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API Reference
Agents

Get agent

agents.retrieve(strid) -> AgentRetrieveResponse
GET/api/agents/{id}/

Get agent

ParametersExpand Collapse
id: str
formatuuid
ReturnsExpand Collapse
class AgentRetrieveResponse: …
id: str
formatuuid
created_at: datetime
formatdate-time
name: str
maxLength255
project: str
formatuuid
slug: str
maxLength255
updated_at: datetime
formatdate-time
active_dataset: Optional[str]
formatuuid
agent_description: Optional[object]
agent_path: Optional[str]
maxLength512
analyzer_model: Optional[str]
maxLength128
backtest_metadata: Optional[object]
backtest_model_suggestions: Optional[object]
consistency_rules: Optional[object]
dataset_has_expected_output: Optional[bool]
dataset_input_keys: Optional[object]
dataset_size: Optional[int]
maximum9223372036854776000
minimum-9223372036854776000
formatint64
description: Optional[str]
display_name: Optional[str]
maxLength512
entrypoint_fn: Optional[str]
maxLength255
eval_dataset: Optional[object]
evaluation_criteria: Optional[object]
fixed_elements: Optional[object]
improvement_metadata: Optional[object]
input_schema: Optional[object]
is_deleted: Optional[bool]
model: Optional[str]
maxLength128
optimizable_elements: Optional[object]
output_fields: Optional[object]
output_schema: Optional[object]
policy_data: Optional[object]
policy_markdown: Optional[str]
proposed_criteria: Optional[object]
status: Optional[str]
maxLength20
structure_weight: Optional[float]
formatdouble
tags: Optional[object]
tool_analysis: Optional[object]
tool_config: Optional[object]
tool_usage_weight: Optional[float]
formatdouble
total_points: Optional[float]
formatdouble
Get agent
from overmind_lab import OvermindLab

client = OvermindLab()
agent = client.agents.retrieve(
    "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
}