POST
/
v1
/
agent
/
execute
!pip install vlmrun

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

# Initialize the client
client = VLMRun(base_url="https://agent.vlm.run/v1", api_key="<VLMRUN_API_KEY>")

# Upload the file to the object store
file = client.files.upload(file=Path("test.pdf"))

# Execute the agent (by name and version)
response: AgentExecutionResponse = client.agent.execute(
  name="<agent-name>:<agent-version>",
  inputs={
    "file": file.public_url
  },
)

# Execute the agent (by prompt)
response: AgentExecutionResponse = client.agent.execute(
  inputs={
    "file": file.public_url
  },
  config=AgentExecutionConfig(prompt="Extract the invoice_id, date and amount from the invoice."),
)
{
  "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, AgentExecutionConfig

# Initialize the client
client = VLMRun(base_url="https://agent.vlm.run/v1", api_key="<VLMRUN_API_KEY>")

# Upload the file to the object store
file = client.files.upload(file=Path("test.pdf"))

# Execute the agent (by name and version)
response: AgentExecutionResponse = client.agent.execute(
  name="<agent-name>:<agent-version>",
  inputs={
    "file": file.public_url
  },
)

# Execute the agent (by prompt)
response: AgentExecutionResponse = client.agent.execute(
  inputs={
    "file": file.public_url
  },
  config=AgentExecutionConfig(prompt="Extract the invoice_id, date and amount from the invoice."),
)

Authorizations

Authorization
string
header
required

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

Body

application/json

Request to execute an agent.

metadata
object

Optional metadata to pass to the model.

config
object

The configuration for the agent execution request.

id
string

Unique identifier of the request.

created_at
string<date-time>

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

callback_url
string<uri> | null

The URL to call when the request is completed.

Minimum length: 1
name
string | null

Name of the agent. If not provided, we use the prompt to identify the unique agent.

batch
boolean
default:true

Whether to process the document in batch mode (async).

inputs
object | null

The inputs to the agent.

Response

Successful Response

Response to the agent execution request.

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)