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# Managed Agent - Code Execution
> For setup, authentication, backends, and background on `ManagedAgent`, see the
> [ManagedAgent guide](../../../../docs/guides/agents/managed_agent/index.md).
## Overview
This sample runs a `ManagedAgent` configured with the built-in **code execution**
tool so it can write and run code server-side to compute answers.
Unlike a regular `LlmAgent` (which enables code execution via
`code_executor=BuiltInCodeExecutor()`), `ManagedAgent` has no `code_executor`
field. Instead you pass the raw built-in tool config
`types.Tool(code_execution=types.ToolCodeExecution())` in `tools` -- the same
config `BuiltInCodeExecutor` produces under the hood. This makes the sample a
demonstration of the raw `types.Tool` server-side tool path.
## Sample Inputs
- `What is the sum of the first 50 prime numbers? Use code to compute it.`
The model writes and runs code server-side; the answer (5117) comes from the
executed code rather than the model guessing.
- `Now do the same for the first 100 primes.`
A follow-up turn that reuses the recovered remote sandbox and the previous
interaction (answer: 24133), demonstrating multi-turn chaining.
## Graph
```mermaid
graph LR
User -->|message| ManagedAgent
ManagedAgent -->|interactions.create| ManagedAgentsAPI
ManagedAgentsAPI -->|server-side code execution| ManagedAgentsAPI
ManagedAgentsAPI -->|streamed events| ManagedAgent
ManagedAgent -->|answer| User
```
## How To
- **Create the agent**: instantiate `ManagedAgent` with an `agent_id`, an
`environment` spec, and
`tools=[types.Tool(code_execution=types.ToolCodeExecution())]`. No `model` is
set -- the model is part of the managed agent on the server.
- **Enable code execution**: `ManagedAgent` has no `code_executor` field, so the
raw `types.Tool(code_execution=...)` config is passed in `tools`. The
interactions converter turns it into the server-side `code_execution` tool.
- **Provision a sandbox**: `environment={'type': 'remote'}` requests a fresh
remote sandbox. The resulting environment id is stored on emitted events, so
subsequent turns automatically recover and reuse it.
- **Multi-turn chaining**: the agent recovers the `previous_interaction_id` from
the session events, so follow-up turns continue the same interaction without
any extra wiring.
- **Drive it**: a `ManagedAgent` is a `BaseAgent`, so a standard `Runner` runs
it just like any other agent.
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# 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.
from . import agent
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# 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 ManagedAgent that runs server-side code execution.
``ManagedAgent`` calls the Managed Agents API directly from its run loop instead
of running a local model loop. It currently supports server-side tools only.
Unlike ``LlmAgent``, ``ManagedAgent`` has no ``code_executor`` field, so code
execution is enabled by passing the raw built-in tool config
``types.Tool(code_execution=types.ToolCodeExecution())`` in ``tools`` -- the
same config ``BuiltInCodeExecutor`` produces under the hood. The model writes
and runs code on the server to compute answers.
A fresh remote sandbox is provisioned via ``environment={'type': 'remote'}``;
the environment id is recovered from prior events so multi-turn conversations
reuse the same sandbox.
Run with ``adk web`` /
``adk run contributing/samples/managed_agent/code_execution``. See the README
for the required environment / auth setup.
"""
import os
from google.adk.agents import ManagedAgent
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'
root_agent = ManagedAgent(
name='managed_code_execution_agent',
agent_id=os.environ.get('MANAGED_AGENT_ID', _DEFAULT_AGENT_ID),
# Provision a remote sandbox for the agent. The environment id is recovered
# from prior events, so follow-up turns reuse the same sandbox.
environment={'type': 'remote'},
# ManagedAgent has no `code_executor` field; enable server-side code
# execution by passing the raw built-in tool config. This is the same config
# BuiltInCodeExecutor appends for a regular LlmAgent.
tools=[types.Tool(code_execution=types.ToolCodeExecution())],
)