CLI

Install the wwai command-line client to script datasets, training, inference, and jobs from your terminal or an agent.

The woodwide-cli package installs wwai — a command-line client for the Wood Wide API. Output is JSON by default so scripts and agents can pipe results to jq.

PyPI

Install, release history, and package README.

Console setup

Guided install steps in the developer console.

The CLI source repository is not public. Use PyPI for the package README, the console CLI tab for setup, and wwai --help for the full command reference.

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Install

Requires Python 3.12+.

uv (recommended)

uv tool install woodwide-cli
wwai --help

pipx

pipx install woodwide-cli
wwai --help

pip

pip install woodwide-cli
wwai --help

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Authenticate

1. Create an API key

   Create a key in the console.

2. Save it locally

   wwai config set --api-key sk_your_api_key_here
   

   Stored in ~/.wwai/config.json. You can also pass --api-key per command or set WOODWIDE_API_KEY.

   Never commit API keys to source control.

3. Confirm

   wwai --format table datasets list
   

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End-to-end example

Upload a dataset, train a prediction model, and run sync inference:

UPLOAD=$(wwai datasets create --file train.csv --name "sales-q4")
DATASET=$(echo "$UPLOAD" | jq -r '.dataset.id')
INGEST_JOB=$(echo "$UPLOAD" | jq -r '.job_id')

wwai jobs wait "$INGEST_JOB"

TRAIN=$(wwai models train \
  --dataset "$DATASET" \
  --type prediction \
  --label-column revenue \
  --wait)
MODEL=$(echo "$TRAIN" | jq -r '.model.id')

wwai infer sync "$MODEL" --file new_data.csv --output-type json

For model types, training options, and task-specific guidance, see Capabilities. For HTTP details behind each command, see the API reference.

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CLI-specific behavior

Use --wait on the command that enqueues a job (e.g. models train ... --wait) to block in the same shell. Use jobs wait JOB_ID to reattach later — useful for agents or a new terminal session.

Output — JSON by default. Add --format table for readable tables:

wwai --format table datasets list

Progress from --wait and jobs wait goes to stderr; final JSON goes to stdout, so you can pipe to jq while watching status.

Exit codes for --wait and jobs wait:

Code	Meaning
0	Job succeeded
1	Job failed, canceled, or rejected
2	Timeout (job still running)

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Data connections

Import from connections configured in the console:

wwai connections list
wwai connections import CONNECTION_ID \
  --mode table --schema analytics --table orders --name "orders snapshot"
wwai jobs wait JOB_ID

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Command reference

Every subcommand exposes flags and examples via --help:

wwai datasets --help
wwai models train --help
wwai infer batch --help

Variable	Description
WOODWIDE_API_KEY	API key (sk_...)
WOODWIDE_BASE_URL	API base URL (default: https://api.woodwide.ai)

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Next steps

API quickstart

Same workflow with curl, JavaScript, or the Python SDK tab.

Python SDK

Typed client for the same workflows from Python.