Building a Multi-Agent System in Python

2026-06-07 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, long

Summary

A tutorial outlines the construction of a Multi-Agent Travel Planning System in Python, leveraging OpenAI's API via OpenRouter.ai and the "gpt-4.1-mini" model. This intermediate-level project employs Object-Oriented Programming to define an Agent class, from which four specialized agents are instantiated: a Travel Research Agent, an Activity Planning Agent, a Budget Agent, and a Final Travel Assistant. The system gathers user input on travel details like origin, destination, duration, number of travelers, budget, and interests. These agents then collaborate sequentially, with each agent's output feeding into the next, to produce a comprehensive, personalized travel itinerary. An example demonstrates a 3-day trip from Islamabad to Istanbul for 4 travelers, generating a detailed plan with an estimated total budget of "~\$3,100 - \$3,800".

Key takeaway

For AI Engineers developing complex applications, consider adopting a multi-agent system architecture to enhance efficiency and organization. By dividing a large problem into distinct roles for specialized AI agents, you can achieve more robust and personalized outcomes than with a single monolithic agent. Implement an Agent class and chain agent outputs sequentially to build scalable, collaborative AI solutions. This approach simplifies development and improves problem-solving capabilities.

Key insights

Multi-agent systems enhance complex problem-solving by distributing tasks among specialized, collaborating AI agents.

Principles

• Complex problems benefit from specialized agent collaboration.
• Sequential agent workflow enables progressive task completion.
• OOP facilitates reusable agent blueprints for system scalability.

Method

The proposed method involves defining an Agent class with name, role, and a run function to interact with an AI model. Individual agents are then instantiated from this class, each with a specific role, and chained in a sequential workflow.

In practice

• Implement specialized agents for distinct sub-tasks.
• Chain agent outputs as inputs for subsequent agents.
• Use "gpt-4.1-mini" for cost-effective agent interactions.

Topics

• Multi-Agent Systems
• AI Agents
• Python Development
• Object-Oriented Programming
• OpenAI API
• Travel Itinerary Generation

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.