What I Built

I built TripSort, an Agentic AI-powered trip planner that helps travelers plan complete trips without the hassle of manually searching and coordinating flights, destinations, activities, and travel information.

Instead of relying on a single AI agent, TripSort uses 5 specialized AI agents that work together to handle different parts of the planning process. It integrates real-time flight data and web search to provide more relevant and practical recommendations, while using LangGraph for agent orchestration and PostgreSQL for persistent workflow state.

I built it to solve a common problem for travelers: turning a vague idea like “plan a trip to Bali” into a structured, personalized, and actionable itinerary.

Code

rajanikant04 / tripsort

AI agent for International Trip Planning

✈️ TripSort AI — A Multi-Agent Travel Planner with LangGraph

An open-source AI travel planner that turns a natural-language trip request into a practical travel plan with flight suggestions, hotel ideas, and a day-by-day itinerary. The project uses a multi-agent workflow built with LangGraph, LangChain, and FastAPI.

Why this project?

Planning a trip usually means jumping between multiple websites, tools, and spreadsheets. This project brings that flow into one experience by combining:

a flight-search agent,
a hotel-research agent,
an itinerary-planning agent, and
a final response agent,

all coordinated through a LangGraph workflow.

Features

✈️ Flight research using AviationStack
🏨 Hotel suggestions using Tavily search
🧠 Multi-agent orchestration with LangGraph
📝 Structured travel itinerary generation
🌐 FastAPI backend with a simple web interface
💾 Conversation state persistence using PostgreSQL
⚡ LLM-powered responses with Groq

Tech Stack

Python 3.10+
FastAPI
Jinja2 + HTML/CSS/JavaScript frontend
LangGraph
LangChain
Groq LLMs
PostgreSQL
Tavily API
AviationStack…

View on GitHub

How I Built It

TripSort is built as a multi-agent AI system using LangGraph and LangChain to orchestrate specialized agents and manage the overall trip-planning workflow.

I used Groq-powered LLMs as the reasoning engine and designed 5 specialized AI agents, each responsible for a specific part of the travel-planning process. LangGraph coordinates these agents, manages state, and controls how information flows between them.

The system integrates AviationStack for real-time flight information and Tavily for web search, allowing the agents to work with up-to-date travel data instead of relying only on the model's knowledge.

The backend is built with FastAPI, with PostgreSQL used for persistent LangGraph checkpointing and workflow state. The complete application is containerized using Docker and deployed through Vercel.

The core idea is to combine an LLM with specialized agents, external tools, persistent state, and a structured workflow to create an end-to-end autonomous trip-planning experience.

Why Does Open Innovation Matter?

Open innovation made it possible to build TripSort as a flexible, modular, and extensible agentic AI system rather than a simple application built around a single closed API.

Using open-source frameworks like LangGraph and LangChain gave me control over how agents are orchestrated, how state is maintained, and how different tools and models interact. I could design the system around 4 specialized agents, customize their workflows, and integrate external services such as AviationStack and Tavily based on the application's needs.

A closed API could provide powerful AI capabilities, but it would limit the level of control and customization over the underlying agent workflow. Open innovation allowed me to experiment, combine different technologies, and build the AI system around the problem rather than around a single vendor's ecosystem.

Most importantly, it made TripSort more transparent, modular, and easier to extend with new agents, models, tools, and capabilities in the future.

DEV Community

🌍 Plan Your Next Adventure with TripSort