Pre-computed metadata context engine for AI-driven data analytics
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-dataraum-dataraum": {
"command": "<see-readme>",
"args": []
}
}
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A rich metadata context engine for AI-driven data analytics.
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The understanding layer that grounds an organization's operating model in its own data.
A semantic layer tells BI tools what columns are called. DataRaum learns what they mean — the concepts, relationships, rules, and measures of the organization — and grounds each one in the actual data, with a measured confidence behind it. See the docs for the full picture.
packages/
├── engine/ # Python — pipeline, detectors, Temporal activity worker
├── cockpit/ # TypeScript — TanStack Start web UI
├── dataraum-config/ # YAML data — entropy config, LLM prompts, verticals (bind-mounted, never imported)
└── infra/ # docker-compose orchestration
Each package has its own README. Start there if you're working in a specific package.
DataRaum runs as a multi-container platform, isolated per workspace:
add_source, begin_session, operating_model) and writes metadata to the workspace's Postgres schema.They share one substrate: Postgres (metadata + cockpit state + catalogs), an S3 object store (the DuckLake data lake + uploads), and Temporal (durable orchestration). No HTTP seam between engine and cockpit — the integration surface is Postgres + Temporal. See the platform architecture.
# Set the LLM key
cp packages/infra/.env.example packages/infra/.env
echo "ANTHROPIC_API_KEY=sk-ant-..." >> packages/infra/.env
# Bring up the full stack (Postgres, object store, Temporal, engine worker, cockpit)
docker compose -f packages/infra/docker-compose.yml up -d --wait
# Engine health = the Temporal worker heartbeat (no HTTP endpoint):
docker compose -f packages/infra/docker-compose.yml run --rm --no-deps \
--entrypoint temporal temporal-admin-tools \
worker list --namespace default --address temporal:7233 # → Status: Running
# Open the cockpit
open http://localhost:3000
For UI iteration, run the cockpit dev server outside docker for hot reload — see packages/cockpit/README.md.
cd packages/engine && uv sync --group dev && uv run pytest --testmon tests/unit -q. See packages/engine/README.md and packages/engine/CLAUDE.md.cd packages/cockpit && bun install && bun run dev. See packages/cockpit/README.md and packages/cockpit/CLAUDE.md.cd packages/cockpit && DATARAUM_WORKSPACE_ID=<id> METADATA_DATABASE_URL=<url> bun run db:pull:metadata. Re-run after the engine adds/changes SQLAlchemy models.Platform docs live in docs/ (workspace root) and are published via Zensical. Start at
docs/index.md, or serve the site locally:
uv run --project packages/engine zensical serve # run from the repo root
MIT — see LICENSE.