If you’re searching for “Google ADK” (Agent Development Kit), you probably want a quick, concrete path to: install it, build a minimal agent, run it, and then deploy it somewhere that won’t disappear when your laptop sleeps.
Zo Computer is a good fit for this workflow because it’s a persistent Linux server with:
a real filesystem (your agent code + logs live in folders)
a terminal for installing dependencies
an always-on “Services” manager so your agent can keep running
This tutorial uses ADK for Python.
Prerequisites
A Zo Computer
A model/API credential appropriate for your ADK setup (depending on the model/provider you use)
Basic comfort running terminal commands
Step 1: Create a project folder
Create a dedicated directory so your agent code, virtual environment, and logs stay together:
Create a folder like:
Agents/google-adk/
(Any folder is fine. The important thing is: keep the agent and its environment together.)
Step 2: Create a Python virtual environment
From your project folder:
Create a virtual environment
Activate it
This keeps ADK dependencies isolated from the rest of your system.
Step 3: Install Google ADK
Install ADK from PyPI:
Package:
google-adk^1
If you already have ADK installed elsewhere, still prefer using a venv per agent project so upgrades are controlled.
Step 4: Write a minimal agent
Create a file like agent.py with a minimal “hello world” agent.
What you want at minimum:
An agent definition (name + instruction)
A runnable entrypoint you can test from the command line
ADK supports richer patterns (tools, workflows, memory/session services), but start minimal and add complexity only after you have an end-to-end run loop.
Step 5: Run it interactively (smoke test)
Before you deploy anything, run the agent once locally to verify:
imports work
credentials are accessible
the agent responds
If you get errors here, fix them now—deploying a broken agent as a background service is just harder to debug.
Step 6: (Optional) Use ADK devtools / web UI while developing
ADK provides developer tooling (CLI and web UI) that can make iteration faster—especially if you’re testing multi-step behaviors. Start here for official guidance on TypeScript devtools concepts; the workflow is similar in spirit (local dev loop, then deploy) ^2.
Step 7: Keep your agent always-on using Zo Services
Once your agent runs locally, the practical next step is making it persistent.
On Zo, you typically want one of these patterns:
HTTP service (recommended): your agent exposes an API endpoint you can call from other tools/agents.
Background worker: your agent runs on a schedule (Zo Agents) or loops continuously.
For “always-on”, use Zo Services (so it auto-restarts). For “runs on a schedule”, use Zo Agents. Agents documentation lives here ^3.
Step 8: Connect your ADK agent to Zo workflows
Once the agent exists, the easiest way to make it useful is to give it real inputs and outputs in your Zo environment:
Read/write files in your workspace (durable state)
Use Zo’s web tools when you need live data:
Fast extraction: https://docs.zocomputer.com/tools/read-webpage
Full browser rendering + screenshot: https://docs.zocomputer.com/tools/view-webpage
Both links are in Zo’s official tool reference ^4 ^5.
Common mistakes
Skipping the smoke test: if you haven’t run it once interactively, you don’t know what “working” looks like.
Mixing environments: install ADK in a venv per project; don’t “pip install” into your global Python.
No logs: always write logs to a file in your agent folder so you can debug restarts.
Summary
You now have a working Google ADK agent project on a persistent server.
The next iteration loop is:
Add one tool (file IO, a simple API call, or a single web action)
Add one durable state file
Deploy as a Zo Service (always-on) or a Zo Agent (scheduled)