---
title: On-call
description: How the on-call rotation works at Langfuse.
---

# On-call at Langfuse

Langfuse Cloud is covered by an on-call rotation around the clock. If you are paged, follow the [incident response plan](/handbook/product-engineering/incident-response).

## Rotation

- **Daytime (level 1)**: newer joiners in the data and platform group take the daytime shifts. Being paged during working hours, with the whole team around to help, is one of the fastest ways to learn how our systems behave in production.
- **Nights and weekends**: more experienced engineers share these shifts.
- **Daytime (level 2)**: the more experienced engineers also act as the escalation level behind the daytime level 1.

## Weekly review

We review all pages weekly — reasons and times, per engineer — to keep the load manageable. Noisy or non-actionable alerts get fixed or removed, and we escalate quickly if alert volume becomes excessive.

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---

## Agent Instructions

This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/handbook/product-engineering/how-we-work/on-call.md`.

### Querying these docs

If the answer is not on this page, query the documentation instead of guessing:

- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:

  ```bash
  curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
  ```

- **Index of every page**: <https://langfuse.com/llms.txt>, with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).

### Before writing Langfuse code

- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.