---
title: Introduction
description: KTX is an open-source, self-improving context layer for data agents.
---

import { ProductMechanics } from "@/components/product-mechanics";

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  <div>
    <h1
      className="max-w-full text-3xl font-extrabold tracking-tight break-words sm:text-4xl lg:text-5xl"
      style={{
        fontFamily: 'var(--font-display)',
        background: 'linear-gradient(180deg, var(--color-fd-foreground) 0%, color-mix(in oklch, var(--color-fd-foreground) 75%, var(--color-fd-primary)) 100%)',
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    >
      Make analytics context usable by agents
    </h1>
    <p className="mt-4 max-w-2xl text-lg text-fd-muted-foreground" style={{ lineHeight: '1.7' }}>
      {'KTX is an open-source context layer for database agents. It turns warehouse metadata, BI models, query history, docs, and approved metric definitions into reviewable files agents can search and execute.'}
    </p>
  </div>
</div>

## Why KTX helps

KTX gives agents a shared context workspace before they write SQL, answer a
question, or update analytics definitions.

- **Context as code.** KTX writes wiki pages and semantic-layer definitions as
  git-based files you can review, diff, and merge.
- **Self-improving ingest.** KTX reads warehouses, BI tools, modeling code,
  query history, and notes, then reconciles new evidence with accepted context.
- **Executable semantics.** Agents can use approved measures, joins, filters,
  dimensions, and segments instead of rebuilding canonical SQL from scratch.
- **Agent-native access.** CLI and MCP tools let agents search context, compile
  semantic queries, run read-only SQL, and propose updates.

KTX complements existing semantic layers by pairing metric definitions with the
surrounding business knowledge, caveats, provenance, and review workflow agents
need for data work.

## How KTX works

KTX has two connected sides: it builds and maintains the context layer, then
serves that context to agents at runtime.

| Side | What KTX does |
|------|---------------|
| **Ingest and auto-maintain knowledge** | Reads your data stack and company knowledge, reconciles new evidence with accepted context, and keeps changes to `semantic-layer/` plus `wiki/` as version-controlled diffs automatically. |
| **Serve agents at runtime** | Helps agents find the right wiki pages and semantic-layer entities, then compile or execute semantic queries through CLI and MCP tools. |

<ProductMechanics />

## Use it for

Use KTX when agents need more than raw database access. Agents can search wiki
context, find semantic-layer entities, compile trusted semantic queries, run
read-only SQL, and use the same tools through MCP.

- Generate SQL from approved metrics, joins, filters, and dimensions.
- Explain metric provenance with wiki context and source evidence.
- Repair context through reviewable YAML and Markdown diffs.
- Work alongside dbt, MetricFlow, LookML, Looker, Metabase, Notion, and
  supported databases.

## Start here

Choose the route that matches what you want to do next. The quickstart is the
best first step for users; contributor setup lives in the community docs.

<Cards>
  <Card title="Quickstart" href="/docs/getting-started/quickstart">
    Install KTX, run setup, build context, and connect an agent.
  </Card>
  <Card title="The Context Layer" href="/docs/concepts/the-context-layer">
    Understand why agents need more than schema access and raw SQL.
  </Card>
  <Card title="Building Context" href="/docs/guides/building-context">
    Refresh context from databases, BI tools, query history, and documents.
  </Card>
  <Card title="Writing Context" href="/docs/guides/writing-context">
    Edit semantic-layer YAML and wiki Markdown safely.
  </Card>
  <Card title="CLI Reference" href="/docs/cli-reference/ktx">
    Complete flag and subcommand reference for every KTX command.
  </Card>
  <Card title="Agent Quickstart" href="/docs/ai-resources/agent-quickstart">
    Machine-readable docs and agent-facing setup notes.
  </Card>
</Cards>