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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.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install am-will/gemini-computer-useExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
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
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
# 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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