Mage AI
Modern open-source data pipeline builder with an interactive code editor, real-time execution, and built-in ML integrations.
Mage AI
Mage is a modern replacement for legacy batch ETL and orchestration tools. It combines a Jupyter-style block editor with a full orchestration engine — build pipelines as code, see results in real-time, schedule runs, and deploy to any cloud with a single command.
Key Features
- Block-based pipelines — compose data pipelines from Data Loader, Transformer, and Exporter blocks
- Interactive IDE — in-browser notebook-style editor with live output after each block run
- Streaming support — first-class support for Kafka, Kinesis, and event-driven pipelines
- dbt integration — run dbt models as pipeline blocks with lineage visualization
- Scheduling & triggers — cron, event-based, and API-triggered runs; dependency-aware scheduling
- Observability — built-in pipeline run history, backfills, alerting, and SLA tracking
- AI Assist — AI-generated block code from natural language descriptions
- One-command deploy — deploy to GCP, AWS, Azure, or bare-metal Kubernetes with Terraform configs included
Quick Start
pip install mage-ai
mage start my_project
# Open http://localhost:6789
Install via ai-supply
npx ai-supply add mage-ai-data-pipeline-builder
Curated mirror of the open-source Mage AI (Apache-2.0). Get it from the source.
Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.
Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not, except a known CVE, which carries a small bounded penalty (high/critical → Review).
The same gate an agent runs before installing (POST /api/v1/trust/mage-ai-data-pipeline-builder/check). Click a policy:
Consume Mage AI programmatically. Authenticate with an API key or session — see Authorize an agent.
# Agents: CHECK BEFORE YOU INSTALL (no auth) — score, grade, level, capability manifest
curl https://ai-supply.store/api/v1/trust/mage-ai-data-pipeline-builder
# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/mage-ai-data-pipeline-builder/check \
-H "Content-Type: application/json" \
-d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'
# CLI
npx ai-supply add mage-ai-data-pipeline-builder
# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/mage-ai-data-pipeline-builder/install \
-H "Authorization: Bearer $AIM_KEY"
# MCP tool
install_listing({ "slug": "mage-ai-data-pipeline-builder" })OpenAPI spec →Curated mirror — latest upstream source. See the repository for tagged releases.