Three signals combined into one view: GitHub star growth, active job listings and monthly download trends. See which tools are gaining ground before everyone else does.
| # | Tool | Stars |
|---|---|---|
| 1 | LangChain | 146.2k |
| 2 | Hugging Face Transformers | 165.2k |
| 3 | PyTorch | 103.0k |
| 4 | Apache Spark | 44.0k |
| 5 | scikit-learn | 67.2k |
| 6 | Apache Airflow | 46.8k |
| 7 | Grafana | 76.7k |
| 8 | Pandas | 49.7k |
| 9 | dbt | 13.8k |
| 10 | MLflow | 27.9k |
| 11 | Apache Kafka | 33.7k |
| 12 | DuckDB | 41.2k |
| 13 | Metabase | 49.2k |
| 14 | Polars | 39.7k |
| 15 | Apache Superset | 74.7k |
| 16 | Dagster | 16.1k |
| 17 | Prefect | 23.8k |
| 18 | Ray | 43.8k |
| 19 | Airbyte | 22.1k |
| 20 | Apache Flink | 26.3k |
| 21 | dlt | 5.8k |
| 22 | Great Expectations | 11.8k |
| 23 | Feast | 7.3k |
| 24 | Redash | 28.8k |
| 25 | Mage | 8.8k |
| 26 | Soda Core | 2.4k |
Momentum Score = percentile composite of GitHub stars, job listings and PyPI downloads. Updated weekly.
GitHub signals (30%)
Total stars as a popularity baseline. Tools with more stars have proven staying power.
Job demand (35%)
Active job listings requiring this tool — the strongest signal that companies are actually using it.
Downloads (20%) + Growth (15%)
PyPI monthly downloads show ecosystem adoption. 4-week star growth catches tools rising faster than their peers.
Data from GitHub REST API and pypistats.org · Job data from active listings · Updated every Sunday