https://github.com/trgiangdo/travel_planner
A travel planner system that utilizes suitable AI agents and tools
https://github.com/trgiangdo/travel_planner
Last synced: 8 months ago
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A travel planner system that utilizes suitable AI agents and tools
- Host: GitHub
- URL: https://github.com/trgiangdo/travel_planner
- Owner: trgiangdo
- Created: 2025-07-17T04:31:27.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-17T16:41:30.000Z (11 months ago)
- Last Synced: 2025-08-22T22:50:31.420Z (10 months ago)
- Language: Python
- Size: 106 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
title: Travel planner
emoji: 🕵🏻♂️
colorFrom: indigo
colorTo: indigo
sdk: gradio
sdk_version: 5.34.2
app_file: app.py
pinned: false
hf_oauth: true
# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
hf_oauth_expiration_minutes: 480
---
# LLM Trip Planner Agent
This project is a conversational AI agent that helps users plan personalized trips. Built with LangGraph and Gradio, the agent intelligently gathers necessary information like the user's desired destination and interests before generating a customized travel itinerary.
## Features
- **Conversational UI:** An intuitive chat interface powered by Gradio for seamless user interaction.
- **Stateful Conversations:** The agent remembers the context of the conversation, such as the user's specified location and interests, across multiple turns.
- **Intelligent Information Extraction:** The agent can parse user messages to find key details, even if they are provided in separate messages.
- **Personalized Plan Generation:** Once all required information is gathered, the agent uses an LLM to create a travel plan tailored to the user's needs.
## How It Works
The application consists of a Gradio front-end and a LangGraph-powered back-end. LangGraph orchestrates the flow of the conversation, managing the agent's state and deciding which action to take next.
The core logic is defined in a state graph, which visualizes the flow of the agent's decision-making process.
### Agent Graph
The graph consists of the following key nodes:
- **`extract_info`**: This is the entry point of the graph. It analyzes the user's messages to extract the `location` and `interests`.
- **`router`**: After the `extract_info` node, a conditional router checks the current state:
- If a `location` is missing, it routes the conversation to the `ask_for_location` node.
- If the `location` is present but `interests` are missing, it routes to the `ask_for_interests` node.
- If both `location` and `interests` are present, it routes to the `generate_plan` node.
- **`ask_for_location`**: If the destination is unknown, this node generates a message asking the user to provide one.
- **`ask_for_interests`**: If the user's interests are unknown, this node generates a message asking the user to describe them.
- **`generate_plan`**: Once all necessary information has been collected, this node calls the LLM to create a personalized travel plan.
## Setup and Installation
1. **Clone the repository**
```bash
git clone https://github.com/trgiangdo/travel_planner.git
cd travel_planner
```
2. **Create and install the dependencies**
This project uses `uv` for package management.
```bash
uv venv
uv sync
```
3. **Set up environment variables:**
Create a `.env` file by copying the example and add your OpenAI API key.
```bash
cp .env_example .env
```
Now, edit the `.env` file to add your credentials.
## Usage
To run the application, use the following command:
```bash
uv run gradio app.py
```
This will start the Gradio server with hot-reloading. You can access the chat interface by opening the provided URL in your web browser.