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Open-model Computer Use companion

This is the provider-portable arm for Experiments 9-6 and 9-7. It runs the same screenshot → structured action → browser execution loop without requiring an Anthropic or OpenAI model account. The documented hosted route uses the open-weight qwen/qwen3-vl-32b-instruct model through OpenRouter. The same runner accepts a self-hosted vLLM/SGLang endpoint or another OpenAI-compatible host.

The Anthropic Computer Use Demo remains a useful reference implementation for its native computer, bash, and editor tools. This companion does not claim that Qwen and Claude are interchangeable. Runs from different models are separate experimental arms and must retain the actual endpoint and model ID.

Current evidence

The canonical open-model run passed on 2026-08-01. OpenRouter returned the requested qwen/qwen3-vl-32b-instruct model for all 16/16 calls. The Agent hit a Google CAPTCHA, recovered through weather.com, and completed in 16 steps. The deterministic validator matched the final 64°F/Sunny answer to the retained browser observation, verified 15 screenshot hashes and the one-action-per-step read-only trajectory, and found no retained credential. This completes the Experiment 9-7 open-model arm only; the Anthropic-native Experiment 9-6 arm remains unrun.

Endpoint contract

An endpoint is eligible when it:

  • accepts screenshot images in OpenAI-compatible chat messages;
  • can produce the Browser Use action schema, either with native json_schema support or with schema-in-prompt JSON;
  • returns enough information for the Agent to choose one browser action per step; and
  • does not silently replace the requested model.

The reference open model is Qwen3-VL 32B Instruct. “Open model” describes the weights/license; OpenRouter is only one hosted API route. Readers can use their own compatible host instead.

Install

Use Python 3.11 or newer. The isolated requirement pins the exact Browser Use commit audited by the chapter (ec9277c…, package version 0.9.5); the PyPI release carrying the same version string is not substituted for that commit:

python3.11 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt
python -m playwright install chromium

Hosted open-model route

cp env.example .env
export OPENROUTER_API_KEY='replace-with-your-key'

python main.py --dry-run
python main.py \
  --task "Open Google, search for San Francisco weather today, and report the temperature and conditions. Do not sign in or change any external data." \
  --max-steps 25 \
  --record-video

The default model is qwen/qwen3-vl-32b-instruct. Override OPEN_MODEL_MODEL to select another explicitly open-weight vision model; do not describe a proprietary model reached through the same gateway as an open model.

Self-hosted or another compatible API

Start a vision-capable OpenAI-compatible server, then configure its URL and served model name. The runner does not require an OpenRouter key in this mode:

export OPEN_MODEL_API_KEY=local
export OPEN_MODEL_BASE_URL=http://127.0.0.1:8000/v1
export OPEN_MODEL_MODEL=Qwen/Qwen3-VL-32B-Instruct
python main.py --dry-run
python main.py --headless

If the host accepts images but rejects response_format: json_schema, set OPEN_MODEL_SCHEMA_MODE=prompt. This is a compatibility fallback, and its reliability should be reported separately because schema adherence can change.

Retained evidence

Every non-dry run creates a new runs/open-model-<UTC>/ directory containing:

  • preflight.json: redacted endpoint, exact model, task, and execution limits;
  • api-receipts.json: credential-free request hashes and raw provider responses, including provider-reported model IDs when supplied;
  • history.json: ordered model decisions, actions, observations, and results;
  • screenshots/ plus screenshots.json: retained per-step visual observations;
  • summary.json or failure.json: outcome and honest failure state; and
  • manifest.json: SHA-256 and byte size for every retained artifact.

No API-key value is written. The Agent's done result is only an agent-reported outcome; manuscript-level completion still requires independent checking of the weather answer and action trajectory. A dry run, model-list lookup, or browser launch alone is not completion evidence.

Validate a retained run against its provider receipts, one-action-per-step limit, final browser observation, screenshot hashes, and credential scan:

python validate_run.py runs/<run-id> --latest validation/latest.json