alvis
b3cf588f2f
Adds ml/agents/ — five specialised sub-agents (overdue_task, momentum, time_of_day, recent_patterns, focus_area) each producing a prompt snippet from user signals. A registry wires them up; the orchestrator prompt in ml/serving/prompts.py synthesises their outputs into one tip via LiteLLM. Also wires /api/agents route in the API and updates the Dockerfile to copy the full ml/ tree with PYTHONPATH=/app so agent imports resolve correctly. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
from __future__ import annotations
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from typing import ClassVar
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from .base import BaseAgent, AgentInput, AgentOutput
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class OverdueTaskAgent(BaseAgent):
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"""Reports the user's overdue tasks by count and age."""
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agent_id: ClassVar[str] = "overdue-task"
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ttl_seconds: ClassVar[int] = 3600 # 1h — overdue status changes infrequently
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version: ClassVar[str] = "1.0.0"
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def compute(self, inp: AgentInput) -> AgentOutput:
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overdue = [t for t in inp.tasks if t.get("is_overdue")]
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top = sorted(overdue, key=lambda t: -t.get("task_age_days", 0))[:3]
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if not overdue:
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prompt = "The user has no overdue tasks at this time."
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elif len(overdue) == 1:
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t = top[0]
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age = round(t.get("task_age_days", 0))
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prompt = (
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f'The user has 1 overdue task: "{t["content"]}" '
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f"({age} day{'s' if age != 1 else ''} overdue)."
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)
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else:
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items = ", ".join(
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f'"{t["content"]}" ({round(t.get("task_age_days", 0))}d)'
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for t in top
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)
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prompt = (
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f"The user has {len(overdue)} overdue tasks. "
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f"Top {len(top)}: {items}."
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)
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snapshot = {
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"overdue_count": len(overdue),
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"top_overdue": [
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{"content": t["content"], "task_age_days": t.get("task_age_days", 0)}
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for t in top
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],
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}
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return self._make_output(inp, prompt, snapshot)
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