semantic-link-labs
Semantic Link Labs for Microsoft Fabric
Decision gist · record as of 2026-08-14
Yes, if you work within Microsoft Fabric notebooks and need to automate semantic model or report management. The library is actively maintained, has no known vulnerabilities, uses a permissive MIT License, and installs with low friction. However, its utility is narrowly scoped to the Fabric environment; it is not a general-purpose Power BI tool and requires Python 3.10 or later.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and is designed for use within Microsoft Fabric notebooks; functionality outside that environment is not documented.
- Low friction install with a pure Python wheel.
- Active maintenance with a release 17 days old and recent commits.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions; you may use, modify, and distribute the package freely provided you include the license notice.
last release 2026-07-28 (17 days) · last repo commit 2026-08-12 · 565 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,146,383 downloads/mo, #3,255 on PyPI
Alternatives
Verify before relying
pip install semantic-link-labs
import semantic_link_labs
# Use within Microsoft Fabric notebooks for semantic model operations- Exact scope of functionality available outside Microsoft Fabric notebooks is unclear.
- Whether semantic-link-sempy is a separate package or a bundled dependency is not specified.
- Performance characteristics and scalability limits for large semantic models are not documented.
- Specific API signatures and usage patterns beyond the featured scenarios listed.
What it is and what it does
Semantic Link Labs is a Python library built on top of Microsoft Fabric's Semantic Link, designed to run in Jupyter notebooks within the Fabric environment. It provides utilities for programmatically managing Power BI semantic models—including migration to Direct Lake, backup and restore, refresh operations, and DAX query analysis—as well as report metadata inspection and capacity management. The library aims to automate repetitive tasks and reduce manual intervention in Fabric workflows.
The package depends on semantic-link-sempy as its primary runtime dependency, along with anytree, polib, jsonpath_ng, anywidget, and sqlglot. It is classified as Alpha and supports Python 3.10 and later. The library is actively maintained by Microsoft with recent releases and a public GitHub repository accepting bug reports and feature requests.
Use it for
- Migrate Power BI semantic models from import or DirectQuery to Direct Lake mode.
- Run best practice analysis on semantic models and reports to identify issues.
- Back up, restore, and deploy semantic models across Fabric workspaces.
- Analyze DAX query dependencies and column usage in semantic models.
- Manage Fabric capacity provisioning and migration between SKU types.
- Auto-generate measure descriptions and manage incremental refresh policies.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work within Microsoft Fabric notebooks and need to automate semantic model or report management.
The library is actively maintained, has no known vulnerabilities, uses a permissive MIT License, and installs with low friction. However, its utility is narrowly scoped to the Fabric environment; it is not a general-purpose Power BI tool and requires Python 3.10 or later.
Install
semantic-link-labs on PyPI
Before you install
Low friction install with a pure Python wheel. Active maintenance with a release 17 days old and recent commits. Depends on semantic-link-sempy plus six other libraries with no compiled dependencies.
Requires Python 3.10 or later and is designed for use within Microsoft Fabric notebooks; functionality outside that environment is not documented.
License in practice
MIT License permits commercial and private use with minimal restrictions; you may use, modify, and distribute the package freely provided you include the license notice.
Quickstart
pip install semantic-link-labs
import semantic_link_labs
# Use within Microsoft Fabric notebooks for semantic model operationsVerify before relying
- Exact scope of functionality available outside Microsoft Fabric notebooks is unclear.
- Whether semantic-link-sempy is a separate package or a bundled dependency is not specified.
- Performance characteristics and scalability limits for large semantic models are not documented.
- Specific API signatures and usage patterns beyond the featured scenarios listed.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagessemantic-link-sempyanytreepolibjsonpath_nganywidgetsqlglot |
| Maintenance | Actively maintained 17 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,146,383 / month, #3,255 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaFramework :: JupyterIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10 |
Evidence: semantic_link_labs-0.17.0-py3-none-any.whl
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See also semantic-link · semantic-link-sempy · semantic-link-functions-validators · pbixray · semantic-link-functions-holidays · semantic-link-functions-geopandas · semantic-link-functions-phonenumbers · dbt-fabricspark · semantic-link-functions-meteostat · xmhuffman