# instrumenting-with-mlflow-tracing

> Claude/Codex skill by [@mlflow](https://omgskills.com/library/mlflow/index.md) · 82 stars · Updated 2026-10-23

Add or repair MLflow tracing in Python or TypeScript applications, including tracing required for evaluation. Covers framework autologging, custom spans, backend setup, and persisted trace verification.

## Install

```sh
git clone https://github.com/mlflow/skills /tmp/skills && ln -s /tmp/skills/instrumenting-with-mlflow-tracing ~/.claude/skills/instrumenting-with-mlflow-tracing
```

## From README

Instrument MLflow tracing Reuse working tracing for investigation or evaluation. Prerequisites Check entry point, language/framework, SDK version, tracking URI, experiment, and deployment identity. Preserve destinations and providers. Explicit application destinations override inherited environment values; assistant telemetry may use another experiment. Read Python or TypeScript for the runtime. Step 1: Check the destination (Databricks only) For OSS or SageMaker, keep the configured backend and proceed to Step 2. Read UC before editing; validate auth/workspace. Reuse the explicit destination; otherwise resolve schema choice before provisioning. Discover an accessible warehouse automatically. Default to UC; legacy storage requires an explicit request. Step 2: Enable framework tracing Enable the matching integration before execution. LangGraph uses mlflow.langchain.autolog(), not mlflow.langgraph.autolog(). Do not wrap automatically traced nodes, tools, or models again.

## Links

- HTML page: https://omgskills.com/library/mlflow/skills/instrumenting-with-mlflow-tracing/
- GitHub: https://github.com/mlflow/skills
- Author: [@mlflow](https://omgskills.com/library/mlflow/index.md)

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