Generated by MidJourney (https://midjourney.com/)If you are someone that enjoy the outdoor, you probably have heard of Komoot. Komoot is a route planning and navigation app that helps you to discover the great outdoors. It is a great tool to plan your next hike, bike ride or even a multi-day tour.
If you are like me, probably you have tough about using the data you generate on komoot to create your own dashboard or to do some analysis. Unfortunately, Komoot does not provide a Python package to access your data.
This is why I created Kompy.
What is Kompy?
Kompy is a Python wrapper for Komoot APIs. It is an easy-to-use Python package that allows you to access your data on Komoot by giving you a straightforward way to download and upload your activities from and to Komoot your Komoot dashboard.
How to install Kompy?
Kompy’s installation process is a breeze:
- Ensure Python 3.11 or newer is installed on your system.
- Simply run pip in your terminal:
pip install kompy
- Import Kompy into your project: import kompy as kp.
What can you do with Kompy?
Kompy’s basic usage involves a few simple steps:
- First, create a KomootConnector:
from kompy import KomootConnector
connector = KomootConnector(password=..., email=...)
- Fetch your activities:
tours_list = connector.get_tours(user_identifier=None)
- For detailed examples, visit this notebook.
Conclusion
Whether you’re a developer looking to explore the potential of fitness APIs or a fitness enthusiast with a knack for programming, Kompy opens up a world of possibilities. It’s user-friendly, efficient, and a great way to integrate your physical activities with your digital world.
Matteo Villosio
AI Lead and Trail Runner
Matteo Villosio is AI Lead at Tinexta Group, where he conceived and launched LextelAI, now Italy’s leading AI assistant for lawyers and legal professionals, and is currently advancing large‑language‑model and agent‑based solutions across the group’s businesses.
In parallel, he co‑founded DatAIMed and drives its AI vision, orchestrating autonomous‑agent pipelines and a multi‑collection MongoDB vector database that indexes more than 150 million scientific papers to deliver real‑time, bias‑checked clinical insights. In this role he recruits and mentors high‑performance AI teams, forges collaborations with hospitals, CROs and universities, and aligns product strategy with clinical and market needs.
Earlier, as the first Data Scientist at Greenomy, Matteo built the firm’s inaugural deep‑NLP system and earned top honours at the Swift Hackathon. He has designed machine‑learning solutions for audit analytics at Generali and data‑engineering pipelines at Flowe, conducted large‑scale social‑media research at SmartData@PoliTO, and led projects at the NGO FAWLTS to narrow the education‑to‑employment gap.
Matteo also serves as a member of GlobalAI, the Swiss‑based non‑profit that represents AI stakeholders before the United Nations and other international bodies, promoting the responsible, sustainable and ethical development of artificial intelligence worldwide.