LLM monitoring without sending your prompts to a third party

Why most LLM tools need your prompts and responses

Tools like Langfuse, Helicone, and LangSmith are tracing / observability platforms. Their whole job is to capture each request and response so you can open a trace later and inspect exactly what went in and what came out. That's genuinely useful — but it means the full prompt and completion are captured and stored on their platform. Self-hosting keeps that data on your own infrastructure, but the content is still recorded; it doesn't make the capture go away.

How ChirpPal is different

ChirpPal isn't a trace store — it's a smoke alarm. You wrap a call in one line, and the checks (valid JSON, no refusal, required keywords, latency, whether the call errored) run inside your own process, against the response you already have in memory. By default, the only thing that leaves your machine is pass/fail metadata: which check failed, latency, the model id, token count. The prompt and the response themselves are never sent.

Even error reporting is content-safe: a failed call's error field defaults to a short signature (the exception type plus a numeric status/code, e.g. RateLimitError (429)), not the raw message — validated against a strict format so a fragment of your own text can't slip through. And because both wrappers (Python and JS/TS) are open source, you can read every line and confirm this yourself rather than take our word for it.

How each tool handles your content

When you actually want the other kind

Be honest with yourself about what you need. If you want to replay a bad conversation to see exactly what the model was told, or you want prompt versioning, datasets, and offline evals, then you want a tracing platform — and you should use one. ChirpPal doesn't replace that; it's an alarm, not an archive. What it gives you instead is a way to know the moment a live call breaks, without your users' content ever leaving your process.

What you still catch without sending content

• Refusals ("I can't help with that") on a call that returned 200 OK.
• Invalid or schema-breaking JSON that would crash the next step.
• Missing required keywords, or forbidden ones (a leaked system prompt, a competitor's name).
• Latency spikes and outright errors / timeouts.
• A model you depend on being scheduled for retirement, via the deprecation radar.

Or start with the free, no-signup Model Deprecation Radar — the same data, nothing to install.

Compare: ChirpPal vs Langfuse · all LLM monitoring tools compared · do you even need a tool?