Adding Vercel AI SDK sample with claude model

nodejs/vercel-sdk/sample-agent/.env.example

@@ -0,0 +1,30 @@
+# Anthropic Configuration
+ANTHROPIC_API_KEY=
+
+# Environment Settings
+NODE_ENV=development 
+
+# Telemetry and Tracing Configuration
+DEBUG=agents:*
+AZURE_EXPERIMENTAL_ENABLE_ACTIVITY_SOURCE=true
+AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED=true
+OPENAI_AGENTS_DISABLE_TRACING=false
+OTEL_SDK_DISABLED=false
+CONNECTION_STRING=
+
+# Use Agentic Authentication rather than OBO
+USE_AGENTIC_AUTH=false
+
+# Service Connection Settings
+connections__service_connection__settings__clientId=
+connections__service_connection__settings__clientSecret=
+connections__service_connection__settings__tenantId=
+
+# Set service connection as default
+connectionsMap__0__serviceUrl=*
+connectionsMap__0__connection=service_connection
+
+# AgenticAuthentication Options
+agentic_type=agentic
+agentic_altBlueprintConnectionName=service_connection
+agentic_scopes=ea9ffc3e-8a23-4a7d-836d-234d7c7565c1/.default # Prod Agentic scope

nodejs/vercel-sdk/sample-agent/README.md

@@ -0,0 +1,43 @@
+# Sample Agent - Node.js Vercel AI SDK
+
+This directory contains a sample agent implementation using Node.js and Vercel AI SDK.
+
+## Demonstrates
+
+This sample demonstrates how to build an agent using the Agent365 framework with Node.js and Vercel AI SDK.
+
+## Prerequisites
+
+- Node.js 18+
+- Vercel AI SDK
+- Agents SDK
+
+## How to run this sample
+
+1. **Setup environment variables**
+   ```bash
+   # Copy the example environment file
+   cp .env.example .env
+   ```
+
+2. **Install dependencies**
+   ```bash
+   npm install
+   ```
+
+3. **Build the project**
+   ```bash
+   npm run build
+   ```
+
+4. **Start the agent**
+   ```bash
+   npm start
+   ```
+
+5. **Optionally, while testing you can run in dev mode**
+   ```bash
+   npm run dev
+   ```
+
+The agent will start and be ready to receive requests through the configured hosting mechanism.

nodejs/vercel-sdk/sample-agent/package.json

@@ -0,0 +1,40 @@
+{
+  "name": "vercel-sdk-sample",
+  "version": "2025.11.6",
+  "description": "Sample agent integrating Vercel AI SDK Agents with Microsoft 365 Agents SDK and Agent365 SDK",
+  "main": "src/index.ts",
+  "scripts": {
+    "preinstall": "node preinstall-local-packages.js",
+    "start": "node dist/index.js",
+    "dev": "nodemon --watch src/*.ts --exec ts-node src/index.ts",
+    "test-tool": "agentsplayground",
+    "eval": "node --env-file .env src/evals/index.js",
+    "build": "tsc"
+  },
+  "keywords": [
+    "vercel-ai-sdk",
+    "microsoft-365",
+    "agent",
+    "ai"
+  ],
+  "license": "MIT",
+  "dependencies": {
+    "@ai-sdk/anthropic": "^2.0.31",
+    "@microsoft/agents-activity": "^1.1.0-alpha.85",
+    "@microsoft/agents-hosting": "^1.1.0-alpha.85",
+    "ai": "^5.0.72",
+    "dotenv": "^17.2.3",
+    "express": "^5.1.0",
+    "node-fetch": "^3.3.2",
+    "uuid": "^9.0.0"
+  },
+  "devDependencies": {
+    "@azure/monitor-opentelemetry-exporter": "^1.0.0-beta.32",
+    "@babel/cli": "^7.28.3",
+    "@babel/core": "^7.28.4",
+    "@babel/preset-env": "^7.28.3",
+    "@microsoft/m365agentsplayground": "^0.2.16",
+    "nodemon": "^3.1.10",
+    "ts-node": "^10.9.2"
+  }
+}

nodejs/vercel-sdk/sample-agent/preinstall-local-packages.js

@@ -0,0 +1,72 @@
+#!/usr/bin/env node
+
+import { readdir } from 'fs/promises';
+import { join, dirname } from 'path';
+import { fileURLToPath } from 'url';
+import { execSync } from 'child_process';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = dirname(__filename);
+
+// Look for *.tgz files two directories above
+const tgzDir = join(__dirname, '../../');
+
+// Define the installation order
+const installOrder = [
+  'microsoft-agents-a365-runtime-',
+  'microsoft-agents-a365-notifications-',
+  'microsoft-agents-a365-observability-',
+  'microsoft-agents-a365-tooling-',
+  'microsoft-agents-a365-tooling-extensions-claude-'
+];
+
+async function findTgzFiles() {
+  try {
+    const files = await readdir(tgzDir);
+    return files.filter(file => file.endsWith('.tgz'));
+  } catch (error) {
+    console.log('No tgz directory found or no files to install');
+    return [];
+  }
+}
+
+function findFileForPattern(files, pattern) {
+  return files.find(file => file.startsWith(pattern));
+}
+
+async function installPackages() {
+  const tgzFiles = await findTgzFiles();
+
+  if (tgzFiles.length === 0) {
+    console.log('No .tgz files found in', tgzDir);
+    return;
+  }
+
+  console.log('Found .tgz files:', tgzFiles);
+
+  for (const pattern of installOrder) {
+    const file = findFileForPattern(tgzFiles, pattern);
+    if (file) {
+      const filePath = join(tgzDir, file);
+      console.log(`Installing ${file}...`);
+      try {
+        execSync(`npm install "${filePath}"`, { 
+          stdio: 'inherit',
+          cwd: __dirname
+        });
+        console.log(`✓ Successfully installed ${file}`);
+      } catch (error) {
+        console.error(`✗ Failed to install ${file}:`, error.message);
+        process.exit(1);
+      }
+    } else {
+      console.log(`No file found matching pattern: ${pattern}`);
+    }
+  }
+}
+
+// Run the installation
+installPackages().catch(error => {
+  console.error('Error during package installation:', error);
+  process.exit(1);
+});

nodejs/vercel-sdk/sample-agent/src/agent.ts

@@ -0,0 +1,62 @@
+import { TurnState, AgentApplication, TurnContext, MemoryStorage } from '@microsoft/agents-hosting';
+import { ActivityTypes } from '@microsoft/agents-activity';
+
+// Notification Imports
+import '@microsoft/agents-a365-notifications';
+import { AgentNotificationActivity } from '@microsoft/agents-a365-notifications';
+
+import { Client, getClient } from './client';
+
+export class A365Agent extends AgentApplication<TurnState> {
+  agentName = "A365 Agent";
+
+  constructor() {
+    super({
+      startTypingTimer: true,
+      storage: new MemoryStorage(),
+      authorization: {
+        agentic: {
+          type: 'agentic',
+        } // scopes set in the .env file...
+      }
+    });
+
+    // Route agent notifications
+    this.onAgentNotification("agents:*", async (context: TurnContext, state: TurnState, agentNotificationActivity: AgentNotificationActivity) => {
+      await this.handleAgentNotificationActivity(context, state, agentNotificationActivity);
+    });
+
+    this.onActivity(ActivityTypes.Message, async (context: TurnContext, state: TurnState) => {
+      await this.handleAgentMessageActivity(context, state);
+    });
+  }
+
+    /**
+   * Handles incoming user messages and sends responses.
+   */
+  async handleAgentMessageActivity(turnContext: TurnContext, state: TurnState): Promise<void> {
+    const userMessage = turnContext.activity.text?.trim() || '';
+
+    if (!userMessage) {
+      await turnContext.sendActivity('Please send me a message and I\'ll help you!');
+      return;
+    }
+
+    try {
+      const client: Client = await getClient();
+      const response = await client.invokeAgentWithScope(userMessage);
+      await turnContext.sendActivity(response);
+    } catch (error) {
+      console.error('LLM query error:', error);
+      const err = error as any;
+      await turnContext.sendActivity(`Error: ${err.message || err}`);
+    }
+  }
+
+  async handleAgentNotificationActivity(context: TurnContext, state: TurnState, agentNotificationActivity: AgentNotificationActivity) {
+    context.sendActivity("Recieved an AgentNotification!");
+    /* your logic here... */
+  }
+}
+
+export const agentApplication = new A365Agent();

nodejs/vercel-sdk/sample-agent/src/client.ts

@@ -0,0 +1,115 @@
+import { Experimental_Agent as Agent } from "ai";
+import { anthropic } from '@ai-sdk/anthropic';
+
+
+// Observability Imports
+import {
+  ObservabilityManager,
+  InferenceScope,
+  Builder,
+  InferenceOperationType,
+  AgentDetails,
+  TenantDetails,
+  InferenceDetails
+} from '@microsoft/agents-a365-observability';
+
+const modelName = 'claude-sonnet-4-20250514';
+
+export interface Client {
+  invokeAgentWithScope(prompt: string): Promise<string>;
+}
+
+const sdk = ObservabilityManager.configure(
+  (builder: Builder) =>
+    builder
+      .withService('Vercel AI SDK Sample Agent', '1.0.0')
+);
+
+sdk.start();
+
+/**
+ * Creates and configures a Vercel AI SDK client with anthropic model.
+ *
+ * This factory function initializes a Vercel AI SDK React agent with access to
+ *
+ * @returns Promise<Client> - Configured Vercel AI SDK client ready for agent interactions
+ *
+ * @example
+ * ```typescript
+ * const client = await getClient();
+ * const response = await client.invokeAgent("Hello, how are you?");
+ * ```
+ */
+export async function getClient(): Promise<Client> {
+  // Create the model
+  const model = anthropic(modelName)
+
+  // Create the agent
+  const agent = new Agent({
+    model: model,
+  });
+
+  return new VercelAiClient(agent);
+}
+
+/**
+ * VercelAiClient provides an interface to interact with Vercel AI SDK agents.
+ * It creates a React agent with tools and exposes an invokeAgent method.
+ */
+class VercelAiClient implements Client {
+  private agent: Agent<any, any, any>;
+
+  constructor(agent: any) {
+    this.agent = agent;
+  }
+
+  /**
+   * Sends a user message to the Vercel AI SDK agent and returns the AI's response.
+   * Handles streaming results and error reporting.
+   *
+   * @param {string} userMessage - The message or prompt to send to the agent.
+   * @returns {Promise<string>} The response from the agent, or an error message if the query fails.
+   */
+  async invokeAgent(userMessage: string): Promise<string> {
+    const { text: agentMessage } = await this.agent.generate({
+      prompt: userMessage
+    });
+
+    if (!agentMessage) {
+      return "Sorry, I couldn't get a response from the agent :(";
+    }
+
+    return agentMessage;
+  }
+
+  async invokeAgentWithScope(prompt: string) {
+    const inferenceDetails: InferenceDetails = {
+      operationName: InferenceOperationType.CHAT,
+      model: modelName,
+    };
+
+    const agentDetails: AgentDetails = {
+      agentId: 'typescript-compliance-agent',
+      agentName: 'TypeScript Compliance Agent',
+      conversationId: 'conv-12345',
+    };
+
+    const tenantDetails: TenantDetails = {
+      tenantId: 'typescript-sample-tenant',
+    };
+
+    const scope = InferenceScope.start(inferenceDetails, agentDetails, tenantDetails);
+
+    const response = await this.invokeAgent(prompt);
+
+    // Record the inference response with token usage
+    scope?.recordOutputMessages([response]);
+    scope?.recordInputMessages([prompt]);
+    scope?.recordResponseId(`resp-${Date.now()}`);
+    scope?.recordInputTokens(45);
+    scope?.recordOutputTokens(78);
+    scope?.recordFinishReasons(['stop']);
+
+    return response;
+  }
+}