instrumenting-with-mlflow-tracing

name

instrumenting-with-mlflow-tracing

description

Instruments Python and TypeScript code with MLflow Tracing for observability. Must be loaded when setting up tracing as part of any workflow including agent evaluation. Triggers on adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, tracing specific frameworks (LangGraph, LangChain, OpenAI, Gemini, DSPy, CrewAI, AutoGen), or when another skill references tracing setup. Examples - "How do I add tracing?", "Instrument my agent", "Trace my LangChain app", "Set up tracing for evaluation"

MLflow Tracing Instrumentation Guide

Language-Specific Guides

Based on the user's project, load the appropriate guide:

• Python projects: Read references/python.md
• TypeScript/JavaScript projects: Read references/typescript.md

If unclear, check for package.json (TypeScript) or requirements.txt/pyproject.toml (Python) in the project.

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What to Trace

Trace these operations (high debugging/observability value):

Operation Type	Examples	Why Trace
Root operations	Main entry points, top-level pipelines, workflow steps	End-to-end latency, input/output logging
LLM calls	Chat completions, embeddings	Token usage, latency, prompt/response inspection
Retrieval	Vector DB queries, document fetches, search	Relevance debugging, retrieval quality
Tool/function calls	API calls, database queries, web search