Create
Create an Agent
agents.create(AgentCreateParams**kwargs) -> agentAPIAgentAgentCreateResponse
post/v2/gen-ai/agents
To create a new agent, send a POST request to /v2/gen-ai/agents. The response body contains a JSON object with the newly created agent object.
Parameters
anthropic_key_uuidstr
optional
Optional Anthropic API key ID to use with Anthropic models
descriptionstr
optional
A text description of the agent, not used in inference
instructionstr
optional
Agent instruction. Instructions help your agent to perform its job effectively. See Write Effective Agent Instructions for best practices.
knowledge_base_uuidlist
optional
List[str]
Ids of the knowledge base(s) to attach to the agent
model_uuidstr
optional
Identifier for the foundation model.
namestr
optional
Agent name
openai_key_uuidstr
optional
Optional OpenAI API key ID to use with OpenAI models
project_idstr
optional
The id of the DigitalOcean project this agent will belong to
regionstr
optional
The DigitalOcean region to deploy your agent in
Returns
AgentCreateResponseclass
Information about a newly created Agent
from do_gradientai import GradientAI
client = GradientAI()
agent = client.agents.create()
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\""
}
}