WrenAI/core/wren

wren-engine

Wren Engine CLI and Python SDK — semantic SQL layer for 20+ data sources.

Translate natural SQL queries through an MDL (Modeling Definition Language) semantic layer and execute them against your database. Powered by Apache DataFusion and Ibis.

Installation

pip install wren-engine              # Core (DuckDB included)
pip install wren-engine[postgres]    # PostgreSQL
pip install wren-engine[mysql]       # MySQL
pip install wren-engine[bigquery]    # BigQuery
pip install wren-engine[snowflake]   # Snowflake
pip install wren-engine[clickhouse]  # ClickHouse
pip install wren-engine[trino]       # Trino
pip install wren-engine[mssql]       # SQL Server
pip install wren-engine[databricks]  # Databricks
pip install wren-engine[redshift]    # Redshift
pip install wren-engine[spark]       # Spark
pip install wren-engine[athena]      # Athena
pip install wren-engine[oracle]      # Oracle
pip install 'wren-engine[memory]'    # Schema & query memory (LanceDB)
pip install 'wren-engine[ui]'        # Browser-based profile form (starlette + uvicorn)
pip install 'wren-engine[main]'      # memory + interactive prompts + ui
pip install 'wren-engine[all]'       # All connectors + main

Requires Python 3.11+.

Quick start

1. Initialize a project — scaffolds a YAML-based MDL project:

mkdir my-project && cd my-project
wren context init

This creates wren_project.yml, models/, and views/. Edit wren_project.yml to set your data_source and add models under models/:

# wren_project.yml
schema_version: 2
name: my_project
catalog: wren
schema: public
data_source: postgres

# models/orders/metadata.yml
name: orders
table_reference:
  schema: mydb
  table: orders
columns:
  - name: order_id
    type: integer
  - name: customer_id
    type: integer
  - name: total
    type: double
  - name: status
    type: varchar
primary_key: order_id

Already have an MDL JSON? Import it directly: wren context init --from-mdl path/to/mdl.json

2. Configure a connection profile:

# Browser form (recommended, requires wren-engine[ui])
wren profile add my-db --ui

# Interactive terminal prompts
wren profile add my-db --interactive

# Import from an existing connection file
wren profile add my-db --from-file connection_info.json

3. Build the manifest:

wren context build

This compiles YAML files into target/mdl.json. The CLI auto-discovers this file when you run queries from within the project directory.

4. Run queries:

wren --sql 'SELECT order_id FROM "orders" LIMIT 10'

wren walks up from the current directory to find wren_project.yml and uses target/mdl.json. You can also pass --mdl path/to/mdl.json explicitly.

For the full CLI reference and per-datasource connection field reference, see docs/cli.md and docs/connections.md.

5. (Optional) Configure security policy — create ~/.wren/config.json:

{
  "strict_mode": true,
  "denied_functions": ["pg_read_file", "dblink", "lo_import"]
}

Key	Default	Description
strict_mode	false	When true, every table in a query must be defined in the MDL. Queries referencing undeclared tables are rejected before execution.
denied_functions	[]	List of function names (case-insensitive) that are forbidden in queries.

6. (Optional) Index schema for semantic search (requires wren-engine[memory]):

wren memory index                              # index MDL schema
wren memory fetch -q "customer order price"    # fetch relevant schema context
wren memory store --nl "top customers" --sql "SELECT ..."  # store NL→SQL pair
wren memory recall -q "best customers"         # retrieve similar past queries

---

Connection profiles

Profiles let you store named connection configurations in ~/.wren/profiles.yml and switch between them easily — useful when working across multiple databases or environments.

# Add a profile (browser form, interactive prompts, or file import)
wren profile add prod --ui                        # opens http://localhost:<port>
wren profile add staging --interactive            # terminal prompts
wren profile add local --from-file conn.json      # import existing file

# List and switch profiles
wren profile list                                 # * marks the active profile
wren profile switch prod

# Inspect a profile (sensitive fields masked)
wren profile debug prod

# Remove a profile
wren profile rm old-profile --force

The --ui flag opens a browser-based form that auto-derives fields from each datasource's schema — including file upload for BigQuery credentials, variant selection for Databricks/Redshift, and sensible defaults for all 20+ supported sources. Requires pip install 'wren-engine[ui]'.

Once a profile is active, wren uses it automatically:

wren profile switch prod
wren --sql 'SELECT COUNT(*) FROM "orders"'        # connects using prod profile

---

Python SDK

import base64, orjson
from wren import WrenEngine, DataSource

manifest = { ... }  # your MDL dict
manifest_str = base64.b64encode(orjson.dumps(manifest)).decode()

with WrenEngine(manifest_str, DataSource.mysql, {"host": "...", ...}) as engine:
    result = engine.query('SELECT * FROM "orders" LIMIT 10')
    print(result.to_pandas())

---

Development

just install-dev    # Install with dev dependencies
just lint           # Ruff format check + lint
just format         # Auto-fix

Command	What it runs	Docker needed
just test-unit	Unit tests (engine, CTE rewriter, field registry, profiles)	No
just test-duckdb	DuckDB connector tests	No
just test-postgres	PostgreSQL connector tests	Yes
just test-mysql	MySQL connector tests	Yes
just test	All tests	Yes

Profile web tests (test_profile_web.py) require wren-engine[ui]:

uv sync --extra dev --extra ui --find-links ../wren-core-py/target/wheels/
uv run pytest tests/test_profile_web.py -v

Publishing

./scripts/publish.sh            # Build + publish to PyPI
./scripts/publish.sh --test     # Build + publish to TestPyPI
./scripts/publish.sh --build    # Build only

License

Apache-2.0