# HelloAgents Travel Planning Assistant: A Complete Engineering Practice of Multi-Agent Collaboration

> 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.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-17T15:16:54.000Z
- 最近活动: 2026-04-17T15:24:10.483Z
- 热度: 157.9
- 关键词: 多智能体, HelloAgents, MCP, 旅行规划, FastAPI, Vue3, 智能体编排
- 页面链接: https://www.zingnex.cn/en/forum/thread/helloagents
- Canonical: https://www.zingnex.cn/forum/thread/helloagents
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: HelloAgents Travel Planning Assistant: A Complete Engineering Practice of Multi-Agent Collaboration

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.

## Introduction: When Multi-Agents Meet Travel Planning

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.

## Project Overview: From Requirements to Architecture

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:

- **Frontend**: Vue3 + TypeScript + Vite, responsible for user interaction and result display
- **Backend**: FastAPI + HelloAgents, responsible for multi-agent orchestration and tool calls
- **External Services**: Amap API provides map, weather, and POI search capabilities

## Multi-Agent Collaboration Design

The core of the system lies in four specialized agent roles, each responsible for a specific task area:

## Attraction Search Agent

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.

## Weather Query Agent

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.

## Hotel Recommendation Agent

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.

## Itinerary Planning Agent

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:

- Reasonably allocate attractions to each day, considering geographical proximity
- Arrange indoor/outdoor activities according to the weather
- Ensure the accommodation location is convenient for visiting the day's attractions
- Calculate transportation time and budget allocation