SML

Judgement

What it is

List, course or other material

What it does

The specification for Semantic Modeling Language, a YAML format for defining semantic models, published by AtScale.

Adopt it when

You want a vendor-neutral way to describe metrics and dimensions, or you use AtScale.

Watch out

Adoption outside AtScale is limited.

Ready for

Usable

Built with

YAML

How established

Established: started 2 years ago, no tagged release yet.

What the license allows

You can build it into a product you sell, as long as you keep its copyright notice.

Judged on 2026-11-03 from the project's README and GitHub data. The license and age lines are worked out directly.

Built from

Follow it down: SML → runs on a microchip

How compilers were built, back to punched paper tape

Open-source alternatives to SML

1. 1
   MetricFlow dbt-labs

   MetricFlow allows you to define, build, and maintain metrics in code.

   Building block Production-ready Your data team uses dbt and wants one governed definition of each business metric.

   ★ 1.9k Apache-2.0 Python Active, pushed 20 days ago
2. 2
   Saiku spiculedata

   Open-source semantic layer: one cube for Excel (MDX/XMLA), dashboards, and AI agents (MCP). Mondrian + Apache Calcite.

   Complete system Usable Your organisation uses OLAP cubes and wants Excel pivot access plus a web front end.

   ★ 1.3k Apache-2.0 Java Active, pushed 16 days ago
3. 3
   SLayer MotleyAI

   An embeddable, expressive semantic layer for AI agents and humans

   Building block Experimental You are building AI analytics and want metrics defined once for agents to use.

   ★ 306 MIT Python Active, pushed 13 days ago
4. 4
   QwenPaw-Data agentscope-ai

   Agentic enterprise data analytics: governed facts (DataBridge), reusable methodology (Skill-Hub), and controllable execution (Host)

   Framework or platform Experimental You research or prototype autonomous data analysis agents.

   ★ 184 Apache-2.0 Python Active, pushed 14 days ago
5. 5
   Data-tools Intugle

   The GenAI-powered toolkit for automated data intelligence.

   Building block Experimental You inherit scattered tables and want a first-pass semantic model generated automatically.

   ★ 152 Apache-2.0 Jupyter Notebook Active, pushed 2 months ago
6. 6
   Sidemantic sidequery

   The universal metrics layer. Compatible with 15+ formats: Cube, MetricFlow, LookML, Omni, BSL, LDM, Cortex, Malloy, OSI, SML, TML, Hex, Rill, Superset

   Building block Experimental You have metrics defined in one BI tool's format and want to query them consistently elsewhere.

   ★ 131 AGPL-3.0 Python Active, pushed 14 days ago
7. 7
   Dex exmergo

   Dex lets your agent build your analytics.

   Developer tool Experimental An analytics engineer wants an AI agent to do routine modelling work in their warehouse.

   ★ 105 Apache-2.0 Python Active, pushed 3 days ago
8. 8
   Zillion totalhack

   Make sense of it all. Semantic data modeling and analytics with a sprinkle of AI. https://totalhack.github.io/zillion/

   Building block Usable You want a lightweight semantic layer inside a Python application.

   ★ 208 MIT Python Slowing, pushed 4 months ago

Commercial semantic layers and metrics stores

1. 1
   dbt Semantic Layer dbt Labs

   Define metrics once and deliver consistent, governed insights across tools with dbt’s Semantic Layer, powered by MetricFlow.

   Building block Usable Your data team already models in dbt and wants metric definitions in the same codebase.

   Commercial
2. 2
   AtScale AtScale

   One metric, defined once, computed in your warehouse and served the same way to every AI, BI tool, and team. AtScale is the semantic layer for trusted analytics.

   Building block Production-ready An enterprise uses several BI tools, including Excel and Power BI, and wants the same metric definitions in all of them.

   Commercial