google--adk-python/contributing/samples/environment_and_skills/daytona_environment

Daytona Environment Sample

Overview

A small data analysis agent that uses the DaytonaEnvironment with the EnvironmentToolset to download public datasets and analyze them inside a Daytona remote sandbox.

Instead of running on the local machine, all commands and file operations execute in an isolated remote sandbox with internet access. Asked a question, the agent downloads a public dataset (a GCS-hosted world population / demographics dataset by default), installs pandas on demand, writes a short analysis script, runs it, and reports the result — all without touching the user's machine. This makes the sandbox a natural fit for running model-generated code safely and keeping the host clean.

Prerequisites

1. Install the daytona extra:

   pip install google-adk[daytona]
   

2. Set your Daytona configuration. Get a server and API key by following the Daytona installation guide (e.g. self-hosted or via Daytona Cloud).

   If you are using Daytona Cloud, you only need to set:

   export DAYTONA_API_KEY="your-api-key"
   

   If you are using a self-hosted Daytona server, also set:

   export DAYTONA_API_URL="your-api-url"
   

Sample Inputs

• Download the world demographics dataset and tell me which country has the largest population.

  The agent downloads the dataset, installs pandas, filters to country-level rows, and finds the maximum. Expected: China (CN), ≈ 1.44 billion, just ahead of India (IN) at ≈ 1.38 billion.

• For the United States, what is the urban vs rural population split?

  A follow-up to the previous turn. Because the sandbox persists across the session, the agent reuses the already-downloaded CSV and the installed pandas — it only writes and runs a new script. Expected for US: urban ≈ 270.7 million vs rural ≈ 57.6 million (out of ≈ 331 million total).

• Using https://storage.googleapis.com/cloud-samples-data/bigquery/us-states/us-states.csv, how many US states are listed?

  Demonstrates pointing the agent at your own dataset URL instead of the default.

Graph

graph TD
    User -->|question| Agent[data_analysis_agent]
    Agent -->|EnvironmentToolset| Sandbox[DaytonaEnvironment sandbox]
    Sandbox -->|download / install / run| Agent
    Agent -->|answer| User

How To

The agent is a standalone Agent (no workflow graph) wired to a single EnvironmentToolset whose environment is a DaytonaEnvironment:

from google.adk.integrations.daytona import DaytonaEnvironment
from google.adk.tools.environment import EnvironmentToolset

EnvironmentToolset(
    environment=DaytonaEnvironment(timeout=300),
)

• timeout bounds the sandbox lifetime in seconds.
• By default, it will spin up a sandbox from the built-in default Python snapshot. If you want to use a custom Docker image instead, you can pass it to the image parameter (e.g. image="python:3.12").