Open-Meteo

Weather API with open-data forecasts, historical and climate data from all major national weather services.

Healthy · 75
75/100
Healthy

Actively maintained with regular releases.

Maintenance 21/40
Releases 25/25
Community 14/20
Momentum 15/15

Commit activity more than 52% of tracked projects; popularity more than 71%. How this is calculated.

Commits, last 11 months

2025-10: 20 commits 2025-11: 29 commits 2025-12: 21 commits 2026-01: 21 commits 2026-02: 20 commits 2026-03: 34 commits 2026-04: 14 commits 2026-05: 13 commits 2026-06: 46 commits 2026-07: 50 commits 2026-08: 32 commits
2025-10 300 total 2026-08
Stars6.3k
Latest release1.6.0 · 10 Sept 2026
Repo updated23 Sept 2026
LicenceAGPL-3.0
Built withDocker

Is Open-Meteo actively maintained?

Open-Meteo is under active development: 300 commits landed over the last 11 months, averaging roughly 43 commits a month in the most recent quarter.

Activity is accelerating: the last quarter carried 110% more commits than the one before it.

The most recent tagged release, 1.6.0, shipped within the last month — a current, installable version exists today.

Of the 77 projects we track in Miscellaneous, Open-Meteo ranks #13 by health score — in the top quarter of its category.

What Open-Meteo actually does

Open-Meteo is a self-hosted weather API that aggregates forecasts, historical records, and climate data sourced from major national meteorological services. It provides programmatic access to meteorological information through open data sets without relying on third-party commercial endpoints. The software is packaged as a Docker container.

Best fit: It is well-suited for developers who want to integrate weather data into their applications while maintaining complete control over their data pipeline and avoiding external API rate limits or subscription fees.

Worth knowing: The AGPL-3.0 licence carries strict copyleft obligations if you modify the code and distribute it over a network. Additionally, maintaining a local instance requires dealing with large data sets and ongoing updates to ensure forecasting accuracy.

Deployment notes

You will typically deploy this via Docker alongside a reverse proxy to handle TLS termination. Persistent storage volumes are usually required to cache historical records and forecast data locally.


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