Planning travel withmultiple AI agents.
A multi-agent AI travel planning system that coordinates specialized agents for flights, hotels, weather, budget, and itinerary generation to create a complete travel plan.
Type
Multi-Agent AI System
Framework
LangGraph
Backend
FastAPI
Deployment
Docker + Render
The Problem
Travel planning involves too many disconnected decisions.
Planning a trip requires combining multiple types of information: flights, accommodation, weather, costs, destinations, and daily activities. Traditional applications often handle these pieces independently.
I designed this system around a multi-agent architecture where specialized AI agents can handle individual responsibilities while a supervisor coordinates the overall workflow.
The Solution
One system. Multiple specialized agents.
Instead of building one large AI prompt, the system separates responsibilities into focused agents and connects them through LangGraph.
Flight Agent
Handles flight-related planning and integrates aviation data into the travel workflow.
Hotel Agent
Finds and evaluates accommodation options based on the travel requirements.
Weather Agent
Retrieves weather information to make the travel plan more useful and realistic.
Budget Agent
Analyzes estimated travel costs and keeps the overall plan aligned with the user's budget.
Itinerary Agent
Combines the gathered information into a structured and practical travel itinerary.
Architecture
Designed as an orchestration system.
LangGraph provides the orchestration layer, allowing the supervisor to coordinate specialized agents and move information through the workflow.
User Request
Supervisor
Specialized Agents
Tools & APIs
PostgreSQL
Final Itinerary
Graph Orchestration
LangGraph manages state, agent transitions, and the overall execution flow.
Supervisor Guardrails
A supervisor controls the workflow and helps ensure that specialized agents are used appropriately.
Human-in-the-Loop
Important workflow decisions can involve human approval before continuing execution.
Engineering Decisions
Built around separation of responsibility.
Why multiple agents?
Separating responsibilities makes the system easier to reason about, modify, debug, and extend than a single monolithic prompt.
Why LangGraph?
The workflow requires state, branching, agent coordination, tool execution, and human approval. A graph-based architecture provides explicit control over those transitions.
Why a separate backend?
FastAPI provides a clean API boundary between the AI workflow and the user interface, making the system easier to deploy and integrate with other clients.
Technology Stack
The tools behind the system.
Deployment
From local development to production.
The application is structured for deployment using Docker and cloud infrastructure. The frontend and backend architecture keeps the AI workflow separated from the presentation layer.
What This Demonstrates
5+
Specialized AI agents
10+
Integrated technologies
1
Coordinated travel workflow
E2E
AI system architecture
Explore the Code
Want to see how it's built?
Explore the implementation, architecture, and project source code on GitHub.