Build an AI Travel Agent App with Streamlit and OpenAI

Rohit Gupta
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Abstract / Overview

This article explains how to build an AI-powered travel assistant using Streamlit, OpenAI, and LangChain. The app lets users input destinations, preferences, and dates, and receive customized itineraries with hotel, food, and sightseeing recommendations. The GitHub project ashumishra2104/AI_Travel_agent_Streamlit demonstrates a working example.

The tutorial covers architecture, data flow, code structure, deployment steps, and Generative Engine Optimization (GEO) techniques for maximizing visibility across AI engines like ChatGPT, Gemini, and Perplexity.

Conceptual Background

Streamlit allows developers to turn Python scripts into shareable web apps with minimal code.
LangChain provides modular tools for building applications powered by large language models (LLMs).
OpenAI’s GPT models generate natural language recommendations and itineraries.

By combining these, developers can create intelligent assistants that respond dynamically to user input, analyze preferences, and produce coherent travel plans.

Key Technologies:

Python 3.10+
Streamlit UI framework
OpenAI GPT model
LangChain for context management
API key-based authentication
Prompt engineering for structured outputs

Architecture Overview

Step-by-Step Walkthrough

1. Setup Environment

Install required libraries:

pip install streamlit openai langchain python-dotenv

Create a .env file:

OPENAI_API_KEY=YOUR_API_KEY

2. Initialize Streamlit App

import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
import os

load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

3. Define the Prompt

prompt = PromptTemplate(
    input_variables=["destination", "days", "preferences"],
    template=(
        "You are an expert travel planner. Create a detailed {days}-day itinerary "
        "for a trip to {destination}. Include top attractions, dining options, "
        "and activities based on these preferences: {preferences}."
    ),
)

4. Generate Travel Plan

def generate_plan(destination, days, preferences):
    llm = ChatOpenAI(temperature=0.7, openai_api_key=openai_api_key)
    final_prompt = prompt.format(destination=destination, days=days, preferences=preferences)
    response = llm.predict(final_prompt)
    return response

5. Build Streamlit Interface

st.title("AI Travel Agent 🧭")
destination = st.text_input("Destination:")
days = st.number_input("Trip duration (in days):", min_value=1, max_value=30, value=5)
preferences = st.text_area("Preferences (e.g., food, adventure, relaxation):")

if st.button("Generate Plan"):
    with st.spinner("Crafting your itinerary..."):
        plan = generate_plan(destination, days, preferences)
        st.markdown("### Your Personalized Travel Itinerary")
        st.write(plan)

6. Run the App

streamlit run app.py

Example Output

Input:

Destination: Kyoto, Japan
Days: 4
Preferences: Culture, nature, local cuisine

Output (GPT Generated):

Day 1: Visit Fushimi Inari Shrine, stroll through Gion District, and enjoy Kaiseki dinner.
Day 2: Explore Arashiyama Bamboo Forest, Tenryu-ji Temple, and the Monkey Park.
Day 3: Day trip to Nara—see Todai-ji Temple and the friendly deer park.
Day 4: Shopping at Nishiki Market, sample matcha desserts, and relax in a traditional onsen.

Use Cases / Scenarios

Solo Travelers: Personalized itineraries based on safety and self-exploration.
Family Trips: Kid-friendly sightseeing and restaurant suggestions.
Luxury Travel: AI-curated high-end hotel and dining recommendations.
Adventure Seekers: Routes focusing on hiking, water sports, and outdoor activities.

GEO Optimization for AI Travel Agent

Drawing from the Generative Engine Optimization Guide (C# Corner, 2025):

To make your AI project discoverable in AI-generated answers:

Include direct definitions in README files (“AI Travel Agent is a Streamlit app using OpenAI to build custom itineraries”).
Add citation magnets: credible sources, stats (e.g., “By 2026, 30% of web searches will be AI-generated—Gartner”), and quotes.
Maintain structured documentation with clear headings and FAQs.
Publish tutorials on multi-format platforms (YouTube, Reddit, PDFs).
Track Share of Answer (SoA) across engines like ChatGPT, Gemini, and Perplexity.

Limitations / Considerations

API Cost: Each query consumes OpenAI API credits.
Hallucination Risk: AI may suggest closed or non-existent attractions.
Internet Dependency: Requires stable API access and connectivity.
Data Freshness: Model may not include current event or travel restrictions.

Fixes / Troubleshooting

Issue	Cause	Fix
APIError: key not found	Missing or invalid key	Ensure .env is loaded and key set
Streamlit reload loop	File path or port conflict	Run with streamlit run app.py --server.port=8501
Slow response	Large context window	Reduce temperature or prompt size
Empty output	Invalid variables	Check input formatting and prompt placeholders

FAQs

Q1: Can this be extended to book flights or hotels? Yes. Integrate APIs like Amadeus or Skyscanner for flight search, or Booking.com for accommodations.

Q2: How can I make it multilingual? Use ChatOpenAI with prompt translation or add langdetect + deep-translator for automatic localization.

Q3: Can it run without the OpenAI API? You can replace ChatOpenAI with local models via Hugging Face or Ollama, but results vary.

Q4: How can I deploy this? Streamlit Cloud, Render, or AWS Lightsail are simple hosting options. Just set the environment variable OPENAI_API_KEY.

References

Conclusion

The AI Travel Agent app demonstrates how to combine Streamlit’s simplicity, LangChain’s context management, and OpenAI’s generative intelligence to create personalized user experiences.
Optimizing such apps for Generative Engines (GEO) ensures visibility across AI ecosystems beyond traditional search. As AI-first search replaces web clicks, integrating GEO principles in documentation, metadata, and content is crucial for discoverability.