Update
Update an Agent
agents.update(strpath_uuid, AgentUpdateParams**kwargs) -> agentAPIAgentAgentUpdateResponse
put/v2/gen-ai/agents/{uuid}
To update an agent, send a PUT request to /v2/gen-ai/agents/{uuid}. The response body is a JSON object containing the agent.
Parameters
uuidstr
anthropic_key_uuidstr
optional
Optional anthropic key uuid for use with anthropic models
Optional update of conversation logs enabled
descriptionstr
optional
Agent description
instructionstr
optional
Agent instruction. Instructions help your agent to perform its job effectively. See Write Effective Agent Instructions for best practices.
kint
optional
How many results should be considered from an attached knowledge base
formatint64
max_tokensint
optional
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_uuidstr
optional
Identifier for the foundation model.
namestr
optional
Agent name
openai_key_uuidstr
optional
Optional OpenAI key uuid for use with OpenAI models
project_idstr
optional
The id of the DigitalOcean project this agent will belong to
- 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
temperaturefloat
optional
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
top_pfloat
optional
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
uuidstr
Returns
AgentUpdateResponseclass
Information about an updated agent
from do_gradientai import GradientAI
client = GradientAI()
agent = client.agents.update(
path_uuid="\"123e4567-e89b-12d3-a456-426614174000\"",
)
print(agent.agent)200 Example
{
"agent": {
"anthropic_api_key": {
"created_at": "2023-01-01T00:00:00Z",
"created_by": "\"12345\"",
"deleted_at": "2023-01-01T00:00:00Z",
"name": "\"example name\"",
"updated_at": "2023-01-01T00:00:00Z",
"uuid": "\"123e4567-e89b-12d3-a456-426614174000\""
},
"api_key_infos": [
{
"created_at": "2023-01-01T00:00:00Z",
"created_by": "\"12345\"",
"deleted_at": "2023-01-01T00:00:00Z",
"name": "\"example name\"",
"secret_key": "\"example string\"",
"uuid": "\"123e4567-e89b-12d3-a456-426614174000\""
}
],
"api_keys": [
{
"api_key": "\"example string\""
}
],
"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\""
}
],
"child_agents": [],
"conversation_logs_enabled": true,
"created_at": "2023-01-01T00:00:00Z",
"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": "\"example string\"",
"functions": [
{
"api_key": "\"example string\"",
"created_at": "2023-01-01T00:00:00Z",
"created_by": "\"12345\"",
"description": "\"example string\"",
"faas_name": "\"example name\"",
"faas_namespace": "\"example name\"",
"input_schema": {},
"name": "\"example name\"",
"output_schema": {},
"updated_at": "2023-01-01T00:00:00Z",
"url": "\"example string\"",
"uuid": "\"123e4567-e89b-12d3-a456-426614174000\""
}
],
"guardrails": [
{
"agent_uuid": "\"123e4567-e89b-12d3-a456-426614174000\"",
"created_at": "2023-01-01T00:00:00Z",
"default_response": "\"example string\"",
"description": "\"example string\"",
"guardrail_uuid": "\"123e4567-e89b-12d3-a456-426614174000\"",
"is_attached": true,
"is_default": true,
"metadata": {},
"name": "\"example name\"",
"priority": 123,
"type": "GUARDRAIL_TYPE_UNKNOWN",
"updated_at": "2023-01-01T00:00:00Z",
"uuid": "\"123e4567-e89b-12d3-a456-426614174000\""
}
],
"if_case": "\"example string\"",
"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,
"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\""
}
],
"logging_config": {
"galileo_project_id": "\"123e4567-e89b-12d3-a456-426614174000\"",
"galileo_project_name": "\"example name\"",
"log_stream_id": "\"123e4567-e89b-12d3-a456-426614174000\"",
"log_stream_name": "\"example name\""
},
"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\"",
"openai_api_key": {
"created_at": "2023-01-01T00:00:00Z",
"created_by": "\"12345\"",
"deleted_at": "2023-01-01T00:00:00Z",
"models": [
{
"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\"",
"updated_at": "2023-01-01T00:00:00Z",
"uuid": "\"123e4567-e89b-12d3-a456-426614174000\""
},
"parent_agents": [],
"project_id": "\"123e4567-e89b-12d3-a456-426614174000\"",
"provide_citations": true,
"region": "\"example string\"",
"retrieval_method": "RETRIEVAL_METHOD_UNKNOWN",
"route_created_at": "2023-01-01T00:00:00Z",
"route_created_by": "\"12345\"",
"route_name": "\"example name\"",
"route_uuid": "\"123e4567-e89b-12d3-a456-426614174000\"",
"tags": [
"example string"
],
"temperature": 123,
"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,
"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": 123,
"updated_at": "2023-01-01T00:00:00Z",
"url": "\"example string\"",
"user_id": "\"12345\"",
"uuid": "\"123e4567-e89b-12d3-a456-426614174000\"",
"version_hash": "\"example string\""
}
}