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rsync of obra/drill@013fcb8b7d into superpowers/evals/, excluding .git/, .venv/, results/, .env/, __pycache__/, *.egg-info/, .private-journal/. The drill repo is unaffected by this commit; archival is a separate manual step after this PR merges. Source SHA recorded at evals/.drill-source-sha for divergence detection.
82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
"""Actor LLM: simulates a user driving an agent session."""
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from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any
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import anthropic
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from jinja2 import Template
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ACTOR_TOOL: dict[str, Any] = {
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"name": "terminal_action",
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"description": "Send an action to the terminal session.",
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"input_schema": {
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"type": "object",
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"properties": {
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"action": {
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"type": "string",
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"enum": ["type", "done", "stuck", "key"],
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"description": "The action to take.",
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},
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"text": {
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"type": "string",
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"description": "Text to type (only for 'type' action).",
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},
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"key": {
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"type": "string",
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"description": "Special key to send (only for 'key' action, e.g., 'ctrl-c').",
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},
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},
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"required": ["action"],
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},
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}
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@dataclass
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class ActorAction:
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action: str
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text: str | None = None
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key: str | None = None
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@classmethod
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def from_tool_result(cls, data: dict[str, Any]) -> ActorAction:
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return cls(action=data["action"], text=data.get("text"), key=data.get("key"))
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class Actor:
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def __init__(self, model: str = "claude-sonnet-4-6", temperature: float = 0.7) -> None:
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self.model = model
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self.temperature = temperature
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self.captures: list[str] = []
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self._system_prompt: str = ""
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self._client: anthropic.Anthropic = anthropic.Anthropic()
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def build_system_prompt(self, posture: str, intents: list[str]) -> str:
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template_path = Path(__file__).parent.parent / "prompts" / "actor.md"
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template = Template(template_path.read_text())
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self._system_prompt = template.render(posture=posture, intents=intents)
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return self._system_prompt
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def append_capture(self, terminal_output: str) -> None:
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self.captures.append(terminal_output)
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def build_messages(self) -> list[dict[str, str]]:
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return [{"role": "user", "content": capture} for capture in self.captures]
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def decide(self) -> ActorAction:
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response = self._client.messages.create(
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model=self.model,
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max_tokens=1024,
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temperature=self.temperature,
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system=self._system_prompt,
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tools=[ACTOR_TOOL], # ty: ignore[invalid-argument-type]
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tool_choice={"type": "tool", "name": "terminal_action"},
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messages=self.build_messages(), # ty: ignore[invalid-argument-type]
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)
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for block in response.content:
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if block.type == "tool_use":
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return ActorAction.from_tool_result(block.input)
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raise RuntimeError("Actor did not return a tool_use block")
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