Most API tests stop at 200 OK. FetchSandbox lets developers and AI agents verify what happens next—webhooks, retries, state changes, async workflows, and failure scenarios. It reproduces the real bug, proves the fix, and remembers what breaks—so your agent catches it before production. Connect via MCP to Cursor, Claude Code, Windsurf, VS Code, and Codex. Explore 60+ APIs—Stripe, GitHub, Clerk, Resend, Twilio, Descope, OpenAI—without burning real API quota or waiting on staging.
@rnagulapalle I get the outline of what it does, but I'd love to understand this better: how can a non-coder like me who builds web-apps just by prompting with Claude Code actually make use of this? I saw it helps avoid burning through API tokens,hitting limits and fix bugs.
Not needing keys or accounts to just try it is underrated, half these tools you have to sign up for three things before you even see if it's useful. And skipping the wait on staging entirely is such a real pain point. Congrats on the launch!
The "remembers what breaks" line is interesting for API integration testing. Does FetchSandbox keep track of the exact failing request/response details, like payloads, headers, status codes, and timing, or is the memory more about patterns across previous test runs? That distinction would help teams understand where it fits beside their current CI checks.
The "beyond 200 OK" framing is exactly right, and I learned it the hard way. I shipped a Paddle integration recently and every test I had was a green 200 — the bug that actually bit me was an idempotency one: a retried/duplicate event creating an orphan customer on a rolled-back checkout. Nothing in my suite could have caught it, because I was only asserting the happy path.
The way I eventually found it was by hand: ngrok pointed at localhost, replaying webhooks, poking state until something broke. A deterministic failure library that just does the duplicate-event / late-event / stale-state cases for me would have saved me an entire evening.
Two questions: (1) does the failure library cover Paddle, or is Stripe the only payments provider so far? (2) for webhook lifecycle bugs specifically — can you replay events out of order (e.g. a subscription.updated landing before the checkout.completed)? That ordering case is the one that actually broke me, and it's the hardest to reproduce on purpose.
The "remembers what breaks" line is the whole reason I clicked. The APIs I integrate never fail cleanly. They pass in dev, pass in review, then throw a 500 once a week in production for reasons I can never reproduce on demand. Does FetchSandbox help with that kind of intermittent break, or is it aimed more at catching hard contract changes when a provider ships a new version? And does it cover webhook and callback flows, or just the requests I make outbound? Those inbound calls are where I get burned most and they're the hardest to test.
The worst production bugs are always the ones that look completely fine until a second system reacts. Your own code passes, the API returns 200, and then three hours later a duplicate webhook fires and someone gets charged twice. Curious what the most common failure pattern is across your 60 plus APIs, is there one class of bug that shows up regardless of which API you're testing?
the most-tests-stop-at-200 framing is exactly right. the bugs never live in the happy path, they live in the retry that fires twice and the webhook that lands out of order. good to see a tool pointed at that part specifically.
The "verify what happens after the 200 OK" framing is the actual gap for me — most of my integration bugs live in retries and webhook ordering, not the happy path. Two things I'd want to pin down before wiring it into a Claude Code loop: when I connect over MCP, does the failure library and my recorded scenarios live locally per-project or in your cloud, and can I pin a specific failure (say a Stripe webhook-out-of-order case) so a CI run replays it deterministically instead of re-deriving it each run? Determinism is what decides whether I'd gate a merge on it.
Congrats on the launch. I integrate several AI provider APIs in my product, and the painful breakages are never the loud ones. It's when the same endpoint quietly starts returning a slightly different shape, or a model name stops resolving one day. Does FetchSandbox catch that kind of quiet drift, or is it focused on hard failures?
The "verify what happens next" framing is exactly the gap — 200 OK tells you nothing about the retry/webhook/state mess that actually breaks in prod.
Two things I'd want to know as someone wiring these into agents:
When an agent drives this over MCP, does it get to discover the failure library as callable scenarios — enumerate and pick which failure to inject — or is scenario selection still human-curated and the agent just runs what you set up?
And "remembers what breaks" — is that memory per-project, and does it auto-replay the known-break scenarios as a regression gate on the next change, so a fix that regresses gets caught without me re-describing the bug? @rnagulapalle
the dropped-vs-mid-response-timeout distinction @omri_ben_shoham1 raised is the one I'd want too. adjacent question: do you simulate variable latency (webhook arrives 3s vs 30s late) or is timing binary right now, on-time vs late?
the dedup/idempotency scenarios are the ones I'd actually use. when two CI runs hit the same sandboxed Stripe API concurrently, is state isolated per run, or can one job's retry storm bleed into another's results?
@Raj shipping duplicate/redelivery with the same event id is the one that matters most imo - idempotency bugs are the ones that actually cost people money in prod, way more than a webhook just being late. nice that you got that out fast. curious if dropped events also simulate a partial timeout (connection dies mid-response) vs a clean non-delivery, since those two failure modes get handled very differently in most retry logic
This is a very real pain point for AI-coded integrations. The first version usually passes the happy path, but the scary bugs are async: duplicate webhooks, late events, retry order, and state that looks correct until a second system reacts.
If I were testing this, I’d want one simple report after each run: what behavior was simulated, what state changed, and which failure is now covered so the agent does not reintroduce it later. That “memory of breakage” angle feels stronger than just another API sandbox.
Hey bro The website icon is showing of Next.js default icon . It would be great if that shows your website's logo .
I have spent time debugging APIs where the request worked perfectly but the webhook created unexpected issues later. A tool that can consistently recreate those situations would have saved a lot of time. Nice work on tackling such a common challenge.
Love that it covers async edge cases most testing tools miss, the MCP integration with Cursor is super smooth. One thing that would make this a no-brainer for me: built-in support for replaying recorded webhook sequences against different environments, so I can validate that a staging deploy handles the exact same payload ordering as production without re-running the whole test suite.
the webhook replay feature is genuinely useful, finally a way to test retry logic without stubbing out half my codebase
About FetchSandbox on Product Hunt
“API integration testing that remembers what breaks”
FetchSandbox launched on Product Hunt on July 12th, 2026 and earned 416 upvotes and 84 comments, earning #3 Product of the Day. Most API tests stop at 200 OK. FetchSandbox lets developers and AI agents verify what happens next—webhooks, retries, state changes, async workflows, and failure scenarios. It reproduces the real bug, proves the fix, and remembers what breaks—so your agent catches it before production. Connect via MCP to Cursor, Claude Code, Windsurf, VS Code, and Codex. Explore 60+ APIs—Stripe, GitHub, Clerk, Resend, Twilio, Descope, OpenAI—without burning real API quota or waiting on staging.
FetchSandbox was featured in API (98.4k followers), Developer Tools (515.9k followers) and Artificial Intelligence (473.7k followers) on Product Hunt. Together, these topics include over 193.9k products, making this a competitive space to launch in.
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stopping at 200 OK is exactly the trap 👏 remembering what broke is the smart bit. solo devs or teams mostly?