{"id":13579877,"url":"https://github.com/mage-ai/mage-ai","last_synced_at":"2026-01-21T19:11:22.780Z","repository":{"id":37022280,"uuid":"493014338","full_name":"mage-ai/mage-ai","owner":"mage-ai","description":"🧙 Build, run, and manage data pipelines for integrating and transforming data.","archived":false,"fork":false,"pushed_at":"2025-05-05T17:49:38.000Z","size":244776,"stargazers_count":8300,"open_issues_count":522,"forks_count":841,"subscribers_count":63,"default_branch":"master","last_synced_at":"2025-05-05T22:17:23.362Z","etag":null,"topics":["artificial-intelligence","data","data-engineering","data-integration","data-pipelines","data-science","dbt","elt","etl","machine-learning","orchestration","pipeline","pipelines","python","reverse-etl","spark","sql","transformation"],"latest_commit_sha":null,"homepage":"https://www.mage.ai","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mage-ai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"docs/contributing/backend/io/adding-a-class.mdx","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2022-05-16T22:11:39.000Z","updated_at":"2025-05-05T21:30:50.000Z","dependencies_parsed_at":"2023-10-02T15:28:09.735Z","dependency_job_id":"4eddecfd-771f-4af2-bf72-cfb373b1e62a","html_url":"https://github.com/mage-ai/mage-ai","commit_stats":{"total_commits":3043,"total_committers":35,"mean_commits":86.94285714285714,"dds":0.6776207689779823,"last_synced_commit":"aee5bbd17e44f815993910363008afd13d688836"},"previous_names":[],"tags_count":58,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mage-ai%2Fmage-ai","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mage-ai%2Fmage-ai/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mage-ai%2Fmage-ai/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mage-ai%2Fmage-ai/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mage-ai","download_url":"https://codeload.github.com/mage-ai/mage-ai/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253968119,"owners_count":21992252,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","data","data-engineering","data-integration","data-pipelines","data-science","dbt","elt","etl","machine-learning","orchestration","pipeline","pipelines","python","reverse-etl","spark","sql","transformation"],"created_at":"2024-08-01T15:01:44.291Z","updated_at":"2026-01-21T19:11:22.768Z","avatar_url":"https://github.com/mage-ai.png","language":"Python","funding_links":[],"categories":["Python","TypeScript","artificial-intelligence","data-science","\u003ca name=\"Python\"\u003e\u003c/a\u003ePython","1. Core Frameworks \u0026 Libraries","🤖 AI \u0026 Machine Learning"],"sub_categories":[],"readme":"# Mage OSS\n\n### Build modern data pipelines locally — fast, visual, and production-ready.\n\n\u003cbr /\u003e\n\nMage OSS is a self-hosted development environment designed to help teams create production-grade data pipelines with confidence.\n\nIdeal for automating ETL tasks, architecting data flow, or orchestrating transformations — all in a fast, notebook-style interface powered by modular code.\n\nWhen it’s time to scale, [Mage Pro](https://mage.ai) — our core platform — unlocks enterprise orchestration, collaboration, and AI-powered workflows.\n\n\u003cbr /\u003e\n\n\u003ca href=\"https://mage.ai\"\u003e\u003cimg alt=\"Mage AI GitHub repo stars\" src=\"https://img.shields.io/github/stars/mage-ai/mage-ai?style=for-the-badge\u0026logo=github\u0026labelColor=000000\u0026logoColor=FFFFFF\u0026label=stars\u0026color=0500ff\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/mageai/mageai\"\u003e\u003cimg alt=\"Mage AI Docker downloads\" src=\"https://img.shields.io/docker/pulls/mageai/mageai?style=for-the-badge\u0026logo=docker\u0026labelColor=000000\u0026logoColor=FFFFFF\u0026label=pulls\u0026color=6A35FF\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/mage-ai/mage-ai/blob/master/LICENSE\"\u003e\u003cimg alt=\"Mage AI license\" src=\"https://img.shields.io/github/license/mage-ai/mage-ai?style=for-the-badge\u0026logo=codeigniter\u0026labelColor=000000\u0026logoColor=FFFFFF\u0026label=license\u0026color=FFCC19\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://www.mage.ai/chat\"\u003e\u003cimg alt=\"Join the Mage AI community\" src=\"https://img.shields.io/badge/Join%20the%20community-black.svg?style=for-the-badge\u0026logo=lightning\u0026labelColor=000000\u0026logoColor=FFFFFF\u0026label=\u0026color=DD55FF\u0026logoWidth=20\" /\u003e\u003c/a\u003e\n\n\u003cbr /\u003e\n\n## What you can do with Mage OSS\n\n- Build pipelines locally with Python, SQL, or R in a modular notebook-style UI\n\n- Run jobs manually or on a schedule (cron supported)\n\n- Connect to databases, APIs, and cloud storage with prebuilt connectors\n\n- Debug visually with logs, live previews, and step-by-step execution\n\n- Set up quickly with Docker, pip, or conda — no cloud account required\n\n- Your go-to workspace for local pipeline development — fully in your control.\n  \n\u003cimg width=\"100%\" alt=\"mage\" src=\"https://github.com/user-attachments/assets/75992872-20a6-4120-8bf0-9c22a3d66450\" /\u003e\n\n\n\u003cbr /\u003e\u003cbr /\u003e\n\n## Start local. Scale when you're ready.\n\nUse 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.\n\n[**Try Mage Pro free →**](https://mage.ai)\n\n\u003cbr /\u003e\n\n### Quickstart\n\nInstall using Docker (recommended):\n\n```bash\ndocker pull mageai/mageai:latest\n```\n\nOr with pip:\n\n```bash\npip install mage-ai\n```\n\nOr with conda:\n\n```bash\nconda install -c conda-forge mage-ai\n```\n\nFull setup guide and docs: [docs.mage.ai](https://docs.mage.ai/getting-started/setup#%E2%9B%B5%EF%B8%8F-mage-oss-overview)\n\n\u003cbr /\u003e\n\n## Core Features\n\n| Feature | Description |\n| :- | :- |\n| Modular pipelines | Build pipelines block-by-block using Python, SQL, or R |\n| Notebook UI | Interactive editor for writing and documenting logic |\n| Data integrations | Prebuilt connectors to databases, APIs, and cloud storage |\n| Scheduling | Trigger pipelines manually or on a schedule |\n| Visual debugging | Step-by-step logs, data previews, and error handling |\n| dbt support | Build and run dbt models directly inside Mage |\n\n\u003cbr /\u003e\n\n## Example Use Cases\n\n- Move data from Google Sheets to Snowflake with a Python transform\n- Schedule a daily SQL pipeline to clean and aggregate product data\n- Develop dbt models in a visual notebook-style interface\n- Run simple ETL/ELT jobs locally with full transparency\n\n\u003cbr /\u003e\n\n## Documentation\n\nLooking for how-to guides, examples, or advanced configuration?\n\nExplore our full documentation at [docs.mage.ai](https://docs.mage.ai).\n\n\n\u003cbr /\u003e\n\n## Contributing\n\nWe welcome contributions of all kinds — bug fixes, docs, new features, or community examples.\n\nStart with our [contributing guide](https://docs.mage.ai/contributing/overview), check out open issues, or suggest improvements.\n\n\u003cbr /\u003e\n\n## Ready to scale? Mage Pro has you covered.\n\nMage Pro is a powered-up platform built for teams.\nIt adds everything you need for production pipelines, at scale.\n\n- Magical AI-assisted development and debugging\n- Multi-environment orchestration\n- Role-based access control\n- Real-time monitoring \u0026 alerts\n- Powerful CI/CD \u0026 version control\n- Powerful enterprise features\n- Available fully managed, hybrid, or on-premises\n\n[**Try Mage Pro free →**](https://mage.ai)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmage-ai%2Fmage-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmage-ai%2Fmage-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmage-ai%2Fmage-ai/lists"}