instrumenting-with-mlflow-tracing
MLflow Tracing Instrumentation Guide
Quick Start
1. Install and Configure
Python:
pip install mlflow>=3.8.0
import mlflow
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("my-agent")
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agent-evaluation
Use this when you need to IMPROVE or OPTIMIZE an existing LLM agent's performance - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
9searching-mlflow-docs
Searches and retrieves MLflow documentation from the official docs site. Use when the user asks about MLflow features, APIs, integrations (LangGraph, LangChain, OpenAI, etc.), tracing, tracking, or requests to look up MLflow documentation. Triggers on "how do I use MLflow with X", "find MLflow docs for Y", "MLflow API for Z".
8querying-mlflow-metrics
Fetches aggregated trace metrics (token usage, latency, trace counts, quality evaluations) from MLflow tracking servers. Triggers on requests to show metrics, analyze token usage, view LLM costs, check usage trends, or query trace statistics.
8searching-mlflow-traces
Searches and filters MLflow traces using CLI or Python API. Use when the user asks to find traces, filter traces by status/tags/metadata/execution time, query traces, or debug failed traces. Triggers on "search traces", "find failed traces", "filter traces by", "traces slower than", "query MLflow traces".
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