This publication presents a production-ready AI-driven Multi-Agent Travel Planner capable of generating end-to-end travel itineraries including geocoding, weather forecasting, point-of-interest discovery, routing, and budget estimation.
Built using LangGraph, Streamlit, Geoapify, and Open-Meteo, the system demonstrates how classical AI agents, rule-based planners, and modern LLM-enabled orchestration can be unified to solve a real-world travel planning problem.
The project prioritizes:
clean architectural separation
Extensive testing (unit + integration)
Robust validation & failure recovery
A polished interactive UI
Production best practices (retry logic, caching, modular pipelines)
This document explains how the system works, why the architecture is valuable, and how it was engineered for correctness, safety, and deployability.
Planning a trip sounds exciting — until you actually try to do it.
You must:
Check weather
Find attractions, restaurants, beaches
Estimate budgets
Plan routes
Build day-wise itineraries
Ensure locations are valid
Avoid API failures
Balance preferences with constraints
Most travel apps do one or two of these, but very few orchestrate all steps end-to-end.
This project solves that:
A Multi-Agent Travel Planner, where each step is handled by a specialized agent orchestrated through a LangGraph state machine.
This project uses a modular multi-agent architecture to automatically generate a complete travel plan based on the user’s destination, trip duration, number of travelers, and budget. The system follows a structured flow of agents, each responsible for a specific task. This ensures reliability, transparency, and ease of debugging.
The core idea of the system is to break the travel-planning process into independent steps handled by specialized agents:
Geocoding Agent – Finds latitude and longitude of the destination
Weather Agent – Fetches weather forecast
Places Agent – Retrieves attractions, beaches, food places
Routing Agent – Computes distance and travel-time matrix
Budget Agent – Estimates total cost and category-wise breakdown
Itinerary Agent – Builds a daily travel plan
Each agent updates a shared TravelState, allowing the next agent to continue smoothly.
The travel planner follows a deterministic, ordered pipeline using LangGraph:
Input Validation-
Ensures destination, days, persons, and budget are valid before the workflow begins.
Geocoding-
Converts the destination text into coordinates using OpenStreetMap/Nominatim.
Weather Retrieval-
Weather data is fetched from Open-Meteo to include temperature trends in the final plan.
Places Discovery-
Nearby attractions, beaches, food spots, and nature points are retrieved using Geoapify.
Routing Matrix-
Haversine-distance–based matrix computes distance and approximate travel time between all selected places.
Budget Estimation-
A custom pricing model calculates accommodation, food, travel, and activity costs.
Itinerary Generation-
Using all gathered data, the system generates a structured, day-wise schedule.
The entire workflow is built on three main components:
A. LangGraph Workflow
B. Independent Agent Modules
C. Shared State Model
Nominatim → Geocoding
Open-Meteo → Weather forecast
Geoapify Places API → Attractions, beaches, food spots
Frameworks & Libraries
Streamlit → User interface
LangGraph → Agent workflow orchestration
Pytest → Testing suite
Requests → API calls
Utility Enhancements
Retry logic for API stability
Input validators
Logging for debugging
Graceful fallback behavior
The system is designed to avoid crashes:
Invalid inputs → Handled by validators
API failures → Retry + safe fallback
Missing data → Defaults or warning messages
Node failures → Do not break the entire chain
This ensures a production-ready user experience.
A Streamlit UI provides:
Clean input controls
Sidebar summary for clarity
Real-time loading indicators
Rendered itinerary with budget and maps
To evaluate the reliability, accuracy, and usability of the Multi-Agent Travel Planner, we conducted a series of small, focused experiments. These experiments tested each agent’s performance individually as well as the end-to-end workflow.
The Multi-Agent Travel Planner achieved reliable and consistent performance across all tested scenarios.
End-to-End Itinerary Generation
A structured, day-wise itinerary
weather snapshot for all trip planned days
Recommended places grouped by category
A budget forecast with clear cost components
A routing matrix used to optimize travel flow
This confirms that the agent workflow—geocoding → weather → places → routing → budget → itinerary—functions smoothly in real-world scenarios.
The system behaved predictably under failure cases
This project demonstrates a production-ready multi-agent travel planning system capable of transforming user preferences into a complete, structured itinerary. By integrating geocoding, weather forecasting, place discovery, routing optimization, budget estimation, and LLM-based itinerary generation, the system delivers a practical end-to-end solution.
Through strong validation, error handling, retry logic, modular agents, and a clean Streamlit interface, the application achieves reliability and usability suitable for real-world deployment. Automated tests further ensure stability across tools, nodes, and workflows.