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ToolLanceDB Inc./10 trustVerified
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LanceDB

Serverless multimodal vector database built on the Lance columnar format.

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About

LanceDB is an open-source vector database designed as a 'multimodal lakehouse': it stores vectors alongside text, images, video, and metadata in the Lance columnar format, and serves vector similarity, full-text, and SQL search from one engine. It runs embedded (no server) or at cloud scale, with zero-copy versioning of datasets. Python, TypeScript, and Rust SDKs plus LangChain/LlamaIndex integrations make it a drop-in vector store for RAG and agent pipelines; the company also sells a managed cloud tier.

What it can do

  • **Embedded + cloud**: zero-server local mode; managed cloud for scale
  • **Multimodal storage**: vectors, text, images, video, point clouds in one table
  • **Three query modes**: vector similarity, full-text search, and SQL
  • **Dataset versioning**: zero-copy version management without extra infra
  • **Lance columnar format**: efficient storage shared with the broader Lance ecosystem
  • **Framework integrations**: LangChain, LlamaIndex, Pandas, Polars, DuckDB

Setup

```bash

pip install lancedb

```

```python

import lancedb

db = lancedb.connect("/tmp/db") # embedded, no server

table = db.create_table("docs", data=[{"id": 1, "vector": [0.1, 0.2]}])

result = table.search([0.1, 0.2]).limit(5).to_pandas()

```

See the quickstart at docs.lancedb.com. Managed cloud available at lancedb.com for production scale.

Prerequisites

Python 3.9+ (or Node.js 18+ / Rust for other SDKs). No server or credentials needed for embedded mode.

Use cases

vector searchRAGmultimodal datarecommendations

Works with

langchainsmolagentscustomGeneric API

Trust score — 8/10

/10 trustVerified

Scored 8/10 (verified tier, metrics-v1). Evidence: 11,652 GitHub stars (log-scaled vs catalog peers); blended with ~7,689,981 monthly package downloads; last push 0d ago; 100 releases/12mo; 745 open issues. Manual review: credible multimodal lakehouse; 7.7M/mo PyPI figure inflated by mirrors/CI; cloud-tier roadmap gravity worth watching; 'billions of vectors' is best-case benchmarking. License: Apache-2.0.

★ 11,652 GitHub stars

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
8.3/10
Maintenance · recency, releases, issue load
8.2/10
Openness · license + docs
10.0/10
Manual review · sanity read + red-flag check
8.0/10
  • · 11,652 GitHub stars (log-scaled vs catalog peers)
  • · blended with ~7,689,981 monthly package downloads
  • · last push 0d ago; 100 releases/12mo; 745 open issues
  • · manual review: credible multimodal lakehouse; 7.7M/mo PyPI figure inflated by mirrors/CI; cloud-tier roadmap gravity worth watching; 'billions of vectors' is best-case benchmarking

Sources

Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.