# lance-format/lance

Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..

Repository: https://github.com/lance-format/lance
Canonical: https://ross.abutalabs.com/products/lance
Homepage: https://lance.org
Language: Rust
License: Apache-2.0
License Family: permissive
Topics: machine-learning, computer-vision, data-format, deep-learning, python, apache-arrow, duckdb, mlops, data-analysis, data-analytics, data-science, dataops, data-centric, embeddings, rust, llms
Last push: 2026-08-26T23:47:42+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 84, longevity 100
- inputs: {"age_days": 1518, "days_push": 7, "days_rel": 26, "gap_med": 9, "n_releases_24m": 62}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6973, forks 817 (observed 2026-08-28T04:09:51.827664+00:00)

## What it is
Lance is an open lakehouse data format (file format, table format, and catalog spec) for multimodal AI, implemented in Rust with Python and Java SDKs. It offers 100x faster random access than Parquet, vector and full-text search, zero-copy versioning with ACID transactions, and integrations with Pandas, DuckDB, Polars, PyArrow, Spark, Ray, and PyTorch.

## Use cases
- convert parquet datasets to a faster format for ML training
- store and query image, video, audio, and embedding data
- run vector similarity search over large embedding collections
- build a feature store with hybrid vector and full-text search
- version ML training datasets with time travel and branches
- serve random-access row lookups for ML inference
- add columns to large tables without full rewrites

## When to choose
- you need fast random access to individual rows in large columnar datasets
- you are training ML models on multimodal data stored in object storage
- you want vector search, full-text search, and SQL analytics over one dataset
- you need dataset versioning and ACID transactions without extra infrastructure

## When to avoid
- you only need simple analytical scans where Parquet is sufficient
- you need a mature, battle-tested lakehouse format like Iceberg or Delta Lake with broad vendor support
- your workload is small-scale and doesn't benefit from the format's optimizations

## Facets
- artifact type: library
- maturity: active
- function: vector-database, search-engine, serialization, database, data-science, machine-learning
- domain: machine-learning, databases, large-language-models, computer-vision
- platform: python, rust, jvm, cross-platform, cloud
- tags: lakehouse-format, columnar-format, parquet-alternative, vector-search, acid-transactions, multimodal-data, apache-arrow, time-travel, data-versioning, pylance, data-engineering, search

## Member repositories
- lance-format/lance (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:51.827664+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:41:10.003576+00:00, confidence not recorded.
  - readme: https://github.com/lance-format/lance (fetched 2026-08-28T04:09:51.827664+00:00, sha 2effff202ca8)
  - homepage: https://lance.org (fetched 2026-08-29T08:36:58.679739+00:00, sha df1945667f78)
  - site_page: https://lance.org/quickstart (fetched 2026-08-29T08:36:58.682411+00:00, sha e150da67eb5c)
  - site_page: https://lance.org/sdk_docs (fetched 2026-08-29T08:36:58.686349+00:00, sha 524ea6d7b9bd)
  - site_page: https://lance.org/quickstart/vector-search (fetched 2026-08-29T08:36:58.687887+00:00, sha 8d5fa9a74955)
  - registry_crates: https://crates.io/api/v1/crates/lance (fetched 2026-08-29T08:36:58.689705+00:00, sha 52ca2e7dfc52)
  - site_page: https://lance.org/integrations (fetched 2026-08-29T08:36:58.684328+00:00, sha 6398acff06fe)
- Data as of 2026-08-30T08:39:29.467469+00:00.