wandb-workspaces
A library for programatically working with the Weights & Biases UI.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an active Weights & Biases account and valid entity/project credentials.
- 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 · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions.
last release 2026-08-04 (10 days) · last repo commit 2026-08-05 · 28 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 345,578 downloads/mo, #7,362 on PyPI
Alternatives
Verify before relying
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()- 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.
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 on this page. Verify before relying on it.
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
wandb-workspaces on PyPI
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.
Requires an active Weights & Biases account and valid entity/project credentials.
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()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 the current Python release ~=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagespydanticwandb |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 345,578 / month, #7,362 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Python Modules |
Evidence: wandb_workspaces-0.4.5-py3-none-any.whl
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