ochakai

ochakai is a context provider for data agents. Semantic layers tell you that revenue = SUM(price) — they don’t tell you whether 100 is good or bad. ochakai keeps that missing half in one knowledge base: metric definitions, verified golden queries, interpretation knowledge (how to read a metric), glossary terms, and table catalog entries — curated by humans and agents together, and served to Claude Code and every other data agent over MCP, REST, a CLI, and a bundled web UI. More at ochak.ai.

One Go binary and Postgres — Docker and nothing else locally, Cloud Run and Cloud SQL when it is real.

Quick start · Why ochakai · Requirements · All docs (Japanese; one page in English) · REST API · Changelog

Quick start

Three steps, in order, and nothing to choose between: read somebody else’s knowledge, then hold your own, then run it for a team.

Sixty seconds: the public demo

A public demo already holds the knowledge base below. It reads no identity (design doc 0066 §3), so there is no account and nothing to configure:

go install github.com/na0fu3y/ochakai/cmd/ochakai@latest
ochakai use https://demo.ochak.ai
ochakai search "なぜ売上が落ちているのか"
ochakai get insights/reading-revenue

The demo knowledge base is written in Japanese, because that is what a knowledge base a Japanese team keeps looks like and this demo is one (goal G5 in docs/surface.md). What stays English is what comes from the warehouse — sale_price, status = 'Complete', the SQL in the # Computation fence — so ochakai search traffic_source still reaches the Japanese concepts that carry the column: the computation that splits revenue by it, and