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):

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.

1. Run the instrumented code — execute the application or agent so that at least one traced operation fires
2. 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"

3. 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