Durable Agent Workflows with Temporal: Crash-Resistant State for Long Tasks

Solution

Temporal is an open-source durable execution platform that wraps agent logic in persistent workflows. Integrating Temporal with the OpenAI Agents SDK requires adding two Python decorators to convert an agent function into a workflow: @workflow.defn on the class and @workflow.run on the entry point. Each agent tool call becomes a Temporal activity, an atomic unit of work that is individually retried on failure. When a worker process crashes mid-execution, the workflow persists in Temporal's state store, and as soon as any worker comes back online, pending activities are resumed from exactly where they left off. For the deep research sample that performs 10 parallel web searches, each search is a separate activity running concurrently. If three searches fail due to a network cut, Temporal retries those three with exponential backoff without re-running the seven that already completed. Bug fixes can be deployed without restarting workflows: update the code, restart workers, and in-progress workflows resume using the fixed code from the point of failure. Interactive workflows are supported via Temporal's workflow update primitive, where a workflow can pause and wait for user input (such as clarifying questions before beginning research), receive the answers as workflow updates, and then continue. Scaling is just adding more worker processes; Temporal distributes activities across available workers automatically.