q792602257andClaude Opus 4.6
ec261d57c2
Host Agent now persists step-level execution detail locally (via a real TaskMetadataStore/Timeline wired into TaskRunner) and reports a bounded in-progress snapshot piggybacked on lease renewal. Cloud persists that snapshot per active assignment and exposes it through the existing task list/detail query path; Cloud Console renders it as a live badge. Host Agent's local console gains authenticated, read-only task list and detail/timeline pages (same-origin, server-rendered) with inlined screenshots. Also fixes a pre-existing gap in the shared Timeline: the actual per-step LLM prompt is now recorded instead of the task goal, benefiting both Runtime and Host Agent consoles. When a host uses the cloud planner transport, each decide call's prompt and resulting tool decision are durably logged in a new planner_decision_log table (with bounded retention) and browsable from Cloud Console; direct-transport hosts explicitly surface a "not reported" state. Includes Alembic migrations 0008 (progress columns on scheduled_tasks) and 0009 (planner_decision_log), bounded Host-Agent-local retention, dual-backend repository parity, and Vitest + pytest coverage. Task 6.5 (manual end-to-end device verification) remains. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass
|
|
from datetime import datetime
|
|
from typing import Any
|
|
|
|
from storage.artifact_store import ArtifactStore
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class TimelineRecord:
|
|
index: int
|
|
scene: dict[str, Any]
|
|
# The prompt actually sent to the LLM for this step. For non-LLM planners
|
|
# (or older records persisted before D9), this falls back to the task goal.
|
|
prompt: str
|
|
tool_call: dict[str, Any]
|
|
result: dict[str, Any]
|
|
timestamp: str
|
|
screenshot_path: str | None = None
|
|
|
|
|
|
class Timeline:
|
|
def __init__(self, artifact_store: ArtifactStore | None = None) -> None:
|
|
self.artifact_store = artifact_store or ArtifactStore()
|
|
|
|
def append(
|
|
self,
|
|
*,
|
|
task_id: str,
|
|
scene: Any,
|
|
prompt: str,
|
|
tool_call: dict[str, Any],
|
|
result: dict[str, Any],
|
|
screenshot: bytes | None = None,
|
|
) -> TimelineRecord:
|
|
index = len(self.read(task_id)) + 1
|
|
record = {
|
|
"index": index,
|
|
"scene": scene,
|
|
"prompt": prompt,
|
|
"tool_call": tool_call,
|
|
"result": result,
|
|
"timestamp": datetime.now().astimezone().isoformat(),
|
|
}
|
|
paths = self.artifact_store.write_step(
|
|
task_id=task_id,
|
|
index=index,
|
|
screenshot=screenshot,
|
|
record=record,
|
|
)
|
|
return TimelineRecord(
|
|
index=index,
|
|
scene=record["scene"],
|
|
prompt=prompt,
|
|
tool_call=tool_call,
|
|
result=result,
|
|
timestamp=record["timestamp"],
|
|
screenshot_path=paths["screenshot_path"],
|
|
)
|
|
|
|
def read(self, task_id: str) -> list[dict[str, Any]]:
|
|
return self.artifact_store.read_steps(task_id)
|
|
|
|
def delete_task(self, task_id: str) -> None:
|
|
"""Delete all timeline records and screenshots for a task."""
|
|
self.artifact_store.delete_task(task_id)
|