# GENAI Travel Agent Gradio

> A modular AI travel assistant powered by LLM tool-calling that can interact with both internal and external data sources. The project integrates an internal travel dataset, external APIs such as Open-Meteo for weather data, and a custom MCP flight search server built from scratch.

- **Type:** MCP server
- **Install:** `agentstack add mcp-lahdhirim-genai-travel-agent-gradio`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Lahdhirim](https://agentstack.voostack.com/s/lahdhirim)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Lahdhirim](https://github.com/Lahdhirim)
- **Source:** https://github.com/Lahdhirim/GENAI-travel-agent-gradio

## Install

```sh
agentstack add mcp-lahdhirim-genai-travel-agent-gradio
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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

---

# 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 ([destinations_infos.xlsx](data/destinations_infos.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**.

---

## External API tool

The assistant can retrieve **live weather information** using the [Open-Meteo API](https://open-meteo.com/). 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.

---

## MCP Server tool (AviationStack)

A local **MCP server** is implemented to expose a flight search tool using the [AviationStack API](https://docs.apilayer.com/aviationstack/docs/api-documentation). 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.

---

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
    ```
2. Create a virtual environment and install dependencies:
    ```bash
    python -m venv venv
    source venv/bin/activate
    pip install -r requirements.txt
    ```
3. 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
    ```
4. Start the MCP flight server:
    ```bash
    python src/services/mcp_flight_server.py
    ```
5. Start the Gradio application:
    ```bash
    python app.py
    ```
6. 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
    ```

2. 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
    ```

3. Build and start the services:
    ```bash
    docker compose up --build
    ```

4. 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](https://github.com/Lahdhirim)
- **Source:** [Lahdhirim/GENAI-travel-agent-gradio](https://github.com/Lahdhirim/GENAI-travel-agent-gradio)
- **License:** MIT

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-lahdhirim-genai-travel-agent-gradio
- Seller: https://agentstack.voostack.com/s/lahdhirim
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.