instrumenting-with-mlflow-tracing

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Instruments Python and TypeScript code with MLflow Tracing for observability. Triggers on questions about adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, or tracing specific frameworks (LangGraph, LangChain, OpenAI, DSPy, CrewAI, AutoGen). Examples - "How do I add tracing?", "How to instrument my agent?", "How to trace my LangChain app?", "Getting started with MLflow tracing", "Trace my TypeScript app"

2installs

Sourcemlflow/skills

Added on2026-02-23

NPX Install

npx skill4agent add mlflow/skills instrumenting-with-mlflow-tracing

SKILL.md Content

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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.

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	External dependency monitoring, error tracking
Agent decisions	Routing, planning, tool selection	Understand agent reasoning and choices
External services	HTTP APIs, file I/O, message queues	Dependency failures, timeout tracking

Skip tracing these (too granular, adds noise):

Simple data transformations (dict/list manipulation)
String formatting, parsing, validation
Configuration loading, environment setup
Logging or metric emission
Pure utility functions (math, sorting, filtering)

Rule of thumb: Trace operations that are important for debugging and identifying issues in your application.

Feedback Collection

Log user feedback on traces for evaluation, debugging, and fine-tuning. Essential for identifying quality issues in production.

See

references/feedback-collection.md

for:

Recording user ratings and comments with
```
mlflow.log_feedback()
```
Capturing trace IDs to return to clients
LLM-as-judge automated evaluation

Reference Documentation

Production Deployment

See

references/production.md

for:

Environment variable configuration
Async logging for low-latency applications
Sampling configuration (MLFLOW_TRACE_SAMPLING_RATIO)
Lightweight SDK (
```
mlflow-tracing
```
)
Docker/Kubernetes deployment

Advanced Patterns

See

references/advanced-patterns.md

for:

Async function tracing
Multi-threading with context propagation
PII redaction with span processors

Distributed Tracing

See

references/distributed-tracing.md

for:

Propagating trace context across services
Client/server header APIs

instrumenting-with-mlflow-tracing

NPX Install

Tags

SKILL.md Content

MLflow Tracing Instrumentation Guide

Language-Specific Guides

What to Trace

Feedback Collection

Reference Documentation

Production Deployment

Advanced Patterns

Distributed Tracing