---
title: "Create multiple metrics in a single request"
method: POST
path: "/test_framework/v2/metrics/bulk_create/"
tags: ["test_framework"]
---

# Create multiple metrics in a single request

`POST /test_framework/v2/metrics/bulk_create/`

## Request

- Base URL: `https://api.cekura.ai`
- URL: `https://api.cekura.ai/test_framework/v2/metrics/bulk_create/`
- Auth: one of:
  - API key in header `X-CEKURA-API-KEY`
  - HTTP bearer
  - HTTP bearer
  - HTTP bearer

Create multiple metrics in a single request

## Request body

- SchemaPostMetricEdit[]
  - `id` integer
  - `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.
  - `agents` integer[]: List of agent IDs to enable this project-level metric for. Only applicable when project is set.
  - `assistant_id` string, required: External identifier for the assistant
  - `type` 'basic' | 'custom_prompt' | 'custom_code' | 'llm_judge', 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
  - `eval_type` 'binary' | 'continuous_qualitative' | 'numeric' | 'enum', required: Output shape of the evaluation score. Accepts canonical (binary, continuous_qualitative, numeric, enum); legacy binary_workflow_adherence and binary_qualitative are accepted and mapped to binary. * `binary` - Binary * `continuous_qualitative` - Continuous Qualitative * `numeric` - Numeric * `enum` - Enum
  - `enum_values` string[]: Possible values for enum-type metrics (list of strings, e.g. ["resolved", "escalated", "abandoned"])
  - `display_order` integer, required: Order in which to display this metric in the UI
  - `configuration` object, required: Per-metric configuration. Some predefined metrics carry **per-agent** configuration, keyed by agent id: `configuration[<key>] = {"<agent_id>": <value>}`. On read, every agent attached to the metric is returned; on write, only the agent ids you include are updated. Call-level keys (not per-agent): * **Detect Silence** — `silence_duration` (int, seconds, default 10): minimum mutual silence before flagging a failure. * **Infrastructure Issues** — `infra_issues_timeout` (int, seconds, default 10): max seconds before the Main Agent must respond after the Testing Agent finishes speaking. Per-agent keys (each value is `{"<agent_id>": <value>}`): * **Dropoff Node** — `nodes`: `{"<agent_id>": [{name, description}]}` — conversation stages used to classify where a call dropped off. * **Topic of Call** — `nodes`: `{"<agent_id>": [{name, description}]}` — topic categories used to classify each call. * **Pronunciation Check** — `pronunciation_words`: `{"<agent_id>": [[word, phonemes], ...]}` — how specific words should be pronounced (e.g. {"42": [["Cekura", "suh-KYUR-uh"]]}). * **Letterwise Pronunciation** — `spelling_word_types`: `{"<agent_id>": ["name", "email", ...]}` — word categories the Main Agent must spell out letter-by-letter. * **Hallucination** — `hallucination_kb_files`: `{"<agent_id>": [file_id, ...]}` — KnowledgeBaseFile IDs used as the source of truth for fact-checking. Any metric (call-level key): * `metric_dependencies`: list of metric IDs (names or slugs also accepted) whose results this metric reads. Those metrics are evaluated first; an LLM-judge prompt can reference them as `{{metrics.<slug>.score}}` / `{{metrics.<slug>.explanation}}` and custom code as `data["<Metric name>"]` or `data["metrics"]["<slug>"]`. Omit the key to infer inputs from those references; pass `[]` to declare none. Cycles, more than 10 inputs, or chains deeper than 5 levels are rejected.
  - `add_to_new_agents` boolean, nullable: 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 its sample_rate, or the project sample rate when unset
  - `sample_rate` number, double, nullable: Sampling rate for this metric (0.0 to 1.0). Overrides the project sample rate when set.
  - `evaluation_trigger` 'always' | 'automatic' | 'custom': 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
  - `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` 'llm_judge' | 'custom_code': 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
  - `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 `201`

- SchemaPostMetricEdit[]
  - `id` integer
  - `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.
  - `agents` integer[]: List of agent IDs to enable this project-level metric for. Only applicable when project is set.
  - `assistant_id` string, required: External identifier for the assistant
  - `type` 'basic' | 'custom_prompt' | 'custom_code' | 'llm_judge', 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
  - `eval_type` 'binary' | 'continuous_qualitative' | 'numeric' | 'enum', required: Output shape of the evaluation score. Accepts canonical (binary, continuous_qualitative, numeric, enum); legacy binary_workflow_adherence and binary_qualitative are accepted and mapped to binary. * `binary` - Binary * `continuous_qualitative` - Continuous Qualitative * `numeric` - Numeric * `enum` - Enum
  - `enum_values` string[]: Possible values for enum-type metrics (list of strings, e.g. ["resolved", "escalated", "abandoned"])
  - `display_order` integer, required: Order in which to display this metric in the UI
  - `configuration` object, required: Per-metric configuration. Some predefined metrics carry **per-agent** configuration, keyed by agent id: `configuration[<key>] = {"<agent_id>": <value>}`. On read, every agent attached to the metric is returned; on write, only the agent ids you include are updated. Call-level keys (not per-agent): * **Detect Silence** — `silence_duration` (int, seconds, default 10): minimum mutual silence before flagging a failure. * **Infrastructure Issues** — `infra_issues_timeout` (int, seconds, default 10): max seconds before the Main Agent must respond after the Testing Agent finishes speaking. Per-agent keys (each value is `{"<agent_id>": <value>}`): * **Dropoff Node** — `nodes`: `{"<agent_id>": [{name, description}]}` — conversation stages used to classify where a call dropped off. * **Topic of Call** — `nodes`: `{"<agent_id>": [{name, description}]}` — topic categories used to classify each call. * **Pronunciation Check** — `pronunciation_words`: `{"<agent_id>": [[word, phonemes], ...]}` — how specific words should be pronounced (e.g. {"42": [["Cekura", "suh-KYUR-uh"]]}). * **Letterwise Pronunciation** — `spelling_word_types`: `{"<agent_id>": ["name", "email", ...]}` — word categories the Main Agent must spell out letter-by-letter. * **Hallucination** — `hallucination_kb_files`: `{"<agent_id>": [file_id, ...]}` — KnowledgeBaseFile IDs used as the source of truth for fact-checking. Any metric (call-level key): * `metric_dependencies`: list of metric IDs (names or slugs also accepted) whose results this metric reads. Those metrics are evaluated first; an LLM-judge prompt can reference them as `{{metrics.<slug>.score}}` / `{{metrics.<slug>.explanation}}` and custom code as `data["<Metric name>"]` or `data["metrics"]["<slug>"]`. Omit the key to infer inputs from those references; pass `[]` to declare none. Cycles, more than 10 inputs, or chains deeper than 5 levels are rejected.
  - `add_to_new_agents` boolean, nullable: 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 its sample_rate, or the project sample rate when unset
  - `sample_rate` number, double, nullable: Sampling rate for this metric (0.0 to 1.0). Overrides the project sample rate when set.
  - `evaluation_trigger` 'always' | 'automatic' | 'custom': 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
  - `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` 'llm_judge' | 'custom_code': 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
  - `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`.

## Other responses

- `400`

## Changes

- **2026-11-03** `a6fb6a739212`: 1 info
  - endpoint added

[Operation history](https://skmtc.dev/cekura/apis/cekura-api/changes/test_framework/v2/metrics/bulk_create/post.md)

---

[API](https://skmtc.dev/cekura/apis/cekura-api.md) - [All operations](https://skmtc.dev/cekura/apis/cekura-api/llms.txt) - [OpenAPI document](https://skmtc.dev/cekura/apis/cekura-api/revisions/a6fb6a739212?raw)