AgentSkillsCN

instrumenting-with-mlflow-tracing

通过 MLflow Tracing 为 Python 和 TypeScript 代码注入可观测性。当用户询问如何添加追踪功能、如何为代理或 LLM 应用进行埋点、如何入门 MLflow 跟踪，或如何追踪特定框架（LangGraph、LangChain、OpenAI、DSPy、CrewAI、AutoGen）时，可触发此技能。例如：“我该如何添加追踪功能？”、“如何为我的代理进行埋点？”、“如何追踪我的 LangChain 应用？”、“MLflow 跟踪入门指南”、“追踪我的 TypeScript 应用”。

SKILL.md
--- frontmatter
name: instrumenting-with-mlflow-tracing
description: 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"

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 TypeExamplesWhy Trace
Root operationsMain entry points, top-level pipelines, workflow stepsEnd-to-end latency, input/output logging
LLM callsChat completions, embeddingsToken usage, latency, prompt/response inspection
RetrievalVector DB queries, document fetches, searchRelevance debugging, retrieval quality
Tool/function callsAPI calls, database queries, web searchExternal dependency monitoring, error tracking
Agent decisionsRouting, planning, tool selectionUnderstand agent reasoning and choices
External servicesHTTP APIs, file I/O, message queuesDependency 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