@mlflow

retrieving-mlflow-traces covers single fetch and search operations via cli, with filter examples across status, time, session, user, tags, and metadata. lacks python api detail and error handling guidance.

mlflow-onboarding branches users into two paths (genai or traditional ml) based on detected imports and explicit questioning, then guides them to relevant quickstart tutorials.

analyzing-mlflow-trace covers single trace inspection to debug mlflow executions. describes trace structure (spans, operations, timing) and one cli command to fetch traces safely to file, lacking systematic analysis procedure.

instrumentation guide for mlflow tracing targeting python and typescript projects, with language detection by dependency file but incomplete procedural coverage and vague operation examples.

searching-mlflow-docs retrieves mlflow documentation by fetching an index file and extracting relevant markdown pages. covers common mlflow topics, apis, and integrations but procedure is incomplete and failure modes are unaddressed.

querying-mlflow-metrics fetches aggregated trace metrics from mlflow tracking servers via a python script. supports token usage, latency, and trace count queries with time-bucketing and aggregation functions.

Analyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation,…

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,…