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Backend update (October 2026)

GitHub Models inference was retired on July 30, 2026. Any references below are retained only for historical context while this lab is migrated. Use Lab 013 — Free LLM backends to configure Foundry Local or Ollama before continuing.

Lab 024: Teams AI Library Bot

Level: L200 Path: Agent Builder — Teams Time: ~60 min 💰 Cost: Free — Microsoft 365 Developer Program (free) + GitHub Models

What You'll Learn

  • Build a code-first Teams bot with the Teams AI Library (JavaScript)
  • Connect the bot to GitHub Models (no Azure OpenAI needed)
  • Run locally with the Teams Toolkit and Teams App Test Tool
  • Handle user messages, adaptive cards, and actions
  • Add AI-powered responses using the AI module

Introduction

The Teams AI Library is a JavaScript/TypeScript SDK for building AI-powered bots that run natively in Microsoft Teams. Unlike Copilot Studio (no-code), Teams AI Library gives you full programmatic control — custom logic, webhook integrations, complex state management.

This lab builds the OutdoorGear customer service bot for Teams.

Prerequisites

Free M365 Developer tenant

The M365 Developer Program gives you a free 90-day sandbox tenant with Teams, SharePoint, and all Microsoft 365 apps — renewable if active.

Lab Exercise

Step 1: Create the project

# Install Teams Toolkit CLI
npm install -g @microsoft/teamsapp-cli

# Create a new bot project
teamsapp new

# Select: Bot → AI Bot → JavaScript → Teams AI Library

Or use VS Code: 1. Open VS Code → Teams Toolkit extension (left sidebar) 2. Click Create a New App → Bot → AI Bot → JavaScript 3. Name: OutdoorGearBot

Step 2: Project structure

OutdoorGearBot/
├── src/
│   ├── app.js          ← Bot application entry point
│   ├── config.js       ← Configuration (model, storage)
│   └── prompts/
│       └── chat/
│           ├── skprompt.txt    ← System prompt
│           └── config.json     ← Model parameters
├── appPackage/
│   ├── manifest.json   ← Teams app manifest
│   └── ...
└── teamsapp.yml        ← Teams Toolkit config

Step 3: Configure GitHub Models

Edit src/config.js:

const config = {
    botId: process.env.BOT_ID,
    botPassword: process.env.BOT_PASSWORD,

    // Use GitHub Models (OpenAI-compatible, free)
    openAIKey: process.env.GITHUB_TOKEN,
    openAIEndpoint: "https://models.inference.ai.azure.com",
    openAIModel: "gpt-4o-mini",
};

module.exports = config;

Edit src/app.js:

const { Application, AI, preview } = require("@microsoft/teams-ai");
const { OpenAIModel, PromptManager, ActionPlanner } = preview;
const config = require("./config");
const path = require("path");

// Create OpenAI model pointing to GitHub Models
const model = new OpenAIModel({
    apiKey: config.openAIKey,
    endpoint: config.openAIEndpoint,
    defaultModel: config.openAIModel,
    useSystemPrompt: true,
    logRequests: true,
});

const prompts = new PromptManager({
    promptsFolder: path.join(__dirname, "prompts"),
});

const planner = new ActionPlanner({
    model,
    prompts,
    defaultPrompt: "chat",
});

const app = new Application({
    storage: new MemoryStorage(),
    ai: { planner },
});

// Handle messages
app.message("/reset", async (context, state) => {
    state.deleteConversationState();
    await context.sendActivity("Conversation reset! How can I help you?");
});

// Default: handled by AI planner
module.exports = { app };

Step 4: Write the system prompt

Edit src/prompts/chat/skprompt.txt:

You are OutdoorGear Assistant — a friendly customer service bot for OutdoorGear Inc.

You help customers with:
- Product questions (boots, tents, packs, jackets, climbing gear)
- Order status and tracking
- Return policy (60-day window, unused items, not worn footwear)
- Shipping info ($5.99 standard 3-5 days, free over $75)
- Warranty claims (2-year standard, lifetime on climbing gear)

Key products:
- TrailBlazer X200 hiking boot — $189.99, Gore-Tex waterproof, Vibram outsole
- Summit Pro Tent — $349, 2-person 4-season, DAC aluminum poles
- OmniPack 45L pack — $279.99, technical backpack with hip belt
- StormShell Jacket — $349, 3-layer Gore-Tex Pro, 20k/20k waterproof
- ClimbTech Pro Harness — $129.99, CE EN12277, 15kN rated

Be friendly, concise, and always recommend visiting outdoorgear.com for purchases.
For order tracking, ask for the order number and direct to outdoorgear.com/orders.

Conversation history:
{{$conversation}}

Edit src/prompts/chat/config.json:

{
  "schema": 1.1,
  "description": "OutdoorGear customer service chat",
  "type": "completion",
  "completion": {
    "model": "gpt-4o-mini",
    "completion_type": "chat",
    "include_history": true,
    "include_input": true,
    "max_input_tokens": 2800,
    "max_tokens": 1000,
    "temperature": 0.7
  }
}

Step 5: Run locally with Teams App Test Tool

npm install
teamsapp preview --env local

This opens the Teams App Test Tool — a browser-based simulator that lets you test your bot without deploying to real Teams.

Try: - What boots do you have? - Tell me about your return policy - I need a tent for 2 people in winter - /reset

Step 6: Add an Adaptive Card response

Adaptive Cards make bot responses rich and interactive:

// In app.js — add after planner setup:
app.message("show products", async (context, state) => {
    await context.sendActivity({
        attachments: [{
            contentType: "application/vnd.microsoft.card.adaptive",
            content: {
                type: "AdaptiveCard",
                version: "1.5",
                body: [
                    { type: "TextBlock", text: "🏔️ OutdoorGear Products", size: "Large", weight: "Bolder" },
                    { type: "FactSet", facts: [
                        { title: "TrailBlazer X200", value: "$189.99 — Waterproof hiking boot" },
                        { title: "Summit Pro Tent",   value: "$349.00 — 4-season, 2-person" },
                        { title: "OmniPack 45L",     value: "$279.99 — Technical backpack" },
                        { title: "StormShell Jacket", value: "$349.00 — Gore-Tex Pro shell" },
                    ]},
                ],
                actions: [
                    { type: "Action.OpenUrl", title: "Shop Now", url: "https://outdoorgear.com" },
                ]
            }
        }]
    });
});

Architecture

Teams Client
    │  (Bot Framework)
    ▼
Bot Framework Adapter
    │
    ▼
Teams AI Library Application
    ├── Message router (custom handlers)
    ├── AI Planner → OpenAI/GitHub Models
    │       └── PromptManager (skprompt.txt)
    └── Adaptive Card renderer

Next Steps

Phase 3 modernization

Verified 2026-10-27

Use the Microsoft 365 Agents SDK and Agents Toolkit. Validate declarative agents against manifest v1.9. Keep Copilot Studio trial and Agents Playground routes optional and clearly bounded.

flowchart LR
  L[Legacy or current lab] --> C[Phase 3 contract]
  C --> V[Local validation]
  V --> R[Optional Real adapter]
  R --> O[Operations and cleanup]

Supporting kit

The site build generates stable PDF and ZIP downloads. The ZIP contains the lab guide and every supporting file declared for this lab.