A Multi Model Orchestrated Agent for Live Flight and Hotel Itinerary Generation

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Abstract

Abstract The evolution of large language models into autonomous, goal-oriented systems capable of advanced reasoning, contextual memory recall, and dynamic tool orchestration is referred to as agentic AI. This work introduces an intelligent travel companion built on LangChain, designed to perform real-time itinerary planning through seamless integration of memory modules, automated email dispatch, and flight and hotel search APIs.The system leverages OpenAI’s GPT framework to perform semantic interpretation, multi-step reasoning, and contextual decision-making, enabling it to translate natural language requests into structured travel plans. Retrieval-Augmented Generation (RAG) is employed to ground responses in stored knowledge, while SERP APIs provide live flight schedules, hotel availability, and other time-sensitive data. The solution features a Streamlit-based frontend for intuitive user interaction and a FastAPI-powered backend for low-latency, scalable orchestration. By inferring user intent, invoking appropriate tools, maintaining session continuity, and optionally delivering results via email, the system demonstrates autonomous, end-to-end task execution. Overall, this work contributes a modular, extensible agentic AI framework that validates the feasibility of real-time, domain-specific applications requiring reasoning, personalization, and dynamic tool integration.
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A Multi Model Orchestrated Agent for Live Flight and Hotel Itinerary Generation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Multi Model Orchestrated Agent for Live Flight and Hotel Itinerary Generation Pooja Gupta, Nethravathi B, Lalitha Shree C P, Srivatsa S, Harshith G N, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7551607/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Apr, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted 10 You are reading this latest preprint version Abstract The evolution of large language models into autonomous, goal-oriented systems capable of advanced reasoning, contextual memory recall, and dynamic tool orchestration is referred to as agentic AI. This work introduces an intelligent travel companion built on LangChain, designed to perform real-time itinerary planning through seamless integration of memory modules, automated email dispatch, and flight and hotel search APIs.The system leverages OpenAI’s GPT framework to perform semantic interpretation, multi-step reasoning, and contextual decision-making, enabling it to translate natural language requests into structured travel plans. Retrieval-Augmented Generation (RAG) is employed to ground responses in stored knowledge, while SERP APIs provide live flight schedules, hotel availability, and other time-sensitive data. The solution features a Streamlit-based frontend for intuitive user interaction and a FastAPI-powered backend for low-latency, scalable orchestration. By inferring user intent, invoking appropriate tools, maintaining session continuity, and optionally delivering results via email, the system demonstrates autonomous, end-to-end task execution. Overall, this work contributes a modular, extensible agentic AI framework that validates the feasibility of real-time, domain-specific applications requiring reasoning, personalization, and dynamic tool integration. Agentic AI LangChain GPT RAG FastAPI Streamlit Autonomous Agents Travel Planning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Apr, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 30 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Editor invited by journal 27 Oct, 2025 Editor assigned by journal 26 Sep, 2025 Submission checks completed at journal 21 Sep, 2025 First submitted to journal 21 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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