This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
sandtable
Rehearse your AI agent on a copy of your customer's world
Your agent sounds fine on the call and books a carrier whose insurance expired. sandtable catches that: simulated callers, mock systems with real state, and every business rule checked on every tool call. Open source, no judge model. Try it running: npx sandtable-ai demo
I built sandtable to fix a specific gap in agent testing: a carrier-sales agent says "Booked L-1001 at $2,100." The call transcript sounds flawless, so an LLM judge model passes it. But the carrier's insurance had expired, a fact living in a database record, not in the dialogue.
sandtable tests conversational agents against stateful, simulated environments to catch these hidden failures before production.
How it works
Simulated callers interact with your agent in real time.
Stateful mocks capture every tool call and evaluate business rules before execution.
Deterministic grading looks at the actual DB records and rules broken. No LLM judge variance—the same transcript yields the same pass/fail result every single time, evaluated over $pass^k$.
Repo-native setups define your domain via simple YAML (generated from your OpenAPI spec, SOPs, and transcripts) that runs in CI on every commit.
Key distinctions
vs. Harbor: Designed specifically for real-time conversational business logic, rather than containerized coding tasks.
vs. LangWatch, DeepEval & IntellAgent: Eliminates subjective LLM judges. Failures pinpoint the precise broken rule and database record.
vs. τ-bench: Adapts the benchmark's underlying methodology into a practical dev tool for custom domains rather than fixed static datasets.
vs. Hosted Voice Platforms: Open-source, runs locally, imports Vapi, Retell, and ElevenLabs agents, and can grade those platforms' test calls.
Current limitations: Text-only (no audio/latency benchmarking yet), no LLM tone scoring, and local file reports instead of a hosted dashboard. This is all work in progress!
Try it in 10 seconds (no API key needed): npx sandtable-ai demo
(MIT-licensed on npm and PyPI, with a Claude Code plugin.)
No comment highlights available yet. Please check back later!
About sandtable on Product Hunt
“Rehearse your AI agent on a copy of your customer's world”
sandtable was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #336 on the daily leaderboard. Your agent sounds fine on the call and books a carrier whose insurance expired. sandtable catches that: simulated callers, mock systems with real state, and every business rule checked on every tool call. Open source, no judge model. Try it running: npx sandtable-ai demo
sandtable was featured in Software Engineering (43.3k followers), Developer Tools (522.6k followers), Artificial Intelligence (483k followers) and GitHub (41.5k followers) on Product Hunt. Together, these topics include over 269.2k products, making this a competitive space to launch in.
Who hunted sandtable?
sandtable was hunted by Uliseis. 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.
Want to see how sandtable stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Uliseis
Hey Product Hunt!
I built sandtable to fix a specific gap in agent testing: a carrier-sales agent says "Booked L-1001 at $2,100." The call transcript sounds flawless, so an LLM judge model passes it. But the carrier's insurance had expired, a fact living in a database record, not in the dialogue.
sandtable tests conversational agents against stateful, simulated environments to catch these hidden failures before production.
How it works
Simulated callers interact with your agent in real time.
Stateful mocks capture every tool call and evaluate business rules before execution.
Deterministic grading looks at the actual DB records and rules broken. No LLM judge variance—the same transcript yields the same pass/fail result every single time, evaluated over $pass^k$.
Repo-native setups define your domain via simple YAML (generated from your OpenAPI spec, SOPs, and transcripts) that runs in CI on every commit.
Key distinctions
vs. Harbor: Designed specifically for real-time conversational business logic, rather than containerized coding tasks.
vs. LangWatch, DeepEval & IntellAgent: Eliminates subjective LLM judges. Failures pinpoint the precise broken rule and database record.
vs. τ-bench: Adapts the benchmark's underlying methodology into a practical dev tool for custom domains rather than fixed static datasets.
vs. Hosted Voice Platforms: Open-source, runs locally, imports Vapi, Retell, and ElevenLabs agents, and can grade those platforms' test calls.
Current limitations: Text-only (no audio/latency benchmarking yet), no LLM tone scoring, and local file reports instead of a hosted dashboard. This is all work in progress!
Try it in 10 seconds (no API key needed): npx sandtable-ai demo
(MIT-licensed on npm and PyPI, with a Claude Code plugin.)