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POST
/
test_framework
/
v1
/
metrics
Create a metric
curl --request POST \
  --url https://api.cekura.ai/test_framework/v1/metrics/ \
  --header 'Content-Type: application/json' \
  --header 'X-CEKURA-API-KEY: <api-key>' \
  --data '
{
  "name": "<string>",
  "description": "<string>",
  "audio_enabled": true,
  "prompt": "<string>",
  "project": 123,
  "assistant_id": "<string>",
  "display_order": 123,
  "configuration": {},
  "agents": [
    123
  ],
  "enum_values": [
    "<string>"
  ],
  "add_to_new_agents": true,
  "simulation_enabled": true,
  "observability_enabled": true,
  "sampling_enabled": true,
  "evaluation_trigger": "always",
  "evaluation_trigger_prompt": "<string>",
  "trigger_type": "llm_judge",
  "evaluation_trigger_custom_code": "<string>",
  "custom_code": "<string>"
}
'
{
  "name": "<string>",
  "description": "<string>",
  "audio_enabled": true,
  "prompt": "<string>",
  "project": 123,
  "assistant_id": "<string>",
  "display_order": 123,
  "configuration": {},
  "id": 123,
  "agents": [
    123
  ],
  "enum_values": [
    "<string>"
  ],
  "add_to_new_agents": true,
  "simulation_enabled": true,
  "observability_enabled": true,
  "sampling_enabled": true,
  "evaluation_trigger": "always",
  "evaluation_trigger_prompt": "<string>",
  "trigger_type": "llm_judge",
  "evaluation_trigger_custom_code": "<string>",
  "custom_code": "<string>"
}

Documentation Index

Fetch the complete documentation index at: https://docs.cekura.ai/llms.txt

Use this file to discover all available pages before exploring further.

Authorizations

​
X-CEKURA-API-KEY
string
header
required

API Key Authentication. It should be included in the header of each request.

Body

​
name
string
required

Name of the metric

​
description
string
required

Description of what this metric evaluates

​
audio_enabled
boolean
required

Whether this metric evaluates audio content

​
prompt
string
required

The evaluation prompt used for this metric

​
project
integer
required

ID of the project this metric belongs to.

​
assistant_id
string
required

External identifier for the assistant

​
type
enum<string>
required

Type of metric (llm_judge recommended; basic and custom_prompt are deprecated)

  • basic - Basic (Deprecated in favor of LLM Judge)
  • custom_prompt - Custom Prompt ( Deprecated in favor of LLM Judge)
  • custom_code - Custom Code
  • llm_judge - LLM Judge
Available options:
basic,
custom_prompt,
custom_code,
llm_judge
​
eval_type
enum<string>
required

Output shape of the evaluation score

  • binary_workflow_adherence - Binary Workflow Adherence
  • binary_qualitative - Binary Qualitative
  • continuous_qualitative - Continuous Qualitative
  • numeric - Numeric
  • enum - Enum
Available options:
binary_workflow_adherence,
binary_qualitative,
continuous_qualitative,
numeric,
enum
​
display_order
integer
required

Order in which to display this metric in the UI

​
configuration
object
required

Custom configuration parameters for specific metrics. For pronounciation metric, you can set words as 2-tuple (word, phonemes) list example:

{
    "words": [["hello", "hɛl.loʊ"], ["world", "wɝɚɚɚld"]]
}
​
agents
integer[]

List of agent IDs to enable this project-level metric for. Only applicable when project is set.

​
enum_values
string[]

Possible values for enum-type metrics (list of strings, e.g. ["resolved", "escalated", "abandoned"])

​
add_to_new_agents
boolean | null

When enabled, this metric is automatically assigned to new agents created in the project.

​
simulation_enabled
boolean

Enable this metric for simulations. Example: true or false

​
observability_enabled
boolean

Enable this metric for observability. Example: true or false

​
sampling_enabled
boolean

Enable sampling for this metric using project-level sample rate

​
evaluation_trigger
enum<string>
default:always

When to run this metric.

  • always — evaluate every call (default)

  • automatic — system decides based on call content

  • custom — only evaluate when evaluation_trigger_prompt condition is met

  • always - Always

  • automatic - Automatic

  • custom - Custom

Available options:
always,
automatic,
custom
​
evaluation_trigger_prompt
string

LLM prompt that decides whether to evaluate this call. Only used when evaluation_trigger=custom and trigger_type=llm_judge. Example: "Did the agent offer a refund?"

​
trigger_type
enum<string>
default:llm_judge

How to evaluate the trigger condition. Only relevant when evaluation_trigger=custom.

  • llm_judge — use evaluation_trigger_prompt (default)

  • custom_code — use evaluation_trigger_custom_code

  • llm_judge - LLM Judge

  • custom_code - Custom Code

Available options:
llm_judge,
custom_code
​
evaluation_trigger_custom_code
string

Python code to evaluate the trigger condition. Only used when evaluation_trigger=custom and trigger_type=custom_code.

​
custom_code
string

Python code that implements the metric evaluation. Required when type=custom_code. Must define a function evaluate(transcript, ...) -> bool | float | str.

Response

​
name
string
required

Name of the metric

​
description
string
required

Description of what this metric evaluates

​
audio_enabled
boolean
required

Whether this metric evaluates audio content

​
prompt
string
required

The evaluation prompt used for this metric

​
project
integer
required

ID of the project this metric belongs to.

​
assistant_id
string
required

External identifier for the assistant

​
type
enum<string>
required

Type of metric (llm_judge recommended; basic and custom_prompt are deprecated)

  • basic - Basic (Deprecated in favor of LLM Judge)
  • custom_prompt - Custom Prompt ( Deprecated in favor of LLM Judge)
  • custom_code - Custom Code
  • llm_judge - LLM Judge
Available options:
basic,
custom_prompt,
custom_code,
llm_judge
​
eval_type
enum<string>
required

Output shape of the evaluation score

  • binary_workflow_adherence - Binary Workflow Adherence
  • binary_qualitative - Binary Qualitative
  • continuous_qualitative - Continuous Qualitative
  • numeric - Numeric
  • enum - Enum
Available options:
binary_workflow_adherence,
binary_qualitative,
continuous_qualitative,
numeric,
enum
​
display_order
integer
required

Order in which to display this metric in the UI

​
configuration
object
required

Custom configuration parameters for specific metrics. For pronounciation metric, you can set words as 2-tuple (word, phonemes) list example:

{
    "words": [["hello", "hɛl.loʊ"], ["world", "wɝɚɚɚld"]]
}
​
id
integer
read-only
​
agents
integer[]

List of agent IDs to enable this project-level metric for. Only applicable when project is set.

​
enum_values
string[]

Possible values for enum-type metrics (list of strings, e.g. ["resolved", "escalated", "abandoned"])

​
add_to_new_agents
boolean | null

When enabled, this metric is automatically assigned to new agents created in the project.

​
simulation_enabled
boolean

Enable this metric for simulations. Example: true or false

​
observability_enabled
boolean

Enable this metric for observability. Example: true or false

​
sampling_enabled
boolean

Enable sampling for this metric using project-level sample rate

​
evaluation_trigger
enum<string>
default:always

When to run this metric.

  • always — evaluate every call (default)

  • automatic — system decides based on call content

  • custom — only evaluate when evaluation_trigger_prompt condition is met

  • always - Always

  • automatic - Automatic

  • custom - Custom

Available options:
always,
automatic,
custom
​
evaluation_trigger_prompt
string

LLM prompt that decides whether to evaluate this call. Only used when evaluation_trigger=custom and trigger_type=llm_judge. Example: "Did the agent offer a refund?"

​
trigger_type
enum<string>
default:llm_judge

How to evaluate the trigger condition. Only relevant when evaluation_trigger=custom.

  • llm_judge — use evaluation_trigger_prompt (default)

  • custom_code — use evaluation_trigger_custom_code

  • llm_judge - LLM Judge

  • custom_code - Custom Code

Available options:
llm_judge,
custom_code
​
evaluation_trigger_custom_code
string

Python code to evaluate the trigger condition. Only used when evaluation_trigger=custom and trigger_type=custom_code.

​
custom_code
string

Python code that implements the metric evaluation. Required when type=custom_code. Must define a function evaluate(transcript, ...) -> bool | float | str.