        return { stopReason: "toolUse" };
+      }
+      break;
+    case "content_filter":
+      return { stopReason: "error", errorMessage: "Provider finish_reason: content_filter" };
+    case "network_error":
+      return { stopReason: "error", errorMessage: "Provider finish_reason: network_error" };
+  }
+
+  return {
+    stopReason: "error",
+    errorMessage: `Provider finish_reason: ${reason}`,
+  };
+}

packages/ai/src/providers/openai-tool-projection.test.ts

@@ -0,0 +1,283 @@
+import { describe, expect, it } from "vitest";
+import {
+  projectOpenAITools,
+  reconcileOpenAICompletionsToolChoice,
+  reconcileOpenAIResponsesToolChoice,
+} from "./openai-tool-projection.js";
+
+describe("OpenAI tool projection", () => {
+  it("keeps healthy tools when sibling descriptors or schemas are unreadable", () => {
+    const projection = projectOpenAITools([
+      {
+        get name(): never {
+          throw new Error("name exploded");
+        },
+        parameters: {},
+      },
+      {
+        name: "bad_schema",
+        parameters: {
+          type: "object",
+          get properties(): never {
+            throw new Error("properties exploded");
+          },
+        },
+      },
+      {
+        name: "lookup",
+        get description(): never {
+          throw new Error("description exploded");
+        },
+        parameters: { type: "object", properties: {} },
+      },
+    ]);
+
+    expect(projection.tools).toEqual([
+      {
+        toolIndex: 2,
+        name: "lookup",
+        parameters: { type: "object", properties: {} },
+      },
+    ]);
+    expect(projection.diagnostics).toHaveLength(2);
+  });
+
+  it("reads optional descriptions once before projecting them", () => {
+    let descriptionReads = 0;
+    const projection = projectOpenAITools([
+      {
+        name: "lookup",
+        get description() {
+          descriptionReads += 1;
+          return descriptionReads === 1 ? "Lookup" : Symbol("invalid");
+        },
+        parameters: {},
+      },
+    ]);
+
+    expect(projection.tools[0]?.description).toBe("Lookup");
+    expect(descriptionReads).toBe(1);
+  });
+
+  it("keeps a healthy pinned Responses function choice", () => {
+    const projection = projectOpenAITools([{ name: "lookup", parameters: {} }]);
+
+    expect(
+      reconcileOpenAIResponsesToolChoice({ type: "function", name: "lookup" }, projection),
+    ).toEqual({ type: "function", name: "lookup" });
+  });
+
+  it("materializes pinned function choices after one name read", () => {
+    const projection = projectOpenAITools([{ name: "lookup", parameters: {} }]);
+    let responsesNameReads = 0;
+    let completionsNameReads = 0;
+    const responsesChoice = {
+      type: "function",
+      get name() {
+        responsesNameReads += 1;
+        return responsesNameReads === 1 ? "lookup" : "broken";
+      },
+    };
+    const completionsChoice = {
+      type: "function",
+      function: {
+        get name() {
+          completionsNameReads += 1;
+          return completionsNameReads === 1 ? "lookup" : "broken";
+        },
+      },
+    };
+
+    expect(reconcileOpenAIResponsesToolChoice(responsesChoice as never, projection)).toEqual({
+      type: "function",
+      name: "lookup",
+    });
+    expect(reconcileOpenAICompletionsToolChoice(completionsChoice as never, projection)).toEqual({
+      type: "function",
+      function: { name: "lookup" },
+    });
+    expect(responsesNameReads).toBe(1);
+    expect(completionsNameReads).toBe(1);
+  });
+
+  it("normalizes omitted parameters to an empty schema", () => {
+    expect(projectOpenAITools([{ name: "lookup", parameters: undefined }]).tools).toEqual([
+      {
+        toolIndex: 0,
+        name: "lookup",
+        parameters: {},
+      },
+    ]);
+  });
+
+  it("quarantines OpenAI tools with unsupported dynamic schema references", () => {
+    const projection = projectOpenAITools([
+      {
+        name: "dynamic",
+        parameters: {
+          type: "object",
+          properties: {
+            value: { $dynamicRef: "#value" },
+          },
+        },
+      },
+    ]);
+
+    expect(projection.tools).toEqual([]);
+    expect(projection.diagnostics).toEqual([
+      {
+        toolIndex: 0,
+        toolName: "dynamic",
+        violations: ["dynamic.parameters.properties.value.$dynamicRef"],
+      },
+    ]);
+  });
+
+  it("quarantines an inventory with an unreadable length", () => {
+    const tools = new Proxy([], {
+      get(target, property, receiver) {
+        if (property === "length") {
+          throw new Error("length exploded");
+        }
+        return Reflect.get(target, property, receiver);
+      },
+    });
+
+    expect(projectOpenAITools(tools)).toEqual({
+      inputToolCount: 0,
+      tools: [],
+      diagnostics: [{ toolIndex: 0, violations: ["tool[0] is unreadable"] }],
+    });
+  });
+
+  it("rejects pinned and required choices when their function tools are unavailable", () => {
+    const projection = projectOpenAITools([
+      {
+        name: "broken",
+        get parameters(): never {
+          throw new Error("parameters exploded");
+        },
+      },
+    ]);
+
+    expect(() =>
+      reconcileOpenAIResponsesToolChoice({ type: "function", name: "broken" }, projection),
+    ).toThrow('requested unavailable tool "broken"');
+    expect(() => reconcileOpenAIResponsesToolChoice("required", projection)).toThrow(
+      "no tools survived schema conversion",
+    );
+    expect(() =>
+      reconcileOpenAICompletionsToolChoice(
+        { type: "function", function: { name: "broken" } },
+        projection,
+      ),
+    ).toThrow('requested unavailable tool "broken"');
+    expect(() => reconcileOpenAICompletionsToolChoice("required", projection)).toThrow(
+      "no tools survived schema conversion",
+    );
+  });
+
+  it("filters official Responses allowed_tools without broadening access", () => {
+    const projection = projectOpenAITools([{ name: "lookup", parameters: {} }]);
+
+    expect(
+      reconcileOpenAIResponsesToolChoice(
+        {
+          type: "allowed_tools",
+          mode: "required",
+          tools: [
+            { type: "function", name: "broken" },
+            { type: "function", name: "lookup" },
+            { type: "web_search_preview" },
+          ],
+        },
+        projection,
+      ),
+    ).toEqual({
+      type: "allowed_tools",
+      mode: "required",
+      tools: [{ type: "function", name: "lookup" }, { type: "web_search_preview" }],
+    });
+  });
+
+  it("disables an auto allowed_tools choice when no allowed tools survive", () => {
+    const projection = projectOpenAITools([{ name: "lookup", parameters: {} }]);
+
+    expect(
+      reconcileOpenAIResponsesToolChoice(
+        {
+          type: "allowed_tools",
+          mode: "auto",
+          tools: [{ type: "function", name: "broken" }],
+        },
+        projection,
+      ),
+    ).toBe("none");
+  });
+
+  it("filters official Chat Completions allowed_tools without broadening access", () => {
+    const projection = projectOpenAITools([{ name: "lookup", parameters: {} }]);
+
+    expect(
+      reconcileOpenAICompletionsToolChoice(
+        {
+          type: "allowed_tools",
+          allowed_tools: {
+            mode: "required",
+            tools: [
+              { type: "function", function: { name: "broken" } },
+              { type: "function", function: { name: "lookup" } },
+              { type: "custom", custom: { name: "shell" } },
+            ],
+          },
+        },
+        projection,
+      ),
+    ).toEqual({
+      type: "allowed_tools",
+      allowed_tools: {
+        mode: "required",
+        tools: [{ type: "function", function: { name: "lookup" } }],
+      },
+    });
+  });
+
+  it("rejects unsupported top-level Chat Completions custom choices", () => {
+    const projection = projectOpenAITools([{ name: "lookup", parameters: {} }]);
+
+    expect(() =>
+      reconcileOpenAICompletionsToolChoice(
+        { type: "custom", custom: { name: "shell" } },
+        projection,
+      ),
+    ).toThrow("custom tool_choice is unsupported");
+  });
+
+  it("disables an auto Chat Completions allowed_tools choice when none survive", () => {
+    const projection = projectOpenAITools([{ name: "lookup", parameters: {} }]);
+
+    expect(
+      reconcileOpenAICompletionsToolChoice(
+        {
+          type: "allowed_tools",
+          allowed_tools: {
+            mode: "auto",
+            tools: [
+              { type: "function", function: { name: "broken" } },
+              { type: "custom", custom: { name: "shell" } },
+            ],
+          },
+        },
+        projection,
+      ),
+    ).toBe("none");
+  });
+
+  it("preserves non-function Responses choices", () => {
+    const projection = projectOpenAITools([]);
+
+    expect(reconcileOpenAIResponsesToolChoice({ type: "web_search_preview" }, projection)).toEqual({
+      type: "web_search_preview",
+    });
+  });
+});

packages/ai/src/providers/openai-tool-projection.ts

@@ -0,0 +1,300 @@
+import { isRecord } from "@openclaw/normalization-core/record-coerce";
+import type OpenAI from "openai";
+import type { ResponseCreateParamsStreaming } from "openai/resources/responses/responses.js";
+import { projectRuntimeToolInputSchema } from "./tool-schema-json-projection.js";
+
+type OpenAIToolDescriptor = {
+  readonly name?: unknown;
+  readonly description?: unknown;
+  readonly parameters: unknown;
+};
+
+type OpenAIProjectedTool = {
+  readonly toolIndex: number;
+  readonly name: string;
+  readonly description?: string;
+  readonly parameters: Record<string, unknown>;
+};
+
+type OpenAIToolProjectionDiagnostic = {
+  readonly toolIndex: number;
+  readonly toolName?: string;
+  readonly violations: readonly string[];
+};
+
+export type OpenAIToolProjection = {
+  readonly inputToolCount: number;
+  readonly tools: readonly OpenAIProjectedTool[];
+  readonly diagnostics: readonly OpenAIToolProjectionDiagnostic[];
+};
+
+type OpenAIResponsesToolChoice = ResponseCreateParamsStreaming["tool_choice"];
+type OpenAIResponsesAllowedToolChoice = Extract<
+  OpenAIResponsesToolChoice,
+  { type: "allowed_tools" }
+>;
+type OpenAICompletionsSdkToolChoice =
+  OpenAI.Chat.Completions.ChatCompletionCreateParamsStreaming["tool_choice"];
+type OpenAICompletionsAllowedToolChoice = Extract<
+  OpenAICompletionsSdkToolChoice,
+  { type: "allowed_tools" }
+>;
+export type OpenAICompletionsToolChoice = Exclude<
+  OpenAICompletionsSdkToolChoice,
+  { type: "custom" }
+>;
+
+function unreadableToolDiagnostic(toolIndex: number): OpenAIToolProjectionDiagnostic {
+  return {
+    toolIndex,
+    violations: [`tool[${toolIndex}] is unreadable`],
+  };
+}
+
+/** Snapshots direct/custom tool descriptors before OpenAI payload construction. */
+export function projectOpenAITools(tools: readonly OpenAIToolDescriptor[]): OpenAIToolProjection {
+  let inputToolCount: number;
+  try {
+    inputToolCount = tools.length;
+  } catch {
+    return {
+      inputToolCount: 0,
+      tools: [],
+      diagnostics: [unreadableToolDiagnostic(0)],
+    };
+  }
+
+  const projectedTools: OpenAIProjectedTool[] = [];
+  const diagnostics: OpenAIToolProjectionDiagnostic[] = [];
+  for (let toolIndex = 0; toolIndex < inputToolCount; toolIndex += 1) {
+    let tool: OpenAIToolDescriptor;
+    try {
+      tool = tools[toolIndex];
+    } catch {
+      diagnostics.push(unreadableToolDiagnostic(toolIndex));
+      continue;
+    }
+
+    let name: unknown;
+    try {
+      name = tool.name;
+    } catch {
+      diagnostics.push({
+        toolIndex,
+        violations: [`tool[${toolIndex}].name is unreadable`],
+      });
+      continue;
+    }
+    if (typeof name !== "string" || !name) {
+      diagnostics.push({
+        toolIndex,
+        violations: [`tool[${toolIndex}].name is empty`],
+      });
+      continue;
+    }
+
+    let parameters: unknown;
+    try {
+      parameters = tool.parameters;
+    } catch {
+      diagnostics.push({
+        toolIndex,
+        toolName: name,
+        violations: [`${name}.parameters is unreadable`],
+      });
+      continue;
+    }
+    const schemaProjection = projectRuntimeToolInputSchema(parameters ?? {}, `${name}.parameters`);
+    if (!isRecord(schemaProjection.schema) || schemaProjection.violations.length > 0) {
+      diagnostics.push({
+        toolIndex,
+        toolName: name,
+        violations:
+          schemaProjection.violations.length > 0
+            ? schemaProjection.violations
+            : [`${name}.parameters must be a JSON object schema`],
+      });
+      continue;
+    }
+
+    let descriptionValue: unknown;
+    try {
+      descriptionValue = tool.description;
+    } catch {
+      // Description is optional; preserve the usable function schema.
+    }
+    const description = typeof descriptionValue === "string" ? descriptionValue : undefined;
+    projectedTools.push({
+      toolIndex,
+      name,
+      ...(description !== undefined ? { description } : {}),
+      parameters: schemaProjection.schema,
+    });
+  }
+
+  return {
+    inputToolCount,
+    tools: projectedTools,
+    diagnostics,
+  };
+}
+
+function requireProjectedFunction(
+  name: string,
+  projection: OpenAIToolProjection,
+  choiceLabel: string,
+): void {
+  if (!projection.tools.some((tool) => tool.name === name)) {
+    throw new Error(`${choiceLabel} requested unavailable tool "${name}" after schema conversion`);
+  }
+}
+
+/** Keeps Responses tool choices aligned with surviving function schemas. */
+export function reconcileOpenAIResponsesToolChoice(
+  choice: OpenAIResponsesToolChoice,
+  projection: OpenAIToolProjection,
+): OpenAIResponsesToolChoice | undefined {
+  if (choice === "auto") {
+    return projection.tools.length > 0 ? choice : undefined;
+  }
+  if (choice === "required") {
+    if (projection.tools.length === 0) {
+      throw new Error(
+        "OpenAI Responses tool_choice requires a tool, but no tools survived schema conversion",
+      );
+    }
+    return choice;
+  }
+  if (choice === "none" || !isRecord(choice)) {
+    return choice;
+  }
+  const choiceType = choice.type;
+  if (choiceType === "function") {
+    const functionName = choice.name;
+    if (typeof functionName !== "string") {
+      return choice;
+    }
+    requireProjectedFunction(functionName, projection, "OpenAI Responses tool_choice");
+    return { type: "function", name: functionName };
+  }
+  if (choiceType !== "allowed_tools") {
+    return choice;
+  }
+
+  const mode = choice.mode;
+  const tools = choice.tools;
+  if ((mode !== "auto" && mode !== "required") || !Array.isArray(tools)) {
+    return choice;
+  }
+  const normalizedAllowedTools: OpenAIResponsesAllowedToolChoice["tools"] = [];
+  for (const tool of tools) {
+    if (!isRecord(tool) || tool.type !== "function") {
+      normalizedAllowedTools.push(tool);
+      continue;
+    }
+    const functionName = tool.name;
+    if (
+      typeof functionName === "string" &&
+      projection.tools.some((projectedTool) => projectedTool.name === functionName)
+    ) {
+      normalizedAllowedTools.push({ type: "function", name: functionName });
+    }
+  }
+  if (normalizedAllowedTools.length === 0) {
+    if (mode === "auto") {
+      return "none";
+    }
+    throw new Error(
+      "OpenAI Responses tool_choice requires a tool, but no allowed tools survived schema conversion",
+    );
+  }
+  return {
+    type: "allowed_tools",
+    mode,
+    tools: normalizedAllowedTools,
+  };
+}
+
+/** Keeps Chat Completions tool choices aligned with surviving function schemas. */
+export function reconcileOpenAICompletionsToolChoice(
+  choice: OpenAICompletionsSdkToolChoice,
+  projection: OpenAIToolProjection,
+): OpenAICompletionsSdkToolChoice | undefined {
+  if (choice === "auto") {
+    return projection.tools.length > 0 ? choice : undefined;
+  }
+  if (choice === "required") {
+    if (projection.tools.length === 0) {
+      throw new Error(
+        "OpenAI Chat Completions tool_choice requires a tool, but no tools survived schema conversion",
+      );
+    }
+    return choice;
+  }
+  if (choice === "none" || !isRecord(choice)) {
+    return choice;
+  }
+  const choiceType = choice.type;
+  if (choiceType === "custom") {
+    throw new Error(
+      "OpenAI Chat Completions custom tool_choice is unsupported because this adapter emits function tools only",
+    );
+  }
+  if (choiceType === "function") {
+    const functionChoice = choice.function;
+    if (!isRecord(functionChoice)) {
+      return choice;
+    }
+    const functionName = functionChoice.name;
+    if (typeof functionName !== "string") {
+      return choice;
+    }
+    requireProjectedFunction(functionName, projection, "OpenAI Chat Completions tool_choice");
+    return { type: "function", function: { name: functionName } };
+  }
+  if (choiceType !== "allowed_tools") {
+    return choice;
+  }
+
+  const allowedConfig = choice.allowed_tools;
+  if (!isRecord(allowedConfig)) {
+    return choice;
+  }
+  const mode = allowedConfig.mode;
+  const tools = allowedConfig.tools;
+  if ((mode !== "auto" && mode !== "required") || !Array.isArray(tools)) {
+    return choice;
+  }
+  const normalizedAllowedTools: OpenAICompletionsAllowedToolChoice["allowed_tools"]["tools"] = [];
+  for (const tool of tools) {
+    if (!isRecord(tool) || tool.type !== "function") {
+      continue;
+    }
+    const functionChoice = tool.function;
+    const functionName = isRecord(functionChoice) ? functionChoice.name : undefined;
+    if (
+      typeof functionName === "string" &&
+      projection.tools.some((projectedTool) => projectedTool.name === functionName)
+    ) {
+      normalizedAllowedTools.push({
+        type: "function",
+        function: { name: functionName },
+      });
+    }
+  }
+  if (normalizedAllowedTools.length === 0) {
+    if (mode === "auto") {
+      return "none";
+    }
+    throw new Error(
+      "OpenAI Chat Completions tool_choice requires a tool, but no allowed tools survived schema conversion",
+    );
+  }
+  return {
+    type: "allowed_tools",
+    allowed_tools: {
+      mode,
+      tools: normalizedAllowedTools,
+    },
+  };
+}

packages/ai/src/providers/openai-tool-schema.test.ts

@@ -0,0 +1,174 @@
+// Verifies OpenAI strict tool schema normalization and cache behavior.
+import { beforeEach, describe, expect, it } from "vitest";
+import { projectOpenAITools } from "./openai-tool-projection.js";
+import {
+  clearOpenAIToolSchemaCacheForTest,
+  findOpenAIStrictToolProjectionDiagnostics,
+  isStrictOpenAIJsonSchemaCompatible,
+  normalizeOpenAIStrictToolParameters,
+  normalizeStrictOpenAIJsonSchema,
+  resolveOpenAIProjectedToolsStrictToolFlag,
+} from "./openai-tool-schema.js";
+
+describe("OpenAI strict tool schema normalization", () => {
+  beforeEach(() => {
+    clearOpenAIToolSchemaCacheForTest();
+  });
+
+  it("repairs top-level object schemas with missing or invalid properties", () => {
+    const schemas = [
+      { type: "object" },
+      { type: "object", properties: undefined },
+      { type: "object", properties: null },
+      { type: "object", properties: [] },
+      { type: "object", properties: "invalid" },
+    ];
+
+    for (const schema of schemas) {
+      expect(normalizeStrictOpenAIJsonSchema(schema)).toEqual({
+        type: "object",
+        properties: {},
+        required: [],
+        additionalProperties: false,
+      });
+      expect(isStrictOpenAIJsonSchemaCompatible(schema)).toBe(true);
+    }
+  });
+
+  it("does not close permissive nested object schemas implicitly", () => {
+    // Nested permissive objects stay incompatible unless callers make them strict.
+    const schema = {
+      type: "object",
+      properties: {
+        metadata: {
+          type: "object",
+        },
+      },
+      required: ["metadata"],
+    };
+
+    const normalized = normalizeStrictOpenAIJsonSchema(schema) as {
+      additionalProperties?: boolean;
+      properties?: { metadata?: { additionalProperties?: boolean } };
+    };
+
+    expect(normalized.additionalProperties).toBe(false);
+    expect(normalized.properties?.metadata).not.toHaveProperty("additionalProperties");
+    expect(isStrictOpenAIJsonSchemaCompatible(schema)).toBe(false);
+    expect(
+      resolveOpenAIProjectedToolsStrictToolFlag(
+        projectOpenAITools([{ name: "write", parameters: schema }]),
+        true,
+      ),
+    ).toBe(false);
+  });
+
+  it("normalizes truly empty MCP tool schema {} for strict mode", () => {
+    const schema = {};
+    const normalized = normalizeStrictOpenAIJsonSchema(schema) as Record<string, unknown>;
+    expect(normalized.type).toBe("object");
+    expect(normalized.properties).toStrictEqual({});
+    expect(normalized.required).toStrictEqual([]);
+    expect(normalized.additionalProperties).toBe(false);
+    expect(isStrictOpenAIJsonSchemaCompatible(schema)).toBe(true);
+  });
+
+  it("reuses normalized strict schemas for stable tool schema objects", () => {
+    // Cache keys include unsupported-keyword policy, not just object identity.
+    const schema = {
+      type: "object",
+      properties: {
+        path: { type: "string" },
+      },
+      required: ["path"],
+    };
+
+    const first = normalizeStrictOpenAIJsonSchema(schema);
+    const second = normalizeStrictOpenAIJsonSchema(schema);
+    const third = normalizeStrictOpenAIJsonSchema(schema, {
+      unsupportedToolSchemaKeywords: ["minimum"],
+    });
+
+    expect(second).toBe(first);
+    expect(third).not.toBe(first);
+    expect(
+      normalizeStrictOpenAIJsonSchema(schema, {
+        unsupportedToolSchemaKeywords: ["minimum"],
+      }),
+    ).toBe(third);
+  });
+
+  it("reports unreadable nested tool schemas instead of throwing", () => {
+    const unreadable = {
+      name: "broken",
+      parameters: {
+        type: "object",
+        get properties(): never {
+          throw new Error("properties exploded");
+        },
+      },
+    };
+
+    const projection = projectOpenAITools([unreadable]);
+
+    expect(findOpenAIStrictToolProjectionDiagnostics(projection)).toEqual([
+      {
+        toolIndex: 0,
+        toolName: "broken",
+        violations: ["broken.parameters is not JSON-serializable"],
+      },
+    ]);
+  });
+
+  it("keeps strict mode for emitted tools when unreadable tools are dropped", () => {
+    const projection = projectOpenAITools([
+      {
+        name: "broken",
+        parameters: {
+          type: "object",
+          get properties(): never {
+            throw new Error("properties exploded");
+          },
+        },
+      },
+      {
+        name: "lookup",
+        parameters: {
+          type: "object",
+          properties: {},
+          required: [],
+          additionalProperties: false,
+        },
+      },
+    ]);
+
+    expect(resolveOpenAIProjectedToolsStrictToolFlag(projection, true)).toBe(true);
+  });
+
+  it("reuses projected schemas for strict checks and normalization", () => {
+    let serializationCount = 0;
+    const projection = projectOpenAITools([
+      {
+        name: "lookup",
+        parameters: {
+          toJSON() {
+            serializationCount += 1;
+            return {
+              type: "object",
+              properties: {},
+              required: [],
+              additionalProperties: false,
+            };
+          },
+        },
+      },
+    ]);
+    const tool = projection.tools[0];
+    expect(tool).toBeDefined();
+
+    expect(resolveOpenAIProjectedToolsStrictToolFlag(projection, true)).toBe(true);
+    const normalized = normalizeOpenAIStrictToolParameters(tool?.parameters, true);
+    expect(normalizeOpenAIStrictToolParameters(tool?.parameters, true)).toBe(normalized);
+    expect(serializationCount).toBe(1);
+  });
+});

packages/ai/src/providers/openai-tool-schema.ts

@@ -0,0 +1,324 @@
+/**
+ * OpenAI strict JSON-schema normalization for tool inventories and request payloads.
+ *
+ * Caches normalized object inputs by provider compatibility so repeated inventory builds preserve identity.
+ */
+import {
+  normalizeToolParameterSchema,
+  shouldOmitEmptyArrayItems,
+  type ToolSchemaModelCompat,
+} from "./agent-tools-parameter-schema.js";
+import type { OpenAIToolProjection } from "./openai-tool-projection.js";
+
+/**
+ * OpenAI strict-tool-schema normalization and diagnostics.
+ *
+ * Strict schemas need all object properties required and `additionalProperties: false`; model
+ * compatibility settings can also remove unsupported schema constructs before strict checks run.
+ */
+type ToolSchemaCompatInput = {
+  unsupportedToolSchemaKeywords?: unknown;
+  omitEmptyArrayItems?: unknown;
+};
+
+const MAX_STRICT_SCHEMA_CACHE_ENTRIES_PER_SCHEMA = 8;
+let strictOpenAISchemaCache = new WeakMap<object, Array<{ key: string; value: unknown }>>();
+
+function resolveToolSchemaModelCompat(
+  compat: ToolSchemaCompatInput | null | undefined,
+): ToolSchemaModelCompat | undefined {
+  if (!compat) {
+    return undefined;
+  }
+  const unsupportedToolSchemaKeywords = Array.isArray(compat.unsupportedToolSchemaKeywords)
+    ? compat.unsupportedToolSchemaKeywords.filter(
+        (keyword): keyword is string => typeof keyword === "string",
+      )
+    : [];
+  if (unsupportedToolSchemaKeywords.length === 0 && compat.omitEmptyArrayItems !== true) {
+    return undefined;
+  }
+  return {
+    ...(unsupportedToolSchemaKeywords.length > 0 ? { unsupportedToolSchemaKeywords } : {}),
+    ...(compat.omitEmptyArrayItems === true ? { omitEmptyArrayItems: true } : {}),
+  };
+}
+
+function resolveStrictOpenAISchemaCacheKey(
+  modelCompat: ToolSchemaCompatInput | null | undefined,
+): string {
+  const compat = resolveToolSchemaModelCompat(modelCompat);
+  return JSON.stringify([
+    [...(compat?.unsupportedToolSchemaKeywords ?? [])].toSorted(),
+    shouldOmitEmptyArrayItems(compat),
+  ]);
+}
+
+function readCachedStrictOpenAISchema(schema: object, key: string): unknown {
+  return strictOpenAISchemaCache.get(schema)?.find((entry) => entry.key === key)?.value;
+}
+
+function rememberStrictOpenAISchema(schema: object, key: string, value: unknown): unknown {
+  const entries = strictOpenAISchemaCache.get(schema) ?? [];
+  strictOpenAISchemaCache.set(
+    schema,
+    [{ key, value }, ...entries.filter((entry) => entry.key !== key)].slice(
+      0,
+      MAX_STRICT_SCHEMA_CACHE_ENTRIES_PER_SCHEMA,
+    ),
+  );
+  return value;
+}
+
+export function clearOpenAIToolSchemaCacheForTest(): void {
+  strictOpenAISchemaCache = new WeakMap();
+}
+
+/** Normalizes a tool parameter schema into the OpenAI strict JSON-schema subset. */
+export function normalizeStrictOpenAIJsonSchema(
+  schema: unknown,
+  modelCompat?: ToolSchemaCompatInput | null,
+): unknown {
+  const schemaInput = schema ?? {};
+  if (!schemaInput || typeof schemaInput !== "object") {
+    return normalizeStrictOpenAIJsonSchemaRecursive(
+      normalizeToolParameterSchema(schemaInput, {
+        modelCompat: resolveToolSchemaModelCompat(modelCompat),
+      }),
+      0,
+    );
+  }
+  const cacheKey = resolveStrictOpenAISchemaCacheKey(modelCompat);
+  const cached = readCachedStrictOpenAISchema(schemaInput, cacheKey);
+  if (cached !== undefined) {
+    return cached;
+  }
+  return rememberStrictOpenAISchema(
+    schemaInput,
+    cacheKey,
+    // Cache by input object and compatibility key so repeated inventory generation preserves object
+    // identity without mixing schemas normalized for different provider limitations.
+    normalizeStrictOpenAIJsonSchemaRecursive(
+      normalizeToolParameterSchema(schemaInput, {
+        modelCompat: resolveToolSchemaModelCompat(modelCompat),
+      }),
+      0,
+    ),
+  );
+}
+
+function normalizeStrictOpenAIJsonSchemaRecursive(schema: unknown, depth: number): unknown {
+  if (Array.isArray(schema)) {
+    let changed = false;
+    const normalized = schema.map((entry) => {
+      const next = normalizeStrictOpenAIJsonSchemaRecursive(entry, depth);
+      changed ||= next !== entry;
+      return next;
+    });
+    return changed ? normalized : schema;
+  }
+  if (!schema || typeof schema !== "object") {
+    return schema;
+  }
+
+  const record = schema as Record<string, unknown>;
+  let changed = false;
+  const normalized: Record<string, unknown> = {};
+  for (const [key, value] of Object.entries(record)) {
+    const next = normalizeStrictOpenAIJsonSchemaRecursive(
+      value,
+      key === "properties" ? depth : depth + 1,
+    );
+    normalized[key] = next;
+    changed ||= next !== value;
+  }
+
+  if (normalized.type === "object") {
+    const properties =
+      normalized.properties &&
+      typeof normalized.properties === "object" &&
+      !Array.isArray(normalized.properties)
+        ? (normalized.properties as Record<string, unknown>)
+        : undefined;
+    if (properties && Object.keys(properties).length === 0 && !Array.isArray(normalized.required)) {
+      normalized.required = [];
+      changed = true;
+    }
+    if (depth === 0 && !("additionalProperties" in normalized)) {
+      normalized.additionalProperties = false;
+      changed = true;
+    }
+  }
+
+  return changed ? normalized : schema;
+}
+
+/** Normalizes tool parameters using strict OpenAI rules only when strict mode is active. */
+export function normalizeOpenAIStrictToolParameters<T>(
+  schema: T,
+  strict: boolean,
+  modelCompat?: ToolSchemaCompatInput | null,
+): T {
+  const toolSchemaCompat = resolveToolSchemaModelCompat(modelCompat);
+  if (!strict) {
+    return normalizeToolParameterSchema(schema ?? {}, { modelCompat: toolSchemaCompat }) as T;
+  }
+  return normalizeStrictOpenAIJsonSchema(schema, toolSchemaCompat) as T;
+}
+
+/** Returns whether a schema already satisfies OpenAI strict tool-schema constraints. */
+export function isStrictOpenAIJsonSchemaCompatible(schema: unknown): boolean {
+  return isStrictOpenAIJsonSchemaCompatibleRecursive(normalizeStrictOpenAIJsonSchema(schema));
+}
+
+type OpenAIStrictToolSchemaDiagnostic = {
+  toolIndex: number;
+  toolName?: string;
+  violations: string[];
+};
+
+/** Returns strict-schema diagnostics for an already materialized OpenAI tool projection. */
+export function findOpenAIStrictToolProjectionDiagnostics(
+  projection: OpenAIToolProjection,
+): OpenAIStrictToolSchemaDiagnostic[] {
+  return [
+    ...projection.diagnostics.map((diagnostic) => ({
+      toolIndex: diagnostic.toolIndex,
+      ...(diagnostic.toolName ? { toolName: diagnostic.toolName } : {}),
+      violations: [...diagnostic.violations],
+    })),
+    ...projection.tools.flatMap((tool) => {
+      const violations = findStrictOpenAIJsonSchemaViolations(
+        normalizeStrictOpenAIJsonSchema(tool.parameters),
+        `${tool.name}.parameters`,
+      );
+      return violations.length > 0
+        ? [{ toolIndex: tool.toolIndex, toolName: tool.name, violations }]
+        : [];
+    }),
+  ];
+}
+
+function isStrictOpenAIJsonSchemaCompatibleRecursive(schema: unknown): boolean {
+  if (Array.isArray(schema)) {
+    return schema.every((entry) => isStrictOpenAIJsonSchemaCompatibleRecursive(entry));
+  }
+  if (!schema || typeof schema !== "object") {
+    return true;
+  }
+
+  const record = schema as Record<string, unknown>;
+  if ("anyOf" in record || "oneOf" in record || "allOf" in record) {
+    return false;
+  }
+  if (Array.isArray(record.type)) {
+    return false;
+  }
+  if (record.type === "object" && record.additionalProperties !== false) {
+    return false;
+  }
+  if (record.type === "object") {
+    const properties =
+      record.properties &&
+      typeof record.properties === "object" &&
+      !Array.isArray(record.properties)
+        ? (record.properties as Record<string, unknown>)
+        : {};
+    const required = Array.isArray(record.required)
+      ? record.required.filter((entry): entry is string => typeof entry === "string")
+      : undefined;
+    if (!required) {
+      return false;
+    }
+    const requiredSet = new Set(required);
+    if (Object.keys(properties).some((key) => !requiredSet.has(key))) {
+      return false;
+    }
+  }
+
+  return Object.entries(record).every(([key, entry]) => {
+    if (key === "properties" && entry && typeof entry === "object" && !Array.isArray(entry)) {
+      return Object.values(entry as Record<string, unknown>).every((value) =>
+        isStrictOpenAIJsonSchemaCompatibleRecursive(value),
+      );
+    }
+    return isStrictOpenAIJsonSchemaCompatibleRecursive(entry);
+  });
+}
+
+function findStrictOpenAIJsonSchemaViolations(schema: unknown, path: string): string[] {
+  if (Array.isArray(schema)) {
+    return schema.flatMap((entry, index) =>
+      findStrictOpenAIJsonSchemaViolations(entry, `${path}[${index}]`),
+    );
+  }
+  if (!schema || typeof schema !== "object") {
+    return [];
+  }
+
+  const record = schema as Record<string, unknown>;
+  const violations: string[] = [];
+  for (const key of ["anyOf", "oneOf", "allOf"] as const) {
+    if (key in record) {
+      violations.push(`${path}.${key}`);
+    }
+  }
+  if (Array.isArray(record.type)) {
+    violations.push(`${path}.type`);
+  }
+  if (record.type === "object") {
+    if (record.additionalProperties !== false) {
+      violations.push(`${path}.additionalProperties`);
+    }
+    const properties =
+      record.properties &&
+      typeof record.properties === "object" &&
+      !Array.isArray(record.properties)
+        ? (record.properties as Record<string, unknown>)
+        : {};
+    const required = Array.isArray(record.required)
+      ? record.required.filter((entry): entry is string => typeof entry === "string")
+      : undefined;
+    if (!required) {
+      violations.push(`${path}.required`);
+    } else {
+      const requiredSet = new Set(required);
+      for (const key of Object.keys(properties)) {
+        if (!requiredSet.has(key)) {
+          violations.push(`${path}.required.${key}`);
+        }
+      }
+    }
+  }
+
+  if (
+    record.properties &&
+    typeof record.properties === "object" &&
+    !Array.isArray(record.properties)
+  ) {
+    for (const [key, value] of Object.entries(record.properties)) {
+      violations.push(...findStrictOpenAIJsonSchemaViolations(value, `${path}.properties.${key}`));
+    }
+  }
+  for (const [key, value] of Object.entries(record)) {
+    if (key === "properties") {
+      continue;
+    }
+    if (value && typeof value === "object") {
+      violations.push(...findStrictOpenAIJsonSchemaViolations(value, `${path}.${key}`));
+    }
+  }
+
+  return violations;
+}
+
+/** Resolves strict mode for the projected tools that will be emitted in the request payload. */
+export function resolveOpenAIProjectedToolsStrictToolFlag(
+  projection: OpenAIToolProjection,
+  strict: boolean | null | undefined,
+): boolean | undefined {
+  if (strict !== true) {
+    return strict === false ? false : undefined;
+  }
+  return projection.tools.every((tool) => isStrictOpenAIJsonSchemaCompatible(tool.parameters));
+}

packages/ai/src/providers/register-builtins.ts

@@ -0,0 +1,164 @@
+// Built-in provider registration installs lazy protocol adapters.
+import type { ApiRegistry } from "../api-registry.js";
+import type {
+  Api,
+  AssistantMessage,
+  AssistantMessageEvent,
+  Model,
+  SimpleStreamOptions,
+  StreamFunction,
+  StreamOptions,
+} from "../types.js";
+import { AssistantMessageEventStream } from "../utils/event-stream.js";
+
+type ProviderStreams<TApi extends Api, TOptions extends StreamOptions> = {
+  stream: StreamFunction<TApi, TOptions>;
+  streamSimple: StreamFunction<TApi, SimpleStreamOptions>;
+};
+
+type RegisterBuiltIn = (registry: ApiRegistry) => void;
+
+/** Source id used for built-in API provider registrations. */
+export const BUILT_IN_API_PROVIDER_SOURCE_ID = "core:built-in";
+
+function forwardStream(
+  target: AssistantMessageEventStream,
+  source: AsyncIterable<AssistantMessageEvent>,
+): void {
+  void (async () => {
+    for await (const event of source) {
+      target.push(event);
+    }
+    target.end();
+  })();
+}
+
+function createLazyLoadErrorMessage<TApi extends Api>(
+  model: Model<TApi>,
+  error: unknown,
+): AssistantMessage {
+  return {
+    role: "assistant",
+    content: [],
+    api: model.api,
+    provider: model.provider,
+    model: model.id,
+    usage: {
+      input: 0,
+      output: 0,
+      cacheRead: 0,
+      cacheWrite: 0,
+      totalTokens: 0,
+      cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
+    },
+    stopReason: "error",
+    errorMessage: error instanceof Error ? error.message : String(error),
+    timestamp: Date.now(),
+  };
+}
+
+// Provider modules load on first use, while callers still receive a stream synchronously.
+function createLazyStream<TApi extends Api, TOptions extends StreamOptions, TStreams>(
+  load: () => Promise<TStreams>,
+  select: (streams: TStreams) => StreamFunction<TApi, TOptions>,
+): StreamFunction<TApi, TOptions> {
+  return (model, context, options) => {
+    const outer = new AssistantMessageEventStream();
+    load()
+      .then((streams) => forwardStream(outer, select(streams)(model, context, options)))
+      .catch((error: unknown) => {
+        const message = createLazyLoadErrorMessage(model, error);
+        outer.push({ type: "error", reason: "error", error: message });
+        outer.end(message);
+      });
+    return outer;
+  };
+}
+
+function createLazyRegistration<TApi extends Api, TOptions extends StreamOptions, TModule>(
+  api: TApi,
+  importModule: () => Promise<TModule>,
+  select: (module: TModule) => ProviderStreams<TApi, TOptions>,
+): RegisterBuiltIn {
+  let streamsPromise: Promise<ProviderStreams<TApi, TOptions>> | undefined;
+  const load = () => (streamsPromise ??= importModule().then(select));
+  const stream = createLazyStream(load, (streams) => streams.stream);
+  const streamSimple = createLazyStream<TApi, SimpleStreamOptions, ProviderStreams<TApi, TOptions>>(
+    load,
+    (streams) => streams.streamSimple,
+  );
+  return (registry) => {
+    registry.registerApiProvider({ api, stream, streamSimple }, BUILT_IN_API_PROVIDER_SOURCE_ID);
+  };
+}
+
+const registerBuiltIns: RegisterBuiltIn[] = [
+  createLazyRegistration(
+    "anthropic-messages",
+    () => import("./anthropic.js"),
+    (module) => ({ stream: module.streamAnthropic, streamSimple: module.streamSimpleAnthropic }),
+  ),
+  createLazyRegistration(
+    "openai-completions",
+    () => import("./openai-completions.js"),
+    (module) => ({
+      stream: module.streamOpenAICompletions,
+      streamSimple: module.streamSimpleOpenAICompletions,
+    }),
+  ),
+  createLazyRegistration(
+    "mistral-conversations",
+    () => import("./mistral.js"),
+    (module) => ({ stream: module.streamMistral, streamSimple: module.streamSimpleMistral }),
+  ),
+  createLazyRegistration(
+    "openai-responses",
+    () => import("./openai-responses.js"),
+    (module) => ({
+      stream: module.streamOpenAIResponses,
+      streamSimple: module.streamSimpleOpenAIResponses,
+    }),
+  ),
+  createLazyRegistration(
+    "azure-openai-responses",
+    () => import("./azure-openai-responses.js"),
+    (module) => ({
+      stream: module.streamAzureOpenAIResponses,
+      streamSimple: module.streamSimpleAzureOpenAIResponses,
+    }),
+  ),
+  createLazyRegistration(
+    "openai-chatgpt-responses",
+    () => import("./openai-chatgpt-responses.js"),
+    (module) => ({
+      stream: module.streamOpenAICodexResponses,
+      streamSimple: module.streamSimpleOpenAICodexResponses,
+    }),
+  ),
+  createLazyRegistration(
+    "google-generative-ai",
+    () => import("./google.js"),
+    (module) => ({ stream: module.streamGoogle, streamSimple: module.streamSimpleGoogle }),
+  ),
+  createLazyRegistration(
+    "google-vertex",
+    () => import("./google-vertex.js"),
+    (module) => ({
+      stream: module.streamGoogleVertex,
+      streamSimple: module.streamSimpleGoogleVertex,
+    }),
+  ),
+];
+
+/** Registers every built-in API provider in one runtime registry. */
+export function registerBuiltInApiProviders(registry: ApiRegistry): void {
+  for (const register of registerBuiltIns) {
+    register(registry);
+  }
+}
+
+/** Restores the built-in provider registry state for tests. */
+export function resetApiProviders(registry: ApiRegistry): void {
+  registry.unregisterApiProviders(BUILT_IN_API_PROVIDER_SOURCE_ID);
+  registerBuiltInApiProviders(registry);
+}

packages/ai/src/providers/schema-keyword-strip.ts

@@ -0,0 +1,44 @@
+/** Recursively remove schema keywords unsupported by a target provider/tool surface. */
+export function stripUnsupportedSchemaKeywords(
+  schema: unknown,
+  unsupportedKeywords: ReadonlySet<string>,
+): unknown {
+  if (!schema || typeof schema !== "object") {
+    return schema;
+  }
+  if (Array.isArray(schema)) {
+    return schema.map((entry) => stripUnsupportedSchemaKeywords(entry, unsupportedKeywords));
+  }
+  const obj = schema as Record<string, unknown>;
+  const cleaned: Record<string, unknown> = {};
+  for (const [key, value] of Object.entries(obj)) {
+    if (unsupportedKeywords.has(key)) {
+      continue;
+    }
+    // Schema containers hold nested schemas under different shapes. Recurse
+    // through each known container while preserving unrelated metadata fields.
+    if (key === "properties" && value && typeof value === "object" && !Array.isArray(value)) {
+      cleaned[key] = Object.fromEntries(
+        Object.entries(value as Record<string, unknown>).map(([childKey, childValue]) => [
+          childKey,
+          stripUnsupportedSchemaKeywords(childValue, unsupportedKeywords),
+        ]),
+      );
+      continue;
+    }
+    if (key === "items" && value && typeof value === "object") {
+      cleaned[key] = Array.isArray(value)
+        ? value.map((entry) => stripUnsupportedSchemaKeywords(entry, unsupportedKeywords))
+        : stripUnsupportedSchemaKeywords(value, unsupportedKeywords);
+      continue;
+    }
+    if ((key === "anyOf" || key === "oneOf" || key === "allOf") && Array.isArray(value)) {
+      cleaned[key] = value.map((entry) =>
+        stripUnsupportedSchemaKeywords(entry, unsupportedKeywords),
+      );
+      continue;
+    }
+    cleaned[key] = value;
+  }
+  return cleaned;
+}

packages/ai/src/providers/simple-options.ts

@@ -0,0 +1,79 @@
+// Simple provider option helpers normalize lightweight provider configuration.
+import type {
+  Model,
+  SimpleStreamOptions,
+  StreamOptions,
+  ThinkingBudgets,
+  ThinkingLevel,
+} from "../types.js";
+
+type FirstEventStreamOptions = {
+  firstEventTimeoutMs?: number;
+  onFirstEventTimeout?: (reason: Error) => void;
+};
+
+export function buildBaseOptions(
+  model: Model,
+  options?: SimpleStreamOptions,
+  apiKey?: string,
+): StreamOptions & FirstEventStreamOptions {
+  void model;
+  const firstEventOptions = options as FirstEventStreamOptions | undefined;
+  return {
+    temperature: options?.temperature,
+    maxTokens: options?.maxTokens,
+    stop: options?.stop,
+    signal: options?.signal,
+    apiKey: apiKey || options?.apiKey,
+    transport: options?.transport,
+    cacheRetention: options?.cacheRetention,
+    sessionId: options?.sessionId,
+    promptCacheKey: options?.promptCacheKey,
+    headers: options?.headers,
+    onPayload: options?.onPayload,
+    onResponse: options?.onResponse,
+    timeoutMs: options?.timeoutMs,
+    firstEventTimeoutMs: firstEventOptions?.firstEventTimeoutMs,
+    onFirstEventTimeout: firstEventOptions?.onFirstEventTimeout,
+    maxRetries: options?.maxRetries,
+    maxRetryDelayMs: options?.maxRetryDelayMs,
+    metadata: options?.metadata,
+  };
+}
+
+export function clampReasoning(
+  effort: ThinkingLevel | undefined,
+): Exclude<ThinkingLevel, "xhigh"> | undefined {
+  return effort === "xhigh" ? "high" : effort;
+}
+
+export function adjustMaxTokensForThinking(
+  // Undefined means no explicit caller cap. Use the model cap and fit thinking inside it.
+  baseMaxTokens: number | undefined,
+  modelMaxTokens: number,
+  reasoningLevel: ThinkingLevel,
+  customBudgets?: ThinkingBudgets,
+): { maxTokens: number; thinkingBudget: number } {
+  const defaultBudgets: ThinkingBudgets = {
+    minimal: 1024,
+    low: 2048,
+    medium: 8192,
+    high: 16384,
+    max: 32768,
+  };
+  const budgets = { ...defaultBudgets, ...customBudgets };
+
+  const minOutputTokens = 1024;
+  const level = clampReasoning(reasoningLevel)!;
+  let thinkingBudget = budgets[level]!;
+  const maxTokens =
+    baseMaxTokens === undefined
+      ? modelMaxTokens
+      : Math.min(baseMaxTokens + thinkingBudget, modelMaxTokens);
+
+  if (maxTokens <= thinkingBudget) {
+    thinkingBudget = Math.max(0, maxTokens - minOutputTokens);
+  }
+
+  return { maxTokens, thinkingBudget };
+}

packages/ai/src/providers/tool-result-text.test.ts

@@ -0,0 +1,118 @@
+import { describe, expect, it } from "vitest";
+import { describeToolResultMediaPlaceholder, extractToolResultText } from "./tool-result-text.js";
+
+describe("extractToolResultText", () => {
+  it("keeps media-only blocks out of provider replay text", () => {
+    const text = extractToolResultText([
+      { type: "text", text: "summary" },
+      { type: "image", data: "image-binary", mimeType: "image/png" },
+      { type: "image_url", image_url: { url: "data:image/png;base64,abc123" } },
+      { type: "input_image", image_url: "data:image/png;base64,def456" },
+      { type: "audio", data: "audio-binary", mimeType: "audio/mpeg" },
+    ]);
+
+    expect(text).toBe("summary");
+    expect(text).not.toContain("image-binary");
+    expect(text).not.toContain("abc123");
+    expect(text).not.toContain("def456");
+    expect(text).not.toContain("audio-binary");
+  });
+
+  it("omits MIME-tagged binary data while preserving textual resource data", () => {
+    const text = extractToolResultText([
+      { type: "resource", mime_type: "application/octet-stream", data: "AAECAwQFBgc=" },
+      { type: "resource", mediaType: "application/json", data: '{"ok":true}' },
+    ]);
+
+    expect(text).toContain('"data":"[binary data omitted: 12 chars]"');
+    expect(text).toContain('{\\"ok\\":true}');
+    expect(text).not.toContain("AAECAwQFBgc=");
+  });
+
+  it("redacts inline data URIs without touching ordinary data-colon prose", () => {
+    const text = extractToolResultText([
+      {
+        type: "json",
+        value: {
+          note: "metadata:ready",
+          prose: "data: is ordinary prose",
+          preview: "thumbnail=data:image/png;base64,abcdef done",
+        },
+      },
+    ]);
+
+    expect(text).toContain("metadata:ready");
+    expect(text).toContain("data: is ordinary prose");
+    expect(text).toContain("[inline data URI:");
+    expect(text).not.toContain("abcdef");
+  });
+
+  it("omits opaque or binary structured fields", () => {
+    const text = extractToolResultText([
+      {
+        type: "json",
+        encrypted_content: "ciphertext",
+        bytes: [1, 2, 3],
+        visible: "safe-value",
+      },
+    ]);
+
+    expect(text).toContain('"encrypted_content":"[omitted encrypted_content]"');
+    expect(text).toContain('"bytes":"[omitted bytes]"');
+    expect(text).toContain('"visible":"safe-value"');
+    expect(text).not.toContain("ciphertext");
+  });
+
+  it("uses structured replay only as a no-text fallback without capping explicit text", () => {
+    const textTail = "explicit-tail-marker";
+    const text = extractToolResultText([
+      { type: "text", text: `${"x".repeat(8_200)}${textTail}` },
+      { type: "json", internal: "extra structured detail" },
+    ]);
+
+    expect(text).toContain(textTail);
+    expect(text).not.toContain("…(truncated)…");
+    expect(text).not.toContain("extra structured detail");
+  });
+
+  it("truncates structured fallback text before provider replay", () => {
+    const tail = "tail-marker";
+    const text = extractToolResultText([
+      {
+        type: "json",
+        data: {
+          payload: `${"x".repeat(8_200)}${tail}`,
+        },
+      },
+    ]);
+
+    expect(text.length).toBeLessThan(8_100);
+    expect(text).toContain("…(truncated)…");
+    expect(text).not.toContain(tail);
+  });
+});
+
+describe("describeToolResultMediaPlaceholder", () => {
+  it("describes image-only tool result media", () => {
+    expect(
+      describeToolResultMediaPlaceholder([{ type: "image", mimeType: "image/png", data: "img" }]),
+    ).toBe("(see attached image)");
+  });
+
+  it("describes audio-only tool result media", () => {
+    expect(
+      describeToolResultMediaPlaceholder([
+        { type: "audio", mimeType: "audio/mpeg", data: "audio" },
+      ]),
+    ).toBe("(see attached audio)");
+  });
+
+  it("describes mixed image and audio tool result media", () => {
+    expect(
+      describeToolResultMediaPlaceholder([
+        { type: "image", mimeType: "image/png", data: "img" },
+        { type: "audio", mimeType: "audio/mpeg", data: "audio" },
+      ]),
+    ).toBe("(see attached media)");
+  });
+});

packages/ai/src/providers/tool-result-text.ts

@@ -0,0 +1,197 @@
+import { isRecord } from "@openclaw/normalization-core/record-coerce";
+import { truncateUtf16Safe } from "@openclaw/normalization-core/utf16-slice";
+import { getAiTransportHost } from "../host.js";
+import { sanitizeSurrogates } from "../utils/sanitize-unicode.js";
+
+const PROVIDER_TOOL_RESULT_MAX_CHARS = 8000;
+const IMAGE_TOOL_RESULT_TYPES = new Set(["image", "image_url", "input_image"]);
+const AUDIO_TOOL_RESULT_TYPES = new Set(["audio", "input_audio", "output_audio"]);
+const MEDIA_ONLY_TOOL_RESULT_TYPES = new Set([
+  ...IMAGE_TOOL_RESULT_TYPES,
+  ...AUDIO_TOOL_RESULT_TYPES,
+]);
+const INLINE_DATA_URI_PATTERN =
+  /(^|[^A-Za-z0-9_])data:([a-z][a-z0-9.+-]*\/[a-z0-9.+-]+(?:;[a-z0-9.+-]+=[^,;"'\s]+|;base64)*,[^\s"'<>)]+)/gi;
+const MIME_KEY_CANDIDATES = [
+  "mimeType",
+  "mime_type",
+  "mediaType",
+  "media_type",
+  "contentType",
+  "content_type",
+];
+const TEXTUAL_MIME_PATTERN =
+  /^(?:text\/|application\/(?:json|ld\+json|x-ndjson|xml|javascript|x-www-form-urlencoded)|[^/]+\/[^+]+\+(?:json|xml)$)/i;
+const OPAQUE_OR_BINARY_FIELD_RE = /^(?:blob|buffer|bytes|encrypted_content|encrypted_stdout)$/i;
+
+function readMimeType(value: unknown): string | undefined {
+  if (!isRecord(value)) {
+    return undefined;
+  }
+  for (const key of MIME_KEY_CANDIDATES) {
+    const mimeType = value[key];
+    if (typeof mimeType === "string" && mimeType.trim().length > 0) {
+      return mimeType;
+    }
+  }
+  return undefined;
+}
+
+function isBinaryMimeType(mimeType: string): boolean {
+  const normalized = mimeType.split(";", 1)[0]?.trim().toLowerCase();
+  return normalized ? !TEXTUAL_MIME_PATTERN.test(normalized) : false;
+}
+
+function describeOmittedValue(value: unknown, label: string): string {
+  const length = typeof value === "string" ? value.length : JSON.stringify(value)?.length;
+  return length ? `[${label} omitted: ${length} chars]` : `[${label} omitted]`;
+}
+
+function redactInlineDataUris(value: string): string {
+  return value.replace(
+    INLINE_DATA_URI_PATTERN,
+    (_match, prefix: string, uri: string) => `${prefix}[inline data URI: ${uri.length} chars]`,
+  );
+}
+
+function redactStructuredTextValue(value: string): string {
+  const host = getAiTransportHost();
+  const redacted = host.redactToolPayloadText(value);
+  const trimmed = redacted.trim();
+  if (!trimmed.startsWith("{") && !trimmed.startsWith("[")) {
+    return redacted;
+  }
+  try {
+    const redactedWrapper = host.redactSecrets({ structuredTextValue: JSON.parse(redacted) });
+    return JSON.stringify(redactedWrapper.structuredTextValue);
+  } catch {
+    return redacted;
+  }
+}
+
+function stringifyStructuredBlock(block: Record<string, unknown>): string | undefined {
+  const seen = new WeakSet<object>();
+  try {
+    const redactedWrapper = getAiTransportHost().redactSecrets({ structuredToolResult: block });
+    const redactedBlock = redactedWrapper.structuredToolResult;
+    const serialized = JSON.stringify(
+      redactedBlock,
+      function structuredToolResultReplacer(this: unknown, key, value) {
+        if (OPAQUE_OR_BINARY_FIELD_RE.test(key)) {
+          return `[omitted ${key}]`;
+        }
+        if (key === "data") {
+          const mimeType = readMimeType(this);
+          if (mimeType && isBinaryMimeType(mimeType)) {
+            return describeOmittedValue(value, "binary data");
+          }
+        }
+        if (typeof value === "bigint") {
+          return value.toString();
+        }
+        if (typeof value === "string") {
+          return redactInlineDataUris(redactStructuredTextValue(value));
+        }
+        if (typeof value === "function" || typeof value === "symbol" || value === undefined) {
+          return undefined;
+        }
+        if (!value || typeof value !== "object") {
+          return value;
+        }
+        if (seen.has(value)) {
+          return "[Circular]";
+        }
+        seen.add(value);
+        return value;
+      },
+    );
+    if (!serialized || serialized === "{}") {
+      return undefined;
+    }
+    return serialized;
+  } catch {
+    return undefined;
+  }
+}
+
+function truncateProviderToolText(text: string): string {
+  if (text.length <= PROVIDER_TOOL_RESULT_MAX_CHARS) {
+    return text;
+  }
+  return `${truncateUtf16Safe(text, PROVIDER_TOOL_RESULT_MAX_CHARS)}\n…(truncated)…`;
+}
+
+export function describeToolResultMediaPlaceholder(blocks: readonly unknown[]): string | undefined {
+  let hasImage = false;
+  let hasAudio = false;
+
+  for (const block of blocks) {
+    if (!block || typeof block !== "object") {
+      continue;
+    }
+    const record = block as Record<string, unknown>;
+    const type = typeof record.type === "string" ? record.type : undefined;
+    const mimeType = readMimeType(record);
+
+    if (
+      (type && IMAGE_TOOL_RESULT_TYPES.has(type)) ||
+      mimeType?.toLowerCase().startsWith("image/")
+    ) {
+      hasImage = true;
+    }
+    if (
+      (type && AUDIO_TOOL_RESULT_TYPES.has(type)) ||
+      mimeType?.toLowerCase().startsWith("audio/")
+    ) {
+      hasAudio = true;
+    }
+  }
+
+  if (hasImage && hasAudio) {
+    return "(see attached media)";
+  }
+  if (hasAudio) {
+    return "(see attached audio)";
+  }
+  if (hasImage) {
+    return "(see attached image)";
+  }
+  return undefined;
+}
+
+export function extractToolResultBlockText(block: unknown): string | undefined {
+  if (!block || typeof block !== "object") {
+    return undefined;
+  }
+  const record = block as Record<string, unknown>;
+  if (typeof record.type === "string" && MEDIA_ONLY_TOOL_RESULT_TYPES.has(record.type)) {
+    return undefined;
+  }
+  if (record.type === "text") {
+    const text = typeof record.text === "string" ? record.text : "";
+    return text ? sanitizeSurrogates(text) : undefined;
+  }
+  const structured = stringifyStructuredBlock(record);
+  return structured ? sanitizeSurrogates(truncateProviderToolText(structured)) : undefined;
+}
+
+export function extractToolResultText(blocks: readonly unknown[]): string {
+  const explicitTexts: string[] = [];
+  const structuredTexts: string[] = [];
+  for (const block of blocks) {
+    const text = extractToolResultBlockText(block);
+    if (!text) {
+      continue;
+    }
+    const record = block as Record<string, unknown>;
+    if (record.type === "text") {
+      explicitTexts.push(text);
+    } else {
+      structuredTexts.push(text);
+    }
+  }
+  if (explicitTexts.length > 0) {
+    return sanitizeSurrogates(explicitTexts.join("\n"));
+  }
+  return sanitizeSurrogates(truncateProviderToolText(structuredTexts.join("\n")));
+}

packages/ai/src/providers/tool-schema-json-projection.ts

@@ -0,0 +1,131 @@
+/** JSON-safe schema value used when projecting runtime tool parameters. */
+export type RuntimeToolInputSchemaJson =
+  | null
+  | boolean
+  | number
+  | string
+  | RuntimeToolInputSchemaJson[]
+  | { [key: string]: RuntimeToolInputSchemaJson };
+
+/** Projected runtime tool schema plus validation violations. */
+export type RuntimeToolInputSchemaProjection = {
+  readonly schema: RuntimeToolInputSchemaJson;
+  readonly violations: readonly string[];
+};
+
+function isJsonValue(value: unknown): value is RuntimeToolInputSchemaJson {
+  if (value === null) {
+    return true;
+  }
+  switch (typeof value) {
+    case "boolean":
+    case "number":
+    case "string":
+      return true;
+    case "object":
+      if (Array.isArray(value)) {
+        return value.every(isJsonValue);
+      }
+      return Object.values(value as Record<string, unknown>).every(isJsonValue);
+    default:
+      return false;
+  }
+}
+
+function isJsonObject(value: RuntimeToolInputSchemaJson): value is {
+  [key: string]: RuntimeToolInputSchemaJson;
+} {
+  return value !== null && typeof value === "object" && !Array.isArray(value);
+}
+
+function serializeToolInputSchema(value: unknown, path: string): RuntimeToolInputSchemaProjection {
+  let text: string | undefined;
+  try {
+    text = JSON.stringify(value);
+  } catch {
+    return {
+      schema: {},
+      violations: [`${path} is not JSON-serializable`],
+    };
+  }
+  if (!text) {
+    return {
+      schema: {},
+      violations: [`${path} is not JSON-serializable`],
+    };
+  }
+  const parsed = JSON.parse(text) as unknown;
+  if (!isJsonValue(parsed)) {
+    return {
+      schema: {},
+      violations: [`${path} is not a JSON value`],
+    };
+  }
+  return {
+    schema: parsed,
+    violations: [],
+  };
+}
+
+const schemaMapKeywords = new Set([
+  "$defs",
+  "definitions",
+  "dependencies",
+  "dependentSchemas",
+  "patternProperties",
+  "properties",
+]);
+
+function findDynamicSchemaKeywordViolations(
+  schema: RuntimeToolInputSchemaJson,
+  path: string,
+): string[] {
+  if (Array.isArray(schema)) {
+    return schema.flatMap((entry, index) =>
+      findDynamicSchemaKeywordViolations(entry, `${path}[${index}]`),
+    );
+  }
+  if (!isJsonObject(schema)) {
+    return [];
+  }
+  const violations: string[] = [];
+  for (const key of ["$dynamicRef", "$dynamicAnchor"] as const) {
+    if (key in schema) {
+      violations.push(`${path}.${key}`);
+    }
+  }
+  for (const [key, value] of Object.entries(schema)) {
+    if (!value || typeof value !== "object") {
+      continue;
+    }
+    if (schemaMapKeywords.has(key) && isJsonObject(value)) {
+      for (const [schemaName, childSchema] of Object.entries(value)) {
+        violations.push(
+          ...findDynamicSchemaKeywordViolations(childSchema, `${path}.${key}.${schemaName}`),
+        );
+      }
+    } else {
+      violations.push(...findDynamicSchemaKeywordViolations(value, `${path}.${key}`));
+    }
+  }
+  return violations;
+}
+
+/** Projects one runtime tool input schema to JSON and reports runtime incompatibilities. */
+export function projectRuntimeToolInputSchema(
+  schema: unknown,
+  path = "parameters",
+): RuntimeToolInputSchemaProjection {
+  const projection = serializeToolInputSchema(schema, path);
+  const violations = [...projection.violations];
+  if (!isJsonObject(projection.schema)) {
+    violations.push(`${path} must be a JSON object schema`);
+  } else if (projection.schema.type !== undefined && projection.schema.type !== "object") {
+    violations.push(`${path}.type must be "object"`);
+  }
+  violations.push(...findDynamicSchemaKeywordViolations(projection.schema, path));
+  return {
+    schema: projection.schema,
+    violations,
+  };
+}

packages/ai/src/providers/transform-messages.ts

@@ -0,0 +1,257 @@
+// Provider message transform helpers convert runtime messages to provider payloads.
+import type {
+  Api,
+  AssistantMessage,
+  ImageContent,
+  Message,
+  Model,
+  TextContent,
+  ToolCall,
+  ToolResultMessage,
+} from "../types.js";
+import { resolveModelBoundThinkingReplayMode } from "./anthropic-model-contract.js";
+
+const NON_VISION_USER_IMAGE_PLACEHOLDER = "(image omitted: model does not support images)";
+const NON_VISION_TOOL_IMAGE_PLACEHOLDER = "(tool image omitted: model does not support images)";
+
+function replaceImagesWithPlaceholder(
+  content: (TextContent | ImageContent)[],
+  placeholder: string,
+): TextContent[] {
+  const result: TextContent[] = [];
+  let previousWasPlaceholder = false;
+
+  for (const block of content) {
+    if (block.type === "image") {
+      if (!previousWasPlaceholder) {
+        result.push({ type: "text", text: placeholder });
+      }
+      previousWasPlaceholder = true;
+      continue;
+    }
+
+    result.push(block);
+    previousWasPlaceholder = block.text === placeholder;
+  }
+
+  return result;
+}
+
+function downgradeUnsupportedImages<TApi extends Api>(
+  messages: Message[],
+  model: Model<TApi>,
+): Message[] {
+  if (model.input.includes("image")) {
+    return messages;
+  }
+
+  return messages.map((msg) => {
+    if (msg.role === "user" && Array.isArray(msg.content)) {
+      return {
+        ...msg,
+        content: replaceImagesWithPlaceholder(msg.content, NON_VISION_USER_IMAGE_PLACEHOLDER),
+      };
+    }
+
+    if (msg.role === "toolResult") {
+      return {
+        ...msg,
+        content: replaceImagesWithPlaceholder(msg.content, NON_VISION_TOOL_IMAGE_PLACEHOLDER),
+      };
+    }
+
+    return msg;
+  });
+}
+
+/**
+ * Normalize tool call ID for cross-provider compatibility.
+ * OpenAI Responses API generates IDs that are 450+ chars with special characters like `|`.
+ * Anthropic APIs require IDs matching ^[a-zA-Z0-9_-]+$ (max 64 chars).
+ */
+export function transformMessages<TApi extends Api>(
+  messages: Message[],
+  model: Model<TApi>,
+  normalizeToolCallId?: (id: string, model: Model<TApi>, source: AssistantMessage) => string,
+): Message[] {
+  // Build a map of original tool call IDs to normalized IDs
+  const toolCallIdMap = new Map<string, string>();
+  const imageAwareMessages = downgradeUnsupportedImages(messages, model);
+
+  // First pass: transform messages (unsupported image downgrade, thinking blocks, tool call ID normalization)
+  const transformed = imageAwareMessages.map((msg) => {
+    // User messages pass through unchanged
+    if (msg.role === "user") {
+      return msg;
+    }
+
+    // Handle toolResult messages - normalize toolCallId if we have a mapping
+    if (msg.role === "toolResult") {
+      const normalizedId = toolCallIdMap.get(msg.toolCallId);
+      if (normalizedId && normalizedId !== msg.toolCallId) {
+        return Object.assign({}, msg, { toolCallId: normalizedId });
+      }
+      return msg;
+    }
+
+    // Assistant messages need transformation check
+    if (msg.role === "assistant") {
+      const assistantMsg = msg;
+      const modelBoundThinkingReplayMode = resolveModelBoundThinkingReplayMode({
+        source: {
+          provider: assistantMsg.provider,
+          api: assistantMsg.api,
+          modelId: assistantMsg.model,
+          responseModelId: assistantMsg.responseModel,
+        },
+        target: {
+          provider: model.provider,
+          api: model.api,
+          modelId: model.id,
+          modelParams: model.params,
+        },
+      });
+      const isSameModel =
+        modelBoundThinkingReplayMode === "preserve" ||
+        (assistantMsg.provider === model.provider &&
+          assistantMsg.api === model.api &&
+          assistantMsg.model === model.id);
+
+      // Public plugin-sdk/llm exports transformMessages; keep accepting legacy
+      // assistant strings from external provider adapters even though session
+      // JSONL replay normalizes them at ingest.
+      const contentBlocks =
+        typeof assistantMsg.content === "string"
+          ? [{ type: "text" as const, text: assistantMsg.content }]
+          : assistantMsg.content;
+
+      const transformedContent = contentBlocks.flatMap((block) => {
+        if (block.type === "thinking") {
+          if (modelBoundThinkingReplayMode === "drop") {
+            return [];
+          }
+          // Redacted thinking is opaque encrypted content, only valid for the same model.
+          // Drop it for cross-model to avoid API errors.
+          if (block.redacted) {
+            return isSameModel ? block : [];
+          }
+          // For same model: keep thinking blocks with signatures (needed for replay)
+          // even if the thinking text is empty (OpenAI encrypted reasoning)
+          if (isSameModel && block.thinkingSignature) {
+            return block;
+          }
+          // Skip empty thinking blocks, convert others to plain text
+          if (!block.thinking || block.thinking.trim() === "") {
+            return [];
+          }
+          if (isSameModel) {
+            return block;
+          }
+          return {
+            type: "text" as const,
+            text: block.thinking,
+          };
+        }
+
+        if (block.type === "text") {
+          if (isSameModel) {
+            return block;
+          }
+          return {
+            type: "text" as const,
+            text: block.text,
+          };
+        }
+
+        if (block.type === "toolCall") {
+          const toolCall = block;
+          let normalizedToolCall: ToolCall = toolCall;
+
+          if (!isSameModel && toolCall.thoughtSignature) {
+            normalizedToolCall = Object.assign({}, toolCall);
+            delete (normalizedToolCall as { thoughtSignature?: string }).thoughtSignature;
+          }
+
+          if (!isSameModel && normalizeToolCallId) {
+            const normalizedId = normalizeToolCallId(toolCall.id, model, assistantMsg);
+            if (normalizedId !== toolCall.id) {
+              toolCallIdMap.set(toolCall.id, normalizedId);
+              normalizedToolCall = Object.assign({}, normalizedToolCall, { id: normalizedId });
+            }
+          }
+
+          return normalizedToolCall;
+        }
+
+        return block;
+      });
+
+      return Object.assign({}, assistantMsg, { content: transformedContent });
+    }
+    return msg;
+  });
+
+  // Second pass: insert synthetic empty tool results for orphaned tool calls
+  // This preserves thinking signatures and satisfies API requirements
+  const result: Message[] = [];
+  let pendingToolCalls: ToolCall[] = [];
+  let existingToolResultIds = new Set<string>();
+  const insertSyntheticToolResults = () => {
+    if (pendingToolCalls.length > 0) {
+      for (const tc of pendingToolCalls) {
+        if (!existingToolResultIds.has(tc.id)) {
+          result.push({
+            role: "toolResult",
+            toolCallId: tc.id,
+            toolName: tc.name,
+            content: [{ type: "text", text: "No result provided" }],
+            isError: true,
+            timestamp: Date.now(),
+          } as ToolResultMessage);
+        }
+      }
+      pendingToolCalls = [];
+      existingToolResultIds = new Set();
+    }
+  };
+
+  for (const msg of transformed) {
+    if (msg.role === "assistant") {
+      // If we have pending orphaned tool calls from a previous assistant, insert synthetic results now
+      insertSyntheticToolResults();
+
+      // Skip errored/aborted assistant messages entirely.
+      // These are incomplete turns that shouldn't be replayed:
+      // - May have partial content (reasoning without message, incomplete tool calls)
+      // - Replaying them can cause API errors (e.g., OpenAI "reasoning without following item")
+      // - The model should retry from the last valid state
+      const assistantMsg = msg as AssistantMessage;
+      if (assistantMsg.stopReason === "error" || assistantMsg.stopReason === "aborted") {
+        continue;
+      }
+
+      // Track tool calls from this assistant message
+      const toolCalls = assistantMsg.content.filter((b) => b.type === "toolCall");
+      if (toolCalls.length > 0) {
+        pendingToolCalls = toolCalls;
+        existingToolResultIds = new Set();
+      }
+
+      result.push(msg);
+    } else if (msg.role === "toolResult") {
+      existingToolResultIds.add(msg.toolCallId);
+      result.push(msg);
+    } else if (msg.role === "user") {
+      // User message interrupts tool flow - insert synthetic results for orphaned calls
+      insertSyntheticToolResults();
+      result.push(msg);
+    } else {
+      result.push(msg);
+    }
+  }
+
+  // If the conversation ends with unresolved tool calls, synthesize results now.
+  insertSyntheticToolResults();
+
+  return result;
+}

packages/ai/src/session-resources.ts

@@ -0,0 +1,28 @@
+/** Cleanup callback for resources tied to an LLM session or all sessions. */
+export type SessionResourceCleanup = (sessionId?: string) => void;
+
+// Process-local registry of cleanup hooks owned by LLM providers/transports.
+const sessionResourceCleanups = new Set<SessionResourceCleanup>();
+
+/** Registers a session-resource cleanup hook and returns an unregister function. */
+export function registerSessionResourceCleanup(cleanup: SessionResourceCleanup): () => void {
+  sessionResourceCleanups.add(cleanup);
+  return () => {
+    sessionResourceCleanups.delete(cleanup);
+  };
+}
+
+/** Runs all registered cleanup hooks, aggregating failures after every hook has run. */
+export function cleanupSessionResources(sessionId?: string): void {
+  const errors: unknown[] = [];
+  for (const cleanup of sessionResourceCleanups) {
+    try {
+      cleanup(sessionId);
+    } catch (error) {
+      errors.push(error);
+    }
+  }
+  if (errors.length > 0) {
+    throw new AggregateError(errors, "Failed to cleanup session resources");
+  }
+}

packages/ai/src/stream.ts

@@ -0,0 +1,59 @@
+// LLM Runtime module implements stream behavior.
+import type {
+  Api,
+  AssistantMessage,
+  AssistantMessageEventStreamContract,
+  Context,
+  Model,
+  ProviderStreamOptions,
+  SimpleStreamOptions,
+  StreamOptions,
+} from "@openclaw/llm-core";
+import { createApiRegistry, type ApiRegistry } from "./api-registry.js";
+
+/** Creates an isolated LLM runtime backed by the supplied provider registry. */
+export function createLlmRuntime(registry: ApiRegistry = createApiRegistry()) {
+  function resolveApiProvider(api: Api) {
+    const provider = registry.getApiProvider(api);
+    if (!provider) {
+      throw new Error(`No API provider registered for api: ${api}`);
+    }
+    return provider;
+  }
+
+  function stream<TApi extends Api>(
+    model: Model<TApi>,
+    context: Context,
+    options?: ProviderStreamOptions,
+  ): AssistantMessageEventStreamContract {
+    return resolveApiProvider(model.api).stream(model, context, options as StreamOptions);
+  }
+
+  async function complete<TApi extends Api>(
+    model: Model<TApi>,
+    context: Context,
+    options?: ProviderStreamOptions,
+  ): Promise<AssistantMessage> {
+    return stream(model, context, options).result();
+  }
+
+  function streamSimple<TApi extends Api>(
+    model: Model<TApi>,
+    context: Context,
+    options?: SimpleStreamOptions,
+  ): AssistantMessageEventStreamContract {
+    return resolveApiProvider(model.api).streamSimple(model, context, options);
+  }
+
+  async function completeSimple<TApi extends Api>(
+    model: Model<TApi>,
+    context: Context,
+    options?: SimpleStreamOptions,
+  ): Promise<AssistantMessage> {
+    return streamSimple(model, context, options).result();
+  }
+
+  return { registry, stream, complete, streamSimple, completeSimple };
+}
+
+export type LlmRuntime = ReturnType<typeof createLlmRuntime>;

packages/ai/src/types.ts

@@ -0,0 +1,2 @@
+/** Shared model, message, tool, and streaming contracts. */
+export * from "@openclaw/llm-core";

packages/ai/src/utils/deferred-event-buffer.ts

@@ -0,0 +1,22 @@
+type EventSink<T> = {
+  push(event: T): void;
+};
+
+export function createDeferredEventBuffer<T>(sink: EventSink<T>, onBufferedEvent?: () => void) {
+  let events: T[] = [];
+  return {
+    push(event: T): void {
+      events.push(event);
+      onBufferedEvent?.();
+    },
+    flush(): void {
+      for (const event of events) {
+        sink.push(event);
+      }
+      events = [];
+    },
+    discard(): void {
+      events = [];
+    },
+  };
+}

packages/ai/src/utils/diagnostics.ts

@@ -0,0 +1,2 @@
+/** Shared provider diagnostics. */
+export * from "@openclaw/llm-core/diagnostics";

packages/ai/src/utils/event-stream.ts

@@ -0,0 +1,2 @@
+/** Assistant message event stream implementation. */
+export * from "@openclaw/llm-core/event-stream";

packages/ai/src/utils/hash.ts

@@ -0,0 +1,13 @@
+/** Fast deterministic hash to shorten long strings */
+export function shortHash(str: string): string {
+  let h1 = 0xdeadbeef;
+  let h2 = 0x41c6ce57;
+  for (let i = 0; i < str.length; i++) {
+    const ch = str.charCodeAt(i);
+    h1 = Math.imul(h1 ^ ch, 2654435761);
+    h2 = Math.imul(h2 ^ ch, 1597334677);
+  }
+  h1 = Math.imul(h1 ^ (h1 >>> 16), 2246822507) ^ Math.imul(h2 ^ (h2 >>> 13), 3266489909);
+  h2 = Math.imul(h2 ^ (h2 >>> 16), 2246822507) ^ Math.imul(h1 ^ (h1 >>> 13), 3266489909);
+  return (h2 >>> 0).toString(36) + (h1 >>> 0).toString(36);
+}

packages/ai/src/utils/headers.ts

@@ -0,0 +1,8 @@
+/** Converts a Headers object to a plain record for provider request handling. */
+export function headersToRecord(headers: Headers): Record<string, string> {
+  const result: Record<string, string> = {};
+  for (const [key, value] of headers.entries()) {
+    result[key] = value;
+  }
+  return result;
+}

packages/ai/src/utils/json-parse.ts

@@ -0,0 +1,153 @@
+// JSON parse helpers recover structured values from partial model output.
+import { parse as partialParse } from "partial-json";
+
+const VALID_JSON_ESCAPES = new Set(['"', "\\", "/", "b", "f", "n", "r", "t", "u"]);
+const JSON_CONTROL_ESCAPES = new Set(["b", "f", "n", "r", "t"]);
+
+function isControlCharacter(char: string): boolean {
+  const codePoint = char.codePointAt(0);
+  return codePoint !== undefined && codePoint >= 0x00 && codePoint <= 0x1f;
+}
+
+function escapeControlCharacter(char: string): string {
+  switch (char) {
+    case "\b":
+      return "\\b";
+    case "\f":
+      return "\\f";
+    case "\n":
+      return "\\n";
+    case "\r":
+      return "\\r";
+    case "\t":
+      return "\\t";
+    default:
+      return `\\u${char.codePointAt(0)?.toString(16).padStart(4, "0") ?? "0000"}`;
+  }
+}
+
+/**
+ * Repairs malformed JSON string literals by:
+ * - escaping raw control characters inside strings
+ * - doubling backslashes before invalid escape characters
+ */
+export function repairJson(json: string): string {
+  let repaired = "";
+  let inString = false;
+  let stringValuePrefix = "";
+
+  for (let index = 0; index < json.length; index++) {
+    const char = json[index];
+
+    if (!inString) {
+      repaired += char;
+      if (char === '"') {
+        inString = true;
+        stringValuePrefix = "";
+      }
+      continue;
+    }
+
+    if (char === '"') {
+      repaired += char;
+      inString = false;
+      stringValuePrefix = "";
+      continue;
+    }
+
+    if (char === "\\") {
+      const nextChar = json[index + 1];
+      if (nextChar === undefined) {
+        repaired += "\\\\";
+        continue;
+      }
+
+      if (nextChar === "u") {
+        const unicodeDigits = json.slice(index + 2, index + 6);
+        if (/^[0-9a-fA-F]{4}$/.test(unicodeDigits)) {
+          repaired += `\\u${unicodeDigits}`;
+          stringValuePrefix += `\\u${unicodeDigits}`;
+          index += 5;
+          continue;
+        }
+        // A \u not followed by four hex digits is an invalid escape: double the
+        // backslash like the other invalid escapes below. Falling through would
+        // hit the valid-escape branch (VALID_JSON_ESCAPES contains "u") and
+        // re-emit the broken \u, leaving the JSON unparseable.
+        repaired += "\\\\";
+        stringValuePrefix += "\\";
+        continue;
+      }
+
+      if (JSON_CONTROL_ESCAPES.has(nextChar) && looksLikeWindowsPathPrefix(stringValuePrefix)) {
+        repaired += "\\\\";
+        stringValuePrefix += "\\";
+        continue;
+      }
+
+      if (VALID_JSON_ESCAPES.has(nextChar)) {
+        repaired += `\\${nextChar}`;
+        stringValuePrefix += nextChar === "\\" ? "\\" : `\\${nextChar}`;
+        index += 1;
+        continue;
+      }
+
+      repaired += "\\\\";
+      stringValuePrefix += "\\";
+      continue;
+    }
+
+    repaired += isControlCharacter(char) ? escapeControlCharacter(char) : char;
+    stringValuePrefix += char;
+  }
+
+  return repaired;
+}
+
+export function parseJsonWithRepair(json: string): unknown {
+  const repairedJson = repairJson(json);
+  if (repairedJson !== json) {
+    return JSON.parse(repairedJson) as unknown;
+  }
+  return JSON.parse(json) as unknown;
+}
+
+function looksLikeWindowsPathPrefix(prefix: string): boolean {
+  const tail = prefix.slice(-160);
+  return /(?:^|[^A-Za-z0-9])[A-Za-z]:(?:[\\/][^"\\/:*?<>|\r\n]*)*$/.test(tail);
+}
+
+function asStreamingJsonRecord(value: unknown): Record<string, unknown> {
+  return value !== null && typeof value === "object" && !Array.isArray(value)
+    ? (value as Record<string, unknown>)
+    : {};
+}
+
+/**
+ * Attempts to parse potentially incomplete JSON during streaming.
+ * Always returns a valid object, even if the JSON is incomplete.
+ *
+ * @param partialJson The partial JSON string from streaming
+ * @returns Parsed object or empty object if parsing fails
+ */
+export function parseStreamingJson(partialJson: string | undefined): Record<string, unknown> {
+  if (!partialJson || partialJson.trim() === "") {
+    return {};
+  }
+
+  try {
+    return asStreamingJsonRecord(parseJsonWithRepair(partialJson));
+  } catch {
+    try {
+      const result = partialParse(partialJson);
+      return asStreamingJsonRecord(result);
+    } catch {
+      try {
+        const result = partialParse(repairJson(partialJson));
+        return asStreamingJsonRecord(result);
+      } catch {
+        return {};
+      }
+    }
+  }
+}

packages/ai/src/utils/llm-request-activity.ts

@@ -0,0 +1,23 @@
+const requestActivityListeners = new WeakMap<AbortSignal, Set<() => void>>();
+
+export function notifyLlmRequestActivity(signal: AbortSignal | undefined): void {
+  if (!signal) {
+    return;
+  }
+  for (const listener of requestActivityListeners.get(signal) ?? []) {
+    listener();
+  }
+}
+
+export function onLlmRequestActivity(signal: AbortSignal, listener: () => void): () => void {
+  const listeners = requestActivityListeners.get(signal) ?? new Set<() => void>();
+  listeners.add(listener);
+  requestActivityListeners.set(signal, listeners);
+
+  return () => {
+    listeners.delete(listener);
+    if (listeners.size === 0) {
+      requestActivityListeners.delete(signal);
+    }
+  };
+}

packages/ai/src/utils/oauth/openai-chatgpt-jwt.ts

@@ -0,0 +1,32 @@
+// OpenAI ChatGPT JWT helpers inspect auth claims for ChatGPT OAuth sessions.
+const OPENAI_CODEX_AUTH_CLAIM = "https://api.openai.com/auth";
+
+export type OpenAICodexJwtPayload = {
+  [OPENAI_CODEX_AUTH_CLAIM]?: {
+    chatgpt_account_id?: unknown;
+  };
+  [key: string]: unknown;
+};
+
+export function decodeOpenAICodexJwtPayload(token: string): OpenAICodexJwtPayload | null {
+  const parts = token.split(".");
+  if (parts.length !== 3) {
+    return null;
+  }
+
+  try {
+    const decoded = Buffer.from(parts[1] ?? "", "base64url").toString("utf8");
+    const parsed = JSON.parse(decoded);
+    return parsed && typeof parsed === "object" && !Array.isArray(parsed)
+      ? (parsed as OpenAICodexJwtPayload)
+      : null;
+  } catch {
+    return null;
+  }
+}
+
+export function resolveOpenAICodexAccountId(token: string): string | null {
+  const accountId =
+    decodeOpenAICodexJwtPayload(token)?.[OPENAI_CODEX_AUTH_CLAIM]?.chatgpt_account_id;
+  return typeof accountId === "string" && accountId.length > 0 ? accountId : null;
+}

packages/ai/src/utils/overflow.test.ts

@@ -0,0 +1,72 @@
+import { describe, expect, it } from "vitest";
+import type { AssistantMessage } from "../types.js";
+import { isConfiguredContextSizeOverflowError, isContextOverflow } from "./overflow.js";
+
+function errorMessage(message: string): AssistantMessage {
+  return {
+    role: "assistant",
+    content: [],
+    api: "test-api",
+    provider: "test-provider",
+    model: "test-model",
+    usage: {
+      input: 0,
+      output: 0,
+      cacheRead: 0,
+      cacheWrite: 0,
+      totalTokens: 0,
+      cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
+    },
+    stopReason: "error",
+    errorMessage: message,
+    timestamp: 1,
+  };
+}
+
+function successfulMessage(
+  contextUsage?: AssistantMessage["usage"]["contextUsage"],
+): AssistantMessage {
+  return {
+    ...errorMessage(""),
+    usage: {
+      input: 12,
+      output: 15_104,
+      cacheRead: 1_100_000,
+      cacheWrite: 93_130,
+      ...(contextUsage ? { contextUsage } : {}),
+      totalTokens: 1_208_246,
+      cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
+    },
+    stopReason: "stop",
+    errorMessage: undefined,
+  };
+}
+
+describe("configured context size overflow", () => {
+  it.each([
+    "400 Prompt has 256468 tokens, but the configured context size is 256000 tokens",
+    "Prompt has 5,958,968 tokens, but the configured context size is 256,000 tokens",
+  ])("detects %s", (text) => {
+    expect(isConfiguredContextSizeOverflowError(text)).toBe(true);
+    expect(isContextOverflow(errorMessage(text), 256_000)).toBe(true);
+  });
+});
+
+describe("usage-based overflow", () => {
+  it("prefers an available context snapshot over aggregate billing usage", () => {
+    expect(
+      isContextOverflow(
+        successfulMessage({
+          state: "available",
+          promptTokens: 148_874,
+          totalTokens: 163_978,
+        }),
+        1_000_000,
+      ),
+    ).toBe(false);
+  });
+
+  it("does not infer overflow from aggregate billing when context is unavailable", () => {
+    expect(isContextOverflow(successfulMessage({ state: "unavailable" }), 1_000_000)).toBe(false);
+  });
+});

packages/ai/src/utils/overflow.ts

@@ -0,0 +1,171 @@
+// Overflow helpers classify provider overflow errors and retryable responses.
+import type { AssistantMessage } from "../types.js";
+
+const CONFIGURED_CONTEXT_SIZE_OVERFLOW_RE =
+  /prompt has [\d,]+ tokens?, but the configured context size is [\d,]+ tokens?/i;
+
+/** Detects DS4-style raw token-count context overflow errors. */
+export function isConfiguredContextSizeOverflowError(errorMessage: string): boolean {
+  return CONFIGURED_CONTEXT_SIZE_OVERFLOW_RE.test(errorMessage);
+}
+
+/**
+ * Regex patterns to detect context overflow errors from different providers.
+ *
+ * These patterns match error messages returned when the input exceeds
+ * the model's context window.
+ *
+ * Provider-specific patterns (with example error messages):
+ *
+ * - Anthropic: "prompt is too long: 213462 tokens > 200000 maximum"
+ * - Anthropic: "413 {\"error\":{\"type\":\"request_too_large\",\"message\":\"Request exceeds the maximum size\"}}"
+ * - OpenAI: "Your input exceeds the context window of this model"
+ * - OpenAI/LiteLLM: "Requested token count exceeds the model's maximum context length of 131072 tokens"
+ * - Google: "The input token count (1196265) exceeds the maximum number of tokens allowed (1048575)"
+ * - xAI: "This model's maximum prompt length is 131072 but the request contains 537812 tokens"
+ * - Groq: "Please reduce the length of the messages or completion"
+ * - OpenRouter: "This endpoint's maximum context length is X tokens. However, you requested about Y tokens"
+ * - Together AI: "The input (X tokens) is longer than the model's context length (Y tokens)."
+ * - llama.cpp: "the request exceeds the available context size, try increasing it"
+ * - LM Studio: "tokens to keep from the initial prompt is greater than the context length"
+ * - GitHub Copilot: "prompt token count of X exceeds the limit of Y"
+ * - MiniMax: "invalid params, context window exceeds limit"
+ * - Kimi For Coding: "Your request exceeded model token limit: X (requested: Y)"
+ * - Cerebras: "400/413 status code (no body)"
+ * - Mistral: "Prompt contains X tokens ... too large for model with Y maximum context length"
+ * - z.ai: Does NOT error, accepts overflow silently - handled via usage.input > contextWindow
+ * - Xiaomi MiMo: Truncates input to fill contextWindow exactly, then returns finish_reason "length"
+ *   with output=0 (no room left to generate). Detected via stopReason "length" + zero output +
+ *   input filling the context window.
+ * - Ollama: Some deployments truncate silently, others return errors like "prompt too long; exceeded max context length by X tokens"
+ */
+const OVERFLOW_PATTERNS = [
+  /prompt is too long/i, // Anthropic token overflow
+  /request_too_large/i, // Anthropic request byte-size overflow (HTTP 413)
+  /input is too long for requested model/i, // Amazon Bedrock
+  /exceeds the context window/i, // OpenAI (Completions & Responses API)
+  /exceeds (?:the )?(?:model'?s )?maximum context length of [\d,]+ tokens?/i, // OpenAI-compatible proxies (LiteLLM)
+  /input token count.*exceeds the maximum/i, // Google (Gemini)
+  /maximum prompt length is \d+/i, // xAI (Grok)
+  /reduce the length of the messages/i, // Groq
+  /maximum context length is \d+ tokens/i, // OpenRouter (all backends)
+  /input \(\d+ tokens\) is longer than the model'?s context length \(\d+ tokens\)/i, // Together AI
+  /exceeds the limit of \d+/i, // GitHub Copilot
+  /exceeds the available context size/i, // llama.cpp server
+  /greater than the context length/i, // LM Studio
+  /context window exceeds limit/i, // MiniMax
+  /exceeded model token limit/i, // Kimi For Coding
+  /too large for model with \d+ maximum context length/i, // Mistral
+  CONFIGURED_CONTEXT_SIZE_OVERFLOW_RE, // DS4 server
+  /model_context_window_exceeded/i, // z.ai non-standard finish_reason surfaced as error text
+  /prompt too long; exceeded (?:max )?context length/i, // Ollama explicit overflow error
+  /context[_ ]length[_ ]exceeded/i, // Generic fallback
+  /too many tokens/i, // Generic fallback
+  /token limit exceeded/i, // Generic fallback
+  /^4(?:00|13)\s*(?:status code)?\s*\(no body\)/i, // Cerebras: 400/413 with no body
+];
+
+/**
+ * Patterns that indicate non-overflow errors (e.g. rate limiting, server errors).
+ * Error messages matching unknown of these are excluded from overflow detection
+ * even if they also match an OVERFLOW_PATTERN.
+ *
+ * Example: Bedrock formats throttling errors as "ThrottlingException: Too many tokens,
+ * please wait before trying again." which would match the /too many tokens/i overflow
+ * pattern without this exclusion.
+ */
+const NON_OVERFLOW_PATTERNS = [
+  /^(Throttling error|Service unavailable):/i, // AWS Bedrock non-overflow errors (human-readable prefixes from formatBedrockError)
+  /rate limit/i, // Generic rate limiting
+  /too many requests/i, // Generic HTTP 429 style
+];
+
+function resolveContextInputTokens(message: AssistantMessage): number | undefined {
+  if (message.usage.contextUsage?.state === "available") {
+    return message.usage.contextUsage.promptTokens;
+  }
+  if (message.usage.contextUsage?.state === "unavailable") {
+    return undefined;
+  }
+  return message.usage.input + message.usage.cacheRead;
+}
+
+/**
+ * Check if an assistant message represents a context overflow error.
+ *
+ * This handles two cases:
+ * 1. Error-based overflow: Most providers return stopReason "error" with a
+ *    specific error message pattern.
+ * 2. Silent overflow: Some providers accept overflow requests and return
+ *    successfully. For these, we check if usage.input exceeds the context window.
+ *
+ * ## Reliability by Provider
+ *
+ * **Reliable detection (returns error with detectable message):**
+ * - Anthropic: "prompt is too long: X tokens > Y maximum" or "request_too_large"
+ * - OpenAI (Completions & Responses): "exceeds the context window" or "exceeds the model's maximum context length of X tokens"
+ * - Google Gemini: "input token count exceeds the maximum"
+ * - xAI (Grok): "maximum prompt length is X but request contains Y"
+ * - Groq: "reduce the length of the messages"
+ * - Cerebras: 400/413 status code (no body)
+ * - Mistral: "Prompt contains X tokens ... too large for model with Y maximum context length"
+ * - OpenRouter (all backends): "maximum context length is X tokens"
+ * - Together AI: "The input (X tokens) is longer than the model's context length (Y tokens)."
+ * - llama.cpp: "exceeds the available context size"
+ * - LM Studio: "greater than the context length"
+ * - Kimi For Coding: "exceeded model token limit: X (requested: Y)"
+ *
+ * **Unreliable detection:**
+ * - z.ai: Sometimes accepts overflow silently (detectable via usage.input > contextWindow),
+ *   sometimes returns rate limit errors. Pass contextWindow param to detect silent overflow.
+ * - Xiaomi MiMo: Truncates input to fit contextWindow then returns stopReason "length" with
+ *   output=0. Pass contextWindow param to detect via the "filled context + zero output" signal.
+ * - Ollama: May truncate input silently for some setups, but may also return explicit
+ *   overflow errors that match the patterns above. Silent truncation still cannot be
+ *   detected here because we do not know the expected token count.
+ *
+ * ## Custom Providers
+ *
+ * If you've added custom models via settings.json, this function may not detect
+ * overflow errors from those providers. To add support:
+ *
+ * 1. Send a request that exceeds the model's context window
+ * 2. Check the errorMessage in the response
+ * 3. Create a regex pattern that matches the error
+ * 4. The pattern should be added to OVERFLOW_PATTERNS in this file, or
+ *    check the errorMessage yourself before calling this function
+ *
+ * @param message - The assistant message to check
+ * @param contextWindow - Optional context window size for detecting silent overflow (z.ai)
+ * @returns true if the message indicates a context overflow
+ */
+export function isContextOverflow(message: AssistantMessage, contextWindow?: number): boolean {
+  // Case 1: Check error message patterns
+  if (message.stopReason === "error" && message.errorMessage) {
+    // Skip messages matching known non-overflow patterns (e.g. throttling / rate-limit)
+    const isNonOverflow = NON_OVERFLOW_PATTERNS.some((p) => p.test(message.errorMessage!));
+    if (!isNonOverflow && OVERFLOW_PATTERNS.some((p) => p.test(message.errorMessage!))) {
+      return true;
+    }
+  }
+
+  // Case 2: Silent overflow (z.ai style) - successful but usage exceeds context
+  if (contextWindow && message.stopReason === "stop") {
+    const inputTokens = resolveContextInputTokens(message);
+    if (inputTokens !== undefined && inputTokens > contextWindow) {
+      return true;
+    }
+  }
+
+  // Case 3: Length-stop overflow (Xiaomi MiMo style) - server truncates oversized input
+  // to fit the context window, leaving no room for output. Returns stopReason "length"
+  // with output=0 and input+cacheRead filling the context window.
+  if (contextWindow && message.stopReason === "length" && message.usage.output === 0) {
+    const inputTokens = resolveContextInputTokens(message);
+    if (inputTokens !== undefined && inputTokens >= contextWindow * 0.99) {
+      return true;
+    }
+  }
+
+  return false;
+}

packages/ai/src/utils/prompt-cache-stability.ts

@@ -0,0 +1,29 @@
+/**
+ * Prompt-cache normalization helpers. They keep generated prompt sections
+ * deterministic across platform newlines, trailing whitespace, and input
+ * ordering.
+ */
+import { normalizeLowercaseStringOrEmpty } from "@openclaw/normalization-core/string-coerce";
+
+/** Normalize structured prompt text before hashing or snapshot comparison. */
+export function normalizeStructuredPromptSection(text: string): string {
+  return text
+    .replace(/\r\n?/g, "\n")
+    .replace(/[ \t]+$/gm, "")
+    .trim();
+}
+
+/** Normalize, de-dupe, and sort capability ids for stable prompt payloads. */
+export function normalizePromptCapabilityIds(capabilities: ReadonlyArray<string>): string[] {
+  const seen = new Set<string>();
+  const normalized: string[] = [];
+  for (const capability of capabilities) {
+    const value = normalizeLowercaseStringOrEmpty(normalizeStructuredPromptSection(capability));
+    if (!value || seen.has(value)) {
+      continue;
+    }
+    seen.add(value);
+    normalized.push(value);
+  }
+  return normalized.toSorted((left, right) => left.localeCompare(right));
+}

packages/ai/src/utils/reasoning-tag-text-partitioner.test.ts

@@ -0,0 +1,297 @@
+// Reasoning tag partitioner tests cover splitting reasoning and visible text segments.
+import { describe, expect, it } from "vitest";
+import { createReasoningTagTextPartitioner } from "./reasoning-tag-text-partitioner.js";
+
+describe("createReasoningTagTextPartitioner", () => {
+  it("routes split inline reasoning tags away from visible text", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+    const deltas = [
+      ...partitioner.push("before <thi"),
+      ...partitioner.push("nk>hidden"),
+      ...partitioner.push("</think> after"),
+      ...partitioner.flush(),
+    ];
+
+    expect(deltas).toEqual([
+      { kind: "text", text: "before " },
+      { kind: "thinking", text: "hidden" },
+      { kind: "text", text: " after" },
+    ]);
+  });
+
+  it("keeps unterminated reasoning as thinking on flush", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect([...partitioner.push("visible <reasoning>hidden tail"), ...partitioner.flush()]).toEqual(
+      [
+        { kind: "text", text: "visible " },
+        { kind: "thinking", text: "hidden tail" },
+      ],
+    );
+  });
+
+  it("emits ordinary angle-bracket text immediately", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.push("<div>visible")).toEqual([{ kind: "text", text: "<div>visible" }]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("reports pending partial tags and active reasoning", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.hasPending()).toBe(false);
+    expect(partitioner.push("before <thi")).toEqual([{ kind: "text", text: "before " }]);
+    expect(partitioner.hasPending()).toBe(true);
+    expect(partitioner.push("nk>hidden")).toEqual([{ kind: "thinking", text: "hidden" }]);
+    expect(partitioner.hasPending()).toBe(true);
+    expect(partitioner.isInsideReasoning()).toBe(true);
+    expect(partitioner.push("</think> after")).toEqual([{ kind: "text", text: " after" }]);
+    expect(partitioner.hasPending()).toBe(false);
+    expect(partitioner.isInsideReasoning()).toBe(false);
+  });
+
+  it("holds possible reasoning opens in visible mode until they are resolved", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("before <thi")).toEqual([{ kind: "text", text: "before " }]);
+    expect(partitioner.push("nk>hidden")).toEqual([{ kind: "thinking", text: "hidden" }]);
+  });
+
+  it("strips complete reasoning tags in visible mode", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("Use <think>literal</think> here")).toEqual([
+      { kind: "text", text: "Use " },
+      { kind: "thinking", text: "literal" },
+      { kind: "text", text: " here" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("keeps split reasoning tags with attributes out of visible text", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+    const deltas = [
+      ...partitioner.pushVisible("Before <think "),
+      ...partitioner.pushVisible("id='x'>secret</think> after"),
+      ...partitioner.flush(),
+    ];
+
+    expect(deltas).toEqual([
+      { kind: "text", text: "Before " },
+      { kind: "thinking", text: "secret" },
+      { kind: "text", text: " after" },
+    ]);
+  });
+
+  it("keeps split antml reasoning tags out of visible text", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+    const deltas = [
+      ...partitioner.pushVisible("Before <antml:reas"),
+      ...partitioner.pushVisible("oning>secret</antml:reasoning> after"),
+      ...partitioner.flush(),
+    ];
+
+    expect(deltas).toEqual([
+      { kind: "text", text: "Before " },
+      { kind: "thinking", text: "secret" },
+      { kind: "text", text: " after" },
+    ]);
+  });
+
+  it("keeps split mm reasoning tags out of visible text", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+    const deltas = [
+      ...partitioner.push("Before <mm:thi"),
+      ...partitioner.push("nk>secret</mm:think> after"),
+      ...partitioner.flush(),
+    ];
+
+    expect(deltas).toEqual([
+      { kind: "text", text: "Before " },
+      { kind: "thinking", text: "secret" },
+      { kind: "text", text: " after" },
+    ]);
+  });
+
+  it("keeps nested reasoning hidden until the outer tag closes", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(
+      partitioner.pushVisible("<think>outer <think>inner</think> still outer</think>visible"),
+    ).toEqual([
+      { kind: "thinking", text: "outer inner still outer" },
+      { kind: "text", text: "visible" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("drops malformed reasoning before orphan close tags in strict mode", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.push("private chain of thought </think> Visible answer")).toEqual([
+      { kind: "text", text: " Visible answer" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("keeps unmatched close-tag prose visible in visible mode", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("Use </think> to close the tag")).toEqual([
+      { kind: "text", text: "Use </think> to close the tag" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("buffers split orphan close tags until the visible suffix arrives", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.push("private chain of thought </think>")).toEqual([]);
+    expect(partitioner.push(" Visible answer")).toEqual([
+      { kind: "text", text: " Visible answer" },
+    ]);
+  });
+
+  it("buffers split orphan close tag prefixes with their hidden prefix", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.push("private chain of thought </thi")).toEqual([]);
+    expect(partitioner.push("nk> Visible answer")).toEqual([
+      { kind: "text", text: " Visible answer" },
+    ]);
+  });
+
+  it("keeps close tags inside hidden code fences private", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("<think>\n```ts\nliteral ")).toEqual([]);
+    expect(partitioner.pushVisible("</think> still private")).toEqual([]);
+    expect(partitioner.flush()).toEqual([
+      { kind: "thinking", text: "\n```ts\nliteral </think> still private" },
+    ]);
+  });
+
+  it("recovers fully wrapped unclosed visible-mode text on flush", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("<think>Visible answer from a malformed local model")).toEqual(
+      [],
+    );
+    expect(partitioner.flush()).toEqual([
+      { kind: "text", text: "Visible answer from a malformed local model" },
+    ]);
+  });
+
+  it("keeps unclosed trailing tags as visible prose in visible mode", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("Use <think> only in this mode")).toEqual([
+      { kind: "text", text: "Use " },
+    ]);
+    expect(partitioner.flush()).toEqual([{ kind: "text", text: "<think> only in this mode" }]);
+  });
+
+  it("does not treat code-span reasoning tag examples as hidden reasoning", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.push("Use `<think>literal</think>` here")).toEqual([
+      { kind: "text", text: "Use `<think>literal</think>` here" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("preserves split code-span reasoning tag examples when active routing begins", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("Use `<thi")).toEqual([{ kind: "text", text: "Use `<thi" }]);
+    expect(partitioner.push("nk>literal</think>` here")).toEqual([
+      { kind: "text", text: "nk>literal</think>` here" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("preserves code-span reasoning tag examples when the closing backtick arrives later", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("Use `<think>literal</think>")).toEqual([
+      { kind: "text", text: "Use " },
+    ]);
+    expect(partitioner.pushVisible("` here")).toEqual([
+      { kind: "text", text: "`<think>literal</think>` here" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("preserves code-span reasoning tag examples when the stream splits after the opener", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("Use `")).toEqual([{ kind: "text", text: "Use `" }]);
+    expect(partitioner.pushVisible("<think>literal</think>` here")).toEqual([
+      { kind: "text", text: "<think>literal</think>` here" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("preserves multi-backtick code-span reasoning tag examples across chunks", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("Use ``<think>")).toEqual([{ kind: "text", text: "Use " }]);
+    expect(partitioner.pushVisible("literal</think>`` here")).toEqual([
+      { kind: "text", text: "``<think>literal</think>`` here" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("reclassifies reasoning tags inside unclosed inline code on final flush", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("Start `unclosed <think>secret</think> end")).toEqual([
+      { kind: "text", text: "Start " },
+    ]);
+    expect(partitioner.flush()).toEqual([
+      { kind: "text", text: "`unclosed " },
+      { kind: "thinking", text: "secret" },
+      { kind: "text", text: " end" },
+    ]);
+  });
+
+  it("keeps buffered unclosed reasoning hidden after strict mode is marked", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("<think>secret")).toEqual([]);
+    partitioner.markStrict();
+    expect(partitioner.flush()).toEqual([{ kind: "thinking", text: "secret" }]);
+  });
+
+  it("preserves fenced reasoning tag examples when the stream splits after the fence", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("Example:\n```")).toEqual([
+      { kind: "text", text: "Example:\n```" },
+    ]);
+    expect(partitioner.pushVisible("\n<think>literal</think>\n```\nDone.")).toEqual([
+      { kind: "text", text: "\n<think>literal</think>\n```\nDone." },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+
+  it("preserves fenced reasoning tag examples when the fence marker is split", () => {
+    const partitioner = createReasoningTagTextPartitioner();
+
+    expect(partitioner.pushVisible("``")).toEqual([]);
+    expect(
+      partitioner.pushVisible(
+        "`xml\n<thinking>literal</thinking>\n```\n<think>secret</think>answer",
+      ),
+    ).toEqual([
+      {
+        kind: "text",
+        text: "```xml\n<thinking>literal</thinking>\n```\n",
+      },
+      { kind: "thinking", text: "secret" },
+      { kind: "text", text: "answer" },
+    ]);
+    expect(partitioner.flush()).toEqual([]);
+  });
+});

packages/ai/src/utils/reasoning-tag-text-partitioner.ts

@@ -0,0 +1,364 @@
+// Reasoning tag partitioner helpers split text around reasoning tag regions.
+import {
+  buildCodeSpanIndex,
+  createInlineCodeState,
+  type InlineCodeState,
+} from "@openclaw/markdown-core/code-spans";
+import type { FenceScanState } from "@openclaw/markdown-core/fences";
+
+export type ReasoningTagTextDelta =
+  | { kind: "text"; text: string }
+  | { kind: "thinking"; text: string };
+
+const REASONING_TAG_RE =
+  /<\s*(\/?)\s*(?:(?:antml:|mm:)?(?:think(?:ing)?|thought|reasoning)|antthinking)\b[^<>]*>/gi;
+const REASONING_TAG_NAMES = [
+  "think",
+  "thinking",
+  "thought",
+  "reasoning",
+  "antthinking",
+  "antml:think",
+  "antml:thinking",
+  "antml:thought",
+  "antml:reasoning",
+  "mm:think",
+  "mm:thinking",
+  "mm:thought",
+  "mm:reasoning",
+] as const;
+
+export interface ReasoningTagTextPartitioner {
+  markStrict(): void;
+  push(chunk: string): ReasoningTagTextDelta[];
+  pushVisible(chunk: string): ReasoningTagTextDelta[];
+  flush(): ReasoningTagTextDelta[];
+  hasPending(): boolean;
+  isInsideReasoning(): boolean;
+}
+
+export function createReasoningTagTextPartitioner(): ReasoningTagTextPartitioner {
+  let buffer = "";
+  let reasoningDepth = 0;
+  let strictMode = false;
+  let emittedVisibleText = false;
+  let inlineCodeState: InlineCodeState = createInlineCodeState();
+  let fenceState: FenceScanState | undefined;
+  let hiddenInlineCodeState: InlineCodeState = createInlineCodeState();
+  let hiddenFenceState: FenceScanState | undefined;
+  let recoverableOpenTagText: string | undefined;
+
+  const consume = (final: boolean, recoverFullUnclosed: boolean): ReasoningTagTextDelta[] => {
+    const output: ReasoningTagTextDelta[] = [];
+    const emit = (kind: ReasoningTagTextDelta["kind"], text: string) => {
+      if (!text) {
+        return;
+      }
+      if (kind === "text" && text.trim().length > 0) {
+        emittedVisibleText = true;
+      }
+      if (kind === "text") {
+        const nextCode = buildCodeSpanIndex(text, inlineCodeState, fenceState);
+        inlineCodeState = nextCode.inlineState;
+        fenceState = nextCode.fenceState;
+      } else {
+        const nextCode = buildCodeSpanIndex(text, hiddenInlineCodeState, hiddenFenceState);
+        hiddenInlineCodeState = nextCode.inlineState;
+        hiddenFenceState = nextCode.fenceState;
+      }
+      const previous = output[output.length - 1];
+      if (previous?.kind === kind) {
+        previous.text += text;
+        return;
+      }
+      output.push({ kind, text });
+    };
+
+    while (buffer) {
+      const activeInlineCodeState = reasoningDepth === 0 ? inlineCodeState : hiddenInlineCodeState;
+      const activeFenceState = reasoningDepth === 0 ? fenceState : hiddenFenceState;
+      const codeSpans = buildCodeSpanIndex(buffer, activeInlineCodeState, activeFenceState);
+      const hasUnclosedCode =
+        reasoningDepth === 0 && Boolean(codeSpans.inlineState.open || codeSpans.fenceState.open);
+      const hasRawReasoning = hasRawReasoningTag(buffer);
+      const tag = findNextReasoningTag(buffer, (index) =>
+        final && hasUnclosedCode && hasRawReasoning ? false : codeSpans.isInside(index),
+      );
+      if (!tag) {
+        if (final) {
+          const recoverAsText =
+            reasoningDepth > 0 && recoverFullUnclosed && !hasRawReasoningCloseTag(buffer);
+          const recoveredText =
+            recoverAsText && recoverableOpenTagText ? recoverableOpenTagText + buffer : buffer;
+          emit(reasoningDepth > 0 && !recoverAsText ? "thinking" : "text", recoveredText);
+          buffer = "";
+          reasoningDepth = 0;
+          recoverableOpenTagText = undefined;
+          return output;
+        }
+        if (
+          reasoningDepth > 0 &&
+          recoverFullUnclosed &&
+          (!emittedVisibleText || recoverableOpenTagText)
+        ) {
+          return output;
+        }
+        if (hasUnclosedCode && hasRawReasoning) {
+          const openCodeIndex =
+            inlineCodeState.open || fenceState?.open ? 0 : findOpenCodeContextStart(buffer);
+          if (openCodeIndex !== -1) {
+            emit("text", buffer.slice(0, openCodeIndex));
+            buffer = buffer.slice(openCodeIndex);
+            return output;
+          }
+        }
+        const trailingFenceStart = findTrailingFenceFragmentStart(
+          buffer,
+          activeInlineCodeState,
+          activeFenceState,
+        );
+        if (trailingFenceStart !== -1) {
+          emit(reasoningDepth > 0 ? "thinking" : "text", buffer.slice(0, trailingFenceStart));
+          buffer = buffer.slice(trailingFenceStart);
+          return output;
+        }
+        const keepFrom = reasoningTagPrefixSuffixIndex(buffer, (index) =>
+          codeSpans.isInside(index),
+        );
+        if (keepFrom === -1) {
+          emit(reasoningDepth > 0 ? "thinking" : "text", buffer);
+          buffer = "";
+          return output;
+        }
+        if (
+          reasoningDepth === 0 &&
+          keepFrom > 0 &&
+          buffer.slice(0, keepFrom).trim().length > 0 &&
+          isReasoningCloseTagPrefix(buffer.slice(keepFrom))
+        ) {
+          return output;
+        }
+        if (keepFrom > 0) {
+          emit(reasoningDepth > 0 ? "thinking" : "text", buffer.slice(0, keepFrom));
+          buffer = buffer.slice(keepFrom);
+        }
+        return output;
+      }
+
+      const beforeTag = buffer.slice(0, tag.index);
+      const afterTag = buffer.slice(tag.index + tag.text.length);
+      if (tag.isClose && reasoningDepth === 0) {
+        if (recoverFullUnclosed && beforeTag.trim().length > 0 && afterTag.trim().length > 0) {
+          emit("text", beforeTag + tag.text);
+          buffer = afterTag;
+          continue;
+        }
+        if (beforeTag.trim().length > 0 && afterTag.trim().length === 0 && !final) {
+          return output;
+        }
+        if (beforeTag.trim().length === 0 || afterTag.trim().length === 0) {
+          emit("text", beforeTag);
+        }
+        buffer = afterTag;
+        continue;
+      }
+
+      emit(reasoningDepth > 0 ? "thinking" : "text", buffer.slice(0, tag.index));
+      buffer = afterTag;
+      if (tag.isClose) {
+        reasoningDepth = Math.max(0, reasoningDepth - 1);
+        if (reasoningDepth === 0) {
+          recoverableOpenTagText = undefined;
+          hiddenInlineCodeState = createInlineCodeState();
+          hiddenFenceState = undefined;
+        }
+      } else {
+        if (reasoningDepth === 0) {
+          recoverableOpenTagText = recoverFullUnclosed && emittedVisibleText ? tag.text : undefined;
+          hiddenInlineCodeState = createInlineCodeState();
+          hiddenFenceState = undefined;
+        }
+        reasoningDepth += 1;
+      }
+    }
+    return output;
+  };
+
+  return {
+    markStrict() {
+      strictMode = true;
+    },
+    push(chunk: string) {
+      strictMode = true;
+      buffer += chunk;
+      return consume(false, false);
+    },
+    pushVisible(chunk: string) {
+      buffer += chunk;
+      return consume(false, true);
+    },
+    flush() {
+      return consume(true, !strictMode);
+    },
+    hasPending() {
+      return buffer.length > 0 || reasoningDepth > 0;
+    },
+    isInsideReasoning() {
+      return reasoningDepth > 0;
+    },
+  };
+}
+
+function hasRawReasoningTag(text: string): boolean {
+  REASONING_TAG_RE.lastIndex = 0;
+  return REASONING_TAG_RE.test(text);
+}
+
+function hasRawReasoningCloseTag(text: string): boolean {
+  REASONING_TAG_RE.lastIndex = 0;
+  for (;;) {
+    const match = REASONING_TAG_RE.exec(text);
+    if (!match) {
+      return false;
+    }
+    if (match[1] === "/") {
+      return true;
+    }
+  }
+}
+
+function findNextReasoningTag(
+  text: string,
+  isIndexInsideCode: (index: number) => boolean,
+): { index: number; text: string; isClose: boolean } | null {
+  REASONING_TAG_RE.lastIndex = 0;
+  for (;;) {
+    const match = REASONING_TAG_RE.exec(text);
+    if (!match) {
+      return null;
+    }
+    if (!isIndexInsideCode(match.index)) {
+      return {
+        index: match.index,
+        text: match[0],
+        isClose: match[1] === "/",
+      };
+    }
+  }
+}
+
+function reasoningTagPrefixSuffixIndex(
+  text: string,
+  isIndexInsideCode: (index: number) => boolean,
+): number {
+  for (let index = text.lastIndexOf("<"); index >= 0; ) {
+    if (!isIndexInsideCode(index) && isReasoningTagPrefix(text.slice(index))) {
+      return index;
+    }
+    if (index === 0) {
+      break;
+    }
+    index = text.lastIndexOf("<", index - 1);
+  }
+  return -1;
+}
+
+function isReasoningTagPrefix(text: string): boolean {
+  const name = normalizeReasoningTagPrefixName(text);
+  return REASONING_TAG_NAMES.some((tagName) => {
+    if (tagName.startsWith(name)) {
+      return true;
+    }
+    if (!name.startsWith(tagName)) {
+      return false;
+    }
+    const rest = name.slice(tagName.length);
+    return rest.length === 0 || /^[\s/>]/.test(rest);
+  });
+}
+
+function isReasoningCloseTagPrefix(text: string): boolean {
+  const normalized = text
+    .replace(/^<\s*/, "<")
+    .replace(/^<\s*\//, "</")
+    .replace(/^<\/\s*/, "</")
+    .toLowerCase();
+  return normalized.startsWith("</") && isReasoningTagPrefix(text);
+}
+
+function normalizeReasoningTagPrefixName(text: string): string {
+  const normalized = text
+    .replace(/^<\s*/, "<")
+    .replace(/^<\s*\//, "</")
+    .replace(/^<\/\s*/, "</")
+    .toLowerCase();
+  const rawName = normalized.startsWith("</") ? normalized.slice(2) : normalized.slice(1);
+  return rawName.trimStart();
+}
+
+function findOpenCodeContextStart(text: string): number {
+  const fence = findOpenFenceStart(text);
+  const inline = findOpenInlineCodeStart(text);
+  if (fence === -1) {
+    return inline;
+  }
+  if (inline === -1) {
+    return fence;
+  }
+  return Math.min(fence, inline);
+}
+
+function findOpenInlineCodeStart(text: string): number {
+  let openStart = -1;
+  let openTicks = 0;
+  let index = 0;
+  while (index < text.length) {
+    if (text[index] !== "`") {
+      index += 1;
+      continue;
+    }
+    const runStart = index;
+    let runLength = 0;
+    while (index < text.length && text[index] === "`") {
+      runLength += 1;
+      index += 1;
+    }
+    if (openStart === -1) {
+      openStart = runStart;
+      openTicks = runLength;
+    } else if (runLength === openTicks) {
+      openStart = -1;
+      openTicks = 0;
+    }
+  }
+  return openStart;
+}
+
+function findOpenFenceStart(text: string): number {
+  const fenceRe = /(^|\n)(```|~~~)[^\n]*(?:\n|$)/g;
+  let open: { marker: string; index: number } | null = null;
+  for (const match of text.matchAll(fenceRe)) {
+    const index = (match.index ?? 0) + match[1].length;
+    const marker = match[2] ?? "";
+    if (open !== null && open.marker === marker) {
+      open = null;
+    } else if (!open) {
+      open = { marker, index };
+    }
+  }
+  return open?.index ?? -1;
+}
+
+function findTrailingFenceFragmentStart(
+  text: string,
+  inlineState: InlineCodeState,
+  fenceState: FenceScanState | undefined,
+): number {
+  if (inlineState.open || fenceState?.open) {
+    return -1;
+  }
+  const lineStart = Math.max(text.lastIndexOf("\n") + 1, 0);
+  const line = text.slice(lineStart);
+  const match = line.match(/^( {0,3})(`{1,2}|~{1,2})$/);
+  return match ? lineStart : -1;
+}

packages/ai/src/utils/sanitize-unicode.ts

@@ -0,0 +1,28 @@
+/**
+ * Removes unpaired Unicode surrogate characters from a string.
+ *
+ * Unpaired surrogates (high surrogates 0xD800-0xDBFF without matching low surrogates 0xDC00-0xDFFF,
+ * or vice versa) cause JSON serialization errors in many API providers.
+ *
+ * Valid emoji and other characters outside the Basic Multilingual Plane use properly paired
+ * surrogates and will NOT be affected by this function.
+ *
+ * @param text - The text to sanitize
+ * @returns The sanitized text with unpaired surrogates removed
+ *
+ * @example
+ * // Valid emoji (properly paired surrogates) are preserved
+ * sanitizeSurrogates("Hello 🙈 World") // => "Hello 🙈 World"
+ *
+ * // Unpaired high surrogate is removed
+ * const unpaired = String.fromCharCode(0xD83D); // high surrogate without low
+ * sanitizeSurrogates(`Text ${unpaired} here`) // => "Text  here"
+ */
+export function sanitizeSurrogates(text: string): string {
+  // Replace unpaired high surrogates (0xD800-0xDBFF not followed by low surrogate)
+  // Replace unpaired low surrogates (0xDC00-0xDFFF not preceded by high surrogate)
+  return text.replace(
+    /[\uD800-\uDBFF](?![\uDC00-\uDFFF])|(?<![\uD800-\uDBFF])[\uDC00-\uDFFF]/g,
+    "",
+  );
+}

packages/ai/src/utils/stream-first-event-timeout.test.ts

@@ -0,0 +1,171 @@
+import { MAX_TIMER_TIMEOUT_MS } from "@openclaw/normalization-core/number-coercion";
+import { describe, expect, it, vi } from "vitest";
+import {
+  createFirstStreamEventAbortController,
+  withFirstStreamEventTimeout,
+} from "./stream-first-event-timeout.js";
+
+function createNeverYieldingStream(onReturn?: () => void): AsyncIterable<unknown> {
+  return {
+    [Symbol.asyncIterator]() {
+      return {
+        async next() {
+          return new Promise<IteratorResult<unknown>>(() => {});
+        },
+        async return() {
+          onReturn?.();
+          return { done: true, value: undefined };
+        },
+      };
+    },
+  };
+}
+
+describe("withFirstStreamEventTimeout", () => {
+  it("fails when the first event never arrives", async () => {
+    vi.useFakeTimers();
+    try {
+      const stream = withFirstStreamEventTimeout(createNeverYieldingStream(), {
+        provider: "local",
+        api: "openai-completions",
+        model: "test-model",
+        timeoutMs: 5,
+        stage: "completions",
+      });
+      const iterator = stream[Symbol.asyncIterator]();
+      const next = expect(iterator.next()).rejects.toThrow(
+        /completions HTTP stream opened but did not deliver a first SSE event within 5ms after streaming headers \(first-event timeout\)/,
+      );
+
+      await vi.advanceTimersByTimeAsync(5);
+      await next;
+    } finally {
+      vi.useRealTimers();
+    }
+  });
+
+  it("calls iterator return on first-event timeout", async () => {
+    vi.useFakeTimers();
+    try {
+      const onReturn = vi.fn();
+      const stream = withFirstStreamEventTimeout(createNeverYieldingStream(onReturn), {
+        timeoutMs: 5,
+      });
+      const iterator = stream[Symbol.asyncIterator]();
+      const next = iterator.next().catch((error: unknown) => error);
+
+      await vi.advanceTimersByTimeAsync(5);
+      await next;
+
+      expect(onReturn).toHaveBeenCalledTimes(1);
+    } finally {
+      vi.useRealTimers();
+    }
+  });
+
+  it("calls iterator return when the consumer closes after the first event", async () => {
+    const onReturn = vi.fn();
+    const source: AsyncIterable<unknown> = {
+      [Symbol.asyncIterator]() {
+        return {
+          async next() {
+            return { done: false, value: "first" };
+          },
+          async return() {
+            onReturn();
+            return { done: true, value: undefined };
+          },
+        };
+      },
+    };
+    const stream = withFirstStreamEventTimeout(source, { timeoutMs: 5 });
+    const iterator = stream[Symbol.asyncIterator]();
+
+    await expect(iterator.next()).resolves.toEqual({ done: false, value: "first" });
+    await iterator.return?.();
+
+    expect(onReturn).toHaveBeenCalledTimes(1);
+  });
+
+  it("aborts the underlying request on first-event timeout", async () => {
+    vi.useFakeTimers();
+    try {
+      const abort = vi.fn();
+      const onTimeout = vi.fn();
+      const stream = withFirstStreamEventTimeout(createNeverYieldingStream(), {
+        timeoutMs: 5,
+        abort,
+        onTimeout,
+      });
+      const iterator = stream[Symbol.asyncIterator]();
+      const next = iterator.next().catch((error: unknown) => error);
+
+      await vi.advanceTimersByTimeAsync(5);
+      const error = await next;
+
+      expect(error).toBeInstanceOf(Error);
+      expect(onTimeout).toHaveBeenCalledWith(error);
+      expect(abort).toHaveBeenCalledWith(error);
+    } finally {
+      vi.useRealTimers();
+    }
+  });
+
+  it("clamps oversized first-event timeouts before scheduling", async () => {
+    vi.useFakeTimers();
+    try {
+      const stream = withFirstStreamEventTimeout(createNeverYieldingStream(), {
+        timeoutMs: Number.MAX_SAFE_INTEGER,
+      });
+      const iterator = stream[Symbol.asyncIterator]();
+      const next = expect(iterator.next()).rejects.toThrow(
+        new RegExp(`within ${MAX_TIMER_TIMEOUT_MS}ms`),
+      );
+
+      await vi.advanceTimersByTimeAsync(MAX_TIMER_TIMEOUT_MS);
+      await next;
+    } finally {
+      vi.useRealTimers();
+    }
+  });
+
+  it("propagates parent aborts through derived first-event signals", () => {
+    const parent = new AbortController();
+    const firstEventAbort = createFirstStreamEventAbortController(parent.signal);
+
+    parent.abort("run-timeout");
+
+    expect(firstEventAbort.signal.aborted).toBe(true);
+    expect(firstEventAbort.signal.reason).toBe("run-timeout");
+    firstEventAbort.dispose();
+  });
+
+  it("passes through events after the first event without adding inter-event timing", async () => {
+    async function* delayedSecondEvent() {
+      yield "first";
+      await new Promise((resolve) => {
+        setTimeout(resolve, 50);
+      });
+      yield "second";
+    }
+
+    vi.useFakeTimers();
+    try {
+      const stream = withFirstStreamEventTimeout(delayedSecondEvent(), { timeoutMs: 5 });
+      const iterator = stream[Symbol.asyncIterator]();
+
+      await expect(iterator.next()).resolves.toEqual({ done: false, value: "first" });
+      const second = iterator.next();
+      await vi.advanceTimersByTimeAsync(50);
+      await expect(second).resolves.toEqual({ done: false, value: "second" });
+    } finally {
+      vi.useRealTimers();
+    }
+  });
+
+  it("returns the original stream when disabled", () => {
+    const stream = createNeverYieldingStream();
+    expect(withFirstStreamEventTimeout(stream, { timeoutMs: 0 })).toBe(stream);
+    expect(withFirstStreamEventTimeout(stream, { timeoutMs: Number.NaN })).toBe(stream);
+  });
+});

packages/ai/src/utils/stream-first-event-timeout.ts

@@ -0,0 +1,134 @@
+import { clampTimerTimeoutMs } from "@openclaw/normalization-core/number-coercion";
+
+type StreamStage = "responses" | "completions";
+
+export type FirstStreamEventTimeoutContext = {
+  provider?: string;
+  api?: string;
+  model?: string;
+  timeoutMs: number;
+  stage?: StreamStage;
+  hint?: string;
+  abort?: (reason: Error) => void;
+  onTimeout?: (reason: Error) => void;
+};
+
+export type FirstStreamEventInternalOptions = {
+  firstEventTimeoutMs?: number;
+  abortFirstEventStream?: (reason: Error) => void;
+  onFirstEventTimeout?: (reason: Error) => void;
+};
+
+export type FirstStreamEventAbortController = {
+  signal: AbortSignal;
+  abort: (reason: Error) => void;
+  dispose: () => void;
+};
+
+export function getFirstStreamEventTimeoutMs(options: unknown): number | undefined {
+  return (options as FirstStreamEventInternalOptions | undefined)?.firstEventTimeoutMs;
+}
+
+export function getFirstStreamEventTimeoutHandler(
+  options: unknown,
+): ((reason: Error) => void) | undefined {
+  return (options as FirstStreamEventInternalOptions | undefined)?.onFirstEventTimeout;
+}
+
+function formatOptionalField(name: string, value: string | undefined): string {
+  return value ? ` ${name}=${value}` : "";
+}
+
+export function createFirstStreamEventTimeoutError(context: FirstStreamEventTimeoutContext): Error {
+  const stage = context.stage ? `${context.stage} ` : "";
+  const details = [
+    formatOptionalField("provider", context.provider),
+    formatOptionalField("api", context.api),
+    formatOptionalField("model", context.model),
+  ].join("");
+  return new Error(
+    `${stage}HTTP stream opened but did not deliver a first SSE event within ${context.timeoutMs}ms after streaming headers (first-event timeout).${details}` +
+      (context.hint ? ` ${context.hint}` : ""),
+  );
+}
+
+export function createFirstStreamEventAbortController(
+  parentSignal?: AbortSignal,
+): FirstStreamEventAbortController {
+  const controller = new AbortController();
+  const abortFromParent = () => {
+    if (!controller.signal.aborted) {
+      controller.abort(parentSignal?.reason);
+    }
+  };
+  if (parentSignal?.aborted) {
+    abortFromParent();
+  } else {
+    parentSignal?.addEventListener("abort", abortFromParent, { once: true });
+  }
+  return {
+    signal: controller.signal,
+    abort(reason: Error) {
+      if (!controller.signal.aborted) {
+        controller.abort(reason);
+      }
+    },
+    dispose() {
+      parentSignal?.removeEventListener("abort", abortFromParent);
+    },
+  };
+}
+
+export function withFirstStreamEventTimeout<T>(
+  stream: AsyncIterable<T>,
+  context: FirstStreamEventTimeoutContext,
+): AsyncIterable<T> {
+  const timeoutMs = clampTimerTimeoutMs(context.timeoutMs);
+  if (timeoutMs === undefined || context.timeoutMs <= 0) {
+    return stream;
+  }
+  const timeoutContext = { ...context, timeoutMs };
+  return {
+    async *[Symbol.asyncIterator]() {
+      const iterator = stream[Symbol.asyncIterator]();
+      let timer: ReturnType<typeof setTimeout> | undefined;
+      let completed = false;
+      const clear = () => {
+        if (timer) {
+          clearTimeout(timer);
+          timer = undefined;
+        }
+      };
+      try {
+        const first = await new Promise<IteratorResult<T>>((resolve, reject) => {
+          timer = setTimeout(() => {
+            const timeoutError = createFirstStreamEventTimeoutError(timeoutContext);
+            timeoutContext.onTimeout?.(timeoutError);
+            timeoutContext.abort?.(timeoutError);
+            reject(timeoutError);
+          }, timeoutMs);
+          timer.unref?.();
+          iterator.next().then(resolve, reject);
+        }).finally(clear);
+        if (first.done) {
+          completed = true;
+          return;
+        }
+        yield first.value;
+        for (;;) {
+          const next = await iterator.next();
+          if (next.done) {
+            completed = true;
+            return;
+          }
+          yield next.value;
+        }
+      } finally {
+        clear();
+        if (!completed) {
+          void iterator.return?.().catch(() => undefined);
+        }
+      }
+    },
+  };
+}

packages/ai/src/utils/streaming-byte-guard.ts

@@ -0,0 +1,87 @@
+/**
+ * Bounded SSE / NDJSON stream reader guard.
+ *
+ * Wraps a `ReadableStreamDefaultReader<Uint8Array>` so the caller's existing
+ * chunk-by-chunk parsing logic is unchanged, but accumulated bytes are tracked
+ * against a hard cap. On overflow the underlying reader is cancelled and a
+ * canonical error is thrown. Mirrors the `readResponseWithLimit` / bounded
+ * JSON response pattern (see `src/agents/provider-http-errors.ts`).
+ *
+ * Internal helper for now. If extensions need it, promote to a plugin-SDK
+ * subpath in a separate, dedicated PR with full SDK metadata sync.
+ */
+
+export type SseStreamOverflow = {
+  size: number;
+  maxBytes: number;
+};
+
+export type ReadSseStreamWithLimitOptions = {
+  maxBytes: number;
+  onOverflow?: (params: SseStreamOverflow) => Error;
+};
+
+export type SseByteGuard = {
+  read(): Promise<ReadableStreamReadResult<Uint8Array>>;
+  cancel(reason?: unknown): Promise<void>;
+  totalBytes(): number;
+  overflowed(): boolean;
+  cancelled(): boolean;
+};
+
+export function createSseByteGuard(
+  reader: ReadableStreamDefaultReader<Uint8Array>,
+  opts: ReadSseStreamWithLimitOptions,
+): SseByteGuard {
+  if (!Number.isFinite(opts.maxBytes) || opts.maxBytes < 0) {
+    throw new RangeError(`maxBytes must be a non-negative finite number: ${opts.maxBytes}`);
+  }
+  const onOverflow =
+    opts.onOverflow ??
+    ((params) =>
+      new Error(`SSE stream exceeds ${params.maxBytes} bytes (received ${params.size})`));
+  let total = 0;
+  let overflowedFlag = false;
+  let cancelledFlag = false;
+  return {
+    read: async () => {
+      if (overflowedFlag || cancelledFlag) {
+        return { done: true, value: undefined };
+      }
+      const result = await reader.read();
+      if (result.done) {
+        return result;
+      }
+      const chunkLen = result.value?.byteLength ?? 0;
+      const next = total + chunkLen;
+      if (next > opts.maxBytes) {
+        overflowedFlag = true;
+        cancelledFlag = true;
+        const err = onOverflow({ size: next, maxBytes: opts.maxBytes });
+        try {
+          await reader.cancel(err);
+        } catch {
+          // best-effort cancellation; caller observes the overflow error
+        }
+        throw err;
+      }
+      total = next;
+      return result;
+    },
+    cancel: async (reason?: unknown) => {
+      if (overflowedFlag) {
+        // overflow already set cancelledFlag; do not overwrite
+        return;
+      }
+      cancelledFlag = true;
+      try {
+        await reader.cancel(reason);
+      } catch {
+        // best-effort cancellation
+      }
+    },
+    totalBytes: () => total,
+    overflowed: () => overflowedFlag,
+    cancelled: () => cancelledFlag,
+  };
+}

packages/ai/src/utils/system-prompt-cache-boundary.test.ts

@@ -0,0 +1,92 @@
+// System prompt cache-boundary tests cover the internal marker that separates
+// stable prompt text from dynamic per-turn additions.
+import { describe, expect, it } from "vitest";
+import {
+  ensureSystemPromptCacheBoundary,
+  prependSystemPromptAdditionAfterCacheBoundary,
+  splitSystemPromptCacheBoundary,
+  stripSystemPromptCacheBoundary,
+  SYSTEM_PROMPT_CACHE_BOUNDARY,
+} from "./system-prompt-cache-boundary.js";
+
+describe("system prompt cache boundary helpers", () => {
+  it("splits stable and dynamic prompt regions", () => {
+    expect(
+      splitSystemPromptCacheBoundary(`Stable prefix${SYSTEM_PROMPT_CACHE_BOUNDARY}Dynamic suffix`),
+    ).toEqual({
+      stablePrefix: "Stable prefix",
+      dynamicSuffix: "Dynamic suffix",
+    });
+  });
+
+  it("strips the internal marker from prompt text", () => {
+    expect(
+      stripSystemPromptCacheBoundary(`Stable prefix${SYSTEM_PROMPT_CACHE_BOUNDARY}Dynamic suffix`),
+    ).toBe("Stable prefix\nDynamic suffix");
+  });
+
+  it("inserts prompt additions after the cache boundary", () => {
+    expect(
+      prependSystemPromptAdditionAfterCacheBoundary({
+        systemPrompt: `Stable prefix${SYSTEM_PROMPT_CACHE_BOUNDARY}Dynamic suffix`,
+        systemPromptAddition: "Per-turn lab context",
+      }),
+    ).toBe(`Stable prefix${SYSTEM_PROMPT_CACHE_BOUNDARY}Per-turn lab context\n\nDynamic suffix`);
+  });
+
+  it("normalizes structured additions and dynamic suffix whitespace", () => {
+    expect(
+      prependSystemPromptAdditionAfterCacheBoundary({
+        systemPrompt: `Stable prefix${SYSTEM_PROMPT_CACHE_BOUNDARY}Dynamic suffix  \r\n\r\nMore detail \t\r\n`,
+        systemPromptAddition: "  Per-turn lab context \r\nSecond line\t\r\n",
+      }),
+    ).toBe(
+      `Stable prefix${SYSTEM_PROMPT_CACHE_BOUNDARY}Per-turn lab context\nSecond line\n\nDynamic suffix\n\nMore detail`,
+    );
+  });
+});
+
+describe("ensureSystemPromptCacheBoundary", () => {
+  it("returns a marker-bearing prompt unchanged", () => {
+    const prompt = `Stable prefix${SYSTEM_PROMPT_CACHE_BOUNDARY}Dynamic suffix`;
+    expect(ensureSystemPromptCacheBoundary(prompt)).toBe(prompt);
+  });
+
+  it("appends the boundary to a marker-free prompt", () => {
+    expect(ensureSystemPromptCacheBoundary("Marker-free override")).toBe(
+      `Marker-free override${SYSTEM_PROMPT_CACHE_BOUNDARY}`,
+    );
+  });
+
+  it("does not add a boundary for an empty prompt", () => {
+    expect(ensureSystemPromptCacheBoundary("")).toBe("");
+    expect(ensureSystemPromptCacheBoundary(" \n\t ")).toBe(" \n\t ");
+  });
+
+  it("uses a per-turn addition directly when the base prompt is empty", () => {
+    expect(
+      prependSystemPromptAdditionAfterCacheBoundary({
+        systemPrompt: ensureSystemPromptCacheBoundary(""),
+        systemPromptAddition: "Per-turn media task hint",
+      }),
+    ).toBe("Per-turn media task hint");
+  });
+
+  it("is idempotent for a marker-free prompt", () => {
+    const once = ensureSystemPromptCacheBoundary("Marker-free override");
+    expect(ensureSystemPromptCacheBoundary(once)).toBe(once);
+  });
+
+  it("lets a per-turn addition split into the uncached suffix for a marker-free prompt", () => {
+    // Marker-free overrides become stable prefixes; additions stay in the
+    // dynamic suffix so prompt-cache bytes remain deterministic.
+    const result = prependSystemPromptAdditionAfterCacheBoundary({
+      systemPrompt: ensureSystemPromptCacheBoundary("Marker-free override"),
+      systemPromptAddition: "Per-turn media task hint",
+    });
+    expect(splitSystemPromptCacheBoundary(result)).toEqual({
+      stablePrefix: "Marker-free override",
+      dynamicSuffix: "Per-turn media task hint",
+    });
+  });
+});

packages/ai/src/utils/system-prompt-cache-boundary.ts

@@ -0,0 +1,66 @@
+/**
+ * System prompt cache-boundary helpers.
+ *
+ * Keeps stable prompt prefixes separate from dynamic runtime additions for provider prompt caching.
+ */
+import { normalizeStructuredPromptSection } from "./prompt-cache-stability.js";
+
+export const SYSTEM_PROMPT_CACHE_BOUNDARY = "\n<!-- OPENCLAW_CACHE_BOUNDARY -->\n";
+
+export function stripSystemPromptCacheBoundary(text: string): string {
+  return text.replaceAll(SYSTEM_PROMPT_CACHE_BOUNDARY, "\n");
+}
+
+// Append the cache boundary when a prompt has none (e.g. a hook systemPrompt override),
+// so dynamic additions route into an uncached suffix instead of the cached prefix (#85203).
+export function ensureSystemPromptCacheBoundary(systemPrompt: string): string {
+  if (systemPrompt.trim().length === 0) {
+    return systemPrompt;
+  }
+  return systemPrompt.includes(SYSTEM_PROMPT_CACHE_BOUNDARY)
+    ? systemPrompt
+    : `${systemPrompt}${SYSTEM_PROMPT_CACHE_BOUNDARY}`;
+}
+
+export function splitSystemPromptCacheBoundary(
+  text: string,
+): { stablePrefix: string; dynamicSuffix: string } | undefined {
+  const boundaryIndex = text.indexOf(SYSTEM_PROMPT_CACHE_BOUNDARY);
+  if (boundaryIndex === -1) {
+    return undefined;
+  }
+  return {
+    stablePrefix: text.slice(0, boundaryIndex).trimEnd(),
+    dynamicSuffix: text.slice(boundaryIndex + SYSTEM_PROMPT_CACHE_BOUNDARY.length).trimStart(),
+  };
+}
+
+export function prependSystemPromptAdditionAfterCacheBoundary(params: {
+  systemPrompt: string;
+  systemPromptAddition?: string;
+}): string {
+  const systemPromptAddition =
+    typeof params.systemPromptAddition === "string"
+      ? normalizeStructuredPromptSection(params.systemPromptAddition)
+      : "";
+  if (!systemPromptAddition) {
+    return params.systemPrompt;
+  }
+  if (params.systemPrompt.trim().length === 0) {
+    return systemPromptAddition;
+  }
+
+  const split = splitSystemPromptCacheBoundary(params.systemPrompt);
+  if (!split) {
+    return `${systemPromptAddition}\n\n${params.systemPrompt}`;
+  }
+
+  const dynamicSuffix = split.dynamicSuffix
+    ? normalizeStructuredPromptSection(split.dynamicSuffix)
+    : "";
+  if (!dynamicSuffix) {
+    return `${split.stablePrefix}${SYSTEM_PROMPT_CACHE_BOUNDARY}${systemPromptAddition}`;
+  }
+
+  return `${split.stablePrefix}${SYSTEM_PROMPT_CACHE_BOUNDARY}${systemPromptAddition}\n\n${dynamicSuffix}`;
+}

packages/ai/src/validation.ts

@@ -0,0 +1,2 @@
+/** Tool argument validation for TypeBox and JSON Schema declarations. */
+export * from "@openclaw/llm-core/validation";

packages/ai/tsconfig.json

@@ -0,0 +1,8 @@
+{
+  "extends": "../../tsconfig.json",
+  "compilerOptions": {
+    "rootDir": "../..",
+    "outDir": "dist"
+  },
+  "include": ["src/**/*", "../../src/agents/**/*", "../../src/shared/**/*"]
+}