GET /api/public/metrics

The Metrics API enables you to retrieve customized analytics from your ABV data. This endpoint allows you to specify dimensions, metrics, filters, and time granularity to build powerful custom reports and dashboards for your LLM applications.

Overview

The Metrics API supports querying across different views (traces, observations, scores) and allows you to:

Select specific dimensions to group your data
Apply multiple metrics with different aggregation methods
Filter data based on metadata, timestamps, and other properties
Analyze data across time with customizable granularity
Order results according to your needs

Query Parameters

The API accepts a JSON query object passed as a URL-encoded parameter:

Parameter	Type	Description
`query`	JSON string	The encoded query object defining what metrics to retrieve

Query Object Structure

Field	Type	Required	Description
`view`	string	Yes	The data view to query: `"traces"`, `"observations"`, `"scores-numeric"`, or `"scores-categorical"`
`dimensions`	array	No	Array of dimension objects to group by, e.g. `[{ "field": "name" }]`
`metrics`	array	Yes	Array of metric objects to calculate, e.g. `[{ "measure": "latency", "aggregation": "p95" }]`
`filters`	array	No	Array of filter objects to narrow results, e.g. `[{ "column": "metadata", "operator": "contains", "key": "customKey", "value": "customValue", "type": "stringObject" }]`
`timeDimension`	object	No	Configuration for time-based analysis, e.g. `{ "granularity": "day" }`
`fromTimestamp`	string	Yes	ISO timestamp for the start of the query period
`toTimestamp`	string	Yes	ISO timestamp for the end of the query period
`orderBy`	array	No	Specification for result ordering, e.g. `[{ "field": "name", "direction": "asc" }]`

Dimension Object Structure

{ "field": "name" }

Metric Object Structure

{ "measure": "count", "aggregation": "count" }

Common measure types include:

count - Count of records
latency - Duration/latency metrics

Aggregation types include:

sum - Sum of values
avg - Average of values
count - Count of records
max - Maximum value
min - Minimum value
p50 - 50th percentile
p75 - 75th percentile
p90 - 90th percentile
p95 - 95th percentile
p99 - 99th percentile

Filter Object Structure

{
  "column": "metadata",
  "operator": "contains",
  "key": "customKey",
  "value": "customValue",
  "type": "stringObject"
}

Time Dimension Object

{
  "granularity": "day"
}

Supported granularities include: hour, day, week, month, and auto.

Example

Here’s an example of querying the number of traces grouped by name:

API

curl \
  -H "Authorization: Basic <BASIC AUTH HEADER>" \
  -G \
  --data-urlencode 'query={
    "view": "traces",
    "metrics": [{"measure": "count", "aggregation": "count"}],
    "dimensions": [{"field": "name"}],
    "filters": [],
    "fromTimestamp": "2025-05-01T00:00:00Z",
    "toTimestamp": "2025-05-13T00:00:00Z"
  }' \
  https://app.abv.dev/api/public/metrics

Response:

{"data":[{"name":"trace-test-2","count_count":"10"},{"name":"trace-test-3","count_count":"5"},{"name":"trace-test-1","count_count":"3"}]}

Python SDK

Install package

pip install abvdev

query = """
{
  "view": "traces",
  "metrics": [{"measure": "count", "aggregation": "count"}],
  "dimensions": [{"field": "name"}],
  "filters": [],
  "fromTimestamp": "2025-05-01T00:00:00Z",
  "toTimestamp": "2025-05-13T00:00:00Z"
}
"""
 
abv.api.metrics.metrics(query = query)

Data Model

The Metrics API provides access to several data views, each with its own set of dimensions and metrics you can query. This section outlines the available options for each view.

Available Views

View	Description
`traces`	Query data at the trace level
`observations`	Query data at the observation level
`scores-numeric`	Query numeric and boolean scores
`scores-categorical`	Query categorical (string) scores

Trace Dimensions

Dimension	Type	Description
`id`	string	Trace ID
`name`	string	Trace name
`tags`	string[]	Trace tags
`userId`	string	User ID associated with the trace
`sessionId`	string	Session ID associated with the trace
`release`	string	Release tag
`version`	string	Version tag
`environment`	string	Environment (e.g., production, staging)
`observationName`	string	Name of related observations
`scoreName`	string	Name of related scores

Trace Metrics

Metric	Description
`count`	Count of traces
`observationsCount`	Count of observations within traces
`scoresCount`	Count of scores within traces
`latency`	Trace duration in milliseconds
`totalTokens`	Total tokens used in the trace
`totalCost`	Total cost of the trace

Observation Dimensions

Dimension	Type	Description
`id`	string	Observation ID
`traceId`	string	Associated trace ID
`traceName`	string	Name of the parent trace
`environment`	string	Environment (e.g., production, staging)
`parentObservationId`	string	ID of parent observation
`type`	string	Observation type
`name`	string	Observation name
`level`	string	Log level
`version`	string	Version
`providedModelName`	string	Model name
`promptName`	string	Prompt name
`promptVersion`	string	Prompt version
`userId`	string	User ID from parent trace
`sessionId`	string	Session ID from parent trace
`traceRelease`	string	Release from parent trace
`traceVersion`	string	Version from parent trace
`scoreName`	string	Related score name

Observation Metrics

Metric	Description
`count`	Count of observations
`latency`	Observation duration in milliseconds
`totalTokens`	Total tokens used
`totalCost`	Total cost
`timeToFirstToken`	Time to first token in milliseconds
`countScores`	Count of related scores

Score Dimensions (Common)

Dimension	Type	Description
`id`	string	Score ID
`name`	string	Score name
`environment`	string	Environment
`source`	string	Score source
`dataType`	string	Data type
`traceId`	string	Related trace ID
`traceName`	string	Related trace name
`userId`	string	User ID from trace
`sessionId`	string	Session ID from trace
`observationId`	string	Related observation ID
`observationName`	string	Related observation name
`observationModelName`	string	Model used in related observation
`observationPromptName`	string	Prompt name used in related observation
`observationPromptVersion`	string	Prompt version used in related observation
`configId`	string	Configuration ID

Score Metrics

Numeric Scores

Metric	Description
`count`	Count of scores
`value`	Numeric score value

Categorical Scores

Metric	Description
`count`	Count of scores

Categorical scores have an additional dimension:

Dimension	Type	Description
`stringValue`	string	String value of the categorical score

Getting Started

Basic Features

LLM Gateway

Guardrails

Evaluations

Prompt Management

Cookbook

SDKs

Platform

Support

Metrics API

Overview

Query Parameters

Query Object Structure

Dimension Object Structure

Metric Object Structure

Filter Object Structure

Time Dimension Object

Example

API

Python SDK

Data Model

Available Views

Trace Dimensions

Trace Metrics

Observation Dimensions

Observation Metrics

Score Dimensions (Common)

Score Metrics

Numeric Scores

Categorical Scores

​Overview

​Query Parameters

​Query Object Structure

​Dimension Object Structure

​Metric Object Structure

​Filter Object Structure

​Time Dimension Object

​Example

​API

​Python SDK

​Data Model

​Available Views

​Trace Dimensions

​Trace Metrics

​Observation Dimensions

​Observation Metrics

​Score Dimensions (Common)

​Score Metrics

​Numeric Scores

​Categorical Scores

Overview

Query Parameters

Query Object Structure

Dimension Object Structure

Metric Object Structure

Filter Object Structure

Time Dimension Object

Example

API

Python SDK

Data Model

Available Views

Trace Dimensions

Trace Metrics

Observation Dimensions

Observation Metrics

Score Dimensions (Common)

Score Metrics

Numeric Scores

Categorical Scores