### Integrations

## Agentic harnesses

* Claude Code: track guide

* GitHub Copilot: track guide

## Storage

Read & write:

* Local file systems

* AWS S3

* S3-compatible storage services, e.g., MinIO & Cloudflare R2

* Google Cloud Storage

Read only:

* http/https: "ln.Artifact("https://a-website.com/a-dataset.txt")"

* Hugging Face:
  "ln.Artifact("hf://datasets/org/repo/dataset.parquet")"

## Ontologies & registries

* bionty: Biological ontologies, with easy import from >20 public
  ontologies

* pertdb: Registries for perturbations (compounds, biologics, genetic
  interventions, etc.)

## Git

Auto-sync with "git": track guide.

## Computational notebooks

You can call "ln.track()" and "ln.finish()" (or the R versions) in
four notebook types:

* Jupyter

* Marimo

* R markdown

* Quarto

For both notebook types, source code is stored in ".py" format and run
reports as html. Jupyter notebooks leverage the "py:percent" format
and Marimo notebooks the Marimo standard.

## MLOps

* PyTorch Lightning: "lightning"

* Weights & Biases: see the Weigths & Biases guide

* MLFlow: see the MLFlow guide

* Croissant format: see the Croissant guide

* scVI: see the guide on the scVI docs

## Workflow managers

* "nextflow" & the Seqera platform: see the Nextflow guide and the nf-
  lamin reference

* "redun": via the Python API, see the redun guide

* "prefect", "airflow", "dagster", etc.: see the workflows guide

* "snakemake": via post-run logic, see the snakemake example

## Tables & arrays

* "pyarrow" & "polars": see the "engine" argument of "open()" and the
  arrays guide

* "tiledbsoma": inhouse guide or cellxgene

* "duckdb": via parquet files, see rxrx

## Visualization

* Vitessce: see the vitessce guide

* DataVisyn: reach out for custom visualization solutions

## ELN systems

* Benchling: sync schema and data from your registries within our
  team/enterprise plan