GET
/
v1
/
agent
/
executions
!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.list(skip=0, limit=10)
[
  {
    "usage": {
      "elements_processed": 123,
      "element_type": "image",
      "credits_used": 123,
      "steps": 123,
      "message": "<string>",
      "duration_seconds": 0
    },
    "id": "<string>",
    "name": "<string>",
    "response": "<any>",
    "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.list(skip=0, limit=10)

Authorizations

Authorization
string
header
required

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

Query Parameters

skip
integer
default:0

Number of items to skip

Required range: x >= 0
limit
integer | null
default:10

Maximum number of items to return

Required range: 1 <= x <= 1000

Response

Successful Response

name
string
required

Name of the agent

usage
object

The usage metrics for the request.

id
string

Unique identifier of the agent execution response.

response
any

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)