Migrate Node.js LangChain sample to GA packages and Microsoft.OpenTelemetry distro

nodejs/langchain/sample-agent/.env.example

@@ -17,32 +17,20 @@ BEARER_TOKEN=
 MCP_PLATFORM_ENDPOINT=
 MCP_PLATFORM_AUTHENTICATION_SCOPE=
 
+# Environment Settings (set NODE_ENV=production to enable JWT validation against Entra)
+NODE_ENV=development
+PORT=3978
 
-# Enable to use observability exporter, default is false which means using console exporter
-ENABLE_A365_OBSERVABILITY_EXPORTER=false
-# Use by the sample to demo using custom token resolver and token cache when it is true, otherwise use the built-in AgenticTokenCache
-Use_Custom_Resolver=true
-# optional - set to enable observability logs, value can be 'info', 'warn', or 'error', default to 'none' if not set
-A365_OBSERVABILITY_LOG_LEVEL=
+# Sample Observability Options
+# When true, the sample uses a custom token resolver + local cache;
+# otherwise it relies on the built-in AgenticTokenCacheInstance.
+Use_Custom_Resolver=false
 
-# Environment Settings
-NODE_ENV=development # Retrieve mcp servers from ToolingManifest
-
-# 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
+# Service Connection Settings (stamped by `a365 setup all` against your Entra app)
 connections__service_connection__settings__clientId=
 connections__service_connection__settings__clientSecret=
 connections__service_connection__settings__tenantId=
+connections__service_connection__settings__scopes=
 
 # Set service connection as default
 connectionsMap__0__serviceUrl=*
@@ -51,4 +39,14 @@ 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
+agentic_scopes=https://graph.microsoft.com/.default
+agentic_connectionName=AgenticAuthConnection
+
+# Agent 365 Observability (stamped by `a365 setup all` for telemetry export)
+agent365Observability__agentId=
+agent365Observability__agentName=
+agent365Observability__agentDescription=
+agent365Observability__tenantId=
+agent365Observability__agentBlueprintId=
+agent365Observability__clientId=
+agent365Observability__clientSecret=

nodejs/langchain/sample-agent/env/.env.playground.user

@@ -4,9 +4,10 @@
 SECRET_AZURE_OPENAI_API_KEY=
 AZURE_OPENAI_ENDPOINT=
 AZURE_OPENAI_DEPLOYMENT_NAME=
+AZURE_OPENAI_API_VERSION=
 
 # Option 2: OpenAI (if Azure OpenAI not configured)
 # SECRET_OPENAI_API_KEY=
 # OPENAI_MODEL=gpt-4o
 
-SECRET_BEARER_TOKEN=
+SECRET_BEARER_TOKEN=

nodejs/langchain/sample-agent/m365agents.playground.yml

@@ -28,6 +28,7 @@ deploy:
         AZURE_OPENAI_API_KEY: ${{SECRET_AZURE_OPENAI_API_KEY}}
         AZURE_OPENAI_ENDPOINT: ${{AZURE_OPENAI_ENDPOINT}}
         AZURE_OPENAI_DEPLOYMENT: ${{AZURE_OPENAI_DEPLOYMENT_NAME}}
+        AZURE_OPENAI_API_VERSION: ${{AZURE_OPENAI_API_VERSION}}
         # OPENAI_API_KEY: ${{SECRET_OPENAI_API_KEY}}
         BEARER_TOKEN: ${{SECRET_BEARER_TOKEN}}
         USE_AGENTIC_AUTH: ${{USE_AGENTIC_AUTH}}

nodejs/langchain/sample-agent/package.json

@@ -25,12 +25,10 @@
     "@langchain/langgraph": "^1.0.2",
     "@langchain/mcp-adapters": "^1.0.0",
     "@langchain/openai": "^1.0.2",
-    "@microsoft/agents-a365-notifications": "^0.2.0-preview.1",
-    "@microsoft/agents-a365-observability": "^0.2.0-preview.1",
-    "@microsoft/agents-a365-observability-hosting": "^0.2.0-preview.1",
-    "@microsoft/agents-a365-runtime": "^0.2.0-preview.1",
-    "@microsoft/agents-a365-tooling": "^0.2.0-preview.1",
-    "@microsoft/agents-a365-tooling-extensions-langchain": "^0.2.0-preview.1",
+    "@microsoft/agents-a365-notifications": "^1.0.0",
+    "@microsoft/agents-a365-runtime": "^1.0.0",
+    "@microsoft/agents-a365-tooling": "^1.0.0",
+    "@microsoft/agents-a365-tooling-extensions-langchain": "^1.0.0",
     "@microsoft/agents-activity": "^1.2.2",
     "@microsoft/agents-hosting": "^1.2.2",
     "@microsoft/opentelemetry": "^1.0.0",

nodejs/langchain/sample-agent/src/agent.ts

@@ -8,8 +8,7 @@ import { Activity, ActivityTypes } from '@microsoft/agents-activity';
 import '@microsoft/agents-a365-notifications';
 import { AgentNotificationActivity, NotificationType, createEmailResponseActivity } from '@microsoft/agents-a365-notifications';
 // Observability Imports
-import { BaggageBuilder } from '@microsoft/agents-a365-observability';
-import { AgenticTokenCacheInstance, BaggageBuilderUtils } from '@microsoft/agents-a365-observability-hosting';
+import { BaggageBuilder, AgenticTokenCacheInstance, BaggageBuilderUtils } from '@microsoft/opentelemetry';
 import { getObservabilityAuthenticationScope } from '@microsoft/agents-a365-runtime';
 import tokenCache, { createAgenticTokenCacheKey } from './token-cache';
 import { Client, getClient } from './client';
@@ -78,7 +77,7 @@ export class A365Agent extends AgentApplication<TurnState> {
 
     const baggageScope = BaggageBuilderUtils.fromTurnContext(
       new BaggageBuilder(),
-      turnContext
+      turnContext as any
     ).sessionDescription('Initial onboarding session')
       .build();
 
@@ -118,12 +117,11 @@ export class A365Agent extends AgentApplication<TurnState> {
       const cacheKey = createAgenticTokenCacheKey(agentId, tenantId);
       tokenCache.set(cacheKey, aauToken?.token || '');
     } else {
-      await AgenticTokenCacheInstance.RefreshObservabilityToken(
+      await AgenticTokenCacheInstance.refreshObservabilityToken(
         agentId,
         tenantId,
-        turnContext,
-        this.authorization,
-        getObservabilityAuthenticationScope()
+        turnContext as any,
+        this.authorization as any
       );
     }
   }

nodejs/langchain/sample-agent/src/client.ts

@@ -15,8 +15,8 @@ import {
   InferenceOperationType,
   AgentDetails,
   InferenceDetails,
-  Request,
-} from '@microsoft/agents-a365-observability';
+  A365Request,
+} from '@microsoft/opentelemetry';
 
 export interface Client {
   invokeInferenceScope(prompt: string): Promise<string>;
@@ -37,11 +37,14 @@ function createChatModel(): BaseChatModel {
   // Check for Azure OpenAI configuration first
   if (process.env.AZURE_OPENAI_API_KEY && process.env.AZURE_OPENAI_ENDPOINT && process.env.AZURE_OPENAI_DEPLOYMENT) {
     console.log('Using Azure OpenAI');
+    const endpoint = process.env.AZURE_OPENAI_ENDPOINT.replace(/\/$/, '');
+    const deployment = process.env.AZURE_OPENAI_DEPLOYMENT;
+    const apiVersion = process.env.AZURE_OPENAI_API_VERSION || "2025-03-01-preview";
     return new AzureChatOpenAI({
       azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
-      azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_ENDPOINT?.replace('https://', '').replace('.openai.azure.com/', '').replace('.openai.azure.com', ''),
-      azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_DEPLOYMENT,
-      azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION || "2025-03-01-preview",
+      azureOpenAIBasePath: `${endpoint}/openai/deployments`,
+      azureOpenAIApiDeploymentName: deployment,
+      azureOpenAIApiVersion: apiVersion,
       temperature: 0,
     });
   }
@@ -152,7 +155,7 @@ class LangChainClient implements Client {
    * @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> {
+  async invokeAgent(userMessage: string): Promise<{ content: string; inputTokens: number; outputTokens: number; finishReason: string }> {
     const result = await this.agent.invoke({
       messages: [
         {
@@ -162,33 +165,45 @@ class LangChainClient implements Client {
       ],
     });
 
-    let agentMessage: any = '';
+    let content = '';
+    let inputTokens = 0;
+    let outputTokens = 0;
+    let finishReason = 'stop';
 
-    // Extract the content from the LangChain response
     if (result.messages && result.messages.length > 0) {
+      // Sum usage_metadata across every AI message in the React loop so the
+      // manual InferenceScope reflects total work, matching the auto-instrumented invoke_agent span.
+      for (const msg of result.messages) {
+        const usage = (msg as any).usage_metadata;
+        if (usage) {
+          inputTokens += usage.input_tokens ?? 0;
+          outputTokens += usage.output_tokens ?? 0;
+        }
+      }
       const lastMessage = result.messages[result.messages.length - 1];
-      agentMessage = lastMessage.content || "No content in response";
+      content = (lastMessage.content as string) || "No content in response";
+      finishReason = (lastMessage as any).response_metadata?.finish_reason ?? 'stop';
     }
 
-    // Fallback if result is already a string
     if (typeof result === 'string') {
-      agentMessage = result;
+      content = result;
     }
 
-    if (!agentMessage) {
-      return "Sorry, I couldn't get a response from the agent :(";
+    if (!content) {
+      content = "Sorry, I couldn't get a response from the agent :(";
     }
 
-    return agentMessage;
+    return { content, inputTokens, outputTokens, finishReason };
   }
 
   async invokeInferenceScope(prompt: string) {
+    const model = process.env.AZURE_OPENAI_DEPLOYMENT || process.env.OPENAI_MODEL || 'unknown';
     const inferenceDetails: InferenceDetails = {
       operationName: InferenceOperationType.CHAT,
-      model: "gpt-4o-mini",
+      model,
     };
 
-    const request: Request = {
+    const request: A365Request = {
       conversationId: this.turnContext?.activity?.conversation?.id || `conv-${Date.now()}`,
     };
 
@@ -202,13 +217,13 @@ class LangChainClient implements Client {
     const scope = InferenceScope.start(request, inferenceDetails, agentDetails);
     try {
       await scope.withActiveSpanAsync(async () => {
-      response = await this.invokeAgent(prompt);
-      // Record the inference response with token usage
-      scope.recordOutputMessages([response]);
-      scope.recordInputMessages([prompt]);
-      scope.recordInputTokens(45);
-      scope.recordOutputTokens(78);
-      scope.recordFinishReasons(['stop']);
+        const result = await this.invokeAgent(prompt);
+        response = result.content;
+        scope.recordInputMessages([prompt]);
+        scope.recordOutputMessages([response]);
+        scope.recordInputTokens(result.inputTokens);
+        scope.recordOutputTokens(result.outputTokens);
+        scope.recordFinishReasons([result.finishReason]);
       });
     } catch (error) {
       scope.recordError(error as Error);

nodejs/langchain/sample-agent/src/index.ts

@@ -9,11 +9,11 @@ configDotenv();
 // Initialize Microsoft OpenTelemetry distro for observability.
 // Must be called before importing other modules so instrumentations can patch libraries.
 // See: https://github.com/microsoft/opentelemetry-distro-javascript
-import { useMicrosoftOpenTelemetry } from '@microsoft/opentelemetry';
+import { useMicrosoftOpenTelemetry, AgenticTokenCacheInstance } from '@microsoft/opentelemetry';
 import { tokenResolver } from './token-cache';
-import { AgenticTokenCacheInstance } from '@microsoft/agents-a365-observability-hosting';
 
 useMicrosoftOpenTelemetry({
+  enableConsoleExporters: true,
   a365: {
     enabled: true,
     // When Use_Custom_Resolver is true the sample populates a local token cache;