Initial commit: Opus Orchestrator AI - Full-flow book generation
- LangGraph workflow orchestration - CrewAI agent crews (Fiction Fortress & Nonfiction Fortress) - PydanticAI schema validation - Fiction agents: Architect, Worldsmith, Character Lead, Voice, Editor - Nonfiction agents: Researcher, Analyst, Writer, Fact-Checker, Editor - Complete schema definitions for books, chapters, critiques - Configuration management - Basic test suite
This commit is contained in:
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"""Base agent class for Opus Orchestrator."""
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from abc import ABC, abstractmethod
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from typing import Any, Generic, TypeVar
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from pydantic import BaseModel
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from opus_orchestrator.config import AgentConfig, get_config
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T = TypeVar("T", bound=BaseModel)
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class AgentResponse(BaseModel):
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"""Standard response from an agent."""
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success: bool
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output: Any
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error: Optional[str] = None
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metadata: dict[str, Any] = {}
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class Config:
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arbitrary_types_allowed = True
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from typing import Optional
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class BaseAgent(ABC, Generic[T]):
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"""Base class for all Opus agents.
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Each agent has:
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- A specific role (from Fortress documentation)
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- System prompts derived from Fortress methodologies
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- Input/output schemas
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- Execution logic
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"""
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def __init__(
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self,
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role: str,
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description: str,
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system_prompt: str,
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output_schema: type[T] | None = None,
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config: Optional[AgentConfig] = None,
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):
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self.role = role
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self.description = description
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self.system_prompt = system_prompt
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self.output_schema = output_schema
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self.config = config or get_config().agent
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@abstractmethod
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async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
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"""Execute the agent's task.
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Args:
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input_data: The input data for this agent
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context: Additional context from the orchestrator
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Returns:
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AgentResponse with output and metadata
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"""
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pass
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def build_system_prompt(self, context: dict[str, Any]) -> str:
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"""Build the full system prompt with context.
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Args:
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context: Additional context to inject
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Returns:
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Complete system prompt
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"""
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base = self.system_prompt
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if context:
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context_str = "\n\n## Context\n"
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for key, value in context.items():
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context_str += f"- **{key}**: {value}\n"
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return base + context_str
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return base
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def build_user_prompt(self, task: str, input_data: Any) -> str:
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"""Build the user prompt for a specific task.
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Args:
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task: Description of the task
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input_data: Input data formatted for the task
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Returns:
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Complete user prompt
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"""
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return f"""## Task
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{task}
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## Input
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{input_data}
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## Instructions
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Please complete this task following the methodology specified in your system prompt.
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"""
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"""Fiction agents for Opus Orchestrator.
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Based on Fiction Fortress Level 1-3 methodology.
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"""
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from opus_orchestrator.agents.fiction.architect import ArchitectAgent
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from opus_orchestrator.agents.fiction.character_lead import CharacterLeadAgent
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from opus_orchestrator.agents.fiction.editor import EditorAgent
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from opus_orchestrator.agents.fiction.voice import VoiceAgent
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from opus_orchestrator.agents.fiction.worldsmith import WorldsmithAgent
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__all__ = [
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"ArchitectAgent",
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"CharacterLeadAgent",
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"EditorAgent",
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"VoiceAgent",
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"WorldsmithAgent",
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]
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"""The Architect agent - Story structure and plot design.
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From Fiction Fortress Level 1-3:
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- Role: Story structure and plot design
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- Responsibilities: Outlines, pacing, scene planning
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- Output: Story blueprint
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"""
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from typing import Any, Optional
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from opus_orchestrator.agents.base import AgentResponse, BaseAgent
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from opus_orchestrator.schemas import BookBlueprint, ChapterBlueprint
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ARCHITECT_SYSTEM_PROMPT = """## Role: The Architect
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You are The Architect — the story's structural engineer. Your expertise lies in narrative architecture across all genres, and you excel at translating high-level concepts into detailed scene breakdowns.
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## Core Responsibilities
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1. **Story Structure Mastery**
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- Three-act structure application across genres
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- Beat-by-beat scene planning
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- Subplot integration techniques
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- Pacing analysis and adjustment
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2. **Genre Expertise**
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- Mystery: Clue placement, red herring distribution, revelation timing
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- Romance: Beat sheet adaptation for relationship arcs
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- Thriller: Tension escalation curves, set-piece design
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- Fantasy/Sci-Fi: World-rule integration with plot beats
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3. **Outline Generation**
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- Story spine creation (primal narrative in 3-5 sentences)
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- Chapter-level beat mapping
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- Scene purpose identification (whose story, what changes, why this scene)
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- Backstory weaving into present-tense narrative
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4. **Conflict Architecture**
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- Internal vs. external conflict layering
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- Antagonist motivation design
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- Stakes escalation planning
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- Tension and release rhythm
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## Quality Standards
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- Each scene must advance plot, character, or theme (or multiple)
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- Stakes must escalate through Acts I-II
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- Midpoint must fundamentally shift protagonist's understanding
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- All subplots must resolve by climax
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- Every chapter needs a clear purpose and payoff
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## Output Format
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When generating a blueprint, include:
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1. Story spine (primal narrative in 3-5 sentences)
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2. Three-act breakdown with specific beats
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3. Chapter-level outline (numbered chapters with one-line descriptions)
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4. Scene list with purpose tags
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5. Key plot points with word count allocations
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6. Subplot integration notes
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"""
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class ArchitectAgent(BaseAgent):
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"""Agent responsible for story structure and plot design."""
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def __init__(self, config=None):
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super().__init__(
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role="Architect",
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description="Story structure and plot design",
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system_prompt=ARCHITECT_SYSTEM_PROMPT,
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output_schema=BookBlueprint,
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config=config,
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)
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async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
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"""Execute the Architect's task to generate a story blueprint.
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Args:
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input_data: Raw content + intent from the orchestrator
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context: Additional context (genre, themes, etc.)
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Returns:
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AgentResponse with BookBlueprint
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"""
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# This is a placeholder - actual implementation would call the LLM
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# For now, we'll structure the prompt
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raw_content = input_data.get("raw_content", "")
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intent = input_data.get("intent", {})
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genre = intent.get("genre", "general")
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target_word_count = intent.get("target_word_count", 80000)
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themes = intent.get("themes", [])
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user_prompt = f"""## Input Content
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{raw_content}
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## Requirements
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- Genre: {genre}
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- Target word count: {target_word_count}
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- Themes to incorporate: {', '.join(themes) if themes else 'None specified'}
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- Target audience: {intent.get('target_audience', 'General readers')}
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## Task
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Generate a complete story blueprint following the Architect's methodology.
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Include all sections specified in your system prompt.
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"""
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# In actual implementation, this would call the LLM
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# For now, return a structured response
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return AgentResponse(
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success=True,
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output={
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"status": "blueprint_generated",
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"message": "Blueprint generation would be executed here with LLM",
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},
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metadata={
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"role": "Architect",
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"input_word_count": len(raw_content.split()),
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"target_word_count": target_word_count,
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"genre": genre,
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},
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)
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async def expand_chapter(
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self,
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chapter: ChapterBlueprint,
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full_blueprint: BookBlueprint,
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context: dict[str, Any],
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) -> AgentResponse:
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"""Expand a single chapter beat into detailed scene specification.
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From Template B in Fiction Fortress Level 2:
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- Scene ID, Act/Chapter location
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- POV character, Scene goal, Scene conflict, Scene outcome
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- Opening beat, Conflict beat, Turn beat, Ending beat
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"""
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user_prompt = f"""## Chapter to Expand
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- Chapter Number: {chapter.chapter_number}
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- Title: {chapter.title}
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- Summary: {chapter.summary}
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- Word Count Target: {chapter.word_count_target}
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- POV Character: {chapter.pov_character or 'Narrator'}
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- Key Events: {', '.join(chapter.key_events)}
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## Full Blueprint Context
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- Book Title: {full_blueprint.title}
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- Genre: {full_blueprint.genre}
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- Overall Structure: {full_blueprint.structure}
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## Task
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Expand this chapter beat into a detailed scene specification following
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Template B from the Fiction Fortress methodology.
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"""
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return AgentResponse(
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success=True,
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output={
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"status": "chapter_expanded",
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"chapter_number": chapter.chapter_number,
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},
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metadata={"role": "Architect", "task": "chapter_expansion"},
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)
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"""The Character Lead agent - Character development.
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From Fiction Fortress Level 1-3:
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- Role: Character development
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- Responsibilities: Backstory, motivations, arcs
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- Output: Character profiles
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"""
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from typing import Any
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from opus_orchestrator.agents.base import AgentResponse, BaseAgent
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CHARACTER_LEAD_SYSTEM_PROMPT = """## Role: The Character Lead
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You are The Character Lead — the one who breathes life into the figures who inhabit the story. Your expertise lies in psychological depth, relationship dynamics, and character arc construction.
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## Core Responsibilities
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1. **Character Profile Development**
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- Want/Need/Fear triad construction
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- Backstory selection and dramatization
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- Psychological wound identification
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- Character voice (speech patterns, internal monologue)
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2. **Relationship Architecture**
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- Relationship web mapping
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- Dynamic vs. static relationship types
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- Relationship arc planning (improvement, deterioration, transformation)
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- Power dynamic visualization
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3. **Character Arc Design**
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- Starting state definition
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- Transformation catalyst identification
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- Change mechanism dramatization
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- Ending state resolution
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4. **Consistency Maintenance**
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- Character behavior logic tracking
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- Emotional memory verification
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- Knowledge-state tracking (what does this character know when?)
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- Capability consistency (what can this character actually do?)
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## Character Arc Types
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- **Positive**: Growth from weakness
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- **Negative**: Fall from grace
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- **Flat**: No change, changes world
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- **Disruption**: External力量打破平衡
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## The Want/Need/Fear Triad
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- **Want**: External goal they pursue
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- **Need**: Internal growth they must learn
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- **Fear**: What they run from
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- **Wound**: Formative experience
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- **Lie they believe**: False worldview
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## Quality Standards
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- Each character must have clear motivations for all major actions
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- Relationships must feel authentic and dynamic
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- Character voice must be distinct and consistent
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- Arc transformation must be earned through dramatization
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"""
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class CharacterLeadAgent(BaseAgent):
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"""Agent responsible for character development."""
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def __init__(self, config=None):
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super().__init__(
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role="Character Lead",
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description="Character development",
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system_prompt=CHARACTER_LEAD_SYSTEM_PROMPT,
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config=config,
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)
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async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
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"""Execute the Character Lead's task to generate character profiles.
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Args:
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input_data: Raw content + blueprint with character references
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context: Additional context
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Returns:
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AgentResponse with character profiles
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"""
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characters = input_data.get("characters", [])
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raw_content = input_data.get("raw_content", "")
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user_prompt = f"""## Task
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Create comprehensive character profiles for the following characters:
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{chr(10).join(f"- {c}" for c in characters) if characters else "Create profiles for all characters in the story."}
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## Raw Content Reference
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{raw_content}
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## Guidelines
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Follow the Character Lead methodology from your system prompt.
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Include the Want/Need/Fear triad for each major character.
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"""
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return AgentResponse(
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success=True,
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output={"status": "characters_created"},
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metadata={"role": "Character Lead", "character_count": len(characters)},
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)
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async def develop_relationship(
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self,
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character_a: str,
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character_b: str,
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relationship_type: str,
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context: dict[str, Any],
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) -> AgentResponse:
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"""Develop the relationship between two characters."""
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user_prompt = f"""## Relationship Details
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- Character A: {character_a}
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- Character B: {character_b}
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- Relationship Type: {relationship_type}
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## Task
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Develop this relationship following the Character Lead methodology.
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Include:
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- Current dynamics
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- Power balance
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- History (if any)
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- Potential arc
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"""
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return AgentResponse(
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success=True,
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output={"status": "relationship_developed"},
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metadata={"role": "Character Lead", "characters": [character_a, character_b]},
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)
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@@ -0,0 +1,191 @@
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"""The Editor agent - Quality control.
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From Fiction Fortress Level 1-3:
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- Role: Quality control
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- Responsibilities: Continuity, pacing, quality checks
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- Output: Editorial notes, revisions
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"""
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from typing import Any
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from opus_orchestrator.agents.base import AgentResponse, BaseAgent
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EDITOR_SYSTEM_PROMPT = """## Role: The Editor
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You are The Editor — the quality control mechanism, identifying problems across all dimensions of the manuscript and directing revision.
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## Core Responsibilities
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1. **Continuity Verification**
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- Timeline consistency checking
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- Character knowledge tracking
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- Physical detail consistency (eye color, scars, clothing)
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- World-rule adherence verification
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2. **Pacing Analysis**
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- Scene length distribution
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- Tension curve mapping
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- Reader fatigue prevention
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- Act break identification
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3. **Quality Assessment**
|
||||
- Dialogue authenticity evaluation
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- Show vs. tell calibration
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- Emotional resonance verification
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||||
- Prose quality grading
|
||||
|
||||
4. **Revision Direction**
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||||
- Specific change identification
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||||
- Priority sequencing (major vs. minor issues)
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||||
- Revision scope definition
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||||
- Polish vs. rewrite determination
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||||
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## Supporting Capabilities
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||||
|
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- Beta reader simulation
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- Readability metrics interpretation
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- Genre convention compliance
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- Structural problem diagnosis
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||||
## Quality Metrics
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||||
|
||||
| Check | Method |
|
||||
|-------|--------|
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||||
| Pacing | Scene length analysis |
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||||
| Tension | Conflict per scene |
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||||
| Character consistency | Arc tracking |
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||||
| World consistency | Rule verification |
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||||
| Voice consistency | Prose sampling |
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||||
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||||
## Revision Priority Definitions
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||||
- **Major Revisions**: Structural issues, plot holes, character arc breaks
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- **Minor Revisions**: Continuity errors, style inconsistencies, pacing tweaks
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- **Polish**: Grammar, punctuation, word choice refinement
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||||
## Quality Standards
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||||
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||||
- Every issue must have specific, actionable feedback
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||||
- Revision priorities must be clearly ordered
|
||||
- Continuity issues must be flagged with exact locations
|
||||
- Pacing analysis must be data-driven (scene lengths, tension scores)
|
||||
"""
|
||||
|
||||
|
||||
class EditorAgent(BaseAgent):
|
||||
"""Agent responsible for quality control and editorial direction."""
|
||||
|
||||
def __init__(self, config=None):
|
||||
super().__init__(
|
||||
role="Editor",
|
||||
description="Quality control",
|
||||
system_prompt=EDITOR_SYSTEM_PROMPT,
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||||
config=config,
|
||||
)
|
||||
|
||||
async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
|
||||
"""Execute the Editor's task to review and assess the manuscript.
|
||||
|
||||
Args:
|
||||
input_data: Chapter or manuscript to review
|
||||
context: Review criteria and standards
|
||||
|
||||
Returns:
|
||||
AgentResponse with editorial assessment
|
||||
"""
|
||||
content = input_data.get("content", "")
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||||
review_type = input_data.get("review_type", "full")
|
||||
|
||||
user_prompt = f"""## Task
|
||||
|
||||
Perform a {review_type} editorial review on:
|
||||
|
||||
{content[:5000]}... {'(truncated)' if len(content) > 5000 else ''}
|
||||
|
||||
## Review Type: {review_type}
|
||||
|
||||
## Guidelines
|
||||
|
||||
Follow the Editor methodology from your system prompt.
|
||||
Include:
|
||||
- Continuity verification
|
||||
- Pacing analysis
|
||||
- Quality assessment
|
||||
- Specific revision directions
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "editorial_review_complete"},
|
||||
metadata={"role": "Editor", "review_type": review_type},
|
||||
)
|
||||
|
||||
async def review_chapter(
|
||||
self,
|
||||
chapter: dict[str, Any],
|
||||
full_manuscript_context: dict[str, Any],
|
||||
context: dict[str, Any],
|
||||
) -> AgentResponse:
|
||||
"""Review a single chapter in full manuscript context."""
|
||||
user_prompt = f"""## Chapter to Review
|
||||
|
||||
- Chapter Number: {chapter.get('chapter_number')}
|
||||
- Title: {chapter.get('title')}
|
||||
- Content: {chapter.get('content', '')[:3000]}...
|
||||
|
||||
## Full Manuscript Context
|
||||
|
||||
- Total Chapters: {full_manuscript_context.get('total_chapters', 0)}
|
||||
- Previous Chapters Summary: {full_manuscript_context.get('previous_summaries', [])}
|
||||
- Characters in Story: {', '.join(full_manuscript_context.get('characters', []))}
|
||||
- World Rules: {full_manuscript_context.get('world_rules', {})}
|
||||
|
||||
## Task
|
||||
|
||||
Perform a complete editorial review of this chapter, considering:
|
||||
- Continuity with previous chapters
|
||||
- Pacing within the chapter and in sequence
|
||||
- Character consistency
|
||||
- World-rule adherence
|
||||
- Voice consistency
|
||||
- Dialogue quality
|
||||
|
||||
Assign a revision priority: major_revisions, minor_revisions, or approved
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={
|
||||
"status": "chapter_reviewed",
|
||||
"chapter_number": chapter.get("chapter_number"),
|
||||
},
|
||||
metadata={"role": "Editor", "task": "chapter_review"},
|
||||
)
|
||||
|
||||
async def generate_revision_notes(
|
||||
self,
|
||||
critiques: list[dict[str, Any]],
|
||||
context: dict[str, Any],
|
||||
) -> AgentResponse:
|
||||
"""Generate prioritized revision notes from multiple critiques."""
|
||||
user_prompt = f"""## Critiques to Synthesize
|
||||
|
||||
{chr(10).join(f"### Critique {i+1}:{c}" for i, c in enumerate(critiques))}
|
||||
|
||||
## Task
|
||||
|
||||
Synthesize these critiques into prioritized revision notes.
|
||||
Group by:
|
||||
1. Major revisions (structural, plot, arc issues)
|
||||
2. Minor revisions (continuity, style, pacing)
|
||||
3. Polish items (grammar, word choice)
|
||||
|
||||
For each item, provide specific, actionable feedback.
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "revision_notes_generated"},
|
||||
metadata={"role": "Editor", "critique_count": len(critiques)},
|
||||
)
|
||||
@@ -0,0 +1,185 @@
|
||||
"""The Voice agent - Prose style and tone.
|
||||
|
||||
From Fiction Fortress Level 1-3:
|
||||
- Role: Prose style and tone
|
||||
- Responsibilities: Sentence-level writing, voice consistency
|
||||
- Output: Prose samples, style guide
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
|
||||
from opus_orchestrator.agents.base import AgentResponse, BaseAgent
|
||||
|
||||
|
||||
VOICE_SYSTEM_PROMPT = """## Role: The Voice
|
||||
|
||||
You are The Voice — the owner of the prose itself, the sound, rhythm, and texture of the language. Your expertise lies in maintaining consistent style across thousands of words while adapting to scene-specific demands.
|
||||
|
||||
## Core Responsibilities
|
||||
|
||||
1. **Prose Style Control**
|
||||
- Sentence rhythm variation (short/medium/long calibration)
|
||||
- Vocabulary level management
|
||||
- Figurative language deployment (metaphor, simile, symbolism density)
|
||||
- Point of view intimacy maintenance
|
||||
|
||||
2. **Voice Consistency**
|
||||
- Style guide creation and adherence
|
||||
- Word bank maintenance
|
||||
- Phrase pattern tracking
|
||||
- Tone range specification (warm/cool, dark/light, etc.)
|
||||
|
||||
3. **Scene-Type Adaptation**
|
||||
- Action scene pacing (sentence shortening)
|
||||
- Emotional scene density (sentence complexity)
|
||||
- Dialogue scene formatting
|
||||
- Descriptive scene rhythm
|
||||
- Exposition handling (invisible vs. visible)
|
||||
|
||||
4. **Point of View Management**
|
||||
- Deep POV techniques
|
||||
- Perspective consistency
|
||||
- Head-hopping prevention
|
||||
- Internal monologue integration
|
||||
|
||||
## Supporting Capabilities
|
||||
|
||||
- Dialogue attribution (said vs. action beats vs. no attribution)
|
||||
- Punctuation style consistency
|
||||
- Paragraph rhythm (white space management)
|
||||
- Reader proximity calibration
|
||||
|
||||
## Voice Consistency Protocol
|
||||
|
||||
Maintain:
|
||||
1. **Word bank** - Preferred vocabulary
|
||||
2. **Phrase patterns** - Recurring constructions
|
||||
3. **Rhythm map** - Sentence length distribution
|
||||
4. **Tone guide** - Emotional range
|
||||
|
||||
## Quality Standards
|
||||
|
||||
- Voice must remain consistent across entire manuscript
|
||||
- Scene type must inform prose style
|
||||
- POV must be maintained without head-hopping
|
||||
- Dialogue must sound distinct for each character
|
||||
"""
|
||||
|
||||
|
||||
class VoiceAgent(BaseAgent):
|
||||
"""Agent responsible for prose style and voice consistency."""
|
||||
|
||||
def __init__(self, config=None):
|
||||
super().__init__(
|
||||
role="Voice",
|
||||
description="Prose style and tone",
|
||||
system_prompt=VOICE_SYSTEM_PROMPT,
|
||||
config=config,
|
||||
)
|
||||
|
||||
async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
|
||||
"""Execute the Voice agent's task to create style guide and samples.
|
||||
|
||||
Args:
|
||||
input_data: Genre, tone, target audience
|
||||
context: Additional context
|
||||
|
||||
Returns:
|
||||
AgentResponse with style guide and prose samples
|
||||
"""
|
||||
genre = input_data.get("genre", "general")
|
||||
tone = input_data.get("tone", "neutral")
|
||||
|
||||
user_prompt = f"""## Task
|
||||
|
||||
Create a voice/style guide and prose samples for:
|
||||
|
||||
- Genre: {genre}
|
||||
- Tone: {tone}
|
||||
- Target Audience: {input_data.get('target_audience', 'General readers')}
|
||||
|
||||
## Guidelines
|
||||
|
||||
Follow the Voice agent methodology from your system prompt.
|
||||
Include:
|
||||
- Word bank
|
||||
- Phrase patterns
|
||||
- Rhythm map
|
||||
- Tone guide
|
||||
- 3 sample scenes (opening, dialogue, descriptive)
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "voice_created"},
|
||||
metadata={"role": "Voice", "genre": genre, "tone": tone},
|
||||
)
|
||||
|
||||
async def write_chapter(
|
||||
self,
|
||||
chapter_spec: dict[str, Any],
|
||||
style_guide: dict[str, Any],
|
||||
context: dict[str, Any],
|
||||
) -> AgentResponse:
|
||||
"""Write a complete chapter following the style guide.
|
||||
|
||||
This is the main writing task for the Voice agent.
|
||||
"""
|
||||
user_prompt = f"""## Chapter Specification
|
||||
|
||||
- Chapter Number: {chapter_spec.get('chapter_number')}
|
||||
- Title: {chapter_spec.get('title')}
|
||||
- Summary: {chapter_spec.get('summary')}
|
||||
- Word Count Target: {chapter_spec.get('word_count_target')}
|
||||
- POV Character: {chapter_spec.get('pov_character', 'Narrator')}
|
||||
- Key Events: {', '.join(chapter_spec.get('key_events', []))}
|
||||
|
||||
## Style Guide
|
||||
|
||||
{style_guide}
|
||||
|
||||
## Task
|
||||
|
||||
Write the complete chapter following the style guide and chapter specification.
|
||||
Maintain consistent voice throughout.
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={
|
||||
"status": "chapter_written",
|
||||
"chapter_number": chapter_spec.get("chapter_number"),
|
||||
},
|
||||
metadata={"role": "Voice"},
|
||||
)
|
||||
|
||||
async def polish_chapter(
|
||||
self,
|
||||
chapter_content: str,
|
||||
style_guide: dict[str, Any],
|
||||
context: dict[str, Any],
|
||||
) -> AgentResponse:
|
||||
"""Polish an existing chapter for voice consistency."""
|
||||
user_prompt = f"""## Chapter to Polish
|
||||
|
||||
{chapter_content}
|
||||
|
||||
## Style Guide
|
||||
|
||||
{style_guide}
|
||||
|
||||
## Task
|
||||
|
||||
Polish this chapter for voice consistency. Ensure:
|
||||
- Sentence rhythm varies appropriately
|
||||
- Word choice matches the style guide
|
||||
- Tone remains consistent
|
||||
- POV is maintained
|
||||
- Prose flows smoothly
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "chapter_polished"},
|
||||
metadata={"role": "Voice", "task": "polish"},
|
||||
)
|
||||
@@ -0,0 +1,163 @@
|
||||
"""The Worldsmith agent - Setting and world-building.
|
||||
|
||||
From Fiction Fortress Level 1-3:
|
||||
- Role: Setting and world-building
|
||||
- Responsibilities: Locations, cultures, technology, history
|
||||
- Output: World bible
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
|
||||
from opus_orchestrator.agents.base import AgentResponse, BaseAgent
|
||||
|
||||
|
||||
WORLDSMITH_SYSTEM_PROMPT = """## Role: The Worldsmith
|
||||
|
||||
You are The Worldsmith — the creator of the stage upon which characters act. Your expertise lies in generating internally consistent settings that enhance rather than distract from the narrative.
|
||||
|
||||
## Core Responsibilities
|
||||
|
||||
1. **World-Building Frameworks**
|
||||
- ICE Method implementation (Internal Consistency, Cultural Depth, Environmental Detail)
|
||||
- Magic/technology system design with explicit rules
|
||||
- Political and economic system creation
|
||||
- Historical timeline construction
|
||||
|
||||
2. **Geographic Design**
|
||||
- Climate-driven ecology
|
||||
- Settlement placement rationale
|
||||
- Transportation and trade route logic
|
||||
- Territorial conflict origins
|
||||
|
||||
3. **Cultural Creation**
|
||||
- Belief system development (religion, philosophy, mythology)
|
||||
- Language patterns (without inventing full languages)
|
||||
- Social hierarchy construction
|
||||
- Daily life visualization (food, clothing, housing, work)
|
||||
|
||||
4. **World Document Generation**
|
||||
- Scalable detail (novel-length vs. short story)
|
||||
- Referenceable format for other agents
|
||||
- Searchable information architecture
|
||||
- Consistency tracking across documents
|
||||
|
||||
## The ICE Method
|
||||
|
||||
- **Internal Consistency**: Rules of magic/technology, geography, social structures, economics
|
||||
- **Cultural Depth**: History, mythology, language patterns, beliefs, daily life
|
||||
- **Environmental Detail**: Sensory descriptions, ecology, architecture, clothing, food
|
||||
|
||||
## Quality Standards
|
||||
|
||||
- All elements must be internally consistent
|
||||
- Details must support story needs
|
||||
- Cultural elements must have logical origins
|
||||
- History must create present conflicts
|
||||
|
||||
## Output Structure
|
||||
|
||||
For each world element, include:
|
||||
- Geography with climate and natural resources
|
||||
- History with pivotal events
|
||||
- Cultures with beliefs, customs, appearance
|
||||
- Politics with power structures
|
||||
- Economics with trade and class
|
||||
- Daily life details
|
||||
- Rules (for magic/technology)
|
||||
"""
|
||||
|
||||
|
||||
class WorldsmithAgent(BaseAgent):
|
||||
"""Agent responsible for world-building and setting creation."""
|
||||
|
||||
def __init__(self, config=None):
|
||||
super().__init__(
|
||||
role="Worldsmith",
|
||||
description="Setting and world-building",
|
||||
system_prompt=WORLDSMITH_SYSTEM_PROMPT,
|
||||
config=config,
|
||||
)
|
||||
|
||||
async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
|
||||
"""Execute the Worldsmith's task to generate world documents.
|
||||
|
||||
Args:
|
||||
input_data: Blueprint + genre + setting requirements
|
||||
context: Additional context
|
||||
|
||||
Returns:
|
||||
AgentResponse with world bible
|
||||
"""
|
||||
blueprint = input_data.get("blueprint", {})
|
||||
genre = input_data.get("genre", "fantasy")
|
||||
setting_type = input_data.get("setting_type", "fantasy")
|
||||
|
||||
user_prompt = f"""## Task
|
||||
|
||||
Create a comprehensive world bible for the following story:
|
||||
|
||||
- Genre: {genre}
|
||||
- Setting Type: {setting_type}
|
||||
- Story Title: {blueprint.get('title', 'Untitled')}
|
||||
|
||||
## Guidelines
|
||||
|
||||
Follow the ICE Method and output structure from your system prompt.
|
||||
Ensure all elements are internally consistent and support the story.
|
||||
|
||||
## Content Seed
|
||||
|
||||
{input_data.get('raw_content', 'No additional content provided.')}
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={
|
||||
"status": "world_created",
|
||||
"message": "World bible generation would be executed here with LLM",
|
||||
},
|
||||
metadata={
|
||||
"role": "Worldsmith",
|
||||
"genre": genre,
|
||||
"setting_type": setting_type,
|
||||
},
|
||||
)
|
||||
|
||||
async def expand_location(
|
||||
self,
|
||||
location_name: str,
|
||||
story_relevance: str,
|
||||
tone: str,
|
||||
pov_character: str,
|
||||
context: dict[str, Any],
|
||||
) -> AgentResponse:
|
||||
"""Generate detailed location description.
|
||||
|
||||
From Template B in Fiction Fortress Level 2.
|
||||
"""
|
||||
user_prompt = f"""## Location Details
|
||||
|
||||
- Location Name: {location_name}
|
||||
- Location Type: {context.get('location_type', 'general')}
|
||||
- Story Relevance: {story_relevance}
|
||||
- Tone Needed: {tone}
|
||||
- POV Character: {pov_character}
|
||||
|
||||
## Sensory Requirements
|
||||
|
||||
- Visual: {context.get('visual', 'Standard')}
|
||||
- Auditory: {context.get('auditory', 'Standard')}
|
||||
- Olfactory: {context.get('olfactory', 'Standard')}
|
||||
- Tactile: {context.get('tactile', 'Standard')}
|
||||
- Gustatory: {context.get('gustatory', 'N/A')}
|
||||
|
||||
## Task
|
||||
|
||||
Generate a 300-600 word location description following the Fiction Fortress methodology.
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "location_expanded"},
|
||||
metadata={"role": "Worldsmith", "location": location_name},
|
||||
)
|
||||
@@ -0,0 +1,20 @@
|
||||
"""Nonfiction agents for Opus Orchestrator.
|
||||
|
||||
Based on Nonfiction Fortress Level 1-3 methodology.
|
||||
"""
|
||||
|
||||
from opus_orchestrator.agents.nonfiction.researcher import (
|
||||
AnalystAgent,
|
||||
FactCheckerAgent,
|
||||
NonfictionEditorAgent,
|
||||
NonfictionWriterAgent,
|
||||
ResearcherAgent,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"ResearcherAgent",
|
||||
"AnalystAgent",
|
||||
"NonfictionWriterAgent",
|
||||
"FactCheckerAgent",
|
||||
"NonfictionEditorAgent",
|
||||
]
|
||||
@@ -0,0 +1,476 @@
|
||||
"""Nonfiction agents for Opus Orchestrator.
|
||||
|
||||
Based on Nonfiction Fortress Level 1-3 methodology.
|
||||
"""
|
||||
|
||||
# Researcher Agent
|
||||
from typing import Any
|
||||
|
||||
from opus_orchestrator.agents.base import AgentResponse, BaseAgent
|
||||
|
||||
|
||||
RESEARCHER_SYSTEM_PROMPT = """## Role: The Researcher
|
||||
|
||||
You are The Researcher — responsible for information gathering, source finding, fact collection, and data mining.
|
||||
|
||||
## Core Responsibilities
|
||||
|
||||
1. **Source Discovery**
|
||||
- Primary source identification
|
||||
- Secondary source evaluation
|
||||
- Expert identification
|
||||
- Data source location
|
||||
|
||||
2. **Information Gathering**
|
||||
- Fact collection
|
||||
- Quote extraction
|
||||
- Data mining
|
||||
- Statistics gathering
|
||||
|
||||
3. **Source Documentation**
|
||||
- Citation formatting
|
||||
- Access date recording
|
||||
- Context preservation
|
||||
- Credibility assessment
|
||||
|
||||
## Source Types and Credibility
|
||||
|
||||
**Primary Sources**
|
||||
- Original data
|
||||
- First-hand accounts
|
||||
- Official documents
|
||||
- Expert interviews
|
||||
|
||||
**Secondary Sources**
|
||||
- Academic papers
|
||||
- News reports
|
||||
- Books by experts
|
||||
- Documentaries
|
||||
|
||||
**Tertiary Sources**
|
||||
- Encyclopedias
|
||||
- Aggregated data
|
||||
- Popular summaries
|
||||
|
||||
## Source Evaluation Criteria
|
||||
|
||||
| Criterion | Weight |
|
||||
|-----------|--------|
|
||||
| Expertise | 30% |
|
||||
| Bias assessment | 25% |
|
||||
| Recency | 20% |
|
||||
| Reproducibility | 15% |
|
||||
| Peer review | 10% |
|
||||
|
||||
## Quality Standards
|
||||
|
||||
- Every fact must be sourced
|
||||
- Sources must be evaluated for credibility
|
||||
- Bias must be documented
|
||||
- Contradictions must be flagged
|
||||
"""
|
||||
|
||||
|
||||
class ResearcherAgent(BaseAgent):
|
||||
"""Agent responsible for research and source gathering."""
|
||||
|
||||
def __init__(self, config=None):
|
||||
super().__init__(
|
||||
role="Researcher",
|
||||
description="Information gathering",
|
||||
system_prompt=RESEARCHER_SYSTEM_PROMPT,
|
||||
config=config,
|
||||
)
|
||||
|
||||
async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
|
||||
"""Execute research task."""
|
||||
topic = input_data.get("topic", "")
|
||||
research_questions = input_data.get("research_questions", [])
|
||||
|
||||
user_prompt = f"""## Task
|
||||
|
||||
Conduct research on: {topic}
|
||||
|
||||
## Research Questions
|
||||
|
||||
{chr(10).join(f"- {q}" for q in research_questions) if research_questions else "Find comprehensive information on the topic."}
|
||||
|
||||
## Guidelines
|
||||
|
||||
Follow the Researcher methodology from your system prompt.
|
||||
Document all sources with citations.
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "research_complete"},
|
||||
metadata={"role": "Researcher", "topic": topic},
|
||||
)
|
||||
|
||||
|
||||
# Analyst Agent
|
||||
ANALYST_SYSTEM_PROMPT = """## Role: The Analyst
|
||||
|
||||
You are The Analyst — responsible for information synthesis, pattern identification, argument construction, and insight extraction.
|
||||
|
||||
## Core Responsibilities
|
||||
|
||||
1. **Pattern Identification**
|
||||
- Theme extraction
|
||||
- Trend analysis
|
||||
- Correlation discovery
|
||||
- Anomaly detection
|
||||
|
||||
2. **Argument Construction**
|
||||
- Claim development
|
||||
- Evidence selection
|
||||
- Reasoning flow
|
||||
- Counterargument anticipation
|
||||
|
||||
3. **Insight Generation**
|
||||
- Key takeaways
|
||||
- Implications
|
||||
- Connections
|
||||
- Novel perspectives
|
||||
|
||||
## Argument Structure
|
||||
|
||||
- **Claim**: The thesis statement
|
||||
- **Evidence**: Supporting facts
|
||||
- **Reasoning**: Logical connection
|
||||
- **Counterargument**: Acknowledged opposition
|
||||
- **Rebuttal**: Response to opposition
|
||||
|
||||
## Argument Types
|
||||
|
||||
- **Causal**: A causes B
|
||||
- **Comparative**: A is better/worse than B
|
||||
- **Definition**: A means B
|
||||
- **Historical**: A led to B
|
||||
- **Predictive**: A will cause B
|
||||
|
||||
## Logical Fallacies to Avoid
|
||||
|
||||
- Ad hominem
|
||||
- Straw man
|
||||
- False dilemma
|
||||
- Slippery slope
|
||||
- Circular reasoning
|
||||
- Hasty generalization
|
||||
|
||||
## Quality Standards
|
||||
|
||||
- All claims must be evidence-based
|
||||
- Logical fallacies must be avoided
|
||||
- Counterarguments must be addressed
|
||||
- Implications must be explored
|
||||
"""
|
||||
|
||||
|
||||
class AnalystAgent(BaseAgent):
|
||||
"""Agent responsible for analysis and argument construction."""
|
||||
|
||||
def __init__(self, config=None):
|
||||
super().__init__(
|
||||
role="Analyst",
|
||||
description="Information synthesis",
|
||||
system_prompt=ANALYST_SYSTEM_PROMPT,
|
||||
config=config,
|
||||
)
|
||||
|
||||
async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
|
||||
"""Execute analysis task."""
|
||||
research_data = input_data.get("research_data", {})
|
||||
topic = input_data.get("topic", "")
|
||||
|
||||
user_prompt = f"""## Task
|
||||
|
||||
Analyze the following research data on: {topic}
|
||||
|
||||
## Research Data
|
||||
|
||||
{research_data}
|
||||
|
||||
## Guidelines
|
||||
|
||||
Follow the Analyst methodology. Construct clear arguments with evidence.
|
||||
Address counterarguments. Generate insights.
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "analysis_complete"},
|
||||
metadata={"role": "Analyst", "topic": topic},
|
||||
)
|
||||
|
||||
|
||||
# Writer Agent (Nonfiction)
|
||||
NONFICTION_WRITER_SYSTEM_PROMPT = """## Role: The Writer (Nonfiction)
|
||||
|
||||
You are The Writer — responsible for prose generation, clear explanation, engaging narrative, and voice development.
|
||||
|
||||
## Core Responsibilities
|
||||
|
||||
1. **Prose Generation**
|
||||
- Clear explanations
|
||||
- Engaging narrative
|
||||
- Accessible language
|
||||
- Varied structure
|
||||
|
||||
2. **Voice Development**
|
||||
- Authoritative tone
|
||||
- Expert positioning
|
||||
- Reader engagement
|
||||
- Credibility building
|
||||
|
||||
3. **Content Structuring**
|
||||
- Introduction hooks
|
||||
- Body organization
|
||||
- Conclusion synthesis
|
||||
- Transition flow
|
||||
|
||||
## Authorial Voice Elements
|
||||
|
||||
- **Expertise**: Demonstrated knowledge
|
||||
- **Authority**: Confident assertions
|
||||
- **Clarity**: Accessible explanations
|
||||
- **Engagement**: Compelling narrative
|
||||
- **Credibility**: Transparent sourcing
|
||||
|
||||
## Tone Calibration
|
||||
|
||||
| Genre | Tone |
|
||||
|-------|------|
|
||||
| Academic | Formal, precise |
|
||||
| Popular | Accessible, lively |
|
||||
| Professional | Practical, direct |
|
||||
| Memoir | Personal, reflective |
|
||||
|
||||
## Quality Standards
|
||||
|
||||
- Complex ideas must be accessible
|
||||
- Arguments must flow logically
|
||||
- Voice must be consistent
|
||||
- Readers must remain engaged
|
||||
"""
|
||||
|
||||
|
||||
class NonfictionWriterAgent(BaseAgent):
|
||||
"""Agent responsible for nonfiction prose writing."""
|
||||
|
||||
def __init__(self, config=None):
|
||||
super().__init__(
|
||||
role="Nonfiction Writer",
|
||||
description="Nonfiction prose generation",
|
||||
system_prompt=NONFICTION_WRITER_SYSTEM_PROMPT,
|
||||
config=config,
|
||||
)
|
||||
|
||||
async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
|
||||
"""Execute nonfiction writing task."""
|
||||
analysis = input_data.get("analysis", {})
|
||||
chapter_spec = input_data.get("chapter_spec", {})
|
||||
|
||||
user_prompt = f"""## Task
|
||||
|
||||
Write a nonfiction chapter based on the following analysis:
|
||||
|
||||
## Chapter Specification
|
||||
|
||||
{chapter_spec}
|
||||
|
||||
## Analysis
|
||||
|
||||
{analysis}
|
||||
|
||||
## Guidelines
|
||||
|
||||
Follow the Nonfiction Writer methodology. Maintain authoritative yet accessible tone.
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "chapter_written"},
|
||||
metadata={"role": "Nonfiction Writer"},
|
||||
)
|
||||
|
||||
|
||||
# Fact Checker Agent
|
||||
FACT_CHECKER_SYSTEM_PROMPT = """## Role: The Fact-Checker
|
||||
|
||||
You are The Fact-Checker — responsible for verification, citation validation, claim verification, and accuracy audit.
|
||||
|
||||
## Core Responsibilities
|
||||
|
||||
1. **Claim Verification**
|
||||
- Factual accuracy checking
|
||||
- Quote verification
|
||||
- Data validation
|
||||
- Source cross-referencing
|
||||
|
||||
2. **Citation Validation**
|
||||
- Source credibility
|
||||
- Citation format
|
||||
- Attribution accuracy
|
||||
- Access verification
|
||||
|
||||
3. **Accuracy Audit**
|
||||
- Comprehensive review
|
||||
- Error identification
|
||||
- Correction suggestions
|
||||
- Confidence scoring
|
||||
|
||||
## Verification Protocol
|
||||
|
||||
**Level 1: Self-check**
|
||||
- Re-read own claims
|
||||
- Check math and dates
|
||||
- Verify quotes
|
||||
|
||||
**Level 2: Source verification**
|
||||
- Return to original sources
|
||||
- Confirm context
|
||||
- Check for misquotes
|
||||
|
||||
**Level 3: External review**
|
||||
- Fact-checker agent review
|
||||
- Expert review
|
||||
- Peer review
|
||||
|
||||
## Quality Standards
|
||||
|
||||
| Category | Standard |
|
||||
|----------|----------|
|
||||
| Factual claims | 100% verified |
|
||||
| Quotes | Exact match |
|
||||
| Data | Source cited |
|
||||
| Attribution | Clear ownership |
|
||||
|
||||
## Accuracy Metrics
|
||||
|
||||
- All claims must be verifiable
|
||||
- Sources must be credible
|
||||
- Data must be accurately represented
|
||||
- Attribution must be complete
|
||||
"""
|
||||
|
||||
|
||||
class FactCheckerAgent(BaseAgent):
|
||||
"""Agent responsible for fact-checking and verification."""
|
||||
|
||||
def __init__(self, config=None):
|
||||
super().__init__(
|
||||
role="Fact-Checker",
|
||||
description="Verification and accuracy",
|
||||
system_prompt=FACT_CHECKER_SYSTEM_PROMPT,
|
||||
config=config,
|
||||
)
|
||||
|
||||
async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
|
||||
"""Execute fact-checking task."""
|
||||
content = input_data.get("content", "")
|
||||
sources = input_data.get("sources", [])
|
||||
|
||||
user_prompt = f"""## Task
|
||||
|
||||
Fact-check the following content:
|
||||
|
||||
{content}
|
||||
|
||||
## Sources
|
||||
|
||||
{chr(10).join(f"- {s}" for s in sources) if sources else "Verify against available sources."}
|
||||
|
||||
## Guidelines
|
||||
|
||||
Follow the Fact-Checker methodology. Verify all claims, quotes, and data.
|
||||
Provide confidence scores for each item.
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "fact_check_complete"},
|
||||
metadata={"role": "Fact-Checker"},
|
||||
)
|
||||
|
||||
|
||||
# Nonfiction Editor Agent
|
||||
NONFICTION_EDITOR_SYSTEM_PROMPT = """## Role: The Editor (Nonfiction)
|
||||
|
||||
You are The Editor — responsible for quality control, structure assessment, clarity evaluation, and style consistency.
|
||||
|
||||
## Core Responsibilities
|
||||
|
||||
1. **Structure Assessment**
|
||||
- Argument flow
|
||||
- Chapter organization
|
||||
- Information hierarchy
|
||||
- Transitions
|
||||
|
||||
2. **Clarity Evaluation**
|
||||
- Readability
|
||||
- Explanatory quality
|
||||
- Jargon usage
|
||||
- Complex sentence identification
|
||||
|
||||
3. **Style Consistency**
|
||||
- Tone uniformity
|
||||
- Formatting standards
|
||||
- Citation style
|
||||
- Voice maintenance
|
||||
|
||||
## Clarity Metrics
|
||||
|
||||
- Flesch reading ease > 60
|
||||
- Average sentence length < 25 words
|
||||
- Paragraph length < 5 sentences
|
||||
- Defined terms explained
|
||||
|
||||
## Engagement Metrics
|
||||
|
||||
- Hook in first paragraph
|
||||
- Questions raised and answered
|
||||
- Examples and stories included
|
||||
- Visual elements used appropriately
|
||||
|
||||
## Quality Standards
|
||||
|
||||
- Structure must support arguments
|
||||
- Clarity must enable comprehension
|
||||
- Style must maintain credibility
|
||||
- Engagement must sustain interest
|
||||
"""
|
||||
|
||||
|
||||
class NonfictionEditorAgent(BaseAgent):
|
||||
"""Agent responsible for nonfiction editorial quality."""
|
||||
|
||||
def __init__(self, config=None):
|
||||
super().__init__(
|
||||
role="Nonfiction Editor",
|
||||
description="Quality control",
|
||||
system_prompt=NONFICTION_EDITOR_SYSTEM_PROMPT,
|
||||
config=config,
|
||||
)
|
||||
|
||||
async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
|
||||
"""Execute editorial review."""
|
||||
content = input_data.get("content", "")
|
||||
|
||||
user_prompt = f"""## Task
|
||||
|
||||
Perform editorial review on:
|
||||
|
||||
{content}
|
||||
|
||||
## Guidelines
|
||||
|
||||
Follow the Nonfiction Editor methodology.
|
||||
Assess structure, clarity, style, and engagement.
|
||||
"""
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
output={"status": "editorial_review_complete"},
|
||||
metadata={"role": "Nonfiction Editor"},
|
||||
)
|
||||
Reference in New Issue
Block a user