usage

name: usage description: "Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI." license: Apache-2.0 metadata: author: wrenai

Wren Engine CLI — Agent Workflow Guide

This guide is served by the wren CLI (wren skills get usage), so it always matches your installed wren-engine version. Pull the deeper reference docs with wren skills get usage --full.

Preflight — Verify environment and installation

Goal: Ensure the wren CLI is available before entering any workflow.

Step 1 — Check Python virtual environment

Run python -c "import sys; print(sys.prefix)" (or equivalent) to determine whether a virtual environment is active.

• If no venv is active, warn the user and ask whether to:
  • Create one (e.g., python -m venv .venv && source .venv/bin/activate)
  • Continue without a venv (not recommended — may pollute global packages)

Step 2 — Check if the wren CLI is installed

Run wren --version. If the command is not found or errors:

1. Tell the user that the wren CLI is not installed.

2. Ask if you should help install it.

3. If the user agrees, determine the datasource extra to install:

   Auto-detect from project: Check whether the current directory is inside a wren project (look for wren_project.yml up to the repository root). If found, read the active profile with cat ~/.wren/profiles.yml or look for a datasource hint in the project's profile configuration. Extract the datasource type from there.

   Ask the user: If no project is detected or no datasource can be inferred, ask the user which database they plan to connect to. Valid extras: postgres (for Aurora Postgres), mysql (for Aurora MySQL), bigquery, snowflake, clickhouse, trino, mssql, databricks, redshift, spark, athena, oracle. DuckDB is included by default — no extra needed.

4. Install with the detected or chosen extra:

   # DuckDB (no extra needed)
   pip install "wrenai"
   
   # Other datasources
   pip install "wrenai[<datasource>]"
   

   To also enable semantic memory, interactive prompts, and web UI (recommended):

   pip install "wrenai[<datasource>,main]"
   # or for DuckDB:
   pip install "wrenai[main]"
   

5. Verify: wren --version

If wren --version succeeds, proceed to the relevant workflow below.

The wren CLI queries databases through an MDL (Model Definition Language) semantic layer. You write SQL against model names, not raw tables. The engine translates to the target dialect.

Two things drive everything:

• Profile — database connection + datasource type, managed via wren profile (stored in ~/.wren/profiles.yml)
• Project — MDL model definitions in YAML, compiled to target/mdl.json via wren context build

The CLI reads the active profile for connection info and datasource. Use wren profile list to see which profile is active, wren profile switch <name> to change it. dry-plan also accepts --datasource / -d for transpile-only use without a profile.

For memory-specific decisions, see the memory reference (run wren skills get usage --full). For SQL syntax, CTE-based modeling, and error diagnosis, see the wren-sql reference (run wren skills get usage --full). For project structure, MDL field definitions, and CLI workflow details, see the documentation.

Workflow 1: Answering a data question

Step 1 — Gather context

If this is the first query in the conversation, also run:

wren context instructions

If it returns content, treat it as rules that override defaults — apply them to all subsequent queries in this session.

Step 2 — Recall past queries

wren memory recall -q "<question>" --limit 3

Use results as few-shot examples. Skip if empty.

Step 2.5 — Assess complexity (before writing SQL)

If the question involves any of the following, consider decomposing:

• Multiple metrics or aggregations (e.g., "churn rate AND expansion revenue")
• Multi-step calculations (e.g., "month-over-month growth rate")
• Comparisons across segments (e.g., "by plan tier, by region")
• Time-series analysis requiring baseline + change (e.g., "retention curve")

Decomposition strategy:

1. Identify the sub-questions (e.g., "total subscribers at start" + "subscribers who cancelled" → churn rate)
2. For each sub-question:
   • wren memory recall -q "<sub-question>" — check if a similar pattern exists
   • Write and execute a simple SQL
   • Note the result
3. Combine sub-results to answer the original question

When NOT to decompose:

• Single-table aggregation with GROUP BY — just write the SQL
• Simple JOINs that the MDL relationships already define
• Questions where memory recall returns a near-exact match

This is a judgment call, not a rigid rule. If you're confident in a single query, go ahead. Decompose when the SQL would be hard to debug if it fails.

Step 3 — Write, verify, and execute SQL

For simple queries (single table or simple MDL-defined JOINs, straightforward aggregation): Execute directly:

wren --sql 'SELECT c_name, SUM(o_totalprice) FROM orders
JOIN customer ON orders.o_custkey = customer.c_custkey
GROUP BY 1 ORDER BY 2 DESC LIMIT 5'

For complex queries (non-trivial JOINs not covered by MDL relationships, subqueries, multi-step logic): Verify first with dry-plan:

wren dry-plan --sql 'SELECT ...'

Check the expanded SQL output:

• Are the correct models and columns referenced?
• Do the JOINs match expected relationships?
• Are CTEs expanded correctly?

If the expanded SQL looks wrong, fix before executing. If it looks correct, proceed:

wren --sql 'SELECT ...'

SQL rules:

• Target MDL model names, not database tables
• Write dialect-neutral SQL — the engine translates

Step 4 — Store and continue

After successful execution, store the query by default:

wren memory store --nl "<user's original question>" --sql "<the SQL>"

Skip storing only when:

• The query failed or returned an error
• The user said the result is wrong
• The query is exploratory (SELECT * ... LIMIT N without analytical clauses)
• There is no natural language question — just raw SQL
• The user explicitly asked not to store

The CLI auto-detects exploratory queries — if you see no store hint after execution, the query was classified as exploratory.

Workflow 2: Error recovery

"table not found"

1. Verify model name: wren memory fetch -q "<name>" --type model --threshold 0
2. Check MDL exists: ls target/mdl.json (or wren context show)
3. Verify column: wren memory fetch -q "<column>" --model <name> --threshold 0

Connection error

1. Check active profile: wren profile debug
2. Verify datasource and connection fields are correct
3. Test: wren --sql "SELECT 1"
4. Valid datasource values: postgres (for Aurora Postgres), mysql (for Aurora MySQL), bigquery, snowflake, clickhouse, trino, mssql, databricks, redshift, spark, athena, oracle, duckdb
5. If no profile exists, create one: wren profile add --ui (or --interactive / --from-file)

SQL syntax / planning error (enhanced)

Layer 1: Identify the failure point

wren dry-plan --sql "<failed SQL>"

Layer 2A: MDL-level diagnosis (dry-plan failed)

The dry-plan error message tells you exactly what's wrong:

Key principle: Fix ONE issue at a time. Re-run dry-plan after each fix to see if new errors surface.

Layer 2B: DB-level diagnosis (dry-plan OK, execution failed)

The DB error + dry-plan output together pinpoint the issue:

1. Read the dry-plan expanded SQL — this is what actually runs on the DB
2. Compare with the DB error message:

For small models: If the error message is unclear, try simplifying the query to the smallest failing fragment. Execute subqueries independently to isolate which part fails.

For the CTE rewrite pipeline and additional error patterns, see the wren-sql reference (run wren skills get usage --full).

Workflow 3: Connecting a new data source

1. Add a profile: wren profile add --ui (or --interactive / --from-file)
2. Test connection: wren profile debug
3. Test query: wren --sql "SELECT 1"
4. Initialize project: wren context init
5. Build manifest: wren context build
6. Index: wren memory index
7. Verify: wren --sql "SELECT * FROM <model> LIMIT 5"

Workflow 4: After MDL changes

When model YAML files are updated, rebuild and re-index:

# 1. Validate changes
wren context validate

# 2. Rebuild manifest
wren context build

# 3. Re-index schema memory
wren memory index

# 4. Verify
wren --sql "SELECT * FROM <changed_model> LIMIT 1"

Command decision tree

Get data back           → wren --sql "..."
Aggregation across dims → wren cube query --cube <name> --measures <m> (if cube defined)
See translated SQL only → wren dry-plan --sql "..." (accepts -d <datasource> if no active profile)
Validate against DB     → wren dry-run --sql "..."
Schema context          → wren memory fetch -q "..."
Filter by type/model    → wren memory fetch -q "..." --type T --model M --threshold 0
Store confirmed query   → wren memory store --nl "..." --sql "..."
Few-shot examples       → wren memory recall -q "..."
Index stats             → wren memory status
Re-index after MDL change → wren memory index
Show project context    → wren context show
Rebuild manifest        → wren context build
Check profile           → wren profile debug
Switch profile          → wren profile switch <name>

Cube Query Workflow

When the user asks an aggregation question (e.g., "total revenue by month", "top customers"), check if the MDL defines cubes before writing raw SQL.

Step 1: Discover cubes

wren cube list

If cubes exist and cover the user's question, prefer cube query over raw SQL. Lower error rate, especially for small / local models — agents don't have to hand-write GROUP BY / DATE_TRUNC.

Step 2: Inspect cube structure

wren cube describe <cube_name>

Shows the cube's baseObject, measures (with expressions), dimensions, time dimensions, and hierarchies.

Step 3: Match user's question to cube measures + dimensions

Step 4: Execute via CLI flags OR JSON input

CLI flags:

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

JSON input (good for agent-generated structured queries):

echo '{"cube":"revenue","measures":["total"]}' | wren cube query --from -

Add --sql-only to print the generated SQL without executing — useful for verification before paying for execution on a remote warehouse.

Step 5: Error recovery

When NOT to use cube query

Fall back to wren --sql when:

• Custom JOINs across multiple models
• Window functions, CTEs, or subqueries
• Queries with no aggregation
• No cubes defined in the MDL

Aggregation decision tree

User question → Is it an aggregation question?
                (SUM, COUNT, AVG, GROUP BY, "by month", "per customer", ...)
  ├── Yes → Are cubes defined? (`wren cube list` once at start of session)
  │         ├── Yes → Does a cube cover the question? (`wren cube describe`)
  │         │         ├── Yes → Use `wren cube query` (preferred — lower error rate)
  │         │         └── No  → Write raw SQL with `wren --sql`
  │         └── No  → Write raw SQL with `wren --sql`
  └── No  → Write raw SQL with `wren --sql` (look for memory recall first)

Things to avoid

• Do not guess model or column names — check context first
• Do not store failed queries or queries the user said are wrong
• Do not skip storing successful queries with a clear NL question — default is to store
• Do not re-index before every query — once per MDL change
• Do not pass passwords via --connection-info if shell history is shared — use profiles (wren profile add) or --connection-file