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.
LanceDB publishes 12 API contracts indexed on the APIs.io network, including MaterializedView API, Namespace API, Table API, and 9 more. Tagged areas include Vector Search, Multi-Modal, Lance Format, Lakehouse, and RAG.
The LanceDB catalog on APIs.io includes 1 JSON-LD context and 2 Spectral governance rulesets.
LanceDB’s developer surface includes authentication, documentation, engineering blog, GitHub presence, support, and 18 more developer resources.
41.5/100developing▬ flatAgent24/100agent awareFront door AI6/6
dominant
Heartbeat94/100
alive
self hosted · Apache-2.0
Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →
Regulatory Posture applies to this provider. Its tags matched the
AI & Biometrics regime, so
Regulatory Posture carries 15 points of the composite.
If this regime is wrong for your business, say so on your
provider repo — the
applicability map is public and we will correct it.
Open Source Surface applies to this provider. This product is open source and we
read its repository directly, so Open Source Surface carries
10 points of the composite. It is scored from what the repository actually
publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the
provider rather than inferred from our own catalog pointers.
This facet adds; nothing was taken away to make room for it. An open-source project is not excused from
the commercial facets, because exemption would strip it of the points it does earn.
If we have the wrong repository, or this product is not open source, say so on your
provider repo and we
will drop the facet rather than have you publish against it.
Create-or-Update Ergonomics applies to this provider. This API accepts writes, so it
carries 10 points of the composite. It is scored from the published contracts
themselves: whether a caller can create-or-update in one call, whether the write accepts a key the caller already
holds, and whether the response says which branch ran. Without that, every write needs a search-and-branch in
front of it, and the first time that check is skipped a duplicate record is created.
Scored against the observed mean rather than raw — a provider at the catalog average is unchanged by this facet,
not penalised by it.
The six quality facets above are damped to 65 points between them,
because both conditional facets apply and carry
35 points together. That is why each facet's contribution is shown against a damped
maximum: raising a quality facet moves the composite by 65% of its nominal
weight, not 100%. The full arithmetic is at apis.io/rating/.
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Agent readiness — checks not met, largest first (exact, from this profile):
MCP Server+12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Idempotency+9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics+8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card+8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
Request/Response Examples+7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link
The Kin Score Market layer reads a provider inside its market:
its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted:
not part of the Kin Score or agent readiness.
Registered MCP server and declares compatibility are report only — not weighted.
Undeclared means we found nothing we can read, so there is no number: we never infer integrations.
Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.
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...
Not yet checked against LanceDB's own pricing page. Compiled by API Evangelist on 2026-05-25; confirm before you rely on a figure. A free plan or tier is listed.
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