diff --git a/docs/integrations/bigquery.md b/docs/integrations/bigquery.md
index 17f9cba157..bc11c1fde0 100644
--- a/docs/integrations/bigquery.md
+++ b/docs/integrations/bigquery.md
@@ -37,7 +37,7 @@ You should use this approach for local development and running on Google Cloud s
 
 ```python
 import google.auth
-from google.adk.tools.bigquery import BigQueryToolset, BigQueryCredentialsConfig
+from google.adk.integrations.bigquery import BigQueryToolset, BigQueryCredentialsConfig
 
 # Load Application Default Credentials
 credentials, project_id = google.auth.default()
@@ -53,7 +53,7 @@ You can explicitly provide a service account file or info.
 
 ```python
 from google.oauth2 import service_account
-from google.adk.tools.bigquery import BigQueryToolset, BigQueryCredentialsConfig
+from google.adk.integrations.bigquery import BigQueryToolset, BigQueryCredentialsConfig
 
 # Load Service Account credentials
 credentials = service_account.Credentials.from_service_account_file('path/to/key.json')
@@ -69,7 +69,7 @@ For applications that need to act on behalf of an end-user, you can pass user cr
 
 ```python
 from google.oauth2.credentials import Credentials
-from google.adk.tools.bigquery import BigQueryToolset, BigQueryCredentialsConfig
+from google.adk.integrations.bigquery import BigQueryToolset, BigQueryCredentialsConfig
 
 # Assume 'user_token' is obtained via an external OAuth flow
 credentials = Credentials(token=user_token)
@@ -84,7 +84,7 @@ bigquery_toolset = BigQueryToolset(credentials_config=credentials_config)
 If you are integrating with an external authentication provider where the token is managed by the platform, such as Gemini Enterprise, use `external_access_token_key`.
 
 ```python
-from google.adk.tools.bigquery import BigQueryToolset, BigQueryCredentialsConfig
+from google.adk.integrations.bigquery import BigQueryToolset, BigQueryCredentialsConfig
 
 # The key used to look up the access token in the session state
 credentials_config = BigQueryCredentialsConfig(
@@ -98,7 +98,7 @@ bigquery_toolset = BigQueryToolset(credentials_config=credentials_config)
 When using the `adk web` interface for interactive sessions, you can provide OAuth 2.0 client credentials to trigger a login flow. This mechanism works for both local development and when your ADK agent is deployed to environments like Cloud Run.
 
 ```python
-from google.adk.tools.bigquery import BigQueryToolset, BigQueryCredentialsConfig
+from google.adk.integrations.bigquery import BigQueryToolset, BigQueryCredentialsConfig
 
 # Provide OAuth 2.0 Client ID and Secret
 credentials_config = BigQueryCredentialsConfig(
diff --git a/examples/python/snippets/tools/built-in-tools/bigquery.py b/examples/python/snippets/tools/built-in-tools/bigquery.py
index 7be429ec76..c44abb6b52 100644
--- a/examples/python/snippets/tools/built-in-tools/bigquery.py
+++ b/examples/python/snippets/tools/built-in-tools/bigquery.py
@@ -15,12 +15,12 @@
 import asyncio
 
 from google.adk.agents import Agent
+from google.adk.integrations.bigquery import BigQueryCredentialsConfig
+from google.adk.integrations.bigquery import BigQueryToolset
+from google.adk.integrations.bigquery.config import BigQueryToolConfig
+from google.adk.integrations.bigquery.config import WriteMode
 from google.adk.runners import Runner
 from google.adk.sessions import InMemorySessionService
-from google.adk.tools.bigquery import BigQueryCredentialsConfig
-from google.adk.tools.bigquery import BigQueryToolset
-from google.adk.tools.bigquery.config import BigQueryToolConfig
-from google.adk.tools.bigquery.config import WriteMode
 from google.genai import types
 import google.auth
 
@@ -62,37 +62,49 @@
 )
 
 # Session and Runner
-session_service = InMemorySessionService()
-session = asyncio.run(
-    session_service.create_session(
+async def setup_session_and_runner():
+    session_service = InMemorySessionService()
+    await session_service.create_session(
         app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID
     )
-)
-runner = Runner(
-    agent=bigquery_agent, app_name=APP_NAME, session_service=session_service
-)
+    return Runner(
+        agent=bigquery_agent, app_name=APP_NAME, session_service=session_service
+    )
 
 
 # Agent Interaction
-def call_agent(query):
+async def call_agent_async(runner, query):
     """
     Helper function to call the agent with a query.
     """
     content = types.Content(role="user", parts=[types.Part(text=query)])
-    events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
+    events = runner.run_async(
+        user_id=USER_ID, session_id=SESSION_ID, new_message=content
+    )
 
     print("USER:", query)
-    for event in events:
+    async for event in events:
         if event.is_final_response():
             final_response = event.content.parts[0].text
             print("AGENT:", final_response)
 
 
-call_agent("Are there any ml datasets in bigquery-public-data project?")
-call_agent("Tell me more about ml_datasets.")
-call_agent("Which all tables does it have?")
-call_agent("Tell me more about the census_adult_income table.")
-call_agent("How many rows are there per income bracket?")
-call_agent(
-    "What is the statistical correlation between education_num, age, and the income_bracket?"
-)
+async def main():
+    runner = await setup_session_and_runner()
+    await call_agent_async(
+        runner, "Are there any ml datasets in bigquery-public-data project?"
+    )
+    await call_agent_async(runner, "Tell me more about ml_datasets.")
+    await call_agent_async(runner, "Which all tables does it have?")
+    await call_agent_async(runner, "Tell me more about the census_adult_income table.")
+    await call_agent_async(runner, "How many rows are there per income bracket?")
+    await call_agent_async(
+        runner,
+        "What is the statistical correlation between education_num, age, and the"
+        " income_bracket?",
+    )
+
+
+# Note: In Colab or another notebook, an event loop is already running, so call
+# `await main()` directly instead of `asyncio.run(main())`.
+asyncio.run(main())