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.
Nethravathi et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: