Data agents can write SQL. The hard part is making sure they write the SQL your analytics team would have written.
KTX is the agent-native context layer for analytics engineering. At its core is a semantic layer: YAML sources that define tables, columns, measures, joins, grain, filters, segments, and computed fields. Around that core, KTX adds the context analytics agents need to work safely: warehouse scans, knowledge pages, ingestion from existing tools, provenance, validation, and MCP access.
KTX projects are plain files — YAML, Markdown, and SQLite — that you commit to git and review in PRs, just like dbt models. Agents can read them, edit them, validate them, query through them, and leave behind a diff your team can review.
## Who KTX is for
KTX is built for analytics engineers and data teams who want data agents to work on real analytics systems, not just generate one-off SQL.
Use KTX when you want agents to:
- Generate SQL from approved measures, dimensions, and joins
- Repair or extend semantic definitions through reviewable git diffs
- Explain where a metric definition came from and what business rules shape it
- Use warehouse scans and relationship evidence instead of guessing join paths
- Work alongside **dbt**, **LookML**, **MetricFlow**, **Looker**, **Metabase**, **Notion**, and BI platforms
- Work with warehouses like **PostgreSQL**, **Snowflake**, **BigQuery**, **ClickHouse**, **MySQL**, or **SQL Server**
If you've ever watched an agent confidently generate a query that joins on the wrong key or invents a metric that doesn't exist, KTX is the fix.
## What KTX gives agents
- **A semantic layer they can edit** — plain YAML sources with measures, dimensions, joins, grain, segments, filters, and computed columns
Exhaustive flag and subcommand reference for every KTX command.
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## Next steps
- **Get hands-on** — follow the [Quickstart](/docs/getting-started/quickstart) to set up KTX with your own database in under 10 minutes.
- **Understand the theory** — read [The Context Layer](/docs/concepts/the-context-layer) to learn why schema access alone breaks on real analytics and how KTX addresses it.
