083cbb1fa7
Scaffold all modules, route stubs, data models, and config. No logic implemented yet — all core methods raise NotImplementedError. Establishes the full directory layout matching the architecture in CLAUDE.md. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
"""
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Orchestrator — stateless LLM call that selects the next speaker or ends the session.
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Called fresh each turn when turn_order is ORCHESTRATED.
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Output is a tool call only; any text is discarded.
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"""
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import logging
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from dataclasses import dataclass
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from typing import Literal, Optional, TYPE_CHECKING
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if TYPE_CHECKING:
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from fellowship.core.session import Session
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logger = logging.getLogger(__name__)
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@dataclass
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class OrchestratorDecision:
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action: Literal["select_speaker", "hold", "end_session"]
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bot_name: Optional[str] = None # set when action == "select_speaker"
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reason: Optional[str] = None # set when action == "end_session"
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# Tool definitions sent to the LLM with every orchestrator call
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ORCHESTRATOR_TOOLS = [
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{
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"type": "function",
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"function": {
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"name": "select_speaker",
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"description": "Choose which bot should speak next.",
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"parameters": {
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"type": "object",
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"properties": {
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"bot_name": {"type": "string", "description": "Name of the bot to speak next"},
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},
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"required": ["bot_name"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "hold",
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"description": (
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"Do not prompt any bot this turn. "
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"Use when the conversation implies bots should stay silent."
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),
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"parameters": {"type": "object", "properties": {}},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "end_session",
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"description": "End the session. Only use when the session goal has been reached.",
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"parameters": {
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"type": "object",
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"properties": {
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"reason": {"type": "string", "description": "Why the session is ending"},
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},
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"required": ["reason"],
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},
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},
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},
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]
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class Orchestrator:
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def __init__(self, session: "Session") -> None:
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self.session = session
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async def decide(self) -> OrchestratorDecision:
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"""
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Build the orchestrator prompt, call the LLM, parse the tool call response.
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Any text output from the LLM is ignored — only the tool call matters.
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"""
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raise NotImplementedError
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def _build_system_prompt(self) -> str:
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"""
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Build the orchestrator system prompt including:
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- Its role and instructions
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- Overview of how Fellowship works
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- Full bot roster (names, roles, system prompts)
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- Session goal (if set)
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- Instruction to always respond with a tool call
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"""
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raise NotImplementedError
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def _parse_tool_call(self, response: dict) -> OrchestratorDecision:
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"""Parse the LLM tool call response into an OrchestratorDecision."""
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raise NotImplementedError
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