| .github/workflows | ||
| assets | ||
| docs/superpowers | ||
| docs-site | ||
| examples | ||
| packages | ||
| python | ||
| scripts | ||
| website | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| AGENTS.md | ||
| biome.json | ||
| CLAUDE.md | ||
| conductor.json | ||
| GEMINI.md | ||
| knip.json | ||
| LICENSE | ||
| package.json | ||
| pnpm-lock.yaml | ||
| pnpm-workspace.yaml | ||
| pyproject.toml | ||
| README.md | ||
| release-policy.json | ||
| tsconfig.base.json | ||
| uv.lock | ||
The context layer for analytics agents
KTX turns warehouse metadata, semantic definitions, and business knowledge into reviewable project files that agents can use while planning, querying, and updating analytics work.
A KTX project is a directory of plain files — YAML semantic sources, Markdown knowledge pages, and SQLite state — that you commit to git and review in PRs, just like dbt models.
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 and joins
- Repair semantic definitions through reviewable diffs
- Explain metric provenance with warehouse evidence
- Work alongside dbt, LookML, MetricFlow, Looker, Metabase, and modern BI platforms
Works with PostgreSQL, Snowflake, BigQuery, ClickHouse, MySQL, SQL Server, and SQLite.
Quick start
Install the CLI and run the setup wizard:
npm install @kaelio/ktx
npm install -g @kaelio/ktx
ktx setup
The wizard walks through six steps: configuring your LLM provider, setting up embeddings, connecting your database, adding context sources (dbt, LookML, Metabase, Looker, Notion), building context, and installing agent integration.
If it exits before completion, rerun ktx setup to resume where you left off.
Check your project status:
ktx status
KTX project: /home/user/analytics
Project ready: yes
LLM ready: yes (claude-sonnet-4-6)
Embeddings ready: yes (text-embedding-3-small)
Primary sources configured: yes (postgres-warehouse)
Context sources configured: yes (dbt-main)
KTX context built: yes
Agent integration ready: yes (claude-code:project)
Generate SQL from a semantic-layer source:
npx @kaelio/ktx sl query --project-dir "$PROJECT_DIR" \
--connection-id warehouse \
--measure accounts.account_count \
--dimension accounts.segment \
--format sql
List and test a configured warehouse connection:
ktx connection list --project-dir "$PROJECT_DIR"
ktx connection test warehouse --project-dir "$PROJECT_DIR"
The connection test prints the configured driver and discovered table count:
Driver: sqlite
Tables: 1
What's in a project
my-project/
├── ktx.yaml # Project configuration
├── semantic-layer/
│ └── warehouse/
│ ├── orders.yaml # Semantic source definitions
│ ├── customers.yaml
│ └── order_items.yaml
├── knowledge/
│ ├── global/
│ │ ├── revenue.md # Business definitions and rules
│ │ └── segment-classification.md
│ └── user/
│ └── local/
├── raw-sources/
│ └── warehouse/
│ └── live-database/ # Scan artifacts and reports
└── .ktx/
└── db.sqlite # Local state (git-ignored)
Semantic sources and knowledge pages are committed to git. The .ktx/ directory
holds ephemeral state and is git-ignored — delete it and KTX rebuilds on the
next run.
Scan the demo warehouse
Scan artifacts are written under
raw-sources/warehouse/live-database/<syncId>/ in the project directory.
SCAN_OUTPUT="$(ktx scan warehouse --project-dir "$PROJECT_DIR")"
printf '%s\n' "$SCAN_OUTPUT"
SCAN_RUN_ID="$(printf '%s\n' "$SCAN_OUTPUT" | awk '/^Run: / { print $2 }')"
ktx scan status --project-dir "$PROJECT_DIR" "$SCAN_RUN_ID"
ktx scan report --project-dir "$PROJECT_DIR" "$SCAN_RUN_ID"
For non-SQLite drivers, prefer credential references such as --url env:NAME
or --url file:PATH over literal credential URLs.
Managed Python runtime
KTX installs its Python runtime only when a Python-backed command needs it.
The runtime lives outside the npm cache, is versioned by the installed CLI
version, and is managed by ktx dev runtime commands.
KTX requires uv on PATH to create the managed runtime. Install uv with
your system package manager or the official installer before running Python-
backed KTX commands. KTX doesn't download uv automatically; run
ktx dev runtime doctor if runtime installation fails:
ktx dev runtime install --yes
ktx dev runtime status
ktx dev runtime doctor
ktx dev runtime start
ktx dev runtime stop
ktx dev runtime prune --dry-run
ktx dev runtime prune --yes
The release artifact manifest contains the public npm tarball and the bundled kaelio-ktx
runtime wheel. The python/ktx-sl and python/ktx-daemon directories remain
source packages for development, not public release artifacts.
Use KTX with agents
KTX integrates with coding agents through CLI skills. The setup wizard configures this automatically.
CLI skills — the agent calls ktx commands directly through a skill file
installed in your agent's config (e.g., .claude/skills/ktx/SKILL.md):
ktx sl query --measure orders.revenue --dimension orders.status --format sql
ktx wiki search "revenue definition"
ktx sl validate orders
Supported agents: Claude Code, Codex, Cursor, OpenCode, and any agent that
reads .agents/ skills.
Workspace packages
| Package | Purpose |
|---|---|
packages/cli |
CLI entry point |
packages/context |
Core context engine |
packages/llm |
LLM and embedding providers |
packages/connector-bigquery |
BigQuery scan connector |
packages/connector-clickhouse |
ClickHouse scan connector |
packages/connector-mysql |
MySQL scan connector |
packages/connector-postgres |
Postgres scan connector |
packages/connector-snowflake |
Snowflake scan connector |
packages/connector-sqlite |
SQLite scan connector |
packages/connector-sqlserver |
SQL Server scan connector |
python/ktx-sl |
Semantic-layer query planning |
python/ktx-daemon |
Portable compute service |
Development
git clone https://github.com/kaelio/ktx.git
cd ktx
pnpm install
uv sync --all-groups
pnpm run build
pnpm run check
Use the development CLI for local testing:
pnpm run setup:dev
pnpm run link:dev
ktx-dev --help
Debug LLM traces
KTX can capture local AI SDK DevTools traces for LLM calls that run through the KTX provider. Enable it with an environment flag when running an LLM-backed command:
KTX_AI_DEVTOOLS_ENABLED=true ktx dev ingest run \
--connection-id warehouse \
--adapter metabase
Traces are written to .devtools/generations.json under the current working
directory. To inspect them, run:
pnpm dlx @ai-sdk/devtools
Then open http://localhost:4983. These traces are local-development-only and
store prompts, model outputs, tool arguments/results, and raw provider payloads
in plain text. Do not enable this in production or for sensitive runs.
The repository uses pnpm for TypeScript packages and uv for Python
packages. See Contributing
for full development setup, testing, and PR guidelines.
License
KTX is licensed under the Apache License, Version 2.0. See LICENSE.