A comparison of advanced workflow engines

Feature | Highway Workflow Engine | DBOS (from Demos) | Apache Airflow | Tork
Core Philosophy | Decoupled Library. A resilient engine you integrate into your app. | Integrated Framework. A full-stack platform that fuses web, DB, and workflows. | Batch ETL Orchestrator. A mature platform for scheduling static, data-processing DAGs. | Task Execution Service. A distributed CI/CD-style runner for Docker/shell tasks.
Primary Use Case | Long-running processes, resilient APIs, event-driven agents. | Resilient web applications, transactional workflows. | Daily/hourly batch ETL, data pipeline orchestration. | CI/CD pipelines, container-based tasks.
Atomic State Guarantee | YES. Guarantees application DB state and workflow state are committed in one transaction via ctx.db_session. | YES. This is its core feature. @DBOS.transaction provides atomic guarantees. | NO. (Critical Flaw). No guarantee. Task state and application state are not in the same transaction. High risk for banking. | NO. (Critical Flaw). No guarantee. Task execution is a black box. State is updated after the fact. High risk for banking.
Durable Execution | Fine-grained (Step-level). ctx.step() provides idempotent, stateful checkpoints. | Fine-grained (Step-level). Each transactional function is an idempotent, durable step. | Coarse-grained (Task-level). Retries the entire task (e.g., a 30-min script). No sub-task checkpointing. | Coarse-grained (Task-level). Retries the entire task (e.g., a Docker container).
Architecture | Broker-less. Uses Postgres for queue, state, and scheduler. Single Dependency. | Broker-less. Uses Postgres for queue, state, and scheduling. Single Dependency. | Broker-based (Optional). Requires DB, Scheduler, Webserver. Often uses Celery/Redis for scaling. High Complexity. | Broker-based. Requires both a message broker (e.g., RabbitMQ) and a datastore (Postgres). High Complexity.
LLM Agent Suitability | Excellent. Event-driven (ctx.wait_for_event), fine-grained state, and dynamic loops are perfect for agents. | Excellent. Event-driven (DBOS.recv), fine-grained state, and imperative code is a natural fit for agents. | Very Poor. Not event-driven. Static DAGs are the opposite of what agents need. | Very Poor. Not event-driven. Coarse-grained tasks. Not designed for stateful, dynamic loops.
Long-Running ETL | Excellent. Step-level checkpointing means a 10-hour ETL can resume from its last transform, not from the beginning. | Excellent. Same principle. Each transaction is a durable checkpoint. | Poor-to-Fair. The industry standard, but inefficient. Task-level retries are costly and risky for long-running jobs. | Very Poor. Task-level retries are a non-starter for heavy ETL.
Fault Tolerance | Advanced. Built-in, configurable Bulkhead & Circuit Breaker patterns. Best in class. | Standard. Provides standard crash recovery and task-level retries. | Standard. Provides task-level retries. | Standard. Provides task-level retries.
Workflow Definition | Declarative (DSL/YAML). High Auditability. | Imperative (Code). High Developer Velocity. | Declarative (Python-as-Config). Python files generate static DAGs. | Declarative (YAML).
Observability/UI | Basic (CLI). A new monitoring CLI is implemented. Full Web UI is on the roadmap. | Excellent. Integrated Web UI, monitoring, and time-travel debugging. Best in class. | Excellent. A mature, feature-rich Web UI for managing batch jobs. | Good. A built-in Web UI is provided for monitoring jobs and workers.