OpenAI + @Temporalio : Building Durable, Production Ready Agents - Cornelia Davis, Temporal

Key Concepts

• Durable Agents: Combining OpenAI Agents SDK with Temporal provides robust, scalable, and reliable agentic applications.
• Temporal Core Abstractions: Workflows orchestrate Activities, ensuring durability, retries, and state management.
• Agentic Loops: LLMs drive application flow through continuous cycles of LLM calls, tool invocation, and result processing.
• Microagents & Orchestration: Building small, specialized agents and orchestrating them enhances maintainability and scalability.
• OpenAI Agents SDK Integration: Simplified tool definition via activity as tool and abstracting the SDK’s runner class for Temporal management.

Introduction & Background

Cornelia Davis (Temporal Developer Advocate) introduces Temporal and its integration with the OpenAI Agents SDK, framing it as a solution for building durable and scalable agentic applications. She highlights the shift from managing complex distributed systems infrastructure (like Kafka and Redis) to focusing on business logic – the agent’s behavior – thanks to Temporal. Her experience with distributed systems, stemming from work on Cloud Foundry, informs her perspective on the challenges Temporal addresses. Several companies, including Snapchat, Airbnb, Pizza Hut, Taco Bell, OpenAI (Codeex & Image Gen), and Lovable, are already leveraging Temporal in production.

OpenAI Agents SDK & Temporal: Independent Overviews

The OpenAI Agents SDK simplifies agent creation, automating JSON blob generation for tools, eliminating manual creation previously required with the OpenAI API. The core concept is the “agentic loop” – a continuous cycle of LLM calls, tool invocation, and result processing. Temporal, conversely, is an open-source, distributed systems platform providing durability and reliability. It handles complexities like retries, failure recovery, and concurrency, allowing developers to concentrate on application logic. The integration leverages a plugin within the Temporal worker and the activity as tool function for streamlined tool definition.

Building Durable Agents: Implementation Details

The core of the integration lies in implementing agent functions as Temporal Activities and orchestrating them within Temporal Workflows. Activities represent individual, idempotent tasks (like calling an LLM or a tool), while workflows define the overall logic and sequence. Temporal manages state, retries, and scaling, ensuring resilience to failures. The OpenAI Agents SDK’s runner class is abstracted to allow Temporal to manage LLM calls and tool invocations durably. Two orchestration methods are presented: “just code” (direct handoffs between agents) and OpenAI’s “handoffs” (context switching within a single agentic loop).

Demonstration & Use Cases

A live demo showcases a weather alert agent querying the National Weather Service API for alerts in California and New York, demonstrating the agent’s ability to interact with external tools. Temporal’s durability is highlighted by intentionally crashing the worker process mid-execution, demonstrating automatic resumption from the point of failure. Further use cases include human-in-the-loop agents (where Temporal manages long-running workflows awaiting human input) and “digital twins” (workflows mirroring real-world entities and responding to events). A customer reported shifting from 25% to 75% time spent on business logic after adopting Temporal, due to reduced operational overhead.

Key Advantages & Perspectives

The speaker emphasizes that durability is crucial for production-ready agents, and Temporal provides a robust solution. Temporal abstracts away the complexities of process management, state handling, and scaling, allowing developers to focus on the logic of their agents. Building small, focused “microagents” promotes modularity and simplifies development, mirroring the benefits of microservices architecture. Temporal’s native application approach provides benefits like durability, visibility, and scalability that are difficult to achieve with other frameworks. Temporal supports running in 15 Amazon regions and 4-6 Google regions, indicating its scalability and geographic distribution capabilities.

Technical Considerations

Key technical terms include: Agentic Loop, Activity, Workflow, Event Sourcing, Idempotency, Dynamic Activity, Worker, Runner, and Handoffs. Temporal adds tens of milliseconds of latency to activity calls, generally acceptable for long-running agentic applications. Retry policies are crucial for handling failures gracefully.

Conclusion

The presentation concludes by advocating for the combination of OpenAI Agents SDK and Temporal as a powerful solution for building durable, scalable, and maintainable AI agents. The emphasis is on leveraging Temporal’s robust infrastructure to handle the complexities of distributed systems, allowing developers to focus on the core logic and behavior of their agents. Viewers are encouraged to explore the provided resources (GitHub repository, documentation, AI cookbook) and consider Temporal for their agentic application development.