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A multi-agent travel planning system built on HelloAgents, using Vue3+FastAPI architecture, integrating Amap MCP services, and demonstrating the complete link from agent orchestration to front-end visualization.
Section 01
A multi-agent travel planning system built on HelloAgents, using Vue3+FastAPI architecture, integrating Amap MCP services, and demonstrating the complete link from agent orchestration to front-end visualization.
Section 02
Travel planning is a typical multi-step decision-making problem: it requires searching for attractions, checking weather, recommending accommodations, planning routes, and finally integrating into an executable itinerary. Traditional single AI assistants often struggle to complete such complex tasks in a single interaction, while the multi-agent architecture, through role division and collaboration, can better handle such scenarios.
The HelloAgents Travel Planning Assistant is a complete multi-agent application example that demonstrates how to combine the reasoning capabilities of large models with external tool services to build practical intelligent applications. This article will deeply analyze the project's architectural design, implementation details, and technical highlights.
Section 03
The goal of this project is to build a system that can automatically generate a complete travel plan based on user input. Users only need to provide the destination, travel dates, transportation mode, accommodation preferences, and interest tags, and the system will output a multi-day itinerary including attractions, hotels, weather, and budget.
To achieve this goal, the project adopts a front-end and back-end separation architecture:
Section 04
The core of the system lies in four specialized agent roles, each responsible for a specific task area:
Section 05
This agent is responsible for calling map search tools, filtering and returning a list of attractions that meet the criteria based on the user's input destination and preference tags (such as natural scenery, historical culture, food shopping, etc.). It comprehensively considers factors such as attraction ratings, popularity, and matching degree with user interests.
Section 06
Weather factors must be considered in travel planning. This agent is responsible for obtaining the weather forecast of the destination during the travel period, including information such as temperature, precipitation, and wind force, to provide a reference for itinerary arrangement. For example, rainy days are more suitable for indoor activities, while sunny days are suitable for outdoor attractions.
Section 07
Based on the user's specified accommodation preferences (such as budget, comfort, luxury) and budget range, this agent searches for and recommends suitable hotel options. It considers factors such as the convenience of the hotel's location (distance from attractions), user ratings, and price range.
Section 08
This is the "brain" of the system, responsible for integrating the output results of the first three types of agents to generate a structured complete travel plan. It needs to solve complex constraint satisfaction problems:
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