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.