# ScreenJob ScreenJob is an autonomous desktop-and-terminal execution service. It lets an LLM use controlled local tools (screen, click, type, shell) to complete GUI-heavy tasks on a real computer. ## What It Solves - Runs agent-driven tasks that require a graphical interface. - Exposes both CLI and HTTP API modes. - Stores job history and events in SQLite. - Streams live monitoring updates over WebSocket. - Returns structured agent output as: - `return`: human-readable completion message - `data`: structured payload (for example command output) - `verification`: final screen-capture metadata for completion accuracy checks ## Core Features - Tool-based agent loop (`execute_command`, `see_screen`, `enhance`, `click`, `type`, `press_key`, `sleep`, `task_complete`) - Safety pre-check with override support - Per-job tool disable list - Live/final usage and cost estimates - Read-only Tailwind monitoring UI - Persistent job and event history ## Project Layout ```text main.py screenjob.py requirements.txt start_backend.ps1 src/ agent.py app_main.py cli.py config.py models.py pricing.py runtime.py safety.py server.py storage.py task_manager.py ui.py utils.py tests/ test_agent_tools.py test_pricing.py test_server_api.py test_storage.py .gitea/workflows/ci.yml ``` ## Setup 1. Install Python 3.11+. 2. Install dependencies: ```powershell pip install -r requirements.txt ``` 3. Create `.env` in project root: ```env OPENAI_API_KEY=... SCREENJOB_TOKEN=choose_a_strong_token # Optional SCREENJOB_DEFAULT_MODEL=gpt-5.4-mini SCREENJOB_SAFETY_MODEL=gpt-5.4-mini SCREENJOB_HOST=127.0.0.1 SCREENJOB_PORT=8787 DISABLE_UI=false ``` ## Usage ### CLI ```powershell python main.py run "Open amazon.de and go to my orders" ``` CLI JSON output includes both legacy and structured fields: ```json { "completed": true, "result": "Task completed successfully", "response": { "return": "Task completed successfully", "data": "file1.txt\nfile2.txt" }, "return": "Task completed successfully", "data": "file1.txt\nfile2.txt", "verification": { "ok": true, "path": "C:/.../screens/screen_final_verification_step_003.png" } } ``` ### Server ```powershell python main.py server ``` Or use the PowerShell launcher: ```powershell .\start_backend.ps1 ``` Auth for all API routes: - `Authorization: Bearer ` - `X-ScreenJob-Token: ` - Query fallback `?token=` (mainly for UI/websocket/artifact fetch) ### Create Job `POST /api/jobs` ```json { "job": "run \"ls -a\" in C:/Users/username/Documents and return output", "model": "gpt-5.4-mini", "disabled_tools": [], "safety_override": false } ``` Response: ```json { "job_id": "job_..." } ``` ### Job Status / History - `GET /api/jobs/{job_id}` - `GET /api/jobs/{job_id}/status` - `GET /api/jobs/{job_id}/events` - `GET /api/jobs` - `POST /api/jobs/{job_id}/cancel` - `GET /api/stats` Each job payload includes: - `result` (compat string) - `response.return` - `response.data` - top-level `return` and `data` aliases - `verification` (final screenshot path + metadata) ### Monitoring UI - URL: `/` - Read-only dashboard (no run controls) - Requires token input - Live updates via `/ws` - Set `DISABLE_UI=true` to disable UI ## Agent Instructions (Practical) - Prefer `execute_command` for deterministic actions (opening URLs, filesystem checks). - Use `see_screen` before UI interaction. - Use `enhance` when text is unclear. - Use `press_key` for non-text keys (Enter, Tab, arrows, Escape). - Use `click` offsets via `offset_up/down/left/right` and optional `sleep_after_seconds`. - When done, call: - `task_complete(return="...", data=...)` - A final verification screen capture is always taken automatically on completion. `data` should contain useful structured output for the requester (text, object, list, etc.). ## Verification Local: ```powershell pytest -q ``` CI: - `.gitea/workflows/ci.yml` runs compile checks + tests on push/PR. ## Compatibility Entry Point - `python screenjob.py ""` remains supported as a wrapper to `main.py`. ## License Apache License 2.0. See `LICENSE`.