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.

Verification

After instrumenting the code, always verify that tracing is working.

Planning to evaluate your agent? Tracing must be working before you run agent-evaluation. Complete verification below first.

Run the instrumented code — execute the application or agent so that at least one traced operation fires
Confirm traces are logged — use mlflow.search_traces() or MlflowClient().search_traces() to check that traces appear in the experiment:

import mlflow

traces = mlflow.search_traces(experiment_ids=["<experiment_id>"])
print(f"Found {len(traces)} trace(s)")
assert len(traces) > 0, "No traces were logged — check tracking URI and experiment settings"

Report the result — tell the user how many traces were found and confirm tracing is working

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

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