Build an AI Travel Planner with Multi-Agent A2A Protocol

Traditional AI systems use single models to handle all tasks, leading to complexity and reduced accuracy. Multi-agent systems with Agent-to-Agent (A2A) collaboration solve this by distributing work among specialized agents that coordinate through structured protocols.

In this project, we'll build an AI travel planner using Python, Google Gemini, and the Agent Development Kit (ADK) that demonstrates multi-agent orchestration through the A2A protocol. We'll create specialized agents for attractions, hotels, flights, and weather data, each powered by external APIs, such as OpenWeatherMap or mock datasets. A root agent coordinates communication between agents, enabling them to collaborate on complex travel planning tasks without requiring a single monolithic model. This architecture improves reliability, modularity, and maintainability compared to traditional single-agent approaches.

We'll implement remote agents with A2A communication protocols, define agent cards for capability specification, and orchestrate workflows through a root coordinator connected to a command-line interface. By the end, you'll have a functional AI travel assistant demonstrating multi-agent collaboration, distributed AI systems, API integration, and agent orchestration patterns applicable to any complex AI automation requiring coordinated workflows.