Add MLflow integration guide (#7563)

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Co-authored-by: Rizel Scarlett <rizel@tbd.email>

documentation/docs/tutorials/mlflow.md

@@ -0,0 +1,77 @@
+---
+description: Integrate goose with MLflow to observe and evaluate agent performance
+---
+
+# 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](https://mlflow.org/) is an [open-source](https://github.com/mlflow/mlflow) 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:
+
+```bash
+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](https://mlflow.org/docs/latest/self-hosting/architecture/backend-store.html) for details.
+:::
+
+## Configure goose to export OTLP to MLflow
+
+goose exports OpenTelemetry data over OTLP/HTTP. Point the exporter to MLflow's OTLP endpoint:
+
+```bash
+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:
+
+```bash
+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):
+
+```bash
+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:
+
+```bash
+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](../assets/guides/mlflow-goose-tracing.png)
+
+## Learn more
+
+- [MLflow Tracing documentation](https://mlflow.org/docs/latest/genai/tracing/)
+- [MLflow OpenTelemetry integration](https://mlflow.org/docs/latest/genai/tracing/app-instrumentation/opentelemetry.html)
+- [MLflow goose integration guide](https://mlflow.org/docs/latest/genai/tracing/integrations/listing/goose.html)