List Eval Agent Templates

curl --request GET \
  --url https://api.bland.ai/v1/evals/agent-templates \
  --header 'authorization: <authorization>'

import requests

url = "https://api.bland.ai/v1/evals/agent-templates"

headers = {"authorization": "<authorization>"}

response = requests.get(url, headers=headers)

print(response.text)

const options = {method: 'GET', headers: {authorization: '<authorization>'}};

fetch('https://api.bland.ai/v1/evals/agent-templates', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.bland.ai/v1/evals/agent-templates",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "GET",
  CURLOPT_HTTPHEADER => [
    "authorization: <authorization>"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"net/http"
	"io"
)

func main() {

	url := "https://api.bland.ai/v1/evals/agent-templates"

	req, _ := http.NewRequest("GET", url, nil)

	req.Header.Add("authorization", "<authorization>")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.get("https://api.bland.ai/v1/evals/agent-templates")
  .header("authorization", "<authorization>")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.bland.ai/v1/evals/agent-templates")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Get.new(url)
request["authorization"] = '<authorization>'

response = http.request(request)
puts response.read_body

{
  "data": {
    "object": "list",
    "data": [
      {
        "key": "hallucination_detection",
        "name": "Hallucination Detection",
        "description": "Detects factual inaccuracies or fabricated information in agent responses.",
        "category": "quality",
        "modality": "text",
        "system_prompt_md": "You are an expert evaluator assessing whether an AI agent fabricated information.",
        "prompt_md": "Review the conversation and determine whether the agent stated anything that was factually incorrect or unsupported.",
        "levels": [
          {
            "level_key": "no_hallucination",
            "label": "No Hallucination",
            "prompt_md": "The agent made no factually incorrect or unsupported claims.",
            "color": "emerald"
          },
          {
            "level_key": "minor_hallucination",
            "label": "Minor Hallucination",
            "prompt_md": "The agent made one or more small inaccuracies that did not materially mislead the user.",
            "color": "amber"
          }
        ],
        "target_level_keys": ["no_hallucination"]
      },
      {
        "key": "call_resolution",
        "name": "Call Resolution",
        "description": "Assesses whether the agent successfully resolved the caller's issue.",
        "category": "support",
        "modality": "audio",
        "system_prompt_md": "You are an expert evaluator assessing call resolution quality.",
        "prompt_md": "Did the agent fully resolve the caller's stated issue before ending the call?",
        "levels": [
          {
            "level_key": "resolved",
            "label": "Resolved",
            "prompt_md": "The caller's issue was fully addressed.",
            "color": "emerald"
          },
          {
            "level_key": "unresolved",
            "label": "Unresolved",
            "prompt_md": "The caller's issue was not addressed or was left open.",
            "color": "rose"
          }
        ],
        "target_level_keys": ["resolved"]
      }
    ],
    "has_more": false,
    "next_cursor": null
  },
  "errors": null
}

GET

evals

agent-templates

List Eval Agent Templates

curl --request GET \
  --url https://api.bland.ai/v1/evals/agent-templates \
  --header 'authorization: <authorization>'

import requests

url = "https://api.bland.ai/v1/evals/agent-templates"

headers = {"authorization": "<authorization>"}

response = requests.get(url, headers=headers)

print(response.text)

const options = {method: 'GET', headers: {authorization: '<authorization>'}};

fetch('https://api.bland.ai/v1/evals/agent-templates', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.bland.ai/v1/evals/agent-templates",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "GET",
  CURLOPT_HTTPHEADER => [
    "authorization: <authorization>"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"net/http"
	"io"
)

func main() {

	url := "https://api.bland.ai/v1/evals/agent-templates"

	req, _ := http.NewRequest("GET", url, nil)

	req.Header.Add("authorization", "<authorization>")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.get("https://api.bland.ai/v1/evals/agent-templates")
  .header("authorization", "<authorization>")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.bland.ai/v1/evals/agent-templates")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Get.new(url)
request["authorization"] = '<authorization>'

response = http.request(request)
puts response.read_body

{
  "data": {
    "object": "list",
    "data": [
      {
        "key": "hallucination_detection",
        "name": "Hallucination Detection",
        "description": "Detects factual inaccuracies or fabricated information in agent responses.",
        "category": "quality",
        "modality": "text",
        "system_prompt_md": "You are an expert evaluator assessing whether an AI agent fabricated information.",
        "prompt_md": "Review the conversation and determine whether the agent stated anything that was factually incorrect or unsupported.",
        "levels": [
          {
            "level_key": "no_hallucination",
            "label": "No Hallucination",
            "prompt_md": "The agent made no factually incorrect or unsupported claims.",
            "color": "emerald"
          },
          {
            "level_key": "minor_hallucination",
            "label": "Minor Hallucination",
            "prompt_md": "The agent made one or more small inaccuracies that did not materially mislead the user.",
            "color": "amber"
          }
        ],
        "target_level_keys": ["no_hallucination"]
      },
      {
        "key": "call_resolution",
        "name": "Call Resolution",
        "description": "Assesses whether the agent successfully resolved the caller's issue.",
        "category": "support",
        "modality": "audio",
        "system_prompt_md": "You are an expert evaluator assessing call resolution quality.",
        "prompt_md": "Did the agent fully resolve the caller's stated issue before ending the call?",
        "levels": [
          {
            "level_key": "resolved",
            "label": "Resolved",
            "prompt_md": "The caller's issue was fully addressed.",
            "color": "emerald"
          },
          {
            "level_key": "unresolved",
            "label": "Unresolved",
            "prompt_md": "The caller's issue was not addressed or was left open.",
            "color": "rose"
          }
        ],
        "target_level_keys": ["resolved"]
      }
    ],
    "has_more": false,
    "next_cursor": null
  },
  "errors": null
}

Headers

string

required

Your API key for authentication.

Response

string

Always list.

array

Array of eval agent template objects.

Show template object

string

Unique identifier key for the template, for example hallucination_detection.

string

Human-readable display name.

string

Brief description of what the template evaluates.

string

Template category. One of voice, sales, support, scheduling, compliance, or quality.

string

Evaluation modality. Either text or audio.

string

The system prompt used for this eval agent, in Markdown.

string

The evaluation prompt, in Markdown.

array

Ordered scoring levels for this template.

Show level object

string

Short identifier for the level, 1-64 characters.

string

Display label for the level, 1-80 characters.

string

Prompt describing this level’s criteria, in Markdown.

string

Optional display color. One of rose, amber, gold, emerald, blue, indigo, violet, or fog.

array

Array of level_key strings that represent the passing threshold for this template.

boolean

Always false. This endpoint returns the full library in a single page.

string

Always null for this endpoint.

{
  "data": {
    "object": "list",
    "data": [
      {
        "key": "hallucination_detection",
        "name": "Hallucination Detection",
        "description": "Detects factual inaccuracies or fabricated information in agent responses.",
        "category": "quality",
        "modality": "text",
        "system_prompt_md": "You are an expert evaluator assessing whether an AI agent fabricated information.",
        "prompt_md": "Review the conversation and determine whether the agent stated anything that was factually incorrect or unsupported.",
        "levels": [
          {
            "level_key": "no_hallucination",
            "label": "No Hallucination",
            "prompt_md": "The agent made no factually incorrect or unsupported claims.",
            "color": "emerald"
          },
          {
            "level_key": "minor_hallucination",
            "label": "Minor Hallucination",
            "prompt_md": "The agent made one or more small inaccuracies that did not materially mislead the user.",
            "color": "amber"
          }
        ],
        "target_level_keys": ["no_hallucination"]
      },
      {
        "key": "call_resolution",
        "name": "Call Resolution",
        "description": "Assesses whether the agent successfully resolved the caller's issue.",
        "category": "support",
        "modality": "audio",
        "system_prompt_md": "You are an expert evaluator assessing call resolution quality.",
        "prompt_md": "Did the agent fully resolve the caller's stated issue before ending the call?",
        "levels": [
          {
            "level_key": "resolved",
            "label": "Resolved",
            "prompt_md": "The caller's issue was fully addressed.",
            "color": "emerald"
          },
          {
            "level_key": "unresolved",
            "label": "Unresolved",
            "prompt_md": "The caller's issue was not addressed or was left open.",
            "color": "rose"
          }
        ],
        "target_level_keys": ["resolved"]
      }
    ],
    "has_more": false,
    "next_cursor": null
  },
  "errors": null
}

Docs for agents: llms.txt

Update Workbench Setup Version Get Eval Agent Template

⌘I

Basic Tutorials

Calls

Voices & Text to Speech

Conversational Pathways

Knowledge Bases

Numbers

Blocked Numbers

Widgets

Tools

Contacts

Memory

Node tests

Agent Testing

Evals

Guard Rails

Web Agents

Live Translation

Custom Twilio Accounts

Batches

Prompts

Account

Organizations

Messaging

SIP Trunks

Custom Dialing Pools

Personas

Citation Schemas

Alarms

Triage

List Eval Agent Templates

Headers

Response

​Headers

​Response

Headers

Response