DAG-based task execution engine with conditional branching, parallel tasks, state persistence, and retry policies. Define workflows in JSON, execute them with full observability.
Browse the actual product documentation and code examples included in this toolkit.
Key features of Workflow Engine
• DAG execution — Tasks execute in dependency order with automatic parallelism • Conditional branching — Skip tasks based on Python expressions • Parallel execution — Tasks without dependencies run concurrently via thread pool • State persistence — Save and resume workflows after failures • Retry policies — Per-task retry limits with configurable backoff • DAG validation — Detects cycles and missing dependencies before execution
DAG execution — Tasks execute in dependency order with automatic parallelism
Conditional branching — Skip tasks based on Python expressions
Parallel execution — Tasks without dependencies run concurrently via thread pool
State persistence — Save and resume workflows after failures
Retry policies — Per-task retry limits with configurable backoff
DAG validation — Detects cycles and missing dependencies before execution
Configure Workflow Engine parameters to see how the product works.
# Run a workflow python src/workflow_engine.py --config examples/workflow_config.json # Preview execution plan python src/workflow_engine.py --config examples/workflow_config.json --dry-run # Resume a failed workflow python src/workflow_engine.py --config examples/workflow_config.json --resume wor