"Temporal has unveiled a public preview integration with the OpenAI Agents SDK, introducing durable execution capabilities to AI agent workflows built using OpenAI's framework. This collaboration enables developers to build AI agents that automatically handle real-world operational challenges, such as LLM rate limits, network disruptions, and unexpected crashes, without adding complexity to their code. At the core of this integration is Temporal's strength in orchestrating distributed, fault-tolerant systems."
"Traditionally, AI agents, whether built with LangChain, LlamaIndex, or the OpenAI SDK, run as stateless processes, meaning a failure mid-execution forces a complete restart and wastes compute and token costs. With Temporal, every agent interaction, including large language model (LLM) calls, tool executions, and external API requests, is captured as part of a deterministic workflow. This approach allows the system to automatically replay and restore the agent's exact state after a crash, timeout, or network failure, dramatically increasing reliability and operational efficiency."
"The integration works by wrapping OpenAI agents inside Temporal workflows, where reasoning loops and tool calls are orchestrated as discrete steps. These workflows persist state in Temporal's event history log, backed by scalable databases like Cassandra, MySQL, or PostgreSQL. Each external interaction is implemented as a Temporal Activity, which runs outside the workflow thread, enabling retries and isolation while keeping orchestration stable."
Temporal's integration with the OpenAI Agents SDK enables durable execution for AI agent workflows by wrapping agents in Temporal workflows. Temporal orchestrates reasoning loops and tool calls as discrete steps, persisting workflow state in an event history log backed by databases like Cassandra, MySQL, or PostgreSQL. External interactions run as Temporal Activities outside the workflow thread, allowing retries, isolation, and deterministic orchestration. The system captures every agent interaction—including LLM calls, tool executions, and external API requests—so workflows can automatically replay and restore exact agent state after crashes, timeouts, or network failures. The integration reduces compute and token waste and improves reliability.
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