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GET
/
v1
/
agent
/
executions
/
{id}
!pip install vlmrun

from vlmrun.client import VLMRun
from vlmrun.client.types import AgentExecutionResponse

client = VLMRun(base_url="https://agent.vlm.run/v1", api_key="<VLMRUN_API_KEY>")
response: AgentExecutionResponse = client.executions.get("<execution_id>")

{
  "name": "<string>",
  "usage": {
    "elements_processed": 123,
    "element_type": "image",
    "credits_used": 123,
    "steps": 123,
    "message": "<string>",
    "duration_seconds": 0
  },
  "id": "<string>",
  "response": "<unknown>",
  "status": "pending",
  "created_at": "2023-11-07T05:31:56Z",
  "completed_at": "2023-11-07T05:31:56Z"
}
!pip install vlmrun

from vlmrun.client import VLMRun
from vlmrun.client.types import AgentExecutionResponse

client = VLMRun(base_url="https://agent.vlm.run/v1", api_key="<VLMRUN_API_KEY>")
response: AgentExecutionResponse = client.executions.get("<execution_id>")

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Path Parameters

​
id
string
required

Response

Successful Response

Response to the agent execution request.

​
name
string
required

Name of the agent

​
usage
CreditUsageResponse · object

The usage metrics for the request.

​
id
string

Unique identifier of the agent execution response.

​
response
any | null

The response from the model.

​
status
enum<string>
default:pending

The status of the job.

Available options:
pending,
enqueued,
running,
completed,
failed,
paused
​
created_at
string<date-time>

Date and time when the execution was created (in UTC timezone)

​
completed_at
string<date-time> | null

Date and time when the execution was completed (in UTC timezone)