{"repo":"laminlabs/lamindb","free":true,"listed":false,"github":"https://github.com/laminlabs/lamindb","clone":"git clone https://github.com/laminlabs/lamindb.git","description":"Open-source data management for multimodal AI. Query, trace, and govern data with a lineage-native, format-agnostic lakehouse. Use biological formats and registries by the creators of Scanpy. 🍊YC S22","language":"Python","stars":361,"topics":["data-lineage","comp-bio-ops","data-lakehouse","ml-ops","lims","eln","feature-store","ontologies","open-source","observability"],"license":"Apache-2.0","category":"analytics","readme_excerpt":"LaminDB: data management for traceable, multimodal AI LaminDB is an open-source data management tool that makes it easy to query, trace & govern datasets across diverse storage formats and locations. It gives you context through annotations, memory through lineage, and governance through branching and versioning. It uses a scalable lakehouse architecture that integrates files, tables, arrays, ontologies, and notes. It supports biological formats and registries by the creators of Scanpy. Why? 1. Untraceable results cannot be trusted, especially in the age of agents. 2. Without effective access to multimodal data, models burn tokens or fail entirely. 3. We want to govern changes to data like we govern changes to code with git. Especially in life sciences, teams need end-to-end traceability for GxP compliance (21 CFR Part 11 and EU Annex 11). How? - lineage → trace results across agent sessions, notebooks, scripts & workflows - lakehouse → query across many datasets, manage tables & arrays schema-based and ACID - LIMS & ELN → unified schema-based records management with support for ontologies & notes - FAIR datasets → validate & annotate files, DataFrame , AnnData , SpatialData , zarr , … - governance → manage changes via branching & by versioning data + code Architecture? - zero lock-in → uses open standards (metadata in SQLite/Postgres, data in parquet , zarr , etc.) - scalable → hit storage & database directly through your pydata or R stack, no REST API involved - simple → pi","default_branch":null,"files":null,"tree":[],"storefront":"/r/laminlabs","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/laminlabs/lamindb/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}