Metrics API
GET /api/public/metricsThe Metrics API enables you to retrieve customized analytics from your Langfuse data. This endpoint allows you to specify dimensions, metrics, filters, and time granularity to build powerful custom reports and dashboards for your LLM applications.
Metrics API v2 (Beta)
The v2 Metrics API is currently in beta. The API is stable for production use, but some parameters and behaviors may change based on user feedback before general availability.
Cloud-only (Beta): The v2 Metrics API is only available on Langfuse Cloud and currently in beta. We are working on a robust migration path for self-hosted deployments.
Data availability note: When using current SDK versions, data may take approximately 5 minutes to appear on v2 endpoints. We will be releasing updated SDK versions soon that will make data available immediately.
GET /api/public/v2/metricsThe v2 Metrics API provides significant performance improvements through an optimized data architecture built on a new events table schema that minimizes database work per query.
Key Changes from v1
The traces view is no longer available in v2. Instead, use the observations view which is both faster and more powerful compared to v1.
Available Views in v2
| View | Description |
|---|---|
observations | Query observation-level data with optional trace-level aggregations |
scores-numeric | Query numeric and boolean scores |
scores-categorical | Query categorical (string) scores |
Row Limit
The v2 Metrics API enforces a default rowLimit of 100 rows per query to ensure consistent performance. You can specify a custom rowLimit in your query to override this default.
High Cardinality Dimensions
Certain dimensions like id, traceId, userId, and sessionId cannot be used for grouping in the v2 Metrics API. Grouping by these high cardinality fields is extremely expensive and rarely useful in practice. These dimensions remain available for filtering.
Example: Most expensive models used in observations
curl \
-H "Authorization: Basic <BASIC AUTH HEADER>" \
-G \
--data-urlencode 'query={
"view": "observations",
"metrics": [{"measure": "totalCost", "aggregation": "sum"}],
"dimensions": [{"field": "providedModelName"}],
"filters": [],
"fromTimestamp": "2025-12-01T00:00:00Z",
"toTimestamp": "2025-12-16T00:00:00Z",
"orderBy": [{"field": "totalCost_sum", "direction": "desc"}]
}' \
https://cloud.langfuse.com/api/public/v2/metricsAPI Reference: See the full v2 Metrics API Reference for all available parameters, response schemas, and interactive examples.
Metrics API v1
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 recordslatency- Duration/latency metrics
Aggregation types include:
sum- Sum of valuesavg- Average of valuescount- Count of recordsmax- Maximum valuemin- Minimum valuep50- 50th percentilep75- 75th percentilep90- 90th percentilep95- 95th percentilep99- 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:
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://cloud.langfuse.com/api/public/metricsResponse:
{
"data": [
{ "name": "trace-test-2", "count_count": "10" },
{ "name": "trace-test-3", "count_count": "5" },
{ "name": "trace-test-1", "count_count": "3" }
]
}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 |
Daily Metrics API (Legacy)
This API is a legacy API. For new use cases, please use the Metrics API instead. It has higher rate-limits and offers more flexibility.
GET /api/public/metrics/dailyVia the Daily Metrics API, you can retrieve aggregated daily usage and cost metrics from Langfuse for downstream use, e.g., in analytics, billing, and rate-limiting. The API allows you to filter by application type, user, or tags for tailored data retrieval.
See API reference for more details.
Overview
Returned data includes daily timeseries of:
- Cost in USD
- Trace and observation count
- Break down by model name
- Usage (e.g. tokens) broken down by input and output usage
- Cost in USD
- Trace and observation count
Optional filters:
traceNameto commonly filter by the application type, depending on how you usenamein your tracesuserIdto filter by usertagsto filter by tagsfromTimestamptoTimestamp
Missing a key metric or filter? Request it via our idea board.
Example
GET /api/public/metrics/daily?traceName=my-copilot&userId=john&limit=2{
"data": [
{
"date": "2024-02-18",
"countTraces": 1500,
"countObservations": 3000,
"totalCost": 102.19,
"usage": [
{
"model": "llama2",
"inputUsage": 1200,
"outputUsage": 1300,
"totalUsage": 2500,
"countTraces": 1000,
"countObservations": 2000,
"totalCost": 50.19
},
{
"model": "gpt-4",
"inputUsage": 500,
"outputUsage": 550,
"totalUsage": 1050,
"countTraces": 500,
"countObservations": 1000,
"totalCost": 52.0
}
]
},
{
"date": "2024-02-17",
"countTraces": 1250,
"countObservations": 2500,
"totalCost": 250.0,
"usage": [
{
"model": "llama2",
"inputUsage": 1000,
"outputUsage": 1100,
"totalUsage": 2100,
"countTraces": 1250,
"countObservations": 2500,
"totalCost": 250.0
}
]
}
],
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"page": 1,
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}
}