LanceDB
LanceDB is the AI-Native multimodal lakehouse built on the open-source Lance columnar storage format. It pairs an Apache 2.0 licensed embedded retrieval library (Python, TypeScript, Rust, Go, C, Java SDKs) with a managed cloud service (LanceDB Cloud) and an enterprise lakehouse (LanceDB Enterprise) that unify vector, full-text, hybrid, and SQL search across billions of multimodal records. The REST surface is governed by the open Lance Namespace specification (OpenAPI 3.1) covering namespace, table, index, tag, and transaction operations with first-class support for materialized views, schema evolution, and time-travel versioning. LanceDB is used in production by Midjourney, Runway, World Labs, Netflix, Character.AI, Uber, NVIDIA, ByteDance, Databricks, and others for RAG, agent memory, training data curation, feature engineering, and large-scale retrieval.
11 APIs
0 Features
Vector DatabaseMultimodalLance FormatLakehouseRAGAgent MemoryOpen SourceEmbeddingsFull-Text SearchHybrid SearchColumnar StorageArrowAI Infrastructure
Apache 2.0 licensed, developer-friendly, embedded multimodal retrieval library. Provides namespace + table CRUD, insert / upsert / delete, vector / full-text / hybrid / SQL sear...
Fully managed serverless LanceDB service. Hosts customer namespaces and tables, exposes the Lance Namespace REST API, and handles compaction, index maintenance, replication, and...
Distributed, multi-tenant multimodal lakehouse. Adds curation and deduplication, Python UDF feature engineering, materialized views, GPU-accelerated index build via cuVS, distri...
Apache 2.0 open lakehouse format for multimodal AI. Columnar Parquet replacement offering 100x faster random access, zero-copy reads, vector indexes, and automatic data versioni...
Open OpenAPI 3.1 specification describing access and operations against a collection of Lance tables in a multimodal lakehouse. Defines a complete REST server contract (50+ oper...
Primary client library. First-class Arrow, Pandas, Polars, and Pydantic integration; pluggable embedding functions covering OpenAI, Cohere, Jina, Hugging Face, Ollama, Bedrock, ...
Node.js / TypeScript / JavaScript client library for LanceDB OSS, Cloud, and Enterprise. Bundles native bindings via napi-rs.
Native Rust client library; the LanceDB core and storage layer are written in Rust.
Official Go client library for LanceDB OSS, Cloud, and Enterprise.
C ABI bindings for LanceDB enabling embedding into C, C++, and other FFI-capable hosts.
Model Context Protocol (MCP) server exposing LanceDB tables as retrieval tools for MCP-aware agents and IDEs.
aid: lancedb
url: https://raw.githubusercontent.com/api-evangelist/lancedb/refs/heads/main/apis.yml
name: LanceDB
kind: opensource
description: >-
LanceDB is the AI-Native multimodal lakehouse built on the open-source Lance
columnar storage format. It pairs an Apache 2.0 licensed embedded retrieval
library (Python, TypeScript, Rust, Go, C, Java SDKs) with a managed cloud
service (LanceDB Cloud) and an enterprise lakehouse (LanceDB Enterprise) that
unify vector, full-text, hybrid, and SQL search across billions of multimodal
records. The REST surface is governed by the open Lance Namespace
specification (OpenAPI 3.1) covering namespace, table, index, tag, and
transaction operations with first-class support for materialized views,
schema evolution, and time-travel versioning. LanceDB is used in production
by Midjourney, Runway, World Labs, Netflix, Character.AI, Uber, NVIDIA,
ByteDance, Databricks, and others for RAG, agent memory, training data
curation, feature engineering, and large-scale retrieval.
image: https://kinlane-productions2.s3.amazonaws.com/api-evangelist-network/lancedb/lancedb-logo.png
tags:
- Vector Database
- Multimodal
- Lance Format
- Lakehouse
- RAG
- Agent Memory
- Open Source
- Embeddings
- Full-Text Search
- Hybrid Search
- Columnar Storage
- Arrow
- AI Infrastructure
created: '2026-05-23'
modified: '2026-05-25'
specificationVersion: '0.20'
apis:
- aid: lancedb:oss
name: LanceDB OSS
description: >-
Apache 2.0 licensed, developer-friendly, embedded multimodal retrieval
library. Provides namespace + table CRUD, insert / upsert / delete,
vector / full-text / hybrid / SQL search, scalar (BTree, Bitmap,
LabelList, FTS) and vector (IVF_FLAT, IVF_PQ, IVF_HNSW_SQ) indexes,
automatic versioning, time-travel, and pluggable embedding functions.
Backed by S3, GCS, Azure Blob, or local disk via object_store.
humanURL: https://docs.lancedb.com/
baseURL: https://github.com/lancedb/lancedb
tags:
- Open Source
- Embedded
- Vector Database
- Apache 2.0
properties:
- type: Documentation
url: https://docs.lancedb.com/
- type: Repository
url: https://github.com/lancedb/lancedb
- type: Quickstart
url: https://docs.lancedb.com/quickstart
- type: License
url: https://github.com/lancedb/lancedb/blob/main/LICENSE
- type: Releases
url: https://github.com/lancedb/lancedb/releases
- type: Issues
url: https://github.com/lancedb/lancedb/issues
- type: OpenAPI
url: https://raw.githubusercontent.com/api-evangelist/lancedb/main/openapi/lance-namespace-openapi.yaml
- aid: lancedb:cloud
name: LanceDB Cloud
description: >-
Fully managed serverless LanceDB service. Hosts customer namespaces and
tables, exposes the Lance Namespace REST API, and handles compaction,
index maintenance, replication, and elasticity. Authenticated via
`x-api-key` header. SOC 2, GDPR, and HIPAA compliant.
humanURL: https://lancedb.com/
baseURL: https://your-tenant.lancedb.cloud/v1/
tags:
- Managed Service
- Serverless
- REST
- Multi-Tenant
properties:
- type: Documentation
url: https://docs.lancedb.com/
- type: API
url: https://docs.lancedb.com/api-reference/rest/
- type: OpenAPI
url: https://raw.githubusercontent.com/api-evangelist/lancedb/main/openapi/lance-namespace-openapi.yaml
- type: Trust
url: https://trust.lancedb.com/
- type: Sales
url: https://lancedb.com/contact
- aid: lancedb:enterprise
name: LanceDB Enterprise
description: >-
Distributed, multi-tenant multimodal lakehouse. Adds curation and
deduplication, Python UDF feature engineering, materialized views,
GPU-accelerated index build via cuVS, distributed query, and direct
training integration with PyTorch / Ray. Deployable in customer VPC on
AWS, GCP, or Azure.
humanURL: https://lancedb.com/
baseURL: https://lancedb.com/
tags:
- Enterprise
- Managed
- Lakehouse
- VPC
- GPU
properties:
- type: Documentation
url: https://docs.lancedb.com/enterprise/
- type: Architecture
url: https://docs.lancedb.com/enterprise/architecture
- type: Sales
url: https://lancedb.com/contact
- type: Trust
url: https://trust.lancedb.com/
- aid: lancedb:lance-format
name: Lance Format
description: >-
Apache 2.0 open lakehouse format for multimodal AI. Columnar Parquet
replacement offering 100x faster random access, zero-copy reads, vector
indexes, and automatic data versioning. Convertible from Parquet in two
lines of code. Maintained by the lance-format community.
humanURL: https://lance.org/
baseURL: https://github.com/lance-format/lance
tags:
- File Format
- Columnar
- Arrow
- Open Source
- Apache 2.0
properties:
- type: Documentation
url: https://lance.org/
- type: Repository
url: https://github.com/lance-format/lance
- type: Specification
url: https://lance.org/format/
- type: Releases
url: https://github.com/lance-format/lance/releases
- aid: lancedb:lance-namespace
name: Lance Namespace Specification
description: >-
Open OpenAPI 3.1 specification describing access and operations against
a collection of Lance tables in a multimodal lakehouse. Defines a
complete REST server contract (50+ operations) covering namespaces,
tables, indices, tags, transactions, materialized views, schema
evolution, and time-travel. Reference implementations exist for Apache
Hive, Apache Polaris, Apache Gravitino, Unity Catalog, and AWS Glue.
humanURL: https://lance.org/format/namespace/
baseURL: https://github.com/lance-format/lance-namespace
tags:
- Specification
- OpenAPI
- REST
- Catalog
- Open Source
properties:
- type: Specification
url: https://lance.org/format/namespace/
- type: Repository
url: https://github.com/lance-format/lance-namespace
- type: OpenAPI
url: https://raw.githubusercontent.com/lance-format/lance-namespace/main/docs/src/spec.yaml
- type: Implementations
url: https://github.com/lance-format/lance-namespace-impls
- aid: lancedb:python-sdk
name: LanceDB Python SDK
description: >-
Primary client library. First-class Arrow, Pandas, Polars, and Pydantic
integration; pluggable embedding functions covering OpenAI, Cohere,
Jina, Hugging Face, Ollama, Bedrock, Sentence Transformers and 20+ more.
humanURL: https://lancedb.github.io/lancedb/python/python/
baseURL: https://pypi.org/project/lancedb/
tags:
- SDK
- Python
properties:
- type: Documentation
url: https://lancedb.github.io/lancedb/python/python/
- type: Package
url: https://pypi.org/project/lancedb/
- type: Repository
url: https://github.com/lancedb/lancedb
- aid: lancedb:typescript-sdk
name: LanceDB TypeScript SDK
description: >-
Node.js / TypeScript / JavaScript client library for LanceDB OSS, Cloud,
and Enterprise. Bundles native bindings via napi-rs.
humanURL: https://lancedb.github.io/lancedb/js/globals/
baseURL: https://www.npmjs.com/package/@lancedb/lancedb
tags:
- SDK
- TypeScript
- JavaScript
- Node.js
properties:
- type: Documentation
url: https://lancedb.github.io/lancedb/js/globals/
- type: Package
url: https://www.npmjs.com/package/@lancedb/lancedb
- type: Repository
url: https://github.com/lancedb/lancedb
- aid: lancedb:rust-sdk
name: LanceDB Rust SDK
description: >-
Native Rust client library; the LanceDB core and storage layer are
written in Rust.
humanURL: https://docs.rs/lancedb/latest/lancedb/
baseURL: https://crates.io/crates/lancedb
tags:
- SDK
- Rust
properties:
- type: Documentation
url: https://docs.rs/lancedb/latest/lancedb/
- type: Package
url: https://crates.io/crates/lancedb
- type: Repository
url: https://github.com/lancedb/lancedb
- aid: lancedb:go-sdk
name: LanceDB Go SDK
description: >-
Official Go client library for LanceDB OSS, Cloud, and Enterprise.
humanURL: https://github.com/lancedb/lancedb-go
baseURL: https://github.com/lancedb/lancedb-go
tags:
- SDK
- Go
properties:
- type: Repository
url: https://github.com/lancedb/lancedb-go
- aid: lancedb:c-sdk
name: LanceDB C Bindings
description: >-
C ABI bindings for LanceDB enabling embedding into C, C++, and other
FFI-capable hosts.
humanURL: https://github.com/lancedb/lancedb-c
baseURL: https://github.com/lancedb/lancedb-c
tags:
- SDK
- C
- FFI
properties:
- type: Repository
url: https://github.com/lancedb/lancedb-c
- aid: lancedb:mcp-server
name: LanceDB MCP Server
description: >-
Model Context Protocol (MCP) server exposing LanceDB tables as
retrieval tools for MCP-aware agents and IDEs.
humanURL: https://github.com/lancedb/lancedb-mcp-server
baseURL: https://github.com/lancedb/lancedb-mcp-server
tags:
- MCP
- Agent
- Tools
properties:
- type: Repository
url: https://github.com/lancedb/lancedb-mcp-server
common:
- type: Website
url: https://lancedb.com/
- type: Documentation
url: https://docs.lancedb.com/
- type: Blog
url: https://lancedb.com/blog
- type: GitHub
url: https://github.com/lancedb
- type: GitHubFormat
url: https://github.com/lance-format
- type: Discord
url: https://discord.com/invite/G5DcmnZWKB
- type: Trust
url: https://trust.lancedb.com/
- type: Support
url: mailto:support@lancedb.com
- type: Contact
url: https://lancedb.com/contact
- type: LLMsTxt
url: https://docs.lancedb.com/llms.txt
- type: OpenAPI
url: https://raw.githubusercontent.com/api-evangelist/lancedb/main/openapi/lance-namespace-openapi.yaml
- type: Capabilities
url: https://raw.githubusercontent.com/api-evangelist/lancedb/main/capabilities/multimodal-retrieval.yaml
- type: Vocabulary
url: https://raw.githubusercontent.com/api-evangelist/lancedb/main/vocabulary/lancedb-vocabulary.yml
integrations:
- name: Apache Arrow
- name: Apache Iceberg
- name: Apache Spark
- name: Apache Flink
- name: Apache Hive
- name: Apache Polaris
- name: Apache Gravitino
- name: DuckDB
- name: Pandas
- name: Polars
- name: Pydantic
- name: Ray
- name: Trino
- name: PostgreSQL
- name: Unity Catalog
- name: AWS Glue
- name: Hugging Face
- name: LangChain
- name: LlamaIndex
- name: GenKit
- name: PyTorch
- name: Databricks
- name: Amazon Web Services
- name: Google Cloud
- name: Microsoft Azure
- name: NVIDIA
- name: OpenAI
- name: Cohere
- name: Anthropic
- name: Jina
- name: Ollama
- name: Sentence Transformers
- name: CrewAI
- name: cuVS
solutions:
- name: Retrieval-Augmented Generation (RAG)
- name: Long-Term Agent Memory
- name: Training Data Curation
- name: Feature Engineering for ML
- name: Multimodal Search (Text, Image, Video, Audio)
- name: Semantic Code Search
- name: Recommendation Systems
- name: Anomaly Detection
- name: Vector Lakehouse
useCases:
- name: Production RAG over multimodal corpora
- name: Agent and assistant long-term memory store
- name: Foundation-model training data curation and deduplication
- name: Semantic search across billions of documents and embeddings
- name: Hybrid (vector + BM25 + SQL) search with reranking
- name: Time-travel and reproducible ML datasets via Lance versioning
- name: GPU-accelerated vector index construction
maintainers:
- FN: Kin Lane
email: kin@apievangelist.com