OpenAI Agents SDK demos made durable and scalable with Temporal

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Steve from Temporal here. Temporal is an MIT open source project for reliable execution at scale. I adapted+extended some of OpenAI's Agents SDK samples to integrate with Temporal. These demo agents can survive process crashes, scale to millions of executions in parallel and have easy-to-implement human interactivity. Just add a couple of Python decorators to your OpenAI agent code, run Temporal workers and you're ready to go. Check the video I did with OpenAI showing this in action (it's linked in the repo). OpenAI actually use us for ChatGPT Images and also their Codex code writing agent so I figure this may be a viable path for others to code something easy that's also reliable at scale. Happy to answer questions.

Visit product View on Hacker NewsJul 30, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, codex · Missing: mac, macos, cursor

98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: video, para · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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