lamindb
Full/meta-package module for the `lamindb` distribution.
Decision gist · record as of 2026-08-14
Yes, with conditions. LaminDB is actively maintained and has low install friction, making it suitable for research teams managing multimodal scientific data at scale. However, the license status is unclear—verify the actual license terms before adopting in proprietary or regulated environments. The package is well-suited for collaborative biotech and academic research but requires careful vetting of compliance obligations.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- A LaminDB instance (local or remote) must be initialized or connected before querying or saving artifacts.
- Low install friction with a single runtime dependency (lamindb-core).
License · maintenance · safety
(unclear) — License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license terms before adopting in proprietary or regulated environments.
last release 2026-08-04 (10 days) · last repo commit 2026-08-14 · 292 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 141,682 downloads/mo, #11,235 on PyPI
Alternatives
Verify before relying
pip install lamindb
import lamindb as ln
db = ln.DB("laminlabs/cellxgene")
df = db.Artifact.to_dataframe()- Actual license terms and compliance obligations—metadata shows 'unclear' treatment with no SPDX or raw license field.
- Whether the package is suitable for production use in regulated industries (e.g., pharma) given the license ambiguity.
- Performance characteristics and scalability limits for the claimed petabyte-scale deployments.
What it is and what it does
LaminDB is an open-source data management platform designed to handle multimodal scientific datasets across heterogeneous storage backends (local, S3, GCP, etc.) with a lakehouse architecture. It combines metadata management (via SQLite or Postgres), file/array storage (parquet, zarr), and a schema-based registry to enable querying and governance of datasets, models, and computational runs. The package provides lineage tracking to trace data provenance across notebooks, scripts, and agent sessions; versioning and branching for data governance; and integrations with biological ontologies and workflow tools.
Typically used by research teams and biotech companies to organize experiments, manage datasets collaboratively, and ensure reproducibility. It ships as a meta-package that depends on lamindb-core and adds optional data-science dependencies. Users connect to a local or remote instance, register artifacts (files, DataFrames, AnnData objects, etc.), and query across federated datasets. The package includes a CLI for common operations (login, save, load) and a skill module for automatic tracking in AI agents.
Use it for
- Organize and query large collections of biological datasets (gene expression, spatial data, biosamples) with schema-based metadata and ontology support.
- Track data lineage and code provenance across multi-step analysis pipelines to ensure reproducibility and audit trails.
- Manage collaborative research projects with versioned datasets, branching for parallel experiments, and federated access across storage locations.
- Integrate AI agent sessions with automatic logging of inputs, outputs, source code, and compute environments for reproducible agent-driven research.
- Govern changes to datasets and models using git-like branching and versioning for regulated or high-stakes biotech workflows.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
LaminDB is actively maintained and has low install friction, making it suitable for research teams managing multimodal scientific data at scale. However, the license status is unclear—verify the actual license terms before adopting in proprietary or regulated environments. The package is well-suited for collaborative biotech and academic research but requires careful vetting of compliance obligations.
Install
lamindb on PyPI
Before you install
Low install friction with a single runtime dependency (lamindb-core). Actively maintained with a recent release (10 days old) and steady repository activity. Supports modern Python versions (3.10–3.14).
Requires Python 3.10 or later. A LaminDB instance (local or remote) must be initialized or connected before querying or saving artifacts.
License in practice
License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license terms before adopting in proprietary or regulated environments.
Quickstart
pip install lamindb
import lamindb as ln
db = ln.DB("laminlabs/cellxgene")
df = db.Artifact.to_dataframe()Verify before relying
- Actual license terms and compliance obligations—metadata shows 'unclear' treatment with no SPDX or raw license field.
- Whether the package is suitable for production use in regulated industries (e.g., pharma) given the license ambiguity.
- Performance characteristics and scalability limits for the claimed petabyte-scale deployments.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagelamindb-core |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 141,682 / month, #11,235 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: lamindb-2.9.1-py3-none-any.whl
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See also lamin_cli · lamin_utils · lamindb_setup · bionty · mudata · dbt-colibri · whylogs · dbl-discoverx · mlflow-tracing · omnigent