Use an agent

The code for querying an agent is the same regardless of whether it is running locally or deployed remotely. Therefore, in this page, the term agent refers to either local_agent or remote_agent interchangeably. As the set of supported operations varies across frameworks, we provide usage instructions for framework-specific templates:

Framework Description
Agent Development Kit Designed based on Google's internal best practices for developers building AI applications or teams needing to rapidly prototype and deploy robust agent-based solutions.
Agent2Agent (preview) The Agent2Agent (A2A) protocol is an open standard designed to enable seamless communication and collaboration between AI agents.
LangChain Easier to use for basic use cases because of its predefined configurations and abstractions.
LangGraph Graph-based approach to defining workflows, with advanced human-in-the-loop and rewind/replay capabilities.
AG2 (formerly AutoGen) AG2 provides multi-agent conversation framework as a high-level abstraction for building LLM workflows.
LlamaIndex (preview) LlamaIndex's query pipeline offers a high-level interface for creating Retrieval-Augmented Generation (RAG) workflows.

For custom agents that are not based on one of the framework-specific templates, you can follow these steps:

  1. User authentication.
  2. Get an agent instance.
  3. Look up supported operations.
  4. Query the agent using supported operations.

User authentication

Follows the same instructions as setting up your environment.

Get an instance of an agent

To query an agent, you first need an instance of an agent. You can either create a new instance or get an existing instance of an agent.

To get the agent corresponding to a specific resource ID:

Vertex AI SDK for Python

Run the following code:

agent = client.agent_engines.get(name="projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID")

requests

Run the following code:

from google import auth as google_auth
from google.auth.transport import requests as google_requests
import requests

def get_identity_token():
    credentials, _ = google_auth.default()
    auth_request = google_requests.Request()
    credentials.refresh(auth_request)
    return credentials.token

response = requests.get(
f"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID",
    headers={
        "Content-Type": "application/json; charset=utf-8",
        "Authorization": f"Bearer {get_identity_token()}",
    },
)

REST 

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

When using the Vertex AI SDK for Python, the agent object corresponds to an AgentEngine class that contains the following:

  • an agent.api_resource with information about the deployed agent. You can also call agent.operation_schemas() to return the list of operations that the agent supports. See Supported operations for details.
  • an agent.api_client that allows for synchronous service interactions
  • an agent.async_api_client that allows for asynchronous service interactions

The rest of this section assumes that you have an instance, named as agent.

List supported operations

When developing the agent locally, you have access and knowledge of the operations that it supports. To use a deployed agent, you can enumerate the operations that it supports:

Vertex AI SDK for Python

Run the following code:

agent.operation_schemas()

requests

Run the following code:

import json

json.loads(response.content).get("spec").get("classMethods")

REST

Represented in spec.class_methods from the response to the curl request.

The schema for each operation is a dictionary that documents the information of a method for the agent that you can call. The set of supported operations depends on the framework you used to develop your agent:

As an example, the following is the schema for the query operation of a LangchainAgent:

{'api_mode': '',
 'name': 'query',
 'description': """Queries the Agent with the given input and config.
    Args:
        input (Union[str, Mapping[str, Any]]):
            Required. The input to be passed to the Agent.
        config (langchain_core.runnables.RunnableConfig):
            Optional. The config (if any) to be used for invoking the Agent.
    Returns:
        The output of querying the Agent with the given input and config.
""",            '        ',
 'parameters': {'$defs': {'RunnableConfig': {'description': 'Configuration for a Runnable.',
                                             'properties': {'configurable': {...},
                                                            'run_id': {...},
                                                            'run_name': {...},
                                                            ...},
                                             'type': 'object'}},
                'properties': {'config': {'nullable': True},
                               'input': {'anyOf': [{'type': 'string'}, {'type': 'object'}]}},
                'required': ['input'],
                'type': 'object'}}

where

  • name is the name of the operation (i.e. agent.query for an operation named query).
  • api_mode is the API mode of the operation ("" for synchronous, "stream" for streaming).
  • description is a description of the operation based on the method's docstring.
  • parameters is the schema of the input arguments in OpenAPI schema format.

Query the agent using supported operations

For custom agents, you can use any of the following query or streaming operations you defined when developing your agent:

Note that certain frameworks only support specific query or streaming operations:

Framework Supported query operations
Agent Development Kit async_stream_query
LangChain query, stream_query
LangGraph query, stream_query
AG2 query
LlamaIndex query

Query the agent

Query the agent using the query operation:

Vertex AI SDK for Python 

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

requests 

from google import auth as google_auth
from google.auth.transport import requests as google_requests
import requests

def get_identity_token():
    credentials, _ = google_auth.default()
    auth_request = google_requests.Request()
    credentials.refresh(auth_request)
    return credentials.token

requests.post(
    f"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID:query",
    headers={
        "Content-Type": "application/json; charset=utf-8",
        "Authorization": f"Bearer {get_identity_token()}",
    },
    data=json.dumps({
        "class_method": "query",
        "input": {
            "input": "What is the exchange rate from US dollars to Swedish Krona today?"
        }
    })
)

REST 

curl \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID:query -d '{
  "class_method": "query",
  "input": {
    "input": "What is the exchange rate from US dollars to Swedish Krona today?"
  }
}'

The query response is a string that is similar to the output of a local application test:

{"input": "What is the exchange rate from US dollars to Swedish Krona today?",
 # ...
 "output": "For 1 US dollar you will get 10.7345 Swedish Krona."}

Stream responses from the agent

Stream a response from the agent using the stream_query operation:

Vertex AI SDK for Python 

agent = agent_engines.get("projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID")

for response in agent.stream_query(
    input="What is the exchange rate from US dollars to Swedish Krona today?"
):
    print(response)

requests 

from google import auth as google_auth
from google.auth.transport import requests as google_requests
import requests

def get_identity_token():
    credentials, _ = google_auth.default()
    auth_request = google_requests.Request()
    credentials.refresh(auth_request)
    return credentials.token

requests.post(
    f"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID:streamQuery",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {get_identity_token()}",
    },
    data=json.dumps({
        "class_method": "stream_query",
        "input": {
            "input": "What is the exchange rate from US dollars to Swedish Krona today?"
        },
    }),
    stream=True,
)

REST 

curl \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID:streamQuery?alt=sse -d '{
  "class_method": "stream_query",
  "input": {
    "input": "What is the exchange rate from US dollars to Swedish Krona today?"
  }
}'

Vertex AI Agent Engine streams responses as a sequence of iteratively generated objects. For example, a set of three responses might look like the following:

{'actions': [{'tool': 'get_exchange_rate', ...}]}  # first response
{'steps': [{'action': {'tool': 'get_exchange_rate', ...}}]}  # second response
{'output': 'The exchange rate is 11.0117 SEK per USD as of 2024-12-03.'}  # final response

Asynchronously query the agent

If you defined an async_query operation when developing the agent, there is support for client-side async querying of the agent in the Vertex AI SDK for Python:

Vertex AI SDK for Python 

agent = agent_engines.get("projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID")

response = await agent.async_query(
    input="What is the exchange rate from US dollars to Swedish Krona today?"
)
print(response)

The query response is a dictionary that is the same as the output of a local test:

{"input": "What is the exchange rate from US dollars to Swedish Krona today?",
 # ...
 "output": "For 1 US dollar you will get 10.7345 Swedish Krona."}

Asynchronously stream responses from the agent

If you defined an async_stream_query operation when developing the agent, you can asynchronously stream a response from the agent using one of its operations (e.g. async_stream_query):

Vertex AI SDK for Python 

agent = agent_engines.get("projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID")

async for response in agent.async_stream_query(
    input="What is the exchange rate from US dollars to Swedish Krona today?"
):
    print(response)

The async_stream_query operation calls the same streamQuery endpoint under-the-hood and asynchronously stream responses as a sequence of iteratively generated objects. For example, a set of three responses might look like the following:

{'actions': [{'tool': 'get_exchange_rate', ...}]}  # first response
{'steps': [{'action': {'tool': 'get_exchange_rate', ...}}]}  # second response
{'output': 'The exchange rate is 11.0117 SEK per USD as of 2024-12-03.'}  # final response

The responses should be the same as those generated during local testing.

What's next