Question 1

How does iWF compare to using Temporal directly?

Accepted Answer

iWF is an abstraction layer on top of Temporal (or Cadence), offering a higher-level workflow-as-code model that simplifies patterns like SAGA. However, it adds an extra layer that can increase latency and reduce flexibility for teams needing fine-grained control over Temporal's native APIs.

Question 2

How to deploy iWF in production?

Accepted Answer

Deploy iWF by setting up the iWF server and a backend engine like Temporal in your infrastructure, using Docker images or Kubernetes configurations. The wiki provides guidelines, but you'll need to manage both services, which requires careful planning for scaling and persistence.

Question 3

What databases does iWF use for state persistence?

Accepted Answer

iWF relies on the backend engine (Temporal or Cadence) for persistence, which typically supports databases like Cassandra, MySQL, or PostgreSQL. You don't configure databases directly in iWF, but through the underlying engine's setup.

Question 4

Can iWF handle event-driven architectures with Kafka?

Accepted Answer

Yes, iWF is designed for event-driven workflows. It integrates with external systems like Kafka through RPC and signals, allowing workflows to wait for and react to events from message queues, as shown in microservice orchestration samples.

Question 5

How to handle errors in iWF workflows?

Accepted Answer

iWF provides automatic retry mechanisms for WorkflowState executions with configurable backoff. You can define error handling in execute methods and use RPC for external notifications, ensuring resilience without complex custom code.

Question 6

Is iWF suitable for AI agent workflows?

Accepted Answer

Yes, iWF is promoted for building reliable AI agents, as it manages long-running, stateful processes with retries and external interactions. Examples show it can handle complex AI pipelines that require durability and event adaptation.

iWF

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