cillin
59b44545d0
- Add agent core modules (agent_core, factory, registry, skill_loader) - Add runtime system for agent execution management - Add REST API for agents, workspaces, and runtime control - Add process supervisor for agent lifecycle management - Add workspace template system with agent profiles - Add frontend RuntimeView and runtime API integration - Add per-agent skill workspaces for smoke_fullstack run - Refactor skill system with active/installed separation Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
# -*- coding: utf-8 -*-
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"""Data models for lightweight process supervision."""
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from dataclasses import dataclass, field
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from datetime import datetime
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from enum import Enum
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from typing import Any, Dict
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class ProcessRunState(str, Enum):
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"""Execution state for supervised runs."""
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PENDING = "pending"
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RUNNING = "running"
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COMPLETED = "completed"
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FAILED = "failed"
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CANCELLED = "cancelled"
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@dataclass
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class ProcessRun:
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"""Represents a supervised process run."""
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run_id: str
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command: str
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scope_key: str
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state: ProcessRunState = ProcessRunState.PENDING
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metadata: Dict[str, Any] = field(default_factory=dict)
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created_at: datetime = field(default_factory=datetime.utcnow)
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updated_at: datetime = field(default_factory=datetime.utcnow)
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def to_dict(self) -> Dict[str, Any]:
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return {
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"run_id": self.run_id,
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"command": self.command,
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"scope_key": self.scope_key,
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"state": self.state.value,
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"metadata": self.metadata,
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"created_at": self.created_at.isoformat(),
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"updated_at": self.updated_at.isoformat(),
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}
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