feat(templates): support vercel typescript.

src/assets/templates/agent-typescript-vercel/README.md

@@ -0,0 +1,22 @@
+This is a project generated by the AgentCore CLI!
+
+# Layout
+
+The generated application code lives at the agent root directory. At the root, there is a `.gitignore` file, an
+`agentcore/` folder which represents the configurations and state associated with this project. Other `agentcore`
+commands like `deploy`, `dev`, and `invoke` rely on the configuration stored here.
+
+## Agent Root
+
+The main entrypoint to your app is defined in `main.ts`. Using the AgentCore SDK `BedrockAgentCoreApp`, this file
+defines an HTTP server that streams tokens from Amazon Bedrock via the Vercel AI SDK's `streamText` API.
+
+# Developing locally
+
+If installation was successful, `node_modules/` is already populated with dependencies.
+
+`agentcore project dev` will start a local server using `tsx watch main.ts` for hot reload on 0.0.0.0:8080.
+
+# Deployment
+
+After providing credentials, `agentcore project deploy` will deploy your project into Amazon Bedrock AgentCore.

src/assets/templates/agent-typescript-vercel/gitignore.template

@@ -0,0 +1,22 @@
+# Environment variables
+.env
+.env.*
+
+# Node
+node_modules/
+dist/
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db

src/assets/templates/agent-typescript-vercel/main.ts

@@ -0,0 +1,35 @@
+import { BedrockAgentCoreApp } from 'bedrock-agentcore/runtime';
+import { createAmazonBedrock } from '@ai-sdk/amazon-bedrock';
+import { fromNodeProviderChain } from '@aws-sdk/credential-providers';
+import { streamText } from 'ai';
+import { z } from 'zod';
+
+const SYSTEM_PROMPT = `You are a helpful assistant.`;
+
+const bedrock = createAmazonBedrock({
+  region: process.env.AWS_REGION ?? 'us-east-1',
+  credentialProvider: fromNodeProviderChain(),
+});
+
+const requestSchema = z.object({
+  prompt: z.string().default(''),
+});
+
+const app = new BedrockAgentCoreApp({
+  invocationHandler: {
+    requestSchema,
+    async *process(payload) {
+      const result = streamText({
+        model: bedrock('global.anthropic.claude-sonnet-4-5-20250929-v1:0'),
+        system: SYSTEM_PROMPT,
+        prompt: payload.prompt,
+      });
+
+      for await (const chunk of result.textStream) {
+        yield { data: chunk };
+      }
+    },
+  },
+});
+
+app.run({ port: parseInt(process.env.PORT ?? '8080') });

src/assets/templates/agent-typescript-vercel/package.json.template

@@ -0,0 +1,24 @@
+{
+  "name": "{{name}}",
+  "version": "0.1.0",
+  "description": "AgentCore Runtime Application using the Vercel AI SDK",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "build": "tsc",
+    "start": "node dist/main.js",
+    "dev": "tsx watch main.ts"
+  },
+  "dependencies": {
+    "@ai-sdk/amazon-bedrock": "~4.0.0",
+    "@aws-sdk/credential-providers": "~3.1126.0",
+    "ai": "~6.0.0",
+    "bedrock-agentcore": "~0.3.0",
+    "tsx": "~4.19.0",
+    "zod": "~4.4.3"
+  },
+  "devDependencies": {
+    "@types/node": "~22.0.0",
+    "typescript": "~5.6.0"
+  }
+}

src/assets/templates/agent-typescript-vercel/tsconfig.json

@@ -0,0 +1,19 @@
+{
+  "compilerOptions": {
+    "target": "ES2022",
+    "module": "NodeNext",
+    "moduleResolution": "NodeNext",
+    "strict": true,
+    "esModuleInterop": true,
+    "skipLibCheck": true,
+    "forceConsistentCasingInFileNames": true,
+    "resolveJsonModule": true,
+    "declaration": false,
+    "sourceMap": true,
+    "outDir": "dist",
+    "rootDir": ".",
+    "types": ["node"]
+  },
+  "include": ["**/*.ts"],
+  "exclude": ["node_modules", "dist"]
+}

src/core/project/templates/runtime.ts

@@ -199,6 +199,23 @@ const getTemplateResolvers = (assetSource: AssetSource, templateRenderer: Templa
       ...(modelScaffold.envEntries.length > 0 && { envEntries: modelScaffold.envEntries }),
     };
   },
+  [buildResolverKey("vercelai", "TypeScript", "HTTP")]: async (input: RuntimeResourceConfig) => {
+    if (input.protocol !== undefined && input.protocol !== "HTTP")
+      throw new InputValidationError("the agent-typescript-vercel template only supports HTTP");
+    const context = { name: toNpmPackageName(input.name) };
+    const tree = await FsTreeNode.fromAssetSource(
+      { assetSource },
+      { assetDir: "templates/agent-typescript-vercel" },
+      {
+        rootDirName: input.name,
+        transformContent: (raw) => templateRenderer.render(raw, context),
+      },
+    );
+    return {
+      tree,
+      spec: { runtimes: [{ ...buildRuntimeSpec(input), protocol: "HTTP" as const }] },
+    };
+  },
   [buildResolverKey("none", "Python", "MCP")]: async (input: RuntimeResourceConfig) => {
     if (input.scaffoldRuntimeInput.modelProvider !== undefined)
       throw new InputValidationError("an MCP runtime does not use a model provider");

src/handlers/project/add/runtime/index.test.ts

@@ -303,6 +303,7 @@ describe("project add runtime", () => {
     ["agent-python-strands", ["SEMANTIC", "USER_PREFERENCE", "SUMMARIZATION", "EPISODIC"]],
     ["a2a-python-strands", ["SEMANTIC", "USER_PREFERENCE", "SUMMARIZATION", "EPISODIC"]],
     ["agent-python-minimal", []],
+    ["agent-typescript-vercel", []],
     ["mcp-python-fastmcp", []],
     ["agui-python-strands", ["SEMANTIC", "USER_PREFERENCE", "SUMMARIZATION", "EPISODIC"]],
   ])("%s ships with its pre-configured memory", async (templateName, expectedStrategies) => {
@@ -349,6 +350,23 @@ describe("project add runtime", () => {
     ]);
   });
 
+  test("agent-typescript-vercel scaffolds a memory-free TypeScript runtime", async () => {
+    const projectRoot = await inProject();
+    await run(["add", "runtime", "--name", "my_agent", "--template", "agent-typescript-vercel"]);
+
+    const spec = await Bun.file(join(projectRoot, "agentcore", "agentcore.json")).json();
+    expect(spec.runtimes).toContainEqual(
+      expect.objectContaining({
+        name: "my_agent",
+        entrypoint: "main.js",
+        build: "CodeZip",
+        runtimeVersion: "NODE_22",
+        protocol: "HTTP",
+      }),
+    );
+    expect(spec.memories ?? []).toEqual([]);
+  });
+
   test.each<[string, string]>([
     ["anthropic", "Anthropic"],
     ["OpenAI", "OpenAI"],
@@ -397,6 +415,17 @@ describe("project add runtime", () => {
       "--api-key is not valid with the agent-python-minimal template",
       ["--name", "my_agent", "--template", "agent-python-minimal", "--api-key", "secret-key"],
     ],
+    [
+      "--model-provider is not valid with the agent-typescript-vercel template",
+      [
+        "--name",
+        "my_agent",
+        "--template",
+        "agent-typescript-vercel",
+        "--model-provider",
+        "Anthropic",
+      ],
+    ],
     [
       "--model-provider without a template requires agent-python-strands",
       ["--name", "my_agent", "--model-provider", "Anthropic"],

src/handlers/project/create/screen.tsx

@@ -140,6 +140,11 @@ const TEMPLATE_OPTIONS: {
     label: "agent-typescript-strands",
     description: "Strands agent on Bedrock with memory, in TypeScript (CodeZip build)",
   },
+  {
+    template: "agent-typescript-vercel",
+    label: "agent-typescript-vercel",
+    description: "minimal Vercel AI SDK agent on Bedrock, in TypeScript (CodeZip build)",
+  },
   {
     template: "mcp-python-fastmcp",
     label: "mcp-python-fastmcp",

src/handlers/project/shortcuts.ts

@@ -83,6 +83,16 @@ export const RUNTIME_TEMPLATE_SHORTCUTS = {
     supportsModelProviderOverride: false,
     runtimeVersion: "NODE_22",
   },
+  "agent-typescript-vercel": {
+    runtimeName: "agent_typescript_vercel",
+    build: "CodeZip",
+    language: "TypeScript",
+    framework: "vercelai",
+    modelProvider: "Bedrock",
+    includesMemory: false,
+    supportsModelProviderOverride: false,
+    runtimeVersion: "NODE_22",
+  },
   "mcp-python-fastmcp": {
     runtimeName: "mcp_python_fastmcp",
     build: "CodeZip",

src/handlers/project/types.ts

@@ -67,7 +67,7 @@ export const ScaffoldRuntimeInputSchema = z
     runtimeName: AgentNameSchema,
     build: BuildTypeSchema,
     language: z.enum(["Python", "TypeScript"]),
-    framework: z.enum(["strands", "none"]),
+    framework: z.enum(["strands", "vercelai", "none"]),
     protocol: ProtocolModeSchema.optional(),
     modelProvider: ModelProviderSchema.optional(),
     apiKey: z.string().min(1).optional(),