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
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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 powered-up platform built for teams. It adds everything you need for production pipelines, at scale.
- Magical AI-assisted development and debugging
- Multi-environment orchestration
- Role-based access control
- Real-time monitoring & alerts
- Powerful CI/CD & version control
- Powerful enterprise features
- Available fully managed, hybrid, or on-premises
2 maintainers
Metadata
2 maintainers
Release files for mage-ai 0.9.80
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mage_ai-0.9.80.tar.gz | 38.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mage_ai-0.9.80-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 78.2 MB
Release files / mage_ai-0.9.80.tar.gz
| Download URL | mage_ai-0.9.80.tar.gz |
|---|---|
| Size | 38.2 MB |
| Tags | Source |
|
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No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Release files / mage_ai-0.9.80-py3-none-any.whl
| Download URL | mage_ai-0.9.80-py3-none-any.whl |
|---|---|
| Size | 40.0 MB |
| Tags | Python 3 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
