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 runs anywhere and captures 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 about a minute to install LaminDB and create a database on your laptop or in the cloud.

Why?

Untraceable results cannot be trusted, especially when non-verifiable tasks are delegated to agents.
Without effective access to multimodal data, models burn tokens or fail entirely.
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 heterogeneous, and teams need end-to-end traceability for GxP compliance (21 CFR Part 11 and EU Annex 11).

Existing data lakehouses aren’t a comprehensive solution. They only address the 2nd and 3rd of these problems for a single table or array format, such as parquet or zarr.

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; load/query with your favorite engine (Polars, DuckDB, annbatch, …)
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 & versioning

Architecture? → docs

zero lock-in → open source, using 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 access → local, S3, GCP, etc. · Postgres, SQLite · ontologies
distributed → zero-copy data sharing across databases & storage
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

Who?

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

Industry → Pfizer, Altos Labs, RefinedScience, 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, hosting? → LaminHub

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

permissions → role-based, fine-grained access management for humans, bots & teams
audit logs → full traceability for compliance
single sign-on → social logins, Okta, Ping, and other providers
secure → SOC2 certified, monitoring ISO27001 & HIPAA compliance
scale → transparently manage many databases and storage locations

Architecture:

zero lock-in → LaminDB ensures data remains accessible even if you cancel LaminHub
permissions on the Postgres & storage layer → no need for an intermediate web service
TRE (Trusted Research Environment) → manage sensitive data from e.g. the UK Biobank
auto-generated REST API → optional REST interface for JavaScript-based web applications

UI:

lineage → interactive graphs for records, datasets, notebooks & pipelines
catalog → browse, search & query records and datasets
notebooks, workflows, runs → visualize & launch executions
versioning → manage data & code revisions
LIMS & ELN → records & notes integrated with ontologies
schemas, features, labels → manage data models

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

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

If you are an agent, see the library skill, the local docs, or llms.txt.

Access sharded datasets¶

Access a validated, versioned collection of artifact shards in parquet format:

Py

db = ln.DB("laminlabs/1000genomes")
collection = db.Collection.get("hVu9puwdRGskm1I6")
collection.schema.describe()

R

db <- ln$DB("laminlabs/1000genomes")
collection <- db$Collection$get("hVu9puwdRGskm1I6")
collection$schema$describe()

Ad-hoc query a sharded dataset.

See the validating schema of the collection (explore):

This example uses polars to filter the collection of parquet files:

chrom = "1"
lo, hi = 150_000_000, 200_000_000

with collection.open(engine="polars") as df:
    filtered = df.filter(
        (pl.col("chrom") == chrom) & (pl.col("pos") >= lo) & (pl.col("pos") <= hi)
    ).collect()

Access a collection of artifact shards in zarr format:

Py

db = ln.DB("laminlabs/arrayloader-benchmarks")
collection = db.Collection.get(key="Tahoe100M_zarr")
collection.artifacts.to_dataframe()

R

db <- ln$DB("laminlabs/arrayloader-benchmarks")
collection <- db$Collection$get(key = "Tahoe100M_zarr")
collection$artifacts$to_dataframe()

Train a model on a sharded dataset.

This example uses annbatch with the pre-shuffled sharded Tahoe100M_zarr collection (explore):

import anndata as ad
import zarr
from annbatch import Loader

paths = [artifact.cache() for artifact in collection.artifacts.all()]
loader = Loader(shuffle=True, batch_size=4096, chunk_size=256, preload_nchunks=64)
loader.add_datasets(
    datasets=[ad.io.sparse_dataset(zarr.open(p)["X"]) for p in paths],
    obs=[ad.io.read_elem(zarr.open(p)["obs"]) for p in paths],
)
for batch in loader:
    pass

Create artifacts¶

To initialize a new database in a development directory, run:

lamin init --modules bionty  # mount module bionty

To connect to an existing database - you can create a hosted one at lamin.ai - run:

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

To save a file or folder:

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

Or via the CLI:

lamin save sample.fasta --key sample.fasta

Load it.

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¶

To track an agent session, run: uvx library-skills --skill lamindb.

To track a script or notebook run with 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

Explore data lineage after running this snippet as a script (python create_fasta.py):

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

R

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

Py

transform.describe()  # context of the transform

R

transform$describe()  # context of the transform

To track a workflow, decorate a function:

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 pipeline managers like Nextflow, see docs.lamin.ai/pipelines.

A richer example.

Here is a 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.

Annotate & query artifacts¶

To annotate and query by entities:

Py

# create entities
my_label = ln.ULabel(name="My universal label").save()
my_project = ln.Project(name="My project label").save()

# annotate
artifact.ulabels.add(my_label)
artifact.projects.add(my_project)

# query
ln.Artifact.filter(ulabels=my_label, projects=my_project).to_dataframe()

R

# create entities
my_label <- ln$ULabel(name = "My universal label")$save()
my_project <- ln$Project(name = "My project label")$save()

# annotate
artifact$ulabels$add(my_label)
artifact$projects$add(my_project)

# query
ln$Artifact$filter(ulabels = my_label, projects = my_project)$to_dataframe()

This also works for entities imported from modules, for example, ontology terms:

Py

import bionty as bt

# import an entity from a public ontology source, e.g., by name or an ontology ID
hek293 = bt.CellLine.from_source(name="HEK293").save()
hras = bt.Gene.from_source(ensembl_gene_id="ENSG00000174775").save()

# annotate
artifact.cell_lines.add(hek293)
artifact.genes.add(hras)

# query
ln.Artifact.filter(cell_lines=hek293, genes=hras).to_dataframe(
    include=["cell_lines__name", "genes__symbol"]
)

R

bt <- import_module("bionty")

# import an entity from a public ontology source, e$g., by name or an ontology ID
hek293 <- bt$CellLine$from_source(name = "HEK293")$save()
hras <- bt$Gene$from_source(ensembl_gene_id = "ENSG00000174775")$save()

# annotate
artifact$cell_lines$add(hek293)
artifact$genes$add(hras)

# query
ln$Artifact$filter(cell_lines = hek293, genes = hras)$to_dataframe(
    include = list("cell_lines__name", "genes__symbol")
)

To annotate & query by features:

Py

from datetime import date

# define features
gc_content = ln.Feature(name="gc_content", dtype=float).save()
seq_date = ln.Feature(name="seq_date", dtype=date).save()

# annotate
artifact.features.set_values({
    gc_content: 0.55,
    seq_date: date.today(),
})

# query
ln.Artifact.filter(seq_date == date.today()).to_dataframe(include="features")

R

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

# define features
gc_content <- ln$Feature(name = "gc_content", dtype = "float")$save()
seq_date <- ln$Feature(name = "seq_date", dtype = date)$save()

# annotate
artifact$features$set_values(list(
    gc_content = 0.55,
    seq_date = date$today()
))

# query
ln$Artifact$filter(seq_date == date$today())$to_dataframe(include = "features")

To query by auto-tracked metadata:

Py

# by storage location and size
ln.Artifact.filter(storage__root=ln.settings.storage.root)
ln.Artifact.filter(size__gt=1e6).to_dataframe()

# by data lineage
ln.Artifact.filter(run=run).to_dataframe()
ln.Artifact.filter(run__transform=transform).to_dataframe()

# include more information in resulting dataframe
ln.Artifact.to_dataframe(include=["run__transform__key", "storage__root"])

R

# by storage location and size
ln$Artifact$filter(storage__root = ln$settings$storage$root)
ln$Artifact$filter(size__gt = 1e6)$to_dataframe()

# by data lineage
ln$Artifact$filter(run = run)$to_dataframe()
ln$Artifact$filter(run__transform = transform)$to_dataframe()

# include more information in resulting dataframe
ln$Artifact$to_dataframe(include = list("run__transform__key", "storage__root"))

Create records & notes¶

You can create record types to model metadata for any entity:

Py

# create record types
experiment_type = ln.Record(name="Experiments", is_type=True).save()
sample_type = ln.Record(name="Samples", is_type=True).save()

# create an experiment
experiment1 = ln.Record(
    name="Experiment 1",
    features={seq_date: date.today()},
    type=experiment_type
).save()

# create a sample that links to "Experiment 1" via a feature
experiment = ln.Feature(name="experiment", dtype=experiment_type).save()
sample1 = ln.Record(
    features={gc_content: 0.5, experiment: experiment1},
    type=sample_type
).save()

# export all experiments and all samples
experiment_type.to_dataframe()
sample_type.to_dataframe()

R

# create record types
experiment_type <- ln$Record(name = "Experiments", is_type = TRUE)$save()
sample_type <- ln$Record(name = "Samples", is_type = TRUE)$save()

# create an experiment
experiment1 <- ln$Record(
    name = "Experiment 1",
    features = list(seq_date = date$today()),
    type = experiment_type
)$save()

# create a sample that links to "Experiment 1" via a feature
experiment <- ln$Feature(name = "experiment", dtype = experiment_type)$save()
sample1 <- ln$Record(
    features = list(gc_content = 0.5, experiment = experiment1),
    type = sample_type
)$save()

# export all experiments and all samples
experiment_type$to_dataframe()
sample_type$to_dataframe()

You can save markdown notes as records:

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

Load it.

To load a record back as a markdown file, run:

lamin load https://lamin.ai/laminlabs/lamindata/record/mNDJgWFrkWQVW3ox

Watch a mini video: youtu.be/NRzVQXJaRH8

Validate datasets¶

Here is how you ingest a DataFrame:

Py

import pandas as pd

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

The simplest way to validate & annotate the content of the dataframe is the built-in schema valid_features:

Py

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

R

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

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.

Watch a mini video: youtu.be/Ji6E7hTnReQ

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.

Use branching & versioning¶

LaminDB versions transforms, artifacts, and collections. If you 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.run.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$run$transform$versions$to_dataframe()  # all versions of the transform that created the artifact

If you create a branch and switch to it as in git, you’ll create new objects and new versions of objects on it:

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

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

Manage ontologies¶

You can use Record to build your ELN & LIMS, your own ontologies, and even a knowledge graph with slow graph queries. If you instead want to build on public biological ontologies: bionty gives you >20 public ontologies so that you don’t have build ontologies for basic biological entities from scratch. For example, the Gene registry was used to validate the ENSG ids in the AnnData object above.

Here is how you import a public ontology into the CellType registry:

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 or Project:

Py

t_cell = bt.CellType.get(name="T cell")  # get a T cell label
artifact.cell_types.add(t_cell)          # annotate an artifact

R

t_cell <- bt$CellType$get(name = "T cell")  # get a T cell label
artifact$cell_types$add(t_cell)          # annotate an artifact

Watch a mini video: youtu.be/3vpWjHj3Kw8