feat: add shared runtime with FastAPI job server and safety pipeline

main.py

@@ -1,4 +1,4 @@
-from src.cli import main
+from src.app_main import main
 
 
 if __name__ == "__main__":

screenjob.py

@@ -2,10 +2,10 @@
 Compatibility wrapper.
 
 Preferred entry point:
-    python main.py "<job>"
+    python main.py run "<job>"
 """
 
-from src.cli import main
+from src.app_main import main
 
 
 if __name__ == "__main__":

src/agent.py

@@ -3,14 +3,16 @@ from __future__ import annotations
 import json
 import logging
 import subprocess
+import threading
 import time
 import traceback
-from typing import Any
+from typing import Any, Callable
 
 from openai import OpenAI
 from PIL import Image, ImageEnhance, ImageFilter, ImageOps
 
-from .models import AgentResult, RunArtifacts
+from .models import AgentResult, RunArtifacts, RuntimeOptions, UsageSummary
+from .pricing import estimate_cost_usd
 from .utils import clamp, draw_global_grid, image_to_data_url, utc_now_iso
 
 try:
@@ -46,32 +48,78 @@ class ScreenJobAgent:
         client: OpenAI,
         logger: logging.Logger,
         artifacts: RunArtifacts,
-        model: str,
-        max_steps: int,
-        command_timeout: int,
-        type_interval: float,
-        click_pause: float,
+        options: RuntimeOptions,
+        cancel_event: threading.Event | None = None,
+        event_callback: Callable[[dict[str, Any]], None] | None = None,
     ) -> None:
         self.client = client
         self.logger = logger
         self.artifacts = artifacts
-        self.model = model
-        self.max_steps = max_steps
-        self.command_timeout = command_timeout
-        self.type_interval = type_interval
-        self.click_pause = click_pause
+        self.options = options
+        self.cancel_event = cancel_event or threading.Event()
+        self.event_callback = event_callback
 
         self.step = 0
         self.completed = False
         self.final_result = ""
         self.previous_response_id: str | None = None
+        self.usage = UsageSummary()
 
         self.last_screen_data_url: str | None = None
         self.last_screen_meta: dict[str, Any] | None = None
         self.click_history: list[tuple[int, int, float]] = []
+        self.disabled_tools = {tool.strip() for tool in (options.disable_tools or set()) if tool.strip()}
+
+    def _emit(self, event_type: str, payload: dict[str, Any]) -> None:
+        if self.event_callback is None:
+            return
+        event = {
+            "ts": utc_now_iso(),
+            "job_run_id": self.artifacts.run_id,
+            "step": self.step,
+            "event_type": event_type,
+            "payload": payload,
+        }
+        try:
+            self.event_callback(event)
+        except Exception:  # noqa: BLE001
+            self.logger.debug("Event callback failed.", exc_info=True)
+
+    def _is_cancelled(self) -> bool:
+        return bool(self.cancel_event.is_set())
+
+    def _register_usage(self, response: Any) -> None:
+        usage_obj = getattr(response, "usage", None)
+        if usage_obj is None:
+            return
+        input_tokens = int(getattr(usage_obj, "input_tokens", 0) or 0)
+        output_tokens = int(getattr(usage_obj, "output_tokens", 0) or 0)
+        total_tokens = int(getattr(usage_obj, "total_tokens", input_tokens + output_tokens) or 0)
+
+        input_details = getattr(usage_obj, "input_tokens_details", None)
+        cached_tokens = int(getattr(input_details, "cached_tokens", 0) or 0) if input_details else 0
+        output_details = getattr(usage_obj, "output_tokens_details", None)
+        reasoning_tokens = int(getattr(output_details, "reasoning_tokens", 0) or 0) if output_details else 0
+
+        self.usage.input_tokens += input_tokens
+        self.usage.cached_input_tokens += cached_tokens
+        self.usage.output_tokens += output_tokens
+        self.usage.reasoning_tokens += reasoning_tokens
+        self.usage.total_tokens += total_tokens
+        estimated_cost, model_for_pricing = estimate_cost_usd(self.options.model, self.usage)
+        self.usage.estimated_cost_usd = estimated_cost
+        self.usage.model_for_pricing = model_for_pricing
+
+        self._emit(
+            "usage_update",
+            {
+                "usage": self.usage.to_dict(),
+                "response_id": getattr(response, "id", None),
+            },
+        )
 
     def _tool_schemas(self) -> list[dict[str, Any]]:
-        return [
+        all_tools: list[dict[str, Any]] = [
             {
                 "type": "function",
                 "name": "task_complete",
@@ -213,6 +261,7 @@ class ScreenJobAgent:
                 },
             },
         ]
+        return [tool for tool in all_tools if tool["name"] not in self.disabled_tools]
 
     def _capture_screen(self, with_grid: bool = True) -> tuple[Image.Image, dict[str, Any]]:
         screenshot = pyautogui.screenshot().convert("RGB")
@@ -378,13 +427,16 @@ class ScreenJobAgent:
                 "recent_similar_clicks": len(near_same),
             }
 
-        pyautogui.moveTo(x, y, duration=self.click_pause)
+        pyautogui.moveTo(x, y, duration=self.options.click_pause)
         pyautogui.click(x=x, y=y)
         sleep_after = self._parse_seconds(args.get("sleep_after_seconds", 0), default=0.0, max_seconds=30.0)
-        if sleep_after > 0:
-            time.sleep(sleep_after)
-        else:
-            time.sleep(0.15)
+        wait_remaining = sleep_after if sleep_after > 0 else 0.15
+        while wait_remaining > 0:
+            if self._is_cancelled():
+                break
+            interval = min(0.05, wait_remaining)
+            time.sleep(interval)
+            wait_remaining -= interval
 
         return {
             "ok": True,
@@ -401,7 +453,11 @@ class ScreenJobAgent:
 
     def _tool_type(self, args: dict[str, Any]) -> dict[str, Any]:
         text = str(args.get("text", ""))
-        pyautogui.write(text, interval=self.type_interval)
+        for char in text:
+            if self._is_cancelled():
+                return {"ok": False, "cancelled": True, "typed_length": 0}
+            pyautogui.write(char, interval=0)
+            time.sleep(self.options.type_interval)
         return {"ok": True, "typed_length": len(text), "message": "Text typed."}
 
     def _tool_press_key(self, args: dict[str, Any]) -> dict[str, Any]:
@@ -410,15 +466,25 @@ class ScreenJobAgent:
         if not key:
             return {"ok": False, "error": "Missing key."}
         repeats = min(repeats, 50)
+        pressed = 0
         for _ in range(repeats):
+            if self._is_cancelled():
+                break
             pyautogui.press(key)
+            pressed += 1
             time.sleep(0.03)
-        return {"ok": True, "key": key, "repeats": repeats, "message": "Key press executed."}
+        return {"ok": True, "key": key, "repeats": pressed, "message": "Key press executed."}
 
     def _tool_sleep(self, args: dict[str, Any]) -> dict[str, Any]:
         seconds = self._parse_seconds(args.get("seconds"), default=0.0, max_seconds=60.0)
-        time.sleep(seconds)
-        return {"ok": True, "slept_seconds": seconds, "message": "Sleep completed."}
+        elapsed = 0.0
+        while elapsed < seconds:
+            if self._is_cancelled():
+                return {"ok": False, "cancelled": True, "slept_seconds": round(elapsed, 3)}
+            interval = min(0.1, seconds - elapsed)
+            time.sleep(interval)
+            elapsed += interval
+        return {"ok": True, "slept_seconds": round(seconds, 3), "message": "Sleep completed."}
 
     def _tool_execute_command(self, args: dict[str, Any]) -> dict[str, Any]:
         command = str(args.get("command", "")).strip()
@@ -426,36 +492,55 @@ class ScreenJobAgent:
             return {"ok": False, "error": "Empty command."}
 
         started = time.time()
+        process: subprocess.Popen[str] | None = None
         try:
-            completed = subprocess.run(
+            process = subprocess.Popen(
                 command,
                 shell=True,
-                capture_output=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
                 text=True,
-                timeout=self.command_timeout,
-                check=False,
             )
-            elapsed_ms = int((time.time() - started) * 1000)
+            while True:
+                if self._is_cancelled():
+                    process.terminate()
                     return {
-                "ok": True,
+                        "ok": False,
+                        "cancelled": True,
                         "command": command,
-                "exit_code": completed.returncode,
-                "stdout": completed.stdout[-12000:],
-                "stderr": completed.stderr[-12000:],
-                "elapsed_ms": elapsed_ms,
+                        "elapsed_ms": int((time.time() - started) * 1000),
                     }
-        except subprocess.TimeoutExpired as exc:
-            elapsed_ms = int((time.time() - started) * 1000)
+                if process.poll() is not None:
+                    break
+                if (time.time() - started) > self.options.command_timeout:
+                    process.kill()
+                    stdout, stderr = process.communicate(timeout=2)
                     return {
                         "ok": False,
                         "command": command,
                         "error": "Command timed out.",
-                "elapsed_ms": elapsed_ms,
-                "timeout_seconds": self.command_timeout,
-                "stdout": (exc.stdout or "")[-12000:],
-                "stderr": (exc.stderr or "")[-12000:],
+                        "elapsed_ms": int((time.time() - started) * 1000),
+                        "timeout_seconds": self.options.command_timeout,
+                        "stdout": (stdout or "")[-12000:],
+                        "stderr": (stderr or "")[-12000:],
+                    }
+                time.sleep(0.05)
+
+            stdout, stderr = process.communicate(timeout=2)
+            return {
+                "ok": True,
+                "command": command,
+                "exit_code": process.returncode,
+                "stdout": (stdout or "")[-12000:],
+                "stderr": (stderr or "")[-12000:],
+                "elapsed_ms": int((time.time() - started) * 1000),
             }
         except Exception as exc:  # noqa: BLE001
+            if process is not None and process.poll() is None:
+                try:
+                    process.kill()
+                except Exception:  # noqa: BLE001
+                    pass
             return {"ok": False, "command": command, "error": f"{type(exc).__name__}: {exc}"}
 
     def _tool_task_complete(self, args: dict[str, Any]) -> dict[str, Any]:
@@ -465,6 +550,8 @@ class ScreenJobAgent:
         return {"ok": True, "result": result}
 
     def _dispatch_tool(self, name: str, args: dict[str, Any]) -> dict[str, Any]:
+        if name in self.disabled_tools:
+            return {"ok": False, "error": f"Tool '{name}' is disabled for this job."}
         handlers = {
             "see_screen": self._tool_see_screen,
             "enhance": self._tool_enhance,
@@ -491,7 +578,7 @@ class ScreenJobAgent:
 
     def _call_model(self, input_items: list[dict[str, Any]]) -> Any:
         return self.client.responses.create(
-            model=self.model,
+            model=self.options.model,
             instructions=SYSTEM_PROMPT,
             tools=self._tool_schemas(),
             input=input_items,
@@ -502,8 +589,18 @@ class ScreenJobAgent:
 
     def run(self, job: str) -> AgentResult:
         started_at = time.time()
-        self.logger.info("Starting run_id=%s model=%s", self.artifacts.run_id, self.model)
+        self.logger.info("Starting run_id=%s model=%s", self.artifacts.run_id, self.options.model)
         self.logger.info("Job: %s", job)
+        self.logger.info("Disabled tools: %s", sorted(self.disabled_tools))
+        self._emit(
+            "job_started",
+            {
+                "run_id": self.artifacts.run_id,
+                "model": self.options.model,
+                "objective": job,
+                "disabled_tools": sorted(self.disabled_tools),
+            },
+        )
 
         self._tool_see_screen({})
         init_input: list[dict[str, Any]] = [
@@ -528,25 +625,37 @@ class ScreenJobAgent:
             )
 
         pending_input = init_input
+        error_text: str | None = None
+        cancelled = False
+
+        while self.step < self.options.max_steps and not self.completed:
+            if self._is_cancelled():
+                cancelled = True
+                error_text = "Cancelled by user request."
+                break
 
-        while self.step < self.max_steps and not self.completed:
             self.step += 1
-            self.logger.info("---- Agent step %d/%d ----", self.step, self.max_steps)
+            self.logger.info("---- Agent step %d/%d ----", self.step, self.options.max_steps)
+            self._emit("step_started", {"step": self.step, "max_steps": self.options.max_steps})
             try:
                 response = self._call_model(pending_input)
+                self._register_usage(response)
             except Exception as exc:  # noqa: BLE001
                 self.logger.exception("OpenAI API call failed on step %d", self.step)
-                raise RuntimeError(f"OpenAI API call failed: {type(exc).__name__}: {exc}") from exc
+                error_text = f"OpenAI API call failed: {type(exc).__name__}: {exc}"
+                break
 
             self.previous_response_id = response.id
             output_items = list(response.output or [])
             text_preview = getattr(response, "output_text", "") or ""
             if text_preview.strip():
                 self.logger.info("Model text: %s", text_preview.strip()[:500])
+                self._emit("model_text", {"step": self.step, "text": text_preview.strip()[:2000]})
 
             tool_calls = [item for item in output_items if getattr(item, "type", None) == "function_call"]
             if not tool_calls:
                 self.logger.warning("No tool calls returned; nudging model to continue with tools.")
+                self._emit("step_warning", {"step": self.step, "message": "No tool calls; nudged model."})
                 pending_input = [
                     {
                         "role": "user",
@@ -566,12 +675,21 @@ class ScreenJobAgent:
 
             next_input: list[dict[str, Any]] = []
             for tool_call in tool_calls:
+                if self._is_cancelled():
+                    cancelled = True
+                    error_text = "Cancelled by user request."
+                    break
+
                 name = str(getattr(tool_call, "name", ""))
                 call_id = str(getattr(tool_call, "call_id", ""))
                 args_raw = getattr(tool_call, "arguments", "{}")
                 args = self._safe_parse_args(args_raw)
 
                 self.logger.info("Tool call: %s args=%s", name, json.dumps(args, ensure_ascii=False))
+                self._emit(
+                    "tool_called",
+                    {"step": self.step, "tool": name, "args": args},
+                )
                 try:
                     result = self._dispatch_tool(name, args)
                 except Exception as exc:  # noqa: BLE001
@@ -587,6 +705,7 @@ class ScreenJobAgent:
                     name,
                     json.dumps(result, ensure_ascii=False)[:2500],
                 )
+                self._emit("tool_result", {"step": self.step, "tool": name, "result": result})
                 next_input.append(
                     {
                         "type": "function_call_output",
@@ -600,26 +719,69 @@ class ScreenJobAgent:
                     next_input.append(
                         self._build_visual_message(title, self.last_screen_data_url, self.last_screen_meta)
                     )
+                    self._emit(
+                        "visual_update",
+                        {
+                            "step": self.step,
+                            "kind": name,
+                            "image_meta": self.last_screen_meta,
+                        },
+                    )
 
+            if cancelled:
+                break
             pending_input = next_input
 
         ended_at = time.time()
         if self.completed:
             self.logger.info("Task completed in %d step(s).", self.step)
+            self._emit("job_completed", {"result": self.final_result, "steps": self.step, "usage": self.usage.to_dict()})
             return AgentResult(
                 completed=True,
                 result=self.final_result,
                 steps=self.step,
                 started_at=started_at,
                 ended_at=ended_at,
+                usage=self.usage,
             )
 
-        self.logger.warning("Stopped due to step limit (%d).", self.max_steps)
+        if cancelled:
+            self.logger.warning("Run cancelled by user after %d step(s).", self.step)
+            self._emit("job_cancelled", {"steps": self.step, "usage": self.usage.to_dict()})
             return AgentResult(
                 completed=False,
-            result=f"Stopped after max steps ({self.max_steps}) without task_complete.",
+                result="Cancelled by user request.",
                 steps=self.step,
                 started_at=started_at,
                 ended_at=ended_at,
+                usage=self.usage,
+                error=error_text,
+                cancelled=True,
+            )
+
+        if error_text:
+            self.logger.error("Run failed: %s", error_text)
+            self._emit("job_failed", {"steps": self.step, "error": error_text, "usage": self.usage.to_dict()})
+            return AgentResult(
+                completed=False,
+                result=error_text,
+                steps=self.step,
+                started_at=started_at,
+                ended_at=ended_at,
+                usage=self.usage,
+                error=error_text,
+            )
+
+        self.logger.warning("Stopped due to step limit (%d).", self.options.max_steps)
+        result_text = f"Stopped after max steps ({self.options.max_steps}) without task_complete."
+        self._emit("job_stopped", {"steps": self.step, "reason": "max_steps", "usage": self.usage.to_dict()})
+        return AgentResult(
+            completed=False,
+            result=result_text,
+            steps=self.step,
+            started_at=started_at,
+            ended_at=ended_at,
+            usage=self.usage,
+            error=result_text,
         )
 

src/app_main.py

@@ -0,0 +1,38 @@
+from __future__ import annotations
+
+import sys
+
+from . import cli, server
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = list(sys.argv[1:] if argv is None else argv)
+    if not args:
+        print("Usage: python main.py run \"<job>\" | python main.py server")
+        return 2
+
+    command = args[0].strip().lower()
+    if command in {"-h", "--help", "help"}:
+        print("Usage:")
+        print("  python main.py run \"<job>\" [options]")
+        print("  python main.py server")
+        print("")
+        print("Examples:")
+        print("  python main.py run \"Open amazon.de\"")
+        print("  python main.py server")
+        return 0
+    if command in {"server", "serve"}:
+        if any(flag in {"-h", "--help"} for flag in args[1:]):
+            print("Server mode:")
+            print("  python main.py server")
+            print("")
+            print("Env:")
+            print("  OPENAI_API_KEY=...")
+            print("  SCREENJOB_TOKEN=...")
+            print("  DISABLE_UI=true|false (optional)")
+            return 0
+        server.main()
+        return 0
+    if command in {"run"}:
+        return cli.main(args[1:])
+    return cli.main(args)

src/cli.py

@@ -2,146 +2,96 @@ from __future__ import annotations
 
 import argparse
 import json
-import os
 import sys
 from pathlib import Path
 
-from dotenv import load_dotenv
-from openai import OpenAI
-
-from .agent import ScreenJobAgent
-from .utils import setup_artifacts, setup_logger
-
-try:
-    import pyautogui
-except Exception as import_exc:
-    raise RuntimeError(
-        "pyautogui is required. Install dependencies with: pip install pyautogui pillow"
-    ) from import_exc
+from .config import load_app_config
+from .models import RuntimeOptions
+from .runtime import create_openai_client, run_job
+from .safety import assess_task_safety
 
 
 def build_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(
-        description="Run an autonomous desktop task agent using OpenAI + UI tools.",
+        description="Run an autonomous desktop task agent using OpenAI + local tools.",
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog=(
             "Examples:\n"
-            '  python main.py "Open amazon.de"\n'
-            '  python main.py "Open amazon.de and search for mechanical keyboard" --max-steps 80\n\n'
-            "Artifacts:\n"
-            "  Each run stores logs/screens in ./screenjob_runs/run_YYYYMMDD_HHMMSS/"
+            '  python main.py run "Open amazon.de"\n'
+            '  python main.py run "Open amazon.de and search for keyboard" --max-steps 80\n'
+            '  python main.py run "Open amazon.de" --disable-tool click --disable-tool type\n'
         ),
     )
     parser.add_argument("job", type=str, help="Task objective for the agent.")
-    parser.add_argument("--model", type=str, default="gpt-5.2", help="OpenAI model name.")
+    parser.add_argument("--model", type=str, default=None, help="OpenAI model name.")
     parser.add_argument("--max-steps", type=int, default=60, help="Max tool-iteration steps.")
-    parser.add_argument(
-        "--command-timeout",
-        type=int,
-        default=45,
-        help="Timeout (seconds) for execute_command tool.",
-    )
-    parser.add_argument(
-        "--type-interval",
-        type=float,
-        default=0.02,
-        help="Seconds between typed characters.",
-    )
-    parser.add_argument(
-        "--click-pause",
-        type=float,
-        default=0.10,
-        help="Mouse move duration before click (seconds).",
-    )
-    parser.add_argument(
-        "--no-failsafe",
-        action="store_true",
-        help="Disable PyAutoGUI fail-safe. Not recommended.",
-    )
+    parser.add_argument("--command-timeout", type=int, default=45, help="Timeout in seconds for execute_command.")
+    parser.add_argument("--type-interval", type=float, default=0.02, help="Seconds between typed characters.")
+    parser.add_argument("--click-pause", type=float, default=0.10, help="Mouse move duration before click.")
+    parser.add_argument("--disable-tool", action="append", default=[], help="Disable a tool by name.")
+    parser.add_argument("--skip-safety-check", action="store_true", help="Bypass pre-flight safety check.")
+    parser.add_argument("--no-failsafe", action="store_true", help="Disable PyAutoGUI fail-safe.")
     return parser
 
 
-def main() -> int:
-    load_dotenv()
+def main(argv: list[str] | None = None) -> int:
     parser = build_parser()
-    args = parser.parse_args()
+    args = parser.parse_args(argv)
+    cwd = Path.cwd()
+    config = load_app_config(cwd)
 
-    api_key = os.getenv("OPENAI_API_KEY", "").strip()
-    if not api_key:
-        print("ERROR: Missing OPENAI_API_KEY (expected in environment or .env).", file=sys.stderr)
+    if not config.openai_api_key:
+        print("ERROR: Missing OPENAI_API_KEY in environment/.env", file=sys.stderr)
         return 2
 
-    pyautogui.FAILSAFE = not args.no_failsafe
-    pyautogui.PAUSE = 0.05
+    model = args.model or config.default_model
+    disabled_tools = sorted({str(x).strip() for x in args.disable_tool if str(x).strip()})
 
-    runs_base = Path.cwd() / "screenjob_runs"
-    artifacts = setup_artifacts(runs_base)
-    logger = setup_logger(artifacts.log_file, verbose=True)
+    if not args.skip_safety_check:
+        safety_client = create_openai_client(config.openai_api_key)
+        safe, reason, parsed = assess_task_safety(
+            safety_client,
+            model=config.safety_model,
+            objective=args.job,
+            disabled_tools=disabled_tools,
+        )
+        if not safe:
+            print(
+                json.dumps(
+                    {
+                        "completed": False,
+                        "result": f"Blocked by safety check: {reason}",
+                        "safety": parsed,
+                    },
+                    ensure_ascii=False,
+                    indent=2,
+                )
+            )
+            return 1
 
-    logger.info("ScreenJob booting. Artifacts: %s", str(artifacts.root_dir.resolve()))
-    logger.info("PyAutoGUI FAILSAFE=%s", pyautogui.FAILSAFE)
-
-    try:
-        client = OpenAI(api_key=api_key)
-    except Exception as exc:  # noqa: BLE001
-        logger.exception("Failed to create OpenAI client.")
-        print(f"ERROR: Could not initialize OpenAI client: {exc}", file=sys.stderr)
-        return 2
-
-    agent = ScreenJobAgent(
-        client=client,
-        logger=logger,
-        artifacts=artifacts,
-        model=args.model,
+    options = RuntimeOptions(
+        model=model,
         max_steps=args.max_steps,
         command_timeout=args.command_timeout,
         type_interval=args.type_interval,
         click_pause=args.click_pause,
+        disable_tools=set(disabled_tools),
     )
-
     try:
-        result = agent.run(args.job)
-        elapsed = result.ended_at - result.started_at
-        logger.info("Run finished. completed=%s elapsed=%.2fs", result.completed, elapsed)
-        print(
-            json.dumps(
-                {
-                    "completed": result.completed,
-                    "result": result.result,
-                    "steps": result.steps,
-                    "elapsed_seconds": round(elapsed, 3),
-                    "artifacts_dir": str(artifacts.root_dir.resolve()),
-                },
-                ensure_ascii=False,
-                indent=2,
+        result, artifacts = run_job(
+            api_key=config.openai_api_key,
+            objective=args.job,
+            options=options,
+            runs_base=config.runs_dir,
+            no_failsafe=args.no_failsafe,
         )
-        )
-        return 0 if result.completed else 1
     except KeyboardInterrupt:
-        logger.warning("Interrupted by user.")
-        print(
-            json.dumps(
-                {
-                    "completed": False,
-                    "result": "Interrupted by user.",
-                    "steps": agent.step,
-                    "artifacts_dir": str(artifacts.root_dir.resolve()),
-                },
-                ensure_ascii=False,
-                indent=2,
-            )
-        )
+        print(json.dumps({"completed": False, "result": "Interrupted by user."}, ensure_ascii=False, indent=2))
         return 130
     except Exception as exc:  # noqa: BLE001
-        logger.exception("Fatal runtime error.")
         print(
             json.dumps(
-                {
-                    "completed": False,
-                    "result": f"Fatal error: {type(exc).__name__}: {exc}",
-                    "steps": agent.step,
-                    "artifacts_dir": str(artifacts.root_dir.resolve()),
-                },
+                {"completed": False, "result": f"Fatal error: {type(exc).__name__}: {exc}"},
                 ensure_ascii=False,
                 indent=2,
             ),
@@ -149,3 +99,16 @@ def main() -> int:
         )
         return 1
 
+    payload = {
+        "completed": result.completed,
+        "result": result.result,
+        "steps": result.steps,
+        "elapsed_seconds": round(result.ended_at - result.started_at, 3),
+        "artifacts_dir": str(artifacts.root_dir.resolve()),
+        "usage": result.usage.to_dict(),
+        "error": result.error,
+        "cancelled": result.cancelled,
+    }
+    print(json.dumps(payload, ensure_ascii=False, indent=2))
+    return 0 if result.completed else 1
+

src/config.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+
+def _env_bool(name: str, default: bool = False) -> bool:
+    raw = os.getenv(name)
+    if raw is None:
+        return default
+    return raw.strip().lower() in {"1", "true", "yes", "on"}
+
+
+@dataclass(frozen=True)
+class AppConfig:
+    openai_api_key: str
+    screenjob_token: str
+    disable_ui: bool
+    default_model: str
+    safety_model: str
+    host: str
+    port: int
+    runs_dir: Path
+    db_path: Path
+
+
+def load_app_config(cwd: Path) -> AppConfig:
+    load_dotenv()
+    openai_api_key = os.getenv("OPENAI_API_KEY", "").strip()
+    screenjob_token = os.getenv("SCREENJOB_TOKEN", "").strip()
+    default_model = os.getenv("SCREENJOB_DEFAULT_MODEL", "gpt-5.4-mini").strip() or "gpt-5.4-mini"
+    safety_model = os.getenv("SCREENJOB_SAFETY_MODEL", "gpt-5.4-mini").strip() or "gpt-5.4-mini"
+    host = os.getenv("SCREENJOB_HOST", "127.0.0.1").strip() or "127.0.0.1"
+    port = int(os.getenv("SCREENJOB_PORT", "8787").strip() or "8787")
+    runs_dir = cwd / "screenjob_runs"
+    db_path = cwd / "screenjob.db"
+    disable_ui = _env_bool("DISABLE_UI", default=False)
+    return AppConfig(
+        openai_api_key=openai_api_key,
+        screenjob_token=screenjob_token,
+        disable_ui=disable_ui,
+        default_model=default_model,
+        safety_model=safety_model,
+        host=host,
+        port=port,
+        runs_dir=runs_dir,
+        db_path=db_path,
+    )
+

src/models.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 from dataclasses import dataclass
 from pathlib import Path
+from typing import Any
 
 
 @dataclass
@@ -21,4 +22,38 @@ class AgentResult:
     steps: int
     started_at: float
     ended_at: float
+    usage: "UsageSummary"
+    error: str | None = None
+    cancelled: bool = False
 
+
+@dataclass
+class UsageSummary:
+    input_tokens: int = 0
+    cached_input_tokens: int = 0
+    output_tokens: int = 0
+    reasoning_tokens: int = 0
+    total_tokens: int = 0
+    estimated_cost_usd: float | None = None
+    model_for_pricing: str | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "input_tokens": self.input_tokens,
+            "cached_input_tokens": self.cached_input_tokens,
+            "output_tokens": self.output_tokens,
+            "reasoning_tokens": self.reasoning_tokens,
+            "total_tokens": self.total_tokens,
+            "estimated_cost_usd": self.estimated_cost_usd,
+            "model_for_pricing": self.model_for_pricing,
+        }
+
+
+@dataclass
+class RuntimeOptions:
+    model: str
+    max_steps: int = 60
+    command_timeout: int = 45
+    type_interval: float = 0.02
+    click_pause: float = 0.10
+    disable_tools: set[str] | None = None

src/pricing.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from .models import UsageSummary
+
+
+@dataclass(frozen=True)
+class ModelRate:
+    input_per_million: float
+    cached_input_per_million: float | None
+    output_per_million: float
+
+
+# Prices are USD per 1M tokens (standard tier).
+# Source: OpenAI API pricing pages (captured 2026-05-27).
+MODEL_RATES: dict[str, ModelRate] = {
+    "gpt-5.4-mini": ModelRate(0.75, 0.075, 4.50),
+    "gpt-5.4": ModelRate(2.50, 0.25, 15.00),
+    "gpt-5.2": ModelRate(1.75, 0.175, 14.00),
+    "gpt-5.2-chat-latest": ModelRate(1.75, 0.175, 14.00),
+    "gpt-5-mini": ModelRate(0.25, 0.025, 2.00),
+    "gpt-5-nano": ModelRate(0.05, 0.005, 0.40),
+}
+
+
+def normalize_model_for_pricing(model: str) -> str:
+    model_name = model.strip().lower()
+    for key in MODEL_RATES:
+        if model_name == key or model_name.startswith(f"{key}-"):
+            return key
+    return model_name
+
+
+def estimate_cost_usd(model: str, usage: UsageSummary) -> tuple[float | None, str]:
+    normalized = normalize_model_for_pricing(model)
+    rate = MODEL_RATES.get(normalized)
+    if rate is None:
+        return None, normalized
+
+    cached = max(0, usage.cached_input_tokens)
+    non_cached_input = max(0, usage.input_tokens - cached)
+    input_cost = (non_cached_input / 1_000_000.0) * rate.input_per_million
+    cached_cost = 0.0
+    if rate.cached_input_per_million is not None:
+        cached_cost = (cached / 1_000_000.0) * rate.cached_input_per_million
+    output_cost = (max(0, usage.output_tokens) / 1_000_000.0) * rate.output_per_million
+    return input_cost + cached_cost + output_cost, normalized
+

src/runtime.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import logging
+import threading
+from pathlib import Path
+from typing import Any, Callable
+
+from openai import OpenAI
+
+from .agent import ScreenJobAgent
+from .models import AgentResult, RunArtifacts, RuntimeOptions
+from .utils import setup_artifacts, setup_logger
+
+try:
+    import pyautogui
+except Exception as import_exc:
+    raise RuntimeError(
+        "pyautogui is required. Install dependencies with: pip install pyautogui pillow"
+    ) from import_exc
+
+
+def create_openai_client(api_key: str) -> OpenAI:
+    return OpenAI(api_key=api_key)
+
+
+def run_job(
+    *,
+    api_key: str,
+    objective: str,
+    options: RuntimeOptions,
+    runs_base: Path,
+    no_failsafe: bool = False,
+    cancel_event: threading.Event | None = None,
+    event_callback: Callable[[dict[str, Any]], None] | None = None,
+    logger: logging.Logger | None = None,
+) -> tuple[AgentResult, RunArtifacts]:
+    pyautogui.FAILSAFE = not no_failsafe
+    pyautogui.PAUSE = 0.05
+
+    artifacts = setup_artifacts(runs_base)
+    active_logger = logger or setup_logger(artifacts.log_file, verbose=True)
+    active_logger.info("ScreenJob booting. Artifacts: %s", str(artifacts.root_dir.resolve()))
+    active_logger.info("PyAutoGUI FAILSAFE=%s", pyautogui.FAILSAFE)
+
+    client = create_openai_client(api_key)
+    agent = ScreenJobAgent(
+        client=client,
+        logger=active_logger,
+        artifacts=artifacts,
+        options=options,
+        cancel_event=cancel_event,
+        event_callback=event_callback,
+    )
+    result = agent.run(objective)
+    active_logger.info("Run finished. completed=%s elapsed=%.2fs", result.completed, result.ended_at - result.started_at)
+    return result, artifacts
+

src/safety.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from openai import OpenAI
+
+
+SAFETY_PROMPT = """
+You are a safety gate for a desktop automation agent that can execute terminal commands, click/type on screen, and browse websites.
+Classify whether the user objective can be executed safely and legally.
+
+Return strict JSON only:
+{
+  "safe": true|false,
+  "reason": "short reason",
+  "risk_level": "low|medium|high",
+  "recommended_disabled_tools": ["optional_tool_names"]
+}
+"""
+
+
+def _extract_json(text: str) -> dict[str, Any]:
+    text = text.strip()
+    try:
+        parsed = json.loads(text)
+        return parsed if isinstance(parsed, dict) else {}
+    except Exception:
+        pass
+    start = text.find("{")
+    end = text.rfind("}")
+    if start >= 0 and end > start:
+        try:
+            parsed = json.loads(text[start : end + 1])
+            return parsed if isinstance(parsed, dict) else {}
+        except Exception:
+            return {}
+    return {}
+
+
+def assess_task_safety(
+    client: OpenAI,
+    *,
+    model: str,
+    objective: str,
+    disabled_tools: list[str],
+) -> tuple[bool, str, dict[str, Any]]:
+    try:
+        response = client.responses.create(
+            model=model,
+            instructions=SAFETY_PROMPT,
+            input=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "input_text",
+                            "text": (
+                                "Objective:\n"
+                                f"{objective}\n\n"
+                                f"Disabled tools: {json.dumps(disabled_tools, ensure_ascii=False)}\n"
+                                "Answer with strict JSON only."
+                            ),
+                        }
+                    ],
+                }
+            ],
+        )
+    except Exception as exc:  # noqa: BLE001
+        return False, f"Safety check failed: {type(exc).__name__}: {exc}", {"safe": False}
+
+    raw_text = getattr(response, "output_text", "") or ""
+    parsed = _extract_json(raw_text)
+    safe = bool(parsed.get("safe", False))
+    reason = str(parsed.get("reason", "")).strip() or "No reason provided by safety check."
+    if not parsed:
+        safe = False
+        reason = "Safety check returned unparseable response."
+    return safe, reason, parsed
+

src/server.py

@@ -0,0 +1,188 @@
+from __future__ import annotations
+
+import asyncio
+import secrets
+from pathlib import Path
+from typing import Any
+
+from fastapi import Depends, FastAPI, Header, HTTPException, Query, WebSocket, WebSocketDisconnect
+from fastapi.responses import HTMLResponse, JSONResponse
+from pydantic import BaseModel, Field
+
+from .config import AppConfig, load_app_config
+from .storage import HistoryDB
+from .task_manager import JobManager
+from .ui import monitoring_page_html
+
+
+class CreateJobRequest(BaseModel):
+    job: str = Field(..., min_length=1)
+    model: str | None = None
+    max_steps: int = Field(60, ge=1, le=400)
+    command_timeout: int = Field(45, ge=1, le=600)
+    type_interval: float = Field(0.02, ge=0.0, le=1.0)
+    click_pause: float = Field(0.10, ge=0.0, le=2.0)
+    disabled_tools: list[str] = Field(default_factory=list)
+    safety_override: bool = False
+    no_failsafe: bool = False
+
+
+class _WebSocketHub:
+    def __init__(self) -> None:
+        self._connections: set[WebSocket] = set()
+        self._lock = asyncio.Lock()
+        self._loop: asyncio.AbstractEventLoop | None = None
+
+    def set_loop(self, loop: asyncio.AbstractEventLoop) -> None:
+        self._loop = loop
+
+    async def connect(self, websocket: WebSocket) -> None:
+        await websocket.accept()
+        async with self._lock:
+            self._connections.add(websocket)
+
+    async def disconnect(self, websocket: WebSocket) -> None:
+        async with self._lock:
+            self._connections.discard(websocket)
+
+    async def broadcast(self, message: dict[str, Any]) -> None:
+        async with self._lock:
+            clients = list(self._connections)
+        dead: list[WebSocket] = []
+        for ws in clients:
+            try:
+                await ws.send_json(message)
+            except Exception:  # noqa: BLE001
+                dead.append(ws)
+        if dead:
+            async with self._lock:
+                for ws in dead:
+                    self._connections.discard(ws)
+
+    def broadcast_from_thread(self, message: dict[str, Any]) -> None:
+        if self._loop is None:
+            return
+        asyncio.run_coroutine_threadsafe(self.broadcast(message), self._loop)
+
+
+def create_app(config: AppConfig | None = None) -> FastAPI:
+    app_config = config or load_app_config(cwd=Path.cwd())
+    if not app_config.openai_api_key:
+        raise RuntimeError("OPENAI_API_KEY is required in environment or .env.")
+    if not app_config.screenjob_token:
+        raise RuntimeError("SCREENJOB_TOKEN is required in environment or .env.")
+
+    app = FastAPI(title="ScreenJob API", version="1.0.0")
+    db = HistoryDB(app_config.db_path)
+    ws_hub = _WebSocketHub()
+    manager = JobManager(config=app_config, db=db, broadcast=ws_hub.broadcast_from_thread)
+
+    app.state.config = app_config
+    app.state.db = db
+    app.state.ws_hub = ws_hub
+    app.state.manager = manager
+
+    @app.on_event("startup")
+    async def _on_startup() -> None:
+        ws_hub.set_loop(asyncio.get_running_loop())
+
+    def _extract_token(authorization: str | None, x_screenjob_token: str | None) -> str:
+        if x_screenjob_token:
+            return x_screenjob_token.strip()
+        if authorization:
+            token = authorization.strip()
+            if token.lower().startswith("bearer "):
+                return token[7:].strip()
+            return token
+        return ""
+
+    def require_token(
+        authorization: str | None = Header(default=None),
+        x_screenjob_token: str | None = Header(default=None),
+    ) -> None:
+        token = _extract_token(authorization, x_screenjob_token)
+        if not token or not secrets.compare_digest(token, app_config.screenjob_token):
+            raise HTTPException(status_code=401, detail="Unauthorized")
+
+    @app.post("/api/jobs")
+    def create_job(payload: CreateJobRequest, _: None = Depends(require_token)) -> dict[str, str]:
+        job_id = manager.submit_job(
+            objective=payload.job,
+            model=payload.model,
+            max_steps=payload.max_steps,
+            command_timeout=payload.command_timeout,
+            type_interval=payload.type_interval,
+            click_pause=payload.click_pause,
+            disabled_tools=payload.disabled_tools,
+            safety_override=payload.safety_override,
+            no_failsafe=payload.no_failsafe,
+        )
+        return {"job_id": job_id}
+
+    @app.get("/api/jobs")
+    def list_jobs(limit: int = Query(default=100, ge=1, le=500), _: None = Depends(require_token)) -> dict[str, Any]:
+        return {"jobs": manager.list_jobs(limit=limit)}
+
+    @app.get("/api/jobs/{job_id}")
+    def get_job(job_id: str, _: None = Depends(require_token)) -> dict[str, Any]:
+        job = manager.get_job(job_id)
+        if job is None:
+            raise HTTPException(status_code=404, detail="Job not found")
+        return job
+
+    @app.get("/api/jobs/{job_id}/events")
+    def get_job_events(
+        job_id: str,
+        limit: int = Query(default=500, ge=1, le=5000),
+        _: None = Depends(require_token),
+    ) -> dict[str, Any]:
+        job = manager.get_job(job_id)
+        if job is None:
+            raise HTTPException(status_code=404, detail="Job not found")
+        return {"events": manager.get_events(job_id, limit=limit)}
+
+    @app.post("/api/jobs/{job_id}/cancel")
+    def cancel_job(job_id: str, _: None = Depends(require_token)) -> dict[str, Any]:
+        job = manager.get_job(job_id)
+        if job is None:
+            raise HTTPException(status_code=404, detail="Job not found")
+        accepted = manager.cancel_job(job_id)
+        return {"job_id": job_id, "cancel_requested": bool(accepted)}
+
+    @app.get("/api/stats")
+    def stats(_: None = Depends(require_token)) -> dict[str, Any]:
+        return manager.stats()
+
+    if not app_config.disable_ui:
+        @app.get("/", response_class=HTMLResponse)
+        def ui_root() -> str:
+            return monitoring_page_html()
+
+        @app.websocket("/ws")
+        async def ws_endpoint(websocket: WebSocket, token: str = Query(default="")) -> None:
+            if not token or not secrets.compare_digest(token, app_config.screenjob_token):
+                await websocket.close(code=1008)
+                return
+            await ws_hub.connect(websocket)
+            try:
+                await websocket.send_json({"event_type": "connected", "payload": {"ok": True}})
+                while True:
+                    await websocket.receive_text()
+            except WebSocketDisconnect:
+                await ws_hub.disconnect(websocket)
+            except Exception:
+                await ws_hub.disconnect(websocket)
+    else:
+        @app.get("/", response_class=JSONResponse)
+        def ui_disabled() -> dict[str, Any]:
+            return {"ok": True, "ui_disabled": True}
+
+    return app
+
+
+def main() -> None:
+    import uvicorn
+
+    app = create_app(load_app_config(Path.cwd()))
+    config = app.state.config
+    uvicorn.run(app, host=config.host, port=config.port, log_level="info")

src/storage.py

@@ -0,0 +1,216 @@
+from __future__ import annotations
+
+import json
+import sqlite3
+import threading
+from pathlib import Path
+from typing import Any
+
+
+class HistoryDB:
+    def __init__(self, db_path: Path) -> None:
+        self.db_path = db_path
+        self._lock = threading.Lock()
+        self._init_schema()
+
+    def _connect(self) -> sqlite3.Connection:
+        conn = sqlite3.connect(self.db_path, check_same_thread=False)
+        conn.row_factory = sqlite3.Row
+        return conn
+
+    def _init_schema(self) -> None:
+        with self._connect() as conn:
+            conn.execute(
+                """
+                CREATE TABLE IF NOT EXISTS jobs (
+                    job_id TEXT PRIMARY KEY,
+                    objective TEXT NOT NULL,
+                    model TEXT NOT NULL,
+                    status TEXT NOT NULL,
+                    created_at TEXT NOT NULL,
+                    started_at TEXT,
+                    ended_at TEXT,
+                    result TEXT,
+                    error TEXT,
+                    steps INTEGER DEFAULT 0,
+                    cancelled INTEGER DEFAULT 0,
+                    safety_checked INTEGER DEFAULT 0,
+                    safety_passed INTEGER DEFAULT 0,
+                    safety_reason TEXT,
+                    safety_override INTEGER DEFAULT 0,
+                    disabled_tools_json TEXT NOT NULL,
+                    artifacts_dir TEXT,
+                    input_tokens INTEGER DEFAULT 0,
+                    cached_input_tokens INTEGER DEFAULT 0,
+                    output_tokens INTEGER DEFAULT 0,
+                    reasoning_tokens INTEGER DEFAULT 0,
+                    total_tokens INTEGER DEFAULT 0,
+                    estimated_cost_usd REAL
+                )
+                """
+            )
+            conn.execute(
+                """
+                CREATE TABLE IF NOT EXISTS job_events (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    job_id TEXT NOT NULL,
+                    ts TEXT NOT NULL,
+                    step INTEGER DEFAULT 0,
+                    event_type TEXT NOT NULL,
+                    payload_json TEXT NOT NULL,
+                    FOREIGN KEY(job_id) REFERENCES jobs(job_id)
+                )
+                """
+            )
+            conn.execute(
+                "CREATE INDEX IF NOT EXISTS idx_job_events_job_id_id ON job_events(job_id, id)"
+            )
+            conn.commit()
+
+    def create_job(
+        self,
+        *,
+        job_id: str,
+        objective: str,
+        model: str,
+        created_at: str,
+        safety_override: bool,
+        disabled_tools: list[str],
+    ) -> None:
+        with self._lock, self._connect() as conn:
+            conn.execute(
+                """
+                INSERT INTO jobs (
+                    job_id, objective, model, status, created_at,
+                    safety_override, disabled_tools_json
+                ) VALUES (?, ?, ?, ?, ?, ?, ?)
+                """,
+                (
+                    job_id,
+                    objective,
+                    model,
+                    "queued",
+                    created_at,
+                    int(safety_override),
+                    json.dumps(disabled_tools, ensure_ascii=False),
+                ),
+            )
+            conn.commit()
+
+    def update_job(self, job_id: str, **fields: Any) -> None:
+        if not fields:
+            return
+        keys = sorted(fields.keys())
+        assignments = ", ".join([f"{k} = ?" for k in keys])
+        values = [fields[k] for k in keys]
+        with self._lock, self._connect() as conn:
+            conn.execute(f"UPDATE jobs SET {assignments} WHERE job_id = ?", (*values, job_id))
+            conn.commit()
+
+    def add_event(self, *, job_id: str, ts: str, step: int, event_type: str, payload: dict[str, Any]) -> None:
+        with self._lock, self._connect() as conn:
+            conn.execute(
+                """
+                INSERT INTO job_events (job_id, ts, step, event_type, payload_json)
+                VALUES (?, ?, ?, ?, ?)
+                """,
+                (job_id, ts, int(step), event_type, json.dumps(payload, ensure_ascii=False)),
+            )
+            conn.commit()
+
+    def get_job(self, job_id: str) -> dict[str, Any] | None:
+        with self._connect() as conn:
+            row = conn.execute("SELECT * FROM jobs WHERE job_id = ?", (job_id,)).fetchone()
+        return self._row_to_job(row) if row else None
+
+    def list_jobs(self, limit: int = 100) -> list[dict[str, Any]]:
+        bounded = min(max(int(limit), 1), 1000)
+        with self._connect() as conn:
+            rows = conn.execute(
+                "SELECT * FROM jobs ORDER BY created_at DESC LIMIT ?",
+                (bounded,),
+            ).fetchall()
+        return [self._row_to_job(row) for row in rows]
+
+    def get_job_events(self, job_id: str, limit: int = 500) -> list[dict[str, Any]]:
+        bounded = min(max(int(limit), 1), 5000)
+        with self._connect() as conn:
+            rows = conn.execute(
+                """
+                SELECT id, job_id, ts, step, event_type, payload_json
+                FROM job_events
+                WHERE job_id = ?
+                ORDER BY id ASC
+                LIMIT ?
+                """,
+                (job_id, bounded),
+            ).fetchall()
+        events: list[dict[str, Any]] = []
+        for row in rows:
+            payload = {}
+            try:
+                payload = json.loads(row["payload_json"]) if row["payload_json"] else {}
+            except Exception:
+                payload = {"_raw": row["payload_json"]}
+            events.append(
+                {
+                    "id": row["id"],
+                    "job_id": row["job_id"],
+                    "ts": row["ts"],
+                    "step": row["step"],
+                    "event_type": row["event_type"],
+                    "payload": payload,
+                }
+            )
+        return events
+
+    def stats(self) -> dict[str, Any]:
+        with self._connect() as conn:
+            totals = conn.execute(
+                """
+                SELECT
+                    COUNT(*) AS total_jobs,
+                    SUM(CASE WHEN status = 'running' THEN 1 ELSE 0 END) AS running_jobs,
+                    SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END) AS completed_jobs,
+                    SUM(CASE WHEN status = 'failed' THEN 1 ELSE 0 END) AS failed_jobs,
+                    SUM(CASE WHEN cancelled = 1 THEN 1 ELSE 0 END) AS cancelled_jobs,
+                    COALESCE(SUM(estimated_cost_usd), 0) AS total_estimated_cost
+                FROM jobs
+                """
+            ).fetchone()
+        return dict(totals) if totals else {}
+
+    def _row_to_job(self, row: sqlite3.Row) -> dict[str, Any]:
+        disabled_tools: list[str] = []
+        try:
+            disabled_tools = json.loads(row["disabled_tools_json"]) if row["disabled_tools_json"] else []
+        except Exception:
+            disabled_tools = []
+        return {
+            "job_id": row["job_id"],
+            "objective": row["objective"],
+            "model": row["model"],
+            "status": row["status"],
+            "created_at": row["created_at"],
+            "started_at": row["started_at"],
+            "ended_at": row["ended_at"],
+            "result": row["result"],
+            "error": row["error"],
+            "steps": row["steps"],
+            "cancelled": bool(row["cancelled"]),
+            "safety_checked": bool(row["safety_checked"]),
+            "safety_passed": bool(row["safety_passed"]),
+            "safety_reason": row["safety_reason"],
+            "safety_override": bool(row["safety_override"]),
+            "disabled_tools": disabled_tools,
+            "artifacts_dir": row["artifacts_dir"],
+            "usage": {
+                "input_tokens": row["input_tokens"] or 0,
+                "cached_input_tokens": row["cached_input_tokens"] or 0,
+                "output_tokens": row["output_tokens"] or 0,
+                "reasoning_tokens": row["reasoning_tokens"] or 0,
+                "total_tokens": row["total_tokens"] or 0,
+                "estimated_cost_usd": row["estimated_cost_usd"],
+            },
+        }
+

src/task_manager.py

@@ -0,0 +1,326 @@
+from __future__ import annotations
+
+import threading
+import time
+import uuid
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Callable
+
+from .config import AppConfig
+from .models import RuntimeOptions
+from .runtime import create_openai_client, run_job
+from .safety import assess_task_safety
+from .storage import HistoryDB
+from .utils import utc_now_iso
+
+
+@dataclass
+class _RunningJob:
+    thread: threading.Thread
+    cancel_event: threading.Event
+    started_at: str
+    objective: str
+    model: str
+
+
+class JobManager:
+    def __init__(
+        self,
+        *,
+        config: AppConfig,
+        db: HistoryDB,
+        broadcast: Callable[[dict[str, Any]], None] | None = None,
+    ) -> None:
+        self.config = config
+        self.db = db
+        self.broadcast = broadcast
+        self._running: dict[str, _RunningJob] = {}
+        self._lock = threading.Lock()
+
+    def submit_job(
+        self,
+        *,
+        objective: str,
+        model: str | None = None,
+        max_steps: int = 60,
+        command_timeout: int = 45,
+        type_interval: float = 0.02,
+        click_pause: float = 0.10,
+        disabled_tools: list[str] | None = None,
+        safety_override: bool = False,
+        no_failsafe: bool = False,
+    ) -> str:
+        job_id = f"job_{int(time.time())}_{uuid.uuid4().hex[:8]}"
+        created_at = utc_now_iso()
+        selected_model = (model or self.config.default_model).strip() or self.config.default_model
+        disabled = sorted({tool.strip() for tool in (disabled_tools or []) if tool.strip()})
+        self.db.create_job(
+            job_id=job_id,
+            objective=objective,
+            model=selected_model,
+            created_at=created_at,
+            safety_override=safety_override,
+            disabled_tools=disabled,
+        )
+        self._publish(
+            job_id,
+            {
+                "ts": created_at,
+                "step": 0,
+                "event_type": "job_queued",
+                "payload": {
+                    "job_id": job_id,
+                    "objective": objective,
+                    "model": selected_model,
+                    "disabled_tools": disabled,
+                    "safety_override": bool(safety_override),
+                },
+            },
+        )
+
+        cancel_event = threading.Event()
+        thread = threading.Thread(
+            target=self._execute_job,
+            kwargs={
+                "job_id": job_id,
+                "objective": objective,
+                "model": selected_model,
+                "disabled_tools": disabled,
+                "safety_override": safety_override,
+                "max_steps": max_steps,
+                "command_timeout": command_timeout,
+                "type_interval": type_interval,
+                "click_pause": click_pause,
+                "no_failsafe": no_failsafe,
+                "cancel_event": cancel_event,
+            },
+            daemon=True,
+        )
+        with self._lock:
+            self._running[job_id] = _RunningJob(
+                thread=thread,
+                cancel_event=cancel_event,
+                started_at=created_at,
+                objective=objective,
+                model=selected_model,
+            )
+        thread.start()
+        return job_id
+
+    def _execute_job(
+        self,
+        *,
+        job_id: str,
+        objective: str,
+        model: str,
+        disabled_tools: list[str],
+        safety_override: bool,
+        max_steps: int,
+        command_timeout: int,
+        type_interval: float,
+        click_pause: float,
+        no_failsafe: bool,
+        cancel_event: threading.Event,
+    ) -> None:
+        started_at = utc_now_iso()
+        self.db.update_job(job_id, status="running", started_at=started_at)
+        self._publish(job_id, {"ts": started_at, "step": 0, "event_type": "job_started", "payload": {"job_id": job_id}})
+
+        if not safety_override:
+            client = create_openai_client(self.config.openai_api_key)
+            safe, reason, raw = assess_task_safety(
+                client,
+                model=self.config.safety_model,
+                objective=objective,
+                disabled_tools=disabled_tools,
+            )
+            self.db.update_job(
+                job_id,
+                safety_checked=1,
+                safety_passed=1 if safe else 0,
+                safety_reason=reason,
+            )
+            self._publish(
+                job_id,
+                {
+                    "ts": utc_now_iso(),
+                    "step": 0,
+                    "event_type": "safety_check",
+                    "payload": {"safe": safe, "reason": reason, "raw": raw},
+                },
+            )
+            if not safe:
+                ended_at = utc_now_iso()
+                error_text = f"Task blocked by safety gate: {reason}"
+                self.db.update_job(
+                    job_id,
+                    status="failed",
+                    ended_at=ended_at,
+                    error=error_text,
+                    result=error_text,
+                )
+                self._publish(
+                    job_id,
+                    {
+                        "ts": ended_at,
+                        "step": 0,
+                        "event_type": "job_rejected",
+                        "payload": {"error": error_text},
+                    },
+                )
+                with self._lock:
+                    self._running.pop(job_id, None)
+                return
+        else:
+            self.db.update_job(
+                job_id,
+                safety_checked=1,
+                safety_passed=1,
+                safety_reason="Safety check bypassed by override.",
+            )
+            self._publish(
+                job_id,
+                {
+                    "ts": utc_now_iso(),
+                    "step": 0,
+                    "event_type": "safety_override",
+                    "payload": {"enabled": True},
+                },
+            )
+
+        def on_event(event: dict[str, Any]) -> None:
+            self._publish(job_id, event)
+            if event.get("event_type") == "usage_update":
+                usage = (event.get("payload") or {}).get("usage") or {}
+                self.db.update_job(
+                    job_id,
+                    input_tokens=int(usage.get("input_tokens", 0) or 0),
+                    cached_input_tokens=int(usage.get("cached_input_tokens", 0) or 0),
+                    output_tokens=int(usage.get("output_tokens", 0) or 0),
+                    reasoning_tokens=int(usage.get("reasoning_tokens", 0) or 0),
+                    total_tokens=int(usage.get("total_tokens", 0) or 0),
+                    estimated_cost_usd=usage.get("estimated_cost_usd"),
+                )
+
+        options = RuntimeOptions(
+            model=model,
+            max_steps=max_steps,
+            command_timeout=command_timeout,
+            type_interval=type_interval,
+            click_pause=click_pause,
+            disable_tools=set(disabled_tools),
+        )
+        try:
+            result, artifacts = run_job(
+                api_key=self.config.openai_api_key,
+                objective=objective,
+                options=options,
+                runs_base=self.config.runs_dir,
+                no_failsafe=no_failsafe,
+                cancel_event=cancel_event,
+                event_callback=on_event,
+            )
+        except Exception as exc:  # noqa: BLE001
+            ended_at = utc_now_iso()
+            err = f"Fatal runtime error: {type(exc).__name__}: {exc}"
+            self.db.update_job(
+                job_id,
+                status="failed",
+                ended_at=ended_at,
+                error=err,
+                result=err,
+            )
+            self._publish(job_id, {"ts": ended_at, "step": 0, "event_type": "job_failed", "payload": {"error": err}})
+            with self._lock:
+                self._running.pop(job_id, None)
+            return
+
+        ended_at = utc_now_iso()
+        status = "completed" if result.completed else "failed"
+        if result.cancelled:
+            status = "cancelled"
+        self.db.update_job(
+            job_id,
+            status=status,
+            ended_at=ended_at,
+            result=result.result,
+            error=result.error,
+            steps=result.steps,
+            cancelled=1 if result.cancelled else 0,
+            artifacts_dir=str(Path(artifacts.root_dir).resolve()),
+            input_tokens=result.usage.input_tokens,
+            cached_input_tokens=result.usage.cached_input_tokens,
+            output_tokens=result.usage.output_tokens,
+            reasoning_tokens=result.usage.reasoning_tokens,
+            total_tokens=result.usage.total_tokens,
+            estimated_cost_usd=result.usage.estimated_cost_usd,
+        )
+        self._publish(
+            job_id,
+            {
+                "ts": ended_at,
+                "step": result.steps,
+                "event_type": "job_finished",
+                "payload": {
+                    "status": status,
+                    "result": result.result,
+                    "error": result.error,
+                    "cancelled": result.cancelled,
+                    "usage": result.usage.to_dict(),
+                },
+            },
+        )
+        with self._lock:
+            self._running.pop(job_id, None)
+
+    def _publish(self, job_id: str, event: dict[str, Any]) -> None:
+        ts = str(event.get("ts") or utc_now_iso())
+        step = int(event.get("step", 0) or 0)
+        event_type = str(event.get("event_type", "event"))
+        payload = event.get("payload") or {}
+        self.db.add_event(job_id=job_id, ts=ts, step=step, event_type=event_type, payload=payload)
+        if self.broadcast is not None:
+            self.broadcast(
+                {
+                    "job_id": job_id,
+                    "ts": ts,
+                    "step": step,
+                    "event_type": event_type,
+                    "payload": payload,
+                }
+            )
+
+    def cancel_job(self, job_id: str) -> bool:
+        with self._lock:
+            running = self._running.get(job_id)
+        if running is None:
+            job = self.db.get_job(job_id)
+            return bool(job and job.get("status") == "cancelled")
+        running.cancel_event.set()
+        self.db.update_job(job_id, status="cancelling")
+        self._publish(job_id, {"ts": utc_now_iso(), "step": 0, "event_type": "cancel_requested", "payload": {}})
+        return True
+
+    def get_job(self, job_id: str) -> dict[str, Any] | None:
+        job = self.db.get_job(job_id)
+        if job is None:
+            return None
+        live = self._running.get(job_id)
+        if live and job["status"] in {"queued", "running", "cancelling"}:
+            job["is_running_thread"] = live.thread.is_alive()
+        else:
+            job["is_running_thread"] = False
+        return job
+
+    def list_jobs(self, limit: int = 100) -> list[dict[str, Any]]:
+        return self.db.list_jobs(limit=limit)
+
+    def get_events(self, job_id: str, limit: int = 500) -> list[dict[str, Any]]:
+        return self.db.get_job_events(job_id, limit=limit)
+
+    def stats(self) -> dict[str, Any]:
+        stats = self.db.stats()
+        with self._lock:
+            stats["live_running_threads"] = sum(1 for job in self._running.values() if job.thread.is_alive())
+        return stats

src/ui.py

@@ -0,0 +1,193 @@
+from __future__ import annotations
+
+
+def monitoring_page_html() -> str:
+    return """<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <title>ScreenJob Monitor</title>
+  <script src="https://cdn.tailwindcss.com"></script>
+</head>
+<body class="bg-slate-950 text-slate-100 min-h-screen">
+  <div class="max-w-7xl mx-auto p-4 md:p-8 space-y-6">
+    <header class="flex flex-col gap-3 md:flex-row md:items-center md:justify-between">
+      <div>
+        <h1 class="text-2xl md:text-3xl font-bold tracking-tight">ScreenJob Monitor</h1>
+        <p class="text-slate-400 text-sm">Read-only monitoring for active and historical tasks.</p>
+      </div>
+      <div class="flex flex-col md:flex-row gap-2 md:items-center">
+        <input id="tokenInput" type="password" placeholder="SCREENJOB_TOKEN" class="bg-slate-900 border border-slate-700 rounded px-3 py-2 text-sm w-72" />
+        <button id="saveTokenBtn" class="bg-cyan-500 hover:bg-cyan-400 text-slate-950 font-semibold px-4 py-2 rounded">Connect</button>
+      </div>
+    </header>
+
+    <section class="grid grid-cols-2 md:grid-cols-6 gap-3" id="stats"></section>
+
+    <section class="grid grid-cols-1 lg:grid-cols-5 gap-4">
+      <div class="lg:col-span-2 bg-slate-900/70 border border-slate-800 rounded-xl p-4">
+        <div class="flex items-center justify-between mb-3">
+          <h2 class="font-semibold">Jobs</h2>
+          <button id="refreshBtn" class="text-xs bg-slate-800 px-2 py-1 rounded">Refresh</button>
+        </div>
+        <div id="jobList" class="space-y-2 max-h-[62vh] overflow-auto"></div>
+      </div>
+
+      <div class="lg:col-span-3 bg-slate-900/70 border border-slate-800 rounded-xl p-4 space-y-3">
+        <h2 class="font-semibold">Job Detail</h2>
+        <pre id="jobDetail" class="bg-slate-950 border border-slate-800 rounded p-3 text-xs overflow-auto max-h-[24vh]"></pre>
+        <h3 class="font-semibold text-sm">Live Events</h3>
+        <div id="events" class="bg-slate-950 border border-slate-800 rounded p-3 text-xs overflow-auto max-h-[36vh] space-y-1"></div>
+      </div>
+    </section>
+  </div>
+
+  <script>
+    const tokenInput = document.getElementById("tokenInput");
+    const saveTokenBtn = document.getElementById("saveTokenBtn");
+    const refreshBtn = document.getElementById("refreshBtn");
+    const jobListEl = document.getElementById("jobList");
+    const jobDetailEl = document.getElementById("jobDetail");
+    const eventsEl = document.getElementById("events");
+    const statsEl = document.getElementById("stats");
+
+    const state = {
+      token: localStorage.getItem("screenjob_token") || "",
+      jobs: [],
+      selectedJobId: null,
+      ws: null
+    };
+    tokenInput.value = state.token;
+
+    function authHeaders() {
+      return { "Authorization": "Bearer " + state.token };
+    }
+
+    async function api(path, opts = {}) {
+      if (!state.token) throw new Error("Token required");
+      const headers = Object.assign({}, authHeaders(), opts.headers || {});
+      const response = await fetch(path, Object.assign({}, opts, { headers }));
+      if (!response.ok) throw new Error(await response.text());
+      return response.json();
+    }
+
+    function renderStats(stats) {
+      const cards = [
+        ["Total Jobs", stats.total_jobs || 0],
+        ["Running", stats.running_jobs || 0],
+        ["Completed", stats.completed_jobs || 0],
+        ["Failed", stats.failed_jobs || 0],
+        ["Cancelled", stats.cancelled_jobs || 0],
+        ["Total Cost (USD)", Number(stats.total_estimated_cost || 0).toFixed(4)]
+      ];
+      statsEl.innerHTML = cards.map(([name, val]) => `
+        <div class="bg-slate-900/70 border border-slate-800 rounded-xl p-3">
+          <div class="text-slate-400 text-xs">${name}</div>
+          <div class="text-lg font-semibold">${val}</div>
+        </div>
+      `).join("");
+    }
+
+    function renderJobs() {
+      jobListEl.innerHTML = state.jobs.map((job) => {
+        const active = job.job_id === state.selectedJobId;
+        return `
+          <button data-job-id="${job.job_id}" class="w-full text-left p-3 rounded border ${active ? "border-cyan-400 bg-slate-800" : "border-slate-800 bg-slate-950"} hover:bg-slate-800">
+            <div class="flex items-center justify-between">
+              <span class="font-medium">${job.job_id}</span>
+              <span class="text-xs px-2 py-0.5 rounded bg-slate-700">${job.status}</span>
+            </div>
+            <div class="text-xs text-slate-400 mt-1">${job.model}</div>
+            <div class="text-xs text-slate-300 mt-1 line-clamp-2">${job.objective}</div>
+            <div class="text-xs text-slate-500 mt-1">$${Number((job.usage && job.usage.estimated_cost_usd) || 0).toFixed(6)}</div>
+          </button>
+        `;
+      }).join("");
+      for (const btn of jobListEl.querySelectorAll("button[data-job-id]")) {
+        btn.addEventListener("click", () => {
+          state.selectedJobId = btn.getAttribute("data-job-id");
+          renderJobs();
+          refreshJobDetail();
+        });
+      }
+    }
+
+    function pushEventLine(obj) {
+      const line = document.createElement("div");
+      line.className = "border-b border-slate-800 pb-1";
+      line.textContent = `[${obj.ts}] ${obj.job_id} step=${obj.step} ${obj.event_type} ${JSON.stringify(obj.payload || {})}`;
+      eventsEl.prepend(line);
+      while (eventsEl.childNodes.length > 400) {
+        eventsEl.removeChild(eventsEl.lastChild);
+      }
+    }
+
+    async function refreshJobs() {
+      const payload = await api("/api/jobs?limit=100");
+      state.jobs = payload.jobs || [];
+      if (!state.selectedJobId && state.jobs.length > 0) state.selectedJobId = state.jobs[0].job_id;
+      renderJobs();
+    }
+
+    async function refreshStats() {
+      const payload = await api("/api/stats");
+      renderStats(payload);
+    }
+
+    async function refreshJobDetail() {
+      if (!state.selectedJobId) return;
+      const [job, events] = await Promise.all([
+        api(`/api/jobs/${state.selectedJobId}`),
+        api(`/api/jobs/${state.selectedJobId}/events?limit=120`)
+      ]);
+      jobDetailEl.textContent = JSON.stringify(job, null, 2);
+      eventsEl.innerHTML = "";
+      for (const ev of (events.events || []).slice().reverse()) pushEventLine(ev);
+    }
+
+    function connectWs() {
+      if (state.ws) {
+        try { state.ws.close(); } catch (_) {}
+      }
+      if (!state.token) return;
+      const scheme = location.protocol === "https:" ? "wss" : "ws";
+      const ws = new WebSocket(`${scheme}://${location.host}/ws?token=${encodeURIComponent(state.token)}`);
+      state.ws = ws;
+      ws.onmessage = async (event) => {
+        try {
+          const payload = JSON.parse(event.data);
+          pushEventLine(payload);
+          if (!state.selectedJobId || payload.job_id === state.selectedJobId) {
+            await refreshJobDetail();
+          }
+          await refreshJobs();
+          await refreshStats();
+        } catch (err) {
+          console.error(err);
+        }
+      };
+      ws.onclose = () => setTimeout(connectWs, 1200);
+    }
+
+    async function fullRefresh() {
+      await refreshJobs();
+      await refreshStats();
+      await refreshJobDetail();
+    }
+
+    async function connect() {
+      state.token = tokenInput.value.trim();
+      localStorage.setItem("screenjob_token", state.token);
+      await fullRefresh();
+      connectWs();
+    }
+
+    saveTokenBtn.addEventListener("click", () => connect().catch((err) => alert(err.message)));
+    refreshBtn.addEventListener("click", () => fullRefresh().catch((err) => alert(err.message)));
+    if (state.token) connect().catch(() => {});
+  </script>
+</body>
+</html>
+"""
+