Merge branch 'main' into feat/function-tools-enum-support

Author: SOORAJTS2001

contributing/samples/agent_engine_code_execution/README

@@ -0,0 +1,18 @@
+# OAuth Sample
+
+## Introduction
+
+This sample data science agent uses Agent Engine Code Execution Sandbox to execute LLM generated code.
+
+
+## How to use
+
+* 1. Follow https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/code-execution/overview to create a code execution sandbox environment.
+
+* 2. Replace the SANDBOX_RESOURCE_NAME with the one you just created. If you dont want to create a new sandbox environment directly, the Agent Engine Code Execution Sandbox will create one for you by default using the AGENT_ENGINE_RESOURCE_NAME you specified, however, please ensure to clean up sandboxes after use, otherwise, it will consume quotas.
+
+
+## Sample prompt
+
+* Can you write a function that calculates the sum from 1 to 100.
+* The dataset is given as below. Store,Date,Weekly_Sales,Holiday_Flag,Temperature,Fuel_Price,CPI,Unemployment Store 1,2023-06-01,1000,0,70,3.0,200,5 Store 2,2023-06-02,1200,1,80,3.5,210,6 Store 3,2023-06-03,1400,0,90,4.0,220,7 Store 4,2023-06-04,1600,1,70,4.5,230,8 Store 5,2023-06-05,1800,0,80,5.0,240,9 Store 6,2023-06-06,2000,1,90,5.5,250,10 Store 7,2023-06-07,2200,0,90,6.0,260,11 Plot a scatter plot showcasing the relationship between Weekly Sales and Temperature for each store, distinguishing stores with a Holiday Flag.

contributing/samples/agent_engine_code_execution/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from . import agent

contributing/samples/agent_engine_code_execution/agent.py

@@ -0,0 +1,95 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Data science agent."""
+
+from google.adk.agents.llm_agent import Agent
+from google.adk.code_executors.agent_engine_sandbox_code_executor import AgentEngineSandboxCodeExecutor
+
+
+def base_system_instruction():
+  """Returns: data science agent system instruction."""
+
+  return """
+  # Guidelines
+
+  **Objective:** Assist the user in achieving their data analysis goals within the context of a Python Colab notebook, **with emphasis on avoiding assumptions and ensuring accuracy.** Reaching that goal can involve multiple steps. When you need to generate code, you **don't** need to solve the goal in one go. Only generate the next step at a time.
+
+  **Code Execution:** All code snippets provided will be executed within the Colab environment.
+
+  **Statefulness:** All code snippets are executed and the variables stays in the environment. You NEVER need to re-initialize variables. You NEVER need to reload files. You NEVER need to re-import libraries.
+
+  **Output Visibility:** Always print the output of code execution to visualize results, especially for data exploration and analysis. For example:
+    - To look a the shape of a pandas.DataFrame do:
+      ```tool_code
+      print(df.shape)
+      ```
+      The output will be presented to you as:
+      ```tool_outputs
+      (49, 7)
+
+      ```
+    - To display the result of a numerical computation:
+      ```tool_code
+      x = 10 ** 9 - 12 ** 5
+      print(f'{{x=}}')
+      ```
+      The output will be presented to you as:
+      ```tool_outputs
+      x=999751168
+
+      ```
+    - You **never** generate ```tool_outputs yourself.
+    - You can then use this output to decide on next steps.
+    - Print just variables (e.g., `print(f'{{variable=}}')`.
+
+  **No Assumptions:** **Crucially, avoid making assumptions about the nature of the data or column names.** Base findings solely on the data itself. Always use the information obtained from `explore_df` to guide your analysis.
+
+  **Available files:** Only use the files that are available as specified in the list of available files.
+
+  **Data in prompt:** Some queries contain the input data directly in the prompt. You have to parse that data into a pandas DataFrame. ALWAYS parse all the data. NEVER edit the data that are given to you.
+
+  **Answerability:** Some queries may not be answerable with the available data. In those cases, inform the user why you cannot process their query and suggest what type of data would be needed to fulfill their request.
+
+  """
+
+
+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.
+You final answer should summarize the code and code execution relevant to the user query.
+
+You should include all pieces of data to answer the user query, such as the table from code execution results.
+If you cannot answer the question directly, you should follow the guidelines above to generate the next step.
+If the question can be answered directly with writing any code, you should do that.
+If you doesn't have enough data to answer the question, you should ask for clarification from the user.
+
+You should NEVER install any package on your own like `pip install ...`.
+When plotting trends, you should make sure to sort and order the data by the x-axis.
+
+
+""",
+    code_executor=AgentEngineSandboxCodeExecutor(
+        # Replace with your sandbox resource name if you already have one.
+        sandbox_resource_name="SANDBOX_RESOURCE_NAME",
+        # "projects/vertex-agent-loadtest/locations/us-central1/reasoningEngines/6842889780301135872/sandboxEnvironments/6545148628569161728",
+        # Replace with agent engine resource name used for creating sandbox if
+        # sandbox_resource_name is not set.
+        agent_engine_resource_name="AGENT_ENGINE_RESOURCE_NAME",
+    ),
+)

pyproject.toml

@@ -32,7 +32,7 @@ dependencies = [
   "click>=8.1.8, <9.0.0",                                   # For CLI tools
   "fastapi>=0.115.0, <1.0.0",                               # FastAPI framework
   "google-api-python-client>=2.157.0, <3.0.0",              # Google API client discovery
-  "google-cloud-aiplatform[agent_engines]>=1.112.0, <2.0.0",# For VertexAI integrations, e.g. example store.
+  "google-cloud-aiplatform[agent_engines]>=1.121.0, <2.0.0",# For VertexAI integrations, e.g. example store.
   "google-cloud-bigtable>=2.32.0",                          # For Bigtable database
   "google-cloud-discoveryengine>=0.13.12, <0.14.0",         # For Discovery Engine Search Tool
   "google-cloud-secret-manager>=2.22.0, <3.0.0",            # Fetching secrets in RestAPI Tool

src/google/adk/cli/adk_web_server.py

@@ -486,6 +486,12 @@ async def get_runner_async(self, app_name: str) -> Runner:
     self.runner_dict[app_name] = runner
     return runner
 
+  def _get_root_agent(self, agent_or_app: BaseAgent | App) -> BaseAgent:
+    """Extract root agent from either a BaseAgent or App object."""
+    if isinstance(agent_or_app, App):
+      return agent_or_app.root_agent
+    return agent_or_app
+
   def _create_runner(self, agentic_app: App) -> Runner:
     """Create a runner with common services."""
     return Runner(
@@ -741,7 +747,7 @@ async def create_session_with_id(
           is not None
       ):
         raise HTTPException(
-            status_code=400, detail=f"Session already exists: {session_id}"
+            status_code=409, detail=f"Session already exists: {session_id}"
         )
       session = await self.session_service.create_session(
           app_name=app_name, user_id=user_id, state=state, session_id=session_id
@@ -763,6 +769,13 @@ async def create_session(
             app_name=app_name, user_id=user_id
         )
 
+      if req.session_id and await self.session_service.get_session(
+          app_name=app_name, user_id=user_id, session_id=req.session_id
+      ):
+        raise HTTPException(
+            status_code=409, detail=f"Session already exists: {req.session_id}"
+        )
+
       session = await self.session_service.create_session(
           app_name=app_name,
           user_id=user_id,
@@ -926,9 +939,8 @@ async def add_session_to_eval_set(
 
       # Populate the session with initial session state.
       agent_or_app = self.agent_loader.load_agent(app_name)
-      if isinstance(agent_or_app, App):
-        agent_or_app = agent_or_app.root_agent
-      initial_session_state = create_empty_state(agent_or_app)
+      root_agent = self._get_root_agent(agent_or_app)
+      initial_session_state = create_empty_state(root_agent)
 
       new_eval_case = EvalCase(
           eval_id=req.eval_id,
@@ -1089,7 +1101,8 @@ async def run_eval(
               status_code=400, detail=f"Eval set `{eval_set_id}` not found."
           )
 
-        root_agent = self.agent_loader.load_agent(app_name)
+        agent_or_app = self.agent_loader.load_agent(app_name)
+        root_agent = self._get_root_agent(agent_or_app)
 
         eval_case_results = []
 
@@ -1430,13 +1443,7 @@ async def get_event_graph(
       function_calls = event.get_function_calls()
       function_responses = event.get_function_responses()
       agent_or_app = self.agent_loader.load_agent(app_name)
-      # The loader may return an App; unwrap to its root agent so the graph builder
-      # receives a BaseAgent instance.
-      root_agent = (
-          agent_or_app.root_agent
-          if isinstance(agent_or_app, App)
-          else agent_or_app
-      )
+      root_agent = self._get_root_agent(agent_or_app)
       dot_graph = None
       if function_calls:
         function_call_highlights = []

src/google/adk/code_executors/__init__.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import logging
 
 from .base_code_executor import BaseCodeExecutor
@@ -29,6 +31,7 @@
     'VertexAiCodeExecutor',
     'ContainerCodeExecutor',
     'GkeCodeExecutor',
+    'AgentEngineSandboxCodeExecutor',
 ]
 
 
@@ -63,4 +66,14 @@ def __getattr__(name: str):
           'GkeCodeExecutor requires additional dependencies. '
           'Please install with: pip install "google-adk[extensions]"'
       ) from e
+  elif name == 'AgentEngineSandboxCodeExecutor':
+    try:
+      from .agent_engine_sandbox_code_executor import AgentEngineSandboxCodeExecutor
+
+      return AgentEngineSandboxCodeExecutor
+    except ImportError as e:
+      raise ImportError(
+          'AgentEngineSandboxCodeExecutor requires additional dependencies. '
+          'Please install with: pip install "google-adk[extensions]"'
+      ) from e
   raise AttributeError(f"module '{__name__}' has no attribute '{name}'")