# Copyright 2026 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. """A coordinator LlmAgent that calls ManagedAgent specialists as single-turn tools. This sample shows a local ``LlmAgent`` orchestrating two server-backed ``ManagedAgent`` specialists exposed as single-turn sub-agents (``mode='single_turn'``). ADK auto-wraps each single-turn sub-agent as an inline tool: the coordinator calls a specialist like a tool, receives the result, and may call several specialists within a single turn before composing the final answer. The specialists' internal events are preserved in the shared session. Each managed call is stateless: single-turn runs are isolated, so the coordinator should pass a self-contained request to each specialist. Two specialists are configured: - ``managed_search_agent`` -- a ``ManagedAgent`` with the server-side ``google_search`` tool, for questions that require web search results. - ``managed_code_execution_agent`` -- a ``ManagedAgent`` with server-side code execution, for questions that require computation. Run with ``adk web`` / ``adk run contributing/samples/managed_agent/single_turn``. See the README for the required environment / auth setup. """ import os from google.adk.agents import LlmAgent from google.adk.agents import ManagedAgent from google.adk.tools import google_search from google.genai import types # The Managed Agent id served by the Managed Agents API. Override with the # MANAGED_AGENT_ID environment variable if your project has access to a # different agent. _DEFAULT_AGENT_ID = 'antigravity-preview-05-2026' _AGENT_ID = os.environ.get('MANAGED_AGENT_ID', _DEFAULT_AGENT_ID) # A ManagedAgent specialist for questions that require web search results. # mode='single_turn' exposes it to the coordinator as an inline tool. managed_search_agent = ManagedAgent( name='managed_search_agent', mode='single_turn', description=( 'Answers questions that require up-to-date information from the web.' ' Uses server-side Google Search.' ), agent_id=_AGENT_ID, environment={'type': 'remote'}, tools=[google_search], ) # A ManagedAgent specialist that solves computational questions by running code # server-side. mode='single_turn' exposes it to the coordinator as an inline # tool. managed_code_execution_agent = ManagedAgent( name='managed_code_execution_agent', mode='single_turn', description=( 'Solves computational, math, or data questions by writing and running' ' code server-side. Use for arithmetic, numeric, and other tasks best' ' handled by executing code.' ), agent_id=_AGENT_ID, environment={'type': 'remote'}, tools=[types.Tool(code_execution=types.ToolCodeExecution())], ) # The local coordinator. No `model` is set, so ADK uses the default model. The # two managed specialists are single-turn sub-agents, so ADK exposes each as an # inline tool; the coordinator calls them and keeps control of the turn (it can # call both before answering). root_agent = LlmAgent( name='managed_tool_coordinator', description='Calls managed specialists as tools and composes the answer.', instruction=( 'You are an assistant with two specialist tools.\n' '- Use `managed_search_agent` to look up current information from the' ' web.\n' '- Use `managed_code_execution_agent` to compute results by running' ' code.\n' 'You may call both tools in a single turn -- for example, look up a' ' value and then compute with it -- and then write the final answer' ' yourself.' ), sub_agents=[managed_search_agent, managed_code_execution_agent], )