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Observability with MLflow

This tutorial covers how to integrate goose with MLflow to trace your goose sessions and understand how the agent is performing.

What is MLflow​

MLflow is an open-source platform for managing the end-to-end machine learning and AI lifecycle. MLflow Tracing provides detailed observability into AI agent execution, capturing LLM calls, tool usage, and agent decisions with a rich visualization UI.

Why MLflow for goose​

  • Detailed trace visualization: Inspect every LLM call, tool execution, and agent decision in a hierarchical trace view.
  • Token usage tracking: Monitor input/output token counts and costs across sessions.
  • Evaluation framework: Evaluate agent outputs using built-in LLM judges and custom scorers.
  • Prompt management: Version and manage prompts used across your AI applications.
  • Open source: Fully open-source with no vendor lock-in, self-host anywhere.

Set up MLflow​

Install MLflow and start the tracking server:

pip install mlflow
mlflow server --port 5000

The MLflow UI will be available at http://localhost:5000.

tip

For production use, configure a SQL backend store (PostgreSQL, MySQL) instead of the default SQLite. See the MLflow documentation for details.

Configure goose to export OTLP to MLflow​

goose exports OpenTelemetry data over OTLP/HTTP. Point the exporter to MLflow's OTLP endpoint:

export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:5000"
export OTEL_EXPORTER_OTLP_HEADERS="x-mlflow-experiment-id=0"

The x-mlflow-experiment-id header specifies which MLflow experiment to log traces to. Use 0 for the default experiment, or create a dedicated experiment:

pip install mlflow
mlflow experiments create --experiment-name "goose-traces"
# Use the returned experiment ID in the header

To export only traces (disable metrics and logs export):

export OTEL_TRACES_EXPORTER=otlp
export OTEL_METRICS_EXPORTER=none
export OTEL_LOGS_EXPORTER=none

Run goose with MLflow enabled​

Start goose normally. With the OTLP environment variables set, goose will automatically export traces to MLflow:

goose session

Open the MLflow UI at http://localhost:5000 and navigate to the Traces tab to see detailed traces of your goose session, including LLM calls, tool executions, and token usage.

goose trace in MLflow

Learn more​