Assembly Line

Mnemiq

Open-source text-to-SQL engine you tune and measure on your own database

Mnemiq is an open-source semantic layer project on GitHub under the Apache-2.0 license (permissive), with 51 stars. It is built with CPython, TypeScript and Postgres. Its last push was 14 days ago.

Stars
51
License
Apache-2.0
Activity
Active

Code on GitHub Website

Judgement

What it is
Building block
What it does
A text-to-SQL engine you tune and evaluate against your own database, answering natural-language questions with measured accuracy.
Adopt it when
You are building a natural-language query feature and want to measure accuracy on your schema.
Watch out
Under four months old with no releases, from a YC startup.
Ready for
Experimental
Runs as
Library and self-hosted server
Built with
Python, TypeScript and PostgreSQL
How established
New: started 3 months 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: Mnemiq → CPython → GCC → LLVM project → runs on a microchip

How compilers were built, back to punched paper tape

Open-source alternatives to Mnemiq

  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
    SML semanticdatalayer

    Open-source repository for Semantic Modeling Language (SML)

    List, course or other material Usable You want a vendor-neutral way to describe metrics and dimensions, or you use AtScale.

    ★ 177 Apache-2.0 Active, pushed 7 days ago
  6. 6
    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
  7. 7
    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
  8. 8
    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

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

Stars, license and activity come from GitHub and refresh on a rolling hourly schedule. The project's own description is quoted as written.