随着 Vertex AI Extensions 的弃用，现有工作流必须迁移到 Gemini Enterprise Agent Platform（Agent Platform）。本指南介绍了三个核心扩展类别的迁移路径：
代码解释器扩展程序
迁移到： Agent Platform 代码执行沙盒 替代方案： Gemini API CodeExecution 工具
代码解释器扩展程序应迁移到 Agent Platform 代码执行沙盒 。此代管式服务提供了一个安全、隔离且有状态的沙盒环境，专门用于运行不受信任的 AI 生成的代码。它非常适合以编程方式执行、财务计算和数据科学工作流。
或者，如果您希望 Gemini 模型在生成过程中自动编写和执行代码，则可以使用 google-genai SDK 中的内置 code_execution 工具。
迁移示例
Vertex AI Extensions：
from vertexai.preview import extensions
extension = extensions.Extension.from_hub("code_interpreter")
response = extension.execute(
operation_id="generate_and_execute_code",
operation_params={"code": "import math\nprint(math.sqrt(15376))"}
)
Agent Platform 代码执行沙盒：
选项 1：使用智能体开发套件 (ADK)
from google.adk.code_executors.agent_engine_sandbox_code_executor import (
AgentEngineSandboxCodeExecutor,
)
from vertexai.preview.reasoning_engines import Agent
root_agent = Agent(
model="gemini-2.0-flash-001",
name="agent_engine_code_execution_agent",
instruction=base_system_instruction()
+ """
You need to assist the user with their queries by looking at the data and the
context in the conversation. Your final answer should summarize the code and
code execution relevant to the user query.
Include all pieces of data required to answer the question, such as a table
from code execution results. If the query can be answered directly without
writing code, generate the textual response directly. Take care not to manually
run commands like `pip install`. When plotting trends, sort data by the x-axis.
""",
code_executor=AgentEngineSandboxCodeExecutor(
sandbox_resource_name=None,
agent_engine_resource_name=None,
),
)
选项 2：使用 GenAI 客户端 SDK
from google import genai
# Initialize Gemini model
model = genai.Client().models
# Ask Gemini to generate code for a calculation
prompt = """
Write Python code to calculate the mean and standard deviation of these numbers:
[23, 45, 67, 89, 12, 34, 56]
Return only the Python code, no explanations.
"""
response = model.generate_content(
model="gemini-2.5-flash",
contents=prompt
)
generated_code = response.text.replace("```python", "").replace("```", "").strip()
# Execute the generated code in Agent Engine Sandbox using new pattern
exec_response = client.agent_engines.sandboxes.execute_code(
name=sandbox_resource_name,
input_data={"code": generated_code},
)
# Parse response with new file handling logic
for output in exec_response.outputs:
if output.mime_type == "application/json" and output.metadata is None:
result = json.loads(output.data.decode("utf-8"))
if result.get("msg_out"):
print(result.get("msg_out"))
if result.get("msg_err"):
print(f"Error occurred: {result.get('msg_err')}")
选项 3：定义为动态手动工具
import json
import re
from google.genai.types import FunctionDeclaration, Tool
def execute_python_code(code: str) -> str:
"""Execute Python code in a secure sandbox.
Args:
code: Python code to execute
Returns:
The output from code execution
"""
# Extract code block if wrapped in markdown
code_match = re.search(r"```python\n(.*?)\n```", code, re.DOTALL)
code_to_execute = code_match.group(1) if code_match else code
# Execute in sandbox
response = client.agent_engines.sandboxes.execute_code(
name=sandbox_resource_name, input_data={"code": code_to_execute}
)
# Parse response using new pattern
for output in response.outputs:
if output.mime_type == "application/json" and output.metadata is None:
result = json.loads(output.data.decode("utf-8"))
if result.get("msg_err"):
return f"Error: {result.get('msg_err')}"
return result.get("msg_out", "Code executed successfully")
return "Code executed (no output)"
# Create a tool from the function
code_tool = Tool(
function_declarations=[FunctionDeclaration.from_func(execute_python_code)]
)
Google 搜索扩展程序
迁移到： 依托 Google 搜索进行接地
迁移到使用 Google 搜索建立依据 。此功能可将模型响应固定到受信任的搜索索引和最新的公开信息，从而提高事实性。
迁移示例
Vertex AI Extensions：
from vertexai.preview import extensions
extension = extensions.Extension.from_hub("google_search")
response = extension.execute(
operation_id="search",
operation_params={"query": "What is the next total solar eclipse in the US?"}
)
依托 Google 搜索进行接地（使用 Google GenAI SDK）：
from google import genai
from google.genai.types import GenerateContentConfig, GoogleSearch, Tool
client = genai.Client()
response = client.models.generate_content(
model="gemini-3.1-flash",
contents="When is the next total solar eclipse in the United States?",
config=GenerateContentConfig(
tools=[Tool(google_search=GoogleSearch())],
temperature=0.0,
),
)
print(response.text)
自定义扩展程序
迁移到： 函数调用（工具使用）
将使用 OpenAPI 规范配置的自定义扩展程序迁移到函数调用 。借助函数调用，您可以在源文件中定义外部平台 API，并将其接口抽象转发给模型。
迁移示例
Vertex AI Extensions：
from vertexai.preview import extensions
extension = extensions.Extension.create(
manifest={
"name": "my_custom_api",
"apiSpec": {"openApiYaml": "..."}
}
)
使用 Google GenAI SDK 使用工具：
from google import genai
from google.genai.types import GenerateContentConfig
def get_order_status(order_id: str) -> str:
"""Returns the current status of a customer order.
Args:
order_id: The unique identifier of the order.
"""
statuses = {"123": "Shipped", "456": "Processing"}
return statuses.get(order_id, "Order not found")
client = genai.Client()
response = client.models.generate_content(
model="gemini-3.0-pro",
contents="Can you check the status of my order 123?",
config=GenerateContentConfig(
tools=[get_order_status],
),
)
print(response.text)