Ktx

The context layer for data agents

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  CLI Reference ·
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  Built and maintained by Kaelio

ktx is a self-improving context layer that teaches agents how to query your
warehouse accurately - from approved metric definitions, joinable columns, and
business knowledge it builds and maintains for you.

Run ktx with your own LLM API keys or a local agent sign-in — a
Claude Pro/Max subscription through Claude Code, or your local Codex
authentication. No extra usage billing from ktx.

Why ktx

General-purpose agents struggle on data tasks. They re-explore your warehouse
on every question, invent their own metric logic, and return numbers that
don't match approved definitions.

Traditional semantic layers don't fix this. They demand constant manual
upkeep and don't absorb the rest of your company's knowledge.

ktx does both, automatically:

- Learns from company knowledge. Ingests wiki content, organizes it,
  removes duplicates, and flags contradictions for human review.
- Maps the data stack. Samples tables, captures metadata and usage
  patterns, detects joinable columns, and annotates sources so agents write
  better queries.
- Builds a semantic layer. Combines raw tables and high-level metrics
  through a join graph that automatically resolves chasm and fan traps, so
  agents fetch metrics declaratively instead of rewriting canonical SQL each
  time.
- Serves agents at execution. Exposes CLI and MCP tools with combined
  full-text and semantic search across wiki and semantic-layer entities.

How ktx compares

Who is ktx for

Use ktx if you:

- Want agents like Claude Code, Codex, Cursor, or OpenCode to query your
  warehouse with approved metric definitions
- Have business knowledge scattered across dbt, Looker, Metabase, Notion, and
 …