---
id: wandb-workspaces
version: "0.4.5"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# wandb-workspaces — A library for programatically working with the Weights & Biases UI.

License: permissive · Maintenance: active · Downloads: 345.6K/mo

## What it is and what it does

wandb-workspaces is a Python library that lets you define Weights & Biases workspaces and reports as code, then save them to your W&B account. It wraps the W&B API to expose workspace sections, panels, and report blocks (headings, text, charts) as Python objects that you instantiate and compose, then call `.save()` to persist. The library is built on pydantic for schema validation and depends on wandb itself for authentication and API communication.

You use it to automate dashboard creation—building standard report layouts, metric visualizations, and workspace organization from scripts rather than clicking through the UI. This is useful for teams that want reproducible, version-controlled experiment dashboards or for generating reports programmatically as part of a training pipeline. The library is in Public Preview and supports Python 3.9 through 3.12.

Use it for:

- Automatically generate standard report templates after each model training run to track validation metrics and loss curves.
- Create workspace dashboards for a team that organize experiments by project phase, with predefined chart layouts.
- Build CI/CD pipelines that generate comparison reports across multiple experiment runs without manual UI work.
- Programmatically populate reports with dynamic titles, descriptions, and chart configurations from experiment metadata.
- Version-control experiment dashboard definitions alongside training code for reproducibility.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Programmatically create and manage Weights & Biases workspaces and reports, including building dashboards with charts and panels from Python code.

Yes, if you use Weights & Biases and want to automate report or workspace creation. The library has low install friction, active maintenance, permissive licensing, and no known vulnerabilities. The only caveat is its Public Preview status—verify that the API and feature set are stable enough for your use case before relying on it in production pipelines.

## Install

pip install wandb-workspaces
uv add wandb-workspaces
poetry add wandb-workspaces

## Installing wandb-workspaces

Before you install: Low friction installation with only two runtime dependencies (pydantic and wandb). Active maintenance with a recent release; last commit 2026-08-05 and marked as actively maintained.

License in practice: Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions.

Quickstart:

pip install wandb-workspaces

import wandb_workspaces.workspaces as ws
import wandb_workspaces.reports.v2 as wr

workspace = ws.Workspace(
    name="Example",
    entity="your-entity",
    project="your-project",
    sections=[ws.Section(name="Metrics", panels=[wr.LinePlot(x="Step", y=["val_loss"])])]
).save()

Requires an active Weights & Biases account and valid entity/project credentials.

Verify before relying:

- Whether workspaces created programmatically are fully editable in the W&B UI after creation.
- Performance characteristics when creating large numbers of panels or sections.
- Backward compatibility guarantees given the 'Public Preview' status.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 345.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

weights and biases workspace automation, wandb report generation, programmatic dashboard creation, wandb ui as code, ml experiment visualization, wandb workspace api, ml-experiment-tracking, dashboard-automation, wandb-integration

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