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Build and run Gemini 2.5 Computer Use browser-control agents with Playwright. Use when a user wants to automate web browser tasks via the Gemini Computer Use model, needs an agent loop (screenshot → function_call → action → function_response), or asks to integrate safety confirmation for risky UI actions.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install am-will/gemini-computer-useInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Source the env file and set your API key:
cp env.example env.sh
$EDITOR env.sh
source env.sh
Create a virtual environment and install dependencies:
python -m venv .venv
source .venv/bin/activate
pip install google-genai playwright
playwright install chromium
Run the agent script with a prompt:
python scripts/computer_use_agent.py \
--prompt "Find the latest blog post title on example.com" \
--start-url "https://example.com" \
--turn-limit 6
COMPUTER_USE_BROWSER_CHANNEL.COMPUTER_USE_BROWSER_EXECUTABLE.If both are set, COMPUTER_USE_BROWSER_EXECUTABLE takes precedence.
function_call actions in the response.safety_decision is require_confirmation, prompt the user before executing.function_response objects containing the latest URL + screenshot.--exclude to block risky actions you do not want the model to take.scripts/computer_use_agent.py
references/google-computer-use.md
env.example
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# Gemini Computer Use
## Quick start
1. Source the env file and set your API key:
```bash
cp env.example env.sh
$EDITOR env.sh
source env.sh
```
2. Create a virtual environment and install dependencies:
```bash
python -m venv .venv
source .venv/bin/activate
pip install google-genai playwright
playwright install chromium
```
3. Run the agent script with a prompt:
```bash
python scripts/computer_use_agent.py \
--prompt "Find the latest blog post title on example.com" \
--start-url "https://example.com" \
--turn-limit 6
```
## Browser selection
- Default: Playwright's bundled Chromium (no env vars required).
- Choose a channel (Chrome/Edge) with `COMPUTER_USE_BROWSER_CHANNEL`.
- Use a custom Chromium-based executable (e.g., Brave) with `COMPUTER_USE_BROWSER_EXECUTABLE`.
If both are set, `COMPUTER_USE_BROWSER_EXECUTABLE` takes precedence.
## Core workflow (agent loop)
1. Capture a screenshot and send the user goal + screenshot to the model.
2. Parse `function_call` actions in the response.
3. Execute each action in Playwright.
4. If a `safety_decision` is `require_confirmation`, prompt the user before executing.
5. Send `function_response` objects containing the latest URL + screenshot.
6. Repeat until the model returns only text (no actions) or you hit the turn limit.
## Operational guidance
- Run in a sandboxed browser profile or container.
- Use `--exclude` to block risky actions you do not want the model to take.
- Keep the viewport at 1440x900 unless you have a reason to change it.
## Resources
- Script: `scripts/computer_use_agent.py`
- Reference notes: `references/google-computer-use.md`
- Env template: `env.example`An open-source platform for orchestrating AI agents — secure, simple, and built for everyone. Multi-agent, real-time task board, web control panel, skills system, browser automation, multi-provider, scheduler,...
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