List

API Reference

Agents

List Agents

client.agents.list(AgentListParams { only_deployed, page, per_page } query?, RequestOptionsoptions?): AgentListResponse { agents, links, meta }

get/v2/gen-ai/agents

To list all agents, send a GET request to /v2/gen-ai/agents.

ParametersExpand Collapse

query: AgentListParams { only_deployed, page, per_page }

only_deployed?: boolean

Only list agents that are deployed.

page?: number

Page number.

per_page?: number

Items per page.

ReturnsExpand Collapse

AgentListResponse { agents, links, meta }

List of Agents

agents?: Array<Agent>

Agents

chatbot?: Chatbot { button_background_color, logo, name, 3 more }

A Chatbot

button_background_color?: string

logo?: string

name?: string

Name of chatbot

primary_color?: string

secondary_color?: string

starting_message?: string

chatbot_identifiers?: Array<ChatbotIdentifier>

Chatbot identifiers

agent_chatbot_identifier?: string

Agent chatbot identifier

created_at?: string

Creation date / time

formatdate-time

deployment?: Deployment { created_at, name, status, 4 more }

Description of deployment

created_at?: string

Creation date / time

formatdate-time

name?: string

Name

status?: "STATUS_UNKNOWN" | "STATUS_WAITING_FOR_DEPLOYMENT" | "STATUS_DEPLOYING" | 6 more

Accepts one of the following:

"STATUS_UNKNOWN"

"STATUS_WAITING_FOR_DEPLOYMENT"

"STATUS_DEPLOYING"

"STATUS_RUNNING"

"STATUS_FAILED"

"STATUS_WAITING_FOR_UNDEPLOYMENT"

"STATUS_UNDEPLOYING"

"STATUS_UNDEPLOYMENT_FAILED"

"STATUS_DELETED"

updated_at?: string

Last modified

formatdate-time

url?: string

Access your deployed agent here

uuid?: string

Unique id

visibility?: APIDeploymentVisibility

• VISIBILITY_UNKNOWN: The status of the deployment is unknown
• VISIBILITY_DISABLED: The deployment is disabled and will no longer service requests
• VISIBILITY_PLAYGROUND: Deprecated: No longer a valid state
• VISIBILITY_PUBLIC: The deployment is public and will service requests from the public internet
• VISIBILITY_PRIVATE: The deployment is private and will only service requests from other agents, or through API keys

Accepts one of the following:

"VISIBILITY_UNKNOWN"

"VISIBILITY_DISABLED"

"VISIBILITY_PLAYGROUND"

"VISIBILITY_PUBLIC"

"VISIBILITY_PRIVATE"

description?: string

Description of agent

if_case?: string

Instructions to the agent on how to use the route

instruction?: string

Agent instruction. Instructions help your agent to perform its job effectively. See Write Effective Agent Instructions for best practices.

k?: number

How many results should be considered from an attached knowledge base

formatint64

max_tokens?: number

Specifies the maximum number of tokens the model can process in a single input or output, set as a number between 1 and 512. This determines the length of each response.

formatint64

model?: APIAgentModel { agreement, created_at, inference_name, 12 more }

Description of a Model

agreement?: APIAgreement { description, name, url, uuid }

Agreement Description

description?: string

name?: string

url?: string

uuid?: string

created_at?: string

Creation date / time

formatdate-time

inference_name?: string

Internally used name

inference_version?: string

Internally used version

is_foundational?: boolean

True if it is a foundational model provided by do

metadata?: unknown

Additional meta data

name?: string

Name of the model

parent_uuid?: string

Unique id of the model, this model is based on

provider?: "MODEL_PROVIDER_DIGITALOCEAN" | "MODEL_PROVIDER_ANTHROPIC" | "MODEL_PROVIDER_OPENAI"

Accepts one of the following:

"MODEL_PROVIDER_DIGITALOCEAN"

"MODEL_PROVIDER_ANTHROPIC"

"MODEL_PROVIDER_OPENAI"

updated_at?: string

Last modified

formatdate-time

upload_complete?: boolean

Model has been fully uploaded

url?: string

Download url

usecases?: Array<"MODEL_USECASE_UNKNOWN" | "MODEL_USECASE_AGENT" | "MODEL_USECASE_FINETUNED" | 4 more>

Usecases of the model

Accepts one of the following:

"MODEL_USECASE_UNKNOWN"

"MODEL_USECASE_AGENT"

"MODEL_USECASE_FINETUNED"

"MODEL_USECASE_KNOWLEDGEBASE"

"MODEL_USECASE_GUARDRAIL"

"MODEL_USECASE_REASONING"

"MODEL_USECASE_SERVERLESS"

uuid?: string

Unique id

version?: APIModelVersion { major, minor, patch }

Version Information about a Model

major?: number

Major version number

formatint64

minor?: number

Minor version number

formatint64

patch?: number

Patch version number

formatint64

name?: string

Agent name

project_id?: string

The DigitalOcean project ID associated with the agent

provide_citations?: boolean

Whether the agent should provide in-response citations

region?: string

Region code

retrieval_method?: APIRetrievalMethod

• RETRIEVAL_METHOD_UNKNOWN: The retrieval method is unknown
• RETRIEVAL_METHOD_REWRITE: The retrieval method is rewrite
• RETRIEVAL_METHOD_STEP_BACK: The retrieval method is step back
• RETRIEVAL_METHOD_SUB_QUERIES: The retrieval method is sub queries
• RETRIEVAL_METHOD_NONE: The retrieval method is none

Accepts one of the following:

"RETRIEVAL_METHOD_UNKNOWN"

"RETRIEVAL_METHOD_REWRITE"

"RETRIEVAL_METHOD_STEP_BACK"

"RETRIEVAL_METHOD_SUB_QUERIES"

"RETRIEVAL_METHOD_NONE"

route_created_at?: string

Creation of route date / time

formatdate-time

route_created_by?: string

Id of user that created the route

formatuint64

route_name?: string

Route name

route_uuid?: string

Route uuid

tags?: Array<string>

A set of abitrary tags to organize your agent

temperature?: number

Controls the model’s creativity, specified as a number between 0 and 1. Lower values produce more predictable and conservative responses, while higher values encourage creativity and variation.

formatfloat

template?: Template { created_at, description, guardrails, 15 more }

Represents an AgentTemplate entity

created_at?: string

The agent template's creation date

formatdate-time

description?: string

Deprecated - Use summary instead

guardrails?: Array<Guardrail>

List of guardrails associated with the agent template

priority?: number

Priority of the guardrail

formatint32

uuid?: string

Uuid of the guardrail

instruction?: string

Instructions for the agent template

k?: number

The 'k' value for the agent template

formatint64

knowledge_bases?: Array<APIKnowledgeBase { added_to_agent_at, created_at, database_id, 10 more } >

List of knowledge bases associated with the agent template

added_to_agent_at?: string

Time when the knowledge base was added to the agent

formatdate-time

created_at?: string

Creation date / time

formatdate-time

database_id?: string

embedding_model_uuid?: string

is_public?: boolean

Whether the knowledge base is public or not

last_indexing_job?: APIIndexingJob { completed_datasources, created_at, data_source_uuids, 12 more }

IndexingJob description

completed_datasources?: number

Number of datasources indexed completed

formatint64

created_at?: string

Creation date / time

formatdate-time

data_source_uuids?: Array<string>

finished_at?: string

knowledge_base_uuid?: string

Knowledge base id

phase?: "BATCH_JOB_PHASE_UNKNOWN" | "BATCH_JOB_PHASE_PENDING" | "BATCH_JOB_PHASE_RUNNING" | 4 more

Accepts one of the following:

"BATCH_JOB_PHASE_UNKNOWN"

"BATCH_JOB_PHASE_PENDING"

"BATCH_JOB_PHASE_RUNNING"

"BATCH_JOB_PHASE_SUCCEEDED"

"BATCH_JOB_PHASE_FAILED"

"BATCH_JOB_PHASE_ERROR"

"BATCH_JOB_PHASE_CANCELLED"

started_at?: string

status?: "INDEX_JOB_STATUS_UNKNOWN" | "INDEX_JOB_STATUS_PARTIAL" | "INDEX_JOB_STATUS_IN_PROGRESS" | 4 more

Accepts one of the following:

"INDEX_JOB_STATUS_UNKNOWN"

"INDEX_JOB_STATUS_PARTIAL"

"INDEX_JOB_STATUS_IN_PROGRESS"

"INDEX_JOB_STATUS_COMPLETED"

"INDEX_JOB_STATUS_FAILED"

"INDEX_JOB_STATUS_NO_CHANGES"

"INDEX_JOB_STATUS_PENDING"

tokens?: number

Number of tokens

formatint64

total_datasources?: number

Number of datasources being indexed

formatint64

total_items_failed?: string

Total Items Failed

formatuint64

total_items_indexed?: string

Total Items Indexed

formatuint64

total_items_skipped?: string

Total Items Skipped

formatuint64

updated_at?: string

Last modified

formatdate-time

uuid?: string

Unique id

name?: string

Name of knowledge base

project_id?: string

region?: string

Region code

tags?: Array<string>

Tags to organize related resources

updated_at?: string

Last modified

formatdate-time

user_id?: string

Id of user that created the knowledge base

formatint64

uuid?: string

Unique id for knowledge base

long_description?: string

The long description of the agent template

max_tokens?: number

The max_tokens setting for the agent template

formatint64

model?: APIAgentModel { agreement, created_at, inference_name, 12 more }

Description of a Model

agreement?: APIAgreement { description, name, url, uuid }

Agreement Description

description?: string

name?: string

url?: string

uuid?: string

created_at?: string

Creation date / time

formatdate-time

inference_name?: string

Internally used name

inference_version?: string

Internally used version

is_foundational?: boolean

True if it is a foundational model provided by do

metadata?: unknown

Additional meta data

name?: string

Name of the model

parent_uuid?: string

Unique id of the model, this model is based on

provider?: "MODEL_PROVIDER_DIGITALOCEAN" | "MODEL_PROVIDER_ANTHROPIC" | "MODEL_PROVIDER_OPENAI"

Accepts one of the following:

"MODEL_PROVIDER_DIGITALOCEAN"

"MODEL_PROVIDER_ANTHROPIC"

"MODEL_PROVIDER_OPENAI"

updated_at?: string

Last modified

formatdate-time

upload_complete?: boolean

Model has been fully uploaded

url?: string

Download url

usecases?: Array<"MODEL_USECASE_UNKNOWN" | "MODEL_USECASE_AGENT" | "MODEL_USECASE_FINETUNED" | 4 more>

Usecases of the model

Accepts one of the following:

"MODEL_USECASE_UNKNOWN"

"MODEL_USECASE_AGENT"

"MODEL_USECASE_FINETUNED"

"MODEL_USECASE_KNOWLEDGEBASE"

"MODEL_USECASE_GUARDRAIL"

"MODEL_USECASE_REASONING"

"MODEL_USECASE_SERVERLESS"

uuid?: string

Unique id

version?: APIModelVersion { major, minor, patch }

Version Information about a Model

major?: number

Major version number

formatint64

minor?: number

Minor version number

formatint64

patch?: number

Patch version number

formatint64

name?: string

Name of the agent template

short_description?: string

The short description of the agent template

summary?: string

The summary of the agent template

tags?: Array<string>

List of tags associated with the agent template

temperature?: number

The temperature setting for the agent template

formatfloat

template_type?: "AGENT_TEMPLATE_TYPE_STANDARD" | "AGENT_TEMPLATE_TYPE_ONE_CLICK"

• AGENT_TEMPLATE_TYPE_STANDARD: The standard agent template
• AGENT_TEMPLATE_TYPE_ONE_CLICK: The one click agent template

Accepts one of the following:

"AGENT_TEMPLATE_TYPE_STANDARD"

"AGENT_TEMPLATE_TYPE_ONE_CLICK"

top_p?: number

The top_p setting for the agent template

formatfloat

updated_at?: string

The agent template's last updated date

formatdate-time

uuid?: string

Unique id

top_p?: number

Defines the cumulative probability threshold for word selection, specified as a number between 0 and 1. Higher values allow for more diverse outputs, while lower values ensure focused and coherent responses.

formatfloat

updated_at?: string

Last modified

formatdate-time

url?: string

Access your agent under this url

user_id?: string

Id of user that created the agent

formatuint64

uuid?: string

Unique agent id

version_hash?: string

The latest version of the agent

links?: APILinks { pages }

Links to other pages

pages?: Pages { first, last, next, previous }

Information about how to reach other pages

first?: string

First page

last?: string

Last page

next?: string

Next page

previous?: string

Previous page

meta?: APIMeta { page, pages, total }

Meta information about the data set

page?: number

The current page

formatint64

pages?: number

Total number of pages

formatint64

total?: number

Total amount of items over all pages

formatint64

List Agents

typescript

• http
• typescript
• python

import Gradient from '@digitalocean/gradient';

const client = new Gradient();

const agents = await client.agents.list();

console.log(agents.agents);

200 example

{
  "agents": [
    {
      "chatbot": {
        "button_background_color": "example string",
        "logo": "example string",
        "name": "example name",
        "primary_color": "example string",
        "secondary_color": "example string",
        "starting_message": "example string"
      },
      "chatbot_identifiers": [
        {
          "agent_chatbot_identifier": "123e4567-e89b-12d3-a456-426614174000"
        }
      ],
      "created_at": "2021-01-01T00:00:00Z",
      "deployment": {
        "created_at": "2023-01-01T00:00:00Z",
        "name": "example name",
        "status": "STATUS_UNKNOWN",
        "updated_at": "2023-01-01T00:00:00Z",
        "url": "example string",
        "uuid": "123e4567-e89b-12d3-a456-426614174000",
        "visibility": "VISIBILITY_UNKNOWN"
      },
      "description": "This is a chatbot that can help you with your questions.",
      "if_case": "if talking about the weather use this route",
      "instruction": "Hello, how can I help you?",
      "k": 5,
      "max_tokens": 100,
      "model": {
        "agreement": {
          "description": "example string",
          "name": "example name",
          "url": "example string",
          "uuid": "123e4567-e89b-12d3-a456-426614174000"
        },
        "created_at": "2023-01-01T00:00:00Z",
        "inference_name": "example name",
        "inference_version": "example string",
        "is_foundational": true,
        "metadata": {},
        "name": "example name",
        "parent_uuid": "123e4567-e89b-12d3-a456-426614174000",
        "provider": "MODEL_PROVIDER_DIGITALOCEAN",
        "updated_at": "2023-01-01T00:00:00Z",
        "upload_complete": true,
        "url": "example string",
        "usecases": [
          "MODEL_USECASE_AGENT",
          "MODEL_USECASE_GUARDRAIL"
        ],
        "uuid": "123e4567-e89b-12d3-a456-426614174000",
        "version": {
          "major": 123,
          "minor": 123,
          "patch": 123
        }
      },
      "name": "My Agent",
      "project_id": "12345678-1234-1234-1234-123456789012",
      "provide_citations": true,
      "region": "\"tor1\"",
      "retrieval_method": "RETRIEVAL_METHOD_UNKNOWN",
      "route_created_at": "2021-01-01T00:00:00Z",
      "route_created_by": "12345678",
      "route_name": "Route Name",
      "route_uuid": "\"12345678-1234-1234-1234-123456789012\"",
      "tags": [
        "example string"
      ],
      "temperature": 0.5,
      "template": {
        "created_at": "2023-01-01T00:00:00Z",
        "description": "example string",
        "guardrails": [
          {
            "priority": 123,
            "uuid": "123e4567-e89b-12d3-a456-426614174000"
          }
        ],
        "instruction": "example string",
        "k": 123,
        "knowledge_bases": [
          {
            "added_to_agent_at": "2023-01-01T00:00:00Z",
            "created_at": "2023-01-01T00:00:00Z",
            "database_id": "123e4567-e89b-12d3-a456-426614174000",
            "embedding_model_uuid": "123e4567-e89b-12d3-a456-426614174000",
            "is_public": true,
            "last_indexing_job": {
              "completed_datasources": 123,
              "created_at": "2023-01-01T00:00:00Z",
              "data_source_uuids": [
                "example string"
              ],
              "finished_at": "2023-01-01T00:00:00Z",
              "knowledge_base_uuid": "123e4567-e89b-12d3-a456-426614174000",
              "phase": "BATCH_JOB_PHASE_UNKNOWN",
              "started_at": "2023-01-01T00:00:00Z",
              "status": "INDEX_JOB_STATUS_UNKNOWN",
              "tokens": 123,
              "total_datasources": 123,
              "total_items_failed": "12345",
              "total_items_indexed": "12345",
              "total_items_skipped": "12345",
              "updated_at": "2023-01-01T00:00:00Z",
              "uuid": "123e4567-e89b-12d3-a456-426614174000"
            },
            "name": "example name",
            "project_id": "123e4567-e89b-12d3-a456-426614174000",
            "region": "example string",
            "tags": [
              "example string"
            ],
            "updated_at": "2023-01-01T00:00:00Z",
            "user_id": "user_id",
            "uuid": "123e4567-e89b-12d3-a456-426614174000"
          }
        ],
        "long_description": "\"Enhance your customer service with an AI agent designed to provide consistent, helpful, and accurate support across multiple channels. This template creates an agent that can answer product questions, troubleshoot common issues, process simple requests, and maintain a friendly, on-brand voice throughout customer interactions. Reduce response times, handle routine inquiries efficiently, and ensure your customers feel heard and helped.\"",
        "max_tokens": 123,
        "model": {
          "agreement": {
            "description": "example string",
            "name": "example name",
            "url": "example string",
            "uuid": "123e4567-e89b-12d3-a456-426614174000"
          },
          "created_at": "2023-01-01T00:00:00Z",
          "inference_name": "example name",
          "inference_version": "example string",
          "is_foundational": true,
          "metadata": {},
          "name": "example name",
          "parent_uuid": "123e4567-e89b-12d3-a456-426614174000",
          "provider": "MODEL_PROVIDER_DIGITALOCEAN",
          "updated_at": "2023-01-01T00:00:00Z",
          "upload_complete": true,
          "url": "example string",
          "usecases": [
            "MODEL_USECASE_AGENT",
            "MODEL_USECASE_GUARDRAIL"
          ],
          "uuid": "123e4567-e89b-12d3-a456-426614174000",
          "version": {
            "major": 123,
            "minor": 123,
            "patch": 123
          }
        },
        "name": "example name",
        "short_description": "\"This template has been designed with question-answer and conversational use cases in mind. It comes with validated agent instructions, fine-tuned model settings, and preconfigured guardrails defined for customer support-related use cases.\"",
        "summary": "example string",
        "tags": [
          "example string"
        ],
        "temperature": 123,
        "template_type": "AGENT_TEMPLATE_TYPE_STANDARD",
        "top_p": 123,
        "updated_at": "2023-01-01T00:00:00Z",
        "uuid": "123e4567-e89b-12d3-a456-426614174000"
      },
      "top_p": 0.9,
      "updated_at": "2021-01-01T00:00:00Z",
      "url": "https://example.com/agent",
      "user_id": "12345678",
      "uuid": "\"12345678-1234-1234-1234-123456789012\"",
      "version_hash": "example string"
    }
  ],
  "links": {
    "pages": {
      "first": "example string",
      "last": "example string",
      "next": "example string",
      "previous": "example string"
    }
  },
  "meta": {
    "page": 123,
    "pages": 123,
    "total": 123
  }
}

Returns Examples

200 example

{
  "agents": [
    {
      "chatbot": {
        "button_background_color": "example string",
        "logo": "example string",
        "name": "example name",
        "primary_color": "example string",
        "secondary_color": "example string",
        "starting_message": "example string"
      },
      "chatbot_identifiers": [
        {
          "agent_chatbot_identifier": "123e4567-e89b-12d3-a456-426614174000"
        }
      ],
      "created_at": "2021-01-01T00:00:00Z",
      "deployment": {
        "created_at": "2023-01-01T00:00:00Z",
        "name": "example name",
        "status": "STATUS_UNKNOWN",
        "updated_at": "2023-01-01T00:00:00Z",
        "url": "example string",
        "uuid": "123e4567-e89b-12d3-a456-426614174000",
        "visibility": "VISIBILITY_UNKNOWN"
      },
      "description": "This is a chatbot that can help you with your questions.",
      "if_case": "if talking about the weather use this route",
      "instruction": "Hello, how can I help you?",
      "k": 5,
      "max_tokens": 100,
      "model": {
        "agreement": {
          "description": "example string",
          "name": "example name",
          "url": "example string",
          "uuid": "123e4567-e89b-12d3-a456-426614174000"
        },
        "created_at": "2023-01-01T00:00:00Z",
        "inference_name": "example name",
        "inference_version": "example string",
        "is_foundational": true,
        "metadata": {},
        "name": "example name",
        "parent_uuid": "123e4567-e89b-12d3-a456-426614174000",
        "provider": "MODEL_PROVIDER_DIGITALOCEAN",
        "updated_at": "2023-01-01T00:00:00Z",
        "upload_complete": true,
        "url": "example string",
        "usecases": [
          "MODEL_USECASE_AGENT",
          "MODEL_USECASE_GUARDRAIL"
        ],
        "uuid": "123e4567-e89b-12d3-a456-426614174000",
        "version": {
          "major": 123,
          "minor": 123,
          "patch": 123
        }
      },
      "name": "My Agent",
      "project_id": "12345678-1234-1234-1234-123456789012",
      "provide_citations": true,
      "region": "\"tor1\"",
      "retrieval_method": "RETRIEVAL_METHOD_UNKNOWN",
      "route_created_at": "2021-01-01T00:00:00Z",
      "route_created_by": "12345678",
      "route_name": "Route Name",
      "route_uuid": "\"12345678-1234-1234-1234-123456789012\"",
      "tags": [
        "example string"
      ],
      "temperature": 0.5,
      "template": {
        "created_at": "2023-01-01T00:00:00Z",
        "description": "example string",
        "guardrails": [
          {
            "priority": 123,
            "uuid": "123e4567-e89b-12d3-a456-426614174000"
          }
        ],
        "instruction": "example string",
        "k": 123,
        "knowledge_bases": [
          {
            "added_to_agent_at": "2023-01-01T00:00:00Z",
            "created_at": "2023-01-01T00:00:00Z",
            "database_id": "123e4567-e89b-12d3-a456-426614174000",
            "embedding_model_uuid": "123e4567-e89b-12d3-a456-426614174000",
            "is_public": true,
            "last_indexing_job": {
              "completed_datasources": 123,
              "created_at": "2023-01-01T00:00:00Z",
              "data_source_uuids": [
                "example string"
              ],
              "finished_at": "2023-01-01T00:00:00Z",
              "knowledge_base_uuid": "123e4567-e89b-12d3-a456-426614174000",
              "phase": "BATCH_JOB_PHASE_UNKNOWN",
              "started_at": "2023-01-01T00:00:00Z",
              "status": "INDEX_JOB_STATUS_UNKNOWN",
              "tokens": 123,
              "total_datasources": 123,
              "total_items_failed": "12345",
              "total_items_indexed": "12345",
              "total_items_skipped": "12345",
              "updated_at": "2023-01-01T00:00:00Z",
              "uuid": "123e4567-e89b-12d3-a456-426614174000"
            },
            "name": "example name",
            "project_id": "123e4567-e89b-12d3-a456-426614174000",
            "region": "example string",
            "tags": [
              "example string"
            ],
            "updated_at": "2023-01-01T00:00:00Z",
            "user_id": "user_id",
            "uuid": "123e4567-e89b-12d3-a456-426614174000"
          }
        ],
        "long_description": "\"Enhance your customer service with an AI agent designed to provide consistent, helpful, and accurate support across multiple channels. This template creates an agent that can answer product questions, troubleshoot common issues, process simple requests, and maintain a friendly, on-brand voice throughout customer interactions. Reduce response times, handle routine inquiries efficiently, and ensure your customers feel heard and helped.\"",
        "max_tokens": 123,
        "model": {
          "agreement": {
            "description": "example string",
            "name": "example name",
            "url": "example string",
            "uuid": "123e4567-e89b-12d3-a456-426614174000"
          },
          "created_at": "2023-01-01T00:00:00Z",
          "inference_name": "example name",
          "inference_version": "example string",
          "is_foundational": true,
          "metadata": {},
          "name": "example name",
          "parent_uuid": "123e4567-e89b-12d3-a456-426614174000",
          "provider": "MODEL_PROVIDER_DIGITALOCEAN",
          "updated_at": "2023-01-01T00:00:00Z",
          "upload_complete": true,
          "url": "example string",
          "usecases": [
            "MODEL_USECASE_AGENT",
            "MODEL_USECASE_GUARDRAIL"
          ],
          "uuid": "123e4567-e89b-12d3-a456-426614174000",
          "version": {
            "major": 123,
            "minor": 123,
            "patch": 123
          }
        },
        "name": "example name",
        "short_description": "\"This template has been designed with question-answer and conversational use cases in mind. It comes with validated agent instructions, fine-tuned model settings, and preconfigured guardrails defined for customer support-related use cases.\"",
        "summary": "example string",
        "tags": [
          "example string"
        ],
        "temperature": 123,
        "template_type": "AGENT_TEMPLATE_TYPE_STANDARD",
        "top_p": 123,
        "updated_at": "2023-01-01T00:00:00Z",
        "uuid": "123e4567-e89b-12d3-a456-426614174000"
      },
      "top_p": 0.9,
      "updated_at": "2021-01-01T00:00:00Z",
      "url": "https://example.com/agent",
      "user_id": "12345678",
      "uuid": "\"12345678-1234-1234-1234-123456789012\"",
      "version_hash": "example string"
    }
  ],
  "links": {
    "pages": {
      "first": "example string",
      "last": "example string",
      "next": "example string",
      "previous": "example string"
    }
  },
  "meta": {
    "page": 123,
    "pages": 123,
    "total": 123
  }
}