querying-mlflow-metrics

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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.

2installs

Sourcemlflow/skills

Added on2026-02-23

NPX Install

npx skill4agent add mlflow/skills querying-mlflow-metrics

SKILL.md Content

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MLflow Metrics

Run

scripts/fetch_metrics.py

to query metrics from an MLflow tracking server.

Examples

Token usage summary:

bash

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m total_tokens -a SUM,AVG

Output:

AVG: 223.91  SUM: 7613

Hourly token trend (last 24h):

bash

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m total_tokens -a SUM \
    -t 3600 --start-time="-24h" --end-time=now

Output: Time-bucketed token sums per hour

Latency percentiles by trace:

bash

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m latency -a AVG,P95 -d trace_name

Error rate by status:

bash

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m trace_count -a COUNT -d trace_status

Quality scores by evaluator (assessments):

bash

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -v ASSESSMENTS \
    -m assessment_value -a AVG,P50 -d assessment_name

Output: Average and median scores for each evaluator (e.g., correctness, relevance)

Assessment count by name:

bash

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -v ASSESSMENTS \
    -m assessment_count -a COUNT -d assessment_name

JSON output: Add

-o json

to any command.

Arguments

Arg	Required	Description
`-s, --server`	Yes	MLflow server URL
`-x, --experiment-ids`	Yes	Experiment IDs (comma-separated)
`-m, --metric`	Yes	`trace_count` , `latency` , `input_tokens` , `output_tokens` , `total_tokens`
`-a, --aggregations`	Yes	`COUNT` , `SUM` , `AVG` , `MIN` , `MAX` , `P50` , `P95` , `P99`
`-d, --dimensions`	No	Group by: `trace_name` , `trace_status`
`-t, --time-interval`	No	Bucket size in seconds (3600=hourly, 86400=daily)
`--start-time`	No	`-24h` , `-7d` , `now` , ISO 8601, or epoch ms
`--end-time`	No	Same formats as start-time
`-o, --output`	No	`table` (default) or `json`

For SPANS metrics (

span_count

,

latency

), add

-v SPANS

. For ASSESSMENTS metrics, add

-v ASSESSMENTS

.

See references/api_reference.md for filter syntax and full API details.

querying-mlflow-metrics

NPX Install

Tags

SKILL.md Content

MLflow Metrics

Examples

Arguments