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Langfuse is an open‑source observability platform for LLM applications that provides tracing, monitoring, and debugging. The integration is built into the Launchpad’s core folder.
You can self‑host for full data control and privacy, which is useful when sensitive data must stay within your infrastructure.

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Why Langfuse?

  • Complete Tracing: Track every workflow step, node execution, and LLM call
  • Performance Monitoring: Monitor response times, costs, and success rates
  • Debug Issues: Detailed logs and traces for troubleshooting failures
Datalumina uses this integration in production to monitor and trace workflows.

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Quick Setup

1

Get Langfuse Account

Create a free account at langfuse.com and get your API keys
2

Update Environment

Add to your .env files:
  LANGFUSE_PUBLIC_KEY=
  LANGFUSE_SECRET_KEY=

  # For EU use: `https://cloud.langfuse.com/api/public/otel`
  OTEL_EXPORTER_OTLP_ENDPOINT=https://cloud.langfuse.com/api/public/otel
  # For US use: `https://us.cloud.langfuse.com/api/public/otel`
  #OTEL_EXPORTER_OTLP_ENDPOINT=https://us.cloud.langfuse.com/api/public/otel
3

Checkout Example Branch

git checkout example/langfuse
4

Test Integration

python playground/workflow_playground.py
5

Verify Dashboard

Check your Langfuse dashboard for workflow traces

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Core integration features

  • Automatic Tracing: Every workflow execution is automatically traced with no code changes required
  • Node-Level Visibility: Individual node executions, inputs, and outputs are captured
  • LLM Call Tracking: All LLM interactions including prompts, responses, and metadata
  • Error Monitoring: Failed executions with full stack traces and context

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Dashboard features

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Workflow traces

View complete workflow execution paths with timing, inputs, and outputs for each node.

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Performance analytics

Monitor average response times, success rates, and cost analysis across workflows.

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LLM usage tracking

Track token usage, model performance, and costs across different LLM providers.

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Debug information

Detailed error logs with full context when workflows fail or perform unexpectedly.
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