--- title: Introduction description: What KTX is, how it works, and where to start. --- import { ProductMechanics } from "@/components/product-mechanics";

Make analytics context usable by agents

{'KTX turns warehouse metadata, semantic definitions, BI usage, and team knowledge into local files and runtime tools that database agents can trust.'}

## Why KTX - Schemas show columns, not business rules. - Agents need trusted metrics, joins, filters, caveats, and provenance. - KTX captures that context before agents write SQL, docs, or semantic edits. ## What KTX creates | Path | What it gives agents | |------|----------------------| | `semantic-layer/` | Measures, dimensions, joins, grain, filters, segments | | `wiki/` | Business definitions, caveats, policies, analyst notes | | `raw-sources/` | Extracted metadata, scan output, relationship evidence | | `.ktx/` | Local indexes, embeddings, setup state, runtime data | ## Use it for - **Generate SQL** from approved measures, dimensions, joins, and filters - **Explain provenance** with wiki context and warehouse evidence - **Repair context** through reviewable YAML and Markdown diffs - **Work alongside** dbt, LookML, MetricFlow, Looker, Metabase, and warehouses Databases: SQLite, PostgreSQL, Snowflake, BigQuery, ClickHouse, MySQL, SQL Server. ## Start here Set up KTX and build your first context in under 10 minutes. Hands-on workflows for scanning, ingesting, writing, and serving. Edit semantic-layer YAML and wiki Markdown safely. Complete flag and subcommand reference for every KTX command. Machine-readable docs and agent-facing setup notes.