Lamindb

---
name: lamindb
description: Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.
license: Apache-2.0 license
metadata:
  version: '1.2'
  category: data-science-and-ml
  maintainer: Kalaris Labs
---

# LaminDB

## Overview

LaminDB is an open-source, lineage-native lakehouse for biology. It makes datasets and models queryable, traceable, validated, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable) while storing data in open formats across local filesystems, S3, GCS, Hugging Face, SQLite, and Postgres.

**Core Value Proposition:**
- **Queryability**: Search and filter artifacts, records, runs, features, schemas, and collections
- **Traceability**: Track inputs, outputs, parameters, source code, and environments for notebooks, scripts, functions, and pipelines
- **Validation**: Curate DataFrame, AnnData, SpatialData, TileDB-SOMA, Parquet, Zarr, and other biological formats with schemas
- **FAIR Compliance**: Standardize annotations with Bionty-backed ontologies and custom registries
- **Change management**: Organize work with projects, branches, spaces, collections, and saved notes or plans

## When to Use This Skill

Use this skill when:

- **Managing biological datasets**: scRNA-seq, bulk RNA-seq, spatial transcriptomics, flow cytometry, multi-modal data, EHR data
- **Tracking computational workflows**: Notebooks, scripts, functions, shell scripts, and pipeline execution (Nextflow, Snakemake, Redun)
- **Curating and validating data**: Schema validation, standardization, ontology-based annotation
- **Working with biological ontologies**: Genes, proteins, cell types, tissues, diseases, pathways (via Bionty)
- **Building data lakehouses**: Unified query interface across multiple datasets
- **Ensuring reproducibility**: Automatic versioning, lineage tracking, environment capture
- **Integrating ML pipelines**: Connecting with Weights & Biases, MLflow, Hugging Face, Lightning, scVI-tools
- **Deploying data infrastructure**: Setting up local or cloud-based data management systems
- **Collaborating on datasets**: Sharing curated, annotated data with standardized metadata

## Core Capabilities

LaminDB provides six interconnected capability areas, each documented in detail in the references folder.

### 1. Core Concepts and Data Lineage

**Core entities:**
- **Artifacts**: Versioned datasets (DataFrame, AnnData, Parquet, Zarr, etc.)
- **Records & ULabels**: Experimental entities, typed records, and simple labels
- **Collections**: Versioned, immutable sets of artifacts
- **Runs & Transforms**: Computational lineage tracking (what code produced what data)
- **Features**: Typed metadata fields for annotation and querying
- **Projects, Branches & Spaces**: Project grouping, change management, and access boundaries

**Key workflows:**
- Create and version artifacts from files or Python objects
- Track notebook/script execution with `ln.track()` and `ln.finish()`
- Track function workflows with `@ln.flow()` and `@ln.step()`
- Annotate artifacts with records, ulabels, projects, and typed features
- Visualize data lineage graphs with `artifact.view_lineage()`
- Query by provenance (find all outputs from specific code/inputs)

**Reference:** `references/core-concepts.md` - Read this for detailed information on artifacts, records, runs, transforms, features, versioning, and lineage tracking.

### 2. Data Management and Querying

**Query capabilities:**
- Registry exploration and lookup with auto-complete
- Single record retrieval with `get()`, `one()`, `one_or_none()`
- Filtering with comparison operators (`__gt`, `__lte`, `__contains`, `__startswith`)
- Feature-based queries, including expression-style queries with `Feature` objects
- Cross-registry traversal with double-underscore syntax
- Full-text search across registries
- Advanced logical queries with `ln.Q` objects (AND, OR, NOT)
- Streaming large datasets without loading into memory

**Key workflows:**
- Browse artifacts with filters and ordering
- Query by features, creation date, creator, size, etc.
- Stream large files in chunks or with array slicing
- Organize data with hierarchical keys
- Group artifacts into collections

**Reference:** `references/data-management.md` - Read this for comprehensive query patterns, filtering examples, streaming strategies, and data organization best practices.

### 3. Annotation and Validation

**Curation process:**
1. **Validation**: Confirm datasets match desired schemas
2. **Standardization**: Fix typos, map synonyms to canonical terms
3. **Annotation**: Link datasets to metadata entities for queryability

**Schema types:**
- **Flexible schemas**: Validate only known columns, allow additional metadata
- **Minimal required schemas**: Specify essential columns, permit extras
- **Strict schemas**: Complete control over structure and values

**Supported data types:**
- DataFrames (Parquet, CSV)
- AnnData (single-cell genomics)
- MuData (multi-modal)
- SpatialData (spatial transcriptomics)
- TileDB-SOMA (scalable arrays)

**Key workflows:**
- Define features and schemas for data validation
- Use `DataFrameCurator`, `AnnDataCurator`, `SpatialDataCurator`, or `TiledbsomaExperimentCurator` for validation
- Standardize values with `.cat.standardize()`
- Map to ontologies with `.cat.add_ontology()`
- Save curated artifacts with schema linkage
- Query validated datasets by features

**Reference:** `references/annotation-validation.md` - Read this for detailed curation workflows, schema design patterns, handling validation errors, and best practices.

### 4. Biological Ontologies

**Available ontologies (via Bionty):**
- Genes (Ensembl), Proteins (UniProt)
- Cell types (CL), Cell lines (CLO)
- Tissues (Uberon), Diseases (Mondo, DOID)
- Phenotypes (HPO), Pathways (GO)
- Experimental factors (EFO), Developmental stages
- Organisms (NCBItaxon), Drugs (DrugBank)

**Key workflows:**
- Import public ontologies with `bt.CellType.import_source()`
- Search ontologies with keyword or exact matching
- Standardize terms using synonym mapping
- Explore hierarchical relationships (parents, children, ancestors)
- Validate data against ontology terms
- Annotate datasets with ontology records
- Create custom terms and hierarchies
- Handle multi-organism contexts (human, mouse, etc.)

**Reference:** `references/ontologies.md` - Read this for comprehensive ontology operations, standardization strategies, hierarchy navigation, and annotation workflows.

### 5. Integrations

**Workflow managers:**
- Nextflow: Track pipeline processes and outputs
- Snakemake: Integrate into Snakemake rules
- Redun: Combine with Redun task tracking
- Lightning: Persist checkpoints and training metadata

**MLOps platforms:**
- Weights & Biases: Link experiments with data artifacts
- MLflow: Track models and experiments
- Hugging Face: Track model fine-tuning
- scVI-tools: Single-cell analysis workflows

**Storage systems:**
- Local filesystem, AWS S3, Google Cloud Storage
- S3-compatible (MinIO, Cloudflare R2)
- HTTP/HTTPS endpoints (read-only)
- HuggingFace datasets

**Array stores:**
- TileDB-SOMA (with cellxgene support)
- DuckDB for SQL queries on Parquet files

**Visualization:**
- Vitessce for interactive spatial/single-cell visualization

**Version control:**
- Git integration for source code tracking

**Reference:** `references/integrations.md` - Read this for integration patterns, code examples, and troubleshooting for third-party systems.

### 6. Setup and Deployment

**Installation:**
- Current stable baseline: `lamindb==2.5.1` (released 2026-06-01; Python >=3.10, <=3.14)
- Basic: `uv pip install 'lamindb==2.5.1'`
- With extras: `uv pip install 'lamindb[gcp,zarr-v2,fcs]==2.5.1'`
- Minimal namespace only: `uv pip install 'lamindb-core==2.5.1'`
- Bionty module: included in the LaminDB docs and available as `uv pip install 'bionty==2.4.0'`
- Optional modules: pin reviewed releases for wetlab or clinical schema modules rather than installing floating latest versions

**Instance types:**
- Local SQLite (development)
- Cloud storage + SQLite (small teams)
- Cloud storage + PostgreSQL (production)

**Storage options:**
- Local filesystem
- AWS S3 with configurable regions and permissions
- Google Cloud Storage
- S3-compatible endpoints (MinIO, Cloudflare R2)

**Configuration:**
- Cache management for cloud files
- Multi-user system configurations
- Git repository sync
- Named environment variables for credentials and connection URLs

**Deployment patterns:**
- Local dev → Cloud production migration
- Multi-region deployments
- Shared storage with personal instances

**Reference:** `references/setup-deployment.md` - Read this for detailed installation, configuration, storage setup, database management, security best practices, and troubleshooting.

## Safety and Security Defaults

When helping with LaminDB setup or integrations:

- Never display, log, or transmit actual API keys, cloud credentials, database passwords, or full connection strings that include secrets.
- Prefer IAM roles, workload identity, secret managers, or named environment variables such as `LAMIN_DB_URL`, `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `GOOGLE_APPLICATION_CREDENTIALS`; only check whether a named variable is present, not its value.
- Before saving content from REST APIs, external databases, or user-provided files, validate and sanitize it with an explicit schema or curator.
- For reproducible installs, pin package versions or use a lock file. Floating installs are acceptable only when the user explicitly wants the latest upstream release.

## Common Use Case Workflows

### Use Case 1: Single-Cell RNA-seq Analysis with Ontology Validation

```python
import lamindb as ln
import bionty as bt
import anndata as ad

# Start tracking a notebook/script run
ln.track(params={"analysis": "scRNA-seq QC and annotation"})

# Import cell type ontology
bt.CellType.import_source()

# Load data
adata = ad.read_h5ad("raw_counts.h5ad")

# Validate and standardize cell types
adata.obs["cell_type"] = bt.CellType.standardize(adata.obs["cell_type"])

# Curate with schema
curator = ln.curators.AnnDataCurator(adata, schema)
curator.validate()
artifact = curator.save_artifact(key="scrna/validated.h5ad")

# Link ontology-backed annotations for queryability
cell_types = bt.CellType.from_values(adata.obs["cell_type"])
artifact.cell_types.add(*cell_types)

ln.finish()
```

### Use Case 2: Building a Queryable Data Lakehouse

```python
import lamindb as ln

# Register multiple experiments
for i, file in enumerate(data_files):
    artifact = ln.Artifact.from_anndata(
        ad.read_h5ad(file),
        key=f"scrna/batch_{i}.h5ad",
        description=f"scRNA-seq batch {i}"
    ).save()

    # Annotate with features
    artifact.features.set_values({
        "batch": i,
        "tissue": tissues[i],
        "condition": conditions[i]
    })

# Query across all experiments by annotated features
immune_datasets = ln.Artifact.filter(
    key__startswith="scrna/",
    tissue="PBMC",
    condition="treated"
).to_dataframe()

# Load specific datasets
for artifact in immune_datasets:
    adata = artifact.load()
    # Analyze
```

### Use Case 3: ML Pipeline with W&B Integration

```python
import lamindb as ln
import wandb

# Initialize both systems
wandb.init(project="drug-response", name="exp-42")
ln.track(params={"model": "random_forest", "n_estimators": 100})

# Load training data from LaminDB
train_artifact = ln.Artifact.get(key="datasets/train.parquet")
train_data = train_artifact.load()

# Train model
model = train_model(train_data)

# Log to W&B
wandb.log({"accuracy": 0.95})

# Save model in LaminDB with W&B linkage
import joblib
joblib.dump(model, "model.pkl")
model_artifact = ln.Artifact("model.pkl", key="models/exp-42.pkl").save()
model_artifact.features.set_values({"wandb_run_id": wandb.run.id})

ln.finish()
wandb.finish()
```

### Use Case 4: Nextflow Pipeline Integration

```python
# In Nextflow process script
import lamindb as ln

ln.track()

# Load input artifact
input_artifact = ln.Artifact.get(key="raw/batch_${batch_id}.fastq.gz")
input_path = input_artifact.cache()

# Process (alignment, quantification, etc.)
# ... Nextflow process logic ...

# Save output
output_artifact = ln.Artifact(
    "counts.csv",
    key="processed/batch_${batch_id}_counts.csv"
).save()

ln.finish()
```

For native Nextflow projects, prefer the `nf-lamin` plugin and current `nextflow.config` patterns when available; use inline Python tracking for small or custom pipeline steps.

## Getting Started Checklist

To start using LaminDB effectively:

1. **Installation & Setup** (`references/setup-deployment.md`)
   - Install pinned LaminDB and required extras
   - Authenticate with `lamin login`
   - Initialize instance with `lamin init --storage ...`

2. **Learn Core Concepts** (`references/core-concepts.md`)
   - Understand Artifacts, Records, Runs, Transforms
   - Practice creating and retrieving artifacts
   - Implement `ln.track()`/`ln.finish()` or `@ln.flow()`/`@ln.step()` in workflows

3. **Master Querying** (`references/data-management.md`)
   - Practice filtering and searching registries
   - Learn feature-based queries and expression-style filters
   - Experiment with streaming large files

4. **Set Up Validation** (`references/annotation-validation.md`)
   - Define features relevant to research domain
   - Create schemas for data types
   - Practice curation workflows

5. **Integrate Ontologies** (`references/ontologies.md`)
   - Import relevant biological ontologies (genes, cell types, etc.)
   - Validate existing annotations
   - Standardize metadata with ontology terms

6. **Connect Tools** (`references/integrations.md`)
   - Integrate with existing workflow managers
   - Link ML platforms for experiment tracking
   - Configure cloud storage and compute

## Key Principles

Follow these principles when working with LaminDB:

1. **Track everything**: Use `ln.track()` at the start of every analysis for automatic lineage capture

2. **Validate early**: Define schemas and validate data before extensive analysis

3. **Use ontologies**: Leverage public biological ontologies for standardized annotations

4. **Organize with keys**: Structure artifact keys hierarchically (e.g., `project/experiment/batch/file.h5ad`)

5. **Query metadata first**: Filter and search before loading large files

6. **Version, don't duplicate**: Use built-in versioning instead of creating new keys for modifications

7. **Annotate with features**: Define typed features and use `artifact.features.set_values()` for queryable metadata

8. **Document thoroughly**: Add descriptions to artifacts, schemas, and transforms

9. **Leverage lineage**: Use `view_lineage()` to understand data provenance

10. **Start local, scale cloud**: Develop locally with SQLite, deploy to cloud with PostgreSQL

## Reference Files

This skill includes comprehensive reference documentation organized by capability:

- **`references/core-concepts.md`** - Artifacts, records, runs, transforms, features, versioning, lineage
- **`references/data-management.md`** - Querying, filtering, searching, streaming, organizing data
- **`references/annotation-validation.md`** - Schema design, curation workflows, validation strategies
- **`references/ontologies.md`** - Biological ontology management, standardization, hierarchies
- **`references/integrations.md`** - Workflow managers, MLOps platforms, storage systems, tools
- **`references/setup-deployment.md`** - Installation, configuration, deployment, troubleshooting

Read the relevant reference file(s) based on the specific LaminDB capability needed for the task at hand.

## Additional Resources

- **Official Documentation**: https://docs.lamin.ai
- **API Reference**: https://docs.lamin.ai/api
- **GitHub Repository**: https://github.com/laminlabs/lamindb
- **Tutorial**: https://docs.lamin.ai/tutorial
- **FAQ**: https://docs.lamin.ai/faq

## Agent operating procedure

1. **Check the environment.** Confirm the Python environment and library versions (`python -c "import pkg; print(pkg.__version__)"`) and inspect the data's shape, types and missing values.
2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. **Run a small version first.** Run on a sample or a single fold first and check runtime and memory.
4. **Execute the full task** using the instructions and references above.
5. **Validate the result.** Use held-out data, fixed random seeds and appropriate metrics; check for leakage; report uncertainty (CIs, std over seeds).
6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

| If this happens | Do this |
|---|---|
| Out-of-memory or very slow execution | Subsample, use chunked or lazy computation, or reduce model size, and tell the user what changed. |
| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |
| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |

**Integrity rules**

- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
- Never report a metric you did not compute in this session; show the code path that produced every number.
- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

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