The AI Travel Agents is a robust enterprise application that leverages multiple AI agents to enhance travel agency operations. The application demonstrates how six AI agents collaborate to assist employees in handling customer queries, providing destination recommendations, and planning itineraries.

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).

ai-travel-agents/
│── src/
│   ├── tools/
│   │   ├── customer-query/
│   │   ├── destination-recommendation/
│   │   ├── itinerary-planning/
│   │   ├── code-evaluation/
│   │   ├── model-inference/
│   │   ├── web-search/
│   │   └── echo-ping/
│   │
│   ├── api/                # API Gateway for backend services
│   └── ui/                 # Frontend application
│
│── infra/                  # azd-related files
│── docs/                   # Documentation files
│
│── README.md              # Project documentation

Ensure you have the following installed before running the application:

• Docker

The application uses environment variables to configure the services. You can set them in a .env file in the root directory or directly in your terminal. We recommend the following approach:

1. Create a .env.dev file for each containerized service under src/, and optionally a .env.docker file for Docker-specific configurations:

   • src/ui/.env.dev
   • src/ui/.env.docker
   • src/api/.env.dev
   • src/api/.env.docker
   • src/tools/customer-query/.env.dev
   • src/tools/customer-query/.env.docker
   • src/tools/destination-recommendation/.env.dev
   • src/tools/destination-recommendation/.env.docker
   • src/tools/itinerary-planning/.env.dev
   • src/tools/itinerary-planning/.env.docker
   • src/tools/code-evaluation/.env.dev
   • src/tools/code-evaluation/.env.docker
   • src/tools/model-inference/.env.dev
   • src/tools/model-inference/.env.docker
   • src/tools/web-search/.env.dev
   • src/tools/web-search/.env.docker
   • src/tools/echo-ping/.env.dev
   • src/tools/echo-ping/.env.docker

2. .env.docker files are used to set environment variables for Docker containers. These files should contain the same variables as .env.dev files, but with values specific to the Docker environment. For example:

# src/api/.env.dev
TOOL_CUSTOMER_QUERY_URL=http://localhost:8080

# src/api/.env.docker
TOOL_CUSTOMER_QUERY_URL=http://tool-customer-query:8080

3. Load the environment variable files in docker-compose.yml using the env_file directive, in the following order:

  web-api:
    container_name: web-api
    # ...
    env_file: 
      - "./api/.env.dev"
      - "./api/.env.docker" # override .env with .env.docker

To run the entire application, use the scripts in the root directory. The scripts will build and run all the services defined in the src/docker-compose.yml file.

./run.sh

On Windows, you may need to use run.ps1 instead of run.sh.

Alternatively, if you're in VS Code you can use the Run Task command (Ctrl+Shift+P) and select the Run AI Travel Agents task.

This command will build and start all the services defined in the docker-compose.yml file.

Once all services are up and running, you can view the messages (currently logging messages) via the Aspire Dashboard at http://localhost:18888. On Structured tab you'll see the logging messages from the tool-echo-ping and api services. The Traces tab will show the traces across the services, such as the call from api to echo-agent.