Built-in tools
These built-in tools provide ready-to-use functionality such as Google Search or code executors that provide agents with common capabilities. For instance, an agent that needs to retrieve information from the web can directly use the google_search tool without any additional setup.
How to Use
- Import: Import the desired tool from the
agents.toolsmodule. - Configure: Initialize the tool, providing required parameters if any.
- Register: Add the initialized tool to the tools list of your Agent.
Once added to an agent, the agent can decide to use the tool based on the user prompt and its instructions. The framework handles the execution of the tool when the agent calls it.
Available Built-in tools
Google Search
The google_search tool allows the agent to perform web searches using Google Search. It is compatible with Gemini 2 models, and you can add this tool to the agent's tools list.
from google.adk.agents import Agent from google.adk.runners import Runner from google.adk.sessions import InMemorySessionService from google.adk.tools import google_search from google.genai import types APP_NAME="google_search_agent" USER_ID="user1234" SESSION_ID="1234" root_agent = Agent( name="basic_search_agent", model="gemini-2.0-flash", description="Agent to answer questions using Google Search.", instruction="I can answer your questions by searching the internet. Just ask me anything!", # google_search is a pre-built tool which allows the agent to perform Google searches. tools=[google_search] ) # Session and Runner session_service = InMemorySessionService() session = session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID) runner = Runner(agent=root_agent, app_name=APP_NAME, session_service=session_service) # Agent Interaction def call_agent(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) for event in events: if event.is_final_response(): final_response = event.content.parts[0].text print("Agent Response: ", final_response) call_agent("what's the latest ai news?") Code Execution
The built_in_code_execution tool enables the agent to execute code, specifically when using Gemini 2 models. This allows the model to perform tasks like calculations, data manipulation, or running small scripts.
import asyncio from google.adk.agents import LlmAgent from google.adk.runners import Runner from google.adk.sessions import InMemorySessionService from google.adk.tools import built_in_code_execution from google.genai import types AGENT_NAME="calculator_agent" APP_NAME="calculator" USER_ID="user1234" SESSION_ID="session_code_exec_async" GEMINI_MODEL = "gemini-2.0-flash" # Agent Definition code_agent = LlmAgent( name=AGENT_NAME, model=GEMINI_MODEL, tools=[built_in_code_execution], instruction="""You are a calculator agent. When given a mathematical expression, write and execute Python code to calculate the result. Return only the final numerical result as plain text, without markdown or code blocks. """, description="Executes Python code to perform calculations.", ) # Session and Runner session_service = InMemorySessionService() session = session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID) runner = Runner(agent=code_agent, app_name=APP_NAME, session_service=session_service) # Agent Interaction (Async) async def call_agent_async(query): content = types.Content(role='user', parts=[types.Part(text=query)]) print(f"\n--- Running Query: {query} ---") final_response_text = "No final text response captured." try: # Use run_async async for event in runner.run_async(user_id=USER_ID, session_id=SESSION_ID, new_message=content): print(f"Event ID: {event.id}, Author: {event.author}") # --- Check for specific parts FIRST --- has_specific_part = False if event.content and event.content.parts: for part in event.content.parts: # Iterate through all parts if part.executable_code: # Access the actual code string via .code print(f" Debug: Agent generated code:\n```python\n{part.executable_code.code}\n```") has_specific_part = True elif part.code_execution_result: # Access outcome and output correctly print(f" Debug: Code Execution Result: {part.code_execution_result.outcome} - Output:\n{part.code_execution_result.output}") has_specific_part = True # Also print any text parts found in any event for debugging elif part.text and not part.text.isspace(): print(f" Text: '{part.text.strip()}'") # Do not set has_specific_part=True here, as we want the final response logic below # --- Check for final response AFTER specific parts --- # Only consider it final if it doesn't have the specific code parts we just handled if not has_specific_part and event.is_final_response(): if event.content and event.content.parts and event.content.parts[0].text: final_response_text = event.content.parts[0].text.strip() print(f"==> Final Agent Response: {final_response_text}") else: print("==> Final Agent Response: [No text content in final event]") except Exception as e: print(f"ERROR during agent run: {e}") print("-" * 30) # Main async function to run the examples async def main(): await call_agent_async("Calculate the value of (5 + 7) * 3") await call_agent_async("What is 10 factorial?") # Execute the main async function try: asyncio.run(main()) except RuntimeError as e: # Handle specific error when running asyncio.run in an already running loop (like Jupyter/Colab) if "cannot be called from a running event loop" in str(e): print("\nRunning in an existing event loop (like Colab/Jupyter).") print("Please run `await main()` in a notebook cell instead.") # If in an interactive environment like a notebook, you might need to run: # await main() else: raise e # Re-raise other runtime errors Vertex AI Search
The vertex_ai_search_tool uses Google Cloud's Vertex AI Search, enabling the agent to search across your private, configured data stores (e.g., internal documents, company policies, knowledge bases). This built-in tool requires you to provide the specific data store ID during configuration.
import asyncio from google.adk.agents import LlmAgent from google.adk.runners import Runner from google.adk.sessions import InMemorySessionService from google.genai import types from google.adk.tools import VertexAiSearchTool # Replace with your actual Vertex AI Search Datastore ID # Format: projects/<PROJECT_ID>/locations/<LOCATION>/collections/default_collection/dataStores/<DATASTORE_ID> # e.g., "projects/12345/locations/us-central1/collections/default_collection/dataStores/my-datastore-123" YOUR_DATASTORE_ID = "YOUR_DATASTORE_ID_HERE" # Constants APP_NAME_VSEARCH = "vertex_search_app" USER_ID_VSEARCH = "user_vsearch_1" SESSION_ID_VSEARCH = "session_vsearch_1" AGENT_NAME_VSEARCH = "doc_qa_agent" GEMINI_2_FLASH = "gemini-2.0-flash" # Tool Instantiation # You MUST provide your datastore ID here. vertex_search_tool = VertexAiSearchTool(data_store_id=YOUR_DATASTORE_ID) # Agent Definition doc_qa_agent = LlmAgent( name=AGENT_NAME_VSEARCH, model=GEMINI_2_FLASH, # Requires Gemini model tools=[vertex_search_tool], instruction=f"""You are a helpful assistant that answers questions based on information found in the document store: {YOUR_DATASTORE_ID}. Use the search tool to find relevant information before answering. If the answer isn't in the documents, say that you couldn't find the information. """, description="Answers questions using a specific Vertex AI Search datastore.", ) # Session and Runner Setup session_service_vsearch = InMemorySessionService() runner_vsearch = Runner( agent=doc_qa_agent, app_name=APP_NAME_VSEARCH, session_service=session_service_vsearch ) session_vsearch = session_service_vsearch.create_session( app_name=APP_NAME_VSEARCH, user_id=USER_ID_VSEARCH, session_id=SESSION_ID_VSEARCH ) # Agent Interaction Function async def call_vsearch_agent_async(query): print("\n--- Running Vertex AI Search Agent ---") print(f"Query: {query}") if "YOUR_DATASTORE_ID_HERE" in YOUR_DATASTORE_ID: print("Skipping execution: Please replace YOUR_DATASTORE_ID_HERE with your actual datastore ID.") print("-" * 30) return content = types.Content(role='user', parts=[types.Part(text=query)]) final_response_text = "No response received." try: async for event in runner_vsearch.run_async( user_id=USER_ID_VSEARCH, session_id=SESSION_ID_VSEARCH, new_message=content ): # Like Google Search, results are often embedded in the model's response. if event.is_final_response() and event.content and event.content.parts: final_response_text = event.content.parts[0].text.strip() print(f"Agent Response: {final_response_text}") # You can inspect event.grounding_metadata for source citations if event.grounding_metadata: print(f" (Grounding metadata found with {len(event.grounding_metadata.grounding_attributions)} attributions)") except Exception as e: print(f"An error occurred: {e}") print("Ensure your datastore ID is correct and the service account has permissions.") print("-" * 30) # --- Run Example --- async def run_vsearch_example(): # Replace with a question relevant to YOUR datastore content await call_vsearch_agent_async("Summarize the main points about the Q2 strategy document.") await call_vsearch_agent_async("What safety procedures are mentioned for lab X?") # Execute the example # await run_vsearch_example() # Running locally due to potential colab asyncio issues with multiple awaits try: asyncio.run(run_vsearch_example()) except RuntimeError as e: if "cannot be called from a running event loop" in str(e): print("Skipping execution in running event loop (like Colab/Jupyter). Run locally.") else: raise e Use Built-in tools with other tools
The following code sample demonstrates how to use multiple built-in tools or how to use built-in tools with other tools by using multiple agents:
from google.adk.tools import agent_tool from google.adk.agents import Agent from google.adk.tools import google_search, built_in_code_execution search_agent = Agent( model='gemini-2.0-flash', name='SearchAgent', instruction=""" You're a specialist in Google Search """, tools=[google_search], ) coding_agent = Agent( model='gemini-2.0-flash', name='CodeAgent', instruction=""" You're a specialist in Code Execution """, tools=[built_in_code_execution], ) root_agent = Agent( name="RootAgent", model="gemini-2.0-flash", description="Root Agent", tools=[agent_tool.AgentTool(agent=search_agent), agent_tool.AgentTool(agent=coding_agent)], ) Limitations
Warning
Currently, for each root agent or single agent, only one built-in tool is supported.
For example, the following approach that uses two or more built-in tools within a root agent (or a single agent) is not currently supported:
root_agent = Agent( name="RootAgent", model="gemini-2.0-flash", description="Root Agent", tools=[built_in_code_execution, custom_function], ) Warning
Built-in tools cannot be used within a sub-agent.
For example, the following approach that uses built-in tools within sub-agents is not currently supported:
search_agent = Agent( model='gemini-2.0-flash', name='SearchAgent', instruction=""" You're a specialist in Google Search """, tools=[google_search], ) coding_agent = Agent( model='gemini-2.0-flash', name='CodeAgent', instruction=""" You're a specialist in Code Execution """, tools=[built_in_code_execution], ) root_agent = Agent( name="RootAgent", model="gemini-2.0-flash", description="Root Agent", sub_agents=[ search_agent, coding_agent ], )