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# Managed Agent - Code Execution
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> For setup, authentication, backends, and background on `ManagedAgent`, see the
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> [ManagedAgent guide](../../../../docs/guides/agents/managed_agent/index.md).
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## Overview
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This sample runs a `ManagedAgent` configured with the built-in **code execution**
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tool so it can write and run code server-side to compute answers.
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Unlike a regular `LlmAgent` (which enables code execution via
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`code_executor=BuiltInCodeExecutor()`), `ManagedAgent` has no `code_executor`
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field. Instead you pass the raw built-in tool config
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`types.Tool(code_execution=types.ToolCodeExecution())` in `tools` -- the same
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config `BuiltInCodeExecutor` produces under the hood. This makes the sample a
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demonstration of the raw `types.Tool` server-side tool path.
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## Sample Inputs
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- `What is the sum of the first 50 prime numbers? Use code to compute it.`
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The model writes and runs code server-side; the answer (5117) comes from the
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executed code rather than the model guessing.
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- `Now do the same for the first 100 primes.`
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A follow-up turn that reuses the recovered remote sandbox and the previous
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interaction (answer: 24133), demonstrating multi-turn chaining.
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## Graph
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```mermaid
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graph LR
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User -->|message| ManagedAgent
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ManagedAgent -->|interactions.create| ManagedAgentsAPI
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ManagedAgentsAPI -->|server-side code execution| ManagedAgentsAPI
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ManagedAgentsAPI -->|streamed events| ManagedAgent
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ManagedAgent -->|answer| User
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```
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## How To
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- **Create the agent**: instantiate `ManagedAgent` with an `agent_id`, an
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`environment` spec, and
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`tools=[types.Tool(code_execution=types.ToolCodeExecution())]`. No `model` is
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set -- the model is part of the managed agent on the server.
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- **Enable code execution**: `ManagedAgent` has no `code_executor` field, so the
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raw `types.Tool(code_execution=...)` config is passed in `tools`. The
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interactions converter turns it into the server-side `code_execution` tool.
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- **Provision a sandbox**: `environment={'type': 'remote'}` requests a fresh
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remote sandbox. The resulting environment id is stored on emitted events, so
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subsequent turns automatically recover and reuse it.
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- **Multi-turn chaining**: the agent recovers the `previous_interaction_id` from
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the session events, so follow-up turns continue the same interaction without
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any extra wiring.
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- **Drive it**: a `ManagedAgent` is a `BaseAgent`, so a standard `Runner` runs
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it just like any other agent.
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# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from . import agent
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# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""A ManagedAgent that runs server-side code execution.
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``ManagedAgent`` calls the Managed Agents API directly from its run loop instead
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of running a local model loop. It currently supports server-side tools only.
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Unlike ``LlmAgent``, ``ManagedAgent`` has no ``code_executor`` field, so code
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execution is enabled by passing the raw built-in tool config
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``types.Tool(code_execution=types.ToolCodeExecution())`` in ``tools`` -- the
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same config ``BuiltInCodeExecutor`` produces under the hood. The model writes
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and runs code on the server to compute answers.
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A fresh remote sandbox is provisioned via ``environment={'type': 'remote'}``;
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the environment id is recovered from prior events so multi-turn conversations
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reuse the same sandbox.
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Run with ``adk web`` /
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``adk run contributing/samples/managed_agent/code_execution``. See the README
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for the required environment / auth setup.
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"""
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import os
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from google.adk.agents import ManagedAgent
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from google.genai import types
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# The Managed Agent id served by the Managed Agents API. Override with the
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# MANAGED_AGENT_ID environment variable if your project has access to a
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# different agent.
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_DEFAULT_AGENT_ID = 'antigravity-preview-05-2026'
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root_agent = ManagedAgent(
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name='managed_code_execution_agent',
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agent_id=os.environ.get('MANAGED_AGENT_ID', _DEFAULT_AGENT_ID),
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# Provision a remote sandbox for the agent. The environment id is recovered
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# from prior events, so follow-up turns reuse the same sandbox.
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environment={'type': 'remote'},
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# ManagedAgent has no `code_executor` field; enable server-side code
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# execution by passing the raw built-in tool config. This is the same config
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# BuiltInCodeExecutor appends for a regular LlmAgent.
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tools=[types.Tool(code_execution=types.ToolCodeExecution())],
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)
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