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Data models for the Stack Doctor Environment.
An overseer LLM diagnoses sick inference stacks by probing subsystems,
reconciling conflicting specialist-agent reports, and selecting the
minimal correct fix.
"""
from pydantic import Field
from openenv.core.env_server.types import Action, Observation
class StackDoctorAction(Action):
"""Agent action — a JSON message selecting one of 4 action types."""
message: str = Field(
...,
description=(
'JSON action. One of:\n'
' {"type":"inspect","target":"logs|config|snippet|metrics"}\n'
' {"type":"ask_specialist","specialist":"runtime|dispatch|kernel|loader"}\n'
' {"type":"apply_fix","fix":"relax_arch_check|add_whitelist_entry|fix_runtime_path|switch_backend|update_model_config|fix_weight_mapping"}\n'
' {"type":"submit","root_cause":"...","fix":"...","justification":"..."}'
),
)
class StackDoctorObservation(Observation):
"""What the agent sees after each action."""
output: str = Field(default="", description="Natural-language feedback")
incident_ticket: str = Field(default="", description="The incident description")
hardware: str = Field(default="", description="Hardware identifier")
model_name: str = Field(default="", description="Model being served")
backend: str = Field(default="", description="Inference backend in use")
log_excerpt: str = Field(default="", description="Log snippet")
code_snippet: str = Field(default="", description="Config or code snippet")
specialist_opinions: dict = Field(default_factory=dict, description="Specialist name -> {opinion, confidence}")
steps_remaining: int = Field(default=6, description="Steps left in episode")
fix_used: bool = Field(default=False, description="Whether apply_fix has been used")
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