# mage-ai/mage-ai

🧙 Build, run, and manage data pipelines for integrating and transforming data.

Repository: https://github.com/mage-ai/mage-ai
Canonical: https://ross.abutalabs.com/products/mage-ai
Homepage: https://www.mage.ai
Language: Python
License: Apache-2.0
License Family: permissive
Topics: machine-learning, artificial-intelligence, data, data-engineering, data-science, python, elt, etl, pipelines, data-pipelines, orchestration, data-integration, sql, spark, dbt, pipeline, reverse-etl, transformation
Last push: 2026-08-13T21:11:32+00:00

## Health v2 (maintenance only)
Score: 78/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 42, longevity 100
- inputs: {"age_days": 1570, "days_push": 20, "days_rel": 224, "gap_med": 115, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8814, forks 990 (observed 2026-08-28T04:10:25.844508+00:00)

## What it is
Mage OSS is a self-hosted data pipeline tool for building, running, and orchestrating ETL/ELT workflows in a notebook-style UI using Python, SQL, or R. It includes prebuilt connectors, cron scheduling, visual debugging, and integrations with Spark and dbt, with a commercial Mage Pro tier for enterprise scale.

## Use cases
- build etl pipelines with python and sql
- schedule data pipeline jobs with cron
- orchestrate data transformations between databases and warehouses
- self-hosted airflow alternative for data pipelines
- integrate stripe salesforce and bigquery data
- run dbt and spark jobs in a visual pipeline tool
- debug data pipelines with live previews and logs

## When to choose
- you want a notebook-style visual UI for building modular data pipelines
- you need self-hosted orchestration with scheduling, retries, and backfills
- your team works in Python, SQL, or R and wants prebuilt data connectors

## When to avoid
- you need only lightweight cron scheduling without a pipeline UI
- you require enterprise features like RBAC and multi-tenant workspaces, which are in the paid Mage Pro tier
- your pipelines are purely streaming with sub-second latency requirements

## Facets
- artifact type: application
- maturity: active
- function: etl, workflow-automation, scheduling, data-science, monitoring, developer-tools
- domain: analytics, self-hosted, developer-tools
- platform: python, self-hosted, cross-platform
- tags: data-pipelines, orchestration, elt, reverse-etl, notebook-ui, dbt, spark, data-integration, airflow-alternative, data-engineering, automation, docker, web-server

## Member repositories
- mage-ai/mage-ai (main) score 78

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:25.844508+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:25:10.485170+00:00, confidence not recorded.
  - readme: https://github.com/mage-ai/mage-ai (fetched 2026-08-28T04:10:25.844508+00:00, sha ad7ff9b2c231)
  - homepage: https://www.mage.ai (fetched 2026-08-29T08:25:27.051767+00:00, sha 34ccb01cca88)
  - site_page: https://www.mage.ai/docs (fetched 2026-08-29T08:25:27.059917+00:00, sha 7d58a3695856)
  - site_page: https://www.mage.ai/integrations (fetched 2026-08-29T08:25:27.054335+00:00, sha dee352df8107)
  - site_page: https://www.mage.ai/pricing (fetched 2026-08-29T08:25:27.056178+00:00, sha 5dd3aae0f281)
  - site_page: https://www.mage.ai/explore (fetched 2026-08-29T08:25:27.058150+00:00, sha 5d47d8ebd211)
- Data as of 2026-08-30T08:39:29.467469+00:00.