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Langfuse

Langfuse

Observe Langfuse instrumented LLM applications with Dynatrace AI Observability.

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Explorer View showing token count, temperature, and full prompt/response content.AI Agents Topology showing the instrumented service connected to the model and provider.
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Overview

Get full-stack visibility into your Langfuse-instrumented LLM applications with Dynatrace AI Observability. Langfuse emits OpenTelemetry traces using its own semantic conventions (langfuse.observation.type, langfuse.observation.model.name, langfuse.observation.usage_details, and others). This integration normalizes them into native gen_ai.* semantic conventions so you can monitor model performance, token costs, and conversation flows in Dynatrace without writing custom instrumentation code.

Use cases

Monitor LLM health and performance

  • Track request latency, error rates, and token consumption across models and providers.
  • Manage AI costs with token-level usage breakdowns by model.
  • Enable alerting based on latency anomalies, cost thresholds, or error rate spikes in your AI workloads.

Debug LLM applications end-to-end

  • Trace LLM calls with distributed traces showing prompt content, model parameters, and response content.
  • Pinpoint exact failure points with span-level detail.
  • Capture full prompt and completion content for root cause analysis.

Track conversations across sessions

  • Group spans that share a session ID under the same conversation thread.
  • Correlate regressions with model version changes or prompt updates.
  • Follow model and provider usage across your application fleet.

Get observability with minimal configuration

  • Run the pre-configured OTel Collector pipeline to normalize attributes before export to Dynatrace.
  • Or export directly to Dynatrace and let OpenPipeline handle normalization at ingestion; no collector process to run.
  • No custom instrumentation code required in either path.

Get started

Requirements

  • Python 3.8+ (Python demo) or Node.js 20+ (Node.js demo).
  • An OpenAI-compatible API key and endpoint.
  • Dynatrace SaaS with Traces powered by Grail and Metrics powered by Grail enabled.
  • A Dynatrace API token with the openTelemetryTrace.ingest scope.
  • Docker (OTel Collector path only).

Details

Normalization paths

OpenTelemetry Collector Run the pre-configured collector alongside your app. The transform/langfuse processor maps langfuse.observation.* attributes into the gen_ai.* semantic conventions that Dynatrace AI Observability queries. See the Python example or the Node.js example.

OpenPipeline Export traces directly from your app to Dynatrace via OTLP (no collector required) and let Dynatrace OpenPipeline handle the attribute normalization at ingestion time. Deploy the pre-configured OpenPipeline definition from openpipeline-langfuse.yaml to your tenant, with the routing matcher isNotNull(langfuse.observation.type), before running the app. Follow these instructions

To install the AI Observability app, see the app installation docs.

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