Connect Your Database

This guide walks you through connecting Wren AI Core to your own database — from creating a profile to running your first query. If you haven't installed the CLI yet, see Installation first.

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Step 1 — Install the data source extra

Each database requires its own connector. Install the extra for your data source:

pip install "wren-engine[postgres,main]"

Replace postgres with your data source (see supported data sources for the full list). If you already installed with the correct extra, skip this step.

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Step 2 — Create a profile

A profile stores your database connection. The browser UI is the easiest way to create one:

wren profile add my-db --ui

This opens a form where you select the data source type and fill in the required connection fields.

Each data source has different fields. To see the exact fields for any data source:

wren docs connection-info postgres

Replace postgres with your data source name. The command prints all required and optional fields with descriptions, derived directly from the Pydantic schema — so it always matches the running CLI.

Example — PostgreSQL:

wren profile add my-db --ui
# Select "postgres", then fill in: host, port, database, user, password

Other options

If you prefer not to use the browser UI:

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

# Import from a JSON/YAML file (recommended for agent-driven flows)
wren profile add my-db --from-file connection.yml

For the --from-file flow, write a flat YAML referencing ${VAR} placeholders so secrets stay in .env:

# connection.yml
datasource: postgres
host: ${POSTGRES_HOST}
port: ${POSTGRES_PORT}
database: ${POSTGRES_DATABASE}
user: ${POSTGRES_USER}
password: ${POSTGRES_PASSWORD}

# .env (in project root; add to .gitignore)
POSTGRES_HOST=db.example.com
POSTGRES_PORT=5432
POSTGRES_DATABASE=wren
POSTGRES_USER=alice
POSTGRES_PASSWORD=...

See Profiles for all options.

Per-datasource setup notes

Most data sources only need the fields shown by wren docs connection-info <ds>. These need an extra step:

BigQuery — credentials must be a base64-encoded service-account JSON:

base64 -i /path/to/service-account.json | pbcopy   # macOS
base64 /path/to/service-account.json               # Linux

Paste the resulting string into BIGQUERY_CREDENTIALS= (or directly into the profile field if not using .env).

Snowflake — account is the account locator from your URL, e.g. xy12345.us-east-1 (not the full hostname). warehouse, database, and schema must already exist and be accessible to the user.

Trino / Presto — catalog and schema are required even though most clients let you omit them. Set both even if you plan to fully-qualify table names in queries.

Athena — Requires s3_staging_dir (an S3 URI for query results) and AWS credentials, either via AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY env vars or the standard AWS credentials chain (~/.aws/credentials).

Databricks — Use a personal access token in access_token. The http_path is the SQL warehouse HTTP path from the workspace UI.

MySQL on macOS — If pip install "wren-engine[mysql]" fails to build mysqlclient, install the system dependencies first:

brew install mysql-client pkg-config
export PKG_CONFIG_PATH="$(brew --prefix mysql-client)/lib/pkgconfig"
pip install "wren-engine[mysql,main]"

Verify mysql-client is actually installed (brew list mysql-client) — brew --prefix returns a path even when the keg is missing, which is misleading.

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Step 3 — Verify the connection

wren profile debug    # show resolved config (secrets masked)
wren --sql "SELECT 1" # test actual connectivity

If the connection fails, check:

• Credentials — run wren profile debug to see what was saved (passwords are masked)
• Network access — can your machine reach the database host and port?
• SSL — some databases require SSL. Add the relevant SSL fields to your profile
• Firewall / IP allowlist — cloud databases (BigQuery, Snowflake, Redshift) may require your IP to be allowlisted

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Step 4 — Initialize a project

Create a directory for your MDL project:

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

This scaffolds:

my-project/
├── wren_project.yml       # project metadata
├── models/                # one folder per model
├── views/                 # reusable SQL views
├── relationships.yml      # join definitions
└── instructions.md        # business rules for the AI

Remove the example placeholders:

rm -rf models/example_model views/example_view

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Step 5 — Generate MDL from your database

With an AI agent (recommended)

Open your AI agent in the project directory and ask:

Use the wren-generate-mdl skill to explore my database and generate
the MDL for all tables. The data source is postgres.

The agent will introspect your schema, normalize types, write model files, infer relationships, and build the manifest.

Manually

If you prefer manual control:

a. Discover tables:

wren --sql "SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'" -o json

b. Create a model file for each table:

# models/orders/metadata.yml
name: orders
table_reference:
  catalog: ""
  schema: public
  table: orders
primary_key: order_id
columns:
  - name: order_id
    type: INTEGER
    not_null: true
    is_primary_key: true
  - name: customer_id
    type: INTEGER
  - name: total
    type: "DECIMAL(10,2)"
  - name: status
    type: VARCHAR
    properties:
      description: "Order status: pending, shipped, delivered, cancelled"
  - name: order_date
    type: DATE

Use wren utils parse-type to normalize database-specific types:

wren utils parse-type --type "character varying(255)" --dialect postgres
# → VARCHAR(255)

c. Define relationships in relationships.yml:

relationships:
  - name: orders_customers
    models:
      - orders
      - customers
    join_type: MANY_TO_ONE
    condition: orders.customer_id = customers.customer_id

d. Validate and build:

wren context validate
wren context build

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Step 6 — Index memory and start querying

wren memory index
wren memory status    # should show schema_items > 0

Now query your data:

wren --sql "SELECT * FROM orders LIMIT 5"

Or ask questions in natural language through your AI agent:

How many orders were placed last month?

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Adding descriptions

Good descriptions significantly improve AI query accuracy. Add them to models and columns via properties.description:

name: orders
properties:
  description: "Customer orders with payment status and shipping details"
columns:
  - name: total
    type: "DECIMAL(10,2)"
    properties:
      description: "Net order total in USD after discounts, before tax"

After adding descriptions, rebuild and re-index:

wren context build
wren memory index

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Adding instructions

instructions.md contains business rules that guide the AI agent:

## Naming conventions
- "revenue" always means net_total, not gross_total
- "active customers" = customers with at least one order in the last 90 days

## Query rules
- Always filter by status = 'completed' unless explicitly asked for all statuses
- Use order_date for time-based filtering, not created_at

Instructions are loaded by the agent at session start and included in memory search results.

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Troubleshooting

wren: command not found

The package is installed but the bin directory isn't on PATH. Run pip show wren-engine to find the install location, then add the matching bin/ directory to PATH — or activate the virtualenv if you used one.

pip install fails with externally-managed-environment

PEP 668 protects system Python on Linux/macOS. Use a virtualenv:

python3 -m venv ~/.venvs/wren && source ~/.venvs/wren/bin/activate
pip install "wren-engine[<your-ds>,main]"

wren profile add reports a missing secret

⚠ Cannot validate: '${POSTGRES_PASSWORD}' is not set in the environment
or any discovered .env file.

The profile references a ${VAR} that isn't defined. Fill in the matching key in your .env and re-run wren profile add. The CLI looks for .env in:

1. os.environ (anything you exported before running wren)
2. $CWD/.env (where you ran the command from)
3. <project_root>/.env (next to wren_project.yml)
4. ~/.wren/.env (user-global fallback)

Driver authentication failed

Examples: MySQL 1044 Access denied, Postgres password authentication failed, BigQuery 403 Permission denied.

wren profile add surfaces the driver error verbatim. Fix the credentials in .env (or the relevant cloud-side IAM permission) and re-run. Use wren profile debug <name> to see the resolved fields with secrets masked.

Pydantic ValidationError: Field required

A required field is missing from the imported --from-file. Run wren docs connection-info <ds> to see the full list, add the missing key to your .env and the corresponding ${VAR} to the profile YAML, then re-run.

unknown datasource: <name>

Check the canonical name with wren docs connection-info (no argument) — it lists every supported source. Most aliases (gcs_file, local_file, etc.) are listed there too.

Connection refused / host unreachable

[Errno 61] Connection refused

• Can your machine reach the host? nc -zv <host> <port> is a quick check.
• Cloud DBs (BigQuery, Snowflake, Redshift, RDS): is your egress IP allow-listed?
• VPN / corporate firewall in the way?

'dict' object has no attribute 'kwargs'

You're on a wren-engine version older than 0.3.1, where validation swallowed driver errors. Upgrade:

pip install --upgrade "wren-engine[main]"

wren context validate warnings

Warnings are grouped by category once there are more than 10 — pass --verbose to see each line. Common categories:

• Missing description — cosmetic; agents can still answer questions without it.
• Missing primary_key — not always a bug; junction / log tables legitimately have no PK.
• Invalid relationship condition — real bug. Regenerate via the wren-generate-mdl skill, or fix manually.

"table not found" after building

Model names in SQL must match the name field in your YAML, not the physical table name. Run wren context show to see available models.

Wrong column types

Always normalize types through wren utils parse-type --dialect <your-datasource>. Raw database types like int8 or character varying may not be recognized.

Memory returns irrelevant results

Re-index after every MDL change: wren context build && wren memory index. Add properties.description to improve search quality.

Switching between databases

Create separate profiles for each database and switch with wren profile switch <name>. The same project can work with different profiles if the schema is compatible.