nlqdb — your autonomous DBA.

Build a real app from day one with no data modeling. nlqdb infers your schema from how you use it, evolves it, and keeps it optimized — and shows you every change before it lands.

Your app talks to its database in plain English — over HTTP, the SDK, the nlq CLI, or MCP from Claude, Cursor and Codex. The first insert creates the shape; later inserts and reads evolve it; the DBA builds the indexes and places data on the right engine, and every change is previewed, versioned and one click to undo. The LLM never emits SQL: it returns a typed plan, our compiler emits the parameterised statement, and you see the exact SQL every time.

No backend to build. You write HTML; each component asks for what it wants in plain English; nlqdb infers the schema, writes the SQL, runs it, and renders the result. There is no backend for you to build.

Two actions. That's the whole product:

1. Create a database — one word: a name (or a goal).
2. Talk to it in plain English.

<script src="https://elements.nlqdb.com/v1.js" type="module"></script>

<nlq-data
  goal="the 5 newest orders, with customer and item"
  api-key="pk_live_xxx"
  template="table"
  refresh="10s"
></nlq-data>

That's the entire backend for a live order list — no API to write, no schema to define, no JSON to parse. Engine choice (Postgres / Mongo / Redis / DuckDB / pgvector / …), schema inference, indexing, backups, and auto-migration between engines based on your real workload are background concerns you never have to see.

Status — early, open

nlqdb is early and built in the open, but fully public — no gate, no invite code. The marketing site, the /v1/ask pipeline, the <nlq-data> / <nlq-action> elements, the chat app, the TypeScript SDK, the hosted MCP server, and the nlq CLI are all live in some form (see the surface table below). Natural-language → SQL accuracy is still climbing toward our public bar (BIRD ≥ 0.65, Spider 2.0 ≥ 0.75 on the free model chain), so answers can be wrong — every response carries a confidence signal and the SQL it ran.

Use it

Connecting an agent over MCP? On Claude Code, one marketplace add wires the hosted server and both memory skills in a single step:

/plugin marketplace add nlqdb/nlqdb
/plugin install nlqdb-memory@nlqdb

On any other MCP host, connect your agent with one browser-OAuth approval; headless hosts skip the browser with npx -y @nlqdb/mcp (0.1.1) and an sk_mcp_* MCP key (MCP setup). @nlqdb/sdk (0.4.0) and @nlqdb/mcp (0.1.1) are both published and importable from npm.

The 60-second walkthrough — plain HTML, CLI, and ten framework wrappers — lives at docs.nlqdb.com. Start with the HTML tutorial or the CLI tutorial.

You don't generate an API key separately: describe your database at nlqdb.com, and the chat hands you a <nlq-data> snippet with the key already inlined.

Examples

examples/ — minimal scaffolds in plain HTML, Next.js, Nuxt, SvelteKit, Astro, plus a CLI-only walkthrough. Each is the smallest valid integration around one <nlq-data> element or one CLI session.

What makes it different

Four things every release has to move, none allowed to regress (GLOBAL-025):

• Engine quality — natural-language → SQL accuracy (measured continuously on BIRD + Spider 2.0 + an internal eval), plus the multi-engine layer that moves your data to the right engine for your workload.
• Onboarding — landing to first answer in under a minute, no card, no config.
• UX — see the diff before any write, see the SQL behind every answer, and on low confidence get a one-click clarify — a guided turn, never a dead-end, and never a silent guess.
• Performance — sub-400 ms cached, sub-1.5 s cold.

The bet: get this right on free, open models and it only gets better on frontier ones — the scaffolding compounds with whatever model is underneath.

Models & plans

• Free forever on the built-in open-model chain — queries, embeds, and the elements, no card required.
• Bring your own LLM key (Anthropic / OpenAI / Gemini / Grok / OpenRouter) on any tier, at no markup.
• Hosted premium models on paid plans, when you'd rather not manage a key of your own.
• Self-host the source — the engine, CLI, MCP server, and SDKs are source-available under FSL-1.1-ALv2: free to self-host for any non-competing use, bring your own LLM key, no per-call fees. The license auto-converts to Apache 2.0 two years after each release.

The hosted-premium model lane went live 2026-08-14. The full model strategy is in GLOBAL-026.

Surfaces at a glance

Full integration matrix in docs/progress.md.

Packages on npm

Published to the public npm registry with build provenance (SK-CIPERM-003). Version badges are live from npm; the table itself is generated from the workspace by scripts/sync-readme-packages.mjs, so it lists exactly the packages that are un-gated ("private" removed) and nothing that isn't.

Package	Version	What it is	Source
@nlqdb/cli		Shim that installs the nlq CLI binary for the host platform.	packages/cli-shim
@nlqdb/mcp		Analytical-memory MCP server for nlqdb — a real database your AI agent can GROUP BY / JOIN / aggregate over in natural language, not just recall.	packages/mcp
@nlqdb/sdk		Typed HTTP client for the nlqdb /v1 API — works in browsers, Node, Bun, Workers.	packages/sdk

Roadmap

The two sections below are the live focus; the numbered phases after them are the engine roadmap. Canonical plan + exit gates: docs/phase-plan.md. Legend: ✓ shipped · ~ in progress · ◯ planned.

This roadmap is yours to shape. Want something added, reprioritised, or dropped? Open a PR editing this section (and docs/phase-plan.md if it's engine-facing), or open an issue to float it first. Say why now — which of the four north-star pillars (engine quality, onboarding, UX, performance) it moves. New to the codebase? Point your coding agent at this repo and paste:

Code

Copy

Read README.md and docs/phase-plan.md, then propose a roadmap change:
add/change "<your idea>" under the right section in one line, with a
"why now" naming which north-star pillar it moves. Open a PR with just
that edit — no code.

Setup, branch naming, and the CLA are in CONTRIBUTING.md.

Now — Phase A: the schema infers itself (GLOBAL-041)

The first insert creates the shape; later inserts and reads evolve it. KPI: first-insert inference rate ≥ 95 % at Phase A exit on the dogfood workload. Build order in GLOBAL-041.

• ~ kind=extend typed plan — a write naming an unseen table or field widens the schema in the same transaction as the insert, never a schema_mismatch (both unseen-table and unseen-column cases merged to main + executor-walked; awaiting a prod deploy, live rate 0 %)
• ~ Extend diff + trace on every surface — trace.widen live on SDK · MCP · <nlq-data>; CLI/web render pending
• ✓ KPI counters asks_extend_ok / asks_extend_failed on the /v1/ask write path (SK-SCHEMA-010; the rate reads on /app/admin)
• ◯ Phase B — pg_stat_* + EXPLAIN collection → typed proposals (index / retype / drop / rename / move-to-engine) → /app/dba dashboard with 1-click apply + undo
• ✓ Rails kept from the prior bet for the expert-knowledge app: agent_memory_v1 preset, nlqdb_remember / nlqdb_read MCP tools, per-agent RLS isolation, the Claude Code plugin, /agents

Next — the expert-knowledge marketplace ("Become AI")