/teams-app-developer

models-router

purpose

Route AI model integration tasks to the correct provider-specific expert. Covers configuring, calling, and managing AI models from any supported provider: Anthropic, OpenAI, Azure OpenAI, AWS Bedrock, Azure AI Foundry (cloud), Foundry Local, and OpenAI-compatible OSS endpoints (Ollama, vLLM, LM Studio, etc.).

interview

Q1 — Model Provider

question: "Which AI model provider are you working with?"
header: "Provider"
options:
  - label: "OpenAI / Azure OpenAI (Recommended)"
    description: "GPT-4o, GPT-4, GPT-3.5. Works with both OpenAI API and Azure OpenAI Service. Best Teams AI SDK support."
  - label: "Anthropic (Claude)"
    description: "Claude 4, Claude 3.5 Sonnet, Claude 3 Haiku. Direct API or via AWS Bedrock."
  - label: "AWS Bedrock"
    description: "Managed access to Anthropic, Meta Llama, Cohere, Amazon Titan, and other models. Uses AWS IAM auth."
  - label: "Foundry / OSS Local"
    description: "Azure AI Foundry (cloud or local), Ollama, vLLM, LM Studio, or any OpenAI-compatible endpoint for open-source models."
multiSelect: false

Q2 — Use Case

question: "What are you building with the model?"
header: "Use case"
options:
  - label: "Bot / agent with chat completions"
    description: "Chat-style interaction in a Slack or Teams bot. May include function calling / tool use."
  - label: "RAG / knowledge retrieval"
    description: "Retrieve-then-generate pattern with embeddings, vector stores, or knowledge bases."
  - label: "Standalone API integration"
    description: "Direct API calls from a service — not tied to a specific bot framework."
  - label: "You decide everything"
    description: "Use recommended defaults and skip remaining questions."
multiSelect: false

defaults table

task clusters

OpenAI / Azure OpenAI

When: OpenAI, Azure OpenAI, GPT-4o, GPT-4, GPT-3.5, openai npm package, @azure/openai, chat completions, OpenAI API key, Azure OpenAI endpoint, deployment name, apiVersion Read:

• openai-azure-openai-ts.md Cross-domain deps: ../teams/ai.model-setup-ts.md (Teams AI SDK model config), ../deploy/azure-cli-reference-ts.md (az cognitiveservices for Azure OpenAI provisioning), ../security/secrets-ts.md (API key management)

Anthropic (Claude)

When: Anthropic, Claude, Claude 4, Claude 3.5, Claude 3, @anthropic-ai/sdk, Anthropic API, ANTHROPIC_API_KEY, Messages API, tool use with Claude Read:

• anthropic-ts.md Cross-domain deps: ../security/secrets-ts.md (API key management)

AWS Bedrock

When: Bedrock, AWS Bedrock, @aws-sdk/client-bedrock-runtime, Bedrock agents, Bedrock Converse API, Bedrock Knowledge Bases, hosted Anthropic, hosted Llama, Amazon Titan, Bedrock guardrails Read:

• bedrock-ts.md Cross-domain deps: ../deploy/aws-cli-reference-ts.md (aws bedrock CLI commands), ../deploy/aws-bot-deploy-ts.md (Lambda/ECS deployment), ../security/secrets-ts.md (IAM auth)

Azure AI Foundry (Cloud)

When: Azure AI Foundry, AI Foundry, Foundry Models, Azure AI model catalog, model-as-a-service, MaaS, serverless API, Azure AI Studio, Foundry cloud, GitHub Models Read:

• foundry-cloud-ts.md Cross-domain deps: ../deploy/azure-cli-reference-ts.md (az cognitiveservices for provisioning), ../security/secrets-ts.md (API key management)

Foundry Local

When: Foundry Local, foundry CLI, flocal, local model, local inference, run model locally, on-device AI, ONNX, Phi-4, Qwen, foundry model run, foundry model list, foundry service, offline AI Read:

• foundry-local-ts.md

OpenAI-Compatible OSS Endpoints

When: Ollama, vLLM, LM Studio, llama.cpp, text-generation-inference, TGI, LocalAI, OpenAI-compatible, self-hosted model, open-source model, Llama, Mistral, DeepSeek, local LLM, /v1/chat/completions custom endpoint, custom base URL Read:

• oss-openai-compatible-ts.md

Transformers.js (In-Process Inference)

When: Transformers.js, @huggingface/transformers, in-process inference, browser inference, WASM inference, WebGPU inference, local embeddings, local classification, local NER, local summarization, pipeline API, HuggingFace Hub, ONNX in browser, serverless ML, no-server AI, feature extraction, zero-shot classification, sentiment analysis, token classification, offline embeddings Read:

• transformers-js-ts.md Cross-domain deps: openai-azure-openai-ts.md (hybrid pattern — local preprocessing + cloud LLM)

Multi-Provider / Provider Abstraction

When: multiple models, fallback model, model routing, provider abstraction, LangChain, LiteLLM, Vercel AI SDK, ai npm package, model switching, cost optimization, A/B test models Read:

• openai-azure-openai-ts.md
• anthropic-ts.md
• oss-openai-compatible-ts.md

combining rule

If the developer uses multiple providers (e.g., Claude for reasoning + GPT-4o for function calling, or Foundry Local for dev + Azure OpenAI for prod), load all relevant provider experts. The multi-provider cluster above covers this.

If integrating a model into a Teams bot, always also read ../teams/ai.model-setup-ts.md — it covers the OpenAIChatModel wrapper that Teams AI SDK uses.

If integrating a model into a Slack bot, the provider experts here cover direct SDK usage — Slack Bolt doesn't have a built-in AI layer, so you wire models directly.

file inventory

anthropic-ts.md | bedrock-ts.md | foundry-cloud-ts.md | foundry-local-ts.md | openai-azure-openai-ts.md | oss-openai-compatible-ts.md | transformers-js-ts.md