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>
29 lines
742 B
Python
29 lines
742 B
Python
from __future__ import annotations
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from .base import BaseAgent
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from .overdue_task import OverdueTaskAgent
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from .momentum import MomentumAgent
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from .time_of_day import TimeOfDayAgent
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from .recent_patterns import RecentPatternsAgent
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from .focus_area import FocusAreaAgent
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_AGENTS: dict[str, BaseAgent] = {
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a.agent_id: a
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for a in [
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OverdueTaskAgent(),
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MomentumAgent(),
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TimeOfDayAgent(),
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RecentPatternsAgent(),
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FocusAreaAgent(),
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]
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}
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def get_agent(agent_id: str) -> BaseAgent:
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if agent_id not in _AGENTS:
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raise KeyError(f"Unknown agent: {agent_id!r}. Known: {sorted(_AGENTS)}")
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return _AGENTS[agent_id]
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def all_agents() -> list[BaseAgent]:
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return list(_AGENTS.values())
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