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.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #1 among LLM repositories ranked this week; #14 of 182 repositories with measured growth this week (top 8%).
The project lists 13 contributors on GitHub.
Peaked at #14 in the weekly ranking the week of Nov 1, its best in the past year.
Use Cases & Benefits
- Builds semantic, graph-based reasoning layers over enterprise data to enable deterministic query generation and explainable AI.
- Provides trusted business context and reconciles inconsistent definitions across systems for more reliable agentic analytics.
- Use for creating AI-ready knowledge graphs from enterprise metadata to improve data discovery and integration.
- Use for enabling natural-language querying of structured data with automatic SQL translation and execution.
- Use for serving semantic context and provenance to LLMs and agent workflows via API, CLI, or MCP server.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
Watch-outs
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
Agents can retrieve documents and query databases, but reliability gates agentic analytics: a revenue metric answered faster is worth nothing if the number is wrong. Agents need a context layer that tells them where data lives, what it means, whether to use it, how to use it, where it came from, and what is similar. NVIDIA Auto Ontology is an open library that builds that layer from enterprise metadata, reconciles definitions across systems, and exposes it to any agent harness through MCP, CLI, and API. It complements the semantic models customers already own.
Learn: fold certified corrections and usage back into the ontology, so shared context improves with every question.
To run a text-to-SQL benchmark such as BIRD, refer to the ontology SQL evaluation repository.
Licensing & contributions. Auto Ontology is distributed under the Apache License 2.0. Third-party open-source components bundled, linked, or otherwise used by this project are listed in
THIRD_PARTY_NOTICES.md. This project is currently not accepting external contributions.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+7,148 stars over 9 days across 3 snapshot days; best day +2,526 on Nov 6.
Forks
596+552
Open issues
57+29
Compared with peers in LLM
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| thedotmack/claude-mem | 113.6K | +8.8K | 35 | today |
| NVIDIA/auto-ontology | 7.5K | +5.6K | 37 | — |
| HKUDS/DeepTutor | 55.5K | +5.5K | 65 | 5 days ago |
| affaan-m/ECC | 297.9K | +5.2K | 63 | 35 days ago |
| DietrichGebert/ponytail | 185.3K | +5.1K | 60 | 7 days ago |
| Graphify-Labs/graphify | 143.0K | +4.3K | 45 | today |
| Category median | 143.0K | +5.2K | 60 | — |
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