GENAI Travel Agent Gradio

AI Travel Assistant using Gradio

This project is a prototype of an AI-powered travel assistant capable of answering travel-related queries while leveraging external tools through LLM tool-calling. The interface is built with Gradio, and the assistant can orchestrate multiple tools to retrieve real-world information.

Supported LLM providers

• OpenAI (openai library)
• OpenRouter (requests library)

Integrated tools

• Internal database tool (Excel-based)
• External weather API (Open-Meteo)
• Local MCP flight search server (AviationStack) built from scratch

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Tools Architecture

The assistant interacts with three different categories of tools, all implemented from scratch, illustrating common patterns used in LLM-based agent systems.

Internal data source

The assistant can retrieve travel information from an internal Excel file ([destinationsinfos.xlsx](data/destinationsinfos.xlsx)). The dataset contains fields such as:

• destination
• country
• keywords
• best_season

This Excel file is used as a simple internal structured data source, but the architecture allows replacing it with other sources such as:

• a SQL database
• a vector database
• an internal REST API
• a larger structured dataset

The goal is to illustrate how an agent can query internal structured knowledge.

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External API tool

The assistant can retrieve live weather information using the Open-Meteo API. Open-Meteo is particularly useful because the API is free, requires no authentication, and provides a large set of weather endpoints. The current implementation retrieves:

• Current weather conditions
• Past weather data (3 days)
• Forecast data (3 days)

A weather plot is generated dynamically and displayed in the interface.

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MCP Server tool (AviationStack)

A local MCP server is implemented to expose a flight search tool using the AviationStack API. For now, the MCP server exposes the following tool:

• search_flights: retrieves scheduled flights between two airports (IATA codes) for today and tomorrow. Access to future flight data requires a paid plan.

> ⚠️ Note (February 2026): > In this project the MCP server is implemented mainly for demonstration purposes. Since the server is built from scratch and used by a > single agent, MCP does not provide a major advantage compared to exposing the tool through a traditional REST API (for example using > FastAPI). MCP becomes more relevant when a team develops and maintains an MCP server that exposes a set of tools, and multiple users can > connect their agents to that server. In that scenario, agents can dynamically discover and use the available tools without having to > implement them locally in their own codebase. As of early 2026, there are still relatively few publicly available and free MCP servers that > can be directly integrated into agent systems.

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https://github.com/user-attachments/assets/66d762bf-74aa-482e-8395-1b3ae05180b4

Possible Enhancements

This project is intended as a base architecture for a more advanced travel assistant. Several extensions can be implemented:

• Extend the Open-Meteo integration to support additional endpoints such as hourly forecasts, precipitation data, climate statistics, or historical weather.
• Extend the AviationStack integration to include flight status, airport information, airline metadata, or route tracking (note that this requires a paid plan).
• Replace the Excel database with a more scalable backend such as SQL, a document store, or a vector database.
• Add new travel-related tools such as hotel search, tourist attraction discovery, visa requirements, currency conversion, or public transportation data.
• Dynamically enable or disable tools through a configuration file.
• Add additional LLM providers as long as they support tool-calling / function-calling methods.

Installation

Choose one of the following options:

Option 1 — Python environment

1. Clone the repository:

``bash git clone https://github.com/Lahdhirim/GENAI-travel-agent-gradio.git cd GENAI-travel-agent-gradio ``

1. Create a virtual environment and install dependencies:

``bash python -m venv venv source venv/bin/activate pip install -r requirements.txt ``

1. Create a .env file in the project root:

``env OPENAI_API_KEY=your_openai_key # or if using OpenRouter OPENROUTER_API_KEY=your_openrouter_key # AviationStack API key AVIATIONSTACK_API_KEY=your_aviationstack_key ``

1. Start the MCP flight server:

``bash python src/services/mcp_flight_server.py ``

1. Start the Gradio application:

``bash python app.py ``

1. Open the interface in your browser: http://localhost:7860

Option 2 — Docker

1. Clone the repository:

``bash git clone https://github.com/Lahdhirim/GENAI-travel-agent-gradio.git cd GENAI-travel-agent-gradio ``

1. Create a .env file in the project root:

``env OPENAI_API_KEY=your_openai_key # or if using OpenRouter OPENROUTER_API_KEY=your_openrouter_key # AviationStack API key AVIATIONSTACK_API_KEY=your_aviationstack_key ``

1. Build and start the services:

``bash docker compose up --build ``

1. Open the interface in your browser: http://localhost:7860

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Lahdhirim
• Source: Lahdhirim/GENAI-travel-agent-gradio
• License: MIT

Install and usage instructions live in the source repository linked above.