Welcome to the TripSmith: Building an AI-Powered Travel Planner repository! 🎉
This project is a collaborative initiative brought to you by SuperDataScience, a global learning community focused on data science, machine learning, and AI. Whether you’re starting with Generative AI or looking to deepen your skills with tool-using LLMs, we’re excited to have you on board!
To contribute to this project, please follow the steps outlined in our CONTRIBUTING.md file.
This project supports two tracks based on experience level:
project-name/
├── beginner/ ← Beginner track files
│ ├── README.md ← Scope of Works for Beginner Track
│ ├── REPORT.md ← Markdown template for beginner submissions
│ └── submissions/
│ ├── team-members/
│ └── community-contributions/
│
├── advanced/ ← Advanced track files
│ ├── README.md ← Scope of Works for Advanced Track
│ ├── REPORT.md ← Markdown template for advanced submissions
│ └── submissions/
│ ├── team-members/
│ └── community-contributions/
│
├── CONTRIBUTING.md
├── requirements.txt
└── README.md ← You are here!
The Beginner Track focuses on building a Python-based travel planner that connects to APIs (Tavily or SerpAPI) to fetch flights, hotels, and points of interest (POIs). Using an LLM, you will synthesize this information into a simple day-by-day itinerary.
At the end of the track, you will:
- Build minimal API wrappers for search queries.
- Prompt an LLM to generate an itinerary in JSON and Markdown formats.
- Deploy your solution with Streamlit or Gradio.
📌 Get started:
➡️ Beginner Track Scope of Works
➡️ Beginner Report Template
➡️ Submit your work
The Advanced Track challenges participants to design a multi-agent AI planner with advanced reasoning. You will explore concepts like:
- Specialized agents (Flight Agent, Hotel Agent, Itinerary Agent).
- Orchestration patterns (central planner vs decentralized negotiation).
- Use schemas (via Pydantic) to structure flight, hotel, and POI data.
- Deployment enhancements (Hugging Face Spaces, Dockerized apps, advanced Streamlit/Gradio dashboards).
At the end of the track, you will have a multi-agent travel planning system that goes beyond simple tool-calling and introduces advanced AI engineering practices.
📌 Get started:
➡️ Advanced Track Scope of Works
➡️ Advanced Report Template
➡️ Submit your work
This project relies on live web data via APIs.
- Search APIs: Tavily or SerpAPI
- LLMs: OpenAI GPT models (or any provider supporting function/tool calling)
- Deployment: Streamlit or Gradio
| Phase | General Activities |
|---|---|
| Week 1: Setup + Exploration | Repo setup, API wrappers, schemas, and mock data |
| Week 2: LLM Planning Pipeline | LLM prompts, JSON schema outputs, itinerary generation, CLI tool |
| Week 3: Streamlit/Gradio + Deployment | Testing, validation, and deployment via Streamlit/Gradio |
This project is open to both official team members and outside community contributors.
- 🧑💻 Team Members should submit their work under
team-members/ - 🌍 Community Contributors are welcome to fork the repo and submit under
community-contributions/
See CONTRIBUTING.md for guidelines on how to participate.