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Agent Rehearsal
Rehearse tool failures before retries reach production
Explore the tradeoffs between completion, duplicate writes, call budgets and virtual latency. Compare recovery policies. Inspect every attempt. A deterministic fixed-workflow simulator, not a benchmark of a named model.
Retrying sounds simple until the first operation might already have committed. A longer timeout can help completion while making latency worse. A repeated write can look successful while duplicating an effect.
Agent Rehearsal lets you compare recovery policies against paired, deterministic fault samples. Inspect each attempt, duplicate writes, call budgets and declared virtual latency. The Python example reproduces a lost payment response with in-memory functions and checks that a guarded retry charges once.
There is also a local trace inspector and a zero-dependency Python SDK. The simulator is a fixed workflow, not an LLM benchmark, and its percentages should not be read as a model leaderboard.
The source is MIT licensed; the public simulator needs no account, API key or payment. I am looking for feedback on which failure details need to be visible before a recovery policy feels understandable.
Try it: https://agent-rehearsal.web.app
Source: https://github.com/shi1720/agent...
Built by Shivam Gupta. More work and contact: https://shivamgupta.web.app/
LinkedIn: https://www.linkedin.com/in/shiv...
About Agent Rehearsal on Product Hunt
“Rehearse tool failures before retries reach production”
Agent Rehearsal was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #6 on the daily leaderboard. Explore the tradeoffs between completion, duplicate writes, call budgets and virtual latency. Compare recovery policies. Inspect every attempt. A deterministic fixed-workflow simulator, not a benchmark of a named model.
On the analytics side, Agent Rehearsal competes within Developer Tools and GitHub — topics that collectively have 561.8k followers on Product Hunt. The dashboard above tracks how Agent Rehearsal performed against the three products that launched closest to it on the same day.
Who hunted Agent Rehearsal?
Agent Rehearsal was hunted by Shivam Gupta. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Agent Rehearsal including community comment highlights and product details, visit the product overview.
Shivam Gupta