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.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles LanceDB the way a machine reads it — 36 machine-readable artifacts across 16 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — LanceDB scores 52.3/100 (developing), with a separate agent-readiness read of 48/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile LanceDB
Each block below is one kind of artifact we hold for LanceDB. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 16
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
LanceDB Enterprise
Distributed, multi-tenant multimodal lakehouse. Adds curation and deduplication, Python UDF feature engineering, materialized views, GPU-accelerated index build via cuVS, distri...
Lance Format
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...
LanceDB Python SDK
Primary client library. First-class Arrow, Pandas, Polars, and Pydantic integration; pluggable embedding functions covering OpenAI, Cohere, Jina, Hugging Face, Ollama, Bedrock, ...
LanceDB TypeScript SDK
Node.js / TypeScript / JavaScript client library for LanceDB OSS, Cloud, and Enterprise. Bundles native bindings via napi-rs.
LanceDB Rust SDK
Native Rust client library; the LanceDB core and storage layer are written in Rust.
LanceDB Go SDK
Official Go client library for LanceDB OSS, Cloud, and Enterprise.
LanceDB C Bindings
C ABI bindings for LanceDB enabling embedding into C, C++, and other FFI-capable hosts.
LanceDB MCP Server
Model Context Protocol (MCP) server exposing LanceDB tables as retrieval tools for MCP-aware agents and IDEs.
LanceDB Data API
Operations that interact with object data and might be computationally intensive
LanceDB Index API
Operations that are related to an index
LanceDB MaterializedView API
The MaterializedView API from LanceDB — 2 operation(s) for materializedview.
LanceDB Metadata API
Operations that only interact with object metadata and should be computationally lightweight
LanceDB Namespace API
Operations that are related to a namespace
LanceDB Table API
Operations that are related to a table
LanceDB Tag API
Operations that are related to tags
LanceDB Transaction API
Operations that are related to a transaction
Scroll within the panel for all 16 ·
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Lancedb Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Lancedb Finops
FINOPSSemantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Lancedb Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
LanceDB API Rules
SPECTRALLanceDB API Rules
SPECTRALJSON Schema 3
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
JSON Structure 1
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Lancedb Table Structure
JSON STRUCTUREExamples 5
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Lancedb Create Table Example
EXAMPLELancedb Create Tag Example
EXAMPLELancedb Hybrid Query Example
EXAMPLELancedb Merge Insert Example
EXAMPLESecurity Posture 3
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Scopes 1
OAuth scopes are the vocabulary of least-privilege access. Profiling them shows exactly what an integration — or an agent acting on a user's behalf — is allowed to do.
OAuth scopes governing access to this provider's APIs.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for LanceDB — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 2
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 1
Pagination, idempotency, versioning, errors, and events
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 5
Authentication, authorization, and security posture
Operate 3
Status, limits, changes, and where to get help
Company 2
The organization behind the API
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/lancedb · machine-readable index on apis.io