Labs to explore AI Models, MCP servers, and Agents with the AI Gateway powered by Azure API Management and Microsoft Foundry 🚀. It is open-source on GitHub.

Is AI-Gateway free to use?

AI-Gateway is open-source under the MIT license, so it is free to use.

What category does AI-Gateway belong to?

AI-Gateway is listed under mcp-servers in the Claudeers registry of Claude-compatible tools.

Platforms	cli, api, web, mobile
Operating systems	—
AI compatibility	claude
License	MIT
Pricing	open-source
Language	Jupyter Notebook

✨ AI Gateway Labs

Explore the enterprise-grade gateway for managing AI Models, Tools, and Agents

📰 New! The AI Gateway Dev Portal is now live — A starting point for building your own developer portal on top of Azure API Management AI Gateway's. Fork it, open it in VS Code with GitHub Copilot (or any coding agent), and shape it to fit your needs!

Why AI Gateway?

Building production-ready AI applications requires more than just calling model APIs. You need security, reliability, observability, and cost control—without slowing down innovation.

AI Gateway powered by Azure API Management provides:

🔐 Security — OAuth 2.0, managed identities, content safety filtering
⚡ Performance — Load balancing, semantic caching, request routing
📊 Observability — Token metrics, built-in logging, tracing
💰 Cost Control — Rate limiting, quota management, FinOps framework
🔌 Extensibility — MCP protocol support, function calling, multi-model routing

📚 Explore the Labs

🔗 Browse all 30+ labs at aka.ms/ai-gateway/labs

Each lab is a hands-on Jupyter notebook with step-by-step instructions, Bicep infrastructure templates, and APIM policies you can deploy to your Azure subscription.

🧠 AI Gateway for Models

Manage and control access to Large Language Models with enterprise-grade policies.

Lab	Description
Backend Pool Load Balancing	Distribute requests across multiple model endpoints
Token Rate Limiting	Control token consumption with rate limiting policies
Semantic Caching	Cache responses using vector similarity for faster, cheaper completions
Model Routing	Route requests to different backends based on model and version
FinOps Framework	Manage AI budgets with automated quota controls

🔧 AI Gateway for Tools

Enable secure tool access with MCP protocol and function calling capabilities.

Lab	Description
Model Context Protocol (MCP)	Plug & play tools with OAuth credential management
MCP Client Authorization	Implement MCP with the client authorization flow
Function Calling	Use OpenAI function calling with Azure Functions backend
Realtime Audio + MCP	Combine realtime voice API with MCP tools

🤖 AI Gateway for Agents

Build and control agentic applications with orchestration frameworks.

Lab	Description
AI Agent Service	Explore Foundry Agent Service with multi-service control
OpenAI Agents SDK	Use OpenAI Agents with Azure OpenAI and APIM-managed tools
Gemini MCP Agents	Integrate Google Gemini models with MCP tools
A2A Enabled Agents	A2A-enabled Agents with models and MCP plug & play tools

🚀 Quick Start

Prerequisites

Python 3.12+
uv (fast Python package manager) — install via curl -LsSf https://astral.sh/uv/install.sh | sh (Linux/macOS) or powershell -c "irm https://astral.sh/uv/install.ps1 | iex" (Windows)
VS Code with Jupyter extension
Azure Subscription with Contributor + RBAC Administrator roles
Azure CLI authenticated to your subscription

Get Started

# Clone the repository
git clone https://github.com/Azure-Samples/AI-Gateway.git
cd AI-Gateway

# Create the virtual environment and install dependencies
uv sync
uv pip install -r pyproject.toml

# Open VS Code and start with a lab
code .

When opening a notebook, select the .venv interpreter created by uv sync as the Jupyter kernel.

Or launch instantly with GitHub Codespaces ☁️

🔨 Developer Tools

The tools/ folder provides utilities for testing and development:

Tool	Description
Tracing	Invoke AI Foundry APIs with tracing enabled
Streaming	Test streaming responses from AI models
Rate Limit Tester	Validate rate limiting configurations
Mock Server	OpenAI API mock for local development and testing
OAuth Client	Test OAuth authentication flows

👩‍💻 Build Your Own Labs with AI

This repository includes Copilot Agent Skills that help you create new labs using AI-assisted development in VS Code.

Available Skills

Skill	Description
`lab-creator`	Scaffolds new labs with notebooks, Bicep, and policies
`apim-bicep`	Generates Azure Bicep templates for APIM resources
`apim-terraform`	Generates Terraform configurations for APIM
`apim-policies`	Creates APIM XML policies for AI gateway scenarios
`apim-kql`	Generates queries in KQL to control models, tools and agents
`mcp-builder`	Builds MCP servers for tool integration

Example: Create a New Lab

Open this repo in VS Code with GitHub Copilot and use this prompt:

Create a new lab called "multi-model-failover" that demonstrates how to 
implement automatic failover between different AI models when the primary 
model is unavailable or throttled. Include:
- A backend pool with priority-based routing
- Retry policy with exponential backoff
- Circuit breaker pattern for unhealthy backends
- Built-in LLM logging to track usage across all backends
- Test the model with a LangChain agent: https://docs.langchain.com/oss/python/langchain/agents
Use gpt-4.1-mini as primary and gpt-4.1-nano as fallback, deploy to Sweden Central.

Copilot will generate the complete lab structure including:

📓 Jupyter notebook with step-by-step instructions
🦾 Bicep infrastructure template
⚙️ APIM policy XML
📖 README documentation
🧹 Cleanup notebook

🏛️ Well-Architected Framework

Labs are designed following Azure Well-Architected Framework principles:

Pillar	Labs
Security	Access controlling, Content safety, Private connectivity
Reliability	Backend pool load balancing, Token rate limiting
Performance	Semantic caching, Model routing
Operations	Built-in logging, Token metrics emitting
Cost	FinOps framework, Semantic caching

📕 Enterprise AI Gateway e-Book

Download the Enterprise AI Gateway e-Book for comprehensive end-to-end view of the Enterprise AI Gateway pattern, explaining why a centralized governance layer is essential for organizations adopting AI at scale and how it can be practically implemented using Azure API Management and Microsoft Foundry.
It describes the AI Gateway as a control plane that mediates all interactions between AI apps and agents and the underlying models, data, and tools, enabling consistent enforcement of security, safety, cost controls, resiliency, scalability, observability, and governance. Overall, the e-Book positions the Enterprise AI Gateway as a foundational architectural component that allows enterprises to innovate rapidly with AI while maintaining trust, compliance, visibility, and control.