Conditional Branching And Error Handling In Pipelines

via “conditional task execution and branching logic”

A lightweight workflow engine built the way it should be: declarative, file-based, self-contained, air-gapped ready. One binary that scales from laptop to distributed cluster. Used as a sovereign AI-agent orchestration infrastructure.

Unique: Conditional execution encoded in DAG structure through task dependencies and exit code conditions — no explicit if-then-else constructs, enabling simple branching logic without adding control flow complexity

vs others: Simpler than Airflow's BranchPythonOperator (no Python code required) and more transparent than Temporal's workflow code because conditions are declarative in YAML