Introduction

LaminDB is an open-source data management tool that makes it easy to query, trace and govern datasets across diverse storage formats and locations. Like git, LaminDB is a distributed system that runs anywhere and captures all relevant context about your work. This includes the data flow through models and analyses, the entities and notes defining your work, and the features & schemas of datasets. It takes a few seconds to install LaminDB and create a database on your laptop.

Why?

1. Untraceable results cannot be trusted, especially when non-verifiable tasks are delegated to agents.

2. Without effective access to multimodal data, models burn tokens or fail entirely.

3. Without governing changes to data akin to governing changes to software with git, it’s hard to evaluate agents, debug their mistakes, and safely merge their contributions.

Especially in life sciences, hard-to-verify tasks are abundant, data formats are very heterogeneous, and teams need end-to-end traceability for GxP compliance (21 CFR Part 11 and EU Annex 11).

Traditional data infrastructure doesn’t solve these issues because it was built for business analytics rather than complex AI workflows. While modern SQL lakehouse solutions (Iceberg, Delta, DuckLake, Lakebase) excel at tabular analytics, they are restricted to structured rows and SQL-centric catalogs. LaminDB generalizes core lakehouse guarantees — ACID transactions, time travel, and schema evolution — to multimodal data (parquet, zarr, AnnData, images) and Python-first workflows, giving you lakehouse governance over non-tabular data while letting you query with your favorite compute engines (Polars, DuckDB, …).

How?

• lineage → trace results across agent sessions, notebooks, scripts & workflows

• lakehouse → manage datasets in any format (parquet, zarr, …) with time travel, schema evolution & ACID guarantees; query with your favorite engine (Polars, DuckDB, …)

• LIMS & ELN → unified schema-based records management with support for ontologies & notes

• FAIR datasets → validate & annotate files, DataFrame, AnnData, SpatialData, …

• 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 → pip install lamindb or install.packages('laminr') - no Docker required, no separate backend

• unified → federate data across storage locations (local, S3, GCP, …) in any database

• distributed → federate data zero-copy & lineage-aware across databases

• reproducible → track agent traces, source code & compute environments

• ACID → snapshot isolation & time travel via transactional metadata records across datasets in any format (parquet, zarr, etc.)

• idempotent → re-run logic without worries about duplications or overwrites

• decoupled compute → run your favorite engine (Polars, DuckDB, data loaders, …) with all its benefits

• integrations → bio ontologies, git, nextflow, vitessce, redun, and more

• extensible → create custom plug-ins based on the Django ORM, the basis for LaminDB’s registries

Read more: docs.lamin.ai/architecture.

Who?

Scientists and engineers at leading research institutions and biotech companies, including:

• Industry → Pfizer, Altos Labs, Ensocell Therapeutics, …

• Academia & Research → scverse, DZNE (National Research Center for Neuro-Degenerative Diseases), Helmholtz Munich (National Research Center for Environmental Health), …

• Research Hospitals → Global Immunological Swarm Learning Network: Harvard, MIT, Stanford, ETH Zürich, Charité, U Bonn, Mount Sinai, …

From personal research projects to pharma-scale deployments managing petabytes of data across:

entities	OOMs
observations & datasets	10¹² & 10⁶
runs & transforms	10⁹ & 10⁵
proteins & genes	10⁹ & 10⁶
biosamples & species	10⁵ & 10²
…	…

UI, permissions, audit logs?

LaminHub is a collaboration hub built on LaminDB similar to how GitHub is built on git.

Platform features:

• infra-as-code → manage many distributed storage locations & databases

• permissions → role-based, fine-grained access management for users & teams

• audit logs → full traceability for compliance

• single sign-on → connect Okta, Ping, and other providers

• secure → SOC2 certified, monitoring ISO27001 & HIPAA compliance

Architecture features:

• zero lock-in → the open-source core ensures data remains yours & accessible even if you cancel LaminHub

• permissions on the Postgres & storage layer → no need for an intermediate web service

• accredited TRE (Trusted Research Environment) → manage sensitive data from e.g. the UK Biobank

• auto-generated REST API → optional REST interface for JS-based web applications

UI features:

• lineage → interactive graphs for datasets, notebooks & pipelines

• catalog → browse, search & query your lakehouse

• notebooks, workflows, runs → visualize & launch executions

• versioning → manage data & code revisions

• LIMS & ELN → records, sheets & markdown notes integrated with ontologies

• schemas & labels → validate & monitor data distributions

• simple dashboarding → auto-generate data summaries

Give it a try by exploring public omics datasets at lamin.ai/explore. It’s free and no account is required.

LaminHub is a SaaS product. For private data & commercial usage, see: lamin.ai/pricing.

Quickstart

To install the Python package with recommended dependencies, use:

pip install lamindb

Install with minimal dependencies.

The lamindb package adds data-science related dependencies through the [full] extra, see here.

For a minimal install of the lamindb namespace, use:

pip install lamindb-core

Agent? See .agents/ in lamindb/. Docs: See docs/ or llms.txt.

Query databases & datasets

You can browse public databases at lamin.ai/explore. To access laminlabs/cellxgene, run:

Py

import lamindb as ln

db = ln.DB("laminlabs/cellxgene")  # a database object for queries
df = db.Artifact.to_dataframe()    # a dataframe listing datasets & models

R

library(laminr)
ln <- import_module("lamindb")

db <- ln$DB("laminlabs/cellxgene")  # a database object for queries
df <- db$Artifact$to_dataframe()    # a dataframe listing datasets & models

To get a specific dataset, run:

Py

artifact = db.Artifact.get("BnMwC3KZz0BuKftR")  # a metadata object for a dataset
artifact.describe()                             # describe the context of the dataset

R

artifact <- db$Artifact$get("BnMwC3KZz0BuKftR")  # a metadata object for a dataset
artifact$describe()                             # describe the context of the dataset

See the output.

Access the content of the dataset via:

Py

local_path = artifact.cache()  # return a local path from a cache
adata = artifact.load()        # load object into memory
accessor = artifact.open()     # return a streaming accessor

R

local_path <- artifact$cache()  # return a local path from a cache
adata <- artifact$load()        # load object into memory
accessor <- artifact$open()     # return a streaming accessor

For broader queries of cellxgene, see docs.lamin.ai/cellxgene.

Save files & folders

You can create a database at lamin.ai and invite collaborators. To connect to an existing database, run:

lamin login
lamin connect account/name  # tip: add flag `--here` to scope to current directory

Or init a new database instead (no login required).

Navigate into a development direcotry, just like you’d do for git init, and run:

lamin init --modules bionty

For more configuration, see docs.lamin.ai/setup.

On the terminal and in a Python session, lamindb will now auto-connect.

To save a file or folder via the API:

Py

import lamindb as ln
# → connected lamindb: account/instance

open("sample.fasta", "w").write(">seq1\nACGT\n")        # create dataset
ln.Artifact("sample.fasta", key="sample.fasta").save()  # save dataset

R

library(laminr)
ln <- import_module("lamindb")
# → connected lamindb: account/instance

writeLines(">seq1\nACGT\n", "sample.fasta")        # create dataset
ln$Artifact("sample.fasta", key = "sample.fasta")$save()  # save dataset

To save a file or folder via the CLI, run:

lamin save sample.fasta --key sample.fasta

To load an artifact via the CLI into a local cache, run:

lamin load --key sample.fasta

Read more about the CLI: docs.lamin.ai/cli.

Trace data, code & agents

The lamindb skill ships with the lamindb package at .agents/skills/. Ask your coding agent to copy it to wherever it reads skills from — .claude/skills/ for Claude Code, .agents/skills/ for GitHub Copilot — so that it automatically tracks agent sessions.

To create a dataset in a script or notebook while tracking source code, inputs, outputs, logs, and environment:

Py

import lamindb as ln
# → connected lamindb: account/instance

ln.track()                                              # track code execution
open("sample.fasta", "w").write(">seq1\nACGT\n")        # create dataset
ln.Artifact("sample.fasta", key="sample.fasta").save()  # save dataset
ln.finish()                                             # mark run as finished

R

library(laminr)
ln <- import_module("lamindb")
# → connected lamindb: account/instance

ln$track()                                              # track code execution
writeLines(">seq1\nACGT\n", "sample.fasta")        # create dataset
ln$Artifact("sample.fasta", key = "sample.fasta")$save()  # save dataset
ln$finish()                                             # mark run as finished

Running this snippet as a script (python create_fasta.py) produces the following data lineage:

Py

artifact = ln.Artifact.get(key="sample.fasta")  # get artifact by key
artifact.describe()      # context of the artifact
artifact.view_lineage()  # fine-grained lineage

R

artifact <- ln$Artifact$get(key = "sample.fasta")  # get artifact by key
artifact$describe()      # context of the artifact
artifact$view_lineage()  # fine-grained lineage

Watch a mini video: youtu.be/yK3ODFZLL1A

Access run & transform.

Py

run = artifact.run              # get the run object
transform = artifact.transform  # get the transform object
run.describe()                  # context of the run

R

run <- artifact$run              # get the run object
transform <- artifact$transform  # get the transform object
run$describe()                  # context of the run

Py

transform.describe()  # context of the transform

R

transform$describe()  # context of the transform

Track a project or an agent plan.

Pass a project/artifact to ln.track(), for example:

Py

ln.track(project="My project", plan="./plans/curate-dataset-x.md")

R

ln$track(project = "My project", plan = "./plans/curate-dataset-x.md")

Note that you have to create a project or save the agent plan in case they don’t yet exist:

# create a project with the CLI
lamin create project "My project"

# save an agent plan with the CLI
lamin save /path/to/.cursor/plans/curate-dataset-x.plan.md
lamin save /path/to/.claude/plans/curate-dataset-x.md

Or in Python:

Py

ln.Project(name="My project").save()  # create a project in Python

R

ln$Project(name = "My project")$save()  # create a project in Python

You can track workflows by decorating functions:

import lamindb as ln

@ln.flow()
def create_fasta(fasta_file: str = "sample.fasta"):
    open(fasta_file, "w").write(">seq1\nACGT\n")    # create dataset
    ln.Artifact(fasta_file, key=fasta_file).save()  # save dataset

if __name__ == "__main__":
    create_fasta()

Beyond what you get for scripts & notebooks, this automatically tracks function & CLI params and integrates well with established Python workflow managers: docs.lamin.ai/track. To integrate advanced bioinformatics pipeline managers like Nextflow, see docs.lamin.ai/pipelines.

A richer example.

Here is an automatically generated re-construction of the project of Schmidt et al. (Science, 2022):

A phenotypic CRISPRa screening result is integrated with scRNA-seq data. Here is the result of the screen input:

You can explore it here on LaminHub or here on GitHub.

Label artifacts

You can label an artifact by running:

Py

my_label = ln.ULabel(name="My label").save()   # a universal label
project = ln.Project(name="My project").save() # a project label
artifact.ulabels.add(my_label)
artifact.projects.add(project)

R

my_label <- ln$ULabel(name = "My label")$save()   # a universal label
project <- ln$Project(name = "My project")$save() # a project label
artifact$ulabels$add(my_label)
artifact$projects$add(project)

Query for it:

Py

ln.Artifact.filter(ulabels=my_label, projects=project).to_dataframe()

R

ln$Artifact$filter(ulabels = my_label, projects = project)$to_dataframe()

You can also query by the metadata that lamindb automatically collects:

Py

ln.Artifact.filter(run=run).to_dataframe()              # by creating run
ln.Artifact.filter(transform=transform).to_dataframe()  # by creating transform
ln.Artifact.filter(size__gt=1e6).to_dataframe()         # size greater than 1MB

R

ln$Artifact$filter(run = run)$to_dataframe()              # by creating run
ln$Artifact$filter(transform = transform)$to_dataframe()  # by creating transform
ln$Artifact$filter(size__gt = 1e6)$to_dataframe()         # size greater than 1MB

If you want to include more information into the resulting dataframe, pass include.

Py

ln.Artifact.to_dataframe(include=["created_by__name", "storage__root"])  # include fields from related registries

R

ln$Artifact$to_dataframe(include = list("created_by__name", "storage__root"))  # include fields from related registries

The query syntax for DB objects and for your default database is the same.

Here is an overview that illustrates how artifacts can be labeled by other entities:

Read more: docs.lamin.ai/organize.

Manage features & records

Let’s define some features:

Py

from datetime import date

gc_content = ln.Feature(name="gc_content", dtype=float).save()
experiment_note = ln.Feature(name="experiment_note", dtype=str).save()
experiment_date = ln.Feature(name="experiment_date", dtype=date, coerce=True).save()  # accept date strings

R

datetime <- import_module("datetime")
date <- datetime$date

gc_content <- ln$Feature(name = "gc_content", dtype = "float")$save()
experiment_note <- ln$Feature(name = "experiment_note", dtype = "str")$save()
experiment_date <- ln$Feature(name = "experiment_date", dtype = date, coerce = TRUE)$save()  # accept date strings

The most basic thing you can do with features is annotating artifacts, records, or runs with them:

Py

artifact.features.set_values({
    gc_content: 0.55,
    experiment_note: "Looks great",
    experiment_date: "2025-10-24",
})

# query
ln.Artifact.filter(experiment_date == "2025-10-24").to_dataframe(include="features")  # query all artifacts annotated with `experiment_date`

R

artifact$features$set_values(list(
    gc_content = 0.55,
    experiment_note = "Looks great",
    experiment_date = "2025-10-24"
))

# query
ln$Artifact$filter(experiment_date == "2025-10-24")$to_dataframe(include = "features")  # query all artifacts annotated with `experiment_date`

You can create records for entities underlying your experiments (samples, perturbations, instruments, etc.):

Py

ln.Record(name="Sample 1", features={gc_content: 0.5}).save()

R

ln$Record(name = "Sample 1", features = list(gc_content = 0.5))$save()

You can create record types and relationships:

Py

# create an Experiments type
experiments = ln.Record(name="Experiments", is_type=True).save()

# create a record of that type
experiment1 = ln.Record(name="Experiment 1", type=experiments).save()

# create a feature that links experiments (a relationship)
experiment = ln.Feature(name="experiment", dtype=experiments).save()

# create a sample record
ln.Record(name="Sample 2", features={gc_content: 0.5, experiment: experiment1}).save()

# export all experiments
experiments.to_dataframe()

R

# create an Experiments type
experiments <- ln$Record(name = "Experiments", is_type = TRUE)$save()

# create a record of that type
experiment1 <- ln$Record(name = "Experiment 1", type = experiments)$save()

# create a feature that links experiments (a relationship)
experiment <- ln$Feature(name = "experiment", dtype = experiments)$save()

# create a sample record
ln$Record(name = "Sample 2", features = list(gc_content = 0.5, experiment = experiment1))$save()

# export all experiments
experiments$to_dataframe()

Watch a mini video: youtu.be/NRzVQXJaRH8

Lakehouse

Here is how you ingest a DataFrame:

Py

import pandas as pd

df = pd.DataFrame({
    "sequence_str": ["ACGT", "TGCA"],
    "gc_content": [0.55, 0.54],
    "experiment_note": ["Looks great", "Ok"],
    "experiment_date": [date(2025, 10, 24), date(2025, 10, 25)],
})
ln.Artifact.from_dataframe(df, key="my_datasets/sequences.parquet").save()  # no validation

R

pd <- import_module("pandas")

df <- pd$DataFrame(list(
    sequence_str = list("ACGT", "TGCA"),
    gc_content = list(0.55, 0.54),
    experiment_note = list("Looks great", "Ok"),
    experiment_date = list(date(2025L, 10L, 24L), date(2025L, 10L, 25L))
))
ln$Artifact$from_dataframe(df, key = "my_datasets/sequences.parquet")$save()  # no validation

To validate & annotate the content of the dataframe, use the built-in schema valid_features:

Py

ln.Feature(name="sequence_str", dtype=str).save()  # define a remaining feature
artifact = ln.Artifact.from_dataframe(
    df,
    key="my_datasets/sequences.parquet",
    schema="valid_features"  # validate columns against features
).save()
artifact.describe()

R

ln$Feature(name = "sequence_str", dtype = "str")$save()  # define a remaining feature
artifact <- ln$Artifact$from_dataframe(
    df,
    key = "my_datasets/sequences.parquet",
    schema = "valid_features"  # validate columns against features
)$save()
artifact$describe()

Watch a mini video: youtu.be/Ji6E7hTnReQ

You can filter for datasets by schema and then launch distributed queries or batch load distributed datasets. For tables, see: docs.lamin.ai/tables. For arrays, see: docs.lamin.ai/arrays.

To validate an AnnData, call:

Py

import anndata as ad
import numpy as np
import pandas as pd

adata = ad.AnnData(
    X=np.ones((21, 10)),
    obs=pd.DataFrame({'cell_type_by_model': ['T cell', 'B cell', 'NK cell'] * 7}),
    var=pd.DataFrame(index=[f'ENSG{i:011d}' for i in range(10)])
)
artifact = ln.Artifact.from_anndata(
    adata,
    key="my_datasets/scrna.h5ad",
    schema="ensembl_gene_ids_and_valid_features_in_obs"
).save()
artifact.describe()

R

ad <- import_module("anndata")
np <- import_module("numpy")
pd <- import_module("pandas")

adata <- anndata::AnnData(
    X = np$ones((21, 10)),
    obs = pd$DataFrame(list(cell_type_by_model = rep(list('T cell', 'B cell', 'NK cell'), 7))),
    var = pd$DataFrame(index = sprintf("ENSG%010d", 1:10))
)
artifact <- ln$Artifact$from_anndata(
    adata,
    key = "my_datasets/scrna.h5ad",
    schema = "ensembl_gene_ids_and_valid_features_in_obs"
)$save()
artifact$describe()

To validate a SpatialData or any other array-like dataset, you need to construct a Schema. You can do this by composing simple pandera-style schemas: docs.lamin.ai/curate.

Branching & versioning

LaminDB co-versions code and datasets for you. If edit and run the create_fasta.py script, you’ll automatically create a new version of the transform and the sample.fasta artifact.

The edited script

Py

# create_fasta.py
import lamindb as ln

ln.track()
open("sample.fasta", "w").write(">seq1\nTGCA\n")  # a new sequence
ln.Artifact("sample.fasta", key="sample.fasta", features={"experiment": "Experiment 1"}).save()  # annotate with the new experiment
ln.finish()

R

# create_fasta$py
library(laminr)
ln <- import_module("lamindb")

ln$track()
writeLines(">seq1\nTGCA\n", "sample.fasta")  # a new sequence
ln$Artifact("sample.fasta", key = "sample.fasta", features = list(experiment = "Experiment 1"))$save()  # annotate with the new experiment
ln$finish()

Py

artifact_latest = ln.Artifact.get(key="sample.fasta")  # pass version for a previous version: ln.Artifact.get(key="sample.fasta", version="1.0")
artifact_latest.versions.to_dataframe()                # all versions of that artifact
artifact_latest.transform.versions.to_dataframe()      # all versions of the transform that created the artifact

R

artifact_latest <- ln$Artifact$get(key = "sample.fasta")  # pass version for a previous version: ln$Artifact$get(key = "sample.fasta", version = "1.0")
artifact_latest$versions$to_dataframe()                # all versions of that artifact
artifact_latest$transform$versions$to_dataframe()      # all versions of the transform that created the artifact

To isolate changes, create a contribution branch and switch to it as in git:

lamin switch -c my_branch

To merge a contribution branch into main, run:

lamin switch main  # switch to the main branch
lamin merge my_branch  # merge contribution branch into main

Read more: docs.lamin.ai/manage-changes.

Watch a mini video: youtu.be/rzRwcMj6-fc

Data sharing

To share data in a lineage-aware way, transfer objects from a source database to your default database:

Py

db = ln.DB("laminlabs/lamindata")
artifact = db.Artifact.get(key="example_datasets/mini_immuno/dataset1.h5ad")
artifact.save()

R

db <- ln$DB("laminlabs/lamindata")
artifact <- db$Artifact$get(key = "example_datasets/mini_immuno/dataset1.h5ad")
artifact$save()

This is zero-copy for the artifact’s data in storage. Read more: docs.lamin.ai/transfer.

Ontologies

Plugin bionty gives you >20 public ontologies as SQLRecord registries. This was used to validate the ENSG ids in the adata just before.

Py

import bionty as bt

bt.CellType.import_source()  # import the default ontology
bt.CellType.to_dataframe()   # your extensible cell type ontology in a simple registry

R

bt <- import_module("bionty")

bt$CellType$import_source()  # import the default ontology
bt$CellType$to_dataframe()   # your extensible cell type ontology in a simple registry

You can then create objects, e.g. for labeling, analogous to ULabel, Project, or Record:

Py

t_cell = bt.CellType.get(name="T cell")
artifact.cell_types.add(t_cell)

R

t_cell <- bt$CellType$get(name = "T cell")
artifact$cell_types$add(t_cell)

Read more: docs.lamin.ai/manage-ontologies.

Watch a mini video: youtu.be/3vpWjHj3Kw8

Manage notes

When in your development directory, you can save markdown files as records:

lamin save <topic>/<my-note.md>