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    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 ( 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 ( 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

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

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

      Where:

    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

    1. 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

      from 
  
 vertexai 
  
 import 
 agent_engines 
 remote_agent 
 = 
 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 .

      from 
  
 vertexai 
  
 import 
 agent_engines 
 remote_agent 
 = 
 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 
 )

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