Find the Wren CLI workflow for answering data questions, connecting databases or SaaS sources, generating model definitions, adding business metrics, or building a shareable analytics app. Guides are retrieved from the installed CLI version.
Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.
Review the instructions the agent will receive before you install.
This is a discovery stub. The actual workflow guides and prompt helpers
live inside the wren CLI itself, so they always match the installed
wrenai version (no skill cache, no version drift).
Install: pip install wrenai.
wren skills list # all available workflow guides
wren skills get onboarding # set up Wren end-to-end
wren skills get usage # day-to-day querying
wren skills get generate-mdl # generate 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 genbi # build & deploy a shareable GenBI web app
# add --full to include the skill's reference docs
# add --script <name> to fetch a bundled script (e.g. dlt-connector / introspect_dlt)
Full reference docs live on the web: https://github.com/Canner/WrenAI/tree/main/docs/core
wren docs connection-info <ds> # required + optional connection fields for a data source
wren ask "<question>" --guided # for weaker LLMs (strict task flow)
wren ask "<question>" --direct # for stronger LLMs (minimal wrapping)
wren --sql '...' # execute SQL through the MDL layer
wren query --sql '...' # same, explicit
wren dry-plan --sql '...' # transpile only, no DB hit
wren context show / build / validate # project / MDL lifecycle
wren profile add / list / switch # named connection profiles
wren memory index / recall / store # semantic memory (needs `[memory]` extra)
Run wren --help for the full surface; load the matching wren skills get <name> guide before driving any multi-step workflow.
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Import SaaS data into DuckDB using dlt, a data loading tool, then generate a Wren project with table definitions and relationships. Verify the setup with real SQL queries, or start from an existing DuckDB file.
Add business knowledge missing from a Wren project's database definitions. Compare source documents with existing context, then add explanations, rules, SQL examples, and proposed metrics through guided questions or an automatic mode with an audit report.
Build an analytics web app from a Wren project's data definitions and business context. Create charts and tables, validate the app, and deploy to Vercel or Cloudflare with a checked URL for sharing.
Create a Wren MDL project, a model layer for database queries, from an existing database. Discover tables and relationships, normalize column types, generate YAML files, and validate and build the project before indexing its schema.
Set up Wren Engine through guided steps covering environment checks, installation, database connection, and project creation. Prepare data definitions and validate the setup, then suggest initial questions to help the user begin exploring their data.
Answer business data questions with Wren CLI by checking model definitions, reusing past queries, and executing SQL or predefined aggregations. The workflow also covers query verification, error diagnosis, and saving successful queries for future use.