ktx vs slayer
Self-improving context layer for data agents — ingests dbt/Looker/wikis, maps your warehouse, builds a semantic layer with approved metrics, and serves Claude Code/Codex via CLI and MCP. — versus — Embeddable semantic layer for AI agents: define metrics once, compose them with expressions and time shifts, row-level security and read-only SQL, over MCP, REST, CLI, Python or a Postgres facade.
Both give data agents a governed semantic layer over the warehouse via MCP; ktx builds and improves it automatically from dbt, Looker and wikis, SLayer is a layer you or your agent curate and embed as a library.
| ktx | slayer | |
|---|---|---|
| Stars | 1.6k | 227 |
| Forks | 106 | 31 |
| Language | TypeScript | Python |
| License | Apache-2.0 | MIT |
| Last activity | 17 days ago | today |
| Topics | rag, agents | data |
| Curated connections | 8 | 5 |
ktx — the curator's take
Reach for it when agents re-explore your warehouse on every question and invent their own metric logic: ktx samples tables, detects joinable columns (resolving chasm/fan traps), absorbs dbt/MetricFlow/LookML/Notion knowledge into one searchable surface, and flags contradictions for human review. Read-only by design; runs locally on your own LLM keys or your Claude Code / Codex login. Skip it if you have no SQL warehouse to sit on, or for one ad-hoc query. It ingests your existing semantic layers rather than replacing them. YC-backed (Kaelio); telemetry is on by default with opt-out.
slayer — the curator's take
Reach for SLayer when agents keep writing plausible-but-wrong SQL against your warehouse: define columns and metrics once, and agents compose them (ratios, time shifts, alternate aggregations, multi-stage queries) through a search, inspect, query flow instead of free-hand joins, with read-only connections and row-level security handled for them. It imports dbt and Cube definitions, embeds as a Python library, and speaks MCP, REST, Flight SQL and the Postgres wire protocol, so BI tools hit the same definitions. It is the core of Motley's product and still young (a few hundred stars, six contributors). Skip it if you want dashboards out of the box (wrenai) or a layer that builds itself from your existing BI assets (ktx).