One platform to build, run, and operate every data workflow your team touches. Source-available, self-hosted.
Most AI tools help you write pipeline code. LeastAction gives the AI an operator — it generates the code, deploys it, runs it, reads the logs, queries the data, spots the issue, fixes the operator, and reruns. No human in the loop at each step. It works against whatever stack you already have — PostgreSQL, Athena, Redshift, BigQuery, S3, Lambda, dbt, Airflow, any API — with no migration.
Everything lives in a catalog: operators, connections, payloads, configs, actions, tasks, reports, and assets. The catalog is the shared context the AI works from — and the same interface engineers use to schedule, monitor, and reuse pipelines across teams. Self-hosted, one instance per team.
Airflow, Dagster, and Prefect are excellent at running pipelines. LeastAction takes a different starting point — an AI operator and a catalog open to the whole team:
- The AI operates, end to end. It generates the operator, deploys it, runs it, reads logs, queries the result, fixes its own bug, and reruns — not just code suggestions, but the full loop. - Orchestrate without Python. Engineers write operators in Python (or generate them with AI); everyone else assembles, schedules, and runs pipelines from the UI or Git. - Custom operators and connections, no package infrastructure. Write an operator, save it to the catalog, use it immediately — no provider packages, no per-worker deploy. Connections take any fields your operator needs. - Granular control built in. N-level config with locked/overridable parameters, per-connection parallelism with priority queuing, native Git CI/CD, and 1-click backfill
From the project README.
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