🛰️ A smoke alarm for your AI features

Your AI feature will break quietly one day —
and you'll be the last to know.

Models get deprecated, output formats drift, answers start refusing, latency explodes. Traditional monitoring only tells you the site is still alive — not that its answers got worse. ChirpPal wraps each real AI call with one line and catches it the moment it breaks.

One-line wrapper · 5-minute setup · it runs on the side and never touches your production path.

AI features break differently from normal websites

None of this throws an error. Your dashboard stays all-green. Usually a customer notices first — and then you're scrambling.

Three steps, five minutes.

1

Add one line — or let your AI tool do it

Paste our setup prompt into Cursor / Claude Code / Copilot and it wires the wrapper in for you. The wrapper watches every real call locally, on a side channel — it never gets in the way of the call itself.

2

In your code, pick what counts as broken

Add checks — valid JSON, no competitor names, latency, no refusals — right in your track() call. It also tracks which models you actually call and warns you before any of them is deprecated.

3

See it on your dashboard the moment it breaks

Which check failed and why, plus how many failures showed up in the recent window, right there on your project dashboard. On Pro, you also get it by email, so you don't have to keep the tab open.

The most common AI failures traditional monitoring misses:

Model deprecated / re-versioned API key expired JSON that won't parse Missing what it should say Mentioning competitors "As an AI…" refusals Latency spikes Empty responses

Every other tool tells you what already broke. This tells you what's about to. 🛰️

The Model Deprecation Radar tells you which of the OpenAI / Anthropic / Gemini models you use are scheduled to retire — and emails you before they do. Free, no signup.

Check out the model list →

Pricing

Pay yearly, get two months free.

Free

For a side project or your first AI feature.

$0

  • ~25,000 events/mo · 1 project
  • Model-deprecation radar
  • Email alert before a model you use retires
  • Deterministic checks
  • See failures on your dashboard
  • Python + JS/TS wrappers
  • 7-day retention
Start free
Compare plansFreePro
Monthly events~25,000~500,000
Projects1Unlimited
LanguagesPython · JS/TSPython · JS/TS
Deterministic checks (rule-based: invalid JSON, refusals, latency…) ?Rule-based checks on each response — crashes, invalid JSON, refusals, missing or forbidden keywords, latency.
Model deprecation radar ?Subscribe to the models you use and we email you before they're deprecated — no wrapper needed. Free for everyone.
See failures on your dashboard ?Every check failure shows up on your project's dashboard — on any plan.
Email alert when a check fails ?Pro also emails you the moment a check starts failing, so you don't have to keep the dashboard open.
Email alert before a model you use retires ?The wrapper sees which models you actually call and flags one scheduled for deprecation automatically — no manual list to maintain.
Data retention7 days30 days
Price$0$19/mo

Not sure? Free is plenty for a side project. Pick Pro when a customer would notice if it broke.

Need more?

Higher volume, more projects, or a team plan — tell us what you need and we'll figure out a price together.

Contact us →

FAQ

Do I need to change my code?

Just one line — wrap your AI call, or paste our integration prompt into your AI tool (Cursor / Copilot / Claude Code) to auto-add it.

Will it affect my production?

No. It watches your calls from the side — it never sits in front of them. If ChirpPal errors or our service goes down, your AI keeps working exactly the same; you just miss monitoring for that window.

How is this different from Sentry?

Sentry catches crashes and errors and gives you the context to debug them — it records, but it doesn't judge whether a call that succeeded actually returned something usable. ChirpPal renders a pass/fail verdict on the response itself (valid JSON, no refusal, required keywords, latency) and tells you when it's wrong. The two work well side by side — and usually should.

How is this different from Langfuse?

Langfuse is a full LLM engineering platform — trace trees, prompt versioning, datasets, experiments, evals. If you need those, use it. ChirpPal does one thing: tell you when a live call breaks. And its checks run inside your process, so by default your prompts and responses are never sent anywhere at all — only pass/fail metadata leaves. Full ChirpPal vs Langfuse comparison →

Do the checks use my OpenAI / Anthropic budget?

No. Deterministic checks run locally in the wrapper — never your keys or token budget.

What data do you see?

Only the metadata you configure — pass/fail, latency, model name, which check failed, plus any tags you add yourself. Even error info defaults to a short signature (an exception type, e.g. "RateLimitError (429)") rather than the raw message. We never see your prompts or the AI's responses, and never the full text of an error, unless you deliberately opt in to one of those in your config. How we monitor without sending your prompts →

Is the wrapper open source?

Yes. Both wrappers (Python and JS/TS) are open source — read every line before you put it in your app: github.com/teprofLLC/chirppal-sdk. The two promises that matter most — it's fail-open, and it only sends metadata by default — are things you can check yourself, not just take our word for.

Which providers does it work with?

The deprecation radar and the built-in response adapters cover OpenAI, Anthropic (Claude), and Google Gemini — the three we've tested end to end. The failure checks run on the response itself, so you can wrap a call to any provider; those three just get the model-retirement tracking on top.

Don't let your customers be your monitoring system.

Start free