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m365-agents-ts

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使用 Microsoft 365 Agents SDK 構建適用於 Microsoft 365、Teams 和 Copilot Studio 的企業級代理,該 SDK 提供 Express 託管、AgentApplication 路由、流式響應以及 Copilot Studio 客戶端整合。

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更新時間 2026-09-12

Microsoft 365 Agents SDK (TypeScript)

使用帶有 Express 託管、AgentApplication 路由、流式響應和 Copilot Studio 客戶端整合的 Microsoft 365 Agents SDK 構建適用於 Microsoft 365、Teams 和 Copilot Studio 的企業級智慧體。

實施前準備

  • 使用 microsoft-docs MCP 驗證 AgentApplication、startServer 和 CopilotStudioClient 的最新 API 簽名。
  • 在連線示例或模板之前,請確認 npm 上的軟體包版本。

安裝

npm install @microsoft/agents-hosting @microsoft/agents-hosting-express @microsoft/agents-activity
npm install @microsoft/agents-copilotstudio-client

環境變數

PORT=3978
AZURE_RESOURCE_NAME=<azure-openai-resource>
AZURE_API_KEY=<azure-openai-key>
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-mini

TENANT_ID=<tenant-id>
CLIENT_ID=<client-id>
CLIENT_SECRET=<client-secret>

COPILOT_ENVIRONMENT_ID=<environment-id>
COPILOT_SCHEMA_NAME=<schema-name>
COPILOT_CLIENT_ID=<copilot-app-client-id>
COPILOT_BEARER_TOKEN=<copilot-jwt></copilot-jwt></copilot-app-client-id></schema-name></environment-id></client-secret></client-id></tenant-id></azure-openai-key></azure-openai-resource>

核心工作流:Express 託管的 AgentApplication

import {
  AgentApplication,
  TurnContext,
  TurnState,
} from "@microsoft/agents-hosting";
import { startServer } from "@microsoft/agents-hosting-express";

const agent = new AgentApplication<turnstate>();

agent.onConversationUpdate("membersAdded", async (context: TurnContext) => {
  await context.sendActivity("Welcome to the agent.");
});

agent.onMessage("hello", async (context: TurnContext) => {
  await context.sendActivity(`Echo: ${context.activity.text}`);
});

startServer(agent);
</turnstate>

使用 Azure OpenAI 進行流式響應

import { azure } from "@ai-sdk/azure";
import {
  AgentApplication,
  TurnContext,
  TurnState,
} from "@microsoft/agents-hosting";
import { startServer } from "@microsoft/agents-hosting-express";
import { streamText } from "ai";

const agent = new AgentApplication<turnstate>();

agent.onMessage("poem", async (context: TurnContext) => {
  context.streamingResponse.setFeedbackLoop(true);
  context.streamingResponse.setGeneratedByAILabel(true);
  context.streamingResponse.setSensitivityLabel({
    type: "https://schema.org/Message",
    "@type": "CreativeWork",
    name: "Internal",
  });

  await context.streamingResponse.queueInformativeUpdate("starting a poem...");

  const { fullStream } = streamText({
    model: azure(process.env.AZURE_OPENAI_DEPLOYMENT_NAME || "gpt-4o-mini"),
    system: "You are a creative assistant.",
    prompt: "Write a poem about Apollo.",
  });

  try {
    for await (const part of fullStream) {
      if (part.type === "text-delta" && part.text.length > 0) {
        await context.streamingResponse.queueTextChunk(part.text);
      }
      if (part.type === "error") {
        throw new Error(`Streaming error: ${part.error}`);
      }
    }
  } finally {
    await context.streamingResponse.endStream();
  }
});

startServer(agent);
</turnstate>

處理 Invoke 活動

import { Activity, ActivityTypes } from "@microsoft/agents-activity";
import {
  AgentApplication,
  TurnContext,
  TurnState,
} from "@microsoft/agents-hosting";

const agent = new AgentApplication<turnstate>();

agent.onActivity("invoke", async (context: TurnContext) => {
  const invokeResponse = Activity.fromObject({
    type: ActivityTypes.InvokeResponse,
    value: { status: 200 },
  });

  await context.sendActivity(invokeResponse);
  await context.sendActivity("Thanks for submitting your feedback.");
});
</turnstate>

Copilot Studio 客戶端(直接對接引擎)

import { CopilotStudioClient } from "@microsoft/agents-copilotstudio-client";

const settings = {
  environmentId: process.env.COPILOT_ENVIRONMENT_ID!,
  schemaName: process.env.COPILOT_SCHEMA_NAME!,
  clientId: process.env.COPILOT_CLIENT_ID!,
};

const tokenProvider = async (): Promise<string> => {
  return process.env.COPILOT_BEARER_TOKEN!;
};

const client = new CopilotStudioClient(settings, tokenProvider);

const conversation = await client.startConversationAsync();
const reply = await client.askQuestionAsync("Hello!", conversation.id);
console.log(reply);
</string>

Copilot Studio WebChat 整合

import { CopilotStudioWebChat } from "@microsoft/agents-copilotstudio-client";

const directLine = CopilotStudioWebChat.createConnection(client, {
  showTyping: true,
});

window.WebChat.renderWebChat(
  {
    directLine,
  },
  document.getElementById("webchat")!,
);

最佳實踐

  1. 使用 AgentApplication 進行路由,並保持處理程式專注於單一職責。
  2. 對於長時間執行的補全操作,優先使用 streamingResponse,並在 finally 塊中呼叫 endStream。
  3. 將金鑰排除在原始碼之外;從環境變數或安全儲存中載入令牌。
  4. 重用 CopilotStudioClient 例項,並在令牌提供程式中快取令牌。
  5. 在記錄或持久化反饋之前,驗證 invoke 有效載荷。

參考連結

資源URL
Microsoft 365 Agents SDKhttps://learn.microsoft.com/en-us/microsoft-365/agents-sdk/
JavaScript SDK 概述https://learn.microsoft.com/en-us/javascript/api/overview/agents-overview?view=agents-sdk-js-latest
@microsoft/agents-hosting-expresshttps://learn.microsoft.com/en-us/javascript/api/%40microsoft/agents-hosting-express?view=agents-sdk-js-latest
@microsoft/agents-copilotstudio-clienthttps://learn.microsoft.com/en-us/javascript/api/%40microsoft/agents-copilotstudio-client?view=agents-sdk-js-latest
與 Copilot Studio 整合https://learn.microsoft.com/en-us/microsoft-365/agents-sdk/integrate-with-mcs
GitHub 示例https://github.com/microsoft/Agents/tree/main/samples/nodejs
在 GitHub 上查看
---
name: m365-agents-ts
description: Build enterprise agents for Microsoft 365, Teams, and Copilot Studio using the Microsoft 365 Agents SDK with Express hosting, AgentApplication routing, streaming responses, and Copilot Studio client integrations.
license: MIT
---

# Microsoft 365 Agents SDK (TypeScript)

Build enterprise agents for Microsoft 365, Teams, and Copilot Studio using the Microsoft 365 Agents SDK with Express hosting, AgentApplication routing, streaming responses, and Copilot Studio client integrations.

## Before implementation

- Use the microsoft-docs MCP to verify the latest API signatures for AgentApplication, startServer, and CopilotStudioClient.
- Confirm package versions on npm before wiring up samples or templates.

## Installation

```bash
npm install @microsoft/agents-hosting @microsoft/agents-hosting-express @microsoft/agents-activity
npm install @microsoft/agents-copilotstudio-client
```

## Environment Variables

```bash
PORT=3978
AZURE_RESOURCE_NAME=<azure-openai-resource>
AZURE_API_KEY=<azure-openai-key>
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-mini

TENANT_ID=<tenant-id>
CLIENT_ID=<client-id>
CLIENT_SECRET=<client-secret>

COPILOT_ENVIRONMENT_ID=<environment-id>
COPILOT_SCHEMA_NAME=<schema-name>
COPILOT_CLIENT_ID=<copilot-app-client-id>
COPILOT_BEARER_TOKEN=<copilot-jwt>
```

## Core Workflow: Express-hosted AgentApplication

```typescript
import {
  AgentApplication,
  TurnContext,
  TurnState,
} from "@microsoft/agents-hosting";
import { startServer } from "@microsoft/agents-hosting-express";

const agent = new AgentApplication<TurnState>();

agent.onConversationUpdate("membersAdded", async (context: TurnContext) => {
  await context.sendActivity("Welcome to the agent.");
});

agent.onMessage("hello", async (context: TurnContext) => {
  await context.sendActivity(`Echo: ${context.activity.text}`);
});

startServer(agent);
```

## Streaming responses with Azure OpenAI

```typescript
import { azure } from "@ai-sdk/azure";
import {
  AgentApplication,
  TurnContext,
  TurnState,
} from "@microsoft/agents-hosting";
import { startServer } from "@microsoft/agents-hosting-express";
import { streamText } from "ai";

const agent = new AgentApplication<TurnState>();

agent.onMessage("poem", async (context: TurnContext) => {
  context.streamingResponse.setFeedbackLoop(true);
  context.streamingResponse.setGeneratedByAILabel(true);
  context.streamingResponse.setSensitivityLabel({
    type: "https://schema.org/Message",
    "@type": "CreativeWork",
    name: "Internal",
  });

  await context.streamingResponse.queueInformativeUpdate("starting a poem...");

  const { fullStream } = streamText({
    model: azure(process.env.AZURE_OPENAI_DEPLOYMENT_NAME || "gpt-4o-mini"),
    system: "You are a creative assistant.",
    prompt: "Write a poem about Apollo.",
  });

  try {
    for await (const part of fullStream) {
      if (part.type === "text-delta" && part.text.length > 0) {
        await context.streamingResponse.queueTextChunk(part.text);
      }
      if (part.type === "error") {
        throw new Error(`Streaming error: ${part.error}`);
      }
    }
  } finally {
    await context.streamingResponse.endStream();
  }
});

startServer(agent);
```

## Invoke activity handling

```typescript
import { Activity, ActivityTypes } from "@microsoft/agents-activity";
import {
  AgentApplication,
  TurnContext,
  TurnState,
} from "@microsoft/agents-hosting";

const agent = new AgentApplication<TurnState>();

agent.onActivity("invoke", async (context: TurnContext) => {
  const invokeResponse = Activity.fromObject({
    type: ActivityTypes.InvokeResponse,
    value: { status: 200 },
  });

  await context.sendActivity(invokeResponse);
  await context.sendActivity("Thanks for submitting your feedback.");
});
```

## Copilot Studio client (Direct to Engine)

```typescript
import { CopilotStudioClient } from "@microsoft/agents-copilotstudio-client";

const settings = {
  environmentId: process.env.COPILOT_ENVIRONMENT_ID!,
  schemaName: process.env.COPILOT_SCHEMA_NAME!,
  clientId: process.env.COPILOT_CLIENT_ID!,
};

const tokenProvider = async (): Promise<string> => {
  return process.env.COPILOT_BEARER_TOKEN!;
};

const client = new CopilotStudioClient(settings, tokenProvider);

const conversation = await client.startConversationAsync();
const reply = await client.askQuestionAsync("Hello!", conversation.id);
console.log(reply);
```

## Copilot Studio WebChat integration

```typescript
import { CopilotStudioWebChat } from "@microsoft/agents-copilotstudio-client";

const directLine = CopilotStudioWebChat.createConnection(client, {
  showTyping: true,
});

window.WebChat.renderWebChat(
  {
    directLine,
  },
  document.getElementById("webchat")!,
);
```

## Best Practices

1. Use AgentApplication for routing and keep handlers focused on one responsibility.
2. Prefer streamingResponse for long-running completions and call endStream in finally blocks.
3. Keep secrets out of source code; load tokens from environment variables or secure stores.
4. Reuse CopilotStudioClient instances and cache tokens in your token provider.
5. Validate invoke payloads before logging or persisting feedback.

## Reference Links

| Resource                               | URL                                                                                                                 |
| -------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| Microsoft 365 Agents SDK               | https://learn.microsoft.com/en-us/microsoft-365/agents-sdk/                                                         |
| JavaScript SDK overview                | https://learn.microsoft.com/en-us/javascript/api/overview/agents-overview?view=agents-sdk-js-latest                 |
| @microsoft/agents-hosting-express      | https://learn.microsoft.com/en-us/javascript/api/%40microsoft/agents-hosting-express?view=agents-sdk-js-latest      |
| @microsoft/agents-copilotstudio-client | https://learn.microsoft.com/en-us/javascript/api/%40microsoft/agents-copilotstudio-client?view=agents-sdk-js-latest |
| Integrate with Copilot Studio          | https://learn.microsoft.com/en-us/microsoft-365/agents-sdk/integrate-with-mcs                                       |
| GitHub samples                         | https://github.com/microsoft/Agents/tree/main/samples/nodejs                                                        |

所有檔案

0 個檔案

安裝 m365-agents-ts

將技能檔案下載並解壓至你的 .claude/skills/ 目錄。

下載 ZIP

複製儲存庫並將技能檔案複製到您的專案中。

git clone https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-typescript/skills/m365-agents-ts # Copy SKILL.md to your .claude/skills/ directory

複製 複製
快速設定: 將技能資料夾複製到 .claude/skills/ 目錄。Claude 將自動檢測並使用該技能。
儲存庫 microsoft/skills

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