Azure AI Travel Agents with Llamaindex.TS and MCP

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Overview • Architecture • Features • Preview the application locally • Cost estimation • Join the Community

The AI Travel Agents is a robust enterprise application that leverages multiple AI agents to enhance travel agency operations. The application demonstrates how LlamaIndex.TS orchestrates multiple AI agents to assist employees in handling customer queries, providing destination recommendations, and planning itineraries. Multiple MCP (Model Context Protocol) servers, built with Python, Node.js, Java and .NET, are used to provide various tools and services to the agents, enabling them to work together seamlessly.

The architecture of the AI Travel Agents application is designed to be modular and scalable:

• All components are containerized using Docker so that they can be easily deployed and managed by Azure Container Apps.
• All agents tools are available as MCP (Model Context Protocol) servers and are called by the MCP clients.
• MCP servers are implemented independently using variant technologies, such as Python, Node.js, Java, and .NET.
• The Agent Workflow Service orchestrates the interaction between the agents and MCP clients, allowing them to work together seamlessly.
• The Aspire Dashboard is used to monitor the application, providing insights into the performance and behavior of the agents (through the OpenTelemetry integration).

• Multiple AI agents (each with its own specialty)
• Orchestrated by LlamaIndex.TS
• Supercharged by MCP
• Deployed serverlessly via Azure Container Apps

Ensure you have the following installed before running the application:

• Docker
• Azure Developer CLI
• Git
• Node.js (for the UI and API services)
• Powershell 7+ (pwsh) - For Windows users only.
  • Important: Ensure you can run pwsh.exe from a PowerShell terminal. If this fails, you likely need to upgrade PowerShell.

To run and preview the application locally, follow these steps:

1. Clone the repository:

Using HTTPS:

git clone https://github.com/Azure-Samples/azure-ai-travel-agents.git

Using SSH:

git clone git@github.com:Azure-Samples/azure-ai-travel-agents.git

Using GitHub CLI

gh repo clone Azure-Samples/azure-ai-travel-agents

2. Navigate to the cloned repository:

cd azure-ai-travel-agents

3. Login to your Azure account:

azd auth login

For GitHub Codespaces users, if the previous command fails, try:

azd auth login --use-device-code

4. Provision the Azure resources:

azd provision

When asked, enter a name that will be used for the resource group. This will create a new folder in the .azure folder, and set it as the active environment for any calls to azd going forward. This will also install the necessary dependencies for the UI and API services, and build the Docker images for the agents.

5. Open a new terminal and run the following command to start the API:

npm start --prefix=src/api

6. Open a new terminal and run the following command to start the UI:

npm start --prefix=src/ui

7. Navigate to the UI in your web browser at http://localhost:4200.

You can run this project directly in your browser by using GitHub Codespaces, which will open a web-based VS Code:

A similar option to Codespaces is VS Code Dev Containers, that will open the project in your local VS Code instance using the Dev Containers extension.

You will also need to have Docker installed on your machine to run the container.

Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. However, you can use the Azure pricing calculator for the resources below to get an estimate.

• Azure Container Apps: Consumption plan, Free for the first 2M executions. Pricing per execution and memory used. Pricing
• Azure Container Registry: Free for the first 2GB of storage. Pricing per GB stored and per GB data transferred. Pricing
• Azure OpenAI: Standard tier, GPT model. Pricing per 1K tokens used, and at least 1K tokens are used per query. Pricing
• Azure Monitor: Free for the first 5GB of data ingested. Pricing per GB ingested after that. Pricing

⚠️ To avoid unnecessary costs, remember to take down your app if it's no longer in use, either by deleting the resource group in the Portal or running azd down --purge (see Clean up).

1. Open a terminal and navigate to the root of the project.
2. Authenticate with Azure by running azd auth login.
3. Run azd up to deploy the application to Azure. This will provision Azure resources, deploy this sample, with all the containers, and set up the necessary configurations.
   • You will be prompted to select a base location for the resources. If you're unsure of which location to choose, select swedencentral.
   • By default, the OpenAI resource will be deployed to swedencentral. You can set a different location with azd env set AZURE_LOCATION <location>. Currently only a short list of locations is accepted. That location list is based on the OpenAI model availability table and may become outdated as availability changes.

The deployment process will take a few minutes. Once it's done, you'll see the URL of the web app in the terminal.

You can now open the web app in your browser and start chatting with the bot.

To clean up all the Azure resources created by this sample:

1. Run azd down --purge
2. When asked if you are sure you want to continue, enter y

The resource group and all the resources will be deleted.

To run the application in a more advanced local setup or deploy to Azure, please refer to the Advanced Setup documentation. This includes setting up the Azure Container Apps environment, using local LLM providers, configuring the services, and deploying the application to Azure.

We encourage you to join our Azure AI Foundry Developer Community​ to share your experiences, ask questions, and get support:

• aka.ms/foundry/discord​ - Join our Discord community for real-time discussions and support.
• aka.ms/foundry/forum - Visit our Azure AI Foundry Developer Forum to ask questions and share your knowledge.

We welcome contributions to the AI Travel Agents project! If you have suggestions, bug fixes, or new features, please feel free to submit a pull request. For more information on contributing, please refer to the CONTRIBUTING.md file.