# What AI Platforms Are Compatible with AI Agents for Beginners?

> Discover which AI platforms work with ai-agents-for-beginners. Explore compatibility with Azure, OpenAI, GitHub Models, and more via MAF.

- Repository: [Microsoft/ai-agents-for-beginners](https://github.com/microsoft/ai-agents-for-beginners)
- Tags: how-to-guide
- Published: 2026-04-22

---

**AI Agents for Beginners supports Microsoft Azure AI Foundry Agent Service V2, Azure OpenAI, OpenAI, GitHub Models, MiniMax, and any OpenAI-compatible API endpoint through its Microsoft Agent Framework (MAF) abstraction layer.**

The **microsoft/ai-agents-for-beginners** repository is designed as a vendor-neutral learning environment for building AI agents. Its architecture centers on the **Microsoft Agent Framework (MAF)**, which decouples agent logic from underlying model providers. This means you can write agent code once and deploy it across multiple AI platforms by changing only configuration parameters.

## Supported AI Platforms and Integration Methods

### Azure AI Foundry Agent Service V2

The **Azure AI Foundry Agent Service V2** is the primary managed platform for deploying agents in this repository. In `02-explore-agentic-frameworks/code_samples/02-python-agent-framework.ipynb`, the `AzureAIProjectAgentProvider` class establishes a direct connection to this service.

```python
from agent_framework import Agent
from agent_framework.azure import AzureAIProjectAgentProvider
from azure.identity import AzureCliCredential

provider = AzureAIProjectAgentProvider(
    endpoint=os.getenv("AZURE_AI_PROJECT_ENDPOINT"),
    deployment_name=os.getenv("AZURE_AI_MODEL_DEPLOYMENT_NAME"),
    credential=AzureCliCredential(),
)

agent = Agent(
    name="TravelRecommender",
    instructions="Help users plan trips based on preferences.",
    provider=provider,
)

```

The `AzureAIProjectAgentProvider` handles server-side agent registration, authentication via `AzureCliCredential`, and runtime management through the Azure portal.

### Azure OpenAI and OpenAI Public API

The repository supports both **Azure OpenAI** and the public **OpenAI API** through the `OpenAIChatClient` class. As documented in [`14-microsoft-agent-framework/README.md`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/14-microsoft-agent-framework/README.md), this client accepts any OpenAI-compatible endpoint.

```python
from agent_framework import Agent
from agent_framework.openai import OpenAIChatClient

client = OpenAIChatClient(
    base_url="https://api.openai.com/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    model_id="gpt-4o",
)

agent = Agent(
    name="OpenAIAssistant",
    instructions="Answer user queries.",
    chat_client=client,
)

```

The same `OpenAIChatClient` works with Azure OpenAI by substituting `base_url` with your Azure endpoint and using `AzureKeyCredential` or `AzureCliCredential` for authentication.

### GitHub Models

**GitHub Models** is a Microsoft-hosted service providing OpenAI-compatible access to models including GPT-4o and Llama 3.1. The [`14-microsoft-agent-framework/README.md`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/14-microsoft-agent-framework/README.md) demonstrates swapping GitHub Models into existing agent code without structural changes.

```python
from agent_framework.openai import OpenAIChatClient

client = OpenAIChatClient(
    base_url="https://models.inference.ai.azure.com",
    api_key=os.getenv("GITHUB_TOKEN"),
    model_id="Meta-Llama-3.1-70B-Instruct",
)

```

This compatibility stems from GitHub Models implementing the OpenAI REST API specification, allowing the `OpenAIChatClient` to consume it transparently.

### MiniMax and Other OpenAI-Compatible Providers

The repository explicitly supports **MiniMax**, a provider offering large context windows up to 204K tokens. As noted in the main [`README.md`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/README.md) and [`14-microsoft-agent-framework/README.md`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/14-microsoft-agent-framework/README.md), MiniMax integrates through the same `OpenAIChatClient` pattern.

```python
client = OpenAIChatClient(
    base_url="https://api.minimax.io/v1",
    api_key=os.getenv("MINIMAX_API_KEY"),
    model_id="MiniMax-M2.7",
)

```

This architectural pattern extends to any OpenAI-compatible endpoint, including self-hosted models with OpenAI-compatible servers like **vLLM** or **Text Generation Inference**.

## Supporting Infrastructure and Services

### Azure AI Search for Retrieval-Augmented Generation

While not a model provider, **Azure AI Search** integrates as a critical infrastructure component for RAG scenarios. Lesson 05 ([`05-agentic-rag/README.md`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/05-agentic-rag/README.md)) demonstrates using `azure-search-documents` as a vector store and retrieval tool.

```python
from azure.search.documents import SearchClient
from agent_framework.tools import RetrievalTool

search_client = SearchClient(
    endpoint=os.getenv("AZURE_SEARCH_SERVICE_ENDPOINT"),
    index_name="knowledge-base",
    credential=AzureKeyCredential(os.getenv("AZURE_SEARCH_API_KEY")),
)

retrieval = RetrievalTool(search_client=search_client, top_k=5)
agent.add_tool(retrieval)

```

### Agent-to-Agent (A2A) and Model Context Protocol (MCP)

The [`requirements.txt`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/requirements.txt) file includes dependencies for **Agent-to-Agent (A2A)** and **Model Context Protocol (MCP)** protocols. These emerging standards enable interoperability between agents built on different frameworks, extending compatibility beyond the core platforms listed above.

## Key Configuration Files and Documentation

| File | Purpose |
|------|---------|
| [`README.md`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/README.md) | Overview of supported platforms and setup requirements |
| [`AGENTS.md`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/AGENTS.md) | Architectural documentation for MAF and provider abstraction |
| [`requirements.txt`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/requirements.txt) | SDK dependencies including `azure-ai-projects`, `azure-ai-inference`, `openai` |
| `02-explore-agentic-frameworks/code_samples/02-python-agent-framework.ipynb` | Azure AI Foundry Agent Service V2 implementation |
| [`14-microsoft-agent-framework/README.md`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/14-microsoft-agent-framework/README.md) | OpenAI-compatible provider configuration (OpenAI, GitHub Models, MiniMax) |
| [`05-agentic-rag/README.md`](https://github.com/microsoft/ai-agents-for-beginners/blob/main/05-agentic-rag/README.md) | Azure AI Search integration for retrieval |

## Summary

- **AI Agents for Beginners** supports **Azure AI Foundry Agent Service V2**, **Azure OpenAI**, **OpenAI**, **GitHub Models**, **MiniMax**, and any **OpenAI-compatible API endpoint**.
- The **Microsoft Agent Framework (MAF)** provides provider abstraction through `AzureAIProjectAgentProvider` and `OpenAIChatClient` classes.
- **Azure AI Search** integrates for RAG scenarios via `azure-search-documents`.
- Configuration changes—not code rewrites—enable switching between platforms.

## Frequently Asked Questions

### How do I switch from Azure AI Foundry to OpenAI in the same project?

Update your environment variables and instantiate `OpenAIChatClient` instead of `AzureAIProjectAgentProvider`. The `Agent` class accepts either provider through its `provider` or `chat_client` parameter, so your agent logic remains unchanged.

### Does AI Agents for Beginners support local LLMs?

Yes, any local model exposing an OpenAI-compatible REST API works with `OpenAIChatClient`. Configure `base_url` to point at your local server (e.g., `http://localhost:8000/v1`) and provide the appropriate `model_id` supported by your local deployment.

### What authentication methods does Azure AI Foundry Agent Service V2 require?

The `AzureAIProjectAgentProvider` uses `AzureCliCredential` by default, falling back to `DefaultAzureCredential` for flexible authentication including managed identities and service principals. Your Azure subscription must have the Azure AI Foundry resource provisioned with appropriate RBAC permissions.

### Are there costs associated with GitHub Models?

GitHub Models offers a free tier with rate limits for prototyping and learning. Production deployments require Azure AI Foundry or direct API provider subscriptions. Check current GitHub Models documentation for specific rate limits and pricing tiers.