mage-ai
Enrichment pending🧙 Build, run, and manage data pipelines for integrating and transforming data.
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Install
pip install mage-aiSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
🧙 Build, run, and manage data pipelines for integrating and transforming data.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Jul 15, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Jul 15, 2026
- Languages
- python
Source: github.language+pyproject.toml · Jul 15, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 15, 2026)
- Build pipelines locally with Python, SQL, or R in a modular notebook-style UISource link
Tags
README
Mage OSS
Build modern data pipelines locally — fast, visual, and production-ready.
Mage OSS is a self-hosted development environment designed to help teams create production-grade data pipelines with confidence.
Ideal for automating ETL tasks, architecting data flow, or orchestrating transformations — all in a fast, notebook-style interface powered by modular code.
When it’s time to scale, Mage Pro — our core platform — unlocks enterprise orchestration, collaboration, and AI-powered workflows.
What you can do with Mage OSS
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Build pipelines locally with Python, SQL, or R in a modular notebook-style UI
-
Run jobs manually or on a schedule (cron supported)
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Connect to databases, APIs, and cloud storage with prebuilt connectors
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Debug visually with logs, live previews, and step-by-step execution
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Set up quickly with Docker, pip, or conda — no cloud account required
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Your go-to workspace for local pipeline development — fully in your control.
Start local. Scale when you're ready.
Use Mage OSS to build and run pipelines on your machine. When you're ready for advanced tooling, performance, and AI-assisted productivity, Mage Pro is just one click away.
Quickstart
Install using Docker (recommended):
docker pull mageai/mageai:latest
Or with pip:
pip install mage-ai
Or with conda:
conda install -c conda-forge mage-ai
Full setup guide and docs: docs.mage.ai
Core Features
| Feature | Description |
|---|---|
| Modular pipelines | Build pipelines block-by-block using Python, SQL, or R |
| Notebook UI | Interactive editor for writing and documenting logic |
| Data integrations | Prebuilt connectors to databases, APIs, and cloud storage |
| Scheduling | Trigger pipelines manually or on a schedule |
| Visual debugging | Step-by-step logs, data previews, and error handling |
| dbt support | Build and run dbt models directly inside Mage |
Example Use Cases
- Move data from Google Sheets to Snowflake with a Python transform
- Schedule a daily SQL pipeline to clean and aggregate product data
- Develop dbt models in a visual notebook-style interface
- Run simple ETL/ELT jobs locally with full transparency
Documentation
Looking for how-to guides, examples, or advanced configuration?
Explore our full documentation at docs.mage.ai.
Contributing
We welcome contributions of all kinds — bug fixes, docs, new features, or community examples.
Start with our contributing guide, check out open issues, or suggest improvements.
Ready to scale? Mage Pro has you covered.
Mage Pro is a powere
For agents
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