Agents TS quickstart - simplier, math question

Author: diberry

.openpublishing.publish.config.json

@@ -50,6 +50,12 @@
       "branch": "master",
       "branch_mapping": {}
     },
+    {
+      "path_to_root": "azure-sdk-for-js-docs",
+      "url": "https://github.com/Azure-samples/azure-sdk-for-js-docs",
+      "branch": "main",
+      "branch_mapping": {}
+    },
     {
       "path_to_root": "samples-luis",
       "url": "https://github.com/Azure-Samples/cognitive-services-language-understanding",

articles/ai-foundry/agents/includes/quickstart-typescript.md

@@ -4,7 +4,7 @@ author: aahill
 ms.author: aahi
 ms.service: azure-ai-agent-service
 ms.topic: include
-ms.date: 07/16/2025
+ms.date: 09/12/2025
 ms.custom: devx-track-ts
 ---
 
@@ -14,8 +14,7 @@ ms.custom: devx-track-ts
 ## Prerequisites
 
 [!INCLUDE [universal-prerequisites](universal-prerequisites.md)]
-
-
+    * [Node.js LTS](https://nodejs.org/)
 
 ## Configure and run an agent
 
@@ -31,19 +30,19 @@ ms.custom: devx-track-ts
 Key objects in this code include: 
 
 * [AgentsClient](/javascript/api/@azure/ai-agents/agentsclient)
-* [ToolUtility](/javascript/api/@azure/ai-agents/toolutility)
 
-First, initialize a new project by running:
+First, initialize a new TypeScript project by running:
 
 ```console
 npm init -y
+npm pkg set type="module"
 ```
 
 Run the following commands to install the npm packages required.
 
 ```console
 npm install @azure/ai-agents @azure/identity
-npm install dotenv
+npm install @types/node typescript --save-dev
 ```
 
 Next, to authenticate your API requests and run the program, use the [az login](/cli/azure/authenticate-azure-cli-interactively) command to sign into your Azure subscription.
@@ -52,7 +51,7 @@ Next, to authenticate your API requests and run the program, use the [az login](
 az login
 ```
 
-Use the following code to create and run an agent which uploads [a CSV file](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/ai/ai-agents/data/syntheticCompanyQuarterlyResults.csv) of data then generates a bar chart from that data. To run this code, you'll need to get the endpoint for your project. This string is in the format:
+Use the following code to answer the math question `I need to solve the equation '3x + 11 = 14'. Can you help me?`. To run this code, you'll need to get the endpoint for your project. This string is in the format:
 
 `https://<AIFoundryResourceName>.services.ai.azure.com/api/projects/<ProjectName>`
 
@@ -68,190 +67,12 @@ Save the name of your model deployment name as an environment variable named `MO
 > * This quickstart code uses environment variables for sensitive configuration. Never commit your `.env` file to version control by making sure `.env` is listed in your `.gitignore` file.
 > * _Remember: If you accidentally commit sensitive information, consider those credentials compromised and rotate them immediately._
 
+Create a tsconfig.json file with the following content:
 
-Next, create an `index.js` file and paste in the following code:
-
-```typescript
-// Copyright (c) Microsoft Corporation.
-// Licensed under the MIT License.
-
-/**
- * This sample demonstrates how to use agent operations with code interpreter from the Azure Agents service.
- *
- * @summary demonstrates how to use agent operations with code interpreter.
- */
-// @ts-nocheck
-import type {
-  MessageDeltaChunk,
-  MessageDeltaTextContent,
-  MessageImageFileContent,
-  MessageTextContent,
-  ThreadRun,
-} from "@azure/ai-agents";
-import {
-  RunStreamEvent,
-  MessageStreamEvent,
-  DoneEvent,
-  ErrorEvent,
-  AgentsClient,
-  isOutputOfType,
-  ToolUtility,
-} from "@azure/ai-agents";
-import { DefaultAzureCredential } from "@azure/identity";
-
-import * as fs from "fs";
-import path from "node:path";
-import "dotenv/config";
-
-const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project endpoint>";
-const modelDeploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "gpt-4o";
-
-export async function main(): Promise<void> {
-  // Create an Azure AI Client
-  const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());
-
-  // Upload file and wait for it to be processed
-  const filePath = "./data/nifty500QuarterlyResults.csv";
-  const localFileStream = fs.createReadStream(filePath);
-  const localFile = await client.files.upload(localFileStream, "assistants", {
-    fileName: "myLocalFile",
-  });
-
-  console.log(`Uploaded local file, file ID : ${localFile.id}`);
-
-  // Create code interpreter tool
-  const codeInterpreterTool = ToolUtility.createCodeInterpreterTool([localFile.id]);
-
-  // Notice that CodeInterpreter must be enabled in the agent creation, otherwise the agent will not be able to see the file attachment
-  const agent = await client.createAgent(modelDeploymentName, {
-    name: "my-agent",
-    instructions: "You are a helpful agent",
-    tools: [codeInterpreterTool.definition],
-    toolResources: codeInterpreterTool.resources,
-  });
-  console.log(`Created agent, agent ID: ${agent.id}`);
-
-  // Create a thread
-  const thread = await client.threads.create();
-  console.log(`Created thread, thread ID: ${thread.id}`);
+:::code language="json" source="~/reusable-content/samples/foundry/azure-ai-agents-quickstart-math/tsconfig.json":::
 
-  // Create a message
-  const message = await client.messages.create(
-    thread.id,
-    "user",
-    "Could you please create a bar chart in the TRANSPORTATION sector for the operating profit from the uploaded CSV file and provide the file to me?",
-  );
-
-  console.log(`Created message, message ID: ${message.id}`);
-
-  // Create and execute a run
-  const streamEventMessages = await client.runs.create(thread.id, agent.id).stream();
-
-  for await (const eventMessage of streamEventMessages) {
-    switch (eventMessage.event) {
-      case RunStreamEvent.ThreadRunCreated:
-        console.log(`ThreadRun status: ${(eventMessage.data as ThreadRun).status}`);
-        break;
-      case MessageStreamEvent.ThreadMessageDelta:
-        {
-          const messageDelta = eventMessage.data as MessageDeltaChunk;
-          messageDelta.delta.content.forEach((contentPart) => {
-            if (contentPart.type === "text") {
-              const textContent = contentPart as MessageDeltaTextContent;
-              const textValue = textContent.text?.value || "No text";
-              console.log(`Text delta received:: ${textValue}`);
-            }
-          });
-        }
-        break;
-
-      case RunStreamEvent.ThreadRunCompleted:
-        console.log("Thread Run Completed");
-        break;
-      case ErrorEvent.Error:
-        console.log(`An error occurred. Data ${eventMessage.data}`);
-        break;
-      case DoneEvent.Done:
-        console.log("Stream completed.");
-        break;
-    }
-  }
-
-  // Delete the original file from the agent to free up space (note: this does not delete your version of the file)
-  await client.files.delete(localFile.id);
-  console.log(`Deleted file, file ID : ${localFile.id}`);
-
-  // Print the messages from the agent
-  const messagesIterator = client.messages.list(thread.id);
-  const messagesArray = [];
-  for await (const m of messagesIterator) {
-    messagesArray.push(m);
-  }
-  console.log("Messages:", messagesArray);
-
-  // Get most recent message from the assistant
-// Get most recent message from the assistant
-  const assistantMessage = messagesArray.find((msg) => msg.role === "assistant");
-  if (assistantMessage) {
-    // Look for an image file in the assistant's message
-    const imageFileOutput = assistantMessage.content.find(content => 
-      content.type === "image_file" && content.imageFile?.fileId);
-    
-    if (imageFileOutput) {
-      try {
-        // Save the newly created file
-        console.log(`Saving new files...`);
-        const imageFile = imageFileOutput.imageFile.fileId;
-        const imageFileName = path.resolve(
-          "./data/" + (await client.files.get(imageFile)).filename + "ImageFile.png",
-        );
-        console.log(`Image file name : ${imageFileName}`);
-
-        const fileContent = await client.files.getContent(imageFile).asNodeStream();
-        if (fileContent && fileContent.body) {
-          const chunks = [];
-          for await (const chunk of fileContent.body) {
-            chunks.push(Buffer.isBuffer(chunk) ? chunk : Buffer.from(chunk));
-          }
-          const buffer = Buffer.concat(chunks);
-          fs.writeFileSync(imageFileName, buffer);
-          console.log(`Successfully saved image to ${imageFileName}`);
-        } else {
-          console.error("No file content available in the response");
-        }
-      } catch (error) {
-        console.error("Error saving image file:", error);
-      }
-    } else {
-      console.log("No image file found in assistant's message");
-    }
-  } else {
-    console.log("No assistant message found");
-  }
-
-  // Iterate through messages and print details for each annotation
-  console.log(`Message Details:`);
-  messagesArray.forEach((m) => {
-    console.log(`File Paths:`);
-    console.log(`Type: ${m.content[0].type}`);
-    if (isOutputOfType<MessageTextContent>(m.content[0], "text")) {
-      const textContent = m.content[0] as MessageTextContent;
-      console.log(`Text: ${textContent.text.value}`);
-    }
-    console.log(`File ID: ${m.id}`);
-    // firstId and lastId are properties of the paginator, not the messages array
-    // Removing these references as they don't exist in this context
-  });
-
-  // Uncomment these lines to delete the agent once done
-  //await client.deleteAgent(agent.id);
-  //console.log(`Deleted agent, agent ID: ${agent.id}`);
-}
-
-main().catch((err) => {
-  console.error("The sample encountered an error:", err);
-});
-```
+Next, create an `index.ts` file and paste in the following code:
 
+:::code language="typescript" source="~/reusable-content/samples/foundry/azure-ai-agents-quickstart-math/index.ts":::
 
-Run the code using `node index.js`. This code generates a bar chart PNG image file in the TRANSPORTATION sector for the operating profit from the uploaded CSV file and provided the file to you. Full [sample source code](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/ai/ai-agents/samples/v1-beta/typescript/src/codeInterpreterWithStreaming.ts) available.
+Run the code using `npx tsx -r dotenv/config index.ts`. This code answers the question `I need to solve the equation '3x + 11 = 14'. Can you help me?`. Full [sample source code](https://github.com/Azure-Samples/azure-sdk-for-js-docs/blob/main/samples/foundry/azure-ai-agents-quickstart-math) available.