技能 编程开发 Microsoft 365 代理 SDK

Microsoft 365 代理 SDK

v20260301
m365-agents-ts
介绍如何在 TypeScript/Node.js 中使用 Express 托管的 Microsoft 365 Agents SDK 构建企业代理,包含流式 Azure OpenAI 结果、Copilot Studio 客户端、invoke 活动处理和 WebChat 集成,适用于 Teams 与 Copilot 场景。
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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

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

Environment Variables

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

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

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

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)

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

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 Files

File Contents
references/acceptance-criteria.md Import paths, hosting pipeline, streaming, and Copilot Studio patterns

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

信息
Category 编程开发
Name m365-agents-ts
版本 v20260301
大小 5.94KB
更新时间 2026-03-02
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