Console
    Contact Us Start free

    Develop and deploy agents on Vertex AI Agent Engine

    This page demonstrates how to create and deploy an agent to Vertex AI Agent Engine Runtime using the following agent frameworks:

    This quickstart guides you through the following steps:

    • Set up your Google Cloud project.

    • Install the Vertex AI SDK for Python and your chosen framework.

    • Develop a currency exchange agent.

    • Deploy the agent to Vertex AI Agent Engine Runtime.

    • Test the deployed agent.

    For the quickstart using Agent Development Kit, see Develop and deploy agents on Vertex AI Agent Engine with Agent Development Kit .

    Before you begin

    1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

    2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    3. Verify that billing is enabled for your Google Cloud project .

    4. Enable the Vertex AI and Cloud Storage APIs.

      Roles required to enable APIs

      To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

      Enable the APIs

    5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    6. Verify that billing is enabled for your Google Cloud project .

    7. Enable the Vertex AI and Cloud Storage APIs.

      Roles required to enable APIs

      To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

      Enable the APIs

    To get the permissions that you need to use Vertex AI Agent Engine, ask your administrator to grant you the following IAM roles on your project:

    For more information about granting roles, see Manage access to projects, folders, and organizations .

    You might also be able to get the required permissions through custom roles or other predefined roles .

    Install and initialize the Vertex AI SDK for Python

    1. Run the following command to install the Vertex AI SDK for Python and other required packages:

      LangGraph

       pip  
install  
--upgrade  
--quiet  
google-cloud-aiplatform [ 
agent_engines,langchain ]>= 
 1 
.112

      LangChain

       pip  
install  
--upgrade  
--quiet  
google-cloud-aiplatform [ 
agent_engines,langchain ]>= 
 1 
.112

      AG2

       pip  
install  
--upgrade  
--quiet  
google-cloud-aiplatform [ 
agent_engines,ag2 ]>= 
 1 
.112

      LlamaIndex

      Preview

      This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms . Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions .

       pip  
install  
--upgrade  
--quiet  
google-cloud-aiplatform [ 
agent_engines,llama_index ]>= 
 1 
.112

    2. Authenticate as a user

      Colab

      Run the following code:

        from 
  
 google.colab 
  
 import 
 auth 
 auth 
 . 
 authenticate_user 
 ( 
 project_id 
 = 
 " PROJECT_ID 
" 
 )

      Cloud Shell

      No action required.

      Local Shell

      Run the following command:

       gcloud  
auth  
application-default  
login

      Express mode

      If you're using Vertex AI in express mode , no action is required.

    3. Run the following code to import Vertex AI Agent Engine and initialize the SDK:

      1. (Optional) Before testing an agent you develop , you need to import Vertex AI Agent Engine and initialize the SDK as follows:

        Google Cloud project

          import 
  
  vertexai 
 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 " PROJECT_ID 
" 
 , 
 # Your project ID. 
 location 
 = 
 " LOCATION 
" 
 , 
 # Your cloud region. 
 )

        Where:

        Express mode

        If you're using Vertex AI in express mode , run the following code:

          import 
  
  vertexai 
 
  vertexai 
 
 . 
 init 
 ( 
 api_key 
 = 
 " API_KEY 
" 
 )

        where API_KEY is the API key you use to authenticate the agent.

      2. Before deploying an agent , you need to import Vertex AI Agent Engine and initialize the SDK as follows:

        Google Cloud project

          import 
  
  vertexai 
 
 client 
 = 
  vertexai 
 
 . 
 Client 
 ( 
 project 
 = 
 " PROJECT_ID 
" 
 , 
 # Your project ID. 
 location 
 = 
 " LOCATION 
" 
 , 
 # Your cloud region. 
 )

        Where:

        Express mode

        If you're using Vertex AI in express mode , run the following code:

          import 
  
  vertexai 
 
 client 
 = 
  vertexai 
 
 . 
 Client 
 ( 
 api_key 
 = 
 " API_KEY 
" 
 )

        where API_KEY is the API key you use to authenticate the agent.

    Develop an agent

    1. Develop a currency exchange tool for your agent:

        def 
  
 get_exchange_rate 
 ( 
 currency_from 
 : 
 str 
 = 
 "USD" 
 , 
 currency_to 
 : 
 str 
 = 
 "EUR" 
 , 
 currency_date 
 : 
 str 
 = 
 "latest" 
 , 
 ): 
  
 """Retrieves the exchange rate between two currencies on a specified date.""" 
 import 
  
 requests 
 response 
 = 
 requests 
 . 
 get 
 ( 
 f 
 "https://api.frankfurter.app/ 
 { 
 currency_date 
 } 
 " 
 , 
 params 
 = 
 { 
 "from" 
 : 
 currency_from 
 , 
 "to" 
 : 
 currency_to 
 }, 
 ) 
 return 
 response 
 . 
 json 
 ()

    2. Instantiate an agent:

      LangGraph

        from 
  
 vertexai 
  
 import 
 agent_engines 
 agent 
 = 
 agent_engines 
 . 
 LanggraphAgent 
 ( 
 model 
 = 
 "gemini-2.0-flash" 
 , 
 tools 
 = 
 [ 
 get_exchange_rate 
 ], 
 model_kwargs 
 = 
 { 
 "temperature" 
 : 
 0.28 
 , 
 "max_output_tokens" 
 : 
 1000 
 , 
 "top_p" 
 : 
 0.95 
 , 
 }, 
 )

      LangChain

        from 
  
 vertexai 
  
 import 
 agent_engines 
 agent 
 = 
 agent_engines 
 . 
 LangchainAgent 
 ( 
 model 
 = 
 "gemini-2.0-flash" 
 , 
 tools 
 = 
 [ 
 get_exchange_rate 
 ], 
 model_kwargs 
 = 
 { 
 "temperature" 
 : 
 0.28 
 , 
 "max_output_tokens" 
 : 
 1000 
 , 
 "top_p" 
 : 
 0.95 
 , 
 }, 
 )

      AG2

        from 
  
 vertexai 
  
 import 
 agent_engines 
 agent 
 = 
 agent_engines 
 . 
 AG2Agent 
 ( 
 model 
 = 
 "gemini-2.0-flash" 
 , 
 runnable_name 
 = 
 "Get Exchange Rate Agent" 
 , 
 tools 
 = 
 [ 
 get_exchange_rate 
 ], 
 )

      LlamaIndex

      Preview

      This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms . Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions .

        from 
  
 vertexai.preview 
  
 import 
 reasoning_engines 
 def 
  
 runnable_with_tools_builder 
 ( 
 model 
 , 
 runnable_kwargs 
 = 
 None 
 , 
 ** 
 kwargs 
 ): 
 from 
  
 llama_index.core.query_pipeline 
  
 import 
 QueryPipeline 
 from 
  
 llama_index.core.tools 
  
 import 
 FunctionTool 
 from 
  
 llama_index.core.agent 
  
 import 
 ReActAgent 
 llama_index_tools 
 = 
 [] 
 for 
 tool 
 in 
 runnable_kwargs 
 . 
 get 
 ( 
 "tools" 
 ): 
 llama_index_tools 
 . 
 append 
 ( 
 FunctionTool 
 . 
 from_defaults 
 ( 
 tool 
 )) 
 agent 
 = 
 ReActAgent 
 . 
 from_tools 
 ( 
 llama_index_tools 
 , 
 llm 
 = 
 model 
 , 
 verbose 
 = 
 True 
 ) 
 return 
 QueryPipeline 
 ( 
 modules 
 = 
 { 
 "agent" 
 : 
 agent 
 }) 
 agent 
 = 
 reasoning_engines 
 . 
 LlamaIndexQueryPipelineAgent 
 ( 
 model 
 = 
 "gemini-2.0-flash" 
 , 
 runnable_kwargs 
 = 
 { 
 "tools" 
 : 
 [ 
 get_exchange_rate 
 ]}, 
 runnable_builder 
 = 
 runnable_with_tools_builder 
 , 
 )

    3. Test the agent locally:

      LangGraph

        agent 
 . 
 query 
 ( 
 input 
 = 
 { 
 "messages" 
 : 
 [ 
 ( 
 "user" 
 , 
 "What is the exchange rate from US dollars to SEK today?" 
 ), 
 ]})

      LangChain

        agent 
 . 
 query 
 ( 
 input 
 = 
 "What is the exchange rate from US dollars to SEK today?" 
 )

      AG2

        agent 
 . 
 query 
 ( 
 input 
 = 
 "What is the exchange rate from US dollars to SEK today?" 
 )

      LlamaIndex

      Preview

      This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms . Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions .

        agent 
 . 
 query 
 ( 
 input 
 = 
 "What is the exchange rate from US dollars to SEK today?" 
 )

    Deploy an agent

    Deploy the agent by creating a reasoningEngine resource in Vertex AI:

    LangGraph

      remote_agent 
 = 
 client 
 . 
 agent_engines 
 . 
 create 
 ( 
 agent 
 , 
 config 
 = 
 { 
 "requirements" 
 : 
 [ 
 "google-cloud-aiplatform[agent_engines,langchain]" 
 ], 
 }, 
 )

    LangChain

      remote_agent 
 = 
 client 
 . 
 agent_engines 
 . 
 create 
 ( 
 agent 
 , 
 config 
 = 
 { 
 "requirements" 
 : 
 [ 
 "google-cloud-aiplatform[agent_engines,langchain]" 
 ], 
 }, 
 )

    AG2

      remote_agent 
 = 
 client 
 . 
 agent_engines 
 . 
 create 
 ( 
 agent 
 , 
 config 
 = 
 { 
 "requirements" 
 : 
 [ 
 "google-cloud-aiplatform[agent_engines,ag2]" 
 ], 
 }, 
 )

    LlamaIndex

    Preview

    This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms . Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions .

      remote_agent 
 = 
 client 
 . 
 agent_engines 
 . 
 create 
 ( 
 agent 
 , 
 config 
 = 
 { 
 "requirements" 
 : 
 [ 
 "google-cloud-aiplatform[agent_engines,llama_index]" 
 ], 
 }, 
 )

    Use an agent

    Test the deployed agent by sending a query:

    LangGraph

      remote_agent 
 . 
 query 
 ( 
 input 
 = 
 { 
 "messages" 
 : 
 [ 
 ( 
 "user" 
 , 
 "What is the exchange rate from US dollars to SEK today?" 
 ), 
 ]})

    LangChain

      remote_agent 
 . 
 query 
 ( 
 input 
 = 
 "What is the exchange rate from US dollars to SEK today?" 
 )

    AG2

      remote_agent 
 . 
 query 
 ( 
 input 
 = 
 "What is the exchange rate from US dollars to SEK today?" 
 )

    LlamaIndex

    Preview

    This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms . Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions .

      remote_agent 
 . 
 query 
 ( 
 input 
 = 
 "What is the exchange rate from US dollars to SEK today?" 
 )

    Clean up

    To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

      remote_agent 
 . 
 delete 
 ( 
 force 
 = 
 True 
 )

    What's next
    Create a Mobile Website
    View Site in Mobile | Classic
    Share by: