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Tracing

Observability & tracing with Langfuse.

Gomus AI includes a built-in Langfuse integration so you can inspect and debug every retrieval and generation step of your RAG pipelines in near real-time.

Langfuse stores traces, spans, and prompt payloads in a purpose-built observability backend and offers filtering and visualisations on top.

REQUIREMENTS

A Langfuse workspace (cloud or self-hosted) with a Project Public Key and Secret Key.

1. Collect your Langfuse credentials

  1. Sign in to your Langfuse dashboard.
  2. Open Settings > Projects and either create a new project or select an existing one.
  3. Copy the Public Key and Secret Key.
  4. Note the Langfuse host (e.g. https://cloud.langfuse.com).

The keys are project-scoped: one pair of keys is enough for all environments that should write into the same project.

2. Add the keys to Gomus AI

  1. Log in to Gomus AI and click your avatar in the top-right corner.
  2. Select API > Scroll down to Langfuse Configuration.
  3. Fill in your Langfuse Host, Public Key, and Secret Key.
  4. Click Save.

Once saved, Gomus AI starts emitting traces automatically — no code changes required.

3. Run a pipeline and watch the traces

  1. Execute any chat or retrieval pipeline in Gomus AI.
  2. Open your Langfuse project > Traces.
  3. Filter by name ~ gomus-ai-*.

For every user request you will see:

  • A trace representing the overall request.
  • Spans for retrieval, ranking, and generation steps.
  • The complete prompts, retrieved documents, and LLM responses as metadata.
tip

Use Langfuse's diff view to compare prompt versions or drill down into long-running retrievals to identify bottlenecks.