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nvidia/auto-ontology
Auto Ontology is a Python framework for building semantic, graph-based reasoning layers over data. It enables deterministic query generation, better context retrieval, and more explainable AI for LLM and agent workflows.
Current momentum
Stars
10.7K
+0 since sync
Commits
17
in period
Active contributors
5
+0%
Issues closed
2
0.3h avg. resolution
Recorded trajectory
Movement you can verify over time.
Loading daily momentum history.
Behind the number
A score you can inspect.
Each signal contributes points to the total. These are capped GitHub samples, not complete activity counts or proof of adoption.
Development activity40.0 / 40 pts
17 commits · 42 opened PRs · 2 closed issues in 7 days
24 + 22 × ln(1 + weekly weighted activity). Activity = commits + 2 × opened PRs + closed issues. Signal bounded to 0–100, then weighted 40%.
Contributor breadth17.2 / 25 pts
5 sampled contributors in 7 days · 0% contributor-growth input
24 + 25 × ln(1 + contributors × √(7 / days)) + 0.15 × growth. Growth uses the 30-day windows; capped samples use a neutral 0 input. Signal bounded to 0–100, then weighted 25%.
Issue resolution20.0 / 20 pts
0.3 hours resolution input
105 − 9 × log₂(1 + hours). Uses sampled closed issues, not first-response time. With no resolved issues, the model uses 168 hours. Signal bounded to 0–100, then weighted 20%.
Release cadence15.0 / 15 pts
1 days cadence input
105 − 5 × cadence days. With fewer than two published releases, the model uses time since the last push as a proxy. Signal bounded to 0–100, then weighted 15%.
What changed in the recorded sample?
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874 forks
Downstream adoption
96 open issues
Community demand
1 day cadence
Shipping consistency