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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
  },
  batch=True  # Required for agent execution
)

# 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."),
  batch=True  # Required for agent execution
)

{
  "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, 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
  },
  batch=True  # Required for agent execution
)

# 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."),
  batch=True  # Required for agent execution
)

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
RequestMetadata · object

Optional metadata to pass to the model.

​
config
AgentExecutionConfig · 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 string 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
Inputs · object

The inputs to the agent.

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)