GraphCanon updated 3w · GitHub synced 3w
Decision brief
Lance is an open lakehouse format built for multimodal AI, offering fast random access and vector index creation with extensive language compatibility.
Good fit when
- Use Lance when you need fast random access to datasets formatted in a way that supports multimodal AI workloads.
- Use if your project requires the ability to quickly convert Parquet files into Lance without significant changes to your codebase.
Avoid when
- Do not use Lance if your application strictly depends on a specific format other than those compatible with it, such as HDF5 or non-supported SQL databases.
- Avoid using Lance if real-time performance is critical for all operations and you do not require vector indexing capabilities.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
cargo add lanceSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Facilitates fast random access, vector index creation, and data versioning with compatibility across Pandas, DuckDB, Polars, Pyarrow, PyTorch.
Capability facts
- Deploy
- Self-host
Source: dockerfile:docker-compose.yml · Aug 3, 2026
- Docker
- Dockerfile present
Source: dockerfile:docker-compose.yml · Aug 3, 2026
- Languages
- rust
Source: github.language · Aug 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Quick Start
Installation
pip install pylance
To install a preview release:
pip install --pre --extra-index-url https://pypi.fury.io/lance-format pylance
[!TIP] Preview releases are released more often than full releases and contain the latest features and bug fixes. They receive the same level of testing as full releases. We guarantee they will remain published and available for download for at least 6 months. When you want to pin to a specific version, prefer a stable release.
Converting to Lance
import lance
import pandas as pd
import pyarrow as pa
import pyarrow.dataset
df = pd.DataFrame({"a": [5], "b": [10]})
uri = "/tmp/test.parquet"
tbl = pa.Table.from_pandas(df)
pa.dataset.write_dataset(tbl, uri, format='parquet')
parquet = pa.dataset.dataset(uri, format='parquet')
lance.write_dataset(parquet, "/tmp/test.lance")
Reading Lance data
dataset = lance.dataset("/tmp/test.lance")
assert isinstance(dataset, pa.dataset.Dataset)
Pandas
df = dataset.to_table().to_pandas()
df
DuckDB
import duckdb
For agents
This page has a .md twin and JSON over the API.