analyzing-mlflow-trace
Analyzing a Single MLflow Trace
Trace Structure
A trace captures the full execution of an AI/ML application as a tree of spans. Each span represents one operation (LLM call, tool invocation, retrieval step, etc.) and records its inputs, outputs, timing, and status. Traces also carry assessments — feedback from humans or LLM judges about quality.
It is recommended to read references/trace-structure.md before analyzing a trace — it covers the complete data model, all fields and types, analysis guidance, and OpenTelemetry compatibility notes.
Handling CLI Output
Traces can be 100KB+ for complex agent executions. Always redirect output to a file — do not pipe mlflow traces get directly to jq, head, or other commands, as piping can silently produce no output.
# Fetch full trace to a file (traces get always outputs JSON, no --output flag needed)
mlflow traces get --trace-id <ID> > /tmp/trace.json
# Then process the file
jq '.info.state' /tmp/trace.json
jq '.data.spans | length' /tmp/trace.json
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mlflow-agent
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Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - 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. IMPORTANT - Always also load the instrumenting-with-mlflow-tracing skill before starting any work. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
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Onboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on "get started with MLflow", "set up MLflow", "onboard to MLflow", "add MLflow to my project", "how do I use MLflow".
1retrieving-mlflow-traces
Retrieves MLflow traces using CLI or Python API. Use when the user asks to get a trace by ID, find traces, filter traces by status/tags/metadata/execution time, query traces, or debug failed traces. Triggers on "get trace", "search traces", "find failed traces", "filter traces by", "traces slower than", "query MLflow traces".
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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".
1querying-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.
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