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CLI Reference

Default command — query​

Running wren --sql '...' executes a query and prints the result. This is the same as wren query --sql '...'.

wren --sql 'SELECT COUNT(*) FROM "orders"'
wren --sql 'SELECT * FROM "orders" LIMIT 5' --output csv
wren --sql 'SELECT * FROM "orders"' --limit 100 --output json

Output formats: table (default), csv, json.

wren query​

Execute SQL and return results.

wren query --sql 'SELECT order_id, total FROM "orders" ORDER BY total DESC LIMIT 5'

wren dry-plan​

Translate MDL SQL to the native dialect SQL for your data source. No database connection required.

wren dry-plan --sql 'SELECT order_id FROM "orders"'
wren dry-plan --sql 'SELECT order_id FROM "orders"' -d postgres  # explicit datasource, no connection file needed

wren dry-run​

Dry-run SQL against the live database without returning rows. Prints OK on success, Error: <reason> on failure.

wren dry-run --sql 'SELECT * FROM "orders" LIMIT 1'
# OK

wren dry-run --sql 'SELECT * FROM "NonExistent"'
# Error: table not found ...

Overriding defaults​

All flags are optional when ~/.wren/mdl.json and ~/.wren/connection_info.json exist.

The data source is always read from the datasource field in connection_info.json (or the inline --connection-info value). Only dry-plan accepts --datasource / -d as an override for transpile-only use without a connection file.

wren --sql '...' \
  --mdl /path/to/other-mdl.json \
  --connection-file /path/to/prod-connection_info.json

Or pass connection info inline:

wren --sql 'SELECT COUNT(*) FROM "orders"' \
  --connection-info '{"datasource":"mysql","host":"localhost","port":3306,"database":"mydb","user":"root","password":"secret"}'

Both flat and envelope formats are accepted:

# Flat format
{"datasource": "postgres", "host": "localhost", "port": 5432, ...}

# Envelope format (auto-unwrapped)
{"datasource": "duckdb", "properties": {"url": "/data", "format": "duckdb"}}

wren profile import dbt​

Import the active dbt target from profiles.yml into ~/.wren/profiles.yml.

wren profile import dbt --project-dir ./jaffle_shop
wren profile import dbt --project-dir ./jaffle_shop --target prod --name jaffle-prod

Common flags: --profiles-path, --profile, --target, --name, --no-activate.

wren context import dbt​

Generate a Wren project from dbt artifacts.

wren context import dbt --project-dir ./jaffle_shop --path ./wren-jaffle
wren context import dbt --project-dir ./jaffle_shop --path ./wren-jaffle --dry-run

Requires target/manifest.json and target/catalog.json; run dbt build and dbt docs generate first. See dbt Integration.

wren docs — Connection Info​

wren docs connection-info <datasource>​

Print the required and optional connection fields for a data source.

wren docs connection-info postgres
wren docs connection-info bigquery
wren docs connection-info snowflake

Use this to check which fields are needed before creating a profile.

wren memory — Schema & Query Memory​

LanceDB-backed semantic memory for MDL schema search and NL-SQL retrieval. Install with the memory extra (separate from main):

pip install 'wrenai[memory]'
# or combine with main for the browser UI and interactive prompts:
pip install 'wrenai[memory,main]'

All memory subcommands accept --path DIR to override the default storage location (~/.wren/memory/).

Note: The memory extra bundles ~800MB of large unsigned native libraries (lancedb plus sentence-transformers/torch). On macOS, the first command that loads the memory stack can trigger a one-time XProtect/Gatekeeper scan and pause for up to about a minute before it finishes; this is normal macOS behavior, not a Wren error, and happens once per install or fresh virtual environment. With lazy memory loading, lightweight non-memory commands are unaffected — the scan is deferred to your first real memory use, not eliminated.

Hybrid strategy: full text vs. embedding search​

When providing schema context to an LLM, there is a trade-off:

  • Small schemas — the full plain-text description fits easily in the LLM context window and gives better results because the LLM sees the complete structure (model-column relationships, join paths, primary keys) rather than isolated fragments from a vector search.
  • Large schemas — the full text exceeds what is practical to send in a single prompt, so embedding search is needed to retrieve only the relevant fragments.

wren memory fetch automatically picks the right strategy based on the character length of the generated plain-text description:

Schema sizeThresholdStrategy
Below 30,000 chars (~8K tokens)DefaultReturns full plain text
Above 30,000 charsDefaultReturns embedding search results

The threshold is measured in characters (not tokens) because character length is free to compute, while accurate token counting requires a tokeniser. The 4:1 chars-to-tokens ratio holds for English; CJK text compresses less (~1.5:1), so a CJK-heavy schema switches to embedding search sooner — which is the conservative direction.

The default threshold (30,000 chars) can be overridden with --threshold.

wren memory index​

Parse the MDL manifest and index all schema items (models, columns, relationships, views) into LanceDB with local embeddings.

wren memory index                          # uses ~/.wren/mdl.json
wren memory index --mdl /path/to/mdl.json  # explicit MDL file

wren memory describe​

Print the full schema as structured plain text. No embedding or LanceDB required — this is a pure transformation of the MDL manifest into a human/LLM-readable format.

wren memory describe                          # uses ~/.wren/mdl.json
wren memory describe --mdl /path/to/mdl.json

wren memory fetch​

Get schema context for an LLM. Automatically chooses the best strategy based on schema size: full plain text for small schemas, embedding search for large schemas.

When using the search strategy, optional --type and --model filters narrow the results.

wren memory fetch -q "customer order price"
wren memory fetch -q "revenue" --type column --model orders
wren memory fetch -q "order date" --threshold 50000 --output json
FlagDescription
-q, --querySearch query (required)
--mdlPath to MDL JSON file
-l, --limitMax results for search strategy (default: 5)
-t, --typeFilter: model, column, relationship, view (search strategy only)
--modelFilter by model name (search strategy only)
--thresholdCharacter threshold for full vs search (default: 30,000)
-o, --outputOutput format: table (default), json

wren memory store​

Store a natural-language-to-SQL pair for future few-shot retrieval.

wren memory store \
  --nl "show top customers by revenue" \
  --sql "SELECT c_name, sum(o_totalprice) FROM orders JOIN customer GROUP BY 1 ORDER BY 2 DESC" \
  --datasource postgres

wren memory recall​

Search stored NL-SQL pairs by semantic similarity to a query.

wren memory recall -q "best customers"
wren memory recall -q "monthly revenue" --datasource mysql --limit 5 --output json
FlagDescription
-q, --querySearch query (required)
-l, --limitMax results (default: 3)
-d, --datasourceFilter by data source
-o, --outputOutput format: table (default), json

wren memory status​

Show index statistics: storage path, table names, and row counts.

wren memory status
# Path: /Users/you/.wren/memory
#   schema_items: 47 rows
#   query_history: 12 rows

wren memory reset​

Drop all memory tables and start fresh.

wren memory reset          # prompts for confirmation
wren memory reset --force  # skip confirmation

wren cube — Pre-aggregation Queries​

For aggregation queries where the MDL defines cubes, use wren cube instead of writing raw SQL. The translator produces correct GROUP BY, DATE_TRUNC, and WHERE clauses from a structured input.

wren cube list​

List all cubes in the loaded MDL with their measures and dimensions.

wren cube list

wren cube describe <name>​

Pretty-print the full cube schema as JSON: baseObject, measures (with expressions), dimensions, time dimensions, hierarchies.

wren cube describe order_metrics

wren cube query​

Build a CubeQuery and translate it to SQL via wren-core, then execute through the same path as wren --sql. Two input modes:

CLI flags:

wren cube query \
  --cube order_metrics \
  --measures revenue,order_count \
  --dimensions status \
  --time-dimension "created_at:month:2024-01-01,2025-01-01" \
  --filter "status:eq:completed" \
  --limit 100

JSON input (--from <file|->):

cat query.json | wren cube query --from -
FlagDescription
--cubeCube name (required unless using --from)
--measuresComma-separated measure names (required unless using --from)
--dimensionsComma-separated dimension names
--time-dimension<name>:<granularity>[:start,end] — one time dimension with optional date range
--filterRepeatable. <dimension>:<operator>[:value]. For in / not_in, value is comma-separated.
--limit / --offsetPagination
--from <file|->Load CubeQuery as JSON from a file or stdin
--sql-onlyPrint the generated SQL and exit without executing
--mdlPath to MDL JSON (defaults to <project>/target/mdl.json)
--outputtable (default), json, csv

Supported granularities: year, quarter, month, week, day, hour, minute.

Supported filter operators: eq, neq, in, not_in, gt, gte, lt, lte, contains, starts_with, is_null, is_not_null.

See the Cube guide for YAML structure and validation rules.

wren skills — Agent Workflow Guides​

The CLI ships its own agent skill content. Use this on any AI client (the content is the same — content travels with the wheel, not the agent cache).

wren skills list​

List the available workflow guides.

wren skills list

wren skills get <name>​

Print a skill's main guide to stdout. Five names ship today: onboarding, usage, generate-mdl, dlt-connector, enrich-context.

wren skills get onboarding              # set up Wren end-to-end
wren skills get usage                   # day-to-day querying
wren skills get generate-mdl            # MDL from a database schema
wren skills get dlt-connector           # connect SaaS sources via dlt
wren skills get enrich-context          # add business context (units, enums, cubes)

wren skills get <name> --full​

Include the skill's reference docs inline (sorted, separated). For skills that have no references/, the output is identical to the non---full form.

wren skills get <name> --script <s>​

Print a bundled script's source to stdout. Currently:

wren skills get dlt-connector --script introspect_dlt > introspect_dlt.py
python introspect_dlt.py --duckdb-path ./pipeline.duckdb --output-dir ./project

wren ask — Prompt Shaping​

Wrap a natural-language question in one of two bundled templates and print the rendered prompt to stdout. Does not execute any query — it produces a prompt for an agent to consume.

You must explicitly pick one mode (no default — silently changing a default would alter agent behavior across an upgrade).

wren ask "<question>" --guided​

For weaker LLMs. Prepends a strict task flow (wren context show → wren memory recall → write SQL → wren dry-plan → wren query).

wren ask "top 5 customers by revenue" --guided

wren ask "<question>" --direct​

For stronger LLMs. Minimal wrapping; the agent decides which wren commands to run.

wren ask "monthly orders trend" --direct

wren genbi — Build & Deploy GenBI Apps​

Turn a project's context layer into a shareable, browser-side GenBI web app (powered by wren-core-wasm) and deploy it to Vercel or Cloudflare Pages.

CLI ↔ agent split: the CLI owns the authoritative build instruction and all deterministic state (the app index, verify, deploy). The agent authors the app code by following the instruction. .wren/apps.yml is only ever written by the CLI — never by hand. The matching agent workflow guide is wren skills get genbi.

wren genbi build <name>​

Print a project-hydrated build instruction (wasm wiring with the pinned wren-core-wasm version, the project's model/column inventory, data-mode guidance, acceptance criteria, and the target folder). Writes no app files; it only compiles target/mdl.json first if it's missing.

wren genbi build sales-overview --prompt "orders dashboard" --data-mode snapshot
# --prompt-file <file> / --prompt -    read a long prompt from a file or stdin
# --data-mode snapshot|live            snapshot (default): bundle data with the app
#                                      live: app calls a CORS endpoint at view time

wren genbi register <name> / list / remove <name>​

Machine-written app index (<project>/.wren/apps.yml).

wren genbi register sales-overview --data-mode snapshot   # record an authored app
wren genbi list                                           # apps + status + deploy state
wren genbi remove sales-overview                          # drop index entry (files kept)

App names must be simple slugs (letters, numbers, _, -); names containing path separators are rejected so they can't escape <project>/apps/.

wren genbi verify <name>​

Deterministic deploy preflight (no browser): required files exist, mdl.json parses, snapshot apps ship a .parquet/.duckdb asset, and a default-deny secret scan flags inlined credentials. deploy gates on this. The secret scan is best-effort defense-in-depth, not a guarantee — never inline secrets.

wren genbi open <name>​

Serve a built app locally for preview (blocking; Ctrl-C stops).

wren genbi open sales-overview --port 8848   # 0 = auto-pick

wren genbi deploy <name>​

Verify, then ship to the user's provider account and return a shareable URL. Preview by default; --prod deploys to production (confirm with the user first).

wren genbi deploy sales-overview --provider vercel        # or cloudflare
wren genbi deploy sales-overview --provider vercel --prod
  • Tokens are discovered from the environment or .env files (VERCEL_TOKEN / CLOUDFLARE_API_TOKEN) — never passed as CLI flags. Cloudflare also needs CLOUDFLARE_ACCOUNT_ID.
  • Cloudflare shells out to the wrangler CLI (npm install -g wrangler, or have npx available) — Pages has no single inline-upload REST endpoint.
  • Vercel Deployment Protection: new Vercel projects return HTTP 401 to logged-out visitors by default. To make the URL public, disable it at Project → Settings → Deployment Protection. The deploy itself succeeded; the URL is just gated.