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Hi I'm Ruddy

Most DuckDB tooling falls into one of three buckets: a browser-based WASM tool
with all the overhead that implies, a thin SQL console bolted onto the engine,
or a Python notebook that happens to use DuckDB on the side.

I wanted something different — a proper Mac app that runs the native DuckDB
binary
, feels at home on macOS, and handles the full data workflow without
switching tools.

That's Ruddy.

What it does

Native DuckDB engine

No WASM. No middleware. The real binary, running locally. This matters for
performance on larger files and for features that WASM builds don't fully expose.

Notebooks powered by Marimo

Python + SQL notebooks with installable native libraries. Not a dumbed-down
cell runner — you can build full analyses and share them.

AI agent — Ruddynie

An agent that runs queries, builds notebooks, and connects to MCP servers.
Bring your own model: Claude, Gemini, OpenAI, or OpenRouter.

Visual query builder

Built on top of the ERD view — you can click together joins without writing SQL.
Useful for exploration and for less SQL-fluent teammates.

Spatial support

Geospatial data renders as maps out of the box, powered by DuckDB's spatial
extension.

Git integration

Version control built into the editor. SQL and notebook files are first-class
citizens in your repo.

Catalog + ERD

Clean schema browser with auto-generated entity-relationship diagrams.

What it connects to

Local files (Parquet, CSV, JSON), cloud data sources, and remote HTTP endpoints.
DuckDB's httpfs and S3 support is exposed natively.

Pricing

Early-access pricing while we're still in this phase, 7-day free trial:

  • $1.25/month — all features, auto-renewal
  • $7.50 — 6 months upfront
  • $15 one-time — pay once, use forever

Why macOS-native

DuckDB is fast. Running it inside a browser sandbox or an Electron shell adds
latency and limits what you can do with native libraries, memory management,
and OS integration. A native Mac app lets the engine breathe.


Download and try it: ruddy.pro

Happy to answer questions about the stack, the product, or how the
AI agent is architected.

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