# dbt Labs — developer ecosystem report

> dbt helps data teams work like software engineers—to ship trusted data, faster.

- GitHub org: `dbt-labs`
- Category: dev_tool
- Overall score: 54.0 / 100
- Overall rank: #160
- Last refreshed: 2026-08-20

## Pillar scores

| Pillar | Score / 100 | Rank | Weight in overall |
| --- | --- | --- | --- |
| Code & Polish | 61.0 | #213 | ~22% |
| Education | 81.0 | #86 | ~18% |
| AI readiness | 66.0 | #48 | part of Reach |
| Builder experience | 0.0 | #1 | ~12% |
| Community | 53.0 | #109 | ~16% |
| Reach | 35.0 | #645 | ~18% |
| Momentum | 76.0 | #45 | ~14% |

## Links

- GitHub: https://github.com/dbt-labs
- Website: https://getdbt.com
- Docs: https://docs.getdbt.com

## Public GitHub totals

| Metric | Value |
| --- | --- |
| Repos analyzed | 12 |
| Stars | 27,075 |
| Forks | 10,314 |
| Open issues | 3,886 |
| Contributors | — |

## Top repositories

| Repo | Stars | Language | Description |
| --- | --- | --- | --- |
| [dbt-core](https://github.com/dbt-labs/dbt-core) | 13,667 | Rust | dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to  |
| [dbt-utils](https://github.com/dbt-labs/dbt-utils) | 1,790 | Makefile | Utility functions for dbt projects. |
| [metricflow](https://github.com/dbt-labs/metricflow) | 1,751 | Python | MetricFlow allows you to define, build, and maintain metrics in code. |
| [dbt-fusion](https://github.com/dbt-labs/dbt-fusion) | 725 | Rust | ARCHIVE: code & issue tracking in dbt-core |
| [dbt-agent-skills](https://github.com/dbt-labs/dbt-agent-skills) | 676 | Python | A curated collection of Agent Skills for working with dbt, to help AI agents understand and execute dbt workflows more e |
| [dbt-codegen](https://github.com/dbt-labs/dbt-codegen) | 669 | Makefile | Macros that generate dbt code |
| [dbt-mcp](https://github.com/dbt-labs/dbt-mcp) | 598 | Python | A MCP (Model Context Protocol) server for interacting with dbt. |
| [dbt-project-evaluator](https://github.com/dbt-labs/dbt-project-evaluator) | 572 | Shell | This package contains macros and models to find DAG issues automatically |
| [jaffle-shop-classic](https://github.com/dbt-labs/jaffle-shop-classic) | 545 | — | A self-contained dbt project for testing purposes |
| [dbt-spark](https://github.com/dbt-labs/dbt-spark) | 447 | Python | This repository has moved into https://github.com/dbt-labs/dbt-adapters |
| [dbt-audit-helper](https://github.com/dbt-labs/dbt-audit-helper) | 419 | — | Useful macros when performing data audits |
| [dbt-external-tables](https://github.com/dbt-labs/dbt-external-tables) | 381 | Jupyter Notebook | dbt macros to stage external sources |

## Methodology

Scores are computed from public signals only (GitHub, package registries, docs sites, community platforms). Pillar definitions: https://www.smoower.com/docs.md

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