Manage deployed agents¶

This page describes how to manage agents that have been deploy to the Vertex AI Agent Engine managed runtime. Deployed agents are resources of type reasoningEngine in Vertex AI.

List deployed agents¶

List all deployed agents for a given project and location:

Vertex AI SDK for Python¶

from vertexai import agent_engines

agent_engines.list()

To filter the list of by display_name:

from vertexai import agent_engines

agent_engines.list(filter='display_name="Demo Langchain Agent"')

REST¶

Call the reasoningEngines.list method.

Before using any of the request data, make the following replacements:

PROJECT_ID: your GCP project ID
LOCATION: a supported region

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)¶

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Execute the following command:

curl -X GET \ 
 -H "Authorization: Bearer $(gcloud auth print-access-token)" \ 
 "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines"

PowerShell (Windows)¶

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token 
$headers = @{ "Authorization" = "Bearer $cred" } 

Invoke-WebRequest ` 
 -Method GET ` 
 -Headers $headers ` 
 -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines" | Select-Object -Expand Content

You should receive a successful status code (2xx) and an empty response.

Get a deployed agent¶

Each deployed agent has a unique RESOURCE_ID identifier. To learn more, see Deploy an agent.

Vertex AI SDK for Python¶

The following code lets you get a specific deployed agent:

from vertexai import agent_engines

remote_agent = agent_engines.get("RESOURCE_ID")

Alternately, you can provide the fully qualified resource name:

from vertexai import agent_engines

remote_agent = agent_engines.get(
"projects/PROJECT_ID_OR_NUMBER/locations/LOCATION/reasoningEngines/RESOURCE_ID"
)

REST¶

Call the reasoningEngines.get method.

Before using any of the request data, make the following replacements:

PROJECT_ID: your GCP project ID
LOCATION: a supported region
RESOURCE_ID: the resource ID of the deployed agent

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)¶

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Execute the following command:

curl -X GET \ 
 -H "Authorization: Bearer $(gcloud auth print-access-token)" \ 
 "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID"

PowerShell (Windows)¶

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token 
$headers = @{ "Authorization" = "Bearer $cred" } 

Invoke-WebRequest ` 
 -Method GET ` 
 -Headers $headers ` 
 -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID" | Select-Object -Expand Content

You should receive a successful status code (2xx) and an empty response.

Update a deployed agent¶

You can update one or more fields of the deployed agent at the same time, but you have to specify at least one of the fields to be updated. The amount of time it takes to update the deployed agent depends on the update being performed, but it generally takes between a few seconds to a few minutes.

Vertex AI SDK for Python¶

To update a deployed agent (corresponding to RESOURCE_NAME) to an updated agent (corresponding to UPDATED_AGENT):

from vertexai import agent_engines

agent_engines.update(
 resource_name=RESOURCE_NAME, # Required.
 agent_engine=UPDATED_AGENT, # Optional.
 requirements=REQUIREMENTS, # Optional.
 display_name="DISPLAY_NAME", # Optional.
 description="DESCRIPTION", # Optional.
 extra_packages=EXTRA_PACKAGES, # Optional.
)

The arguments are the same as when you are deploying an agent. You can find details in the API reference.

REST¶

Call the reasoningEngines.patch method and provide an update_mask to specify which fields to update.

Before using any of the request data, make the following replacements:

PROJECT_ID: your GCP project ID
LOCATION: a supported region
RESOURCE_ID: the resource ID of the deployed agent
update_mask: a list of comma-separated fields to update

HTTP method and URL:

PATCH https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID?update_mask="display_name,description"

Request JSON body:

{
"displayName": "DISPLAY_NAME",
"description": "DESCRIPTION"
}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)¶

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X PATCH \ 
 -H "Authorization: Bearer $(gcloud auth print-access-token)" \ 
 -H "Content-Type: application/json; charset=utf-8" \ 
 -d @request.json \ 
 "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID?update_mask="display_name,description""

PowerShell (Windows)¶

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token 
$headers = @{ "Authorization" = "Bearer $cred" } 

Invoke-WebRequest ` 
 -Method PATCH ` 
 -Headers $headers ` 
 -ContentType: "application/json; charset=utf-8" ` 
 -InFile request.json ` 
 -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID?update_mask="display_name,description"" | Select-Object -Expand Content

You should receive a successful status code (2xx) and an empty response.

Delete a deployed agent¶

Vertex AI SDK for Python¶

If you already have an existing instance of the deployed agent (as remote_agent), you can run the following command:

remote_agent.delete()

Alternatively, you can call agent_engines.delete() to delete the deployed agent corresponding to RESOURCE_NAME in the following way:

from vertexai import agent_engines

agent_engines.delete(RESOURCE_NAME)

REST¶

Call the reasoningEngines.delete method.

Before using any of the request data, make the following replacements:

PROJECT_ID: your GCP project ID
LOCATION: a supported region
RESOURCE_ID: the resource ID of the deployed agent

HTTP method and URL:

DELETE https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)¶

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Execute the following command:

curl -X DELETE \ 
 -H "Authorization: Bearer $(gcloud auth print-access-token)" \ 
 "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID"

PowerShell (Windows)¶

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token 
$headers = @{ "Authorization" = "Bearer $cred" } 

Invoke-WebRequest ` 
 -Method DELETE ` 
 -Headers $headers ` 
 -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID" | Select-Object -Expand Content

You should receive a successful status code (2xx) and an empty response.

Manage deployed agents¶

List deployed agents¶

Vertex AI SDK for Python¶

REST¶

curl (Linux, macOS, or Cloud Shell)¶

PowerShell (Windows)¶

Get a deployed agent¶

Vertex AI SDK for Python¶

REST¶

curl (Linux, macOS, or Cloud Shell)¶

PowerShell (Windows)¶

Update a deployed agent¶

Vertex AI SDK for Python¶

REST¶

curl (Linux, macOS, or Cloud Shell)¶

PowerShell (Windows)¶

Delete a deployed agent¶

Vertex AI SDK for Python¶

REST¶

curl (Linux, macOS, or Cloud Shell)¶

PowerShell (Windows)¶

What's next¶