Update agent
agents.update(strid, AgentUpdateParams**kwargs) -> AgentUpdateResponse
PATCH/api/agents/{id}/
Update agent
Parameters
id: str
formatuuid
active_dataset: Optional[str]
formatuuid
agent_path: Optional[str]
maxLength512
analyzer_model: Optional[str]
maxLength128
dataset_size: Optional[int]
maximum9223372036854776000
minimum-9223372036854776000
formatint64
description: Optional[str]
display_name: Optional[str]
maxLength512
entrypoint_fn: Optional[str]
maxLength255
model: Optional[str]
maxLength128
name: Optional[str]
minLength1
maxLength255
policy_markdown: Optional[str]
project: Optional[str]
formatuuid
slug: Optional[str]
minLength1
maxLength255
status: Optional[str]
minLength1
maxLength20
structure_weight: Optional[float]
formatdouble
tool_usage_weight: Optional[float]
formatdouble
total_points: Optional[float]
formatdouble
Returns
Update agent
from overmind_lab import OvermindLab
client = OvermindLab()
agent = client.agents.update(
id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(agent.id){
"id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"created_at": "2019-12-27T18:11:19.117Z",
"name": "name",
"project": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"slug": "slug",
"updated_at": "2019-12-27T18:11:19.117Z",
"active_dataset": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"agent_description": {},
"agent_path": "agent_path",
"analyzer_model": "analyzer_model",
"backtest_metadata": {},
"backtest_model_suggestions": {},
"consistency_rules": {},
"dataset_has_expected_output": true,
"dataset_input_keys": {},
"dataset_size": -9007199254740991,
"description": "description",
"display_name": "display_name",
"entrypoint_fn": "entrypoint_fn",
"eval_dataset": {},
"evaluation_criteria": {},
"fixed_elements": {},
"improvement_metadata": {},
"input_schema": {},
"is_deleted": true,
"model": "model",
"optimizable_elements": {},
"output_fields": {},
"output_schema": {},
"policy_data": {},
"policy_markdown": "policy_markdown",
"proposed_criteria": {},
"status": "status",
"structure_weight": 0,
"tags": {},
"tool_analysis": {},
"tool_config": {},
"tool_usage_weight": 0,
"total_points": 0
}Returns Examples
{
"id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"created_at": "2019-12-27T18:11:19.117Z",
"name": "name",
"project": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"slug": "slug",
"updated_at": "2019-12-27T18:11:19.117Z",
"active_dataset": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"agent_description": {},
"agent_path": "agent_path",
"analyzer_model": "analyzer_model",
"backtest_metadata": {},
"backtest_model_suggestions": {},
"consistency_rules": {},
"dataset_has_expected_output": true,
"dataset_input_keys": {},
"dataset_size": -9007199254740991,
"description": "description",
"display_name": "display_name",
"entrypoint_fn": "entrypoint_fn",
"eval_dataset": {},
"evaluation_criteria": {},
"fixed_elements": {},
"improvement_metadata": {},
"input_schema": {},
"is_deleted": true,
"model": "model",
"optimizable_elements": {},
"output_fields": {},
"output_schema": {},
"policy_data": {},
"policy_markdown": "policy_markdown",
"proposed_criteria": {},
"status": "status",
"structure_weight": 0,
"tags": {},
"tool_analysis": {},
"tool_config": {},
"tool_usage_weight": 0,
"total_points": 0
}