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m365-agents-ts

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使用 Microsoft 365 Agents SDK 构建适用于 Microsoft 365、Teams 和 Copilot Studio 的企业级代理,该 SDK 提供 Express 托管、AgentApplication 路由、流式响应以及 Copilot Studio 客户端集成。

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更新时间 2026-09-12

Microsoft 365 Agents SDK (TypeScript)

使用带有 Express 托管、AgentApplication 路由、流式响应和 Copilot Studio 客户端集成的 Microsoft 365 Agents SDK 构建适用于 Microsoft 365、Teams 和 Copilot Studio 的企业级智能体。

实施前准备

  • 使用 microsoft-docs MCP 验证 AgentApplication、startServer 和 CopilotStudioClient 的最新 API 签名。
  • 在连接示例或模板之前,请确认 npm 上的软件包版本。

安装

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

环境变量

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 托管的 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 进行流式响应

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>

处理 Invoke 活动

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 客户端(直接对接引擎)

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 集成

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

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

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

最佳实践

  1. 使用 AgentApplication 进行路由,并保持处理程序专注于单一职责。
  2. 对于长时间运行的补全操作,优先使用 streamingResponse,并在 finally 块中调用 endStream。
  3. 将密钥排除在源代码之外;从环境变量或安全存储中加载令牌。
  4. 重用 CopilotStudioClient 实例,并在令牌提供程序中缓存令牌。
  5. 在记录或持久化反馈之前,验证 invoke 有效载荷。

参考链接

资源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
与 Copilot Studio 集成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                                                        |

所有文件

0 个文件

安装 m365-agents-ts

将技能文件下载并解压至你的 .claude/skills/ 目录。

下载ZIP

克隆仓库并复制技能文件到您的项目中。

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

复制 复制
快速设置: 将技能文件夹复制到 .claude/skills/ 目录。Claude 将自动检测并使用该技能。

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