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‎contributing/samples/core/input_output_schema/tests/weather_cupertino.json‎

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‎contributing/samples/core/input_output_schema/tests/weather_sanjose.json‎

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‎contributing/samples/core/input_output_schema/tests/weather_tokyo.json‎

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‎contributing/samples/environment_and_skills/daytona_environment/README.md‎

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# Daytona Environment Sample
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## Overview
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A small data analysis agent that uses the `DaytonaEnvironment` with the
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`EnvironmentToolset` to download public datasets and analyze them inside a
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[Daytona](https://daytona.io) remote sandbox.
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Instead of running on the local machine, all commands and file operations
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execute in an isolated remote sandbox with internet access. Asked a question,
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the agent downloads a public dataset (a GCS-hosted world population /
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demographics dataset by default), installs `pandas` on demand, writes a short
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analysis script, runs it, and reports the result — all without touching the
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user's machine. This makes the sandbox a natural fit for running
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model-generated code safely and keeping the host clean.
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## Prerequisites
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1. Install the `daytona` extra:
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```bash
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pip install google-adk[daytona]
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```
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1. Set your Daytona configuration. Get a server and API key by following the
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Daytona installation guide (e.g. self-hosted or via Daytona Cloud).
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If you are using Daytona Cloud, you only need to set:
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```bash
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export DAYTONA_API_KEY="your-api-key"
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```
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If you are using a self-hosted Daytona server, also set:
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```bash
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export DAYTONA_API_URL="your-api-url"
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```
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## Sample Inputs
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- `Download the world demographics dataset and tell me which country has the largest population.`
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The agent downloads the dataset, installs `pandas`, filters to country-level
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rows, and finds the maximum. Expected: China (`CN`), ≈ 1.44 billion, just
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ahead of India (`IN`) at ≈ 1.38 billion.
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- `For the United States, what is the urban vs rural population split?`
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A follow-up to the previous turn. Because the sandbox persists across the
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session, the agent reuses the already-downloaded CSV and the installed
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`pandas` — it only writes and runs a new script. Expected for `US`: urban
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≈ 270.7 million vs rural ≈ 57.6 million (out of ≈ 331 million total).
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- `Using https://storage.googleapis.com/cloud-samples-data/bigquery/us-states/us-states.csv, how many US states are listed?`
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Demonstrates pointing the agent at your own dataset URL instead of the
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default.
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## Graph
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```mermaid
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graph TD
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User -->|question| Agent[data_analysis_agent]
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Agent -->|EnvironmentToolset| Sandbox[DaytonaEnvironment sandbox]
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Sandbox -->|download / install / run| Agent
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Agent -->|answer| User
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```
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## How To
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The agent is a standalone `Agent` (no workflow graph) wired to a single
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`EnvironmentToolset` whose `environment` is a `DaytonaEnvironment`:
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```python
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from google.adk.integrations.daytona import DaytonaEnvironment
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from google.adk.tools.environment import EnvironmentToolset
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EnvironmentToolset(
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environment=DaytonaEnvironment(timeout=300),
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)
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```
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- `timeout` bounds the sandbox lifetime in seconds.
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- By default, it will spin up a sandbox from the built-in default Python snapshot.
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If you want to use a custom Docker image instead, you can pass it to the
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`image` parameter (e.g. `image="python:3.12"`).

‎contributing/samples/environment_and_skills/daytona_environment/__init__.py‎

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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

‎contributing/samples/environment_and_skills/daytona_environment/agent.py‎

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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 data analysis agent that runs Python in a Daytona remote sandbox."""
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from google.adk import Agent
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from google.adk.integrations.daytona import DaytonaEnvironment
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from google.adk.tools.environment import EnvironmentToolset
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root_agent = Agent(
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name="data_analysis_agent",
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description=(
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"A data analysis agent that downloads public datasets and analyzes"
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" them inside a Daytona remote sandbox."
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),
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instruction="""\
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You are a data analysis assistant. You work inside an isolated Daytona remote
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sandbox that has internet access, where you can safely download data and run
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Python, so you never touch the user's machine.
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To analyze a dataset:
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1. Download it from the internet into the working directory, e.g. with
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`curl -O <url>` or `wget <url>`.
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2. Install whatever you need on demand, e.g. `pip install pandas`.
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3. Write a short Python script that loads the data and computes the answer.
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4. Run the script and report the result, showing the numbers you found.
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Prefer writing a script and executing it over guessing. If a command fails,
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read the error, fix the script, and try again.
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""",
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tools=[EnvironmentToolset(environment=DaytonaEnvironment())],
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)

‎contributing/samples/multi_agent/single_turn_sub_agent/tests/gaming_1000.json‎

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‎contributing/samples/multi_agent/single_turn_sub_agent/tests/gaming_1000_large.json‎

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