Tutorial: Agent Platform API in express mode

Gemini Enterprise Agent Platform in express mode lets you try core generative AI features available on Agent Platform. This tutorial shows you how to complete the following tasks by using the Agent Platform API in express mode:

  • Install and initialize the Google Gen AI SDK for express mode.

  • Send a request to the Gemini for Google Cloud API, including the following:

    • Streaming request
    • Non-streaming request
    • Function calling request

Before you begin

Before performing the tasks described in this document, sign up for express mode.

Install and initialize the SDK for express mode

The Google Gen AI SDK lets you use Google generative AI models and features to build AI-powered applications. When using Agent Platform in express mode, install and initialize the google-genai package to authenticate using your generated API key.

Install

To install the Google Gen AI SDK for express mode, run the following commands:

# Developer TODO: If you're using Colab, uncomment the following lines:
# from google.colab import auth
# auth.authenticate_user()

!pip install --upgrade google-genai

If you're using Colaboratory, restart the runtime after installation if prompted.

Initialize

Configure the API key for express mode and initialize the client with enterprise=True. For details about getting an API key, see Agent Platform in express mode overview.

from google import genai
from google.genai import types

# Developer TODO: Replace YOUR_API_KEY with your API key.
API_KEY = "YOUR_API_KEY"

client = genai.Client(
    enterprise=True, api_key=API_KEY
)

Send a request to the Gemini for Google Cloud API

You can send either streaming or non-streaming requests to the Gemini for Google Cloud API. Streaming requests return the response in chunks as the request is processed. Non-streaming requests return the complete response after processing finishes.

Streaming request

To send a streaming request, call client.models.generate_content_stream() and print each chunk as it arrives:

from google import genai
from google.genai import types

def generate():
  client = genai.Client(enterprise=True, api_key="YOUR_API_KEY")

  config = types.GenerateContentConfig(
      thinking_config=types.ThinkingConfig(
          thinking_level="MEDIUM",
      ),
      seed=5,
      max_output_tokens=1024,
      stop_sequences=["STOP!"],
      safety_settings=[
          types.SafetySetting(
              category="HARM_CATEGORY_HATE_SPEECH",
              threshold="BLOCK_ONLY_HIGH",
          )
      ],
  )
  for chunk in client.models.generate_content_stream(
      model="gemini-3.8-flash",
      contents="Explain bubble sort to me",
      config=config,
  ):
    print(chunk.text, end="")

generate()

Non-streaming request

The following code sample defines a function that sends a non-streaming request to Gemini 3.8 Flash (gemini-3.8-flash). It shows you how to configure thinking level, output parameters, and safety settings:

from google import genai
from google.genai import types

def generate():
  client = genai.Client(enterprise=True, api_key="YOUR_API_KEY")

  config = types.GenerateContentConfig(
      thinking_config=types.ThinkingConfig(
          thinking_level="MEDIUM",
      ),
      seed=5,
      max_output_tokens=1024,
      stop_sequences=["STOP!"],
      safety_settings=[
          types.SafetySetting(
              category="HARM_CATEGORY_HATE_SPEECH",
              threshold="BLOCK_ONLY_HIGH",
          )
      ],
  )
  response = client.models.generate_content(
      model="gemini-3.8-flash",
      contents="Explain bubble sort to me",
      config=config,
  )
  print(response.text)

generate()

Function calling request

The following code sample declares a function tool, sends an initial prompt to 3.8 Flash, receives a function call part in the response, and then sends the matching FunctionResponse back to the model to generate a final response:

from google import genai
from google.genai import types

client = genai.Client(enterprise=True, api_key="YOUR_API_KEY")

get_weather_declaration = types.FunctionDeclaration(
    name="get_current_weather",
    description="Gets the current weather in a given city.",
    parameters={
        "type": "OBJECT",
        "properties": {
            "location": {
                "type": "STRING",
                "description": "The city and state, such as Boston, MA.",
            },
            "unit": {
                "type": "STRING",
                "enum": ["C", "F"],
            },
        },
        "required": ["location"],
    },
)
tools = [types.Tool(function_declarations=[get_weather_declaration])]

prompt = "What is the weather in Boston?"
first_response = client.models.generate_content(
    model="gemini-3.8-flash",
    contents=prompt,
    config=types.GenerateContentConfig(tools=tools),
)

function_call = first_response.function_calls[0]
contents = [
    types.Content(role="user", parts=[types.Part(text=prompt)]),
    first_response.candidates[0].content,
    types.Content(
        role="user",
        parts=[
            types.Part(
                function_response=types.FunctionResponse(
                    id=function_call.id,
                    name=function_call.name,
                    response={"weather": "sunny", "temperature": "72F"},
                )
            )
        ],
    ),
]

response = client.models.generate_content(
    model="gemini-3.8-flash",
    contents=contents,
    config=types.GenerateContentConfig(tools=tools),
)
print(response.text)

Clean up

This tutorial does not create any Google Cloud resources, so no cleanup is required to avoid charges.

What's next